From 17096411a3d120695a94e1e136914edcca877c5f Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Tue, 19 Jun 2018 10:04:02 +0200 Subject: [PATCH 01/42] Small/insignificant modifications and corrections of typos --- README.rst | 2 +- wwdata/Class_HydroData.py | 10 +++++----- wwdata/Class_OnlineSensorBased.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/README.rst b/README.rst index d70018c99..092dc5f92 100644 --- a/README.rst +++ b/README.rst @@ -20,7 +20,7 @@ wwdata :target: https://doi.org/10.5281/zenodo.1288581 -Data analysis package aimed at data obtained in the context of (waste)water +Data analysis pckage aimed at data obtained in the context of (waste)water * Free software: GNU General Public License v3 * Documentation: https://ugentbiomath.github.io/wwdata-docs/ diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 04e74e8b9..eb4e04095 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -405,7 +405,7 @@ def get_avg(self,name=None,only_checked=True): Parameters ---------- - name : arary of str + name : array of str name(s) of the column(s) containing the data to be averaged; defaults to ['none'] and will calculate average for every column @@ -420,7 +420,7 @@ def get_avg(self,name=None,only_checked=True): df = self.data.copy() df[self.meta_valid == 'filtered']=np.nan - if name == None: + if name is None: mean = df.mean() elif isinstance(name,str): mean = df[name].mean() @@ -429,7 +429,7 @@ def get_avg(self,name=None,only_checked=True): mean.append(df[name].mean()) else: - if name == None: + if name is None: mean = self.data.mean() elif isinstance(name,str): mean = self.data[name].mean() @@ -465,7 +465,7 @@ def get_std(self,name=None,only_checked=True): df = self.data.copy() df[self.meta_valid == 'filtered']=np.nan - if name == None: + if name is None: std = df.std() elif isinstance(name,str): std = df[name].std() @@ -474,7 +474,7 @@ def get_std(self,name=None,only_checked=True): std.append(df[name].std()) else: - if name == None: + if name is None: std = self.data.std() elif isinstance(name,str): std = self.data[name].std() diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 9c139570d..3044feb95 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -99,7 +99,7 @@ def drop_index_duplicates(self): Note ---- This operation assumes the dropped rows have the same data in them and - therefor no data is lost. + therefore no data is lost. """ #self.data = self.data.groupby(self.index()).first() #self.meta_valid = self.meta_valid.groupby(self.meta_valid.index).first() From a0142d2e3b3fb73104921978de9f66d3385cdb32 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Tue, 19 Jun 2018 10:35:49 +0200 Subject: [PATCH 02/42] Small/insignificant modifications and corrections of typos --- wwdata/Class_HydroData.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index eb4e04095..0aaf6abf9 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -170,7 +170,7 @@ def drop_index_duplicates(self): Note ---- - It is assumed that the dropped rows containt the same data as their index- + It is assumed that the dropped rows contain the same data as their index- based duplicate, i.e. that no data is lost using the function. """ #len_orig = len(self.data) @@ -199,7 +199,7 @@ def set_index(self,keys,key_is_time=False,drop=True,inplace=False, Notes ---------- key_is_time : bool - when true, the new index will we known as the time data from here on + when true, the new index will be known as the time data from here on (other arguments cfr pd.set_index) From 574574459e3c10146d3174ec7e0e1365b3b0062d Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 10:11:55 +0200 Subject: [PATCH 03/42] Fixing bugs for pandas.interpolate --- Showcase_OnlineSensorBased.ipynb | 3557 ++++++++++++++++++++++++++++- wwdata/Class_HydroData.py | 18 +- wwdata/Class_OnlineSensorBased.py | 9 +- 3 files changed, 3469 insertions(+), 115 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 8e61148e1..a126afe6c 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 3, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 4, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,13 +206,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", "start_time": "2017-05-09T11:54:55.790638+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -228,13 +227,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", "start_time": "2017-05-09T11:54:55.796021+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -250,13 +248,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", "start_time": "2017-05-09T11:54:55.815534+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -265,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -275,9 +272,9 @@ "outputs": [ { "data": { - "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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m+wytsx7THCf1Qjtcq20bjLk1GDIMwHTcYIW+ZWWRaU/4SNKlVVuFWWZbG6SGODIAiCsEVIY6MF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\nX78ugYGB8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+ "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -319,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -329,9 +326,9 @@ "outputs": [ { "data": { - "image/png": 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89dZbWLp0KRQKRZMAV6vVQhAEeHl5QaFQAGgaBNfV1cGzPj+5uW0YX3s6Y2I+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Pjw4fJ82xUemUyGIU21q/LMKriQMD9zLHxhCQBu+JExrL2hvtrkNd79clWpTQ2Y8Nd2W9xWrHpz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\n", 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\n", 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" ] }, "metadata": {}, @@ -352,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -382,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -412,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -442,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -465,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -482,9 +479,9 @@ }, { "data": { - "image/png": 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Lly/H6NGjtcddtmxZsx6/Zs0aAMB3332HTp06AQCio6MxYcIEw785NdhDhIiIiIiMSxQh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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -500,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -508,19 +505,19 @@ "text/plain": [ "array(['TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2',\n", - " 'TSS_line1', 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns',\n", - " 'Turb_ns', 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es',\n", - " 'Temp_es', 'NH4_infl', 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz',\n", - " 'PO4_mixinggutter', 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST',\n", - " 'CODsol_efflPST', 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT',\n", - " 'CODsol_efflRBT', 'Cond_line1', 'Turb_line1', 'Cond_line2',\n", - " 'Turb_line2', 'Cond_line3', 'Turb_line3', 'NH4_efflPST',\n", - " 'PO4_efflPST', 'PO4_sandtrap', 'NH4_splittingworks',\n", - " 'PO4_splittingworks', 'Flow_line1', 'Flow_line2', 'Flow_line3',\n", - " 'Flow_total'], dtype=object)" + " 'TSS_line1', 'NO3_line1', 'CODtot_line1', 'CODsol_line1',\n", + " 'Cond_ns', 'Turb_ns', 'Temp_ns', 'Ammonium_ns', 'Cond_es',\n", + " 'Turb_es', 'Temp_es', 'NH4_infl', 'NH3_line3', 'Turb_rz',\n", + " 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter', 'TSS_efflPST',\n", + " 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST', 'TSS_efflRBT',\n", + " 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT', 'Cond_line1',\n", + " 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", + " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", + " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1',\n", + " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 18, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -538,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -549,18 +546,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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7HyQ/P59vvlnK9evXaNasOVZWVowZM5b333+X+vW96NSpC7t2befs2TPY2Oi/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\nMnjwYHbt2sWSJUsICwsDlHd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1/Tl4bb/O/W3LmWZe08lAWQghTCDCp73WD9HU3BRSciRH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\nfiH7OtAjCNG+Hewn+gBg8P9b2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GMNqUhXq8lYLvOg71uDsI88uyab1WOQSvAylmZ6kZhX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\nSQAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -598,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -607,7 +604,7 @@ "4895" ] }, - "execution_count": 20, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -618,14 +615,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 39.46857910106997%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 38.794057317612484%. This value is also saved in self.filling_error.\n" ] } ], @@ -639,14 +636,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 54.261283673154466%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 53.919222841147075%. This value is also saved in self.filling_error.\n" ] } ], @@ -688,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -701,17 +698,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:324: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:324: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n", - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:367: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:367: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", " 'filling of gaps in the data during rain events.')\n" ] }, { "data": { - "image/png": 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d++67j2+++YaWLVua2rt06cJLL73EoEGDrltDUlJ6+d5UNdawobPeD7E66vdi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HAAAg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\nu9DfRrwGBJ4CfC5wK3wuAIGncG/vUV17YIQQQkgbK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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -719,7 +716,7 @@ } ], "source": [ - "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],\n", + "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='index',\n", " plot=True)" ] }, @@ -733,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 76, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", @@ -745,7 +742,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py:1593: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1593: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", " 'representative average day and/or high standard deviations.')\n" ] } @@ -757,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 77, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", @@ -769,15 +766,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:675: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:674: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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Bl156ib59+142hrS0nBt7UzcxLy93PQ+xOxr3Ym805sUeadyLvdGYt+bl5W7r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vTs2RNpaS0LVBMcoyO/eFzhPqlftVMqlIITsgjvKFGybL7+8wte29tDl/MmojRF4//6v2TUdTBz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\ndjO6q+tLFb3zAIkEuB3FKVvEgr5shte2L9jjb3Vhj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\n", 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" ] }, "metadata": {}, @@ -799,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 78, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", @@ -811,8 +808,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " interactivity=interactivity, compiler=compiler, result=result)\n" + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: \n", + ".ix is deprecated. Please use\n", + ".loc for label based indexing or\n", + ".iloc for positional indexing\n", + "\n", + "See the documentation here:\n", + "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { @@ -823,7 +828,7 @@ " dtype='object')" ] }, - "execution_count": 26, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -840,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 79, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", @@ -852,15 +857,3333 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:811: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:810: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" ] }, { "data": { - "image/png": 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xMf32PlQ5Vru2q96HVDjq91LRqM9LRaR+LxWN+ry12rVdbR2ClFMxMTEMGzaM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\nAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -885,7 +4208,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 80, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.917107", @@ -897,10 +4220,10 @@ { "data": { "text/plain": [ - "(2.450642327196896, 0.672153214085126)" + "(2.4506423271968965, 0.6721532140851265)" ] }, - "execution_count": 28, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -919,7 +4242,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 81, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", @@ -931,7 +4254,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best ratio (2.53282188261064 ± 0.16586491872475553) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" + "Best ratio (2.5328218826106403 ± 0.16586491872475548) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" ] } ], @@ -948,7 +4271,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 82, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", @@ -960,15 +4283,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:454: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:453: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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UXaxmTVe9D6lwNO+lotGcl4pI814qGs15SzVrulo7BLlLJSYmMmzYML7//nsM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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -991,32 +4314,32 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "slope: 0.405512924986 intercept: 0 R2: 0.973774656376\n" + "slope: 0.4055129249855649 intercept: 0 R2: 0.9737746563763395\n" ] }, { "data": { "text/plain": [ - "(,\n", - " )" + "(
,\n", + " )" ] }, - "execution_count": 13, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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pyGQyLl36By+vWTRvbkrnzl24dOkfrWCkUinR0Xmy3427eTOPhIR4LCzuP+1N\nTSopKSE4OJDTp3/H3r4NQUGrsLVtJUnZNU2MGQk4ODiSn38LgNat7Wnd2h5b21Zs376Zf/65gJmZ\nGVZW1ly+fFFzjkqlIjo66p7Xa93aHl1dXSIjr2kd/+qrYzhx4gequlmsWNL8zrIALl26qPlDXtvu\nrp+DgyNJSYnY2Nhqvj86Ojps3PghmZnp972OiUlTnn/+P5w8+TO//HKCIUOGafZ98cUuRo0ay8KF\nSxk92oOnnupKamrKfQPi3SwsLLl5M09r27Ztm9mz5zMMDAywsChfjfP333+lZ8/eAOzf/xUjRw5B\noVBozklPT+PmzTzN9/1OJ078gKGhEc8+2+//vy8yzdLTCoWiUl1v3rxZa+n5DUF6ejpxcXGSvcia\nm5vD3LnenD79O926dWfDhs0NNhCBCEYC4O7ei06dnmLp0gVcvHiB5OQkVq0K5NSp33B0LB+XeO21\n1/n66684duwoycmJrFsXQkbGvf8QGxsbM2bMK2zfvoUzZ06RkpLM2rWruX07n6efdtd0G0ZHR3H7\n9m2tc+3sWvPCC4NZsyaYv/76k6SkRDZtWkt0dCTjxr1Wu9+I/3d3/caOfYWCgnyCgvyIi4slMvIa\ny5YtIiUlGXv7tlVea+jQ/3L8+DGuX0/lxRdf0my3tm7JpUv/EBMTTUpKMjt2bOPnn3+stNLx/XTo\n0InY2Gitbba2rdi9eydnzpzi+vVU1q8PISLiGm+++RYAzz7bj6KiIlauXE5SUiKXLv3D4sXz6dKl\nm9aLsVAebD75ZCvvvz9dq8zDhw8SGRnBhQvn6dz5Ka1zoqOjGmT3UE1ITk7kxo1UyTLmEhMTmDHD\nk6ioSAYPHsKKFSGazM6GSnTTPYBcXn4XWVqqR1lZqaRlSkUmk7Fy5Ro++mg9CxbMQS4vw9nZlbVr\nQzV3zK+8MgGVSsW2bZu5desmAwa8wHPP9b/vNT09Z6Cjo8PKlQEUFRXi5taRDz8MpUULC5o3N2XA\ngIH4+S1i1CiPStf54ANfNm/eyPLlSyguLsLFpbwud48j1ZZ27Ry06uftPYf16zezZcsmpk59E0ND\nI55+2p2AgFX37aqs4O7e6/8Xf+yJqem/XWQ+PvNZtSoQT8+3MDIypmPHTsybt4iQkBWkp9//aavC\ns88+x5o1K4mLi9Vk1A0fPoqcnGxCQlZSUJCPm1tHNm7coknPt7Nrzbp1H/Hxx6G8++6b6Onp0a/f\n80yf7lNRcyzRAAAgAElEQVTp+ocOHaB16zZ07dpds23mzDn4+/ty7NhRxo+foBV4bt26SXx8LIsX\n+z2w7o2JSqUiNjaG27cLJEtU+PvvcPz9l1JUVMjkye8wYcLrjWLqMLGERBXunJtO6mnupZqbrjFO\n3w+Ns113t2nJkgW0bGmjSQypS//73xf89tuvhIZue+hzG+rPSi6XExUViUIhr/S7am5uQl5ezc94\n8t13R9i4cS06OjrMnbuAgQNfrPEy7kcsIVGHZDKZZn44IyMjjIyq14UiCFKYMuVdfHymMWXKO1pp\n+1JTKBSEhZW/Y/WkKCoqIiamfMxUqoy5nTs/4auv9tK8uSn+/oGS9RRUUKvVyGSyWpuhRIwZCUID\n5ejoxMiRY9mz5/M6rce334bRo0dPrXesGrObN/OIioqQrLzS0lKCgvz56qu92Nm1ZsOGzZIHIpVK\nSfPmprRoUXsTB4snI0FowN56a2pdV6HK6YUam4yMDFJTUyRLVMjLy2PZskVERFzjqae64OcXSPPm\npg8+sQYpFEpatrSmdes2tfoUKIKRIAhCNSQnJ5KVlSXZ9DpJSYn4+i4gPT2NF14YxOzZ8zEwqPx+\nXG1SKJS0adMWK6vaT9kXwUgQBKEKarWa2NgYCgryJQtEFy78jb//EgoLbzNp0mQmTZosecacSqXC\n2dlZsicxEYwEQRDuQy6XEx0dhVxeJlnq9g8/fM+6dSHIZDLmz1/EoEEvPfikGqZWg5tbR83MHVIQ\nwUgQBOEeiouL/38xPGky5tRqNZ9/voO9e3fRrFkzli0LrNbKxDVdB319A9zcOkgWfCuIYCQIgnCX\n/PxbxMXFSja1T1lZKWvWrOKXX36iVSs7AgODsbdv8+ATa5BKpaRp0+a0b+9cJy/RimAkCIJwh6ys\nLJKTkyTLmLt16ybLlvly9eplOnbszPLlQVqzdUhBoVBgbW39wOmtapMIRoIgCP8vJSWJzMxMyRIV\nUlNTWLz4A27cuM6AAQOZN28BBgaPPsPBo1AolLRu3YaWLVtKWu7dRDASBOGJp1ariYuLpaDglmSB\n6NKli/j5LaagoIAJEybx5ptvSdYtWEGtVkuaMVcVEYwEQXiiKZVKIiMjkMvL0NGRpmvuxx9/YO3a\n1ajVaubM+YAhQ16WpNw7qdXg6tpB0oy5qohgJAjCE6ukpISoqEhALVnG3O7dn7F792eYmDRl2bIA\nund/utbLvbsOenr6uLq6PXDWeSmJYCQIwhNJ+oy5MtauXc1PP/2IjY0NgYGraNu2nSRlV1AqlTRr\nVncZc1URwUgQhCeO1Blz+fn5+Pn5cvnyRTp06Ii//wrMzc0ffGINUiqVWFpaata3qm9EMBIE4Yly\n/XoK6ekZkgWi69dT8fVdQGpqCs8/P4D58xdhaFgXGXP2dZ4xVxURjARBeCJoZ8xJE4iuXLnMsmWL\nyc+/xauvTmTKlHckz5hTqVQ4OTlhZibtk9jDEsFIEIRGT6lUEhUVQWlpqWQZcz//fII1a4JRKlX4\n+Mzl5ZeHS1Lu3VxdO9CkSZM6KfthSL64XmxsLK6urpX+hYeHA3Dq1ClGjhxJly5dGD58OCdPntQ6\nPycnB29vb9zd3enTpw8hISEoFAqpmyEIQgNRUlLClSuXkcvlkjyVqNVqPv30U1auDEBf34AVK1ZJ\nHojUajW6unp07Ni5QQQiqIMno+joaMzNzTl8+LDWdjMzM2JjY/H09GTatGkMHjyYw4cP4+XlRVhY\nGM7OzgDMmDEDmUzGnj17yMjIYMGCBejp6eHj4yN1UwRBqOfy8/OJj4+VLHNMLpezfv0ajh8/hrV1\nSwIDg3FwcJSk7ApKpZKmTZvRvr2z5F2Cj0PymkZHR9O+fXusrKy0/unr67Nr1y66deuGp6cnTk5O\nzJo1i+7du7Nr1y4ALly4wPnz5wkODsbNzY3+/fszf/58du/eTVlZmdRNEQShHsvOziYmJlqyQFRQ\nUMCiRfM4fvwYHTt2ZNOmLXUSiCwtLXFxcW1QgQjqIBjFxMTg6HjvH1B4eDi9evXS2ta7d29NF154\neDh2dnbY29tr9vfq1YvCwkIiIqRbk14QhPrtxo1UkpISJEtUSEu7gbf3NP755wJ9+z7Htm3baNHC\nQpKyKygUSlq1al1vU7cfpE6C0Y0bN3jllVfo27cvkydP5tKlSwCkp6dXSj20trYmPT0dKF9/3tra\nutJ+gLS0NAlqLwhCfVa+KmssGRnpks0xd+3aVWbO9CQlJRkPj/EsWeKPkZGRJGVXqMiYs7GxkbTc\nmiTpmFFJSQkpKSm0aNGC+fPL13Pfs2cPr7/+OmFhYZSUlFRa493AwIDS0lKgfLGru/Pz9fX1kclk\nmmPux9y8yWPfJVlZNXus8+sr0a6GozG2CWqmXUqlkoiICPT0FFhYNK+BWj3YiRMnWLZsGQqFggUL\nFuDh4aHZZ25uIkkdANzc3DAxkaa82voMShqMjIyMOHfuHAYGBpqgExwczNWrV/niiy8wNDRELpdr\nnVNWVqaZyM/IyKjS2JBcLketVj8wYyQvr+ix6m5l1YysrILHukZ9JNrVcDTGNkHNtOvOOeakoFar\n2bfvSz799GOaNGnCsmUB9OzZm7y8QqA8EFX8vzbroKurh5tbB4qKVBQV1f5n43F/VlUFMsmz6Zo2\nbar1tY6ODu3btyctLQ1bW1syMzO19mdmZmq67mxsbCqlelccX5/fLBYEofYUFBQQGxuDjo40iQoK\nhYKNG9fy/fdHsbKyIjBwFY6OTpKUXUGpVGJi0hRnZ5cGl6hwP5K24sqVKzz99NNcuXJFs618+vZI\nnJ2d6dGjB+fOndM65+zZs7i7uwPQo0cPUlJStMaHzp49i4mJCW5ubtI0QhCEeiMnpzxjTqpAVFh4\nm8WL5/P990dp396ZjRu31kkgatHCAldXt0YTiEDiYOTm5oadnR1Lly7l4sWLxMTEsHDhQvLy8njj\njTd4/fXXCQ8PZ+PGjcTFxbFhwwYuXrzIm2++CUD37t3p1q0bPj4+XL16lZMnTxISEsKUKVMqjTUJ\ngtC43biRSmJiArq60vwZS09Pw9vbi7//Ps8zzzzL2rUbsbS0lKTsCgqFEltbO9q1c5C0XClIGoz0\n9PT45JNPcHBw4P3332fcuHFkZ2ezZ88eLCwscHV1JTQ0lB9++IFRo0bx888/s3XrVpycyu88ZDIZ\noaGhWFhYMHHiRBYtWsS4cePw8vKSshmCINQhtVpNfHwc6enSZcxFRkYwc+Y0kpISGT3aAz+/QIyN\npZ3ZQKlU4ujoiK2traTlSkWmVqulGfGrY487QCoGjxuWxtiuxtgmeLh2qVQqoqMjKS4ulqyL6vff\nf2PVqkDkcjmentMZNWrsA8+p6QSG8uXBXSXLmLufRpXAIAiC8CjKysqIjIxApVJKNsfc11/vY/v2\nrRgaGuHvH8Qzzzxb6+XeXQcdHV06dOjQ6IciRDASBKHeKygoIC4uBplMJsn0Pkqlgk2bNnD06LdY\nWFgSGLiS9u1dar3cO6lUKpo0aYKzc8Ob2udRiGAkCEK9lpubQ0KCdFP7FBYWEhTkx7lzf+Ho2J7A\nwJVYWVk/+MQapFQqMTdvIfncdnVJBCNBEOqtGzdukJZ2Q7JAlJmZia/vByQkxNOr1zMsXrxM8iUY\nyjPmWtGqVStJy61rIhgJglDvqNVqEhMTyMvLlSwQRUdHsWTJQnJzcxgxYjTTpk1HV1faP5FKpQoH\nBwfJJ1mtD0QwEgShXlGpVMTERFFUVISurjSB6I8/TrNy5XJKS0vx9JzO6NEeki098S81Li6ulWap\neVKIYCQIQr1RVlZGVFQkSqVCsoy5sLBv2Lo1FENDQ/z8Ann22X61Xu7dddDR0cXNrfFnzFVFBCNB\nEOqFwsJCYmKiJM2Y27IllEOHwmjRogXLl6/E1VXaacWetIy5qohgJAhCncvNzSExMVGyqX2KiooI\nCvLnr7/+pF07B4KCVmFtLe1kyxUZc+3aOdRBl2D9I4KRIAh16vr165KmbmdlZeLru5D4+Fh69OjJ\nkiX+ks9soFQqsbF58jLmqiKCkSAIdSYxMQG1ukSyQBQbG4Ov7wJycrIZNmwE06d7Sza/XQWFQkm7\ndu2wsJB2ktX6TgQjQRAkV54xF01h4W0sLaVZlfXs2TMEBvpTWlrC1KmeeHiMl7x7TK0uz5hr1qxx\nrtj7OEQwEgRBUnfOMSdV6vahQ2Fs3rwRfX19lixZznPPPS9JuXeSyXSeiDnmHpUIRoIgSEb6jDkl\n27Zt4cCB/ZiZmRMQsBI3tw61Xu6dVCoVxsbGuLg0rsXwapoIRoIgSOLmzTzi4+MkexoqLi5m5coA\nzpw5Tdu27QgMDMbGRtq1gJRKJWZm5jg4OIqMuQcQwUgQhFqXnp7O9eupkiUqZGdns3TpQmJioune\nvQdLl/rTtKm04zQKhQJbW1tatWotabkNlQhGgiDUqsTEBHJzcyQLRPHxcfj6LiArK5MhQ4bh7T1b\n8ow5pVJJu3YOImPuIYhgJAhCrbgzY06qrrlz584SGOhHUVERb789lfHjJ9RJxlyHDh0oKZG02AZP\nBCNBEGqcXC4nMjICpVIhWSA6cuRbNm1aj66uLr6+y+jff6Ak5d5JJtPB1bU8dbukpPEtEV+bRDAS\nBKFGFRUVERMTBSDJU4lKpeKTTz5m//6vMDU1ZfnyFXTs2LnWy727DkZGRri4uEkWfBsbEYwEQagx\nN2/mkZAQL1kKc0lJCatWBXHq1G/Y27chKGgVtrbSTrGjUilp1swMJycnkTH3GEQwEgShRmRkZJCa\nmiJZokJubg5Lly4iKiqSrl27sWxZoOQzGygUCmxsbLCzs5e03MZIBCNBEB5bcnIi2dnZkgWixMQE\nfH0XkJGRzqBBQ/DxmYu+vr4kZVdQKJS0adMOKysrScttrEQwEgThkanVamJiorl9u0CysZK//w7H\n338pRUWFTJ78NhMmTKqTjDlnZxeaN5dmXr0ngQhGgiA8ErlcTlRUJAqFXLJA9N13R9i4cS06Ojos\nXOjLwIGDJClXmww3tw4YGRnVQdmNlwhGgiA8tOLiYqKiIpHJpMuY27nzE776ai/Nm5vi7x9I585d\nar3cu+tgaGiIq2sHkTFXC0QwEgThody6dZO4uFjJ/iCXlpayevUKfvvtV+zsWhMYuIrWraWdYqc8\nY84UJ6f2ImOulohgJAhCtUmdMZeXl8eyZYuIiLjGU091wc8vkObNTSUpu4JCocTGpqXImKtlIhgJ\nglAtKSlJZGZmSjbPW1JSIr6+C0hPT+OFFwYxe/Z8ydcCKs+Yaysy5iQggpEgCFVSq9XExsZQUHBL\nskD0zz9/4++/hNu3bzNp0mQmTZosefeYSqXC2dlZ8iexJ5UIRoIg3JdSqSQyMgK5vAxdXWn+XPzw\nw/esWxeCTCZj/vxFDBr0kiTlapPh5tYRY2PjOij7ySSCkSAI9yR1xpxarebzz3ewd+8umjVrxrJl\ngXTt2q3Wy727Dvr6Bri5iYw5qYlgJAhCJfn5t4iLi5VsjrmyslLWrFnFL7/8hK1tK4KCVmFv30aS\nsiuoVEqaNm1O+/bOImOuDtTpguz//PMPHTt25OzZs5ptp06dYuTIkXTp0oXhw4dz8uRJrXNycnLw\n9vbG3d2dPn36EBISgkKhkLrqgtBoZWVlERsrXSC6efMm8+fP4ZdffqJjx85s3LhF8kCkUCiwtLTC\n2dlFBKI68sBPm1qt5syZMxw8eJCrV6/e85jc3Fz27dv3UAUXFRUxf/58lEqlZltsbCyenp4MGTKE\nsLAwXnjhBby8vIiJidEcM2PGDLKzs9mzZw/BwcEcOHCATZs2PVTZgiDcW0pKEsnJSejqShOIUlNT\nmDx5MlevXmbAgIGEhKzFzMxMkrIrKBRKWrdug719W0nLFbRV+YkrLCzktdde46233mLBggV4eHjw\n/vvvc/PmTa3jUlJS8PPze6iCg4ODadmypda2Xbt20a1bNzw9PXFycmLWrFl0796dXbt2AXDhwgXO\nnz9PcHAwbm5u9O/fn/nz57N7927KysoeqnxBEP5VkTGXnZ0l2TtEly5dZOZMT1JTU5kwYRILFy7B\nwMBQkrIrlM8x51zpb5EgvSqDUWhoKAkJCaxZs4aDBw/i6enJH3/8waRJk8jNzX3kQk+ePMmvv/6K\nr6+v1vbw8HB69eqlta13796Eh4dr9tvZ2WFv/+/LZ7169aKwsJCIiIhHro8gPMmUSiUREVe5fbsA\nHR1pAtGJE8f54IPZFBUVsXTpUqZMeUeybsEKajW4unYQqdv1RJU//Z9++omZM2cybNgw3NzcmDlz\nJjt27OD69eu89957lDzCIu+5ubksXryYwMBATE21PwTp6emV7lCsra1JT08Hyt/+tra2rrQfIC0t\n7aHrIghPupKSEq5cuYxcLpcsY2737s9YtSoIQ0MjVq5cw4gRI2q93LvroKenT6dOnUXqdj1SZTZd\nVlYWTk5OWtvc3d3ZtGkT7733Hj4+PmzevPmhCly2bBkDBw7k+eef1wSZCiUlJZXesDYwMKC0tBQo\nTzU1NNR+jNfX10cmk2mOuR9z8yaP3f1gZSXtwl1SEe1qOGqyTbdu3SIxMRFz8yY1ds2qlJWVERAQ\nwPfff0+rVq3YsGEDDg4OpKYaMO8DF+LjTHB0KiRkVSKtW9dOt7tSqaR58+a4urrWevBtjJ8/qL12\nVRmMWrVqxaVLl3jmmWe0tvft25eFCxcSEBBAQEAAI0eOrFZhYWFhXLt2jW+//fae+w0NDZHL5Vrb\nysrKNHcvRkZGlcaG5HI5arWaJk2q/oXKyyuqVh3vx8qqGVlZBY91jfpItKvhqMk2ZWVlkZycJNn4\nUH5+Pn5+vly+fBE3t44sX74CMzNz8vIKmfeBC/otkxk8OIGkiw7MntuGj7dcrPE6KBQKrKyssLCw\nIzv7do1f/06N8fMHj9+uqgJZlcFo5MiRbNmyBT09PQYOHEi7du00+yZOnEhSUhK7du3in3/+qVZF\nDhw4QEZGBv369QPKH5cB3n33XUaNGoWtrS2ZmZla52RmZmq67mxsbCqlelccLwYgBaF6rl9PIT09\nXbKpfa5fT8XXdwGpqSk891x/PvhgsVYPR3ycCYMHJ6Crr6Rt1wSO/9GhxutQkTEn/k7UX1V+GidP\nnkxycjKrV68mNTWVpUuXau1ftGgRBgYGfPrpp9UqbM2aNVrjTFlZWUycOJHAwED69u3L+vXrOXfu\nnNY5Z8+exd3dHYAePXqwZs0a0tLSsLW11ew3MTHBzc2tWnUQhCeVWq0mLi6OgoKbkgWiK1cus2zZ\nYvLzbzF+/ATeeuvdSokKjk6FJF10oG3X8iejtu1q9olCpVLRvn17TE2lTRkXHk6Vn0gDAwMCAwPx\n9vamqOje3Vxz585l8ODB/PDDDw8s7O67koq7o5YtW2JhYcHrr7/O2LFj2bhxI8OGDePIkSNcvHhR\nkzbevXt3unXrho+PD0uWLCE7O5uQkBCmTJki+Wy+gtCQKJVKoqIiKC0tlSxj7uefT7BmTTBKpQof\nn7m8/PLwex4XsiqR2XPbcPyPDrRtV4Df0qgaq4NarcbVtcMDu/GFulet26O7p09XKBTk5eVhbm6O\nnp4eXbp0oUuXx1910dXVldDQUEJCQti+fTuOjo5s3bpVk0Qhk8kIDQ3Fz8+PiRMnYmJiwrhx4/Dy\n8nrssgWhsSopKSE6Ogq1WiVJ+rRareaLL/bw2Wef0KSJCQEB/vTo0fO+x7duXVbjY0QVGXOurm7o\n6+vX6LWF2iFTVwzcVENkZCQffvghZ8+eRaFQsH//fvbs2UO7du147733arOej+1xBxPFgGTD0hjb\n9ShtKigoIDY2Bh0daaa4kcvlrF+/huPHj2Ft3ZLAwGAcHByrPMfc3IS8vMIaq4NSqaRp02Z1OrVP\nY/z8Qe0mMFT7NunKlSu8+uqrpKSkMGHCBE3ygampKevXr2f//v2PXEFBEGpednY2MTHRkgWigoIC\nFi2ax/Hjx3BxcWXTpi0PDEQ1TalUYmlpiYtL7aduCzWr2qOYa9asoUuXLuzcuRO1Ws1nn30GwIIF\nCygsLGTv3r2MGzeutuopCMJDuHEjlbS0NMkSFdLSbrB48QekpCTTt+9zLFjgi5GRkSRlVyjPmLMX\nGXMNVLWfjC5evMibb76Jrq5upTuOl19+maSkpBqvnCAID0etVhMfHydp6va1a1eZOdOTlJRkPDzG\ns2SJv+SBSKVS4eTkJAJRA1btT6uent59l2ooKCgQg4SCUMeUSiUxMVEUFxdLtjDcyZO/sHr1ChQK\nBTNn+jB8+ChJyr2bi4sbJiYmdVK2UDOqHYx69+7Nli1b6NOnj+aHLpPJUCgU7N69W/MukCAI0isr\nKyMyMkLSjLl9+77k008/xtjYmGXLAujV65kHn1jDddDV1cPNrYO4GW4Eqh2M5syZw/jx4xk0aBA9\ne/ZEJpOxdetWYmNjSU9P56uvvqrNegqCcB8FBQXExcVINmCvUCjYuHEt339/FCsrKwICgnFyai9J\n2RWUSiUmJk1xdnaRfLZvoXZU+6fo4ODAN998Q//+/fnnn3/Q1dXl3LlzODs787///Q8XF5farKcg\nCPeQk5NNdHSUZIGosPA2ixfP5/vvj9K+vTMbN26tk0DUooUFrq5uIhA1Ig81wmlvb8/q1atrqy6C\nIDyEGzeuk56eJtlkp+npafj6LiApKZFnnnmWRYuWYGws7cwGCoWSVq3sNNOBCY3HQ6fbZGZmUlxc\njEqlqrTPwcGhRiolCML9qdVqEhLiuXkzT7JEhaioCJYsWUReXi6jR3vw3nvTJCu7glKpwtHREXPz\nFpKWK0ij2sEoISGBuXPncu3atfseI1ZbFYTapVKpiI6OlDRj7tSp3wgODkQul+PlNZNRo8ZKUu6d\nyueYExlzjVm1g1FQUBCpqalMnz4dGxsb0VcrCBIrKyvjypXLqFRKyTLmvv76f2zfvgVDQyP8/YN4\n5plna73cu+ugq6uHq6ubmAy5kat2MAoPD2f58uWSLxEsCALcvn2bpKTyyU6lSFZQKhWEhm7gyJFv\nsbCwJDBwJe3bS5ukVJ4xZ4Kzs6u4+X0CVDsYGRsbY2FhUZt1EQThHnJzc0hMTMTSUpplrAsLCwkK\n8uPcub9wdGxPYOBKrKysJSm7gkKhoEULC8nnthPqTrVvN4YOHcqBAwdqsy6CINzlxo0bJCQkoKsr\nzZNBZmYmPj7TOXfuL3r1eoZ16zbVQSBSYmtrJwLRE6baT0ZOTk5s2LCBcePG0a1bN4yNjbX2y2Qy\nfHx8aryCgvCkSkiIJy8vV7LU7ejoKJYsWUhubg7Dh4/Cy2sGurrSzG9XQalU4eDgQIsWohfmSVPt\nT1pAQAAAly9f5vLly5X2i2AkCDVDpVIRExNFUVGRZBlzf/xxmpUrl1NaWoqn53RGj/aogyUY1Li4\nuNK0aVOJyxXqg2oHo8jIyNqshyAIlGfMRUVFolQqJBu0P3Dga7ZuDcXQ0BA/v0CefbafJOVWUKvV\n6Ojoioy5J5xIUREqSUyUMWCgIa1amTBgoCGJiWKRMikUFhZy7doVVCqlhBlz69myZRPm5uasWbNB\n8kCkUqkwNjamU6fOIhA94ap8MpozZw6zZs3C3t6eOXPmPPBiH374YY1VTKg7k98ygBYxDPJMIOmi\nA5PfcubXn0vrulqNWl5erqSJCsXFRQQFLefs2TO0a+dAUNAqrK2lXQtIqVRibt6Cjh07kp19W9Ky\nhfqnymB04cIFCgsLNf+viljit/GIjtRjkGcCuvpK2nZN4MctHQARjGpLWloaN25clyxRITs7C1/f\nBcTFxdKjR0+WLPGXfGYDpVKJjU0rWrVqJf52CMADgtHPP/98z/8LjZuLm4Kkiw607Vr+ZOTidu9F\nFYXHl5iYQG5ujmSBKDY2hiVLFpKdncWwYcOZPn2WZCvCVlAolCJjTqhEjBkJlXy2owxynflxy8uQ\n61z+tVCjVCoVUVGR5ObmSJYxd/bsGXx8ZpCdncXUqZ54e8+RPBCp1eUZcyIQCXer8pP46quvPtTF\nxAJ7jUO7dur/HyMSXXO1QS6XExkZgVKpkCwQHToUxubNG9HT02Pp0uU891x/ScqtUJEx16FDB5Go\nINxTlcFILOUrCDWrsLCQ2NhoQJpxVqVSybZtWzhwYD9mZuYsX76CDh061nq5d6rImHNxEYvhCfdX\nZTDavXv3Q1/w9u3bRERE0LNnz0eulCA0Rjdv5pGQEC/ZH+Ti4mJWrgzgzJnTtG3bjsDAYGxspF2U\nTqlUYmZmjoODo0hUEKpU478VcXFxvPHGGzV9WeExiXeH6lZ6ejrx8dIFopycHObMmcmZM6fp3r0H\n69eHSh6IFAoFNjY2ODo6iUAkPJB4Zn5C/Pvu0HfQIqb8a0ESycmJ3LiRKtk7RAkJccyY8T4xMdEM\nGTKMFStW07SpNDN+V1AqVbRt60CrVq0lLVdouEQwekJER+rRtuu/7w5FR0qbRfUkKl+VNYrs7GzJ\nEhXOnfuLWbOmk5WVydtvT2X27Hl1kjHn7OyCpaWlpOUKDZv4i/SEEO8OSUsulxMVFYlCIZcsEB05\n8i2bNq1HV1cXX99l9O8/UJJytclwc+uAkZFRHZQtNGTiyegJId4dkk5RURHXrl1BqVRIMlaiUqnY\ntm0LGzZ8SLNmTQkJWSd5IFKpVBgYGNC581MiEAmPRDwZPSHEu0PSkDpjrqSkhFWrgjh16jfs7dsQ\nGBhMq1Z2kpRdQaVS0qyZGU5OIlFBeHQiGAlCDcnIyCA1NUWyqX1yc3NYunQRUVGRdO3ajWXLAmnW\nTNpEhYqMOTs7e0nLFRofEYwEoQYkJyeSlZUlWbJAYmICvr4LyMhIZ9CgIfj4zJX8JXWFQknbtg4i\nUUGoESIYCcJjUKvVxMbGUFCQL1kg+vvvcPz9l1JUVMjkyW8zYcIkybvHKjLmmjdvLmm5QuNV7Y7t\nc98osO4AACAASURBVOfOaZaTuFt+fj5Hjx4FoEWLFowaNeq+10lPT2fmzJn06tULd3d3fHx8yMjI\n0Ow/deoUI0eOpEuXLgwfPpyTJ09qnZ+Tk4O3tzfu7u706dOHkJAQFAqRGSZITy6Xc/XqFQoLb0uW\nMff990dZtGg+cnkZCxf6MnHiG3UwTiPDza2jCERCjap2MHrjjTeIi4u7575r166xcOFCAOzt7Vm5\ncuU9j1Or1UydOpX8/Hx27drFnj17yMrKwtPTE4DY2Fg8PT0ZMmQIYWFhvPDCC3h5eRETE6O5xowZ\nM8jOzmbPnj0EBwdz4MABNm3aVO0GC0JNKC4u5upVaTPmQkNDWbt2NSYmTVm9ei0DBw6q9XLvroO+\nvr7ImBNqRZX9CvPnzyc9PR0oDyR+fn40bdq00nGJiYnV6jfOzs7GycmJOXPm0Lp1+ZvZkydPxsvL\ni1u3brFr1y66deumCU6zZs3i/Pnz7Nq1i4CAAC5cuMD58+c5ceIE9vb2uLm5MX/+fAICAvDy8hKz\nAQuSyM+/RVxcrGQZc6WlpaxevYLffvsVO7vWBAau0vz+SKU8Y84UJ6f2ImNOqBVV/ja9+OKLlJaW\nUlpaikwmQy6Xa76u+CeXy+nYsSNBQUEPLMzKyop169ZpfpHS09PZt28fTz31FKampoSHh9OrVy+t\nc3r37k14eDgA4eHh2NnZYW//b+ZOr169KCwsJCIi4qEbLwgPKyMjg5iYGMkCUV5eHvPmzeK3336l\ne/fubNy4WfJApFAosbZuSfv2ziIQCbWmyiejwYMHM3jwYAAGDhxISEgIbm5uNVLwtGnT+OmnnzA1\nNWXXrl1AeXBq2bKl1nHW1taap7OMjAysra0r7YfypZu7du1aI3UThHtJSUkiMzNTskSFpKREfH0X\nkJ6exgsvDCIgwJ/CQrkkZVdQKJS0adMWKysrScsVnjzV/q26c9nxuLg4CgoKMDc3p23bto9UsLe3\nN++//z6bN29mypQpHDx4kJKSkkpdbQYGBpSWlr+oWVxcjKGhodZ+fX19ZDKZ5pj7MTdv8tjvf1hZ\nSfsOh1REu6qmVquJjo5GoSjCysq0Rq75IOHh4cybN4+CggLeffddpk6dikwmk7QrWqVS4eLigqlp\n7be5MX4GG2OboPba9VC3eN999x3BwcFkZWVptllbWzN37lyGDx/+UAW7uroCsG7dOgYMGEBYWBiG\nhobI5dp3fmVlZRgbGwNgZGREWZn2NDZyuRy1Wk2TJk2qLC8vr+ih6nc3K6tmZGUVPNY16iPRrqop\nlUoiIyOQy8sk66L64YfvWbcuBJlM9n/snXt8VOW197/7NpdcEQghIQkZSCaJCiFyCci1WgViW2x7\n1CpQre2pIrbW+lpPa23p9bSH1lMritrWt/Vufau1KqBW5RZCIFzCNZkkTBJCQhJumYTMZGbv2e8f\nO9nJ5EaABBHm9/n48UP2nmc/z8zez9prrd/6LX7wgx9yww0LOHWqlSuuiOTkyd4ZrYMPAaczA79f\nHPL741K8By/FNcH5r6s/QzZgY7Rx40YeeughsrOzWbZsGXFxcdTX1/POO+/wgx/8gGHDhjF79ux+\nxzh27BiFhYXcdNNN5t/sdjvJycnU19eTkJBAQ0NDyGcaGhrM0N3o0aN7UL07zu8e3gsjjPOF1+vF\n5SoF9AtiiHRd529/e56XX36B6OhofvrTX5KdPWnIr9t9DhaLhYyMrAtGVw8jDDgLY/T000/zuc99\njqeffjrk74sXL+a+++7j2WefPaMxqq2t5fvf/z4pKSlMmDABgObmZtxuN1/+8pdRVZXt27eHfKaw\nsJApU6YAMHnyZH73u99RV1dHQkKCeTwyMnLQcllhhAEXnjHn97fxu9/9lk8++YiEhER+9avfkpyc\nckGu3YEwYy6MTxMDftIOHjzIbbfd1uux2267jQMHDpxxjKuvvpopU6bw4x//mD179nDgwAG+973v\nmYWyS5YsoaioiD/+8Y9UVFTwxBNPUFxczJ133glATk4OkyZN4sEHH2T//v1s2LCBlStX8o1vfCNM\n6w5j0NDY2HhBGXNNTad45JGH+OSTj7jyyqv54x9XX3BDpKoqI0fGhRlzYXxqGPDTFhsbS2tr73mX\n06dPD8ilF0WRJ598kqysLO655x6WLFlCZGQkL730EpGRkWRkZLBq1Sref/99br75Zj7++GOeeeYZ\nxo8fD4AgCKxatYoRI0awePFifvSjH3HLLbewfPnygS4jjDD6xZEjh6murhpUsdO6Ohv3LMtmwcJZ\n3LMsm7q6zoLRmprDfPe797Fv317mzbuOlSsfZ9iwYYN27YHAYMylkpx8bmSkMD77qKwUmHedlcTE\nSOZdZ6Wy8sK/kAi6rusDOfHBBx+koqKCl156KUQGxOPxsHjxYpKSkli9evWQTfR8cb7JxHBC8rOF\ns12XrutUVJTT3NyEKA5uruSeZdnYEmvMxoa+2iSeXV3Mnj3FrFjxY5qbPdx++xLuuuub/XpjQ0Fg\nCAaDjB+fRkzMhWEJ9oZL8R78rK1p3nVWGF5m3qOcSG9vOROKi4LA8P3vf5+vfOUrfP7zn2f27NmM\nHDmSY8eOsWnTJjRN4/HHHz/nCYYRxqcJTdMoLT1IW1vbeRmiujobK36eQVVlNGNTm1nxk1IA3BXR\nLFjY2fL9gy1Z/PvfH/D73/8WXdd56KEfsGDBTWcc64orzmuZPaDrkJl5pclWDePyhatE5oZlnffo\nh6uzuNC9zwbsGYFRX7Rq1Sq2bdtGU1MTsbGx5Obmsnz5cjOUdrEi7Bn1jst9XT6fj9LSEmDAj0Gf\n6M0DAjjaKJMysRLHJDeHdqZSt+8VPE3/DcRisb2K6p+P1R6gzaeQ6jAMz4qfZ5hjuXc5KC904hjn\n47FHD5KQ4Duveeq6jqJYcDozhqTtRGWlwF13W3CVyDgzVf76vJ/U1L6/30vxHvysreli8IzOyhj1\nh6NHjzJ69OjBGGpIEDZGveNyXpfH4+HQofJzTtjX1dl45L+yaGy0oaoSsqxxzRe2I0pBdrw7lUCb\nAjpEj2im1ROBFlARhG+h66+gWMeQ+x8/4ljVXOrKEklIr6WuLJG4lEaOHBiLr1VhztL1FH+Qg6cx\nBkEM4pjkJngynmdXF/c7p+4eVVfjpWka0dExAyIqnMmo9HV8oBtbBy7Fe/CztqaBvkAMpTEaMIEh\nKyuLPXv29HqsqKiIhQsXnv3MwgjjU4LBmHP1uyEXF8fypZtzufHGWXzp5lyKi0PzKg/9nytpaLSh\nAwIgWwPseHcqhf+4FllRiR7ejCCApspk3/gRonQDuv4KkMvsJf/NsPgxOHLcNB+LMf/fWB3H2Jxy\nYkY1sf3tXEan1zL/vrWk57porI6jqrL/6vcOj+rGZWuwJdaw4udGcXldnY177s3mpi/M5T/vmUBV\n1Zkf/bvutsDwMm5YtgaGlxn/HsBxV4nM2OzOkI+rJNw27WJHaqrO+o/bqK09zfqP2/r1ZIcK/d4l\nf/nLX/B6vYDh2r/xxhts3Lixx3m7du0KU6vD+MzgyJHDHD16tF+Nubo6G//16JWkTXXhyDFCZf/1\noytJSPBSeySKxDEtnDxpxRrhN0Nw7l0Oyrc5mXrzVjyNsVQVp7Lg/jWUbpHY+d4DQBnwVSTleWpL\nD5vjRo/04N7lIHJYCy0nonAVZBI1vJnWJjuOScam7shxU7olC8e4/t9KqyqjubFbfgpgxc8ysI2p\n4cY8w1u56+7+vRU4cx6hr+POTJWqYofpGTnGqcy7zjrgsN3lhLMNaV7K6NcY+Xw+Vq1aBRi06jfe\neKPX8+x2O/fff//gzy6MMAYRBmOugubmU2cUO13x8wxUv0T8uHoK3phphsrqjylEjWiipjoWUdZo\n9diocyXi2pJJ9EgPakBi97rJXP+tDynNz2Ltk7EIws3ACWJH/yctx58AZErzsygrdAKgBSRajkch\nCBDUJGLimhiR3EirJwL3LodptCRFM0kRfWFsanOIIRib2kwwGKS6Kpob8s4uQd3dqDgz1QEd/+vz\nfu66O50PV2fhzFSNbNzwMm5YNnBDeLmg07sMfzdnzBn5/X50XSc7O5uXXnqJiRMnhhwXRfGCqRif\nD8I5o95xodZ1od8Au69L0zRcrhJ8Pp9Jn66rs/FfP7yS+gYrQVXCag/w4AOHeOmVMdTW2gmqEpJF\nJcF5BE/DMJrqY5EVjbRcF/Hj6tn+di5ejx17jJepiwqpPxRPRVEagTaFzFkHOVS0Hb/3XkBFkFYh\nid/E0sWTKi1wUlXsMK+TMqGSjBku3LscHClJouV4NJISQFMVJFlD9UuMS+uZB+qK7jmjnz5Wwty5\nqeR94Yoz5nG6/0a/+kWARx9Tzjpn1B2JiZHcsGwNvtNWdr43BU9DLFlXdZ5/KT5bA11Tx3cjKRpa\nQOLD1XnU1l4o/cGzx0VBYDhy5AijRo0aEvbNhUDYGPWOC7Wus01qny+6rqsvxtw9y7JDmG7uXQ7K\nCp2Ios74qWWmR1K+zYmmSqZBiI1vouVEFOm5nSG8skInUcNbaGqIRZJU4L/R1BVADBHD/i9ez82g\ngyDqzL9vLZKisenlOSSk15pj1JUlMnvxRrSAxLpVedhjvAR8CroukDLRTcYMF2VbnRw5MBZ/m9Ir\nQaEDuq4jywoZGZkoijIgwzFUv1HHuEdKE831dh3/Uny2BrqmC/1cnC8+dQKD1+tl+/bt/OxnP+Oe\ne+7hnnvu4Sc/+QnvvffeGVs3hBEGfHpJ7ebmZg4ePEBv1O2qymh8LTYzLxM/rh4ANSDjyOnM1Wiq\nxILla0ib5kKUNFpPRaIFJMq3OVn3VB51ZYloAYkRyY1IshdN/Xa7IUpBlNeTcnUGC5avwR7jxRbl\nw73bQfOxaDM/VPDGTOLH1eNpjEULSLh3OxBljamLCgn4ZdSARMYMF5KimQSH7gSFrtA0jYiISK68\n8irz5XEgCeqh+o3++rwfTqTjaYg1v9cwscFAx3fz4eo8OJFu/PsyxRk9ow0bNvCjH/2I48ePI4qi\nKVXS1NSEpmmMGjWK3/72t8yYMeOCTPhcEfaMesel7BmVlLipqqpi374reOynmfhaFRSLSlAXUAMG\nFVsNSEiKhq5jhMsUDSDE6zlSksT4KS72fTwJLSBhj/EiKSpjMmsMYkGBk+o9DlS/B4T/AH09MAV7\n9Gv4TqeyYLnhCTUfi2bjy7ORRNBUCVuUF9mi0nIyGllRjTloErYoH5KsMiarhvJCJ4iQOtHNmKwa\nNr44jwX3d4Z1Plidx7q1m811a5rGyJEjSUlJPevvbKh/o77G73oPXioJ/fB+0ffn+0K/ntGePXtY\nvnw5CQkJPPvss+zdu5ctW7awZcsWduzYwTPPPMPo0aO59957cblc5zzBMC59XIg3wK76Wlde7eX5\n51v48ldn8PDDExBklak3b0UH0qa5WHj/GuLGHUVWNIKqhCCAKBsbPDpUFafy/tMLqd6bSlurhX0f\nTSJtmosF968hZUIlLcejzbf844fjSJn4EZFXTAJ9PVHDr+OGex9h7CQfilXFvduBFpCoPxSPJEFa\nrjHO2OxKvJ4Ioq5oJnlCJYKoo+sCfq+F5uPRlG9zcs0XtpM+zcWRA2PJf3U29hivOZ57l0FQ6ICq\naiQmJp2TIYKh/40GMv6Z6OTni4tBgy2M3tGvZ/Td736XmpoaXn/99T5zRaqqcscddzB27FhWrlw5\nZBM9X4Q9o95xqayrY5PxthpstJEpjVQVO0ib1unhlBc60TQj5CYpGutW5XUe3+2gek8qis1PQnot\nFUVpTP7Cdg5uuoqmhlhESSNyWCunT0YRPdITkjNa++RwBGERun4M+D4Lls+hzRvBzvem0FQfa3pb\nQVVClDVm3b6J6JHNRm7oqTzs0Yan1Xw8GsUaYHR6LUfLElH9Mjd9713TAwKYdUdnIawoBvnNfx/g\n6WdSqaqMJi29jRf/pn3mPImu9+BQJ/QvlId+qTxX3fGpeUY7d+7kzjvv7Je0IMsyt912G0VFRec8\nwTDCOF/cdbeFlEkuFixfw4hkwxCpAYnD+5P5959uoCTfqLcRRY3SAicb/jYPNSB15oYmufG12Ghq\niDUYcT6FHe9OJSG9loX3r8EW6WdMZg3z71tLQnot6FBZ7OD9p5uB69H1E4jyHxGElXz4XB6bXprD\niORGokc0I4o66e3eUHqui+1v56IFJCp3O4iJa8LXYqOl3RAF2hSOliWSfHUV9mifeZ7FGiCowfa3\nc3HOOIglog0dePjhCdQ1yMy6Yz1S3KFB9yT6wlB5GB10cS0g9UonP1+EC3IvXvRrjE6dOkViYuIZ\nB0lJSQlpRR5GGBcarhI5JGyWNs3F3KXr8bfaGD+lnIX3ryEt1wUCVO124PVEIMka7l0OkzRgi/IR\nO6qJ8VPKkS0agTaFurJEfKetBtEhhNQggv4/BLU7kGQBSX4L5/TrmLN0PdYIP1pA4fC+sZxuiuhB\niPB67Kx7Ko9aVyIjkhuxRfkQZY3kq6uQZY2AT6GudAySrLLuqTzKtjkZc2WVGSLc+e5Uw8BNNwxc\ngvMIBW/MpCQ/E1epMKShpw4jlDs9gsrDAWbesX5Qw2lDHSocamMXxrmj39cCVVWxWq1nHMRisaBp\n2qBNKoww+kLXBHfqOBWfD44clk3D4shx42mMZdL8XRS8MZOAX6bmQDIV29MI+BVkWUO0qozLOWTW\nCpXmZyHKGoIQJPerW7BFtlGSn8XC+9fg3uVg53tTDBZc+/iHdiQhCN/G1/I8tqiRKNY3aTk5E0fO\nGvJfm0XKhMoQyrdkCeAqcOJsryGKGtGMJAfxNMbQciIKxRZA1+HwvrGoASN/Ne+uT0zSw+ZXZ1Ox\nI43KYgeqX0JWNLzNneoMxw/HMW5yuXnNoSic7PjeDx6QsUd7mbNkM/XueIo/yGHGLfl88HQW867j\nvIkHHay/oVKM7l6Qezmz1y42XJhWlmGEcR6orBSYOctK/OhIZsy0cqhKZeYd66mtDxCVUmZ4DNlu\nyrens/bJPERJY8vrswi0KUQPN3IzgTYFWdFQVYmgX+bwvmSTPi3KBolBtmgUvT2NdU/lISsavtNW\nHDlumhpi8bVYKC90svbJmbgKHkbXnwcmgV5Ay4mZSJJhDD2NMdSVJfL+0wtNyvf4KeVU7xvLuqfy\nKN/mJC61nhm35OOcUQKA5pexR3kZP6UcxRZA7OKxbX871wjxtVPLo0c2Y4nwI0qd53gaY0M8r6EI\nPXUQCxYsX0PKxEqKP8jBMcmNpzGGqmIHVrt2VsSDrmG+7EnqeXlzZxMyvBg02MLoHWe8a4uLi/F4\nPP2eU1FRMWgTCuPMuFTorwPFXXdbkEaVsWB+J9Fg19pr8HrsOHLc+E5bqa8YTVATTLLA+KllxI+r\nZ8vrsxg/tcxUxu7wHiq2p7PzvSkEfBbS2xUVCt+aTqsnwihJEnS2vD4DTTU2VVGEsdnrqT/0LVpO\nVCMINyFIL5KaU4sjZw2lBU4qtqcjKxoJ6bVce2s+7l0OTp+IwpHjpmRzForNyAlVFzs4VJSGYgug\nBSQiRjYz5UvbsEW2UZqfhS2qlfJCJ6Xtea6uhqY0P4uMWQeJd3R6dVa7FiIb5Bg3+KGnrjp0jklu\nXFsyce9yIIpBOJGOzytxpDSRknxDFqnlmEx376brfWuxaSRmublhmeu8ZXDCkjqXBs5ojH7961/T\nXymSIAjoun7OMvxhnD0ut4evt40wqNlRrEZORbYEkBUVW6QhtePakokjx22G6erKEmlqMBS3E5yG\nQSrJz6KpIRYBY7Pf+NIc1DYFTZVMRltdSRIp2YbywdpVcRw+cCv+1lOMzf4iVcX/QBSgdEsW5duc\nZvhMDfTUsystcKLYAoyfUh5SuzQms4aK7eloARlbZJtZ7Bpos5CW68JVkGmKqHZ8TpQ1Mzw39+uf\nsG5VHklJOtV7U3EVZGKL8pEY3/d3ebYvMh3na0HY8MLnTNkjUQwinkpnS77hXaSOE0KMcPXpqB5j\ndb1vOxQnMmcfPO9mbmfTGO5ye5H7LKFfave2bdvOarBp06ad94SGCpcStXsw6a8X07r6wrzrrASH\nlYXowUmyRkq2m+OH4/A0xoIOOjqKVSXQZhS3BvwSilUNMQIdHlLF9nRsUV68ngg0TUKSNNPwuHc5\nqChKQ/UrxMQ1kTbtf9jxzhNAG1lzvokefICKorRexy0vdKLY/IydFKrkrQYkFnYpVl27Ko/YUU14\nGmNAFxBE3Sx2bT4RzcL711DwxkyGJzUauSS/gmwJoKkizumddPXq3U78PmnA98PZUpu7nt+RAxOA\nVIfOKy91buQJiZHcuCy0GLeu2xy637frnspjwfI1502xPps19XXuYBupz8JzdS64KLTpPuu4lIzR\nYNZKXEzr6guVlQJz5lkJBDBDaptfnY2ug9Xux9tsN70S2aqiBYwke9tpC5omETuqieZjMUSP9Jhi\np9ZIL2pAwZFzqDN0V5SGFpDN82JGncLT+DzoP0AQ7QjCywS1L5m5p9hRTVxzUxG2yDbef3oh8+9b\ny7pVRj1QV5WEdU/lETW82VRsMD2jrBqq96Ti91rQVAnZEiD56iqq9zhQbH58LXZkRSXQJmOP1PC1\nSgiShiiCGpBQLBp/f83Po48pA74fzvZFpj8D4j+ajsWCGXZLvcbV7xy637eVOw1DeuVVQf78J985\nbf6VlQJ3LLFQ6RbQNIm0NJWXX+rbkPQl2ur3g2X04NUffRaeq3PBUBqjAWc6i4qK+PDDD6mpqQEg\nMTGRG2644aL2hi5VXG6MoNRU3diIBcwQWFCTEEQNQQRBAEuEH3u7hI4p0VPsQJJDczitTRHIikpr\nkxFGCsnHbMliwXKDQddywoauLwf9NSARSXoLVZ2CbNHQ2g1RzKhTbH5ljum1lBY4TWMR0vpB0jh9\nKoLybU5Kt2QhKSpqm0zZVie2KC9quwcX8CkcPxyHbA0wdmInI89Xk0ZElICrBCw2aPNKXNlF9fpX\nvwiweKmTg5uysEdqvPxi3/dDb20fOryC0hIZq02jzSuRkWWM3/V89y6jLsoMh7UzBG9Y5qZsq5PK\nnU5cW0Lvye7sR7U6nQ8LnF0MRytTp0bR2Hhu78R33W3BMrqMG+Z3GpH+jFrHejpEW6+9Nd8wjAVO\nbph/di02whhcnNEzOn78OA8//DAFBQUAxMTEoCgKJ06cQNd1pk6dyu9//3vi4uIuyITPFZeSZzSY\nuJjXVVkpcOutFg4fEQzdOFkzQmDZxkb9wTPzSZ/u6mzHsNsBgqF0IFuNHI2rINNUyTbCYwuJHeXp\nsx2EYgsw4fqN7Hj398D7wEQk+Z9o2ljj/HbFhsrdDsq3h4bqyrc5QdBJmVBphg9li0FaiIjx4vXY\nEWUNPQjWCD9trTYjNKeozLp9E2ufzMNiD+D3KSjWAJO/sJ0rEk7x4TPzcc7o6XV0p1tPXVRIY2V8\nv2/1puE5KGO1a7T5JKztZIL06S4z5Dgmo9as8+lOOkifbpAODm7OMtUs+vKyevOGuntQ+/dazvke\nPFtPz/zO9ss9vNesWQcHLXx3MT9X54NPTYHB7/fz7W9/m/379/PjH/+YgoICCgsL2bx5M9u2beMX\nv/gFZWVl3HPPPQQCgXOeYBhhdEVlpcC06VamTYvg8JFOYoxOEG+znSMHk/jgmfkEVclM5je64xGl\nTqUD1a/gyHETE+dh3/qreP/pBaxdlYesBA11bSUAok5Jfhb5r84mZUIlC+5fQ/LVm9jx3veB9xk+\nJhdR/oT06a3EjmpCUzsVG1InuQm0KT3UvTW/TMYMF7MXb2TBcmMeBk3cYlLIRRESMo4w/761pEyo\nBAxPSrZojJtsFOiOn1LOjnenGvRtVSIutb5HYWtvdOux2W4O7pf7pDh3UJszslRSr3Fx47I1pEwy\nWpp3rKP5WIypTtCdCm3xppoFqWlpZy4g7a544D0t9aqAcK6KDmdbxNqxnqyrQj+Xlqb2Wmw71Fp5\nYXSiX8/o5Zdf5vHHH+fvf/8748eP7/Uct9vNLbfcwoMPPsjixYuHbKLni7Bn1DsupnV1bLAH9slI\nioaAoW5tjfIhEMTvsxo9gSQdtc0odO3whKR2Be7YeIMUIMlBNFUiIqYF32k7EHqerGgE0QkGjGsF\nVQlBKkLXFqHrRxHEe9GDTyApAoIQNI1KV5JD2VaDJef3WrFF+RBEDV+L3cxrmWQLRUOxhpIauvYu\n6iAzNDXE9iA5RMR40VULghza3lw8lU5piRxCGlj3VB4Z1x4M8WwGmjvqyAX15xl19wzOpUdSX57R\nVRP855QHPVfP5WwbA54tWehieq4GE5+aZ/Svf/2LO+64o09DBOBwOFi8eDHvvPPOOU8wjDAqKwVm\nzbHicoEoaQiCoW49Z+l6/D6ZQJsVrV2dQPXLyBYNa6SfsdluYkY1oWlG0eqI5EZi4jykTHQbum+n\n7Vgj/KZ0Tlqui9j4JtJyXYiCgCQbb+g5N61AD85D1+sRxN+B8ASyRSeoSZiPiQCHitL44Jn5uLY6\nESSd1Elu5t+3ltHpR2g7bXSHLd+ezuZXZuNtthMzqomUiW58LZ2KCY4cN56GWJqPRePe5SB2VBMJ\n6bXIShd5ol0OZMUoyvX7pBDFhY7CVqst9HxJ0qgrS+Sam4rOqLvW3aOwR2h8sDqP6t1OWo7Fhhii\nvjyDrl5Tx7ndPZtf/SJA5U4n61blUbnTye9XBnr1QAaqGdfdgwLOqYh1oMWvYfmgC4d+PaPc3FxW\nrlzJnDlz+h1ky5YtPPjggxQWFg76BAcLYc+od1wM6+rYYHw+EEUddAFVlZh4wy4OrJ+IFjC8Iy0g\nmWQB1a8gCDrRIz0kOmtJ7eJxdDDnEp21lG7JBGD24g2m2rUgBtG69DFCX0VQewhRVtCDr5IxM4Pq\nPakAzP36JyF1QR0U7rqyRDyNsWbOZNPLhjCqmStSVHLyitj3cbZJRU/r1hlWABCCqAHD64pzCOGF\naQAAIABJREFUHOVY1ShTLSLOcZRjlfGoARl7RE+2WulBmaiRoUxBQYSYuCaGjTpFozsJv08alJbh\nZ/IM+mJ4DoT5GRcXPWDP6EL3xQrnjELxqeaMBqJNJ0lSWJsujAHDlPeJjyR+tI1rZxqtHwQBLPYA\nCZk1yIrG3g9zsNj9zFm6HgGd8VPKzTxMTFwTtigfnsZYUrt0am05EUVQE2g5EUXJ5iwEMYggaWx+\ndRbNx6PQgwIWmx/ZEiBt2gH04PcIat8H4km+6gUU603EO+rxtdjwNttNT6Sjf1FTQ2y7IYohJq6J\n0gInm16eQ1NDLIf3jSXBWcuC5WsYP7WM3esmkzKxslOuqNDwEKr3pjLr9k2k5brQddGgivtF0xBF\nD28mIvY0R11jUGwB5ixZz+hMN66tnR7Gd5arWO0GU7BDSTwi1suC5WtISK+l1pVE6jWuPnMdg+0Z\n9OXZDNTjGahA6oVW3Q7LB1049GuMkpKSKC4uPuMgxcXFJCcnD9qkwrj4cT4tBO6624IWU0nMqCbQ\nJSwRfqYu2gpAq8fO0bJE0qYZITqAjS/Ow9tsp6IojbWr8pBkjYhhLfhOW0xNOC0gUfjWdBRbwMgH\naRJRI5pRbAGCARlBEBib7Tab2gWDrZw48i304NMgXAVsobb0ZpKuqmL727nYonzIihrSBrxrSE2S\ngrS1Wqne4zDbTKh+JSSUFvAplG7OIv+1WSRfWYOmSQiiztyvf0L0yGYcOW70oEjaNBex8R5Ttbvl\nRDSqX2HO0vUmMSFjhstQebAGaD0t8Z3vKsSOOUpdWaKh7F3oZOqiQvPaqr93osDZYqBGoi+jNVBj\nFg6bhdGvMbr++ut58cUXaWpq6vOc48eP88ILL7BgwYJBn1wYFwbnYljOh2XkKpFpODSahPRa0+Ds\neHcqY7PdyEqAgE/h8P5kNr86m9Z2AoCsaJ2tIKa5aHSPRhAgJdttbsh+r4Lql9FUCVEyWkCAUYCa\nNs3F8cMGYywhvYigeh2NlUUI4udB34SsJKG2M+FaPXYk2Sg2Xbcqj+o9qeg6VBSlcc1NRQZzTpNI\nzXajdemJFBPXFJLDscd4WXD/GhIzjrD97Vxi4pqIifN0dmrd7SAmzmMy2I4fjjO7wHYXI+1g23Vt\nh3GsapTJ2gOoPxTfmW+yaD027XP5nQdqJPoyWoPdEuJCdAwO49NBv8bo7rvvRpIk7rzzTnbv3t3j\neFFREUuWLCEiIuKiZtKF0T/OxbCcT7jEmaly+qQhIFr8QQ4pEyvRgyLHD8ehWFVkRUMPGm/2HZRq\ntZ1W7Tttpa4sETUgAVC5y0HLiSiDZSdA2lRDxTt9ugtR1An4lE7SQGMsJ2ur2PTyI8BuBPFbpE76\nA7IlCtGiIltU3LscKNYAY7JqkC1GTqmt1YIAJF9dZWrIxY5qwpHjDlHYHpHcaCh7r+rpqXg9dkYk\nN5J94y6q96SaRi77xl24dzmIHukJVd/uYoQEMWjIE3VtBphjUMs7jA06PdTCu2/aQ0lT7stoDXaY\nKxw2GzxcbC3Y+zVGMTEx/PnPf6alpYXbb7+da6+9lltvvZXbb7+dOXPmsHTpUgRBYPXq1URF9RRG\nDOOzgXMxLOcTLvnr835zE/c0xhDvqEdSVDwNsfha7KiqhK/FxvHDcYxMbiQmrgkBQ6iz6F/TGJHc\nSOyoJoKaUbMjWzpr3OLH1ZububfZYLd1eAuC8A75r/0I1V+PIP4GhKc4WZvIzK9tQvVZzGZ6AZ/x\nf9UvodgCJGbWgAA1+8eGGpHdDiJiW6krS2Ttqjyqih3MvH0TFlsAa4SfenenpyLKGtXFDja8OI9A\nm8xo5xH8XgsbXpxHeaHTaE8uh7LjBDFI+TYngqCbpIbubLu1qwxj4xinMyajM38kSkYOd8uWVnPT\n/qx1Ob3YNsvzxcW2nouthmpA2nRtbW28+eabbNy4kZqaGnRdJykpieuvv54vfelLAyI5fNoIs+l6\nx9kwmbrifOo77liiUFFmeCzBINgi/YxOP0KjO57Wpgh0QBQw9d862HKlBU6qih3ouvEZX4uhYOA7\nbSF6RAsjkhs5WjbGZMCVFToNKnhAQpSeJKh9H0FQEIS/gfAVxmTVcPW8/WZbCklRQxhzFUVpzL9v\nnVnzs/D+NabMkKZK2GO8XH1dscmYky0qyRMqGZl8jB3vTgWM2iZblJdAm0LatDJTvaHWZbD+OvTs\ndCB6eDOtngj0oEhMnIfsG3ex8cV5ZMw6iGOSm33rr6KuJAm1i7L4sUNJBNokUsepCEB5uaHGYM6r\n2U7WlWqIZzRYTLTBEhft7dnq2LhTJhkMxAvBnBtM9LamC80EPBPOpYbqUxdKPX36NM3NzYwePTrk\n7//4xz+YP3/+Z8IrChuj3hEXF8327S0XTFZ/xkwFJb4ihOIcVCViRjXhb7UgtPvqKRMrqXMZ9Ok5\nS9azc81kWpsiDAWD9tqgjBku3LuN4lPALE4VBLDHGPI4RytG4ip4Hj34BJaIWCbf9BgF/+9Bgzb+\nyUQjvyRrpmyPJAcNL0TWSMhsN1btUj+aKiErKpJFRUAn4FfQgwLpXSjb5YVOs1BXVoxwZIfyQnf5\nGVHSsEb4GXWFwpE6gfFTXT3UwMsKndx47/shnzPYdwY1XbIECLQX3SbGK1Qekpl5x3q2/H1myDha\nQzovv9R3ASucvXEZrM21r4374AH5jHJDFyt6W9Ngqu0PBs7l9/vUqN0AH3/8Mddddx2vvPJKyN8b\nGhp49NFHmTdvHps3bx7wZI4dO8YjjzzCrFmzmDJlCt/85jdxuVzm8c2bN7No0SImTpzIF7/4RTZs\n2BDy+ePHj/PAAw8wZcoUZsyYwcqVK1HVMKPmfHAh4vD/+IdEYrKNinJLSN6jw7g0tYfovM022lot\n7T2LRGRLgMK3ppshN1HWUGwBqveMNfIjLiM/EtQkBEFAtqiIstHrxx7byMnab7cbovHMuOX3nKxb\ngGxRKS/MRG03EAu/s4br7v4YXZNCDJGnYZjRnbXQiRoQiR3VxLW3bcbfaqWt1Ybmlw1Jom6SQCtX\n7iUxPoDXE4VjXDNJY/zYor2hBartenQpEypRLBDUpHYWnNxOyFjYriIuseGFz9F8LJrK3Q5kS4Dx\nUw2Sg8Xux9FedJsyoRK3W8Bi09j2z1xTDqljXuXlPaV9gJCw0R1Lzi5sM5RhP1eJTExcE5W7O7+z\nzzpz7mJjAl5sZJB+jVFJSQkPPPAASUlJzJ07N+TYyJEjee6550hJSWHZsmWUlZWd8WLBYJD777+f\nyspKnn76aV577TWioqK46667OHnyJOXl5SxbtowFCxbw1ltvcf3117N8+fKQsb/zne9w7NgxXnrp\nJX7zm9/w5ptv8uSTT57j8sMYalRWClw7y8p3vmsk2xWroW5dvS+JD5+djw6ggyRpSLLBmkvPdTH/\nvrWMyapBDwoEfEoPYoKui2Z+RFY0s7ZH1w1R1bLCKLa8/mMa3IUgXIfmL2Dji9+gtnQM6IaYaeyo\nLuy33Qb7TbEGUFWJutIkPA1GHke2BcicWcKMW/Kpd8dji/K1C5xqIQQGg8Wm8sSTDp5dXcy776zn\nzX/U8sbfdXTVQvXeVN5/eiHVe1OxR3lNerf7kExG+0YVE+chIb2WmDiPwZz7zhpSJlSy+dXZ1LoS\nQ/TwfC22EIOjB0VSr3Hha7b32MglqWcdYPecQaVbOCvjMpSbqzNTJS6lkVqXwZSs3u381DfL88XF\ntvlfbGSQfo3Rc889R0ZGBq+++iqTJ08O/aAoMmfOHF5++WVSUlJ47rnnznixkpISdu3axa9//Wsm\nTpxIWloaK1eupLW1lQ0bNvDCCy8wadIkli1bxvjx4/ne975HTk4OL7zwAgC7du1ix44d/OY3vyEz\nM5O5c+fygx/8gBdffBG//7N9o16quOtuCzW1qkFZXm4IgFbtdnDgk4mkTXOxsN3A6Bi5HdUvhzDK\n1O7exyQ3vhaboTPXxRvxnbaanpY9divl25biaShHsS3GkfMEgjCMjGsPMvNrm9FUY0O/5qYik3xQ\nttWJJKskX11F7Kgm1IBRE6QGJHK/vJXa0jGsaz+v7bSF1iY7ug6SRTWKWZ/Ko64skeQJldTV2tG0\nIKmpDhITk0hN1VH9EnO//gl5D7zL3K9/QsvJaLSARGmBE0nRcJUKHNyURcuJKCp3OvE0xPbwIFuO\nxxIR2UnZtkX5QoxpTJzHMCayIYvUsZFXbE8n1dFzo+nu2WiqZI43EOMylJvrX5/3Y/Gmcvp4LFlX\nqhfFZnm+uNg2/4sN/Rqj3bt3s3TpUiyWvt11u93O17/+dXbu3HnGiyUkJPDss8/icDjMv3W0K29q\naqKoqKhHf6Tc3FyKiooAg0o+ZsyYkALbadOmcfr0aQ4ePHjG64dx4WCoLFg4sF+m7XToGzwCaFqn\ngYkfV2/keaK9SO306q71MvYYb2dtTjvLTFI0Q9ut3aPZ+d6U9mNr8XpuBGpQrCsI+P5GnWsckqWN\nsnYFhI5CWVtkm+FZWYxNt+VENI2V8YxIbkSxqO2Frxr1h+KZ+bXNZMw8iCDA2EluFn5nDem5LoIB\n2Qj3LV/D7MUbyZjhMnJG1ixGjBhpfh9dvQiDCacaXtKeVIJB3awvSs91oQbAHqn18NquuipohNfa\nDUBivILWkG54Du0Mv6piB6kOHcmTiqcxFknSGDsWXnmpp6HoPidbtNes2arceWZPZCg31/DGfXGh\ng1CiWIJDxgTs1w8/fvw4iYmJZxwkNTWVY8eOnfG8K664gnnz5oX87cUXX8Tn8zFr1iyeeOIJ4uPj\nQ46PGjWKo0ePAlBfX8+oUaN6HAeoq6sjOzv7jHMIY+hQWSmweImF8nLZpBbLsvH/jsZsu9ZMMZvT\nlRY4DS23Y1HomkTQqqNrAtV7UynNz0KyqKgBiZlfK6T4gxxKN2dhj/Ey6/ZN1B+KZ/Ors4ke0cLU\nRYVseHEezcdfIRh8AHQZeIWIYQtoqhfQVImgpmCNMDbX0elHOFKSRGl+ltFfqH2fE2WN5uPReJsi\n0IKgnTZewsoKncZ82hW/M2a4TMNakp9F7CgjJNahj2eP8XLjguEENYmMTJVf/SKAxwNH9mdRWuAE\nHTRVZNhoj7H2YGjeyVWQSWJWBeWFTlwFmcTEeZi6qJD8V+eZm3TXxm+VlQYxIf+VeTgz1S7twPtP\nRndt0oigMfP2TUSPbDaT62EDEEYHzJDuvQbZ4a67B58J2K8xGjlyJHV1dWccpLGxkeHDh5/1xT/6\n6CMef/xxvvGNbzB+/Hh8Pl8PL8xisdDWZiza6/X2oJErioIgCOY5feGKKyKQZems59gV/TFBPss4\nn3UdOgRf/orKgQMiVptGfIabBcsNllvlbge+ZhtgFGSWFzpJy3VRV5bIiORGqoodKLYAAiBIGlqb\nFU0zwlkbX5qDr8WOAGx+dTYRMa3oGMKlkqIREdtK6ZYsZtySz6GdYxGEhwmqjwMjsUa+xvSvCtQf\nquX0iSizyZ6vxcaULxWye91k/D6DqKCqImCodwNY7G3oQZGM3PIQhhyAPbYVb7M9pIurrHSGxEry\ns1CsAWbcks/GF+cB4K4OsHiJhZQcFwsWuU0aeaBNZnR6Lc3HnUTHdTFm7eG29OkuKnakmQ3f3Lsc\nWG0azc3RjBvX/feD/Xs7/mUBLCG/y5VXBnnrTbnfz2VPCtJYGU9EbCtVxQ6uvCp4Qe/3S/HZupTW\n5CoNcsO9XTrhPpNFXNzg1iX1a4ymT5/Om2++yZe+9KV+B3nzzTe56qqrzurCb775Jo899hh5eXk8\n/PDDAFit1h5N+vx+P3a70Y/GZrP1yA0FAgF0XSciIqLf65082XpW8+uOS5nafa7r6nDdW08bxITW\nFoWqYgeH944lJ28HAZ9RqKkFofVUpKmikOCsZed7U8xxOlqHG9RpIzzV0RfIkdO5gcvtZAGznbes\nsXbVPGAp6P8EMhCkf6L5x7PhRckwNgHJKJoVgwBs/+d0bFFe5i7Np/5QPBXb00HQUdsUdDDrknq0\nI7/f6PVTsjmLurJESrdkIltUVL9E9R4j1NVBJ68/FI89xmvWO5XmZ4XkvFxbMgkGBYMJqEr4Wy3U\nHEiipN3zm7qo0Gz4VrnTycHNWcTENZGQ6eaLi1IH9Eb6xUXWkDfZLy7q/032z38SQlrZ//l53zm3\nAj9bXIrP1qW2JmeGNbRdfYZKY+O50fj7Qr85o6VLl1JUVMQvf/nLXj2PtrY2fvWrX1FQUHBWckCr\nV6/mhz/8IV/72tf4n//5H0TRmEZCQgINDQ0h5zY0NJihu9GjR9PY2NjjONAjvBfG0OOuuy2MznSj\nWI0aHdkaIKhJ6LrIjnenoAUkNFVCEmH81DKTvdaRqwmqEn6vggCkTXMxddFW9CBm7VF30oKmSSHK\n11MXvY0t8lrQ/4kozebz/7mCjGs1LBF+bFE+EHUEAXa+N9VoQxFhKICPzW7XfWunUo9Or0WUNRbe\nv4YF968JZdmZRm8hNQeSkWQNT0MsomTkreZ+fT2aKjFn6Xosdj+bXp4bIgUUP64+VFlht8PIRcma\nKbA6NrsSr8co9tVVC5tfmQcnjLogv68zH5U+3TVg+vTZ0q4vhxzNUCogdB/70KFBG/qigElWeWbo\nmID93qGZmZn89Kc/5Wc/+xnvvfceM2bMICkpCU3TqKmpYevWrTQ3N/PQQw8xY8aMAV3wT3/6E3/4\nwx/47ne/y/Lly0OOTZ48me3bt4f8rbCwkClTppjHf/e731FXV0dCQoJ5PDIykszMzAEvOozBgatE\nRolIAF1AABSryrW3Gh5HWaHT0JiDHh5RSX4WgqiZxX+6bGzaBW/MxNquxlBV7OjMKbX3BwpqAtYo\nL22nrfhOl1Pwxv8BqkBYysyvLcYW7cMxyU3pZiPflDa1zPSi6soSSXDWUvxBDjNuycdVkGnK9NSW\nJIEOZVudpE93EZfSSPk2J6VbDI8kZaKb44fjSEivpXpvquHxtHtrxR/kEBPXRP2heGbcko97l4OK\n7ekcPpBkzL0hFluUlyMHkyjdkoUkaeiCHtLCvMP7Wnj/mh7Fhx0kA/ONdID06e6fc4xTmXed9YIU\nNl+s6KSyD37eo/vYX/5KBv/+cFCGvijQ8bISF2c5J49oIDhj0estt9zCyy+/zOTJk/noo4947rnn\n+Mtf/kJ+fj4zZ87k9ddf51vf+taALlZSUsL//u//8tWvfpVbb72VxsZG87/W1laWLFlCUVERf/zj\nH6moqOCJJ56guLiYO++8E4CcnBwmTZrEgw8+yP79+9mwYQMrV67kG9/4Rr+MvzCGBklJKgGvjfHt\nNUAd9TAdQp2aKmGxtZnhNVtkGyOSG5FlDUk0vKEOBtmW12fh9ym0tVpodMej2AJUFTsI+CwIgm6o\nHgjg9UQiSh8SVOcCVcDPkaQ/01iV1KWgVEMLyCGbffOxmF7FR8dmu7nx3vdJn+7iyIGxvP90HjX7\nU1EDoQw5T2OMKXjaUWzrbbbR1BCDv9VCaX4WHzwzn8P7kxHadehGJDcSM8roNtvWamPOEsOLioj2\nMiy+KYQhGBPX1KsX05U+LTdlDPiNtDvtWoeLSofs08BQF+l2HfvAgTNurWF0w4DkgLrixIkTyLJM\nTEzMWV/s8ccf59lnn+312AMPPMB9993H+vXrWblyJdXV1YwbN45HHnmEa6+91jyvsbGRFStWkJ+f\nT2RkJF/96lf53ve+Z4b6+kJYDqh3nM+6ksfaaPMaYqIdygVaEBw5bqqLHehgJt8lSUfXjbBIx9t6\niAbbS/NYsHyNKYEz6/ZNbH51NunTXTgmudnwwudImVCJbHmWfZ+sBl0k+8bv0Ob9Bof3JeP3Wgm0\nKaZmW11pEmnTXKGeUXotZe1kBAQdPSiAbszBOeMgO9v15NJye5flsUb4CQYFPnfXxwaxYXs6BIUe\nHVwBo8DXFjAbAXYQNkRRR9MEZt+xie1v5+JttmOP0EjMcpM+3dWvLMv5/FYXmxRNV1yoZ2soteG6\njy03ZfDvD72DMvbFhE9dm+5SQNgY9Y5zXZfX6yV13BWGh9NlM67Ynk7EsNN4GmKZs3Q9W16fhaYJ\niIJxnqsgk4jY0yRdeTiEnKDY/MxevNHQX1uVR8bMg5TmZ5l6bu/9IQ9Hzh0c2vH/UGzRBHzvIAiz\nQ0gDZYVO5ixdz873ptDUEGvkstpJFGpAImp4C6PGHaW+YjS+FrupiTciuZHDe1MZP7UMV0Em8+9b\ny9pVC1GsKmqbEnKNiqI0Zt2xEVtkG+tW5YGoMWdxJyV63ao85ixdT/5rs01j2CGM6mmIJWPWQcq2\nOhH0znbgwIA04c7nHrzYRDq74kI9W4Ml7DqQsd9520J0dHi/6O3zfeHi1pAP46KEx9NERUU5kjjT\nzAd1rbvxNMYiyoZUjuqXiRreTPPxaCqK0ggGBVOupoOcULo5i9jRJ3j/6QVmq4TD+5JNqZ2UCSWI\n0m0c2vEWEbEJjE77M8drJnLNTR+x870pbHhxHvYoL4otQNHb09BUGVHUUawqkqJ1ejKFTo6WjcHv\nU0ymXoehCLQrPxwtT8S924FiVU1KeHc6+c73ppDgrMUe4yXgU9j2Vi6WCL+R27KobH51dkjTvdRJ\nxvciyp3rra8P9Uq61w6dD3rbdLvWFHU1gpcTeqvRGqqxjdzKoF/mkkY4sBnGWaGxsZGysjJEUUTt\nQsXuyH0oFhVZMZLsO9+bgj3GS/Px6HalaYXYUU2kZLvZ+e5UNrwwj9ICJ6Ks0XhoNMlXV5mdXNtO\n2xGlICWbR/DBMyvRAm+BMBNv825qDlzPiOTGTgUFWSPgs9DWquD1ROBtthM90sPo9CMhzfVU1ahh\n6io5lDrJaLpnjzByVB3N7wJtCvHj6s2Ge52SO000NcRytCyRqYsKCfhlvM12EtJrDbmjaYaOomwJ\n/V4kRSUiphX3Lgdp6UMrkNlbn5rLgS0XxmcbYc8ojAHj8OEqGhoakGXjtrHZVXytMhVFaZTkZ5k5\nI1EATYWWE1FoqoisaL3mVQJtRl3S5C9sx9MYS11ZItKcg6aH5ZiwlqPl38brqUe23ora9lcyZh0i\n3rGP7W/n4t6RhmxRUWw+QERrsaOLGlFXNDMyuZFjh+NCmusp1oBRqNqtXslqC3DFsACuAicHNxlM\nPFnW2P52LklXVbXXFWUhWwIkX11FwGcxmXOKRUUPCiE09NLNWehgfi8d57SeikY8lc4LvUjzDCZc\nJTI3LOtSoLg6i6HwBsIIYzAR9ozCOCN0Xae8vIxjxxpNQwTw4AMVyBbNUJe+fw0p2W5EQ9AASYH0\nXBfRw1t60JiDmmSqbwO4CrLMtuBmZ1TxIw7vvwOvp560abej+l8heqSf6j2pbHhhHv5Wi8EQE3QC\nbRbGZleaqt6aKnOsnRIuyhrrVuVRvs1pdnCdsqjQ0GBblYf/aBJxcW0MG1/FDfe+jzXKhx4UUAMS\nXo+djBkuZi/eyILla4xOsK4xSLJqtJbY5iQnrwi1i8Coe5cDW5QXWen8XsZPK0MA6uoujFdysbUq\nCCOMgSBsjMLoF5qmcfDgflpamhHFUDml199IJDGjhoqiNNauyqN6jwNrhN8wCAGJurJEWj0RPYRO\nFWsgRH27g25tFJfmUbZ1K0HtJlR/GxM//yCy5VFkOUjzsWgkRcUe7SUt11D8Hj+lHF0XexTIdoiE\njorz8be/FbH2vQLGpTUzJqOWKxJOMSajFsf4Zp5dXUztkSiTlpv75a0EVYPs0H3ekqwx785PmHvn\nehYsX4OmSsZ1ZI2q4lRD1bvQid9rQfWHFu1q2vlJUZ0NLrZWBWGEMRCEw3Rh9Amfz0dpaQmgm+rq\nXVHpjkZSbCRfXcXxw3E0NcSi6xbiHfWUb3WSkF5LaX4WUxcZQqeuLZkIYpCgJrZ7ED78PqNbakVR\nGrao07Sc+D2a+kskJRpb1Cvs+SjPaOmgisTGG/kaAXqQJj54Zj4RMa3EOeqxRfnQVYVnV+8mIcFn\nznfFT0pZ8fMMPtiSxdjUZlb8pBSAsanNZoFovTseUdYYNe4o1XvGUrbVSenmLGzRXgSBUDkiSaN6\ntxMtIDHv3k86u7GuyiM2PlQ8NS1t4N7J+bK+hjJRH0YYQwVpxYoVKz7tSVwItLae39thZKT1vMe4\nGNHXujyeJlyu0l6NEEBdnY1/vRePFpBR2xQSM44wddF2RCnI/vUTUAMSJ46MQA+KHD6QTFurBUuE\nH0kOogdFThwZQVAVUP0K1kg/uV/eQIP7v2hr/QuKLYkZt/4C2ZJNoM3CiORGWk7E4PVEIMkagqhT\nVphBfcVoWj12VL9C6iQ3J2uHc/LISPSgSMAvs3tPNFMmNxEdrVJXZ+PHP8miyh2FKGlERKpcf90x\noqNVpkxu4t/vjqPo/atpPRWFYvXTcjwWv8+CJBvGMz1doLFexu+zcGDD1fh9Fvyn7Wze5OP//k1E\nlIKmjNCpo8NJcB6hwT2a/esn0Hp8OO+87WfYsIH9Jjd/xaBhX/OF7TR5gvzztVHcdad22d2Dn2Vc\nimuC819XZKS1z2PhOqMB4nKqM2psbKS6uqpflfNv/mcOh6sjiYlrwtMQa9YDaQGJD56ZjzXCT8rE\nShyTOkkLRodWnZSJVabMjyhpCGIDatvXgM3AdET5H+jaaARJo8MWpnchQJiSPO2Fp0FNIHpEC031\nscb5AkQNbybQptDWYsMxvhmfV+KkR8DXYkeUNQRBJyHBx/N/3g2Aqqr4/WP44aMOSg/KWO0abT6j\nBUSHZ9JXrU5+vsjipRZDMNaikjV3L+WFmXib7WRdefaeTV8FqpfTPfhZx6W4JhjaOqNwziiMEBw5\ncrhfQ1RXZ+Pub02iujIS2RKgqcEgCexbfxXvP72AtU/mAdDqseOYFNoS22gLLlLVLpUjKyrX3PQK\ngnAtsBlBvAX4CFGIY87S9cSMbDG6wAbFkLCcr8XW2eXVL4Mumu3H7TFeFixfw5jMGkRrwgL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obE5mFXN4J6A6r9DAbTQrTaN+jU7Rz2SinbEf0z0eoVKspNRA1Od63Mmjh276VApFcI7Gxm7dq9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\n7rv06NGD1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4QQl5FgdBXY7XY8PT2JiZHF8IQQojoSjJqZoih06BBAZGQPyZgTQogaSDBq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\n", 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\n", 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" ] }, "metadata": {}, @@ -1039,7 +4362,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.016129", @@ -1051,15 +4374,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:561: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:560: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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YPHiwrUP5xwwbNox69eoxceJEW4dyy9h8aWpxnD9/nuHDh+Pu7s7TTz8N5B39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IpEFVu7zUFJDc6r+lODY1Yw4rm0BXB39MAI/ziLvvPPWT+hts180CvcPQK7HjyA1Lw9eP3kEv3A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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1084,7 +4407,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 85, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.731819", @@ -1096,15 +4419,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:955: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:954: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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YPHiwrUP5xwwbNox69eoxceJEW4dyy9h8aWpxnD9/nuHDh+Pu7s7TTz8N5B39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\n", 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" ] }, "metadata": {}, @@ -1120,7 +4443,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 86, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", @@ -1130,9 +4453,9 @@ "outputs": [ { "data": { - "image/png": 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2Y7id75mXl3dH3/9Bc98kygpPogBYsWLFLZ/39vYudm1voUGDBhVZUywiIiIi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qXIk+VhhP8o8rq4JgLcGgAAn6/Gxy9Pph3LHC6zzwKlNSS9V1hdgOEHWr7+owUk8mR5+Pnvn/BX\nyh8YHN2PoW+oHxlrThACAofHxuLz3ivwee8V9U4MWPDvAsHn42ZoKK6FhuJmaCgIvtHYlBYNjVkf\nj8dD7969aX9ubm6ora2FtbU1PD09weFwkJ6ezjiGKel2zs7OKCsz3XXaHNAQWvrC9bppkk5OTrC2\ntoZCoWCcv4+PD2QyGaytGyf50lxwcnKCWCxGamoqY1taWhoAoFWrVnB0dARBEPVeM3P8BqWl5HdU\nPxKuJaP5qNgmoLy8HHv27MGXX36Jrl27IjQ0FHPnzsX9+/dx7do1pKWl4YsvvkBAQABmz56NLl26\n4MCBAwCA6OhotG/fHrNmzUJAQABWrFiB3NxcXLt27TmflQUWNAz6IrBKKPG4JMmsdQQ6BdGiQlZc\n/wJr+39vsLy7jbvRdL6rOZdRVMOeg/+49BHi8+Ow5U6dS5+oEhgxR1ugKBAoCEaWNBNj/xyJQdF9\nGzUYOZ52tP5C+tAbOBeUVuJqjuFc+06uIeDXWb7zOXya3tXdgvhn2hk/m3GKmrHWuGullaXibAYz\nNFrXUGHI/ojnPtibFETXtZzZeQ6uTY6DiGNFW/9n8u/N2o5hfiMh5rOH+3sTDY9GJtM86Z0u/egx\nh/J+iBq5BZjVDZjZg/y3Lt25XF5u8vXJqqB3PpsrgtEY0XgroRonP/kMqKmTX9AhxAxpHwLAyutf\nGicE9TtdOhOkfA4fXrbeyKrIgILF0AAALuVeQMTeXtTvSBGzetciQD7aKAloCkzR8zIXnmVd9Wmq\nVfMKaPewUliGmJTDkIgluDP9Ad7y+gFQiQAACgUHRZl0XcL6viktATEphylXXADIlGYwjEgsaHnI\nk+Why/YOWHxxPt6InYY8GZP0vfX0RrPVrzE5+vTKUux68CtsBKanlFnw7wDB56OHnd0/miQDADc3\nN3Ts2BG///47FTUGkNlfS5cuxbx586BQKODk5IRu3brh8OHDNKLpzp07uH+/fm1HDw8PyOVyFBQ0\nfyaGBq6u5DcpMVE7YfP06VPcuXOHWubz+QgPD8f58+fx8OFD2v4rV67E22+/3eB0Qt00yeYAj8dD\nv379cPnyZdpvr1ar8dNPP4HD4SAiIgIcDgeRkZG4ePEiJe4PkCTZuXPnqGVz/AZPn5LvYA+P5umv\nNAdaJFF2+/ZtWFtbU44QADB27FhERUUhISEBHTp0oIkEdu3aFfHx8QCAhIQEdOvWjdpmbW2N4OBg\n2g1vgQVNwbMSggXAEPrWjx4xB2oV2gF9SmkyfB18Ycu3ZS1bpahGpbySdRsAZJZnMNbxQH4MfO38\n8O6Zt7A5QYeI87xlcBCdVpaKY6lHGnIqkMql+O72WpPK2gl00pRYUuCSSx6z7wjUDc7JgZNCrUBW\nhfa82fbTT1szF6RyKRaee4912xux00jDBB3oRxA+70gOX3s/XJ8cj/dCF+D65Hj42vvB194PkzrQ\no06MXQtTIZVLEbk/nNVplRAQiBl7knW/rQmbG/zM60Zf+dr5gQsugzCaFtEDo9qOxtmpJ8HxukkT\nOAeAnMrseq+PVC7Fr/eiqGUBV4AR/qNMaqM58fW6atAiUUWl2mdZVAnh7H4MMhAg9Q8PPdpv8LiK\nkFAoXLXEigBk6iVAPnePS5LqjbLNqEinhP9fDhhLrtS5Fv4BCvTq7IBDo2OwfsBGHBod06g0QI2e\nlydITQtvXvMNjDR1tQYgBODE4aKkmYSiNZpq7TkCOHO42NjKm6apFugUBBc7a8DrBnVtNaYZErEE\nfbzo2oIcDdtZ921T1bR88qC1HfMeu5ZtXLTYguePU+mxBkl0DU6kHTO6vSnQ/95GP9zT6MmplhQJ\nbsF/Ex999BFqa2vxyiuvYNOmTdi9ezemT5+OhIQEzJ07F46O5Hfhww8/hFwux/jx47Ft2zZs3LgR\ns2bNorYbQ8+epKFTQkJCs56LLjSZce+//z527NiBn376CRMnToREQo/+XLBgAQiCwOTJk7Fu3Trs\n3bsXc+bMQWxsLCZMmIC2bds2qF7N73H48GHs37+f4TBpDixYsAB2dnaYOnUq1q9fj127dmHGjBk4\ndeoUZsyYgYCAAADAu+++Czs7O0yZMgVbtmzB1q1bMWnSJIZeXFN/Aw1X06tXL7Ofa3OhRRJlGRkZ\n8PDwwJEjRzBixAgMGDAA33zzDWpra1FQUAA3N3pKkbOzM8VSGtquy4BbYEFjkSfLQ+iOYLx/di5C\ndwQ3L1lWSzBStS5knTdrR4lNS8mT8MJnfVawli+tKcGAvb0NnvcI/1EUMaaBsk4TSCqXIrNCj0gT\nVbJG1Gjw9unZOJkea/I57038jaGPxgZ/hwBcnnwLK/vVkWosUS+FMsPuNLqpdbomC1K5FD/Eb6SV\n9SS8mi1i4lhqDIprDJOnW+I30JYDnYLg70B+GP0dAlpEJIevvR+W9vyEFtk4PfgNWpnoR78xSL+G\nIj4/DimlyQBIQljfNdFFz4FTg9iMYwjd0QHvn52LkO1BJrfjm/7rcOjlIzg94RISZiRhSNtw2r0u\ntCbT0YJdOuLAS4dZj6FWGdfqSCpOpKIJAeDXYb81W2qPsWjE0D56moAvvkd7lpf0f59GpOgiV2ok\nZZAgUHLkJNR1s/FKPo9KvQSA+efmmTTQfVJKphmUaJ4VUSUwPQJvfZqAP36vAkRk5Mf7Z+di7B8j\nGv2O1eh5KaDVDrtY0TxpJE58PjIB1AIoVqswM+cJDpc0DyHfWihCilqBIrUK7z/NRJ5cO7lCCAiM\naTeOVl7znAFAtdsFwLkuEto5CU5+aSRJtvU2EHUdeeuPID6LJBOUUiVktyuhlNIF/583enn0gYsV\n/f3Q07O3gdIWtBSQemDGpWF6NoPxiQaBTkHwtw+glhdfnM+YpDEFLS0S3IL/Jrp06YI9e/agY8eO\n+OWXX7B69WpUVVVh5cqVmD1b6zrdsWNH7Ny5E61bt8bGjRuxf/9+zJ07l+agaKwOOzs73L59uzlP\nhYb27dvj22+/hY2NDVatWoXo6GjMmjUL48ePp5Xz9vZGdHQ0IiIiEB0djRUrViAzMxNLlizBp59+\n2uB6/f39MXXqVNy7dw8rVqxATo755RM0be7fvz/27t2L1atXo6KiAsuXL8fixYupcu7u7tizZw9C\nQ0MRFRWFX375BWPGjDH7bxAXF4d27doxSMiWDI66Iap5zwibN2/Gtm3bEBAQgIULF6KyshKff/45\nBg0ahMrKStTU1GDtWm3UyIEDB7B582acOXMGgwcPxuzZs2kXd9GiReByuVi5cqXRehUKJfj8f47A\nnAXPHtvitmHmXzOp5aiXovBG6BtG9mg8zl6SYWA/HfHPmT0ArxsIcArA1pFb0c2zGyUi31hIa6Xw\n/84f+TLtQDfqpSj4Ofph4I6BBvdrY98G9966x1r/0cdHMeK3EU1qF1t912ZeQyuCmQakwVPpU7iv\ndTe4XYN53edh+aDlIIQEpLVSdPupGx4WPmQKrQNI+F8COrXqxDjG9azr6LmtJ7V87Y1r6OHVg7Ee\nAAb5DMIfk/5o8rXSh7RWCs+1niivLTdY5o2QNxD1sjbi6Kn0Kbr/1B2Z5Zlo59wOt2ffNnu7zIGz\naWcZ99+crnOwZeQWsx3zzLQzGOA7gFrWf7YNwdnaGU/ee2Lwd5PWStF1a1c8KnpE+43XXVmH+Sfn\nU+XWRq7FB70/oJZH/TYKfz3+i3YsO6EdsudnG61Lc/+2d2mPm7NuNtv1NHTPA8DTYim82hVDWeQN\nOKQAb3amniFna2fM7zkfS88uZT3u+A7jsW/cPuOVP30KxMTgrwA1Rp2bRdvUt3VfXMq8ZGBHEmHu\nYfjrtb+QWJBI3gM6bqPt2wOb/7iJgXu7s55bQzDi7l0cLaYT173t7HA5NLTBx6oPMxITsV1vAtBf\nJEJyM8zUbsvNxcwkbdp/VGAg3tCxkU8pTkHABi0hkPxOMvyd/PFU+hSt17WGoloEFATDL7AaYzu9\niDW7bwI7zlHld/6RjYmDJIjrFgfZQxnE7cUIvRkKPtFy0pWeSp+i69auyKnIgYetB27Pvm30e2TB\n84cpfQIvOy8kvp3YbO9Ntm9ZzKQYDG833ORjGHv3WmDBvw0rVqzAiRMncPbs2WbRQLfg+UAqlaJP\nnz6YP38+pk1rHgOV5kDL6YXogM/nQyqVYvXq1ZRd6aJFi7Bo0SKMGTMGUil9NqW2thZWVqSmjUgk\nYgjy1dbWwsHBod56S0pkZjqDfz5cXW1RUFDxvJvR4tDDuT8EXCHkqloIuEK8YBeG3+NjAIAmGm0O\n2DjlAy61ZCqgTlpicnEyBu4YiLYO7RhaQQ2FVC6FiKfVgxJwBejh3B82Ahs4W7mgqJo9qiq9LB2X\nHt1AV0k3xrYgmy5ws3ZrkvMkhTryKr3mPrpv7YHzE68ZPN+t8b+YdEhnfitUlalRBfL+PjrmDJKK\nE3E67STWxNHJ9IXHF2PXCOYg3o3rTXNQdON6o6CgAjZKpkDl6Sen0WFDBxx99YxZo31OpscaJckA\n4PjjWKTl5IIQEJDKpejzWxhyK8lZq0dFjwxew2cFqVyKpOJEBDoF0a5rbhEzMmb77R24kXELy3p+\nii6turLuZww+ovbwtw9ASlky/O0D4CNqT3vH9XDuz9yJhTwtqirCjht7MC6Q3fXnUvYFPCoiI2Qe\nFT3CyQfn0dczHC96jgKf8yEUagX4HD5e9BxFq3+oN5MoK68tp/Y3BM39G+gURLuvTYWp73o3rjf8\nHQKQUpoMf4cA6p4HAB6A+KtCnLp5CyHBIkT+WQOFmky7Pjr2NA4b0ZibETi7/vp5NsCo8fjrwiLa\najuBHWx59adz3Mq9hdbrWuPXob+RK3RSrR8+BNL+pvcPONVWjfr+zbVzYRBl8x3cmuVbOsPGEdtB\nJ8qWOLs3S1091EIIwIEcagjAQQ+1kFYPVy6Gj50vnpSnwcfOF9xqMQoKKrA1/pe6FHVSo2xiu6l4\n2S8Sazg3ace/mnYD/S71guwh2QeTPZQh+1IhxF2bLy2zoX0cHmwQ+8p5DNzXBzkVOej2Y3dcmHS9\nxbp1WlBPn6Du3Z7ler9ZvoOab5uXrTd87f1okcij9ozClcm3TXY2NvbubQgs/Xo6XF3ZJUYseL6Y\nPn06du/ejWvXrv2jUvQsMI5jx45BJBLh1Vdffd5NaRBaZOqlm5sb+Hw+RZIBgK+vL2pqauDq6soQ\n+SssLKTE+CQSidHtFljQFEjEEsRNu4/1Azbi0qQbmHhkLMb+ObJJ4vOGcDw72mhaojk0puLz42jp\nkD9EboNELAEhIPB6x1kG97PmiZFamsp6voSAwL6X/gCPQ0Zn8jkCzOvyAaMcAHDAMexoqCewn1lY\nZPR8a5Q1jHVvdnoHrXU0jPhcAcbqpQkRAgJdJd3QuzUzLPxKzkWD58gmbJ6ln1pah0xpJoYfNJ9T\nmlQuxbn0+sWksyuzKFOCqzmXSZKsThuoFb+tWVIvpXIpTqbH4ue/f2pQKrKxdBJrPtM5pwoyxBXc\nwit/vYR+e7o3OA2FEBA4Of4Cjr1yGifHX2AMcCViCa5Pjoctr67zzOZSWYelFxc1+FqS4uaJWD9g\nI+5MT2SQpu4Ee+RDbOrxes+rq6Rbsw/YC2T5KKsmHYtUaqb4rMTBBpMjAxHs4UOdZ/yMh/C190MH\nPZdYXZTUmi6A29OTniZVIa/A8Sem6Rgq1ArSbRegpVq3batEljX9N26sUHuYjS0OegdAVJfu5ckX\noIu4ea5LsLUNzvq1R0+hGO48HqI8fDDKsXmcpCQCIeLadcR69zaIa9cREgHd0TWpOBFPysn01ifl\naUgqToRULiVTv3Weo61zZ0CslmDXzA+1jsfODzFuQABEgVYQtiUnbYRtrSAKpBt6tAQcTIpGQd0E\nUJY0E78/OkjbLpVKcfv2TeTl5eH27ZvUpK5mvf4kb3OgulaBlJwyVNc2j2bd86qrMSiqMiCfoPdu\nd+I23LDFGHT1MEf9PoTxvlRCiRGHIhv2DanL/amWG9eJtcCCfzo8PT0xadIkbH0NkHsAACAASURB\nVN269Xk3xQIzQalUYtu2bXjzzTchFovr36EFwWBE2fDhpocFa8DhcBATE9OkBgFASEgIFAoFkpKS\nEBgYCABISUmBjY0NQkJCsG3bNshkMurHvn37NkJCSNe5zp0749atW9Sxqqqq8ODBA7z55ptNbpcF\n/03oR73I5JVIL3uCgsoC2ixhWlkqfn90AB1cghsU6cKGPFkevrjyMSBSkto+OrAX2qOstqzR7my6\nMGYOQAgNz7ZVKWV4+/Qs+Nz0xZkJl2nnKpVLMfvEDCjVSrhZu+HXob9h+O+DWY+jhhobBv0AAHj7\n1Gwq2gkAq8B+kcywBo+/gz9jXSvCHecnXsPVnMvILM/ACP9RBqO6QtxC4SJ2oWmTSeVSXM25jMg2\nQ2hlpXIpQ+MKIDVJ3MUeyJUxtQYyKzKQVJzY5JlrDcGkEQmuD9/fWodqRTUeFj2gpZzJWxcAr1mR\nCulNaEv/PT2RKSUJwo8vL0bctAcmRc6xGQtofpsQt1A4CB1RaoBEyZZmUfuxXZ/GwtfeD1emxiFi\nT08UZfkz7j/Ns1hWW4r4/DjWSK8Qt1AqgsDX3g8hbtq0O4lYgslB7CHnIW6haCV2x1NZLm39j39v\nxMSg1xBshGxqbuTJ8tB7d1dKbzCtLNXg+QPM8+zl0Qc2fBtUKpgDvIXn3sOl126a9L4c4D0IDiJH\nlNaQ94UaTOUILrhQQQ2wbKNQp424zH8/JoZ3wd0yOknJJt5uKsQ8Pmrq6s5WyJFUU42u4uaJjAq2\ntsHhts9GZ1AiEGKykwvrNo15hSbCNtApCPH5ceS9XNCdeo4KM10wfPM4fDd9BjA7jIrWrOYdBo/w\ngV9se9QkVUMUaAUe0bJkMPJkefjs6jLauuik3zA1eDoAkgwbMiQCjx8/gkAghFxei7Zt2+HQoRiM\nHTsCjx8/Qtu27RAbe45mRmVOVNcq8OX2W8gtksHdWYyPp4fBStg8iSPPsq56IZWCn5QIRWAQoPPb\nFlUZ6C/o9S2O3ziDN0e4sZdtBHT1MA1pWhZWFZjcH0gqTkRKGXm87MosDD84yGh0vQUW/NPx3nvv\nYcSIEbh58ybNoM+CfyYOHz4MsVj8j0q51MBgRBlBELC1tW3Qn7k+/j4+Phg0aBCWLFmCe/fu4dat\nW1izZg3Gjx+PXr16wcPDA4sXL8bjx4+xdetWJCQkYNw4MkrklVdeQUJCArZs2YLk5GQsW7YMHh4e\nlvBNCxoF3aiXyOhw7Lz/K3rsDsG3cWuw4sbnjPLzz89jddVrKGJSDlODUn3wOQJsGvQTvum/rtHH\n1yC1NIXmrJlamkJtG9tuHEOYXx9PytMYhJEuAZJflY/fkw+y7UrBk/BCX89wnBh3Hu42OpbBLAL7\nU46NNxi15GjlRFvmgIOx7caBEBCIbDMEr78wyyiBQwgIfNCTGfl2v/AebVkql2JQdF8qklD3WhMC\nAifGn4eHjScAoLWtNxUxZw5iE6D/vqbget5VvBE7FatvfU0bIBRluiIpqWlBxVdzLlMkGQDIVXKc\nSo81aV9dZ0j934YQEFg74HtDu4KjI9A849hrJkWyGXO91IVELMG5Sddg65Fp0JEVAKoUVQbr4tZ9\nWrkNCNrWRLwRXOZ39Nvba0w+TnPA2PvIFBACAkcMuIrmVGbjz+RDJr8veXq/qSZylQMOlvX4FAkz\nkvB57+X1H0hUieVZwzH2aH8EOLQFn0MO8vkcPjq5hpjUFjYEiqzQmsuvayuQXVNN236xogx9HiSg\n36N7ZhH6T6upwuS0RwhOvIPoIno0/f2qSryTmYb7VeaLQDlcUoTuD+/SjAMIAYHvXjqOF/qfhjw0\nCldkOk6Deu/xTOtjqFJUQWAtB7xuQGAtr9e5tCWAzZ3VQycaOikpEY8fk+9leZ3ZwePHj3DqVCy1\n/vHjR0hKaj6n4ezCSuQWkemruUUyZBc2X+TRs6zLKPLy4NinKxyHDYLdwF6IT7sAqVwKqVyK0xkn\n2PfRuydrnJiTXk2BsW+DBhxwTL7vNRNwGmgm3Syw4N8KgiBw/vx5C0n2L8GYMWNw6NAh8HgtawLM\nFBic/omOjn6W7WBg1apVWL58OaZPnw4+n4/Ro0fjgw8+AI/Hw+bNm7Fs2TKMHTsW3t7e2LhxI7y8\nyA6Ll5cXNmzYgK+//ho//PADOnfujM2bN4PLbZFZpha0cOiSEillyZh/fp5J+2lc9YxpCxmDgCtg\n1UcCgKKaQrx9mkyL9HcIwMlxzDQyU1EhBRVhBJdE1HTTaglJxBLEz3iIzy4tw8Fkw++Dd07PwcVJ\nN6g26EYX+NsH4PfHzAGGLq7kXIKvvR8kYgkuv3YLV3MuY87x11EhqiBTTvV+g+33fsai7ksYx9FP\n4fQiWsNG0LBIjs6tOjPWJZc8hlQupc4vPj+ONkucUppMmxmWiCW49NpNSp/kcQkpgm0uDTvd31cf\n73aZj+/urGXupLmX7J+QA4TCIDi3LkBgE9ObMsuZqaa9Pep3NgK06auGtMYGeA+CmCeGTMnUjtSN\nJNKQc4YitTS4mnOZ4Xpp6Pm8WxCPCm4u6/2nAVt6KECf/U8pS25QFKFELMGPw37B5Bh6enAbO1+T\n9tdAqZSipiYRIlEQeLym33P6EVatxO5UpJypdQW7dMTZ8Vcw+o/hKKstpW17/+xcbL7zfb2ai/H5\ncSjSc7VVqkkCTw01BrcZAolYgrHtxuGzKx9BDWaKqD4elz7C2YzTdVpaZIrm45KkRusJptdWI1NF\nHksJYGbOE+zicvGivSMuVpThlYw6R0g56Yp50DsA/WztG1VXWk0VeiQ/oJbnPiWfx/HOrrhfVYkB\nqWR6477yIhz19kGYLXtEmKk4XFKEmTlPAJDnFQVglKMz7ldVYnhGOgAuoFRiSlYqoloFULpKus+R\nj6sbrPnWkKtIMkmuqkVWRQZcVC5IjUxEbUoNhP4i+J0MalFRZWyp/SP9X6L+HxgYhLZt21GkGADw\neHyEhITC3z8AKSnJ8PcPQGBg80UAerrYQOJkjbziKkicrOHp0nwab8+yLoOQSuHwYjj4uWQUruhJ\nOtZ9NxJJ3fywNuJ7RnQuBY3bdt096e/WeJMYRpPkUtbvoj7UUONG7lW85D+63rKV8krkV2kng3zt\n/VqEY7UFFlhgwb8dZmWPUlJS6i9kIgiCwNdff43bt2/j+vXrWLJkCYRCUhejTZs22LVrF/7++2/E\nxMQwLGf79++P48ePIyEhATt27KBpnf2TIZVLcTvvpsUa+hlCN+qFBp0oLEMwZVbREB7mZjD1kVjq\n1Az4GwOpXIrd52/RUhBsy+iOjRKxBJ/1NR6dkS3NorVBV79rdcS3KGQxBNBEBAm4wjoLd+2+kW2G\n4IehP5MrRJVkupsOSfFDwkbWZ0BfVyhT2vBZ1/A24XDTGyBHP/qNpj+nf109bDwZnVZCQCDQKQij\n943D2E1fYOGJjxrUDmPQ/L5vdaaTti5WLujvPYC5g64ey/ZzwPQIYGYPjFu9Hk0NAu7hzozUTS59\n3LSD1oEQEIh55ZRJZV2tjKfNSOVSvH9mLm2dseeTGuiw3H8aOIqcGOsA8p3h70A6APo7BDR4QNPL\now/crOn3YCsb0931lEopUlMjkJY2CCkp4ZBKL0CpbNo3o5dHH7Sx8yHbInanNN5060pNjai3nmCX\njrgz/QFmBDGdgk3RXDSWKg4Aa258DYB8b92dkYT+Xoade91tyHTLtg7t4Co2X9rVD4VME5Mvn5Kp\nwivzmCnZbOtMxZ4S5u+xPD+bpR0cvHprj9G+Q54sD7sTdxiNzvwqL5t1me2c1xYV4+S4C+R7Suc5\nqqgpR1vHQEY0aVV8JWpTSDKqNqUGVfEtS4dJP/XZxcoVA7y1kgIEQSA29hxWrtROVCiVCkyZMh4q\nVf2ErQUNBz8pEYJcOhnmU0qmO+ZKcyDgCg3sCdo9qR+N3lhoopYXX5xff2EAFzMvmFRub+IuakIA\nAF5tO8GSdmmBBRZY8AxgMlGmUCiwYcMGjB8/HiNHjsTw4cOpvyFDhqBv374YOXJkc7b1Pw1jwtcW\nNB80pMTKfjpROkZEvnVR3QSirKdgNl0fKSdMW+fWW0Bqf6rek09iG3U/JBUnosj2HC0FYWh3pqit\nRCzBnE7vMA+gQ9zpD2A1AuMhbqGQWDMH+TFjTmL9gI2Im3afNXKjl0cf+NqxO0JJ5RUMcpASjtaB\nj51vg0kKQkhg30imQ59GkwlgXtdlPT9l7bTGZz1CyurfgKjrSFn9G+KzTE+XNAV/pfxBW94xbC+p\ns2alZ1yir/VW5gN43YBSQI/qaQziC5gkbXKJaUSZKamQwS4dERW5g76ShTCeFTsDl7IvGHwOruZc\nps3I14cR/qPqLbM/aa/hjWq9fxsAQkDg6/DVtHVLLy00qHWjj5qaRNTWalLAkpGePtIkEqs+aFIT\nbQQ2VKSmbl21tY9QU1M/MU0ICLjaMIkpLrhwsjIuRl8gKzC63UZHV1EilmB2Z8PapAKuEIdePoJD\no2Ow4po2jV5fV66hmOPCPLdJjmQk12KJB2Mb2zpTMcmROcBf5kamfU93sAXUdTegGpB9PRLHHp5j\nPU6eLA+hO4Lx/tm5CN0RbJAs+0jiybrMds7LJJ7kd6BVGG19UU0RHpcksZihcPSOoL/8fNHJNYR6\nBnjgIeaVk6zv/U2bvqMtZ2dnIS2NfHZTUpIRH2/eND9dpOWWI6+Y/D7lFVchLde4K/I/pS5DUAQG\nocBNG42pAnC8Tqr0j0eHqKhFNmjS4nngoa1joFnaoxu1bMpkKo9j2hAsv5L+PJZWm26AYoEFFlhg\nQeNhMlG2YcMGbNq0CdnZ2VAqlUhLS4ONjQ2qq6uRnp4OqVSKBQsWNGdb/9NgE7624NmAEBD0qDIW\nkXk2/HI3ClviNzbICVADvzY8gFfXyePVQKh01NZZ1B7YcY4i6bYkbEDYjhdMHkhr4GXrDZ5VNc1Z\ns1iVzlq2X2u9FDU9sjAln/0cCQGBRd2XMdYnlT7E5KBpBtObCAGB0xMu4dDLRzC/64eM7frRQEnF\niUiveEJbt7zfqkbNul7Pvcq6fv65eZDKpYzBekUtu916VY4f7T6pyjHNCt4UJBUn0rTBAPI3JQQE\nxrTVs15m0XoDgJmd/tfkdrClWbpYm5bepSt4bCwy0tNOZ3BugKSuUsmMOs+ykXeGUicBrQMm37A6\nAcQCMWtdbKmXDYUVS9uiEn4waV+RKAhCIT0KVpfEksvzUFy8A3K56e8lQ+ekW5dQ2A4iEZ2YNlSX\nkMeM9FBBhVcPjzJK+o/wH0XTp7OpAbpnkf8CQP/WEbTybNF5GmRUpMOab42sigzq3ABgbcT3TYrW\nCLa2wVGfdrDmkO304AswzZkkkvrZ2uOgdwDacvgIFIialHYJAL4ia1wP6IBIsS1cuVxsbOWN8c4k\nUc6RpQF/bAaOSYD/6wrEd8KiPb+x/r6n0mNpqZCGdAZHOTojysMHPjwBzWVT48DZR2QDX74Au7z8\n8KK9IwCmdqQG+m6t1iFiCPxFAACBvwjWIS3LGSurIoNKz1VCieJqplB8UlIiMjONp93Nnz8PUqnU\n7E6Y1bUK/HKM/q759dhDVNcqzO5O+SzrMgqCwHevv0AtcgG41XUNTmZqnWztBMxnTFWXlq2EEncL\n4pvcFKlcikXn3iMXdL9Tm/8GKtgjVvclGY/y1OC1DtOMLltggQUWWNA8MJkoO3r0KLp27Ypz587h\nl19+gVqtxsqVK3HmzBls2LABcrkc9vaN7/BZYBzGhK//q3iWqagb73yrXTBAPOjjUu4FfHplKbps\nD2oQWSaVSzF+57uAsm4wqRShn093bZ0a6JB0xTVF6LE7hCE8bwyPS5LIcP66FARPZ0eD91Uvjz5o\nrSs8q0cWSrOYkWia6/N3QQJtPZfDpaVbGgIhINDXMxyhehEJAPDRpQ9p1z3QKQieNp60WVxjRIgx\nGHK8SytLRVJxIkb4jyI15EBqyRmKPrL2SKXfJ27s90ljwBZ5oyGtGASYRo+ljgyFqBKT208zS7qZ\nxn1SF4VVzFTbpiDQKQj+9mQqY30kdVpZKq7mXGYcQ5+8c7V2qzdqyNfeD4fHHDe4fc2tlRiwrzfj\n/dPU1EtDsLdyNKkcj0fAz+8c3N3p1uocjjXk8jw8ehSM3Ny5ePQo2GSyzNA5aepq0+YI3N3p5iLG\n6upgwMGzPpFqiViCqCFkhKFPEfD4e+B6FHBrK0mWuRP06CxCQODT3l+yHkuTMq3/LOlrHTYGYTa2\nuB/YGcd82+NSQDAIHQHbfrb2uNyhMy6269gkkkwDX5E1dvu2w/2gLhRJBpDXTFxZDawKAtLJSLvK\n2gqczWCmMwtAJy5t+XYG6xvl6Iwb7TtRJJkGwdY2+D2gPa4HdqJIMkDrAku110DEHo/gwf9kEHyP\ntYd/C9MnA0zrg3l5eYPLNd7utLRUxMfHITIyHMOGDUK/ft2Rl8d8DhtKpGUXVqKwlK6jVlBajbTc\ncnz+600s33EbH0VdR6mUqbXWUHLLWF1fbr+F5TtuY/EPV5FXzNSXNDeRNuSVz5BY93pPdAHuuzLL\n9PTobdRYpSH9JkNIKk5EdmVdarLud6rMF4i6xhpZJlWwP4/6qFbSJwZLaoynoFtggQUWWGAemEyU\nPX36FEOHDoVAIECrVq3g5OSEuDgyAiAyMhIvv/wy9u41kopiQZOgq/tUn+DxfwH6qah5srxmI82k\ncike1QmyA6ARD05zh2JBn3mw4hgWRVeoFYhJOWxyffH5cSggztJIlvkvDwR3Vk9SX8o5iVoP+ye0\n8P4B0b1xMt20VMxcKV0b54OuiwzeV4SAwPmJ17B7xH583nsFCHe6I+C23HlIK0ulroHu9YlJpZ/7\n6vBvGySUzSaMqyGtdNt3aPh58LfdAaKuQ7AtAW1tuppchy4CHNqyrudxeHCycoZELEHctAd1qaMP\nDJ5LiFc7+C6YSBFUn9x8x2z3p74eGwAqwsHX3g/XJ8djRtAbGNQ6ktyop7W1++EOREY3zZnVEEx9\nN4W4hVIEmL99gEHiSuMGubb/9yaR1HfymJFp+uTdrE5vmtTOMPfuODv+CiYETsa7XZi6M+nlT3As\n9QhjvUaTqLHaRGwkbxdJw9IBc3PpEd6pqQOQnf0BAE06Ui2Kin6EQmHiPWAknTQn5x2kp4/Eo0cv\noKrqHqTSCygs3ECrq6JCG6XUyTWEFhmmgbuNe73E4gDvQQiSOyNpI+BeJ2PVvggYVuzCeg9lS7MZ\n6wDg99ExIAQEjqcdpa3XX24sKlVKbCt8itCku9hZ0PCo4oYgT16LtzJS0O7BHaouQkBgZD937ffC\nOQnwvIXYNDr5K5VLMf88PbX+x7ubDNYlVSoRlZ+HocmJJrl2EgICp8eT0cGHXj6C0+MvGXz2eAQP\n4q42LY4kA0zrg2VlZUCl0mpJrV37PYM44/P5KCkpRkoKGcWYnZ2FoUMH0AgxqVSKIUMiMGzYIAwZ\nEmESWebpYgN7QkBbx+UA0io5lSJZXF6Dr3bcopFU1bUKitz6cvstkwgsQ3XVylWUE2a5TM4g5hpT\nV31o36Y79v24GD1mAt1mAZUiZpm7BfE4Pf4SpT/qa+dHI85W3VjeqMh/XQQ6BcFOUEcw2z8BuDpp\nn2W+BjMPfozfXO932MvWGxKxVsJi4fn3LPIrFlhggQXPACYTZSKRCCKR9gvk7e2NpCQtedClSxdk\nZmaat3UW0KCfqvBfhn4q6vCDg1j128wRdUbOFOpFzogqsW7yVNyadQ2Lui/Bphe3su9cB6OisnpI\nK01lRAFxrCqR8L/bWP/6OExa/x25fnoEKc6ul4Y2OWacQc0nXcTn36EtP6wnRUwjtP9myFx8PnAp\nrX2V/Kfo/VtX6hrE58dR16egWiv03NrWG2PavWqoClZQ6VY60WI8Do9hrZ6dag9FPklyyfP9kZVi\ny3a4enEl5xLreqVaSaWG2Qhs0N4pyKirJiEgsHbICoqg0rhjNhVpBflYceA4bYZaX4/N194Pqwas\nx09DtxuMkEkpS2aNvtLA2LOjEf72JLzQSuxO27bg/LvIk+XV++xpCLBjr5ymxOENgRAQ5HFYouP0\nEXX3B0adAY508lNfmNsYgl06YsOgLeju0ZN1+zun59AGWfH5cUgrJ9Og08pTG2W2oR+F08bOB708\n+rCWVSqlkMlu0jTIystjAOhrBtWgsvIv2pqiojWIi+tWr36ZsXRSqfQ05PI0AIBKVYTU1N5ITx+J\n4uLvacewttaSWI9LkmjOpRoMaTOi3u8bISBwwm4+hHq7r+7MrhUo4rGMnKE1ndB3M2RzN2wo8uS1\neOHR3zhQUYpStQrz87OajSwzVteQwH7A7K7k8zK7KyCqxB/JB2hp+knFiahR0c/53VB2MXKpUok+\nD+9iaUEW4mpkeCUj2WSyrK9nOPp6hv+j+y/19cE0zpcA0LZtO4wZ8yp++ukXWhmFQoGCAnr6fnZ2\nFpKStM9UfHwc5Z75+PEj2jZDsBLyMWNoe9o6lRoor6TrdBWX1yC7UPveTMstp8it3CIZbVtD6xIK\nuLATawk0pUqNuynaFNXG1GUKvD064IYXO0kGAE9luSipKca1yXdw7JXTODw2FvZCB2q7Qq3AoUfG\n3blNAUddN6Qq8wFUOn0++zSDmQc38q6h/56eBr+TUrkUIw9GIk/2lFpnrr6EBRZYYIEFxmEyURYY\nGIhLl7QDSD8/PyQkaFOqCgoKoFY3QrnYgv8MzJkqqZsG0ZpojcwKMupIV7/NXAYIgU5BrGRDkEsH\nqsM8wHsw5QrHhgXn55k0YymVS/HZlTqHxLooIK5VVd2MogSTg6ZhafgHJPlS5mMwDc0UN8yeHr2M\nLhuDXFXLiFLSuDJpCDI2t9CV4WsbPFCSiCWY3+krmjaVstoKR5L/pMpI5VK8f38AFW3kH6BAYGDj\nonkGtxliME0jsyID8flxJt9XbR0DKZJUwBUyyL2G4n7OE/QMV6F8ywlg622KLJsRPJP1dyUEBC5O\nuoFtQ3bgrc7zsGnQT7Tti86/z9p+Y89OniwPXbYH4f2zc9F7d1fwuXQdLzXU+CnhB/Tf27N5zEeM\nOFECQGltCePe7+XRhyKefO39DJJOxtDJNYR1vQoqWsRoiZ7Qsv6yKdCPwjk74Qrr9TXkOllYuMXk\numSyh/WK8BtLOysu3m5SPWVlB+stYy2wNuleEQ0fBzWHHpHmVM4u3D223TjW51kTqeqsl3qpv9wY\nnKpgCpuvKGi8u2Vj6xrgPRgOhID2vNSqatFjdwi+u70WebI8BDoFoTXRmra/s5j9N0iqqUYu6O/V\nprh2/tugcb48duw0YmPPgSAIDBgwGL6+WtLb3d0DAwYMQps2PtQ6Ho8HJyfyN5dKpZg/X+to7O8f\ngMBA09K3A70d4eqojW53sBWio68zXBy0DBKXAxBWJJlVXavAr8cfUtskTtbwdDE8+WOsLk0db499\ngVbOp5Vtk+uqD1kVTAkAfVQpqiiiM6siAyW19PTF2iYS5PH5cShT1Bnk6EY+26cBM3sa/F4BpEP3\n74/Y34/x+XFILyygZQ6wTRRaYIEFFlhgfphMlE2aNAknTpzAjBkzIJVKMXToUPz999/49NNPsWPH\nDmzfvh0dO5o+S2/Bfwvmdu3UTYM4+uoZ1kGcuQwQCmT5DC0mV2s32mCREBA4O+EK0x2yLgpKXSPG\ntzfX1FvX1ZzLqJDTBz4qtQpZFdr0Q4lYgrPjr7CnoRlxotTHAO/BlO5Ya1tvmtV9fTDmCtjWoR1C\n3EJxaHQM3uo8j7atsbphvjUvMUjBL699Qt1HV3MuI736HhVttHTbXyAaGbggEUtwejx7VJlGp8nU\n+yqrIoMmkq17HRuKPFkeBq57F+qiuuiookAgm9Rv+/3xAYP7EQICL/mPxmd9vkJYq260bdnSLNb2\n6z870Q+1osM77v1ME7XOkjIjiX+8u5FGXrORtg19J4xtN47ShuNxeDg65hR4MC1FS0M8HXvltNHU\nL2Mwdu1shXY65ei/h/6yqTAlCofNdbKy8gZqaxsSxSYEh2P8uTSUdlZVdQ8yWf0aOwBQVLSe0ikL\ncQtFa4I50NuSsAGR+8ORVpaK3Yk7DE8uSCTIOnESijqurJYLlAwZxFrURmDD0OPjc/jUO4xyqauD\n/nJjMNiWqfG11LXx7paNrYsQEBjXfpJ2g873Yfn1z9H510BUyitx9NUz1LfAmAZqoMgK7nrdxqa4\ndv4bQRAEunbtBqLuA0QQBA4fjoW7O/k75ebmYPTo4ZgyZTq1j1KpxKuvjoJUKsXVq5cpl0wA+OKL\nr6lj1QcrIR9LJneFoy05OVNaUYtVe+IQ3kl7jVRqYM2+eFTXKpCUUYqCkmpq28SBAbASGjYwYavL\nyY4kyIrLarB6Tzw2HfqbVm7Dob+bXFd90I8YZoNu3yPQKYjhDu1kZZoJjVFoni9AG/n81guArTaq\n3knETkLPPz+P1ZCppLyWYWCjVCub1JdoKJ6lHrAFzQO1Wo3Vq1ejR48eCAkJwe7duzF16lQMHDiQ\nKlPfclPRkOPJZDJERETg9u3bZqvfXFi8eDECA83jlPsssGHDBgQGBiIrq/4JhYbi1q1biIiIgEzG\n1KP8t8BkomzkyJH46KOPkJWVBSsrK4SHh+PVV1/Fvn37sGLFCohEInz4IdOdzgLz4Z/8sWoO107N\n7KBELGEdxHnZepslmmf7vZ8Z61aGr2EMXgkBgUU9lmh1KvQc+rbf2VfvtWNz52PT7Ql26YizU0+C\nM6uHNg0NoNV3Nyul3nMT1v0+wgakhgI6ZJ0eeOBh14hoAMDYP0Zgc4I2/YrPETTahr3Q9hyDFJQp\nZNR9ROmY1UUbFSjSGlWPBvriuRqs7v8tI7qQTVhfA3OacMSkHIaaoxclV0cUTGg/2aRj6Gub6RO+\nGtAE9AEsvjgf/fZ0x/3Ce1h962vDFdQNFGpk9AHQB2eZ+mwNfSfoasPFoTBKIQAAIABJREFUT3+I\nMPfu+LjXF4xyXHAbfZ8ZQ6BTENrY+rBuq6jVkttetvToHP1lc4LNdTI3d1EDj1KL1NTeqKkx7prL\nlnb29OlnDahHRdMpq1Kwd6xSSpPR57cwvH92LkJ3dDBIlt1vxYHnB8Dro4DW7wOJfKYLIUCS6Lpp\nS3ZCO1x+7RalLTi94+u08vrLjYFEIMTf7V7Aq7YOcOBwsdbNC1NdTddlNGddlLkHi2OsCipsv/cz\nJGIJzk+8Vq8GKsHj4XL7Tljh6oVQkbjJrp3/dOgK7hsT38/KykBurjbyLjc3B8uXfw6ejslDZmYG\n4uPjsGjRe7R9ra0bNrlUVF6NkgptdGVJRS0OXUiDbgBmURkpvL8z9iFtX6GgYdpwReXVKC4nI7FU\ndQkl5TI5TX3QXHUZQy+PPjQNLzbofrcJAcGY7HtY/KBJbfAUtgcvKk77fAGMyOdlPT7F+UnXIOax\nObqqMeJQJOM7WZDuxpgk9LX3e2aGXuae5Lbg+eDcuXOIiopCSEgIli1bhl69emHOnDlYunTp824a\nKzTkTteujdMabk5MmDABq1atet7NaBEICwtDQEAANm7c+Lyb0mwwmSgDgClTpuDUqVPg88lB0Fdf\nfYXjx49j7969OHHixD+KYf2nQSqXIjI6HMMODmo2Ee7mRHO7drIN4swVzdNVz3XR1drNYPSVRncJ\nAMOhT5HfzqgmFMA+qP6/jrNZBy7BLh1x939xGN5PQnbG9Oo7d/up0fvEmO6QKWBzXlJCiSs5l2gk\niAYKtbzR1yBA4s6qTaUhqUb4jwKfQ76XdKNFGotApyD42vkx1nsSXgyyiU1YXwNCQGDXiGi8F7oA\nu0ZEN0mfx1ZoB3jcApzrBhzODwGPW3AUOmFi0GsmHUPf0ZON8NW0e3XEt7R12dIsjPlzOKOsFbcu\n/YZlIK7Bk/I0xv3VmHeCJv1YQ3J0cuvMKKOCCjdyr9LWmaOzTwgIrAhfzbqtk4u2HY567pT6y+aE\nxnXS1/c0/PzOQaWqRE1NvF4pJ5OOlZe3vMH1y+VsaXeGuhUC2NqSbrdJxYkorNYxWNCJdAJARSzK\nVXKDRiiBTkGwb90Ov4QC9q0N3z/6ZiBCrpAWYeYqdqMI0Da2PmZxgwVIAmuVpw8m2Dvi8/xsROXl\n4kRZCbrdv4PI5Pu4VVlhlno0dW329sci51b4LD8Ln2al43BJEbrdv4PZBVX4dHC0QcfYxCJSO8lU\nDVSCx8NMNwmOBwT950kyjeB+ZGQ4Bg3qS/1fnyzz8vIGny9gHEOpVFJkmUbbLDtbaz7h6emFkJCG\nmXg421mBy/IIqtVk2iUAuDuTRE2xDqHmZCeCr7tht1NDdfF4TFMONcxflzEQAgKnxl806lirrz3a\nvVUP2nKIW5dG1y+VSzH2pw+hLKiTm6h7vlysXOBqTb5P2tj54I1O/4NELMGXfb9hPU5hVQHjOzmi\npz8EbnWTnnWThMYcPM2N5pjktuDZQ6Mp/sEHH2DcuHHw8/NDnz59MHiw6ZkkzwqZmZnYsWMH5syZ\n87ybwoouXbrg5Zdfft7NaDGYM2cOtm/f/q/VqTf5bTtr1ixcv36dsd7HxwchISG4fv06xo4da9bG\nWaBFfH4cjdRojED088TzcO0MdAqiUuU8CS942XpTIuQNcTjq6NKJthz90h9G2+9r71eXGvmAEQVV\nnw25FZ/pnmlMeFwilmByh2nkgl4qZgJnFyL29jJICuj+Pv4OAQ0mLw2ldoa4htJIEA2aEtXXy6MP\nnOysGDO0fyb/Tv3fxZpMpfC09TIqsm8KCAGBtQO+Z6zfn7SPocWom3anjzxZHvr81g3fxq1Bn9+6\nNdpZSyqX4vMrH5HnPjusTpw7DBBVYmPkjyY/T708+lCkQCuxO7q7G9ala+sYCD6HPrgrrSlllKtW\nVcPFygWcghcMaubZ8AnG/WWOd4Kuc6YuLmSdpy2bq7NvKHV41B9DqWtrqpunucDjERCLyYjSx497\nA3oaUj4+0WjX7jEkkrVwcfkKXG4H1uNUVzfsNykrOw65nP4+c3VdjXbtkuDuvhFeXtEQifqCIMbA\n1fVTtGv3AAIBSXDSovOMEKwAk9zVwNT7Z4T/KFqKbmF1Ie36JxUnIr3iCQAgveKJ2QaCUqUSYQ/j\n8WNpEcqhxtLCHEzJSkU6VEioqcbwJ4/MSpZF5eViaWEOKgBsKSvEzJwnVF2fy13xv9GzWR1jh/u9\nZLY2/JeQlJRICe6npCRT6ZIpKcmIj6f3z7KyMqBQyFmPo1QqsX79RsTGnkNISChFmLVu3RrHj581\nOe1Sg6Lyahgy21WpgRnD2uPj6WHwdbejSCxnOxE+mhbW4FTIovJqKJXs2sTmrqs+SMQSXJx0A2Pb\njmfdHuhANx9wJzyMLjcEScWJyLY+Tnu+Vr7yBm5MvYvrU+Jx7JXTNJ3JMe1egZ2QnWTWj1CXONgg\n7pIN3ttygJokfJZjgOae5Lbg2UAuJ98/Njbm0QVsTuzcuRPu7u7o0qXx5LUFzw5hYWHw9vbGrl27\nnndTmgUGibLa2loUFRVRfxcvXkRqaiptneavoKAAFy9eRHJy07U9LGCHPilRn/5USwQhIAfLScWJ\nZo+ISytLxYprX+B+4T1aeqpCSUYmZEuzMPJQJEJ3dKhL6Qk2mbQ4nnaUtnxdL1qFDcEuHXH99UsQ\nze5Hi4KS1hpPn9UfiEvEreoVHu/l0Qd2fDtWR8CMinTjHSq13r8NQIGsgHX99dyrIAQEDo2OgYNI\nG03TlKg+QkDg4Mt/Mdb/mLAJebI8DN0/AE9luQCA9PInZulEtnUMJN02dbDm1tdYemkhbZ1u2p0+\nYlIOQ6EmOygKtbzRzlpJxYnIr6q7X3XE7N3EkgYL03Prwg2eynIx+o9hBu/FrIoMqu0aOAnZo5MK\nqwvxfxG9WAfiAFCpkKJAls/Yr6lOvpoIzvHtJtHW66e2mKuzH+IWCmcWjRmFWkGLfFod8S0OvXyk\nXjdPc0IqPQ21mv5MikQRsLHpDoFAAheXWZBI5iEo6BpatWK69MrlqfWmX2pQU5OKrCz9AakQzs6T\nIRBI4OQ0Dfb2QxEQcBRt2myHm9t8iiQDyOv2ZkidnqOBSCcNAhwM6w+Zcv9IxBJcmXwbbnVRiPrX\n31wp+vpIqqlGfV/pdflP6ylhOlYW5hrdnuLaBfN/PET7Prhau2GY3wizteG/BF2HSy6XnkZYVVVl\nsKyrK10by8XFFW3a+KCyshJJSYk4dCgGx46dxvnz1yGRNDxd19PFhiKldB0oNcuuDlaorlUiu7AS\nCyd1wbJpXfHlzB5wIAxYRraQukwBISDwYXf2VLIjqfTIVNJoR6t5aSwarT4EOgVB4mhL63+1dfMA\nISBY31GEgMDJcTqTOToRtbsf7GQcX+JggzeGhYAn0hoOzD8375lkljyPSW4LzIuBAwdSqXGDBg2i\ndMIao0GWnJyMt99+G2FhYejcuTMmTpyIixcvMspduXIFEydOREhICAYPHoz9+03r+1ZXV+PQoUMY\nNIiuOTp16lTMmDEDZ86cwfDhw9GpUyeMHj0asbGxjHJvvPEG1q9fjy5duqBXr15UNF19bd+6dSsC\nAwNx/z7ToXbgwIGYNo0MSmDTKMvOzsbChQvRs2dPvPDCCxg1ahSio6NpZQxpm+mvV6vV2LhxI4YM\nGYIXXngBvXv3xsKFC5Gba/wbDwAZGRl455130K1bN/To0QPffPMNRZLq4v79+3jnnXfQu3dvBAcH\no1evXpg/fz6ePiX7JKmpqQgMDGRNMV2zZg06duyIsjKt4/WLL76IgwcPorq6mlH+nw6DRFlZWRle\nfPFF9O3bF3379gWHw8EXX3xBLev+hYeHY9euXRb2txmRWppidPmfgPuF99D5xzAM+24J+u8YbLaP\n/P3Ce+ixOwTfxq3BgOjeZHrq/nBS4L0uUgAgCRS5inxhyFW1OJUea+CIWkjlUmy8Q09BcxW7GihN\nh6+9H+b0mEGLgtr54Bej6V/6nbW9Iw/VnwojIHBywgUyHJ/FEdBQh6qpqZcj/EcxiCQAsBWSLlcX\nMs+itEbr+NdUpyY23bCi6kKcSo9FdiVdpLJKwa4x1hBkVWRAzcIg6q7jgms0zVM/GubHhE2Nuu+t\neOyRTF/3W92gjmtScSJNMNiYzbwuueRp44ndI/ZjQpBhLbQDGT9rBwrTI0jCQyc6iE3rzxwgBAQC\nHOnRi7sfbqcR4ebq7BMCAqv0UlI12HTnO+TJ8hC5Pxxj/xyJheffYy3XXJDJbrKtZS3r7DwRPj6n\nADjTyiYnd6EE942hpIQ5cygUdgCPZ/rvSqZLCwD7JwCvbgDIqyGXdaCfMtUY+Nr74drkO6zX/25B\nvNkMN3QRKLKqN+n1AzfjukoNwWIX93rrervn6/DtUAiIKuEu9sCZCZctA99GQuNwuX79RqhUSto2\nfV0xXTfMI0dOQiAgiVkulweCIDB27Eh06RKEYcMGYfjwgfDy8m5wJJkGVkI+Pp4ehmXTumLJlK5U\naiSHA4iEPKzeE4+Fm69g+Y7bWL7jFpztrBod3fUs6zIVvvZ+uD45HkO8h9HW60tokNIcZH9QqVZi\n7J8jG90nrZRXolBWQOt/bUkwrtnja++HXcOiGRG131/7gVXU/3FJEpRQUMtpZanPLA2yqRNaFjxf\nLF26FJGRkQCAJUuWNFqXLCkpCRMmTEBycjL+97//4f3334dCocDs2bNx9Kg2oODKlSuYNWsWKioq\n8N5772H48OFYvnw57t0znlEDALdv30ZFRQUiIiIY25KTkzFv3jx069YNCxYsAJfLxbx58/DXX/RJ\n9Li4OBw7dgwLFy7EmDFjEBAQYFLbR44cCQ6Hg2PHjtGOl5CQgOzsbLz0Env0dWZmJl599VWcPn0a\n48ePx6JFi2Bvb4+PP/64UVpmP/zwAzZt2oR+/frhk08+wbhx43Dq1Cm8/vrrUCqVBvcrLCzExIkT\nce3aNUyfPh2zZs1CbGwsdu6kk+9JSUl47bXXkJ6ejtmzZ+OTTz5BeHg4YmJiMHfuXACAn58fgoOD\ncfz4cUY9R48eRb9+/WBvr42K7dGjByoqKhAX98/KdjMFBr9Yrq6u+Oabb5CQkAC1Wo2oqChERESg\nbVvm7C6Xy4WTkxNGjWqaLpAFhiHkiYwut3SklaViwM5IskNQGIRMl0T8FLQT4f5hCHQKatQHOE+W\nh5iUw1hx7XPGtpTSZIYwvkTcCsXVRZCr5BBwhRjcZki9dcTnx6GgihkJYypshPTz0uh6adK/ukro\nLoT60WsXss4ZTb3UwNfeD1cnx2Hg3r6oVNI7e5oOlX5dGiLkcemjRkXZSMQSbBz0I94+PZu2vqKW\nTCc6mnKEtl7j1KTRl2ooAp2C4C72QK5Mq4vEAw+9Pfoy1jfWXVO/Pn/7AIpMZMORMSeMnk8vjz5w\nt/FAbiXZtpzKbNZrUR++j1vHut7RyjT9KQ3YjAeMmRF81mc5Fpx7F9mV2Zh3cg687XwMli2vLYMj\n4YgS3Keec7gkUtErrZvRzl7/GSmvLceL+/vj8mu3qHeLprPfVAzwHgQ3sQT5ehGpmdIMxKQcplwT\nU0rJ9Ji+nuFNrlOplKKmJhEiUZBBMoogIlFcTE8XtrHpZ/CYQmEbAPoC+GoUF++CrW240bpsbPqj\nqIju4ksQ7K6ThiARS3Bn+gO89ctPuKis+54pRUCZD80lrrdH3wYd1xDYrn+aNBfTLn4BcK0AVbVZ\nRbIJHg+32ofgm6eZ2FZaBD6AUKE1ntTWwFUkxNfu3gizsTVLXQAwU0ISZZ8U5hit6/QEUkOysd9d\nC7QgCAIvvzwWGzd+i5QU8rn39fVj1RXTuGECQFzcfZw6FQs3NwkmTx4HAFAoSBIkMzMTw4cPwvnz\n15pElvl7kIOY1W/2xt2UItjbCPHdgbsAAGWd8n5ReQ2W77yNL9/o3iSyzNS6vtpxC1/N7PFMyLIt\nQ7ZhwL7eSC9/gjZ2Pgxd2UCnIHjaeCK7ktSEy5ZmNep9LZVLEfFbTyhrrMjJIdf7gKgSH3RdWO++\nBdX5rBG12+/9jM/6fGV0X0/Cy5IG+Q9AVY0CGU/L4d3KDtai5r3vDWHw4MFITEzEyZMnMXjwYHh5\nNS568quvvoKTkxN+//13iMVkJOmUKVMwffp0LF++HIMHD4ZQKMSaNWvg6uqKffv2Ue+w3r17Y/r0\n6XB0NK7ZqnG5ZIu8KigowJIlSzBjxgwAwPjx4zFq1CisWrUKI0aMoLIlZDIZVq9ejc6dtdqxprTd\nw8MDYWFhOH78OBYsWEDte/ToUQiFQgwZwj5mXLduHUpLS3HgwAEEB5MR8ZMnT8Zbb72Fn3/+GWPG\njGHlTgzhr7/+Qnh4OD766CNqnbu7O/bs2YPs7Gx4e7P3pbdt24bi4mIcPHiQaseYMWMwcuRImivl\nb//P3nmHR1Gtf/y7u9mUzaSQtqSTTghCgBAEQomU0C/FgIAIIggioIj32svPK0URUQT0ioVqoSkI\nRKT33tQYNiGENGBJSJ3ULfn9MdnNzs5sstnMpsD5PI+PzJnZOWeTycyZ97zv9/vDDxCJRNi0aRNc\nXV0BMAYFKpUK+/btQ1FREVxdXTF69GgsX74cf/75J7p0YSSIrl69itzcXNbPBwDCw5nF6kuXLqFP\nnz5mf9e2QL0aZYMHD8bixYvx6quvYsSIEXj++eexePFizn+LFi0y6w+AYDnjwxP1YuViiNHfb2DL\nDshMdE6dS87+H2dCsGzvTovNCZTlSnTf1Amvn1yMEhV/6VulukKvTSOBBHvG/Y5Tky/i5e6v4tTk\nC2YFbPgyk0yVHPJhKsgV4sKvCValqap3uz6CXIIxJfJpTruHg6fJCdX7fZdgeb+V2DV2n0UvTa48\nQuXxAcwLM5+2UH1BmYagpBQ+7Lec1aaBBjeL0mAjqZuA2IhsBHE9pKQUPoirx+ERgEjMzagzPscf\nicf1QaKGApKmnG3TClM5x8pl7Rutf8WXncPXphO/n7ovUR/ke1D9AFfzTVt1+1J+iA8cbLKUTvGA\nu/otlJNvb5++cDMqibxbdqdB8wxLoKQUfhvHzUaViCRmZ5s2Bo2Gxq1bA5GRMQi3bg2ERsP/syor\nO85p8/AwLYZr6EBpSEHBp4L3ZQq5TI4Pxj1tsmQXAAoq+d0sm4qyRINhqVnQdFsNdP8KENtjTpcX\nBQ0eURIJwm0doAZQCeBMdQWU0GJLYJigQTIdzjY2rL7u8fRFskOEhaIoHDx4Art27cWuXXtx+PCp\nBgNccrkcU6c+g969+8Lfn2vgk52dBYVCmGwhV8oO/bv6ICLAFe7O3AXWB8WVyM0v4/mk8H0VlFQh\n465pqQIhoaQUjk46w9EHM9z/5uPvsdoyiswrPTdEUZCCB6WVrKywIPsuiPGObfCzgwMTeLVs997a\nzXkmRnt1R5ALYzDk7eiD3588Sv6GWzkVVWq88tlxvLr6JF757DgqqtQNf6iVUlhYiAsXLmDAgAGo\nrKxEQUEBCgoKUFJSgiFDhiA/Px9//fUXHjx4gOTkZIwcOZJ1H3z88cfNMvzLzs6GTCaDmxt3EdjJ\nyQlTptQZV9nb22Py5Mm4f/8+K1vN3t4ejz32WKPHDgCjR49Gdna2/nw1NTVISkrCwIED4ezM1SPW\naDQ4duwY4uLi9MEpgEkgmjt3LmpqanDkyJEGv7ch7du3x/nz57Fx40bk5zOmR0899RR2795tMkgG\nACdOnMBjjz3GGoe7uztGjmTLK7z//vs4cuSIPkgGMOY0dnbMPVsXVBsxYgTEYjErw27fvn2QyWSI\nj49nndPDwwMODg7IyWFX9zwMmC3m/+mnn6J7d+al7MaNGzh8+DBOnDiBtLS0Bj5JEAK5TI6DiScg\nEUmghRZDdwy0WBi8uaBVNIZsZ5w699z6hSM2r3shSi++iaRbe+s5E5ddqdv1afOmWHbhv9CASVPV\nBVSm7HsSn135BFP2PWnWy3mlml1vLRFJGuWo2NunL9ztPDjtWvCr7Ya4hrC2zckmM2RWV+7L6is9\nXuNMqHQuqlP3JeL1k4sx5pcEi4IVfJlbuTRzo3Rz4AbFmlpGZc/T36mcE8g2KJdS16iRVqhoUj86\nGspMM1USaYij1BGfP7EOu/61t96yv/qcbed2mc861sXOFYcmnmz0RHlwYAJERrf9aE9usI3PtRQA\nx52QdR6PboxAsYm/86TMfazvJKTtPCWl0M2TayP+n+OL9Oe1xMjDFHzBG00NNyW+Kbo3OqqqUlBd\nzfwuqqtTUVXF/wLdrh07SN6hwyGWLpgxjAMl14lPqy1l9cUXzGxsX/VRKcnjdbQFTC8oNBWaBkbM\nq0GhboXfMRBil6gmu+XysTSP7QyqAfBjQT7/wU1kyf1c1rYWwPp84XTQCPxQFIW4uP6Ii+vfqCww\niqKwY8dv+kwIHb6+foiIEPa6t7e1watPdeOIJbg528HXQ1iBb11fDawjWZ2GgsL5Fey/w1ePv9To\n54ObvTtncWiQwyKzPiuXyXF02h+82rJ8ZZVikZj1f0LrJuteCXLuM8/NnPs0su41T5DYGugcDTdv\n3ozevXuz/lu2jFlQvnv3rt61ly+gExzMdZI3pqioyKThQEBAAGxtbVltgYGBANhuwa6urqx7qrlj\nB4Bhw4ZBKpXqSw4vX74MpVKJUaNG8Y6psLAQ5eXlCAoK4uwLCQnhjM0c/vOf/6Bdu3ZYunQp4uLi\nMGHCBKxduxZ5efUnaZjKNjP+uYtEIhQWFmLZsmWYMWMGnnjiCcTExGDXrl0AAG2tG4xcLkdsbKxe\nB06r1eL333/HoEGDOPICAPM8Kyws5LS3dRp1tz116hQGDx6McePGYf78+ZgzZw7GjBmDwYMH84r5\nEYTlWt4V/cuYuRpbLcm1+1f0ZUiocmQmE9MH8r4QvXj4eV5dBlM0JtNKx5dX1yBdeRfIiUW68m6D\nwTlaReO1Y+wJz396vtWo0kFKSmFUaK2NsEGQgU9fglbRWHruA/12oHOHRgu1B7kEY1ZndrBs2dkP\nOEEIQ30ygCnPtEQAP9qrO7wd+d2i+IJ8QpVRGbIpmat9JYRGGcAI/tZnxb5d8VO9n9cFg8bvHoWX\nDr+AMpXplXtTzrbKciVeOvoC69jvh22xqIRVLpPj/T7sko5redzfu67slEUD7oSRHlF4odt8XlMJ\n5nvcY11jQtvOt6e4ek+5dA4UBSm1GahRjTbyMEWEWyS8HLxYbS62Lrh+/zqrbY+BK6slaDQ0tNoK\n2Noyvwtb23DY2fG/QNvYeEEiYSZJEkkA7O353S11SKVyhIf/g3bthvPut7UNh1oSwBvMbGxf9RHh\nFgm5K8XWVqy9V6qruC7AQqBQiJFtU8o2Men4NmBG4LuxvOnJvT8uzb+LjCph7lGGvOXly2lbXZCH\n5AphMoYIwlNQ8ED/YqLj449XWVx2WR90pYqjujltaLhVSiHpShW0Rp25OdshyNu0S3RzE9qOXQ5V\ngxq8fnwxDmYegLJcaVa289Gsw5zFofgY87UHozw649sxX3G0ZY0X4RQFKfr5dC6dgxE7BzWLmD/B\ncgLaO8PPi/k79vOiENC+9Vz7jUWnjTV16lR8//33vP/FxsZCJGKi43yi7sb3OT7EYjHHWV6HVMq3\nsMecUyKpM1Qx/Hdjxg4ALi4u6Nevnz5Qtn//fjg5OXEyqHSYGqvh2IyDe8YY64517NgRBw4cwLp1\n6/Dkk08iPz8fq1evxvDhw5GeblqfXCQS8f7cjce4f/9+jB49GgcOHED79u3x9NNPY9OmTZgzZw7n\ns6NGjUJubi6uX7+OixcvIi8vz2TQUKvVcn72DwNmB8quXr2KuXPnoqKiAi+++CJWrlyJTz75BPPm\nzUNlZSVeeOEF/Pnnn9Yc6yPP4MAEvUuPVCw1S2OrJckoymD+YfiCvfEYI9ZsJPQNAKsv8esw8RHi\nGtrwQUacyrjEetF/cf+ieoNzioIU5FexVxxP5nJLjhoiol1HTpBBqnLjZEoYB69Wxa+xKLXeeBG3\nVFOCn1K2str8nALqDQCZCyWl8OvY/fqyYKlYqi971OlzGdLUMiq+DK8ydRk87D0aPM4SckqzTGb/\nAQ1n/BkGg7LpbAzaFqcP0hhn6hgH93Tbu1K36zMjAaaUtrEll4YYl23zZZRRUgqvxLzGbjRaNXcs\nfFz/e7cR22B65+f0Qsqze0yDc9AN1sTf8DsBwtvOL+zxCqdNAgnc7N1xKPMAS7C9qYsMlJTCz6N/\nZbUVVxfju7/YbpL3yywPyGk0NG7ejENm5iio1TT8/bcjOPiYSd0wmj4MjSar9rNZqKhoOPAtlcrR\nsSM30OzsPBPBwceQVpTFG8wsKzvd6L5MQUkpvNvnv3UNBvfKzE+24ezt66Y/bCEREVqIns9k3Sy1\ntq7YlctniNA0pnnK4cxjevJjofDO1RPdPeHGk23yVb7lOpuPOjRN4/Lli6Bp6wQlIiIiERJSN58J\nCgpG796NWyAzF18PR8jd6p6Nnu3sERFgHckU477cnO3w9jMxVtcnawwcR90qR+w7eQ9Td81A9MZI\nDN85CIO2xdUbkPJ3DmAtDnm9NBq9O3Q1eTwf8QGD4Gh0X9+Q/C1rO8ItEv5UXZludmlWs4n5EyzD\nwc4Gn748AJ8s7IdPXx7QYhplQuDryyzCSCQS9OnTh/Wfl5cXqqur4eDgAF9fX4hEImRmZnLOYU5Z\nnru7O8tN0fjzxkGf27dvA6jLLGvK2HXoyi9TUlLwxx9/YOjQoSaDXW5ubpDJZLh1i/sumZHBvAO3\nb88EznVZbtXV1azjdOWVABM0S05Oxt27dzFo0CB8+OGHOH78OFatWoXS0tJ63UP9/Px4f+66jDod\nK1euRGBgIPbv34/ly5dj5syZiI2N5c0GS0hIgK2tLY4cOYLDhw/D1dUVffvyP5+Ki4vh7m65vE5r\nxew35TVr1kAul2Pv3r2YP38+RowYgZEjR2LBggXYt28fvL29sW7fru2fAAAgAElEQVTdOmuOlYA6\njScfyheOUmHT5RuiMXpCl+5ewOLjC5gNY82ib87xZqX8pNiK5PyGXVEAoB2PNlaD8GgnvXvyDZzK\nPcH7nSLcIjm6R+NCn2x0tzml2UBuDKtvlTIUV++x9Z6M9bssLdviK7/88Nx7rO+YVqhgBYC8HX0s\nDr4UVD6AuobRXlBpVXrXuMbqc5lDtFd3TlBMBBE+i18HDwdGHyrEJbRJgSRDeDOrDODTaDP+vOHk\n9n65EiN2DoKyXMnJ1DEO7pkK9j3fZV6TtEmMM8jO3z3Le1xy/l/sBqNV8w/GTcXV6SlYFb8GV59J\n0We4BbkEY0n/j3Fw4gleV1QdlJTClpHb8HL3V7Fl5LYm663IpI6c4K8GGoz7dST6+MRBKmYmOuYa\neTQEnwsrrS5lbfP9LZpLWdlpqNXM5EurvYe7d027aKpUSuTkTGe1abXmZSzZ2bWHs/NkVhtN7wTA\nBNQNf25+TgHQaGjk5s5jHa9WNy3oozMAAcC5T99MrX811hIoClgS6A0YTrqr8lFVIkzJtjFL23N1\nqCa3a5wRh7l87M0tvZjr4cVzJKEhaJpGQsJADB8+CAkJA60SLLNE48xS7G1t8N6Mnvj35Gj8e3I0\n/u9Zy0X8G9vXh7N6wZVqXSZUR7MO120YLWZqKpmxZhTfwqIj800uqnbxjGYWjOzKIPG7jN+e2tno\nZ1mZqoxjwlShqrt/0yoaioIU7PjXb/r5lD/l3yQXcULz4GBng4hAtzYdJAMALy8vdO7cGb/88guU\nyroFQJVKhTfffBMLFy6EWq2Gm5sbevbsiT179rACQFevXkVycjLfqVn4+PhApVLxlhnm5+ez9LLK\ny8vx448/okOHDvXqn5k7dh1PPPEEHB0d8fnnnyMvL8+k2yXABN/69euH06dPs75fTU0N1q9fD5FI\npHfw9PRk3lFSUuoC3Pfu3cPVq1f12xqNBs888wyWLl3K6kdnTGBcpm/I0KFDkZaWhhMnTujbSktL\nsXv3btZxRUVF8PHx0ZsaAEzp6R9//KEfgw5nZ2cMGDAAx48fx/Hjx5GQkMCb2ZeXlwe1Wg1v7/rd\nt9sijcoomzRpEq9gv4uLCxITEx9KW9DWAq2iMWz7QCjLGb2RzJLbFpXKNaX/IVtGYPjnb2DIlhH1\nBssyim9hxC8GDkOGL9guGUBxbS23gdA3wLzUxm/rY1YJpimx9jhv0y5vfNpJB7KSMH73KAzZzjUU\nKFOVobiqbmXD29EH48InNDg2YxKDZgH7vqprcFcAnsmYun8iq0/WpI1n21w8ZV7wcmCX5ZWry1mr\nj8bZSx/GLbc4UFFfZpBcJsfxp84hacLhevW5zIWSUtg+Zg+rrQY1eDppIvIr8uBL+eHXcUmCidzy\nCf4a0lDmGiWlsONfv0EiqktHzi7Nwrd//o+TqRPt1V0flDMM9o0PT4RNbSapjViKyTyGDY3BOINs\n3bXVvH/PnDJZo5LK9m5OkMvkmBr5DG8ZaJBLMNYMYmdYVRpcd8pyJeJ+jMVnVz5B3I+xTS6HPJR5\ngDf7705ZLi7du4ANw7dieb+VuPJMssXOq4ZEuEXC1Zb7PFwatwKTIqbi6MQzevFlS6ioYC8aqNW5\nJvXJioq2A0bfXSw2P6vS1rYDa1urLUZFxRWkFSpYmXg5pVkoKzsNrZY9iVWrzTc44WNkyBi98Yrx\nfTo0vNr0B5vALF85ZkoeAJUPgFsbgAvTENUupMHPWcJEd0+saR8ADwAJMmecD+2EIDvhyzwBYEw7\nd3zj0wHtIUIfexmOBndElEPzLqo9LCgUKUhLq71Pp6UKJrBvjKUaZ5Zgb2uDyEA3RAa6WT27qzn7\nsgSW4ZAJExoA2J2+C722RuNk9nHOgnFaoUK/UKiBRq/R2hj4Mpz3Z/yGjOJbLC3PKXufxOuxb8PT\nwQvZdDbG/zqyWcovhTLdIbRt3n77bVRXV+s1s7Zu3Yrp06fj+vXrmD9/vj4+8Nprr0GlUmHixIn4\n9ttvsWbNGsyePdssw7/HH38cAHD9OjeTXCqV4o033sCKFSuwceNGPPXUU1AqlXjnnXcEGzvAmAEM\nHToUR48ehZeXF3r16lXvuV999VU4Oztj2rRpWLVqFbZs2YIZM2bg0KFDmDFjBkJDmXn98OHDIRKJ\nsGjRImzatAnr16/HU089Bbm8bj5qa2uLadOm4dixY3jxxRfx008/YcOGDZg1axYcHBwwYYLpd9Bn\nn30WgYGBWLBgAT799FNs2LABEydO5GTh9e/fH6dOncK7776L7du3Y9WqVRg/fjwqKpj5eVkZuxJk\n1KhRSElJwe3bt00GDXW/r969e9f7s2qLmP3kqqmpgY2N6cNtbGygUtUvrk6wHEVBit7GWodQOkzm\ncC0nFekrfgDyI5HukYJrA1MRF8SftbPxb6NSHt0Ldl4UU3a58RgzETF0ONNpmHkmY+WFj7BmyP/q\nHc+fedc4bQu7LUZXr644ddeEXp7hOGotvHWkF92EoiAFPeQ99W2HMg9Ag7pVhpe6L7YoAFOY7Q08\nMMiCGjUHsCtDpQasPo1dIvlcI81BUZCC+xXcoEONsWCIAXwi+eZCSSkcSDwGRUEKItwied2lDH+u\nTYUvk0dHLp2DtEKFIIEQHXnl/GVLAU6BZmWuFVQ+YAm924hs8NmVTyAV20KlrdYHFykphYMTT3B+\njo5SR/hSvsgsuQ1fATJJjTPKskozse3Gj5jYcTLrd/dL2g7uh+3KGC0VmFveyr7m/n3sZcR694Zc\nJucth5wa+UzjvowBTJaYiNMnwGggAkzwbmLHyZz9lkBJKTzV8Wl89ecXrPa11z5HLp2DK8qLTQoO\ni8Xc7AuNphzl5RdhZxfJKsHUatmajWKxOxwczM+q5Du2mL6GdRe+gr0YqNQy5e4RbpGoKNpgPFK4\nuDRNBF8uk+PM1MuI/6kPyg3u0yLPFHTxtd6C0KLAaGzcGAlNjRoSkQ26eEZbra+J7p6Y6C68Kyof\nY9q5Y0y7h68EornRlUWmp99ESEio4AL7hJaF9feuC9Abz00B/fx0wo6n4OcUgJxbTggKq8Thp383\nKZnQGCJcO3LaaFUp+v4Qg40jftQvqqUX39Q/y4C6RTYh51fccTCBurSiVIS5hguy4Elom3Tr1g0/\n/vgjvvjiC3z//fdQq9UICgrC8uXLMW7cOP1xnTt3xubNm7Fy5UqsWbMGzs7OmD9/Pv7+++8GE2q6\ndesGZ2dnXL58GYMHD2bt8/LywptvvomPPvoIeXl5iIqKwvfff4+ePRu+/s0du47Ro0fjl19+wciR\nI+vN4gIYk4Ft27bhs88+w08//YTKykqEhIRgyZIlePLJuiqkjh074rPPPsPatWvx8ccfw9vbG7Nn\nz0ZlZSU+/vhj/XELFy6Eq6srdu7ciY8++ggSiQTdu3fHihUr9AYBfFAUha1bt2LFihX4+eefodFo\nMGLECISFheHDD+u0id9//33IZDIcOXIEu3fvRvv27TF27FgMGTIEkydPxrlz59CpU53ubHx8PCiK\nAkVRiImJ4e378uXLcHFxQXS09eZQLYXZgbLOnTtj165dmDp1qt5CVEdFRQV27tzJsiQlCEuEWyTk\nDnIoDQIglc0YKKu4Ewzk15aP5Eei4g4FcE0+AACeMjkr8AW7Mv0Ldie3zvjHOFilS3mvnaBsm90T\ni2NfM5mNQatovHJ0AatNDDFmd50LR6kjvB19cLfsDu9nDV/0jTEWTzWevHTxaJzuhB6vZMDDq24C\n5nNJv8uw3LKLZzQksIEGakhg+UubTmj8fgU7wDNuzyiceOocglyCOddOU68loYNh9RHhFglfRz/k\nljWPDXF8wCDe9jt0LspUZQ1OGo2vq7oy1Wos77eSFaDi+zleu38FmSW3AdRlksb59rfkqwBgAko2\nIinUNXULG6+fXIz1f32Jg4kn9GN5InAwdt7cxvqsBBJooDG7vNW4lLqgqgBDtw/A6SmX9JqLKq1K\nEM1FuUyO/eMOsrNZjcgovoWjWYcwOmRsk/rSoalh273LJDJ9RkFTX2JcXRNx//6brLasLGY1z9Y2\nnKVX5uDA1srz9l5lUsuMD0fHvgDcAdRpCBY+eBtvhQOZvsDcK8CKAZ+BklKolrBLnz09P7LY8dIQ\nmdSxzqSl9j5dA0YnUMjAtyF/5l3T/w41NWr8mXcNQ1q59iehedEJMpsjRE1oW7DcsXUB+jsx7LUW\nw/mp+w3k1IiBgnBkuCtwNu46HGTmSSbUx95be3jb1TVq3CxMQ5hrOK8Ltb9TgFVcgQ0xNt1p6vyD\n0DIsWLAACxaw35s2b97cqG0AiIqKwldffcVpN6ZLly7YuHFjo8cpkUgwbtw4JCUl4T//+Y/eHEDH\n4MGDOQG0hsasw9yxA0Dfvn2hUPBLMSxfvhzLly9ntQUGBmLVqlUNnnfYsGEYNmwYp/25557T/1ss\nFmPGjBmYMWOGWWM1xNPTkxV00zFt2jT9v11cXLBkyRLez/N9Z5FIBJFIhFGjRnF+HwDzbNy/fz/G\njRv3aIv5z5s3D+np6RgzZgy2bt2K06dP4/Tp09i8eTPGjh2LjIwMzJ1ruR4LoX4oKYVpUc+y2m4V\nmXa/EBoHn1uschgHH/7ySFpF45OTX5h0x/tkwGcI8fIG/C7Axr72pYgn5X3Aj71NlmBeu39FX4Kq\nY33CBshlclBSCqenXMJbvUyXy5nih382sbb/yPy93m1zifYLR8i/pwCzesF1fgIrk+3MnVP6f+eU\nZukz2DRQ67W+Gguf0DgAVGkq0WdrDyjLlcgrZ5dKGW+3Zigphd8Tj8LTgT87w1JtN1OYMiBQ16gb\nFIWnVTQm/WY6KLP6yqfYduNHkwL/tIrGmdzTrM80NZNULpPj9JSLcLVjp8Hrsip1DA8exfpZtpd5\n48zUy0iacBgHJ54wa1U5MeIpTtvdsjvYnLwBAKO1qPu/EJqLHT06wcmmfmep104sFqyEZFYXtkuQ\noRtvkEtwk15ipFI5ZLIhvPuqq1NZZZiOjn1hY8MsLNjYBMPJyfREkg+JhIKjI7u8QDcdCnQE4uR+\n+sCoRsM2OBGJhMkkZzJ42e5PHZyDrPoimF2SVe824dHm2rUryMhg5iEZGbdw7RqRF3no2fclsOlY\n3dzVcH76oCNQEF777wicv6QyKZnQGHq058/SAAA/Jz8cSDyGrSO3681zAOZ5vH/CYatnd0W4Rep1\n0QDg38dfJiWYBKsyffp05OXl4dy5cy09FAKAffv2obS0FOPHj+fdf/78eeTn52P69Om8+9s6ZgfK\nevfujU8//RQ0TeO///0vZs2ahVmzZmHJkiUoKSnBRx99hLi4uIZPRGgC7EhulcY62i18GAZ7Qv49\nBdF+4bzHnb1zGmX3AjiBrwjXjjg68QxivGNxcOIJJE04jKvTU7B20NdsTRr3G0C1AyorxOjzQw9e\n3SLjQIG3ozfiA+peDCkphee6zNHrZgU5B+P/+izFtwmbsLzfSu6gqxyBnFjs+ud31gTgX6Hsm4Lx\ntrlQUgoHn96PpJeW4ZeJP7P2GepARbhF6t08dWVOlmKqPFEDDfal7+Fkx/Xyblt15eWqMuRV8Af3\nfs/YL2hfEW6RaGfH1VaQiCQNZkExZbCmHefulOXi9ZOL0X1TJ2QU38KQ7f0xfOcgDNneH8pyJQb9\nHIdPLi1jfaZSzbV/biwFlQ9QVMV2uDFenaakFL4YVLf6dq/8LgoqH6CHvKfZk3NT1+F7Z97EsO3x\nnEy5pnL2zmmUqkvqPSa/Ik8wt7Agl2CsHbRev20Y6KkW4P7s4NCFt10qDYCdXd3vSiKhEBp6CkFB\nhxEaeqpR2WR1ffFnzN574IGUfzqhTFVW2zc7EG28bSmDAxMgMpqSjAgabdUXwZEhY2AjqtX/E0kx\nMqRpJaSEhwudZoupbULbxjDIBYBfp8xwfgr2PT35bjrj/D0uCavi11isjxofMBiBzh1M7qekFNzs\n3fTZ6ACg1qpNHi8keeX3kW2waGu8oEYgCI2vry8mT56Mr7/+uuGDCVbju+++w/z58/Hee+8hPj7e\nZNnn//73P0yePBk+Pj7NPMLmwexAGcAI0R09ehRbtmzBsmXLsHTpUmzatAnHjx+v1xWCIAxOtk71\nblsTw2DPwaf3m5wMJOf/zSua/27f/yLKo7P+XD3kPSGXyRHsGlKX8j59IACRfjVPU2mPfen8KemG\nfBj3Ea8u1oHEY0iacBiHJ53CC9HzMTpkLCZ2nAx/ykD7y8Dp6MHq/biWU5fefquYnbF3x0gjrjHo\nvnNhFdsdzlj4VaVRsf5vKRFukfCW8d+0HlTkY9r+Saw2Y92q1g5HB8+KUFIKu/61j9O++okvGywJ\n83MKgKjUB7jyLFBq2nlOpVVh3bUvkF50EwAzGd2XvgcZJdysSlOaaY1BV75qyJ3SXH0wBIA+aKwL\n3lriWlqfK5c1SmfNyQjydvQWNEvJ1d6Vtz2XzmnyC4VM9jhvu0qVBa2WLbgqkVCQyXpaFCQDADe3\nmbztcrd8aH5ei36vrwKtojkmAY0xDagPuUyOzcN/qmuockQo/TSsYDTI6vPq9H8Y59bp/1itxJPQ\nNnFwcKh3m9C20emCJk04jPd7L+Gdu+rnp2NmAmA78LZ3dQWtojH+15FYdHS+xeL6lJTC0UlnMDqY\nq5WUU8o8J41d0fMr8zBsR7zVs7uM51pikZi4bRKszssvv4xbt27h4sWLLT2URxaNRoNTp06ha9eu\nLI0zQy5cuICMjAy8/LJpV/a2jslA2RtvvMHrOmFra4uYmBiMHTsW48aNQ2xsLGxthbdvJ3AZH54I\naa37nUQkwbCgEc3avy7YU9+KWVk1zXHHC/T0RG+fvrzH6x0T7coAaQXwoNbNMj8SuBOj/76scVQD\nsTmAY22VUzt7N7PHS0kpvNjtpbqDjFYQC7OY4BKtovHasUWs890sTDP5vc2lPuHXo1mHkVWaCYAR\nWLfU9RJA7Sonf2bVikvL8KCqrpzQnMyo1kZ9EzVr/F1EeXTGpwPYou3eVMOrJ39m3EXNZ7eAPd8B\nn2Vxg2W12YyocoSohp0x6u8cgPYyrtWyKc20xkBJKXwQx7af1mUbAsz1H/9zH4zfPQrVmmrs+tde\ni0R8Gyof1jkd2oikJp1sG8PIkDEsh1E+no6cIWiWkrG+n7j2sSoVS5v8QlGnHcaFcboUDqlUDi+v\nTzjtIhEwfvwXKPppDZLO3YZGU8Tar9UKl2WTV1kbBK5dwHhlWk8kJMisHiwz5dxKeLSJju6OkJDa\nLO+QUERHN76sjtC60c0Tn+n8LOzsNay5q14mw64MiNrGVDzoaHcTC0f342h4Wbo4QkkpxLTn6llS\nUmZB3FCmQ0cunSNIJnZ9GJeFamu0FsuCEAjmQlEUjh8/rhfq37x5M44cOdLCo3q0mD17Nq5du4bN\nmzfDw8OD95jY2FgcP37c6m7NLYnJQNkvv/yCrCxyM2xNyGVynJp8ER4OntDUaDBl75OtSiuAVtHY\n+Pe3zEatGPPUrhNwdNIZky+musyvrSO3M6t3hhOR377GodQzrOPLipQYMPVlHP7GEd+si4Uz7dzo\nF+yRIWMgFdcGd41WEFMkzMunoiAF+VVsLZ7QdmGN6qexnDPSojLebiymtLWMcZI6C6IP1ZzUN1Gz\nxJ69IWgVjbXXPtdvd3AOMkuLJPvyY4Cm1vxEYwekjazbaZDNiPUXMdh7vD4wLBVL0cUzGivjV3PO\nae7vtT5oFY33Tr/Fadc5rR7NOqQvi8wuzUJhZYFFwaUIt0i42fEHsoG6UkV1jUqQybdcJseZKZfh\nVU/QgxI4E9dY308LRvRbpVWxxaItQCKh4OQ0gHefRlPapHPzoVbz/w7KyhwBiJC0OQD37r1h9Bnh\n9A0Zgwdb1gJGWpoECkWjkt8JBEGgKAoHD55AUtJhHDx44qF+GXjUoaQUlvb/uM7wyY6dsQu7MmDG\nAMCFWcxsJ3OBp4MX/JwCWM/tpiyOjA9P5LTdeJAMWkXDSybXL8IY8srRBVZ9D4gPGAxvR/aioHF2\nG4FAIDyskNlnGyOXzkF+rTZTevFNq68mNYazd06jSMXONuhuhp4RJaUwJDABR6cdBIYaZHEVhCPp\nzF2czD4OgHm5X7RuACS3i9ATFzG5+Dyq15/Dn7k3GzVOuUyOK88kY3m/lXCkRKwVxKxKxqUvt4Rd\nZunp4GUyK04oOrp3Ym0/7tunSedjyut8GzyuqLqwzWlOTO/MXyZmLRQFKUgvrrvOVFrzSmNHJkhg\nI63VrZJUAWEGJZxG2Yy/nEnRn1cXZAl1ZQdnhRI3P5p1GDl0NqtNAglCXcOgLFdi3dU1rH1HMg9Z\n1A8lpfDcY3MaPE4ishGsnCPIJRjnpl7FvK4LefcLnXHYy7s3KzNQaNzd5wl+TlO4unLNFwBArWYW\nFjpEnoJWa7iAIIGLi3C6Xvp784TnEBTC6AGFhWkQEUEcBwktA0VR6NGjJwmSPQKMC38SrnbsUvp5\nXRcyZZkAUNwBKA4EABTmeuLaNTEu3D3HeW5bilwm52SuR8t7IGH7QEzdlwh3ngDV7ZIMq74HUFIK\nL3VfzGrjy24jEAiEhxESKCMIBl9pYnqR+eWKUR6dMbUrWzsLNcDbp18HwATiDjrcwX7nKNwAEyyo\nLI7E+WuNz6yQy+SY+dhsvBrzOmsFcXvqT1CWK7H8Its6183eTZByLeMyrfN3zoJW0VCWK/HvP97R\nv2z7Un4sgwJLoKQUtoxsuDzLSya3usW40AS5BOObIZs47e72Hha5TjVEhFsk/Cl//ba5+lNyOXDw\ndCYw5jng5QDAyUBfzCib8XjVFyxXq8XHFnLKb+d2nS/IdciXraiBBmN/HYFuGyNx+f4Fo71cS2hz\niZab+H0YBJc0NZa7vPJBSSm80G0BRDzjFiIjz5DzmX/xuvz6Ovo1+VrUaGjcucMfKCsq+gYajXCZ\nBBoNjZycGbz7Ro/+FvayAnR/wgG2toxJikTihdDQy5BKhS1ZlMvkmNljMg4frEJSUhkOHCgHiVEQ\nCARrQ0kpHHjymP45LBVL8UK3BXim87Pwo/wBz2SIPOrmtK+8KsWsPez7c1NdqceGT0AH5yAAgLOU\ncXDWlXbmVbaMO7lhFYZUbNvmpDoIBALBUmzq23np0iVoNJr6DuEwduzYJg2Ij7fffhuZmZnYvHkz\nACA3NxfvvPMOrly5Am9vb7z++usYMKCuPOXcuXNYsmQJsrKy0KVLF3z44YcIDAwUfFwtQbRXdwS5\nBCOj+BaCXIKtEhSwFJ2WgiGNzfx5orcLtrorGK0ydwXgewk3CsqhLFfiqvIyACBMkoyOSMENRELk\nnoLb9r8BGGbRmI2F0WtQg41/f4ebRexVwX/HvGnR+bn9sSc6q69+ip8VP2Ba6AJo159lMow8UjD9\n231NDojQKhpT9k5o8LhZj821usW4NTiYdYDTtmPMHqt8F0pKYf+TRzBi5yBkl2Y1Sti+0uE20J3H\nfMCujDGwSBsJhO1DvpZ9LWYU34KnzJPVJoQ+GQA87tsX6//+itN+t+wO7/F9fC3Ppuzt0xdyWXso\ny+/VNerKTmuvd6cXBwserJXL5Fg5YDVeOb5A3+bt6CN4P/6Vw4H82iCqzinN7wKivbo3+VqsqkpB\ndXUq7z6NJg8lJfvQrt0k3v1C9iWX56D3kv4Y2OkgHMTHUFWVAju7SIuNA8yBooAePUgmGYFAaD6C\nXIJxdXoKDmUewODABL124YnJ56EoSEFBdzdMra2QvH3LFsiLZBZaa3GwaZrhAyWl8P2wrYjf1gcl\nqhK8eHg2OjgH4XZJBrxlPrhbzn5Gu9sxWWa0irbaPE4uk+OPJ4/hq+trMbfri0TPkUAgPDLUGyjb\ntm0btm3bZtaJampqIBKJBA+UnT17Ftu3b0dsbKy+n3nz5iEkJAQ7duzAkSNHsHDhQuzduxf+/v64\ne/cuXnjhBcybNw/x8fFYu3Yt5s2bh99++w1i8cORQCcWiVn/by3ceJDM2p4YNhlBLsGNOkd86OOg\n5vUAfddf7zhUA2Bf+h7kl+cjJhfoVliGi+iJZETh1aHJWNT7oMVjnt55JtZdZ+tAXbx3nnOcm8y0\nzlJj4At0KMvv4cuDR4D82mBcfiREebeb3JeiIAV3y+82eJzOjbStMbfri/hZsZXVVqkRTljcGLlM\njuNPnYOiIAURbpFmT0p1JbC5Bq6pDmIHVFSIgY3H9MEilngwULuqzc6IyqVzGv03xUdnj8d429vZ\nuqGwuoDTbo5xgSkoKYVDE09i2I74Ov04o7LTOd5rrTLJ7+AaxNr+ZODngvfTu6srXH3voSi3fZ1T\nGoBI96gmn9vOLhK2tuGork6FSCRHTY2Stb+oaJdggTLDvsRib2i1dfeOGgBfjl2v/9nJZFzR6bYG\nTQMKhRgREVqSsUYgEPToDD4M0Yn+045ASIgG6ekSyP2LofRMZn1OiMXr7YqfWNsDfZ9A1x7d0Mcn\nDiN2DsaDyrryd4nEBuN3j0KYa7hFhjvmoCxXYsj2AVDXqLAzdRtxCCYQCI8M9QbKJk6ciOjo6OYa\nC4fy8nK888476N697sFz7tw5ZGRkYOvWraAoCqGhoThz5gx27NiBRYsWYdu2bejYsSNmz54NAFi6\ndCn69u2Lc+fOoU+fpmk+tQYUBSlIL2K0ktKLbkJRkIIe8tbx0hLkGsra7uXT+J83JaXw2+SdiN/G\n/qxULMXh7D8Qpa49DmXohQtY0/8T+DQh0BPkEowBvk/geG6dm4pGo+Yc19R0eh2myr7KXM8xL9m1\nQZPgsMom9xXhFokg52BklNwyeYxEJEEXz5b7G28KUR6dsX/cIUzaNx6l1SWNyvKyFN1kubGf+T3x\nmD5QFOISih9G7UDCZ6+jyCBYpMtE0qGuUePGg39Y5xLqOvw9g98Rla9UkbKhmjz5l8vkODn5AjYn\nb8B7Z96sKzutvd4T+/EH7ppKtFd3hLiEIr34JkJcQq2iMzF2hswAACAASURBVEhRwLx1W7D0t+36\n4D4AjAwe3eRzSyQUgoOZDC6pNACpqd0A1JVbarU0aPoEHBy6Nzm7y7iv9PS+0GiYLEcRgKqSn0DX\nDBWkr5aGpoGEBBnS0iQIC9OQ8k4CgdBoJGK2w/Kn8WsECVT1aB8DXK/bPpC1HxtSvkWYazjmdn0R\nS87/n37f/XJm8UTnuGmN94F96XugrmF02NQ1KuxL34OZj80WvB8CgUBobdQbKIuJicHo0U2f7FvK\nqlWrEBsbC09PT1y5wohVXr9+HZ06dWIJq/bo0QOXLl3S79fZyQKAg4MDoqKicPXq1YciUObnFAAb\nkRTqGhVsRE1z2BESWkVjxYWlrDaVttqic0V5dMZL3Rbj86sr9W1HMg8huzQLwUZXbIC8I7hhrcaR\nEDSCFSi7nn+Vc0xT0+l1RLhFwsPOg+OoaWuvQvXsnkywxDMZ7Zx/bnJflJTC4UmnGG23W79jQ8q3\nnGM0NRrklGa12dXBGO9YXJ9+o9FZXs2NLlBkOM61U17C1J/rgkXwTGZKEmuvAdiV4evrXzbrOAuq\nuYHcxT3fEOTnSkkpjA9PZAJldmVMBl3tdy3Q7kEQvJrcB1+fByeesPr1MbnrWCy/+pre8RIAruVd\nESRbUyKh9BlcXl7v4/79V/X7KitPIjPzJAAHdOjwGxwdYwXrKyjoD9y8GQPU3mELClajoGA1bGwC\nEBp6rk0HyxQKMdLSmJdcnasmKfMkEAgNoVCIkZ7O3DvuZFKsBS5j8x1LiQ8YDLlNKJS33eDmr8Td\nMsZpM60oFZ08OsNGZAN1jRoSSBDgEoiM4ltWXSg0loAw3iYQCISHldZVu2fA1atX8fvvv+O1115j\ntefl5cHLi/1C5e7ujnv37tW7X6lkl6y0VdIKFayVnaY47DSEslyJrSmboKxdsaJVNC4rL/JaUR/N\nOsQq2RJDjJEhlruhxfo8ztred3sPAOCSL6CoNf5Rh4RCHd30NHexiJ1FU6pimwMIKRBPSSl8NHAV\np726ppplKtDOTphST52j6JBgfg23tijkb4wuy6u1Bsl0GI+zd4euCFw8Ue+4CoAjCl9s5CIrFOPD\nEyERSRo+EJYHvPlgCfbXXu8hcm+rXoPNcX04Sh3h7cguT+3jEyd4P2KxKVOFCty+PRgVFX8L1ped\nXTDCw1Pg4sIuQVKrs1BaapkLamOgaeDyZTFo4fwK9EREaBEWxuivEldNAoFgLhERWoSEMPcOD78H\n+lJ7ABzzHUvJzMuH8rM9wDfnUfBFEmxUjBOnVGyLUNcw+DszC+QBLoH4adQurIpfg11jm65rawp7\no4XiSnXTKx4IBAKhLVBvRllLUV1djbfeegtvvvkmXFxcWPsqKioglUpZbba2tlCpVPr9tra2nP3V\n1Q2/7LVrJ4ONjXkvjy2FXSH7RclOJoKnJ1dEv6nco++hx+YoVGuqYSO2weXZlzHpl0m4kX8DHT06\n4uLsi6Bs6x7K12sz+nQ8G/0sOgeGGp/WbDprwnnby+yAHs8DXwctxJTJS+ApQL3M9NgpeOPkq6hB\nDSejBwA6uAYiyMe7yf3oCKJ9Gzzm4J29GBjZW7A+vWmurTgAvNb3P4J+t4cBa/w98fYDJ/z9yll8\ncvoT/N+JC4wDZD2lmADg7e4uyPg84QTFfAV6fdMLDyrqd4F0d3EW7GcS5xKLjh4dcSP/Bvyd/fHV\nqK/QP7A/617SFrmV8w9yy3JYbTX2lYJfS87OU3Dv3mKT+0tL1yIgYEujz2t6nE548IAbqdJqz8LT\nc1qj+zEXmgb69wdu3AA6dgQuXoSgpZGensCVK0ByMhAVJQFFNc/fPKF10Vz3esLDg4MDIKl9TbCR\nsF+hfN29BLmmvtxyEMh/hdnIj4RaGQ74XYBKW42/Si4ho5iR08i4r8SoNW8jT3YU4T6rcfn5yw0+\nSy0Zn2uRjLW98MgLGB89Gu2p9o0+F4FAILQlTAbKxo0bh4CAlinrW7t2LQIDAzF8+HDOPjs7O9BG\nS8zV1dWwt7fX7zcOilVXV8PV1bXBfgsLy5sw6uahqKScs52XV2riaMv55NxXqM6MBjyTobYrQ9x3\n/VCqKgEA3Mi/gVOpF1haCF3bxbA+30c+oEnj+t85bpmgjjI7oPKxGORV1AAVTf/uEjjijdh3sfTk\nJywnPp24+qJurwn6M+5g1xFeDnLcrzCd5Rjn+YTgfQY6dUBm6W19m41YiqG+Y6xy/bRVPD2dmv3n\nMT1iDj4+9TEqjHS7DFeqAUAua48Odh0FG58zvLB+6EaM3z3K5DFikQRDfYS9RvaPO8IqhaworkEF\n2vY16Khx15fEA4z2oZc4wArXkiM8PVcgL+/fvHvF4l6N7rOha16r5Qb2VSovq/6dXL4sxo0bjgCY\nYNmpU2VWKY0MDgYqKpj/CI8WLXGvJ7R9Ll8WIzWVuTfdy3RhLWjdup8tyDUV2KGSdy4Q5hqOx5xj\nmGdNpS2w/iLyao9Jnd0TB/85jjjf/ibPa+k1X1VWw9rW1Gjw9dnv8UL0fFY7raJx7T4jkyOE67O1\nIYFyAoHQECYDZcuWLWvOcbD47bffkJeXh27dugEAVCoVNBoNunXrhjlz5uDGjRus4/Pz8+HpydTM\ny+Vy5OXlcfaHhQmjHdDSGGtlCaWdZcilzH+wcuYkIP99fcCoFCWQiCTQ1GggFdtytNGCXdjZY509\nujRpDD3a92SJmRpjnAreVPLKlRwnPt0EyF3Gn41lKZSUwovdXmK0mnQYZbIpim4gxrtpekPGfR59\n6gzO3jmN5Py/YSexw/jwxDarTfYwodPu2npjE0u3y9ABEwCW9vtY8IlntFd3uEhdUKwq5t2/ov8q\nwa8RSwwRWjs5pVn6IBkArBy42movCe7uU5GX9wHAE1y0tRU+O1QqNT6nCG5uTwvejyG60kid2D4p\njSQQCK0BXelleroEnv5FyDNY0AptJ8x7xjPdJ2LF7GjWXKCHVyw2jNha96zJ68adr1qJaK/ucLVt\nh6LqQn1btaaKdQytohH/cx9kltwGwEiWHHvqLJljEgiENk2r1CjbvHkz9u7di19//RW//vorEhMT\n0blzZ/z666/o2rUrbty4gfLyusyqy5cv6905u3btqhf+B5hSzH/++adF3TuFJKxdBGxETHzTRmSD\nsHYRgp5fWa7Ewp/X8T6ANTWMLoNKW83SGqJVNP71Kzv7b7uiaWL08QGD4CQxvdpTKZD7n46O7lF1\nTnyAfhXP08HLKvpJ48MTIdb9+VU5srSpxNXOGByYIHifOr2yl3ssxgvR88kEphWxsEdtmYWBTp0x\nleoqTltToaQUxoUl1jVUOTIloFXMinmQa7DgfT6MRLhFIsyVKRcPcw0XTNPQFDY2/MF7sVj4hRNX\n10QAOrkDMYKDT0Mqte69g6KAAwfKkZRURhwpCQRCqySvvK4qIMApUDBXZblMjt4dollzgcv3L2Ds\nr8PhZu/OzB155quFlYW8GsJNhZJSeKf3B6w2H4qdaXz2zml9kAwAHlTmI/7nPlYZD4FAIDQXrTJQ\n5uvri8DAQP1/zs7OsLe3R2BgIGJjY+Hj44PXX38daWlp+Prrr3H9+nUkJjIvexMmTMD169fx5Zdf\n4ubNm3jrrbfg4+OD3r2F03tqSRgxf8aFTF2jFlTMPzn/b3TdEIGb0p2cB7AhQS7BrODR2TunUVLN\nzkhJLWRn/TUWSkpheIjpkrD0ovQmnd8Ylba6zolv+kBgxAsQQYy94/+wSmaIXCbH2alXYAs7Tibb\nZPdlJIj1iBHkEozzU6/h5e6vorc3/2Q7Of8vq/T9Qrfa8gmjgK2oyknwQPzDCiWlcCDxGJImHMaB\nxGNWLTmpqkqBWn2bZ48UdnbC/77EYkfY2PgDAGxsOsDWtoPgffBBUUCPHloSJCMQCK0GQ9dLPIjQ\nLyRPipgi6H3fn8fRPr3oJs7cOcW4K+vmqzozILsyPHdgGhK2D7RKcMrY1Ke0mp3RfLMwrW4jKwbY\nsgf5ikB9KSaBQCC0RVploKw+JBIJ1q1bh4KCAowfPx67d+/GmjVr4OfnBwDw8/PDF198gd27d2PC\nhAnIz8/HunXrIBa3ua9qFoWVBQ0fZAbKciXit/Ux+QA2pFzF1knLLsmCMYt68GvoNIb2jqbLiOwk\ndk0+vyEjQ8ZAgtrJz74vgU3H0P6HbHhKrJdRE+QSjJNTz3NWBp+IIeL6jyJBLsF48/F3sbTfCt79\n0zvPtFq/56deQ0fVRFbAtiYvku1SSaiX5nJflUoDAPCZzqigUgn/+2ICc4x4tFp9C1VVKYL3QSAQ\nCG0BPz8tpNJazS5JFeByGwBQVFlo+kMWkBDE1Wh2s3fH4MAEeNp7mfxcWlEqFAXC36N7efdmZZz3\n8mYnH9iKa03UsmKA7y4AN0cD313A6XPCZ8ITCARCc9EqXS+NWbRoEWs7MDAQW7aYdvYaMGAABgwY\nYO1htQjRXt3h7xSA7NoX2Dl/zETs9N5NzkBaf/0rdoOuBIwHZfk9XLt/RS8a2sWjK2v/mvivEeXR\nuUnjAQB3Bw/edhFEGB+eyLvPUuQyOc5MvYyEz15HUW2w4G6mCxQK64hI6whyCcb5macxwn44HmTL\nERhajvjQP6zWH6H1E+XRGUcnnsGqyyvgae8FsViMWV3mIMjFukHb8f06YemGOgFh94D7Vik7JjSN\nioprADQGLTYA1LC1DYednfC/Lzu7SNjahqO6OtVqfRAIBEJbICdHDJWq1n1eYwcUdwCc7mNc2JOC\n9hMfMBjONs4oUZfo22pqauAodUQf3zjs/ucAr/mUv1OAVZ7b5zP/quvPJQM/dNyENwZ30C8Mnbtz\nmjnwxLsAan8+EGH7+nC8NkHw4RAIBEKz8HCmWT3kVFTXZXSpa9TYl76nSefLKL6F1ee+YmkTcTDS\nLqow0Aj7I/N31qE3i1ObNB4dLB0vA45MPG2V0sQgl2CcfOl7+AcxGXTNJSId5BKMi7POIumlZTg6\nzTqlnoS2RZRHZ3yTsBHLBqzAkn4fWTVIpmNIWF9WJunmf31DrsVWSHU1O2vMw+MtBAUdRnDwMUgk\nwv++JBIKwcHHrNoHgUAgtAV0Yv4AAPcbemkSRVHT5EaMoaQUpnSazmorrCqAoiAFc7rM45pP3WGc\n5zcN/0nw5zatolGa61/XX3EQ1i98BkO2jNCXeUbLezD7+n8AQOeSWYN3X5dyzkcgEAhtBRIoa2Mo\nClKQX5XPaqupqTFxtHl8eX4DS5vIMFg2LGAER7sIVY6sNPPJkWwHNONtS5HL5Lg+Q4E3e72HqR2n\n461e7+GvGWmCZKuZ7NPVEfv3aLFqVQV27Wo+EenmKtsiEExx/u5ZlpnAn/n12M4SWgwXlzGoE9eX\nws3tachkPa0awJJIKKv3YQxNA5cvi0ETLWgCgdAqYTKnpGKpVQyYjE2rXGxdEOEWCZFYxATo3A2C\nc3v/B1Q5YunZDwTVKKNVNBK2D8SS9ETAJaNuR3EQ0tNsoShIgbJcif+efZdpD7gEzIyFZ9dL+GZb\nKsYM9BFsLAQCgdDctInSS0IdEW6RcLJxQqm6Tkhz2fkPMCnSMiFRZbkS205e57pc1pZdTnvsWbjk\nJ+Bnw/3JE/EiFiG1QIEaAA8q8iGGGFpoIYYEMqmJrDQLkMvkeLnHYsHO1xA0DYwfL0NamgRhYRri\nuEZ4ZPCUebK2/Z25YsKElkcqlSM8/B+Ulh6Ak1OC1R0oWwKaBhISyH2YQCC0Ljhi/skT0f7x83AU\ncN6ro5//AGz45xv99tJ+n4CSUohwi4Sbkz0KRs4FNh2rG0teFA7a/Y4nfu6LI5NOC7LwqihIQVpR\nKmAHYNbjwDfngOIgwCMFYi8F/JwCsCt1O6NvrCPgEv63QIk4X2IGRCAQ2jYko6yNQUkpzI2ez2or\nUZVY5CxDq2iM2PEEyt0u8LpcBrkEo7dPX7wycmTdfkkVsOc74OtL+HznJaw+9xW23tiof0hqocGh\nzAOWf8EWRqEQIy2NmQSlpUmgUJA/EcLDD62isfRcnf27kFb3BOGRSuVwc3vmoQySAeQ+TCAQWicR\nEVoEBTPO87r5cPanO3D2tvAZ2PEBg9DBOQgA0ME5CMODRwJg3gOSEg9D5HuFd+5+uyRDMEH/CLdI\nhLmGAwAcnEuBeY/p5Rm0tsU4kX0MVRq2YL+bnTuivboL0j+BQCC0JGT22QZ5MmKSIOe5dv8Ksuls\njsult5sLjjxzBIcnngIlpRDk6YX9SSXAmJmMeCkAPOjIrGQZlWoCQB+fOEHG1xIY6k+EhDSPRhmB\n0NIoClKQXnxTv62p0dRzNIFgXSIitAgLY67B5tKKJBAIBHOo1tYGhnTz4fxI3Ey1FbwfSkrhyKTT\nSJpwmJMhFuQSjHMzT8J94Qheh3p7iYNgYziQeAxJEw6jR/sYljwDALx69CWEuIayPrNi4CoiI0Ig\nEB4KSKCsDXKzKI21LZfJG716oyxXYs4fM+saDB5+L3VfjPigeNaDLiawE1a+0K9u9UqHrlTTgFw6\np1FjIRAILUuEWyR8pRF6w45cOscqFvMEgjlQFHDgQDmSkspI2SWBQGg1KBRi5N42KrP0SEFoeLVV\n+qtPvzbIJRgXnzuDiU+EsIJkADDmlwRBtMpoFY2zd07j+v1reMwrmrO/QluOrJJMVluwSyjnOAKB\nQGiLkEBZGyS7hO16ptY2LvuDVtEYtn0g8iruc/aJIMLIkDG8nxPLyplVq+kDAXcF02iQ7q2jwkiA\ntC1hqD+Rnk5KfgiPCFUUbL/7U2/YEeIQbRWLeQLBXCgK6NFDCwo0bC5fhNCq/rSKxmXlRUGFrwkE\nwsONX0gpxJ61zu7uN4BnBqLd/GHo3aFri4yHklL4V9h4TnupqhS/pO5s0rkv3b2ATt8EY+q+RLx+\ncjG+vr6O97hv//wfa3v3zV1N6pdAIBBaCyQK0AYZGTIGYoNf3YPK/EZplCkKUpBblsu7b2zok5DL\n+HVvBgcmMKtWQceB53sw6d7TBzIZZQbllw42wqR8twSk5IfwKKJQiJGRXls6kh+JFZ0OktIJQsuj\nVMJtwONoN3wQ2iUMFCxYpnNyG75zEBK2DyTBMgKBYBZpZZehndWdmf8+HwMEH8eIjgNb9HnZxZOb\n6QUAi48vQEbxrQY/b7hoQKtonMo9gc3JGzDil8GorKnUH6eBBq/GvAEfmR/r8zll2aztoYHDLPgW\nBAKB0PoggbI2iFwmxycDPme1FVYWmv35Gm2NyX2v93qr3n6PTjwDEcRMwMwzGdh4TJ+FgirHNi/i\nSVHArl3lWLWqArt2kZIfwqOBsTZfdJRdC4+I8MhD02g34glIspkMapu0VNgohCkH1ju5AUgrSiVl\nxgQCwXyMdLqiPB5rsaHQKprfQKvKEciJxZDNI6EsVzKBsGruggCtojHo5zgM/2EMOr8/FRFrozB+\n9ygsPr5Qfw7DhXAnWyd8PODTesekKLrR5O9FIBAIrQGblh4AwTKqtWw9hLxybhklH7SKxpR9T/Lu\nWzvoawS5BNf7+SiPzvhzhgL70vfgzg0/rM6vLc+q1Sqb9ni/Np2JolQCI0Y4IjtbjLAwDdHHITwy\naLXs/xMILYmNIgU22XWZChr/AKgjhCkH1jm5pRWlIsw1nJQZEwgEs/Cl/DhtOaXZPEdaH11mbFpR\nKqRiW6h07wVVjszidX4kSjxSMMR2BO6p0+Dv7I/l/T5FF89o/Jl3DefvnMPB20nIyFMC6y+iPD+S\nkVOZ3ZM5T+059G12ZRgfnlivs71EJGGqTwgEAuEhgATK2igjQ8bg7VOvQ12jgo1IalJXzBhFQQqK\nqos47R4OnhgePMqsc8hlcsx8bDYy2t/Hao+UugepZzJq0K9R36M1QdPAiBEyZGcziZZpaYxGWY8e\nJHJAeLi5dk2MjAxGmy8jQ4Jr18SIiyPXPaHlKPLrhH/8n0TX7CTY+7uhcP9hCLVqoXNyUxSkIMIt\nsk0v7hAIhObjzJ1TnLbpnWfyHGl9DDNjVdpqzH7sBaz/60tGDsVgEfve7XaAH5Bdko2p+xK5J8qL\nZR2P5ImA6y12W14U5o7oBblMXm8g7An/ISblWwgEAqGtQUov2yhymRw/j9qFnvJe+HnULrMfTG72\n7pw2e4k9jk460+iXhTP5vzOrTAbW1BXq8kadozWhUIiRnS3Rb/v7a4lGGYFAIDQzNA0kjPdEXPZ2\ndPdXInv/BUAu7MtXfW5yBAKBwMfgwARIxYyepwhi7B93qMFKDGuhy4wFgDDXcCzs8Qra2bkxsig6\nh3qd4ZZhGaVxSaXh8ZIqYM93wL6vjEy7/sGL3RcCYN4/Vg74gndMd4jrPYFAeIggGWVtlOT8vzHh\nt9EAgAm/jcbRiWcQ5dG5wc/9nrGf0za/2yKLVoD6+MTVaTXUMqvLnEafp7Xg56eFVFoDlUoEiaQG\nO3aUkbJLwiNBdDSjUZaeLmE0yqJJgJjQcigUYqSlMYsWadmOUOQAPeTkmiQQCC2LXCbHlWeScSjz\nAAYHJrRo9hRfZuzvTx5Br63RzOJ1XlSdK72ujNIpExCJgJIAVkklZvdkMsn2fMcc/6AjY9YlrYDM\n+zaOPnOK9V3HhU/AJ5eW4W7ZHdaYpnaa3kzfnkAgEKwPyShro3x1fW2926YoqHjAabM0bbygkn2u\nbxM2tdjKmhDk5IihUokAABqNCAUF5M+D8GhAUcDBg+VISirDwYNEl4/QsrDch/3LEOFX2sIjIhAI\nBAa5TI6pkc+0ihJD48zYIJdgHJ14hm04YFiKWRrIBMkAfUklAOa4qG3sTDSfS3APvYXzz53mzO0p\nKYXTUy5h7aCv4ShmMtO8HX3wVORUq39nAoFAaC5IJKCNMrfri6zt6Z2ebfAztIrGhr+/ZZ+nywKL\nH/bGad/xAYMtOk+joGnYXL7I1OYIjLHzHym7JBAIhOaHooADu/Jwyj8RV7Ll8B8/wCr3fAKBQHjY\niPLojJ2jf6tr8EwGXDK4B7pk6DPORBBhy9jvIX95DDCrFzxfGoWt4zfg4rQ/Tb4jUFIKiRFP4a/n\n0pA04TBOT7lEStkJBMJDBQmUtVF0D0KZjQwAsODoXNCq+l8kzt45jWIVW8ifsrX8oaZL+06acBgH\nEo9Z/wFJ02iXMBDthg9Cu4SB5MWJQBAImgYSEmQYPtwRCQky8qdFaHFcc/5B3+wdoFAGm7RU2ChS\nWnpIBAKB0Cbo5z8AW4ZvYzbsyoBZjwPOt+sOcM5k2uzK8FK3xfhzRiqGBg3D2WdPIOmlZTg/8xSG\nBCaYNa8neo8EAuFhhWiUtVFoFY2FR15Aea14fnrRTVy7fwVxvv05x+n0C64qr3DO42Tr1KRx6B6Q\nzYGNIgU2aYzDj+7FSd1DuL4VCjHS0xldnPR04nhJeHRgaUIRt1dCK0AdEQl1WDhs0lKhDguHOiKS\nfQBNM8+AiEjB3DAJBALhYWFo0DAcnXgGY3YloNTpPvBiZ+BODIYGjkBwpyJopBMwq8scVlllc87p\nCQQCobVDAmVtFEVBCnLL6neXoVU0ErYPRFpRKvwpf3R0j2LtF0GE8eE8VtGtlAZfnJqIThcnLU2C\nsDBSekl4dIiI0CIkVI30mzYICVWTa5/Q8lAUCg8c4w+G1WYX654FhQeOkWAZgUAgGBHl0RnXn1Xg\n7J3TKNLeR3/50FahrUYgEAhtARIoa6NEuEXC19GPFSyzF9uzjlEUpCCtiMnAyqazkU1ns/ZP6/hs\n23pg1vfiJMzpsWtXOQ4dssHgwWry3kV4dLCjgdn9gTRbIKwasNsPgPwBEFoYiuLNGrZ2djGB0BzQ\nNA2FIgUREZGgrDzhqKxWIze/DL4ejrC3te7Uvzn7IjQMJaUwJDABnp5OyMsjxigEAoFgLuQJ1kah\npBR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" ] }, "metadata": {}, @@ -1162,7 +4485,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 89, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", @@ -1171,11 +4494,31 @@ "scrolled": false }, "outputs": [ + { + "ename": "TypeError", + "evalue": "float() argument must be a string or a number, not 'Timestamp'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m 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\u001b[0;36mcalc_daily_average\u001b[1;34m(self, column_name, arange, plot)\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtslib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTimestamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 229\u001b[0m ax.errorbar([pd.to_datetime(x) for x in to_return['day']],to_return['mean'],\n\u001b[1;32m--> 230\u001b[1;33m yerr=to_return['std'],fmt='o')\n\u001b[0m\u001b[0;32m 231\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 232\u001b[0m ax.errorbar(to_return['day'],to_return['mean'],\n", + "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1853\u001b[0m \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1854\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1855\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1857\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n", + "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36merrorbar\u001b[1;34m(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)\u001b[0m\n\u001b[0;32m 3185\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_line\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3186\u001b[0m \u001b[0mdata_line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_line_style\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3187\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_line\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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KwocOHdL999+v4uJiDRo0SD179lRISIhOnz6trVu36sMPP9TYsWP17rvvKjw8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\nkksuqff50PSYqc97e3urd+/eNm1ff/2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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1195,7 +4538,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 88, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", @@ -1228,7 +4571,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" }, "latex_envs": { "bibliofile": "biblio.bib", diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 0aaf6abf9..417af8ac4 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -555,7 +555,7 @@ def add_to_meta_valid(self,column_names): DataFrame, where all tags are set to 'original'. This makes sure that also data that already is very reliable can be used further down the process (e.g. filling etc.) - ++ Parameters ---------- column_names : array @@ -984,7 +984,7 @@ def moving_slope_filter(self,xdata,data_name,cutoff,arange,time_unit=None, _print_removed_output(len_orig,len_new,'moving slope filter') elif type(log_file) == str: _log_removed_output(log_file,len_orig,len_new,'filtered') - else : + else: raise TypeError('Please provide the location of the log file as '+ \ 'a string type, or leave the argument if no log '+ \ 'file is needed.') @@ -1323,12 +1323,18 @@ def calc_ratio(self,data_1,data_2,arange,only_checked=False): if only_checked == True: #create new pd.Dataframes for original values in range, #merge only rows in which both values are original - data_1_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]=='original'].values, + try: + data_1_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]=='original'].values, index=self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]=='original'].index) - data_2_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_2][self.meta_valid[data_2]=='original'].values, \ + data_2_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_2][self.meta_valid[data_2]=='original'].values, \ index=self.data[data_2][arange[0]:arange[1]][self.meta_valid[data_2]=='original'].index) - ratio_data = pd.merge(data_1_checked,data_2_checked,left_index=True, right_index=True, how = 'inner') - ratio_data.columns = data_1,data_2 + ratio_data = pd.merge(data_1_checked,data_2_checked,left_index=True, right_index=True, how = 'inner') + ratio_data.columns = data_1,data_2 + except KeyError: + + wn.warn('only_checked cannot be fulfilled') + + mean = (ratio_data[data_1]/ratio_data[data_2])\ .replace(np.inf,np.nan).mean() diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 3044feb95..0631af78d 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -284,7 +284,7 @@ def add_to_filled(self,column_names): ### FILLING ##################### - def fill_missing_interpolation(self,to_fill,range_,arange,method='index',plot=False, + def fill_missing_interpolation(self,to_fill,range_,arange,method='index', order=None, plot=False, clear=False,*kwargs): """ Fills the missing values in a dataset (to_fill), based specified @@ -397,7 +397,12 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index',plot=Fa # the limit argument makes sure that only the values that can be filled by # interpolation are filled; needed to prevent other, already present NaN values # from also getting filled!! - self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) + + + #if order is None: + self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs, order=order) + #else: + #self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_,*kwargs, order=order) # Adjust in the self.meta_filled dataframe self.meta_filled.loc[indexes_to_replace[0],to_fill] = 'filled_interpol' From f6acbba5f574f69328a11f4bdd409d5a2aef5a74 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 10:38:42 +0200 Subject: [PATCH 04/42] Fixing bugs for pandas.interpolate --- Showcase_OnlineSensorBased.ipynb | 64 +++++++++++++++---------------- wwdata/Class_OnlineSensorBased.py | 13 ++++--- 2 files changed, 40 insertions(+), 37 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index a126afe6c..517f25a59 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 38, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 39, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 53, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,10 +546,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 54, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ "4895" ] }, - "execution_count": 55, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -615,14 +615,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 38.794057317612484%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 38.08767869958925%. This value is also saved in self.filling_error.\n" ] } ], @@ -636,14 +636,14 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 53.919222841147075%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 54.01010122892707%. This value is also saved in self.filling_error.\n" ] } ], @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -716,7 +716,7 @@ } ], "source": [ - "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='index',\n", + "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='spline',order=3,\n", " plot=True)" ] }, diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 0631af78d..748afe6a7 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -284,7 +284,7 @@ def add_to_filled(self,column_names): ### FILLING ##################### - def fill_missing_interpolation(self,to_fill,range_,arange,method='index', order=None, plot=False, + def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=False, clear=False,*kwargs): """ Fills the missing values in a dataset (to_fill), based specified @@ -398,11 +398,14 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', order= # interpolation are filled; needed to prevent other, already present NaN values # from also getting filled!! + if method is 'polynomial' or 'spline': + order = input('Please specify an order:') + self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) + else: + self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) + + #self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) - #if order is None: - self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs, order=order) - #else: - #self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_,*kwargs, order=order) # Adjust in the self.meta_filled dataframe self.meta_filled.loc[indexes_to_replace[0],to_fill] = 'filled_interpol' From d11d345d5fee84da609fdf2a87f04ad499fe046b Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 11:03:32 +0200 Subject: [PATCH 05/42] Fixing bugs for pandas.interpolate --- Showcase_OnlineSensorBased.ipynb | 38 ++++++++++++++++++------------- wwdata/Class_OnlineSensorBased.py | 2 +- 2 files changed, 23 insertions(+), 17 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 517f25a59..29056b9a4 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -705,18 +705,24 @@ ] }, { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "ValueError", + "evalue": "invalid method 'polynomial' to interpolate.", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='polynomial',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, order, plot, clear, *kwargs)\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m'polynomial'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'spline'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please specify an order:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36minterpolate\u001b[1;34m(self, method, axis, limit, inplace, limit_direction, limit_area, downcast, **kwargs)\u001b[0m\n\u001b[0;32m 6028\u001b[0m \u001b[0mlimit_area\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimit_area\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6029\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdowncast\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6030\u001b[1;33m **kwargs)\n\u001b[0m\u001b[0;32m 6031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6032\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36minterpolate\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 3700\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3701\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3702\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'interpolate'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3704\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mshift\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m 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interpolate.\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1170\u001b[0m def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,\n", + "\u001b[1;31mValueError\u001b[0m: invalid method 'polynomial' to interpolate." + ], + "output_type": "error" } ], "source": [ - "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='spline',order=3,\n", + "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='polynomial',\n", " plot=True)" ] }, @@ -4497,7 +4503,6 @@ { "ename": "TypeError", "evalue": "float() argument must be a string or a number, not 'Timestamp'", - "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", @@ -4512,7 +4517,8 @@ "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 2048\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2049\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2050\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2052\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m 490\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 491\u001b[0m \"\"\"\n\u001b[1;32m--> 492\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'Timestamp'" - ] + ], + "output_type": "error" }, { "data": { @@ -4576,9 +4582,9 @@ "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", - "current_citInitial": 1, + "current_citInitial": 1.0, "eqLabelWithNumbers": true, - "eqNumInitial": 0 + "eqNumInitial": 0.0 }, "nav_menu": {}, "toc": { diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 748afe6a7..cd0bcdccb 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -399,7 +399,7 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=F # from also getting filled!! if method is 'polynomial' or 'spline': - order = input('Please specify an order:') + order = int(input('Please specify an order:')) self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) else: self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) From 45199e0de9643f897a36730f80c16aac04de2499 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 11:39:35 +0200 Subject: [PATCH 06/42] Fixing bugs for pandas.interpolate in fill_missing_interpolation --- Showcase_OnlineSensorBased.ipynb | 18 +++++++++--------- wwdata/Class_OnlineSensorBased.py | 20 ++++++++++++-------- 2 files changed, 21 insertions(+), 17 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 29056b9a4..b48c0ec32 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -707,18 +707,18 @@ { "ename": "ValueError", "evalue": "invalid method 'polynomial' to interpolate.", + "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='polynomial',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, order, plot, clear, *kwargs)\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m'polynomial'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'spline'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please specify an order:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='polynomial',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, order, plot, clear, *kwargs)\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m'polynomial'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'spline'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please specify an order:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 404\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 405\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36minterpolate\u001b[1;34m(self, method, axis, limit, inplace, limit_direction, limit_area, downcast, **kwargs)\u001b[0m\n\u001b[0;32m 6028\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36minterpolate\u001b[1;34m(self, method, axis, index, values, inplace, limit, limit_direction, limit_area, fill_value, coerce, downcast, mgr, **kwargs)\u001b[0m\n\u001b[0;32m 1166\u001b[0m downcast=downcast, mgr=mgr, **kwargs)\n\u001b[0;32m 1167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1168\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"invalid method '{0}' to interpolate.\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1170\u001b[0m def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,\n", "\u001b[1;31mValueError\u001b[0m: invalid method 'polynomial' to interpolate." - ], - "output_type": "error" + ] } ], "source": [ @@ -4503,6 +4503,7 @@ { "ename": "TypeError", "evalue": "float() argument must be a string or a number, not 'Timestamp'", + "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", @@ -4517,8 +4518,7 @@ "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 2048\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m 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492\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'Timestamp'" - ], - "output_type": "error" + ] }, { "data": { @@ -4582,9 +4582,9 @@ "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", - "current_citInitial": 1.0, + "current_citInitial": 1, "eqLabelWithNumbers": true, - "eqNumInitial": 0.0 + "eqNumInitial": 0 }, "nav_menu": {}, "toc": { diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index cd0bcdccb..ad1b5d8d3 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -285,7 +285,7 @@ def add_to_filled(self,column_names): ##################### def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=False, - clear=False,*kwargs): + clear=False,*kwargs, order=None): """ Fills the missing values in a dataset (to_fill), based specified interpolation algorithm (method). This happens only if the number of @@ -308,7 +308,9 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=F clear : bool whether or not to clear the previoulsy filled values and start from the self.meta_valid dataset again for this particular dataseries. - + order : int + Both of the methods ‘polynomial’ and ‘spline’ require that you also + specify an order. Returns ------- None; @@ -398,13 +400,15 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=F # interpolation are filled; needed to prevent other, already present NaN values # from also getting filled!! + #if method is 'polynomial' or 'spline': + # order = int(input('Please specify an order:')) + # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) + #else: + # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) if method is 'polynomial' or 'spline': - order = int(input('Please specify an order:')) - self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) - else: - self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) - - #self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) + if order is None: + raise(ValueError('Please specify order')) + self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) # Adjust in the self.meta_filled dataframe From 78507262f2e62db1faa687fe09e37831acc8d854 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 12:06:48 +0200 Subject: [PATCH 07/42] Fixing bugs for pandas.interpolate in fill_missing_interpolation --- Showcase_OnlineSensorBased.ipynb | 90 +++++++++++++------------------ wwdata/Class_OnlineSensorBased.py | 9 ++-- 2 files changed, 42 insertions(+), 57 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index b48c0ec32..d0ef99170 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 5, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 19, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,10 +546,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ "4895" ] }, - "execution_count": 21, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -615,14 +615,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 38.08767869958925%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 37.68527528743143%. This value is also saved in self.filling_error.\n" ] } ], @@ -636,14 +636,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 54.01010122892707%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 54.25518830362069%. This value is also saved in self.filling_error.\n" ] } ], @@ -698,31 +698,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:324: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:326: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:367: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:369: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", " 'filling of gaps in the data during rain events.')\n" ] }, { "ename": "ValueError", - "evalue": "invalid method 'polynomial' to interpolate.", + "evalue": "Please specify order", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='polynomial',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, order, plot, clear, *kwargs)\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m'polynomial'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'spline'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please specify an order:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 404\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 405\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36minterpolate\u001b[1;34m(self, method, axis, limit, inplace, limit_direction, limit_area, downcast, **kwargs)\u001b[0m\n\u001b[0;32m 6028\u001b[0m \u001b[0mlimit_area\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimit_area\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6029\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdowncast\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6030\u001b[1;33m **kwargs)\n\u001b[0m\u001b[0;32m 6031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6032\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36minterpolate\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 3700\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3701\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3702\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'interpolate'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3704\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mshift\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[0;32m 3579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3580\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mgr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3581\u001b[1;33m \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3582\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3583\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36minterpolate\u001b[1;34m(self, method, axis, index, values, inplace, limit, limit_direction, limit_area, fill_value, coerce, downcast, mgr, **kwargs)\u001b[0m\n\u001b[0;32m 1166\u001b[0m downcast=downcast, mgr=mgr, **kwargs)\n\u001b[0;32m 1167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1168\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"invalid method '{0}' to interpolate.\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1170\u001b[0m def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,\n", - "\u001b[1;31mValueError\u001b[0m: invalid method 'polynomial' to interpolate." + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, plot, clear, order, *kwargs)\u001b[0m\n\u001b[0;32m 408\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m'polynomial'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'spline'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 409\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0morder\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m \u001b[1;32mraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please specify order'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 411\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: Please specify order" ] } ], "source": [ - "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],method='polynomial',\n", + "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',\n", " plot=True)" ] }, @@ -4567,18 +4563,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index ad1b5d8d3..41137b683 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -285,7 +285,7 @@ def add_to_filled(self,column_names): ##################### def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=False, - clear=False,*kwargs, order=None): + clear=False,order=None, *kwargs): """ Fills the missing values in a dataset (to_fill), based specified interpolation algorithm (method). This happens only if the number of @@ -405,9 +405,10 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=F # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) #else: # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) - if method is 'polynomial' or 'spline': - if order is None: - raise(ValueError('Please specify order')) + + #if method is 'polynomial' or 'spline': + # if order is None: + # raise(ValueError('Please specify order')) self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) From ea6b48ac43201b3687795010a9e45507f122dc62 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 12:08:18 +0200 Subject: [PATCH 08/42] Fixing bugs for pandas.interpolate in fill_missing_interpolation --- wwdata/Class_OnlineSensorBased.py | 1 + 1 file changed, 1 insertion(+) diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 41137b683..43cbd4da6 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -409,6 +409,7 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=F #if method is 'polynomial' or 'spline': # if order is None: # raise(ValueError('Please specify order')) + self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) From c62885668746bd416d77e1dd5b5695f168ff545c Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 12:13:02 +0200 Subject: [PATCH 09/42] Fixing bugs for pandas.interpolate in fill_missing_interpolation --- Showcase_OnlineSensorBased.ipynb | 78 ++++++++++++++++++------------- wwdata/Class_OnlineSensorBased.py | 4 -- 2 files changed, 45 insertions(+), 37 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index d0ef99170..bc3e5cd03 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 5, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 9, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 23, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,10 +546,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ "4895" ] }, - "execution_count": 25, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -615,14 +615,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 37.68527528743143%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 38.343418407969835%. This value is also saved in self.filling_error.\n" ] } ], @@ -636,14 +636,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 54.25518830362069%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 54.41203321861795%. This value is also saved in self.filling_error.\n" ] } ], @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -711,8 +711,8 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, plot, clear, order, *kwargs)\u001b[0m\n\u001b[0;32m 408\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m'polynomial'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'spline'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 409\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0morder\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m \u001b[1;32mraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please specify order'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 411\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, plot, clear, order, *kwargs)\u001b[0m\n\u001b[0;32m 408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 409\u001b[0m \u001b[1;31m#if method is 'polynomial' or 'spline':\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m \u001b[1;31m# if order is None:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 411\u001b[0m \u001b[1;31m# raise(ValueError('Please specify order'))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Please specify order" ] } @@ -4563,6 +4563,18 @@ "language": "python", "name": "python3" }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 43cbd4da6..62632969d 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -406,10 +406,6 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=F #else: # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) - #if method is 'polynomial' or 'spline': - # if order is None: - # raise(ValueError('Please specify order')) - self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) From 8f853c6aa1f050492655789ae3ef4f0a8ddb1777 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 12:17:04 +0200 Subject: [PATCH 10/42] Fixing bugs for pandas.interpolate in fill_missing_interpolation --- wwdata/Class_OnlineSensorBased.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 62632969d..3f840060c 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -284,8 +284,8 @@ def add_to_filled(self,column_names): ### FILLING ##################### - def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=False, - clear=False,order=None, *kwargs): + def fill_missing_interpolation(self,to_fill,range_,arange,method='index',order=None, plot=False, + clear=False, *kwargs): """ Fills the missing values in a dataset (to_fill), based specified interpolation algorithm (method). This happens only if the number of @@ -406,7 +406,7 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index', plot=F #else: # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) - self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) + self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,order=order, limit=range_, *kwargs) # Adjust in the self.meta_filled dataframe From e09a39476e036a08cc2f4afc9a22e1a17c0baa82 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 28 Jun 2018 12:19:40 +0200 Subject: [PATCH 11/42] Fixing bugs for pandas.interpolate in fill_missing_interpolation --- Showcase_OnlineSensorBased.ipynb | 6 +++--- wwdata/Class_OnlineSensorBased.py | 1 - 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index bc3e5cd03..f3f389929 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -711,8 +711,8 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, plot, clear, order, *kwargs)\u001b[0m\n\u001b[0;32m 408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 409\u001b[0m \u001b[1;31m#if method is 'polynomial' or 'spline':\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m \u001b[1;31m# if order is None:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 411\u001b[0m \u001b[1;31m# raise(ValueError('Please specify order'))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',order=None,\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, plot, clear, order, *kwargs)\u001b[0m\n\u001b[0;32m 408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 409\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 412\u001b[0m \u001b[1;31m# Adjust in the self.meta_filled dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Please specify order" ] } diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 3f840060c..446edfd0d 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -408,7 +408,6 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index',order=N self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,order=order, limit=range_, *kwargs) - # Adjust in the self.meta_filled dataframe self.meta_filled.loc[indexes_to_replace[0],to_fill] = 'filled_interpol' From 48e1fb9007ef9ed294bc57bc7107fbcf9d585950 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Fri, 29 Jun 2018 14:09:05 +0200 Subject: [PATCH 12/42] Fixing bugs shown in showcase for fill_missing_model --- Showcase_OnlineSensorBased.ipynb | 3569 ++--------------------------- wwdata/Class_OnlineSensorBased.py | 4 +- 2 files changed, 156 insertions(+), 3417 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index f3f389929..88089d928 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 40, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 41, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 55, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,10 +546,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 56, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ "4895" ] }, - "execution_count": 57, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -615,14 +615,14 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 38.343418407969835%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 38.215709508155975%. This value is also saved in self.filling_error.\n" ] } ], @@ -636,14 +636,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 54.41203321861795%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 52.98648788031606%. This value is also saved in self.filling_error.\n" ] } ], @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 73, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -705,21 +705,19 @@ ] }, { - "ename": "ValueError", - "evalue": "Please specify order", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',order=None,\n\u001b[1;32m----> 2\u001b[1;33m plot=True)\n\u001b[0m", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_interpolation\u001b[1;34m(self, to_fill, range_, arange, method, plot, clear, order, *kwargs)\u001b[0m\n\u001b[0;32m 408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 409\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_fill\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 412\u001b[0m \u001b[1;31m# Adjust in the self.meta_filled dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: Please specify order" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='index',\n", - " plot=True)" + "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n", + " order=2, plot=True)" ] }, { @@ -732,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", @@ -744,7 +742,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1593: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1599: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", " 'representative average day and/or high standard deviations.')\n" ] } @@ -756,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", @@ -768,13 +766,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:674: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:683: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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M/PlmFdyCg4MrT548WQ4A/fr1q4iJiWnSda1xPj4+VbWPmT9/frcBAwYUJycn/2Idu+eee4oCAwNDlixZ4v3+++9fWSl36dIlVWJi4vHo6Ohy69g333zj9PXXX7usWbMmffbs2QUAMGHChGJXV1fjzJkzA/bt26eLjo4uf/fdd93Onj2rTUpKOmHNYeLEiZd79eoVnJubq76R74XarjaxjJSIiIiIiIiI2rDkZGdUVytgNgNGowLJyW1uh8+mOnr0qDYrK0v70EMPFVRXV8P6cnZ2NoeFhZXu37/fyTbex8enyrbQBgBff/11J7VaLadMmXLR9hzjx48vAoCdO3c6A8D+/fudvL29ryr2KZVKjB8/vvBm3Cu1Dna2EREREREREVHDYmKKsXKlGUajAiqVGTExt+zunufPn1cBwHPPPad/7rnn9LXnu3TpUmX72dPTs7p2TH5+vqq6ulp06tQprK5rFBQUqAAgNzdX7ebmZqw97+Xldc0YtR8sthERERERERFRw2JjS5GY+HNrPLPN3jw8PEwAsHDhwnOjR48uqj2v1Wqv2mlUCHHNzqOurq5GrVYrv/vuuxN1XcPPz68aALy8vKpPnjzpUHs+NzeX9Zh2jL9cIiIiIiIiImpcbGzprVZkU6vVsry8/KpHaIWGhlb4+PhUHT9+XLdkyZKc6znvPffcU7R+/XrvixcvKsePH19vl9+gQYNKPv/8c7fk5OQO1qWkJpMJX331lev1XJduDSy2EREREREREVG7FBgYWJGUlNRpy5YtRW5ubkY/P79qvV5f/fbbb2c+8sgjgffee6+YOHFioYeHh/H8+fPqffv2Ofn5+VW99tpruQ2dd8yYMcVjxowpfPTRRwOfeuqp3EGDBpUqFAqcPn1a880333RasWLF2f79+1fOmjWrYNWqVd6TJk0KfOmll855eXkZN2zY4FFSUqK8Wd8B3XzcIIGIiIiIiIiI2qV33nknw9HR0fzwww/3GDp0aJ/Vq1d7AMBDDz10+ZtvvkkrKytTPPPMM/r7778/6NVXX+2al5enHjJkSElTzr1169Yz8+bNy962bZvL5MmTe0yZMqV7QkKCZ2BgYKWvr68RABwcHOS33377c58+fcoWLFjgN336dL2/v3/l888/n92S902tS0h5zdJjqiUyMlIePHiwtdMgIiIiIiKiFiCEOCSljGztPOzpyJEj6aGhoRdaOw+i9urIkSPuoaGh+rrm2NlGRERERERERERkJyy2ERERERERERER2QmLbURERERERERERHbCYhsREREREREREZGdsNhGRERERERERERkJyy2ERERERERERER2QmLbURERERERERERHbCYhsREREREREREZGdsNhGRERERERERERkJyy2ERERERERERER2QmLbURERERERETU5q1atcrN39+/n1qtDnd2dr4DAHx9fUPi4uL01pjVq1e7CSEi0tLSNNax2jE3auDAgb0GDhzYqymxu3fvdtTpdGFnzpxR32g+aWlpmjlz5vgcP35c03h060pMTHQWQkQkJiY6N/fYOXPm+Gzbtu2a4+Li4vS+vr4h9snwWhkZGWqdThf2/fffO97ouVT2SIiIiIiIiIiIqKWkp6er586dqx83blxBQkLCBZ1OZwaATz/99FTnzp3NrZ1ffebNm9d14sSJBQEBAdXWsevN+eTJk9qVK1d2ueuuu4r79u1bZd9M246VK1d2MRqNGDduXLHt+KJFi85funQpt6Wu6+/vX/3www9fmD9/frcffvgh7UbOxWIbEREREREREbVpx44d05pMJkydOrVg1KhRJdbxwYMHl7dmXg3Zs2eP44EDB5zXrl2baTvelnI2Go2QUkKtVjce3MqCg4MrW/oas2fPzo+MjAz+/vvvHYcNG1Z2vefhMlIiIiIiIiIiarPi4uL0Y8aM6QUA48ePDxJCRFiXYV7vkswTJ05oxo0bF+Di4hKq0WjCe/fu3Xfjxo2da8clJCS4BAQEBGs0mvAePXoE1xVTn/Xr17sHBQWVR0ZGVtiO17f0NTk5ucO4ceMCnJycwjw9PftPnTq1W1lZmQBqlmWOHTs2CADuu+++ICFERO1lmitWrHDv1atXX61WG+7i4hL64IMP+ufm5iptry2EiHj66ad9X3jhBW9fX98QrVYb8e9//1tnXfb517/+tXNcXJy+Y8eOdzg5OYWNGzcuICcn56pzFBYWKqZMmeLn6enZX6PRhOv1+n6vv/66p9nccLPel19+2XHo0KE9PDw8+ut0urCePXsGv/rqq15Go/Gq/ABgzZo1Xaz3OGfOHB+g7mWkGRkZ6vvuu09v/T0GBQX1XbdunattTFO+X6uIiIiKnj17lickJHg0eDONYGcbERERERERETUqKQkdkpPhHBOD4thYlN6s6y5atOh8eHh42UsvvdQtPj4+c8CAAWXe3t7Gxo+s26lTp9TR0dF93NzcjG+++WaWl5eX8ZNPPnGdOnVqoFKpPPXII49cBoCtW7c6T58+vbvBYLgcHx9/Ni8vT7VgwYJuRqNRBAQENNpllZKS0ikmJuZyU/N6/PHHA+67777Cxx9//NTevXud3n77bR8XFxfTypUrs6Ojo0vj4+MzFy5c6PfGG29kDRo0qBQAwsLCygFg5syZvgkJCV7Tpk3Li4+PP5uVlaV+8803fUeMGKE7fPjwCZXq1/LP5s2b3bp161b55ptvZjk5OZn9/PyqL168qAKAP/zhD35Dhgwpev/990+npaU5LFmyxHf8+PHqAwcO/AwAJpMJI0aM6Hn8+HHH+fPnZ4eGhpZv376902uvvdYtPz9fvXbt2nMNfO9ag8FQPGvWrDydTif//e9/O7711ls++fn5qnXr1p0DgKSkpBOxsbG94+LiCmbMmJEPAHq9vs4ls0VFRYqhQ4f2unz5svLFF1885+fnV/Xhhx+6zZo1K6CsrEwxd+7cC039fm3jBg0aVLJjx45OTf291YXFNiIiIiIiIiJqUFISOowZg6DqaihWroQ5MRE/36yCW3BwcOXJkyfLAaBfv34VMTExN3TdF154wUdKid27d5/w9vY2AUBcXFxRdHS0evHixb7WYtuiRYt8AwICKnbs2HFKqaxp7rJcv3djxbasrCxVdna2JjQ0tMlLEe+///5Ca+FnwoQJxQcPHuywZcsW15UrV2a7urqa+/XrVwEAwcHB5bbfQVpammbDhg3ezz33XPZbb7113jrep0+filGjRvX++9//3vnRRx+9ZHutXbt2/ezk5CStn48cOQIA6NmzZ/nnn3+ebhkucnV1Nc6cOTPgq6++ch4/fnzxp59+2unw4cNO77zzTvozzzxTYMm7qKysTJGQkOD14osv5nbp0qXOQuj8+fPzrT+bzWaMHj26uKqqSqxfv957zZo155RKJaz35ePjU9XY73nt2rVuGRkZ2u3bt/88ZsyYYgB48MEHi6Kjo9VLlizxffbZZy/YFhkb+n5tzxsWFla2adMmj/T0dLVer6/GdeAyUiIiIiIiIiJqUHIynKuroTCbAaMRiuRkNHuXybYiJSWl07Bhwy67ubmZqqurYX3FxsYWpaWl6QoLCxVGoxFHjx51HDt27EVroQ0Ahg8fXurj49Po5gSZmZlqAPD09GxyB964ceOuKoj17du3/Pz5843uPJqYmNjRbDZj2rRphbb3M2zYsFInJyfTrl27nGzjhw4dWmRbaLMVFxdXaPt52rRpFxUKBfbu3esEALt27XJWKBR44oknrop79NFHC6urq8X333/fob48MzIy1JMnT/b38fEJ0Wg04RqNJmLZsmW+xcXFynPnzjW7GWzPnj3Onp6e1dZCm9XDDz9ccPHiRdXhw4d1tuNN/X49PT2rgV9/h9eDnW1ERERERERE1KCYGBSvXAmz0QiFSgVzTAyKGz+qbSosLFRt2bLFTaPRuNU1n5eXpyotLTUbjUbh5eV1TWeTu7t7o91O5eXlCgDQarVN3nXUw8PDZPtZq9XKqqoqUV+8bb4A0K9fv351zRcWFl5V+/H29q43/9rLcx0cHGTHjh2N586dUwPAxYsXlR07djTqdLqrinW+vr7VAFBQUFBnnclkMuHee+/tkZeXp16wYEF2cHBwhaOjo/nzzz/vvGbNmi7W76s5Ll26pPLw8LjmXnx8fKoBID8//6pnzTX1+3V0dJQAUFZWdt0Naiy2EREREREREVGDYmNRmpiIn1vjmW321rlzZ9OAAQOKFy5cmFPXvL+/f7VarZYqlUrm5uZe09104cIFta+vb4PdbdaOttqFrpbg5uZmAoAvv/zypJub2zWddLW764QQdXa1AUBOTs5V+VZUVIiioiKVtZjm4uJiKioqUlVUVAgHB4cr57EW49zd3evs5Dt+/Lj22LFjju++++6ZmTNnXumK27JlS5M3nKitc+fOxtOnTzvUHs/Ozm52V6GtCxcuKG/keKCNLCMVQtwlhNgmhDgnhJBCiKk2c2ohxB+FEP8VQpQKIc4LIT4WQvjVOodWCLFGCHHBErdNCNG1VoyfEGK7Zf6CEGK1EKLRlkwiIiIiIiKi211sLErj45FzKxfaAGDo0KGXf/rpJ8fw8PDyu+66q6z2S6fTSZVKhZCQkLLt27e7mEy/NkTt3LmzQ3Z2dqN1hKCgoCqtVitPnz6ttVfeDg4OZuDajqt77723SKFQID09XVPX/fTu3bvRZa9WX3zxxVU7eX7wwQcuZrMZgwcPLgEAg8FQbDab8cEHH7jYxn344YeuarVaGgyGOv+3UVJSogAAtVp9pUBXWVkpal/PGtOUTrc777yzODc3V/3dd99dtXR18+bNrq6ursawsLCK+o5tyJkzZ7RqtVr27t270U0w6tNWOtucAPwPwEbLy5YjgHAAbwL4D4BOAFYA+EYI0V9Kaa00rgIwHsAkAAUA3gaQKISIkFKahBBKAF9b5u4E4AbgbwAEgKdb8N6IiIiIiIiIqI1YunRpdlRUVJ9Bgwb1fuqpp/K6d+9eWVhYqDp69KjuzJkz2s8++ywdAF555ZVz999/f9CIESN6PPnkk/l5eXmqpUuX+jRlGamDg4Ps379/6cGDB+t9hllz9evXr0KpVMoPPvjA3d3d3ejg4CBDQkIqgoODK2fMmJGzcOFCv7S0NAeDwVCs0+nMGRkZmqSkpI5PPPHEhbFjxzZp2e/Jkyd1DzzwgH7SpEmFJ06ccIiPj/cdMGBAyfjx44sBYOLEiZeXL19eMnfuXP/8/HxVSEhIRWJiYqfNmze7z5o1K6e+zRHCwsIqfHx8qhYvXuyrUqmgVqvl6tWrveqKDQwMrEhKSuq0ZcuWIjc3N6Ofn191XRsVzJo1q2DDhg1ekyZN6mG7G+m+ffs6Ll++PMN2c4Tm+OGHHzqEhISUWpeTXo820dkmpfyHlPIFKeXnAMy15i5LKUdIKTdLKdOklP8G8BSAPpYXhBCdAPwWwDwp5Q4p5WEAjwLoDyDWcqqRAIIBPCqlPCyl3AFgPoAnhBAdb8Z9EhEREREREVHr6tmzZ9WBAweOBwcHly1evNh3woQJQXPmzPHbs2eP07Bhw4qscRMmTChev379mdOnTztMmTIlcPXq1d5Lly7NamwnUqu4uLjC/fv3OxcVFdml9uLt7W2Kj4/P/Omnnxzvueee3kOHDu2zd+/eDgCwdu3acytWrEhPTU11mjZtWvdJkyb1WLVqlXfnzp1Nffv2bXKH1x//+MdMKSWmTp3afcmSJb7Dhg279NVXX/1inVcqldixY8fJuLi4gjVr1ng/+OCDPZKTkzu99tprWatXrz5X33kdHBzkZ599dsrDw6N65syZ+ueff94vOjq6+Nlnn71mKe8777yT4ejoaH744Yd7DB06tM/q1as96jpnx44dzbt27Uq78847ixYvXuw7efLkHsePH9e9++67Z+bOnXuhqfdsq6SkRKSmpnasvVFEcwkpr7tQ1yKEECUAZksp/9pAzCAAqQC6SSnPCiGGA0gG4CmlzLeJOwbgcynlq0KIRQDipJTBNvMeAPIADJdSfl/f9SIjI+XBgwdv9NaIiIiIiIioDRJCHJJSRrZ2HvZ05MiR9NDQ0OsqOJB9FBYWKvz8/EKXLVuWYfucsrYoMTHReezYsUFGsBydAAAgAElEQVRbtmz5ecKECbfs5hc36s9//rPLs88+q8/IyPivu7u7qaHYI0eOuIeGhurrmmsTnW3NYXnG2goA26WUZy3D3gBMAGr/Q5JrmbPG5Naav2A5zhtERERERERERHbi6upqnjVr1vlVq1Z5m81N3pSUWtHKlSu9Z8yYkdNYoa0xbeWZbU0ihFAB+BBAZwDjmnIIANvWvfra+K4ZF0I8CeBJAPDz87vmACIiIiIiIiKihrz88su5JpNJZGZmqut67hi1HZmZmaq777770quvvlq7UavZbplim6XQ9ncAIQAMUsoCm+kcAEoA7gDybcY9AfzLJmZwrdO6W4675ouUUiYASABqlpHa4RaIiIiIiIiI6Dbi5OQk33rrrfOtnUdjxowZUyylPNTaebQmPz8/44oVK+zyu7ollpEKIdQANqNmw4NhUsraD9A7BKAawAibY7qiZgOFfZahVAB9LONWIwBUWo4nIiIiIiIiIiK6IW2is00I4QSgh+WjAoCfEOIOAIUAsgF8BmAAgLEApBDC+oy1y1LKcinlZSHEewCWCyHyABQAeBvAfwEkWWK/A3AMwEYhxPMA3AAsB/BnKeWV3UaIiIiIiIiIiIiuV1vpbIsE8KPlpQPwuuXnRQC6AhgPwAc1HWjnbV4P2ZzjOQBfoqYDbi+AEgBjpZQmALC83wugzDK/2RI/t2VvjYiIiIiIiIiIbhdtorNNSpmCms0M6tPQnPUcFQCetrzqi8kEMKa5+RERERERERERETVFW+lsIyIiIiIiIiIiuuWx2EZERERERERERGQnLLYRERERERERERHZCYttRERERERERNTmrVq1ys3f37+fWq0Od3Z2vgMAfH19Q+Li4vTWmNWrV7sJISLS0tI01rHaMTdq4MCBvQYOHNirKbG7d+921Ol0YWfOnFFbx+bMmeOzbds2Z3vlUx9737et5nwHtdX1O7Kn119/3TMoKKivyWRqidM3SZvYIIGIiIiIiIiIqD7p6enquXPn6seNG1eQkJBwQafTmQHg008/PdW5c2dza+dXn3nz5nWdOHFiQUBAQLV1bOXKlV2MRiPGjRtX3JLXbuvfTUt5/vnn89esWdNl7dq1br///e8LWiMHFtuIiIiIiIiIqE07duyY1mQyYerUqQWjRo0qsY4PHjy4vDXzasiePXscDxw44Lx27drM1rh+W/5uWpKTk5N84IEHCtasWePdWsU2LiMlIiIiIiIiojYrLi5OP2bMmF4AMH78+CAhRIR1eeT1LpU8ceKEZty4cQEuLi6hGo0mvHfv3n03btzYuXZcQkKCS0BAQLBGownv0aNHcF0x9Vm/fr17UFBQeWRkZIV1TAgRAQBr1qzpIoSIEEJEzJkzx+eVV17x0mg04dnZ2Vc1RZnNZnTt2jVk7NixAQCQlpamEUJELF261ON3v/tdV1dX11CdThc2bNiwHrWXZdb13Zw4cUIzYcKEAHd391CNRhPetWvXkGnTpnWzzu/atctx9OjR3b28vPo7ODiE6/X6frNnz/YtKSkRTb1vW8ePH9cYDIYeOp0uzMXFJXTatGndKisrrzlXQkKCy6BBg4JcXFxCHR0dw/r06dN3zZo1brYxQUFBfUeMGBFY+9jExERnIUTEF1980dE69pvf/Kbwl19+cdixY0eH68n7RrGzjYiIiIiIiIgadfFiUoeLF5OdXVxiil1cYktv1nUXLVp0Pjw8vOyll17qFh8fnzlgwIAyb29v4/We79SpU+ro6Og+bm5uxjfffDPLy8vL+Mknn7hOnTo1UKlUnnrkkUcuA8DWrVudp0+f3t1gMFyOj48/m5eXp1qwYEE3o9EoAgICKhu7TkpKSqeYmJjLtmNJSUknYmNje8fFxRXMmDEjHwD0en2Vk5OTedmyZb7r1q1ze+ONN3Kt8Vu2bOl47tw5zZ/+9KcLtudZtWpVl759+5atW7cuPTc3V/XGG2/4jho1KigtLe2YVquVdeVz4sQJTVRUVB+dTmdesGDBuV69elVmZGRoduzYcaVIdebMGU3//v3LH3vssYKOHTuajh49qnvrrbd80tPTtYmJiaeb8z1XVFSIUaNGBVVWViqWLl2a6eXlZUxISPD45z//6VI79vTp09oJEyZcDAoKylEoFDIlJcX5ueee8y8vL1fMnz8/HwAef/zx/Jdeeqlbenq6Wq/XX1mWu2HDBndfX9+q++67r8g6FhUVVebk5GT6+uuvO40YMeKm/W/VisU2IiIiIiIiImrQxYtJHf773zFBUlYrsrJWmvv3T/z5ZhXcgoODK0+ePFkOAP369auIiYm5oeu+8MILPlJK7N69+4S3t7cJAOLi4oqio6PVixcv9rUW2xYtWuQbEBBQsWPHjlNKpRI21+/dWLEtKytLlZ2drQkNDS2zHbfm7uPjU1X7Pu69997CjRs3eixatChXoahZiLhhwwYPvV5fMWbMmKue79ahQweTbV59+vSpGDVqVO9169a5Pffcc1cV5qwWLlzoU1lZqfjxxx+P2xarnn766StLLadOnXoJwCWgpqtu5MiRJR07djTNnj07ICcnR2n9vpri3XffdTt79qw2KSnphPVeJ06ceLlXr17Bubm5atvYpUuX5lh/NplMuPfee4tzcnLU7733noe12PbUU08VvPHGG13fffdd9+XLl58HgPPnz6u+/fZbl7lz52ZbvzMAUCqVCAoKKv/hhx9apbONy0iJiIiIiIiIqEEXLyY7S1mtAMyQ0qi4eDG5xXfTbCkpKSmdhg0bdtnNzc1UXV0N6ys2NrYoLS1NV1hYqDAajTh69Kjj2LFjL1oLWgAwfPjwUh8fn6rGrpGZmakGAE9PzyZ34M2ePTsvKytLa92pNCMjQ71z585OU6dOza8dWzuvkSNHlnp5eVXv37+/3uLS7t27Ow0fPvyybaGttsLCQsWMGTN8u3Xr1k+r1YZrNJqIWbNmBUgpcezYMYem3gsA7N+/38nb2/uqoqJSqcT48eMLa8cePXpUO3bs2ABPT8/+Go0mQqPRRGzevNk9PT39yjVdXFzMEyZMKPjoo4/crTuNrlu3zk1KiRkzZlxTYHRzczPm5eW1yI6njWFnGxERERERERE1yMUlpjgra6VZSqNCCJXZxSWmRXfSbEmFhYWqLVu2uGk0Gre65vPy8lSlpaVmo9EovLy8rilMubu711ussiovL1cAgFarbfJuoMOGDSsLDg4u+9Of/uQxYcKE4rVr17qrVCpMnz79mof815fX+fPn6y0uXbp0SdlYoXDSpEkB+/btc54/f352eHh4mbOzs3nfvn0dFi5c6Ge9p6bKzc1Vu7m5XVNs9PLyumrs8uXLitGjRwc5ODiYX3311bNBQUGVWq1Wrl271uOzzz5zt439/e9/n/fRRx95fPrpp50eeuihyxs3bvQYOXLkpW7dul1zHQcHB3NFRcV1PWvuRrHYRkREREREREQNcnGJLe3fP/Hn1nhmm7117tzZNGDAgOKFCxfm1DXv7+9frVarpUqlkrWXOwLAhQsX1L6+vg0WrawdbYWFhc2qu/zud7/Lmzt3rv+ZM2fUH330kfvdd99d6OXldc3SzfryCg4OLqs9buXi4mI8f/78NcdZlZWVieTk5M5z5szJfvnll/Os4z/++KOuOfdg5eXlVX3y5MlruuFyc3Ov+k527tzplJ2drfnmm2/SbHeaXbVq1TWFsgEDBlRERESU/PnPf/bQ6XTmzMxM7Zo1azLquv6lS5dULi4u1/1svxvBZaRERERERERE1CgXl9jS7t3jc27lQhsADB069PJPP/3kGB4eXn7XXXeV1X7pdDqpUqkQEhJStn37dhfrkkUA2LlzZ4fs7OxGlyYGBQVVabVaefr0aW3tObVaLevrEvvd735X2KFDB/ODDz7Y/fz585qZM2des4QUAGrn9d1333XIzc1VDxo0qN7fzZ133lm0c+fOzhkZGXUW3MrLyxUmkwlqtfqqDRY+/PBD97riGzNo0KCSnJwcTXJy8pWlrSaTCV999ZWrbVxpaakCwFXXzc/PV+7YsaPOnV+ffPLJvH/961+dFi9e7OPv7185bty4Orsss7KyNIGBgRV1zbU0FtuIiIiIiIiI6LaxdOnS7JKSEuWgQYN6r1mzxu3rr7922rRpU+f58+d3mThxot4a98orr5w7c+aMw4gRI3p88sknnVavXu32m9/8pntTlpE6ODjI/v37lx48ePCaZ6gFBgZWJCUlddqyZUvHf/3rX47p6elXil9OTk5y4sSJFw4ePOjUs2fP8vp20iwtLVXa5vXII48E+vv7V86cOfOaJadW8fHx2RqNxhwdHd17xYoV7tu3b3det26d6/jx4wMAwM3NzRQaGlq6fv16r7Vr17pt3ry50+jRo7vX1UXXFLNmzSro2rVr5aRJkwJXr17ttnnz5k4jRozoUVJSorSNGz58eImTk5PpmWee8fvkk086/eUvf3EZPHhwr/q60h577LFLnTt3Nh4+fNiprufZAcCFCxeUGRkZDnfeeWdJXfMtjcU2IiIiIiIiIrpt9OzZs+rAgQPHg4ODyxYvXuw7YcKEoDlz5vjt2bPHadiwYUXWuAkTJhSvX7/+zOnTpx2mTJkSuHr1au+lS5dmNbYTqVVcXFzh/v37nYuKiq6qvbzzzjsZjo6O5ocffrjH0KFD+6xevdrDdv7hhx++CADTpk2rs5AEAM8+++z57t27V8yYMUO/YMECv+Dg4LJvvvnmZ61WK+s7plevXlV79uw5ER4eXvLGG2/4xsXF9VyyZImvu7v7laLW5s2bT/fr169swYIFfjNmzNB7enoaly1bltWU+63NwcFBfvvttz/36dOnbMGCBX7Tp0/X+/v7Vz7//PPZtnE+Pj7Gjz766BeTySSmTp0a+Prrr/tOmTLlwgMPPHDNRgoAoNVq5ciRIy9pNBo5ffr0Onde/eyzzzqp1Wo5efLki9eT+40SUtb7eyCLyMhIefDgwdZOg4iIiIiIiFqAEOKQlDKytfOwpyNHjqSHhobWWYigm6OwsFDh5+cXumzZsoyZM2fWWTiqy9NPP+373nvveZ49e/aIq6vrVRsspKWlaXr37h2yYsWKjDlz5tyWv9/q6mro9fqQAQMGlGzduvVMXTF33XVXT1dXV2N98/Zw5MgR99DQUH1dc9wggYiIiIiIiIjIzlxdXc2zZs06v2rVKu/p06cXKhQNLy7cu3ev7tixYw7vvfee5+TJk/NrF9pud4WFhYpDhw7pNm3a5JaTk6OZP39+nRtc7Nu3T3fgwAHnQ4cOHbvZOVqx2EZERERERERE1AJefvnlXJPJJDIzM9V6vb7BZ709+OCDPQoKCtRDhgy5vHz58uyGYm9H+/bt6zB27NggV1dX4+LFizOjo6PL64rLzs5Wr169Or1fv35NWu7bEriMtAm4jJSIiIiIiKj94jJSImquhpaRcoMEIiIiIiIiIiIiO2GxjYiIiIiIiIiIyE7sXmwTQnQUQvjZ+7xERERERERE1GRms9ksWjsJovbI8t9WvRtYNKnYJoQIFEJ8JYS4LIQoEEJ8KIQIqCf8OQAttrUqERERERERETVMCJFTXl7u0Np5ELVH5eXlDkKIOndDBZpQbBNCeALYA2AsAGcALgAmA/hRCHGvvRIlIiIiIiIiIvswGo2vp6ena0pLS3XscCOyD7PZLEpLS3Xp6ekao9H4en1xqiacayEALwAbACwCUAXgdwBeAfClEOIhKeVWeyRNRERERERERDcuPDz828OHD8/+5ZdfXpVSeoPPbCeyB7MQIsdoNL4eHh7+bX1BTSm23Q3giJRyhs3YH4UQOwFsB/CJEOIBKWXiDSZMRERERERERHZiKQbUWxAgopbRlMq2P4CdtQellD8AuAtAAYDPhBB32zk3IqJbWmpWKuJ3xyM1K7W1UyEiIiIiIqKbpCmdbeUATHVNSCl/FkIYAOwC8IUQYpwdcyMiumWlZqUiZmMMqkxV0Cg1SJ6SjKhuUa2dFhEREREREbWwpnS2ZQAIrW9SSnkSQAyAYgBbAQy2T2pERLeulPQUVJmqYJImVJmqkJKe0topERERERER0U3QlGLbHgB3CSE61RcgpfwJQCyACtQU3ppFCHGXEGKbEOKcEEIKIabWmhdCiNeEENlCiHIhRIoQIrhWjIsQYpMQ4rLltUkI0blWTIgQYpflHOeEEK8IIbgrCxHZnUFvgEapgVIooVFqYNAbWjslIiIiIiIiugmaUmz7GoAWwMyGgqSUR1FTcLt0HXk4AfgfgN+jZtlqbfMBPA/gaQADAOQB2CGEcLaJ+RhAOGo2dBht+XmTdVII0RHADgC5lnM8A2AegDnXkS8RUYOiukUheUoyFg9bzCWkREREREREtxEhpWw8SAgtAJOU0tiE2M4AOkkpM64rISFKAMyWUv7V8lkAyAawVkr5pmVMh5qC21wp5QYhRB8AxwEMkVLutcQMAbAbQG8pZZoQYgaAPwLwklKWW2JeAjADQFfZwBcRGRkpDx48eD23Q0RERERERG2cEOKQlDKytfMgovahKZ1tkFJWNqXQZom9dL2FtnoEAPAG8J3NNcoB/AtAtGUoCkAJgH02x+0FUForZre10GbxLQAfAHo75ktEdAV3JCUiIiIiIrq9NGU30joJIToACALgJKXcbb+UruFtec+tNZ4LwNcmJt+2O01KKYUQeTbHewM4W8c5rHNnbCeEEE8CeBIA/Pz8biR/IrpNcUdSIiI7SE0FUlIAgwGI4r+hRERE1PY1qbPNlhCiqxDiCwAXARwE8L3N3BAhxHEhhMF+KV5Re5mnqDVW1zLQxmJEPeOQUiZIKSOllJEeHh7NzZWICBuPbESFsYI7khIRXa/UVCAmBnj55Zr3VHYJExERUdvXrGKbEKILgAMAxgNIBJCKXwtWsMx5AnjIXgkCyLG8e9ca98SvnWk5ADxtdxa1/OxRK6aucwDXds0REd2Q1KxUvP+f9yEttXyVQsUdSYmImislBaiqAkymmveUlNbOiIiIiKhRze1sexU1BapYKeX9qNnd8wopZTVqNiUYbJ/0ANQs78wBMMI6IIRwAHAnfn1GWypqdjS1XVsQBaBDrZg7LcdajUDN5gvpdsyXiAgp6SkwmmsedSkgMO2OaVxCSkTUXAYDoNEASmXNu8HQ2hkRERERNaq5z2y7B8A2KWVKAzGZqCmENZkQwglAD8tHBQA/IcQdAAqllJlCiFUAXhRCnADwM4CXULMhwscAIKX8SQjxDYANQognUNNttwFAopQyzXLej1FTLPyrEOIN1Dxv7g8AXm9oJ1Iiouvh5ugGszQDACQkwrqEtXJGRES3oKgoIDmZz2wjIiKiW0pzi21eAE42ElONmo6y5oiEzbPfALxuef0NwFQAywDoALwLwAU1y1VHSimLbY55BMBq/Lpr6TYAs62TUsrLQogRlnMcRM0z51YAeLuZuRIRNaqgrAAKKGCGGQooUFBW0NopERHdmqKiWGQjIiKiW0pzi22FALo1EhOEX5+z1iSWTjnRwLwE8JrlVV9MIYDfNHKdowDuak5uRETXw6A3QKvSXtmJlM9rIyIiIiIiuj00t9i2F8A4IYS3lPKagpoQoieA0QA+tEdyRES3qqhuUUiekoyU9BQY9AY+r42IiIiIiOg20dxi23LU7ES6SwjxLABHABBCdEBNx9hKAGbULM8kIrqtRXWLYpGNiIiIiIjoNtOsYpuU8oAQ4kkAfwKQaDNVZHk3AnhcSnnMTvkRERERERERERHdMprb2QYp5QdCiD0AZgIYBMANwGUA+wGstdn9k4jotpealcqlpERERERERLeRZhfbAEBKeRLAc3bOhYioXUnNSoXhjYWo/mUw1IELkfJSPAtuRERERERE7dx1FduIiKhxyzbvRtX7/wBMGlTtqsLGOz5H1AwW24iIiIiIiNqz6yq2CSGUAHoBcAGgrCtGSvmvG8iLiOiWlpqVim3fXgZMGkCqAJNEzrHerZ0WERERERERtbBmF9uEEC+jZglpp0ZC6yzCERHdDjYe2Qiz/38A5YuASQLKanxdNQ+pWUu5lJSIiIiIiKgda1axTQgxH8DrqNkQYROALNTsQEpERLV12w88FgOkGwB9Coy+B5CSnsJiGxERERERUTvW3M62JwCcAxAupcxvgXyIiNqFsC5hNT9021/zAqBRamHQG1ovKSIiIiIiImpximbGdwOwlYU2IqKGFZQVQEBc+TzQZyC+f+x7drURERERERG1c80ttuWCO5gSETXKzdENEvLK59+G/5aFNiIiIiIiottAc4ttnwIYIYTQtkQyRETthW1nm4BAQVlBK2dERNS2pGalIn53PFKzUls7FSIiIiK7am6X2isABgH4XAjxjJTyTAvkRER0y7PtbJOQcHN0a+WMiKjVpKYCKSmAwQBEscMVqCm0xWyMQZWpChqlBslTktn9S0RERO1Gc4ttxwCoAfgAuEcIcRnApTripJQy8EaTIyK6VRWUFUAhFDBLM8TZaHyREISQqfw7m+i2k5oKxMQAVVWARgMkJ/MfAgAp6SmoMlXBJE2oMlVxp2YiIiJqV5q7jFQBwAgg0/K6DEDU8WrueYmI2hWD3gCtUgvF2cGQf9uBpPeGIiam5u9uIrqNpKTUFNpMppr3lJTWzqhNMOgN0Cg1UAolNEoNd2omIiKidqVZnW1SSn0L5UFE1K5EdYtC8pRkvPZGJZLMOphN4srf2WxqIbqNGAw1HW3WzjaDobUzahOs/0ampKfAoDewq42IiIjaFe4sSkTUQqK6RSHu7qNI/ms1hFRCpQYMBmVrp0VEN1NUFLBqFfDFF0BcHKvtNqK6RbHIRkRERO0Si21ERC0kNSsVM/97J0yPDgDSDTB33wd0XQqAf1wS3TZSU4Fnn63pbNu9GwgJYcGNiIiIqJ1rsNgmhJhi+XGLlLLY5nOjpJQbbygzIqJb3LK9y2CSJqDbfqDbflQDfAg40e2mrme2sdhGRERE1K411tn2VwASwH4AxTafGyIsMSy2EdFtLbs4+6rPCij4EHCi2w2f2UZERER022ms2PY4agpn5y2fp7VsOkRE7cdvw3+Lf2f/G8gaBByZghDvO4CRUUC31s6MiG6aqCggObmmo81gYFcbERER0W1ASNlYoxpFRkbKgwcPtnYaRHQLWvDBVix/8m5IowaAgFYLfP89/94mIiIiakuEEIeklJGtnQcRtQ+K1k6AiKg9K/r3BEijFjUr7H99ZBMRERERERG1Tyy2ERG1kNRU4P33rx7jI5vatoRDCRi1aRQSDiW0dipERERERHSLamw30tPXeV4ppQy8zmOJiNqFjVszUG3sBuv/r9G1K/DyywC6piJ+dwoMegN3Jm1DEg4l4KnEpwAA353+DgDwZMSTrZkSERERERHdghrbIEGBxncfrYu4jmOIiNqN1KxUvH9pIaTiH4BZA0CJ7GyBZ35vgjy0ECbfPdAoNUieksyCWxvxxfEvrvnMYhsRERERETVXg8tIpZR6KWXA9bxu1g0QEbVFKekpMPnuAR6LAQKTIYSE2VzzzLbqXwbDJE2oMlUhJT2ltVMli7i+cQ1+JiIiIiIiaoqb8sw2IUR/IcSUGzheKYRYLIQ4I4SosLy/IYRQ2cQIIcRrQohsIUS5ECJFCBFc6zwuQohNQojLltcmIUTnG7k3IqK6GPQGaJQaKIQSStdMqNSAUlnzzDZ14F4ohRIapQYGvaG1UyWLJyOexIYxGzCy+0hsGLOBXW1EN0FqVirid8cjNSu1tVMhIiIishsh5fWsEm3mRYR4FcArUkrldR7/AoC5AB4DcBRAfwB/A/C2lHKxJWYBgJcATAWQBuAVAEMA9JJSFlti/gnAD8ATqFke+xcAp6WUYxu6fmRkpDx48OD1pE5Et7GErUcx++HeMFYroVCaMfahC5g/0xtHNQn44vgXiOsbx4IOEd0+UlNrtmM2GICoKKRmpSJmYwyqTFVcVk9ErU4IcUhKGdnaeRBR+9DYM9vaimgA26WU2y2f04UQ2wD8H1DT1QbgWQBLpZRfWMYeA5AHYDKADUKIPgBGAxgipdxniXkKwG4hRC8pZdpNvSMiavcKfgqBySghzQImsxlfpR5BhwFf4MvieagyVWF35m6EeIbwj8s2JDUrFSnp3LyCyO5SU4GYmJq19BoNkJyMFGMKqkxVVy2r5393RERE1B7clGWkdrAHwDAhRG8AEEL0BTAcwD8s8wEAvAF8Zz1ASlkO4F+oKdQBQBSAEgD7bM67F0CpTQwRkd249TkKKCsBGAEoIU/H4KPnp6EiPYzPbGuDrF02L3//MmI2xnBZG5E9paTUFNpMppr3lJQry+25rJ6IiIjam1ul2PZHAJsAHBdCVAM4BuBvUsp1lnlvy3tureNybea8AeRLm3Wzlp/zbGKIiOwiNSsVzx77P8hHY4DAJECYAKkCTGqI9GH847INSkm/tsuGiOzEYAA0GkilAtUqBY72cUNUtygkT0nG4mGLuYSUiIiI2pVbZRnpQwCmoGZJ6DEAdwB4RwhxRkr5nk1c7QfQiVpjdT2grnZMzaAQTwJ4EgD8/PyuP3Miui1ZCzey2z7A8DqQcRdgkoCqGnMnR6Jzj8VcqtjGuDm6QSEUkJAshFLdaj1zjJohKgpHP16Fz9bNwk5/Ew4fexbJETXL6PnvIBEREbU3t0qxbTmAt6SUn1g+HxVC+ANYCOA9ADmWcW/g/9k79/goynv/v5/dXADlIhEEymJAEUUjKCoMiKxCvdIelFNPFQ2ibVBESz1t8Nra2iqktgfrBYkikoq15/ywaFEUCSwgLFK5pAgKCAQWkYJBFLkk2Z3n98fsbHZ2Zm/JJiTwvHnxys7MszPPXHfmM5/v90sg6nudqXO77QU6CyGE6W4L53rrhN0Rh5SyFCgFo0BCZldHoTj5ONlyYXnzvbhdbkKhEHhW4cuwok4AACAASURBVLruAbrtvo9bb27F1HGjgFHHu4uKKErXlDLx3YkE9SBul5tp1047KY5TRRo45BxTglt6zM+r4snLJSGp4w7VUFZRdlL9LigUCoVCoTh5aClhpG2AUMy4EHX934Ehpn3fnCiEaAUMpS5Hmx84FSN3m4kGnII1j5tCocgwpWtKGfbqMB5d8uhJkwtL82jc2f9OBAICg9AX/IkvKs7l2d+ehf/EX/0WhT/g595376VWr0Ui0aVO1ZGq490tRXPDIeeYIj2ic7S5XW5mrZ/Fo4sf5YpXr6B0Tenx7p5CoVAoFApFxmgpYts/gAeFEDcIIfKFEDcCDwB/h0jutWnhNjcJIS4AXsUoiPB6uM2nwHsYlUkHCSE0YAYwX1UiVSgaj2ghQ5c61aHqkyYXVmG/QrLd2VDphVAOUndTXSPVM3ozw1fpQ9f1yLBAqBBShZ1wzjHcbuOv13u8e9Ti0HbDp9+O5ZVOP+XO/ndSG6pFRyeoB5n47sST4kWMQqFQKBSKk4OWEkZ6H/AE8AJGaOiXwEvAb6PalACtgeeB04CPgKullIei2owB/kxd1dK3gYmN2nOF4iTnZBYyTHfbi5VLwV0DIYk7C7ze7OPdNUUU3nwvWe4sakI1ABgZBhSKGDTNCB1VOdvqRzgM98yaGgpzcrjo9Wm87HJFfh9CMoSv0qfCSRUKhUKhUJwQNJWzTYT/1wsp5SEp5SQp5ZlSytZSyl5SyoellMei2kgp5eNSyq5SylZSymFSyk9i5nNASnmblLJd+P9tUsqDDVgvhUKRBFPIMDnZhIzCfoXk5q+FsSNwD/8tz73xmXpGb2ZoHo3re18fGQ7qQUpWlBzHHimaLZoGDz2khLYE+AN+nlr+lN2lFhOGW/BpFc9f/zzZrmxcwkWuO/ekeRGjUCgUCoXixCctsU0I8YoQ4odJ2owUQrwSPS4sgrWUkFWFQpFBnISMsoqy49ijpie06zKouB35Tffjsvy4D7+KCBv3bbQMv73lbbW9FIo08Qf8DC8bzmNLHrPn53QIwy0aUMTSO5byw3N+SEHnAjbs23Dc+q5QKBQKhUKRSdINI70DqMQIv4xHP2AscGf9uqRQKE44TuJ6vmXztxJ8ZSGEctGBCTeHKFjadMYY8+G3JlRDjjuH8sJyFaYVQ+maUrYe2GoZp0tdhbQpFGniq/RRE6ohJEPUhGqs51CcMNx5i/7NvDnnQv5eVu8Zz7avtzF1xNTjtg4KhUKhUCgUmaAx3Ga52CuHKhSKkxR/wM87W9+JDGe7sinsV3gce9R0+AN+1q5qB6FszGh6Pehu0gIJTg+/Cisz1860jXMLtwppawE0e9em3w9PPcXJUoI4utpojjvHfg7FhOH6/fD03dfC4idgdjkEBvH0yqeb7/5UKBQKhUKhSJH6FEiI61ERQuQCVwB7690jhUJxQlGysoRavTYyfEPvG04Kt5DpKKvOuRjc10LIeLeRkyPIyzOev5six7r58Gs625SAZKdb2262cZ52npPiOG3JRM6xYDUul4vnr3+eogFFx7tbdYQLAlBTY4RNlpefeLne/H6LU03zaJQXluOr9OHN9yY9h3w+kMEckC4ISaNys2e1cpUqFAqFQqFo8SQV24QQ22NG/VwIMc6hqRvohOFsezEDfVMoFC0cf8DP258lijo/cSmrKONY8Biy+wpc40bQ54vf0za7LV6tPZMmndVkz9/pPvyejBQPKWbe5nmWca2zWx+n3ihSxVfpozpYjY6OrutMfHciBZ0Lms8xHlMQAJ/vxBLb6ikm+gP+yPXI69VoleviWHUI6apF5C8jN0sVSlAoFAqFQtHyScXZ5qLOzSaJX1m0FtgAlAO/y0jvFApFi8ZX6UPGmGG7nNrlOPWm6fAH/Lyy/pXIugsEW2oXIb+3hLWLr0Kv+Q16SJyQz9+ZJPqhvDEFFM2jMaZgDHM2zImM87Tz8NTyp5RA2Yzx5ntxuVzoug4YxVealSPKLAhgilFe7/HuUWZxEBP93eHK2VdGnLRLxi6x7A+nHJLl5Rpl83azt9Pf6HLuhRT2e7r57EOFQqFQKBSKepJUbJNS5pufhRA68D9Syt82ZqcUCsWJgTffS5YrKxJGerLka/NV+gjqQWMgoCHLPkAPZoH7IeR1PycrK4ggu0mev1tqgYSm7vf5nc63DC/cvpBFOxaR685tMdvseNNU4qiJ5tF4QHuAkhUlAEgkeW3yGn25KaNpMG0azJ0Lo0efeKq6g5hYVlFGdagagOpQNWUVZZZjYev8Mn6+5BiL8yX/7GHkkMyrOpWX1/0f+pmLya1ee1L8RigUCoVCoTjxSTdn25UY1UgVCoUiJYQwjLBu4ea56587KUSLvDZ56NJw21A5DBnKBumGkMR97Ayee+Mzqj4taJKcbQmrAzZjovt9LHjM9tCeaUxhOCKSYlQkrQ5Wt5htdjw5XqJuh9wOuIQLXeq4hIuqI1WNvsyU8fth0iRDjFq+HAoKTizBzaG66N43SuK39/sZ88AsZLXkETdcO06QVzWSiT8+l2DNr8D9INV3XK3ON4VCoVAoFCcEaVUjlVIulVLuNIeFEO2EEB4hRLvMd02hULR0fJU+QnoIAoMILfsl099cf7y7BDR+BcMFWxfUDeT7EFlBXG5Jdg48cMvFVOXNx3ubv0meu735XtwuNwKB29VyKmx6870RoVYimbluZqNXKBzZeyRu4baM09Gbl1uqmVI2fyvHlvyc0K5Lm7TqrTffS647F7dwk+tuZrm+nHK2nWhEVRf1B/y8vdmao3P711Fpf30+XLW1ZEnIDsGwHZIFcztRW5MFMgtC2YhKL7u+2aWqkSoUCoVCoWjxpF2NVAjhBn4J/AToGTV+B/Ay8LSUMhjn6wqF4iTCm+9FBgbB7IUQymH90hpuO+N5Xrv/3uPWp6Zw4Ow5tKduwLOKPj+fwO3tZ5J33mfc/8mPqdnlnM+osRDhNJvCMd1m8yWkhyKfa/XaRnO3RR8TsbhoOrdU6ZpS5m6ay+i+o5tXVc0k+P0w8+e3ImsA9yO477y+yUQvzaMx7dppke3WrBxRJ3rOthh8lT50dMu4D7Z/gD/gN/aL10swyw26Tq0b3mo9kI1v5IWzAksQOvpBD6Vv+ZhdMVyFbysUCoVCoWjRpOVsE0LkAB8AvwfygQCwOvw3Pzx+UbidQqE4gUnFHaZ5NE7dMxJCORHnwj/e/7YJe2nHKawy03h7ei3DnvN34+t+DXMOTqS68iLk8slUV15EWUVZxpcdi5k/TiIjCeRbAmUVZbbiGo1F9DERkiHrREGTCEela0oZP388C7cvZPz88ZSuKW30ZWaKsnk7qa0hco5fn13SZCKJP+Bn0nuTKN9RzqT3JjUPR5TfD089ZXwuL4cnnmj8ssPHC3Nd/X5HB6hE1l1zNI3P3niO8ZdcTt/+D7Fp71ik7sKouSVBupFr7kJ/dSHVlRe3mGuVQqFQKBQKhRPpOtseALzAfOC/pZRbzQlCiLOAPwI/CLebkqE+KhSKZkY67rBzB+xl9cIaCElw1/Jdt3fwB45fhUdvvpccd06k740hpHTI7WAZXrh9ofEhMAhmlxvio7uGvec+ByMzvngLTbG+TcWhmkONMl9zGx0LHrMJfFkiq0mO1bmb5tqGW4q7bVXWVHA/HTnHD3R+E7isSZbtmJNwN5Y8Yk2K328st7YWsrONfjz0UNP2oanw+2H48Ihzb/3P+4DDq9boa853ZxTx+oa7qKkRZGVJcAlCerjQvXQbf0MS186rWvS1SqFQKBQKhSItZxtwK/AJMCpaaAOQUm4DbgI2AmMy0z2FQtEc8VX6qA5VE5IhqkPVcR0I/oCftdnPw9jhcNWvYOxwZPeVx9WxoHk0ygvLeeLKJ5h27TR8lb6Mu2Hi5viq9Na5/IK5sP72jC7XCTPMbnjP4Uy7dlqLCctq18qeCvSj3R81yrLMY2Joj6G2aTV6TZO4zPp37Z9wuLniD/hZnzPdco7vave3Jlu+KZS6hZscdw4jq/IMAeixx4y//iZ2upWVGeKTlMbfssZ3rx43YnLSXVb+GQ8uh0EBa7N5m+dZvlJb60LqLkIhFwMudmGkZgyHuAsjt+XzE37UYq5VCoVCoVAoFE6kK7adDSyQUupOE8PjFwBnNbRjCoWi+RJdbVOX8RPI+yp96LoOnlUwdAp4Vh13d5U/4MdX6SOvTR6T3pvEo4sf5YpXr7AJKg0porDg8wXOE/J9uLMkRpIiFwv+X9dG1wLMMLtF2xdx77v3tpjwRN8On23cwO4DG215mkfjWPCY47RY11ljEO2GFAibO7K5EhHOo87xpkwNGC2elxeWU/BpVeMVJYgKmVRQl5POZdxK3vrxMZ5YDOWzrYLbrHWzIp/zztuA7joKohbddRTvjdvIzg6HkQIul+S5P2dTNKqgCVdEoVAoFAqFIvOkG0ZaA5yapM0pQG39uqNQKFoCVUeqcOFCR0+YQN6b7yXLnRVJPO8SLv583Z+Pm2PBH/Bz5ewrqQnV4BIudKkjkei6zoR3JlDQuQDNozWoiELpmlLmfTbPMm5MwRj2H97P6JGjWZedzYszJEhBbVDH53M1aqSbr9JHdbAaHR1d15n47sTIejZnurXtZhvXNqdtoy6zRrcXSAA4eOxgoy4X4GB13TIkssVUQPXmexEIS/htj3Y9mrQPmkerO569NKgogSnGe/NjQt1jQibj5mArLIRZs4x2WVl13z0R87VpGtx3H/Lpp5GhENkYOqsIgrcSVnmMZtEVfqvy5uMa+w76jqG4ei6nw5AbuP7mccx7rRPgRteDrNsWAM5s+vVRKJqQuNcahUKhUJwwpOts+xfwn0KITk4ThRCnA/8JVDS0YwqFovnizfeSm5WLCxculyuuMKB5NAZ1H2QZl0plx4a4yhJRVlFGdagaiSQkQxaBICRDkYIFDSmi4OSCapvTlvdvf5+iAUW0u2weuMPODnGMg13mOcwlc+S1ybM4jUIy1CISjxcPKcYV8xM1c93MRkuA7w/4+erwV47TVu9ZnVFHYOzx7Q/4+ePKP1razPnXnIwtrzHRPJot/LZj646Nv+Aol1npmlKu+cs1xj7StHoXJTBF9seWPMbwsuHWYy0mZDKuY07TYMkSGD/ecHy99NLxCWdtCvx+9D/9EXQ9cqYanl1oF2US7dK2S+RzXtVIXDuvwtVzObn5aw2Xc5e14AoZKp27FvKXNuFKKBRNT8JrjUKhUChOGNIV254DOgGrhRB3CSF6CSFaCyF6CiHGAR+Fpz+X6Y4qFIrjg5PwZeYBc7vc6FKPWwWwdE0py3YuiwwnCjmNXl5j3YTu/W5vSu1i80ClE/Y6uu/ohMtdn2XNb7U+a3rK804XM4TUDPkVCHLduS0i8bjm0fjhuT+0jKvVaxtFKDSPuS8OfRG3TaZCSf0BP8NeHcbDix9m2KvDIu6G2Cqoy3YtazEhvx3bxIhrjR1GarrMHnuM4JXDmDU9poqrphlFCdJ0kyUU2b1ecNc5tMhLcB3TNOjRA4LBxglnbS74fBAMURcEWrfrL4q61Oa4jKoJfj9MurUAffFvEGWLuTR0PyV/W847z14NuguETtYNv6BwZO8mXIl6cjxCilUY8wlDWUUZR4NHjby3wfh5bxUKhULRsklLbJNS/i9GldEzgVJgK/Ad8DnwMtAT+EO4nUKhaOFMXjSZy2ddzsOLH2borKHc+LcbI+JX1ZEqdKmjSz2u+8tJoFj35bqEy2yIqywZB44eSDi9Xat2EfFj2rXTInmg0gnxKBpQRO+O8R8W+3ftb8lv5STOZQpfpc+Sh0wiuW/gfS0iZMUf8PPOlnds4zfu35jxZZnHXGwl0mgyFUpasrKEWt3ItFCr11KysgRvvpdsV7at7cy1MzOyzEYnZrN1OaWLc7tMEe0yq63FW1k3yWmbpeqU9eZ7yXJlIRBkubLsorQMr2goBPffn1j0MPOZud31CmdtEeTlWYQ2oj7rUSO9Pb2AsduqayR6SBCqFSxbJpj33tfU1gBkgXTRfffPKJu/1bqvmpvI5PfDsGHIhx+mdugQ5s2a3DTLvPJKeOQR429z2RaKtPEH/Ly87uXIsE7yl5AKhUKhaJmk62xDSvkwMBh4BVgHbA//fQUYIqV8MKM9VCgUx4XSNaWUrCiJuKJCMsS8z+ZF3DipuL+chKRZ62clfOhtiKssGVsObEk43bfDF3HVTXpvUr1yqfgDfrZ9vc06UtRNm7ZqWmS0W7gp6Nx4icC9+V6EsNqM1n+53tLXxgjXzQRlFWURUSqaORvmZLy/0SJLtisb4WDNWr1ndUaWu37vesvwnm/3ADgKfcnE4eaAP+Dn3c/fjQy7hZvCfoWNu9AoISuU5cKXXzcpNtefmafxkcWPcOXsK5PuQ3M/2PaHz2c41UySudUaEM7aYqiqQgrr2WJ+PjtKm35m1TP4A35LcQQjXNRn/HfXAEGQbirXnsWL9/8n3t89ZOyrKBdjswnHLSlB1tYigKyQRC8paXwXalkZVFcbgm919Yld5fYEx1fpI6TXOZkFIqX0GgqFQqFoeaQttgFIKVdJKX8qpbxEStk7/PenUspmcBekUCgyQbywuVq9lrKKMlsVQCdRqmhAETNGzqB72+513w/V8rjv8bgPvWaI6vCew5l2rSFMZUIQ8gf8ScNIu7Xr1mBXna/SFxEoTUynj6/SR22oTkDSpd6o4SOaR+OWC26xjOvftT/QsnPGxAtbbghmsQyAXw75pWMbM6dffSldU0rlwUrLOG9PL75KH0E9aGsfrzpqc8JX6SMYsve9UYkSsrb8bTprzjRcgdmubIqHFFuaRudprA5VJ9yHZRVl1IZqjZyOekxuw7y8OmcbGMUPkrnV6hnO2mLwetFzspjBTxiEn5uYy0qMHJ1zz6trVh0ywuSM4ghXR0Lo8awy/o8dDmctAhECmQWhbGq3DTa2f6q58pqSzZstg+d81QAXanNz7SkaHW++l2x3nZPZ7XK3iNQOCoVCoUiftMQ2IcSvhBBXJGkzVAjxq4Z1S6FQJKOxXUnJwhtTraRV0LmAkeeMjITJ6egs2rEorsBj5hkr31HOfQvu48rZV2ZEEEokanVs1ZHiIcUUDy62uerS3c6x4SDRTp/Ym2zHULUM4g/4efPTNy3jTJdJY4brZoJ2rdrFnbZ6z+qMCoTRLrpavZZvj31Lfof8jMw7GicB+5lVz8QNIRqWPyzjfcg03nwvLlfdrYRE1utYSvt6FhayCkYV8dz1z3F1r6t57vrnbNeiWIE9nuDuD/iZuW5mRHC1nZtVVUbBAwAh4K67jM8ns1CiaQy/6lHuppTVDGQeN+JlKe/mDuLh71ub5rXJM46VHh9FQugjeFaB9zeGwy3senP1XG5s/+YYjtunDwB+BvEUD/KPUwax/eD25N+LFdZKS2HYMHj00eSuvcJCY/3B2BYXXdTAlVAcLzSPxqRBkyLDQT3Ihn0bjmOPFAqFQtFYZKXZ/vHw/2UJ2lwB/Br4bf26pFAokmG6kmpCNeS4c9LOK5YKBZ0LEAjH8LZ2rdqltHyzn9XBanTq3F661CNJgWO/Fy0C6SHjOxIZEYTqu55meGpNqMY27cCxA0xbNY1RfUZRXlgeERGBtLdz1ZGqyHYTCH568U8t37n+7Ot5e/PbxnTRuJnkzW0ZjenuKexXGNkemQ7XbSj+gJ8/+f+UsE1Dj4dk3Hz+zZSsKIkMC0SDwyP7d+3Pwu0LLeOqQ9VUHamiy6ldbELQ4ZrDDVoepC6K1xfNo/H89c8z8d2JhGSoXgU4GnI98wf83LfgPmpDtfh2+ijoXJDwu/FCc2PDlgd+b6B1Pl4v5OYa7qqcHEPsGD68bvhEDRVNgv/zy8OfjGtZLVkUD/gBbvFPS9GPqiNVaB6Nc/LOYdP+TfYZmQ63Si/kL+WFu+8wtr8HY9v6fMY+aA7buLiY5W/t5VpZTg05yN016JsN8T/usWeGw5rHy7RpcO+9daHJ1dXGOsZbP02DZ5+FiRMNl9+kSVBQ0Dy2hyJtfDt8luGZa2dSNKDo+HRGoVAoFI1GvcJIk5AF6ElbKRSKetMUriRfpS9uwvi/VPwlpeWb/dQdLgnxkgJH52zLdmdnLH+b5tF49rpnHfNxgSHemOGxDw19CM2j1Ws757XJs+R9uqir4UAoXVPKsFeH8dbmt9DRnUPVMoy5LZ1IJQz4eBGb08aJTAqEh2oOWYbbtWrHt8e+tYyTyAa7DzrkdnAcv3H/Rv793b9t4/cc2tOg5U1eNJnBrwyOFDhpLBds0YAilt6xlN9d+buUj6VoJ1tDrmdlFWWR4hbmORzNjoM7LMMfBj503A6xAtDyXcut7TTNEEiGDzf+VlXVhTceO2Y4j0pbRvXYTJKvrQl/koBEuIKM/Elfhvccbmm3cf9G/AE/W6pi8mYGBsHyB42/4cIxM+65wyo8NLdwXE1j/MDrOUYOIbLQ9Wyo9CY+bqPDYaur4Q9/MD6buN3JXXtVVaDrxv/mElKrqBexuSVjhxUKhUJxYpCusy0VBgBfNcJ8FQpFmLw2ebiEC4lsNFeSTQgLDAq7Dnx86VlFrjsX9MSihyn2HA0edZy+4PMFtre5pggU7S7LlDOn6khVwoqT87fMh/nG54u6XsSub3aR5cpKup7RLPh8gW24oHMBE96ZYHF6QOPnajG3ZcmKkoibLtudHXFoaR6tWYlsJmZhBynj76ubzrspI333B/z89ZO/Wsat/3I9vU7rZWs7d9PcBrkPDlY7VzSds2GO4/gDRw9w4xs30uXULhT2K0xrfc0CJyYhGeLB8gdZesfS9DqdIukcS7FOtmnXTqu3y9K/O76A6A/4qfh3hWWcLnXKKsps15TY/HgSGRHfjZn5DTdRTQ0sX24Ibjk5htAmJXz+OYwfb7QtOnkcKrN/dzlDvr4buXYctN3Drfd8ydRx99L7z9a8hx/t/ojzO51vPacDg2B2OYRyjBDSsSMYc30vqo5UJXaJNQMG3NKRzz6ugZAMF3tYijf/j/G/YIbDVlcbYtm2bcZxI4QhtD33XHIx0ZyH6Y5rDiG1inpxXe/rmLd5nmVYoVAoFCceScU2IcTimFF3CCG8Dk3dGIb/M4G/OkxXKBQZwMxpFtSDCCG4b+B9jfJQsu7LdXUDtoei4Ywb1Z8e7XskFMHMYgePLn6U/Uf226ablRijQ93ALq5lav3i5cYy2X1oNy+uedEyTiD4j3P/g+LBxSn1Y/NXm23DZRVlNqHNnHdT8P629yNhq5MGTWrWD7EAG/ZtsBWZiGXhtoUJp6eKU0GL/l37M6rPKF5a+5JlvyXLY5h0WTGhQ8n4/OvP+fzrzwGjiu+SsUtS3ndOCdv/ufufaS0/VUrXlDJ301xG9x2dkhjpq/RxdEc/qPRyNN/Hgq0LLAJ7qus4edFkm5gWnesvntPopbUvoUudHHdOZJvedfFdrN6zOkGnfXVCSXW14TIqL4cf/Qi++KKu3cyZJ5XYpnk0VjwPvsol4X13EwADuw+MHLvmsDffi9vlJmQ6uioKIZgLuA3RqtLLnA1PMXi34OhON6dOeJ6CUc1zW55/0bdRYa8+8Kxi3uZ58Y9ds6jH44/DokXGceRywYgRxrhUXHvmPJpTSK2iXljurxyGFQqFQnFikIqzzRv1WQL54f+x6EAV8Dfg5w3sl0KhiIOv0kd10KiuJ6Xkf/z/w6g+oxpXQKn0GkKbzIo8FC34/K9UTqpM+DVTGIxXVbF3Xm+Ly8XtciOlJKgHLQ/CmSI6n1qqSCRvb36b4sHFyRsD31Zbww9zs3Ljtq3Vaxs17xiEhY2ws7DJjpcGkkplv/1H9mfE/eLN9+IWbouo1iG3A5pH44UbXuDu+XdHjpdtX29r0LIaEipkVnRMdX1bZbeyjXMSfBtK6ZpSxs83HF1mPrpkgtu8D/5tEe/nMZzrem/goaEPpbXsNze9aRsXfXw77Vuo2w5m/kLNoznuWzMEHDCqkephUVbXjWFNg7POsoptrezb/UTHydXYNqetbVjzaNzZ/07jhUZgEKy9EyObiQRXCPKXMCgAH8yW5ISCyGUTYUnzzEvmzfeC52FLoYdnVz3L1BFT7Y39fkMgy8uDXr0gO9vI1ZaTk7rQZqJpzXJ7KBQKhUKhsJM0Z5uU0mX+x8iA+3j0uKj/WVLKM6SUt0op7RYWhUKREWKr/zVW3q/CfoW4hdsYyPdZKsWR72PnNzu57c3bEs7DzMUUT9zaf3i/JV9TbaiWWr0WGRhIte/njH32Rcfv1Ze8Nnn1cpPpUk9pG5euKeWLQ19YxuW4cqwP7VG4ReOGkYI9dDGoB5td5dFYYoWieHnnosMkG0J0oYroBP9zNsyxHLslK0qYvGhyvZdTPCQ1wRaw5rIKk86x0vf0vvZxne3jGkpshVWniqula0q55i/XULrGyGm2YXVelHhv5Lt6aFF6Qht+P2/+r8D/Evzk47rRsdfDc/LOSWl2TsJd1ZGqqIGoaqQulzEM0LGj9UuxwycpkRx44eO4fNkRwPhdcQmX4WrTczALK9D7XfCswlsJOSHIkpAVDDXbvGSaRzPWI4oa3V58J1IY4dFHjTDjGTMMoe0HPzhpi2o0V2KvU41JbJ7Q2GGFQqFQnBikm7NtHLC+MTqiUChSQ/NoPKA9EBEa4hUayMRyXrjhBX695NfstVSK80Xe5v/fxv9j/+H9ccPHoiuA6lK3iW79u/a3VQkN7bo04nrZurSGa057nPcferzB62O67JyKNaTCxv0bk7Z55qNnbOPuuvguqo5U4cJlWbZLuHju+uca3WG2/kvrJVsI0awqjzrR9/S+LNu5zDK8/t/2n57NVZtt49LFV+mL5JESCMb1HxfZJ05VE83zztHBkgLxKuJacAjbdvVIEOLoQKzAKxC8cP0L6XY3KbEVVvt37W+ZHut8m7tpLq3PzuZoeTjflSsE3/TgwNZzL9BV0QAAIABJREFUUs9P5/fD0KFcEA5HHBjWt1++xPjrzfdGHLPxXLVg7Aszf2Gv03pZwh7BEOfNEPeR5+VREF2NVOXLSsj+I/stx/HWpTWUDt5AwQDDYct3Zzh+z5cPNW6QIXBlZ+Nuxtu5VwfrMdOzQ097I7MwgumKlNIojPDOO3DddamHhJruOBU+2ihMXjQ5cm1P1aHbED7a/VHCYYVCoVCcGKQltkkpZzdWRxQKRerEVkpsjHwfpjhVE6oxQi89qywhM2C8yV+4fWHcm9PoYgela0upPFhpmb4qsIoOuR2Ydu00qo5U4av0sXDZxXWul6Bg4Ztn0P+0/ky/YXqDhClfpS/hg3cyzCT2r930Wtw2h2sOW4Y7tupI0YAi/AE/uVm5HAseiwiOyXKSZQJ/wE+b7DaWcb8Y/ItmHUIK9rf853c+n0/2f0JQD1rG9zm9T4OXFS32RosvAN3bdeerI/Z6PyUrSjjrtLPSfhjzVfps6xAhqgCJU9i27lnF5a9czod3fpjS/ou9JvxHn/9olP3eIbdDJDRbIGwVV6etmmZZt4UshE4Y4n1FIawbB2t+CuvHMo/h4JnHzHUzWXrH0vj9LSuDUCjiUZXA6E8NsU1HZ8O+DVQdqaI6VB3XVXtZt8uYdu20yDK2HAhXyYzq64KtCyLXwCfcOXz0+jQKPq2yCh5dujRg67Uw0hB8+pzeh08rz7Ucx3MXVFGV5zf2yakx1XdP3QfAKg8MHwveSljZS2dKd2iuV6uyG8u4/JXLIy9RBnYfaG8UWxjBJBiEiRONcTk5iV1upjvOFHqVIy6j+AN+m0u6ocVwknFT35ssy7yp702NtiyFQqFQHD+ShpFGI4T4oRDib0KIHUKI74QQh4QQ24UQfxVC3NBYnQwvu6sQYrYQYr8Q4pgQYpMQYljUdCGEeFwIsUcIcVQI4RNCnB8zj9OEEH8RQnwT/v8XIUQH+9IUiubNqi+sopeTA6ehmLnhQjKUUo6zeHm2zNxJuw7usk37cNeHPLbkMSa9N4m8Nnl8sP0DQ2xwBTEeoV2wbhwVH7fm8lcu55759+APxK8+mIiD1QfTytUWISqcb86GOQlDTC7qYnUTXXHmFUCd6NipTSfL9FRyk9UXf8DPFa9eYal4lu3KZlSfUXHbP7X8qXpv30zi9Nb/gs4X2NodqTnS4GWZ++aJK5+gvLDcIvAM+t6guN9zCpdMRl6bPGeR1XQALX7C+Nv6K1vYNhhC0oR3JqS0rNhrwoGjB9Lubyp48720ymqFW7hpldXK5prc/eZEmLkCyp+EWUvrwmI9q6D9LtCzLOGkYOQyLKsoi7/QvXsjH80zeu55dZNnrp0Zf1uHueviuyL7unRN+EVAzH7YvL5jJMS9JlTD/LwqeOghq9BRWGjk4DJZsMAQR040TMHnsceMv0nWsXhwsWP6AVPcpsvacEtjD159+em2eZg5LZsrmkfjloJbIsNzNsxxDjMfOxZ++EOj6qiJEIbgFgoZIlqicFnTHZdKW0VKlK4pZeBLA7nxbzcy4V37NbXTKZ0cvpU5RvUZxRU9rqB72+4UDymut1NaoVAoFM2blMQ2IcQpQoh3gL8DP8KoONoGOAWjWMJ/AW8LId4WQrTOdCfDgtgKjOQeNwDnAfcB+6KaFQP/HR5/aXjaB0KI6Cy9rwMXA9cB14Y//yXT/VUoGhN/wM/6vdaQup3f7Mz4cvLa5FnCHs8+7eyE7VtlxU8MXlZR5hi+KZGEZIjqYDV/WPEHQwzzrIKLZmHUXBGguw1nDzovrnmRYa8Oq5cglG4lSMAuggQG8dTyp+I2v673dQmH9x3ZZxlOGk7YAEpWlNhcVLV6LT95+ye27WeG3D225DGGlw0/7oJb7Fv+m/re5BgCuXD7wkbNrxPtcoulPiGsZjixDYuTLZtOoq/h/LrqV8bfKEfp+n+vT2mdY12cDXF1JiKRWDl5yjYOLb4H46dbgJ4NK35Z92UHMcYk4QuEJG6yVlmtrPnWHIgW7iPCacx+6PPdeHLcOQzZ7eLhDwUjqxzC9TUN7rrLEE/AEFBORDEkTcFH82h06rPNchxvzH0ZzaPhG+vjvJCZ79PYboHNhrgxKABLXoXflcPiV3He5s2Itz57yzL8wuqo65QpUL70EvzjH8a2iyYcvk5WVuKwZNMd53arEOYMYIa2r96zmnmfzbPdT4EhnDbW76D5e7sisIKqo1VxX4ApFAqFouWTqrNtBoZA9RXwO2AEhuDVN/z5SYxKpDcAmc1oblAMfCmlLJRSrpZS7pBSlkspPwXD1QZMAqZIKedKKT8BxgJtgVvDbc7DENiKpJQrpZR+YDwwUgjR8FgkxUlPUyXXLasosyVPD3wbyPiNYWwYmpOzqKFEQirRrfmS+pVBVrXjQ3itXuv4JjoZew7tSaldtivKpRLz8E2ll6+O2sMKTcxqp2DkyIp+4HdyaMRL/J8JYt2PJpu+2mQTLKOLVNSEao67m2TqiKkUDynm7NPOjrz11zxaxCkYTX0cZtH4A36unH0ljyx+hCtnX2nZLppHi1vUwBS40znvvflecrNycQs3Wa6oLA4xotPv7hzBjHvGwdApttBtSG2dvT29CYebgjllpgAfVZTkUFRFVjMXpIOoGAnrdKKwEIRARs159Kd1kzu27pg0j6WOHnHPRfLMRe2H7BxB8ZjLeL3bfSwqg8fLQxTcOsnZ0VVYaFQhPZHFkHoIPrnuXGOfho9j89qoeTT65Flvu47WGhWTCytgbWgQJTzI2tAgTv9/CzK9Jhkl9oWJZThaoIwntAEMHJg4LFTTjNDRJ55QIaQZINXfjITu2gYQ/XtbHaxm0nuT6u3ab06OdIVCoVDYSSq2CSH6YwhW/wIukFL+Skq5WEq5WUr5Wfjzo8AFwCfAbUKICzPcz1HAR+EQ1n1CiPVCiImiroRcT6ALEMnSLKU8CiwDBodHacB3wMqo+a4ADke1USjqhfmmdOH2hYyfP75RBbe9n/W0ua10qTPpvUkZveFyCkNzEjtMlu9aHne9C/sVOjt64pHgIRxg/d71aTncJi+azO5Du5O2y3XnsvSOpcwYOYPubbs7Om8O1xyOu9y8NnkRAVEiLQ/8TgJWYwkgpWtK2fvd3rjTY8OzvPleslxZCARZrqxmUUBh6oipbL1/qyW8ZkzBmIwvp6yiLJLbqzpUbXvASuQ6GPjSwLTOe82jMe3aaQzvOZznr3+eGSNncGr2qbbjvSpvPkUDiri619WO80nFVWfmUgMcc6llikSuyLN6RLtdw+LCRXWh022y2ljEmGj2frc3/vmtafDLXyJwDiN9Z+s7TP94etK+L9q+CIjKgelZBdf+jM4XfMJzf86C7n72Tf8j2TU6Ll0ia6qdHV0ngxjSgHUcFIAHl8PFldWRcQfOmQbuaiAE7mo6XPY2AB/u/wlelvIoTzCCct7f2byru8ZW+LUMe73W0NFooiogs2wZTE5S5VjT7CHMinoxuu/o47p8M5TaLJq0es9qXlzzou1lTzKamyNdoVAoFHZSefq9FeN+tlBKuT9eIynlPqAQ40XzrZnpXoRewARgO3AN8AwwBbg3PN2MKYnJuMu/o6Z1AfZLWfc6Mfx5X1SbCEKIIiHEx0KIj/fvj7vaCgVgf1PaULdNIrrs/y+b2wpg9Z7VDJ01NGNCn1MY2pThU+K2l0gmvjvR8YZP82jpJxuO8xBusmznspTW1yn5sROt3K1YMnZJpK//+6P/JevMj22in0QydNZQx/WsOlKFSxiXVZdwWZxtq3bb1yO20EWm+LXv10nbRAtqG/ZtiISc1iuvXRPhVAjkw8CHjfqQkcjlt3qPtUJoshx8/oCf+xfczwfbP+D+BfdT0LmAvp3CD+fh493lWR3ZN+/f/j5Zwl7HaOc3O7ntzdts46NJlkstUyRyRU75dR7uLB0jJFyHIVPgkpcj0zuf0jnpvOMydSrMmIG4+mqeLuwdqUQKhpi8tWpr0r5v+3qb9foRGATvPcP+jRcyaRIs+PNyCteGcGHcBOkQ39GlxBAbt154Kz/5GJbOgicWw/+Vfg1+P6VrSlmmT4U7roThj+Ie931y8tdAYBB/3fU8tWSjk0U1OazLH3+8VyMhL1z/Am5hCGouXAz63qC669GGDUZYcSzZ2XDuudZxTz99Yub6a8HEVnTOFGbo/YheIyIvRACqQ9VpucqbmyNdoVAoFHZSEdsGAmullP9K1lBKWQGsAeJnla4frnAfHpJSrpNSzgL+TJ3YFulCzLCIGef0FBnbxmgoZamU8hIp5SWdOjVuolRFyyf2TWn/rv15avlTlK4pzbjFv9256+LmOQrJUFzBK1165/W2DHt7etE8Gpd1uyzud0J6KO4NX2G/wogQZZKW2y2acBhtaNelSdc31VCQVlmtLPmmNI/GsjuW0b3vbpvoF5LO6+nN95LrNsIEc925FoEjNyvX1v6ltS9l3AWZzNVmsmHfBsAQgCa8MyFSCCMYCjaLm3an8Bin9TpSe4TBrwwmb2oekxdNTvt8K+xXSI47B4GwVSMFnEWqmDBukx0HdyRclpOL7q6L77K0+cUQa7XYBwY/4DivZDmFEuVSyySJXJGaBsuXubm7OMCoP/wR99WPRaa5hbsufDMOyUJBKSqC99/nDxcctE06rdVpSfsukYyfP561X4aT9YfDxqXupqYGdi3pQFaoLlTVFdINAeVkJM0CCQC3fXcWz78L2TpkSciu1cHnMyrURtG9XXfjPKj0IqUL87ZMuiQ/nhD/96Y5oHk0lo9bTu/Tekfyinpne9kwrxQmTLBWIAXD0XbXXTBypHW8lCdmrr9mSMKXIlHX9mR5H+uD+bsGzg67jfs3pjyv5uhIVygUCoUV+ytzO72Bf6QxzzXAD+rXnbh8CcRmS/4U+Fn4s/kE1gUIRLXpTJ3bbS/QWQghTHdbOAy1E3ZHnEKRFqZra+6mufTv2p9nVj1DdcgImREIWmW1ytgD7/qs6TB2qvFgmO+zOb9MIaghy/IH/Pz1k79axplhaN3adnP6CmDkQfrz6j+T1ybP5mTTPBq3XHALczbMiYy7pcA6HM1l3S6zOYeAuqIFoRxw11A7djhlFWVpra8Ll23ZRZfYnXeaRyPwQADxG2Gb5iQEmAKHr9KoupesT6Y4WtC5ICPHhj/g5w8r/pBS22mrplE0oIiyijJCMmRs10over4vuciRYl9KVpSw59Ae7rr4rrScjWZ4TE2ohhx3TuTc6XJq/MT4B44doGRFSdrnm5mwPd4+0zwaM0bOYPz8sMMm5viLDnPef2Q//oA/rX0Zfe0Y3Xe0bTtNHTGVaaumORbTSHbcax6t0US2aIJ60BBqdbuLR9NA084EivEHhkbEb1PUnPfZPNt3TFJ92D2v03ns32l1oH9x6IsUex/lUDTDxnXIycmi6rwlsNbQQSJv5ebONUS+kw2nAglJXHxVC+bi0uu2nY5k03l5fFbxmeU82rWsFoZ+zmWDN7F6aQ0EAZfO9+9/G037z8Zftwby/D+fZ+vXdU7KmlAN2/4+k4LYPG1CGLn9CgvtwpoQJ2auv2bI10e/dp4Qc20/OGQhDM3ccs38oDWhGrJcWbiEy+Ykf33D69x76b0pX7dl+F9QD7Jh34Ymud4rFAqFInVSsZW0B9KJo/wKyHRymBVAbBGDcwCzBOMODDHt++ZEIUQrjJ9JM0ebHzgVI3ebiYZRUTU6j5tCkRJOzpuDxw4yc+3MiNAGxs3Q0eBRHvc9njmHW5wQSxcum6OqPpSsKEGXdW/kXcIVmWeyQgN7v9vrmL/KH/Dz5qdvIhC4hIviIcV8tPujuPPp1q4bK+9cac+Z5VC04MU1L8bdtk6hIO1y2/HaTa/ZkvDHo3fH3rZx8UTCDfs24Kv0RZxjJud1Os+xfTyXXLqYApWl0EQCtny1hfNfON8opBBTdXXBErtTKN2+XPHqFczbPI/Ve1annccwXnhMKrn/zPPtwUUPNmQVLBQNKGLlnSvpf0Z/x+MvmkQhyxd1vQi3cNtcdEUDinj/9vfjCpKTBk1yHF++vbxe65NJHix/0BBrMY7lB8vjb3fNozF95HSmj5weEQL7d4nvbjtYndpx6BTe7hQOndRJG86d1/aaEqa9voGsG47xh8Hm/MKMPr75no4b9SiQkHfdaILuum0nhIsFny8w9k3UeSSDWdz7wv9x8WXVMHYEDH8Mxnnx3hQWsPx+eOqpZhtm+cYnb9jGbSnoZmwnE7cbxo+vy3fn9RpVSE30k9g12VjEOW6OBo86t4+5ts9//1BGuxPtbK7Vay33iSYSmfL9gK/SRzBkvODIZFSDQqFQKDJHKmJba8D+Sj0+tUCrpK3S43+AQUKIR4QQZwshfgTcDzwPkdxr04AHhRA3CSEuAF7FKIjwerjNp8B7wAwhxCAhhIZRZXW+lDJ5tmmFIoroxLTe2V6GzRoWKSVfddTZjbFw+8K0E+A6LXfh9oVxp7fJbsM1Z19T7/mbrN+73hJO0blN58gb09iwt3jEhgqZAopEIhB8e+zbhMLQui/XoXk0/v7jv1srQjoULQDiiitO7hjTxeaUhN+J4T2H28b9c/c/beMSFcqYMnxKpAKlCxdu4cYlMiOOQt32TZUQITbt32Ts65iHjD0bzmlwX2JdTs989EzK3zcTSLuFmxx3TmT7aB6ND+/8MKV5LNu1jMmLkiQdJ3E10mg0j8YLN7wQ9/gz2VLlXEXTH/Az6b1JSClxu9w8e92zKbsQpo6YSmt3a9v4wLcBh9ZNy6f7P004nIwXrn8h7rQ3N72Z0vVS82hGQZMEFA8p5sM7P7TkSHLEs4pDAx9h0saBnHP6OTz8fSgaCe+fBcseHnNiuNrqI17Vo0BCwagiDo8wiuoIwBUKMXBx+HYr5jwKnVluhIl7/OEXSX7DYVuP8NWmxB/wR8TmaIb+V7HhXrv7buP/8uUwfXrddtM0OCfmOjszcc5HRRr4/ehDhyIffhh96OWUTa+r9jksf5jzd2KOySPfOz6VcFN9yeDN91oKPWfqxZ1CoVAoMkc9EyY1LVLKf2JUJL0Zo+Lp74HHgOi79BLgTxgC3MdAV+BqKWX0q6kxQAVG1dL3w59vb+z+K048Yp03y3YtS+l7TtUO06FkZUncfFEA39V+x7zP5qVVqdOJY5UXWSue7q57sCoaUMSMkTO4rNtlCR9cP/3qU0sfYgWUTV/FRoZbOSXnlMjnqSOmMmPkDGMgTqXSeC652BtXF66EFSadiM3jBXA0dNTm1ooVlKJzw5g54J686kk+vPNDXrjhBUb0HMG0a6dlJPTD3L5OdGwVp6KfeSy1/srykHHXjWc1uC+xHK45nPL3E+Ub0zwa7XLapTSf0o+Tu+mSVSONpmRFSdJKuefkOQuV5jVDR0dKmXY+oLH9x9rGtclpk9Y8vvnGz86dT/HNN5kTLM47/TzH4VSXZYbpxuZzBPj8689TrrL3Xe13caeZ57zm0fh+r+/HbRdNTagG3w4fAC9fAtfdDm9obVP6brPG74crrkA+/DChIUPYNiW5IB0R5yDtIhB5PftafiX65PUh151rO49k95Ug6tyHLsIFZpzCV5sRTteLtjltjWuWphkCW7TIFk2s2NYtfooGRXp8M+keRCiEAERIp8fUFyMFlc7vdL7zl2KOyR4XpB6KngqpFlz42yd/S6ndhn0bLBEIbuFWedsUCoWimZGq2HaHEGJxKv8B+xNBBpBSviOl7CelbCWlPEdK+efYyqJSysellF3DbYZJKT+JmccBKeVtUsp24f+3SSkbFi/VDHAKZ1Q0LqawYRObEghhJqkkr4/H5nUdrSJYYJDjMmv12gaJeq12X2txOrXafa1letGAIj766Ue0zrK7baKJ7kOsgLL/cOLo9J8N/JlluGhAUZ1I5hBGW61XO54D679cbxnW0dN++6t5NMYUjLGNv+edurfl/oDf5uqJzW+neTQeGvoQAPeVvs4Hsy/hvtLXM3Lumtv37NPOtowXCG4+/2b7F6JDR997Bq79WeQho2BAfOEiFWJDaAFOa508YX2q/OHq1PLSHaw+mDR8ddH2RZbhRCJwJIQ6QaXcI7VHHL/b0GTWToKvWQUxFb75xk9FxXB27HiM9eu9bN58T0ZEtzEXjolcB93CzZQRUyzLqqgYnnQ5RQOK+HDch5ze5nTbtGPBY0mvZf6An4PH4v+U6+iRebx/+/s2gTAW84VAt3YnoPhRUoIMBlnFIKbKyex5aJmRzD8OG+aVErzicsMhNOyK9J1lhYWQm2vkJMvNpcuEYpaMXcLVva62nUcHjhwgNytcYCYr7PitR/jq8ebpq59OrWFxsVGZFIy/xcWJ2ytSJrTNWo241wHD+XXP/HsSO8eijslIpegMULqmlInvTkypbbJUHSZmoQWTvNZ5KmebQqFQNDNSFdvyAW+K//Mz0zVFKkSHM6bqAFA0HFPYGD9gfN3ImLxX8QS3yoOV9V5un+/GR4lgOVBRWLfMV5fA/Bciy311/av1Ph56XLjd4nTqceF2x3aXdLvEPjJK/PvHFmttFVNs0jwanU6xV/m9rNtlXN3ramaMnOGYv6p4SHFc9xY4uwxil1Pft79XnHmFbZwu9UiOrrKKMlueqOt6X+fcz/lbqXnlXeTi31DzyruUzd/q2C5dNI9Gr9N6WcZd2u1SCvsV2oWZ2NxjR0+PPGQ0NBRl7qa5tnEHj6b+XiNZaGfRgCLjYT0WB+H5iaVPxD0PSteU2kKZE4nAqYRQr9ptF+BMzOPDKZ9YMjSPxqhzrY7M/Uf2c9ubt6X0/YMHfeh6DRBCyhq+/HJGSkJYIszQWFNAfOGGF9A8mmVZul7DwYO+pPPSPBqn5pxqGy+RzFw3M+G1LN3j9fYLExvaf3rxTykvLOe6s63nb6rOlGbNnj34GYSXJTzK7xjBEkqfftOxqT/gZ9vDd+MO6oZDqDZI1W/SzIWoabBkCfz+98ZfzcjV97j3cVvTXd/ssjta6xG+2pTEHhNjCsakXgxG02DpUnjySeNvM1u3lsw/exvOePNK68s3/uroPL0yNTH0UE1mcraZFb9r9dqUXsjW6rUp5Tg9XGt1izuFMysUCoXi+JKK2HZlPf5f1RidVdiJl0hc0fhoHs16M5YkcbrJ+n+v54ynz0gpn1Qsp7Q/CtINSJBuxHddopaZCx8XRYS+Y6FjDH5lcMoP4xZ6+C3hFB37OKc1nDIiJjF5jOC499OecRfxbfW3tnEdWnVImCjerBz55FVPcmb7M23TnVxJW6usQtY5eefU6+1vdEhoNPM2z4t7YxwvVHDvxnMtx8rejeem3Z94bNpv3QYHjh5A82gM6DrA2jBO7rFMhKL072pPer/r210pi7+phHba+hhH7N59aHfcsGqnfdonL7YWTx1FA4qs+QMdCMmQ47J8lT5CegiJJKTXL7dO8WD7sp2ETSc6dPDicuVQl+RHouvVESFsz55SKiquYc+e9AtZxIbG1i3LjcuVQ4cOXsv34i2rR7sejstJ5tT15nstYvKgADy43PgLhrsz2hnozfc6hq1G+tG+B5pHY92X6yLjBCLt0N9myV13UUYhNeQicVNDLv9b9R+O17Ct88u4aptVGNY/Sy8nH2CISDHhp5pHI79DvqVZm+w2lhcyib7fXKg6UhU5llzCFT9EMR7mukGzLgLR0tjWNRcd42qnA592rpsWHXqZiDkb5qRV2Ccelorfs8uh/AmYtRQ+/knc7zy5/Mmk8736rKsTDisUCoXi+JNUbJNSLq3P/6bovCJ+IvGTnaYKrV2wNSqBbpLE6dHsO7yPkhUlaQlu/oCf1z9aCIQwbiFD5LY/aCwT842m2yb0zdkwh4EvDUxrOct3LreEU3Q5pYtjW82jWcWHGMHRvcteWMBcxpav7InkR/dNXunPfBirnFRpCztbvnO5bZ93a9vN8jY5kZiSiEQhZXM3zaWwXyHZruzIuESFD7qc/5nlWOly/mf16lMs/oCfLw5Z88yYTjebKytO7rH6ipHRfHvMLqTqMv3w3UR4873WMOYEYnetXutYJbNVlr2WTzw3osnUEVOdXXVhDtce5vJXLrcdhw0NIwXj2I8+xiB1l1z79hr9+pXTpk2/qLE6X301jz17StmyZTxff72QLVvGpyy4xVsnc1k9ez7B2WdP4+BBX8RBl2hZ9Q3b0jway8ctB+AnH8PymfD7cvDNMgS3oWcOteX9u/CMCx3nZVZe9gf8vLT2pch4iTQS9rd0iopYc/YpllH7ToWHyx+2tvP7ufXeF2kbU3NF9EkcgpsOD13+kGX4Z4N+Fqdl88Wb7yXXHQ59rW+xm9giEKWlSnhrIN1+cCvVWVAroDqrztkWjVMYfmxqkFRfZiQikjqk0gvBHCAL9Gx49/m4Dred3+xMev8aK+y2zTkBckoqFArFCUaLKJCgiE+iROInK7GVQu+Zf0+jiW6WMMokidOdmPOvOSkvq6yiDJm/GLLCIk1WDVf8x05jWZeUgru6Tuhr/ZUlVGH1ntUpF00oWVFie4BPFD5lFi847/TzbIJjjed9rvnLNRbh09w/R4LW3Fa9T+udevhNmNgbY4m0OWCua/Vbi+Ppula/TWsZJk6uomg0j8bSO5Zy94C7uXvA3SwZuyTu+XjRZccsx8pFlx2rV59icQplNTELW3Q5pQut3GGRySH32JaqLY12vqQqVhT2K4zkRMxx5zjmKzOvff3PCLvokojdy3Yus62Xk7gT7WaKx/u3v8+YgjF0bNXRUXjT0ZnwzgTb+JAMO9saEO4Tmwewy6nOQng8jhyx5jA8dGg1W7bcaxlXWflEyuGl8UJj27fXOHx4I1u2jGfHjodZt+4Ktm2bzPbtVoHlyy/r3IVO+9kkWQin5tH4z4PfY/p8cGPc3OTo8MsV0Pd0+36uDlY7zueWC25B82h1bpQoUjk2WgJtbt9k/F4QMv72K6PqaJX1/CgpwS2tnoM/AAAgAElEQVSN1zoCIxxPB05/fIrjPOuDeU1KlDqguZORe7DoIhDV1TBxYrOtvtpSGDVuKj++53R+dRUMHwurPPY2IRni6l5X4xIuXLhondWaS7tdamnjlO4iXQ4cPWB8aP0VxtVJAgJ0d9wICCCSoiIeeW3yLILhy+teVqlkFAqFopmRttgmhBgmhHhYCPGcEOLZ8Oc4dbQVTYFj2MVJTGxo7Yw1M2z57DLlfLO9xfas4rwb3+LJsT9k5Z0rHfN81Ze93+21CXptelUw455xXH3/PL533+3G+Gt/ZiS8jwmnW7ZzmWP+q1i2HLA7zpKFTxUNKGLTvZso/vEVNsFx4faFPLL4kcg+8FX6bA+6LlzMvnF2mlsE2ue2Nz5EOddi8ztVfVqA0FuBzMIlW1H1aUHaywHjPOuQ28Fx2qIdiyLL7NG+B4X9ChOej1VHqhCej2DoFITno4yEp5WuKeXFeett+WCiQzqLBhTx5S++5OijRx0LPoDxAJLsJj/e+VO6ppRr/nIN7Vq1c3QNzPnXnJTOPTNc+PdX/R7fWF/cbal5NDqfEo4PSkHsjl2vwn6FCcMJE/HaTa9RNbkqrpNl/b/XW0KQyirKCOpBAIJ6sN4FTB4e+nDCYROnaqDbt8fLtxW0DNXU7Gb9+iuTCm6JQmO3bZvMvn3RLxOCBAIlBIMHLPP47ru1luXEq3CcyjnydM4PcIFlDv33Oot4fU53dri+8ckb+AP+BhWyae70vfgbuONKGP6o8Td8rlw+63LmzZpsuKq21P0OmDLqc97WGQ/lLBpQlDB1QEugwfdg0UUgXC5DdGum1VdbEu7BlzNlqLPQZlK+o5zpN0znd1f9jvLCcvYd2WeZviKwosH9iNxTHT2dusgECa5QwgiItza/Ffd30h/wc9+C+ywvBIJ60KhYr1AoFIpmQ1aqDcOC2nTAvEOtS/xiTP8MmKBCSBX1wRRhvPneBouGZmjtseAxZPifmc9O82gRZ1VNqIYcd06DHIEb92+0jbv9wtsjFSenDJ/CsFeHGYlxHfji0BdMXjSZqSOmJl3WjoM7jA+eVZGHo81fncff/+vvFA0owu/1M/iVwYbYEgmnk8ab03B7M/9VovU9p+M5lrxfZlhVKkwdMZWnV7rRY4SO6H3gzfcihCDaCPPDPj+s1z7o2Loj/CucByWUA+4aascOt6zjxlOeR7rGgcxGd9WSd942oH6C24VdLmTZzmX2CdIQU2ZXzE7puMprk2dxBDU0PG3yosmUvLHMKJIRyjacXeGH6HgC4Ws3vcb32n2POf+agy51vvzuy8i0t7e8jT/gd+x/vPOndE0p4+cbBUMWbl/oKLYt27WMYa8OI6gHyXHnxHX/pXM9GN13NAu3LzQGos4NJzZXWXMPah6N6TdMZ8I7E9ClHtdFl4hE58bMtTMjIkJsPsFEVU8TYc5v7qa5jO472lGkMKuB6noNLlcO/fqV0769xjffpP7gKKWRz619+/jb37zWmsdC9LYIBP6U4nKCkeUkCjNOWEEwzJmjCtFLXjR9IwB0OK0LvRyOoeLBxbz12Vs2R54Z7uxUyCbdY6O5UtivkBlrZiBjzpXLdulc+9sS9BC4hLEFza3zlwL47L8bpdi8wiwCUVYGe/fCggUQDLaY6qvNlVRcvyEZoupIVeSeLbaQz1eHv2pQHyzCfb7PiEwIAi4drr834e+VRHLz/91M4IGAbVpZRRk1oRrjxVql15i3Z9UJ475VKBSKE4WUXukLIUYDHwDnAl8CfwWmAiXhz18C5wEfCCFuapyuKk5UMl1RNbpSqJlLJfpB0HRWhWSI6mB1vfNI+QN+5mywhoEKhOWB0wwtdMx5FnZjlbxhD29zWlbFvyts46PdGZpHY8bIGc7hdFHOr0TVEsGo+GkKJQLB9BumpyWE9erQy3G8uQ80j8aQHkMs09INhTPpndfbMVeXeXNbuqaUOV9PjDieROH3qcqbX69lAXHdYNluI4/WseAxQjLEseCxhMdVbFLthjjbSteUGo6tikKjSAZu42+FIQwkEoOmjpjK7gd22yoD6lKP67yKPn+OBY9F2k1bNc3SLl6oZK1eGyl84OSgS/d6UDSgKO5+icXJzVQ0oIjl45bz+6t+nzD0Nx6aRyO/fb7jtOpQnYMztsppoqqnyUjmBnKqBrpt22TqcjumRrIqovFC6IxlBRN+N5rDh42XFt58L26XXaQF+OPKP+IP+CPuScfE5ZqGq9gI9zZFokcv/MrxGNI8GkPPHGobb16nvjpifcju1KbTCeMe1zyavZL28ge5fMUgckPGjaGURgzpP78nGD8Sim7OPWHExmbLrFkwbx7U1sKQITBWiZsNIZXj1YX1ZWJsHtjDtYcbdE/qq/TV3X+BcS8y/DEYNwwueTnSLp6jd/eh3fGLXDkUBTol+xTnto1EU+VHVigUipZKUrFNCNENmI1x53wPcKaU8jYp5UNSygellLcBPYDxQC1QFv6Ooolo6T92jVFRVfNoTB85nSVjl9geBPPa5KFjVKPS0evtKnISI9rntrc9kGkejTf/601rw5ibpAdnvZVwWfG2SWwesaIBRcy4Z5w1nA4sy1r/caukx4rb5UYgyHZnU9A5PRfYL4f80nH8TefdFHEWrgystExLlo8pHvsP70+YqyuS3Dicm0z0WNWgIiLxRLFx/cfRrlU7i1stkRMnI0m1w8Srkgrgxp2SQJDO2/Do80ciKV1bSumaUrZ/vT3+l6LE3mhMB1009bkevHbTa5bcT/3O6OfY7py8c2zjMuGqvfmCmx3H14TqssvH5v7JRC6geDhVA/3iixfSns/XXy9k06bE1YydQujSqWgK8O23H0Xm5bSPwBBvJ7wzgfHzx7Nw+0LGzx/vLLhNncqCX4xi4VlQNBJKL5aOx5A/4OfDnR9axuW3z48IrrdeeKtl2riLxqW1Ts2diBAR9Vv07OZy/BjnqACEhDPGjKdn8ZP1EqIVaVBWZuRrA9B1WLbMKJSg8rbVG82jJU3lMaLXCMtxbb44M5E4Xz9S5eDn51kFMbDlSe16aldW3LmCvNbO96JzNsyx/U4W9itEVF5le9HYlEVGMv2iXKFQKE5EUnG2TQLaAGOklDOktNsVpJS6lPIlYEy4bcsrKdVC8Qf8eGd7eWTxI3hne1vkj11jVlR1ehDMpKsolqJLnJ0mEdeZSYwb61+rOyacr5MgeGr2qY4PP0UDilj56z9xxZiVxg2dg/MrUU6uRHmYUiGecGPOx5x/NPXdB6P7jnbM1TV/63z8Ab+tuukvBv+iQQ+MZvXFWA7VHMK3w2cZFzscjebRuG/gffTs0JP7Bt7XoD5F3FP9ymxJz/97yH/Xa54uXHFdAbH7SpdGMYBoF5cFh7fv0d+NPb7qez2Idntp3Z2359Mrn7blbszEw0K8UN1od1Rskn6npP2ZIroaqBlCqutHkn/RgX375qRcLMFE1w+n1b5TpzpD/Dkdo8S2GJF2/b+txR3iVQo87f5ibhzXmlmXxj+GfJW+iGhs0r9L/8i5OKrPqIjD1y3cjOozKq11au5oHs1whEb9PgRlNj68QCQ/CGeOKlQ5YY8Xuq7ytjWQKcOn2Co4R3PwmPWlmJP7ub4vZP0BP398/WPHKtmmk80t3My9eW5cp61J7MtdzaPxyzGXWl40unouT/vlaENojBflCoVCcaKRith2LfCRlPLvyRpKKecBHwHXNbRjitQw8zaYObGSJTZvjjR1RVVvvjdy8yUQkRuphOFJDrRr1c4yfFm3yxLmXisaUMTKO1fS9dSuDlU7FyZclpOAdXHXi+O21zwaS8eF0yc6OL/mbZ4Xdz1NQUkgyHJlpS1+7j3snFh873d7DXE432tLSl/fm9miAUWGiymmqmZQD0Zu/KJvahv6wKx5NH5y0U9s4+dsmFOXUy9MjV5ja2dihn5+/vXnlKwoSfmYi8USXuxZZUl63v+SYynlAoS66p8mtxTckjDfXCwJq2s6iL3RxB5fmbgexK6PSay4l6mHBW++F5fDz+n+I/uZvGiypU+JKqxmkvbtNc488yHat9eoqLgGYoQlAI+nmLZtL6NVq94kSuGaLJw0mk2bbkNKe47Kzp3HcNppV9O58xjc7o6AwOVqjcdTzFln1R2n1/UO3z4kEGlNYsV0k1SOIW++15ZXMDqc3VfpszhVT8QHyddueo0z+1VGfh/cohYvvroGQ4dmvCCCIg6FhUZxhGiEUHnbGoiZyqNtTlvH6V8f/doyXDy42HJ/IhD1fhnoq/QROnOx5f7rssFHWXnnSlbcuYInr3qS5eOWR65PxYOL44aTOlUanTpuFKOeeg6u+jWMHY7svrLehXfqQ2O+KFcoFIoThVTEtjOBlUlb1bESyK9XbxRpEytsJKpedDIyedFkev+5N5MXTY6E227YtyFSFTAkQ9y/4H4mL5qcPDwphkTOpXhoHo25N8+1ubGOdCln8qLJaYUDTxkxJWmb77X9XtwqjYnCD6MfMtPFMT8dxrY2H1h1aX3wb4i7cMKlExzHb9y/kQnvTIisQ0iGMnIjWtiv0PGGeP8Raw6u2Iqr0cRu+9h8Z6liEwDMcFnPR7xwQ+phg5pHY9KgSZHh/8/evcdHUZ5/4//cu8mGc4IcjMhCBAEBI0gQGKiwEMRjWwq/52srGhSerlbtt/QEYmvVokKo3xYfD0jUqhFs+/TBeqAKasoCwiIVAakg4iEYRZTzGZLdvX9/zM5m57Cbze5skt183rx47c7s7Mwke8jMNdd9Xcu2L4v5GYjODAVi15rROC94J+YwXwB4edfLlvuTSjaN1s3UKuMsenivXScLilvBRd0usnxs2QfLIss8evWjuKLPFXj06kebLFNo794KHD5sDuZ36jQWffuWo6TkXYwa9TE8njp07jzJch3ffPNCQts6etRv6ECqysvrh0GDlmLIkFUYNGgpLr/8IDyeEMaOPaULtAFR3wXVHiAQDtIGXKYg7aQ+k+J2sGzoPaS4FTxx7RNwCnXIfJ5TX5OsS7suke+pkEy+3EBLV/0/f8H5d04HJvwO/a4tRYljI0IAgk4nsKDhvzFkIyHM04sWMeCZIsWt4CeX/cTysaNnj5qW/dXoX0WmU2lg5CnyAG6/4fhLbTxk9f2kuBU8ed2T9SuIyuyNvoAY7erxBcDl8wH3RkhIUzf2dGrqC+VERJkokWBbLoDYKRpmdQCsKxyT7YyBDQmZca2/b3zpRoy+/xe4+/5j+M7vf23bgcKNL92oyx4a/efR+M2/foPb/3m7LhOnNliLl3boa6rFGp4UzZi5FC+TKVpk+I4hG+vh9Q/HHM5mzKKbVjwtoQObv/+vv6t3DNsCgM1fb7b8Xac6jDRWMEprHnHX23eZgniJdBqMJVag7t0v3zVlXEW6gqVAcSv4/oDvN7jcgC7m4SiaHp30ZS13HdyV1Ps+1u/twnMubPSBrzF4HOszoNWbc8ABp3Di+xd9Hz079oy53mDPd+pPNq76mRo0icpSenbLs43az0QpbsUyePbw+vqhpHaeLPxspHX1hL3H98Jf44e/xo//fuO/8dZnb+G/3/jvJjsh2r/f+nVs3948jHXIkFVwu2eb5p86tTOcHRdfrAw4l6tzg8/VRIZqtz0A9VBCqrdtU+sKaCVec4zo75VUslsywd9/8Qu4PH/Eh8M3YvwtwKoZY5Gzbh2DPE3J5wOk4eJWKAQsX86abTYon1iO2WNmI8+Zp5t/UVfzRZJjZ47pplPu8Bl1/LX92+1xF/WWeDF7zGzLzF6rYzHjvsUKyqVLqhfGiIiyXSLBtq8BNKYIwGAAqZ/RUkKshiLtPba3GfYkOXPenoNlr38aOagIPfcmrimfl1LDh4rNFRj51EhTp1BADUYaAzAO4cCUQfomurGGJ0Xbd3xf3Ol4rIY0hBCKOZxtxcf6Dppb9iV28Ke4FWyYsQHd2pmLsUdnmkVLNdsnVjCqd0FvKG4F278xH2xu/XqraV6irIaDAUCfzn3iDhNLRWSoWwwOONQD5hiMQ1WA2E0w4nlhm3XGkbEOTUP8NX78e++/E1pWqzcHob6HXv7oZew93sB3jnujmtG28hHTsMCTtY2r79UYVq9BCCHdcHu7Tha8JV5M6mPODJOQqNxWicptlTgbPBvpxGpHluXRo37s2TO/0TXVAKCw0HoYq5ppZg6WHz5c1eC2tK6iRuedNzPh/VLcCtbevBbdxSBABNV9EUHgtL5L4NDzhia8zoa2Z/X6RweyG2p4kum0TNCHJjyE/7l3A65+Zg0DbU3N4wHy8szz33xTfYwBt5SVTyzH6umrdbUYrUYIGC/KJXuR7r/+Hm6cE5WhlkhNtfKJ5XDuKTWVX/B/2fB7wCEcHM5JRNSCJBJsWwvgCiGE9RiZKEKIgQCuDD+HmoDiVkwnlDOHJX5i01y0IZ2PvvuoqabTkY+G4O5/3Z1Uw4eKzRW4dcWt2LR3U8LP+eHFP4xc9byw84WYPWZ23OFJmpN1+iDBmcCZRu2rkQj/czqcpoMlY0CiMQEKxa3g0kLrbp9WwyPsKN5vFdTSCp87jHVpkFpnRsWtYIx7jGn+6urVKL2gVDfPmCGYrIayXL7T6ztxf2+KW8Hiaxcj15ELh3A02JE0Vsfho2eOWi4/sOvAuPtnFF2fShOru6i/xo8/+v+oGwpsLDRv1NHVMWbttlOBU6afy64Oy4pbUYdSG7y6y9wF1Q6rblqF/Lx80/x9J/bZklUZ7ehRP7ZtK8Xnn9+DbdtKYwbBTp82v45u92zk58f7XFsVFA82uC2tq2i07t2noUePhr9PoyluBTf/4IKo4ce1uuHHAiJmUwo7+P3ASxUDdBmYqVwQyATMUGlmigJUVQGTJpmHk9bWqt1KKWWKW8G6W9aZ6qVFO3T6kG76w2+tLyI0ZO+JvaYMtR5HG76QCwDdBu8wlV84WXfSsiupVoPYIRx44ton+BkmImpBEgm2PQb1yHuFECJm+7RwoO01qOM+Hrdn9ygRfTv3be5daBR/jR/jnx+Pu/91N04HTlsW8AfU4Z23/9O6Hlcsj2x8pNH789f//BVz3p6DP/n/hM+OfIZH3320wRNxf40fx2uP6+Zdcu4lCW/TKiNRhv9ZDcE0BstiBc9iiZWpZzU8wo7i/VZDSd/+/G34a/zo3r67afndB3c3ehvRzmlr7uZaF6ozZQCu2LXCtFwyGrpyvPaLtQ3+3oq7F2PmpTPhHeaNO4QxXsfhi7tfbFpeQCRUzy+ap8hjer36dO5juaxVN1lj10gjp3DG/JwD+k5rdnUI1VgFRkMIRbbZ2MYoDRlfNN4079CZQ3hl1yu6eZee17jPsNGRIz6EQrUAggiFamMO4Yzu9AkAnTtPMtVJM3K7Z8V4RL8tY1DUaluDBi1t6EexVHDhTstak0B6szf8fqC0FPh0+XRdBmYi2c5EKVEU4L77gDZtzAG3FSuACnu+o1q7hgLLxvqruw/vjjS6SZS/xq9ekDJcZNq7vX+DzwWA+2+6yvT9Z9W9G1C/DwUEnMKJLV9vYd1mIqIWpMFgm5RyM4A/AOgD4H0hxItCiJlCiElCiCvC9/8CYEt4mT9KKd9L725TNGNtpUTqjTUnbTgVAPVEptqj1nKyOKna+s1WXPlCw7WCNMkU9A/KIBb+dS3q1vwSoS9G4HTgdIND+iLDA6I0JsChuBUUtAlnZhgCFXWhOtP2jcMWGxrGaOQt8eLCzhea5u/Yv8M0z1i8P14jhVgUt4Jfj/m1fqZUAzXRhfg1bXLaNHob0WINDzU2KThVdyql7URrqDFAvM+hFlCq2FyBp7c8HbeOi7HjsBYk8tf48d7X+q/aovwirJ+xvtFXthW3gst6XKabF2soqinIkUDXyEsKL4FwvxszeLLxq/r7dnUI1XRuG7temJYJ25jGKA2xei/+55v/mL6bUqkBdvSoH2fOfAEhcgA44XC4UFDgsVy2a9fJqP9T70BR0X0Nrr9v33K0a2edHaltyyoomsy2YvEUedT3jKHWJGpGIbR2DrZv7pD0uuPx+YCztRIy5IQI5KLXNk/C2c5EKdMy3G69FciNyjD98kt1HgNuaTegq7ne6sL1C+H+oxs3vnRjQlnXkQtIhotMM3+Q2MVxb4kXY7/jMn3/fbhfn2Xnq/YhEApAQqIuVIclm5fYcpGKiIjskUhmG6SUcwDcF17+hwAqALwBYGX4/vVQM9rmAYhdqIjSwnjFvaVfgY+cWEefpK98pD7TxZAh8+ZnbyZ8EvzdAd9t/A5ZBAuMBzRGxhpVAqLRAY5u7bpZbltrJBAtOgMt2WLdVsNc136x1nRQZmz00CY3uUDY5AGTdTXTcp258BR51IPI3mN1yw7qFjNpNiFWmYIAkJejr4EztNCeOk++ap+p5ppRvM+hr9qHs4GzCCGEQCiAn/zzJ5H3uDFbyNhxWJuu3FZp6uo6tHBo0kNIPBd4dNPv73vf8oB9+7fb9YGjGMNDNTmOHCwoXYD1M9Zj7BgXul35jD54AuCrY1/V74dNHUI193nus5y/7+Q+UyDZjgsVVhlrh87ohyVZfcYTdfSoH1u3evD1109CyrPIzx+DIUOqYg4L3bevEogM8w2Fpxs2YsQOmHsdOSLb0t7DQRnE2cBZ+Kp9SW/LiuJW8P2LDLUfw9+X8l/34yfX909LGSuPB3CIs3CiDm1Qh+ff90Fs2Njg84hsoyjA4sXAmjXA+YZh8M80/uIXNU7/Lobss/AF0S939MSy7ctw97/uxrjnxiUW0IrqBj/tf56Fd3LiJbAXlJov4L7ykT5DWvt7qV380y7KNWWTBCIiii2hYBsASCl/D6Af1IDaagAfAdgFwBee119Kea+UxnZK1BQc4ZeyoQBAOjSmvpK/xo+t+8K1b4wn6dvKYmbI/HzlzxPal6Tq+FgEC/76n7/iB3/7QcyfyVjrrEfHHpbLxdO5TWfDtl1AtcfUwMFf48dT7z8VmU62Ff0Nl9xgOd84hO+DfR/oHh/UNblAWPTBnoDALUNviQSCFpQuQJ4zDwICec68mMGyRCluxTJg17Vd18hnI9eRG7dpQWNoB7ixnNPmnLiZMJ4ij652XUiGcOfrd6Jic4UpW8jYcdg4rXsshQYQxs9OrK5mpoCUxfDQsb3G4raS23BbyW1Ye/NaKG4FilvBmlvW4Ntff4t+5/TTreL8TvUnlHbUDIwWqyD1yx+9jPf3va+bZ8eFikQC4TcU35D0z7VvXyWkrA+IHz26FidPxs6MPHz4bd30sWOJB45crvMMc0I4cOBlAOp3oFarL4QQurTrYlp3Y7ZlZfZow+c16vsyFHDC50tp9ZYUBbhn0Pfwe/wOVSjFuNBGDH3rg4afSGQ3RQEu02cco0fjjzWocXSduWNkbteF6jDlb1Mw8qmRlheDy4aU1V9sdG+Ec+wfcMcPhqW8byfqTkSOS/01fviqfVh01SLcWnIrch25Mev+EhFR82hUZEZKuSccUJsopRwspRwkpSwNz/s8XTtJsflr/Ljj9TvqT3rCJ+1NlULur/HD88Bc/Ob+E/A8MLfB7Zb9IyqoYjxJB2JmyJwKnMLIp0Y2uD+xglBxh/xZBAu0LotWVy/9NX4cPnNYN+93437X4L4ZzRw2E2h7AJBOAFK9bXsAgD4AVrmt0hSASyazrXxiOXrn9467TOW2Sl2xewccSQfCojOU2uS00a1HcStYPX01HpzwIFZPX21LQd/r+l9nmrfzwE6EEIJTOPHYNY/ZVjhYcStYdNWimI8bM/esnv8L5Re6eYFQAH9Y/wecDZ7VDaEsG1IWuXLtcroiv0fdwTzUYGIqQUurz05CHRijrtxrw0MHdRuExdctxuLrFlv+zgd3G6ybrg3URj5ndtQMjBbvCn8gFMDArgMxoscILLluiS1DBWOd5HR0dcQ5bc7BtOJpWDoluTpmsXz5pXWtSnW46Se6eWfPfmW5rJVzzzUH6Pftexb+Gj+W71ge+V51CAcOnjqI06f12zpzZk/C27KiuBUMOXdI/Yyo72pHThAeT0qrj+mybrsxFwugQA0W5qexGQNRXLNn1w8nzc1VpymtdBdP42Ru7zu5D5v2boqUIDBeeI7OAJeQjc42i7X81P87VXdhbtbKWTheexzBUDBm3d90sauZERFRtmow2CaEyBNCbBJCVAkhrFqUacu5wstsjLcc2cuqWHmsjJR0qFyxG7V/fh3yX/ej9s+vo3JF7EL3/ho/PjkcdTJmPEkfUmldQD2cwr/pXUeDf9CtCv4DwJDCIZbzLfcjaoibVf00rUZGtGSCX94SL8Z2nwIgCECot6e7Nvg8p0j+quXVF5prva3ZsyZy39gxsaGumvEobgVVZVWYN36eZQMAu7vfxctqDMpgzPdGsmK95gIioQw64/5KSHx6+FOEZAgO4YgMoVTcCnzTfXhwwoPwTffpfl9aJqtDOFIOJlr9PC9+8KLpM2dV5w/ujZHaMk7hbDDoV32kWr/OAzsizR/srkHZ0Gdl54Gdptp3qVDcCoryi0zzT9SewNGzR/HSzpdSOjEpLDT/bk+frsaePfNNXUKthnHGqsVmpW/fcjgc7XTz6uqOYEnVWOw98BYkZKSb7tjCLgiFTuiWzclJvfvv4msX1588Rn1X/+rJN6Ckqene2Wn/hVqHOiC21qFOEzU5v18tIvjYY8BDD6nDStP1pqcI3d/vOI19ouvt/uqZ5fjOjH/iN5UrUFpZioUbFurKPCRTOiDWxa6vT3yN2/95e+TC3JnAGSzbvixyobQuaD5uTQe7mxkREWWjRDLbpgEoAfA/Usq6WAtJdVzLHwCMCD+HmoCnyGMaOprsMMNk7PvwIt1Vv30fXhRz2buq7jJ3LYw6SbcMehlS+Kc/+mTc/bEKBMweMxtPXPNE/B8kej8MjActxnpuqQS/Bg3fD+SED+RyaiMHcp3a1J+kGmtA/XL0L21t7b7zwE7c+NKNAMwt75IrVo8AACAASURBVFNld0AtHk+RR5fplW6xPmMSMm7DA41VXUDtSnifgj5YdNWiyO/N6vdYua0SdSH1KzkkQykHEz1FHuQ4cnTzvjz+JcY/P153EB2rS6mAQI4jB09c+0SDr/eBUwdM87TmD3bXoFTcCqYVx/+TFJIh3LbiNttOFoaeZ64NKCHV+mbBsymdCOXnK8jJ6aZftzyFzz//DbZtKzUF3Iz69m1cp9qcHH2nX4k6lPUO4A+XSFzcSWDiBRNRVVaF/LNvmJ7bu/fdjdqWFcWt4Io+V9TPCH9XF1y4M+V1x7J3w5twhJNSpAA2fsmabdTE/H5g/Hjg7ruB228HunRhoK2JKG4FHVzhBizacWnJ08DQ5+sXij42fW41ji95DaG374d8tgpnqi/F3mP6ur7f7f/dRh8Hbf16a8zHgjIIp3DCKZwQxs61ouGLTHawqttJRER6iQTbpgD4TEr5ekMLSilXAtgN4H+lumOUGMWtoPhcc02iVDrdNUbh4I90V/0KB38Uc9kNG2TMmmwDuw5Ua2AZg16GFP7d7/eI253UeBJf2L4Q5RPLobgVbJixAWN7jYXLEbvWlpV/ff4v3fS7X76r30aHwqSDSWXX9YNrxjWmrLrog6yDpw7W1+SDI7m6dGFWxdsBYNn2ZZjz9hxTEwWrpgotleJW8MS1T8SsW/jZ4c9s3V68z1hD2Vj+Gj/+8p+/xHz8k8Of4LYVt2HO23N0z9GGa/hr/HjjE31ww5iV2FiKW8Hj1zxuGoJiLLa8YOKCyPsRqM/ke3DCg1h789qEhmLGqh9Y9XkVtny9JbJ+p3DGrLnWGHdcdkeDy0hILFy/MOVtAWqtsVhDeUIylPLFkFDIqquuRCh0FkeO+CJzCgvLIIT2fedE//5LYjZSiCUQ0A+ZFwAcAsh1AJd2duA+z31Q3ApOndqlWy43txA9etjTwXPXQf26U2kw0SC/H95ntiJHqgdIuUGg1xZ7vzuIGlRZCZwNd9MOBtWAWzo6gpClYYWG+mpbpwObf1x/7GocXhrKBeAEgnmQ7/wKM4fN1JV/SKZebLwLTQICj13zGOaNn4cfXfwj3WM/uvhHTXKB88P9H5rqdhIRkV4iwbZLoTZBSNRaAPa0/KOE1AZrTfOa6o+eFiwSE+6Da8Y1KLuun+VyFZsrEPhsjGXti4FdB2LHHTvgm+7DQxMewpLrlmBsr3DNKy2FHwFASKDtAbz52Zu6IEQ0V44+kBbdVUor0O672Ye2OW3hgANO4cTQwqEY0WMEurQ1/M7CWXiBL4br6kaN7KmvHZfKSZ/iVuD77XyMnbZBl1UXnRnjKfIg16kWvtU6eiYrXoDo4Q0Po6CNPpDXr4v169mSGTt0at767C1bhzl4ijwxAyoNZWP5qn1oqJeMFvyp2FyBis0VGPfcOPx29W/hed6Dcc+Nw56j+npYnx9JvWym1fvDGNhQ3Aq8JV5dva6CvIJGZTCWTyxHz449TfN3H9qNJZuXRA7ggzKoq1+YrETXYewynCzLTppRUs1CbNs21ucyhECgfuhRfr6CoUN9uOCCh3DppeuSCn5ZbUuGA1F5/74Nt//8KPw1fuTm6rPt2rXrb3pesk4HTuumO7g6pO9ksrISTqkGFSXUn3PwwHHp2RZRokIhpKUjCFlaMDEqA9iqblv0sanxVGrXd1Fc68WjVz+KK/pcgUevfjSp7ytviTdmkE4IgeLuxZh7+VycrD2pe8w4nQ4VmyuwbPsy3Ty7S3UQEWWDRIJtXQF804h1fgOAlzeaUP9zzCc1TZXZpgWLHry3A3y/nR/zgGL5juUxuxbuuGNHZF1zL58Lb4kXV114lXoy794IXPUzwBECpANY+QhQMwrPbnnWtA1/jR/bv9EP37PqUKnVEntgwgNYd8s6bLl1C9798bu46sKr6hcyDF9d9Pf6bLaTdYYDm7rUDmysOmkeO3NMNx0IBSAhTbXiGstq2LEmJENYX7NeN2/3wdg1+FqiZ95/JuZjyRQojkdxK7ih2JyhNfTcoQ1md0Veh/f+N/DCG+ptDPPfmY87Xr8DdaE6hGQIdcG6yPDRaHYEiqzeHyGEdMNitYCl1vxCqy3XWCfqTljOjy4q3dRmDptp38rS+GP07x97WPy33/5NN52fr6B377mNzmiLty1t1NL4MSuwdfmVuHzaOzhmeEsah5+mQjeMtGYUTlT9FBUvNzxUO1nCcDuuw+BYixKlR1kZkBM1rN/lQto6gpCJNhqik6uTdd02bXhp37dRX3dXQvvWqHx5D2atnIWqz6swa+WspC/0lU8sR0dXR9P8kAzBV+2Dv8aPV3a9onvs1Y9fTXv9NKtjrVSz64mIslEiwbbTADo0Yp0dAGTO2LMsMHvMbFNXwqZs+51wXS6Lmmy6q4dRPEUetMlpo06c7qoG2mQOEMgDtpVZDm/0Vfsw4osg7loHjKpB3ELtVvu8/+T++gUMVzJ3vndu5ODFWEcjXl2NRBkPUqKn76q6K9KNNCiDau27JCluBYuvXRzzcWPgMPIaZIg2ubH3Nx2fi6VTluq7JQIY1XNUjKXrKW4F7T74GbCiAvj0SvU2OuAWVdvwZO1JhEL6Qsu5DnMPGmNWYjIUt4IxvcaY5msH1v4aP8Y9Nw5Pbn4StcFafHfAdy2bXySijTOx91asoc+NUTakLGaQWdMup50t3UgTkerPlJ+vID/fuuNtXZ29F1ry8xV07TrZ8rGCAvU7M7jRiyOnvtA9FgjYV/8x0r02fBFE/ut+3PnDi9Izqu5S/WsjcnMZ5KCmpyjA2rXAbbep/1evZs22Jqa4Fay8cWXsJlrujYDnfrXeLrQLoSEIRxAoWhNpYJBqnc6HJz1sOd9T5FGz5A1XdkIyhPt896U14NajUw/TvMIOhWnbHhFRpkok2FYD4LJGrHM4gC8aXIpsE6lVldDL2fT8NX689dlb6kRUTbbZY2bHPEnXss9uK7lNvYroCCAyqOf9GTj+6WDd0E5/jR9HV6/Eg8+NQoequ/DQc6PwSMEPGxUE0A39M13JXB2p5/TtyW91z2ub2zbhbSQqulHBzv36QuDG6cbylnjrh+lmmUFdzZmMmmv7XZuWoWe3X3a7bjrRQIrzo+vD98L5MzvD7z9DVmW7faXIcdZnODgdTjx2zWPokKu/BtK5Teek9j+av8aPd/a8Y5qvHVgvXL8wklUnIfHqrleT3laiB+Z2DE3RgszxGmi0c7WL+Vgy4v18xnp7yWjXLtZ73XpocyrcbuuhTE5n+AQz2Ab5hvhvXd1+8xOS5CnyqDU9oy6CBAM56RlVt8Xwfrv2WgY5qHkoCrB4sfqf78FmobgVDC0cGruJlhaIG/4U4KgDIJHjzEWnNp0iJS1SrdPpLfGibY75OHP7t9vxxVHr0623PnsrrR1CreqSRjf2IiIiVSLRGR+AUUKI4Q0tKIQoATAawOoU94saacvXW+rbfofqbKlzZJfKbZWmK2/5efkon1ge93mKW8Hi6xZj9g/HAv20/hwCCLmAbWW4dcWtqNhcEcm22ftaLa4NVuFezMO1wSocX5nXqP30lnix5LolmNRnEi685IDpSube43tx5QtX4lRAX5x82HnDYqwxccYT83e+eCdykDSw60DdY8bpZFgNr7WSSQ0SADWDKVYdtTTEIACYg0GJBoe8N2oH3+HPxsBwUwVDVuXX/xmAGUNnRJ4XCAXwxu43TMMw7RgCeVfVXZHvkWj9u/SHv8aPVZ+u0s3XhrIkw6rWpBWrDsPJ8JZ4se6WdRjRY4Tl41f2jd14JRmRoKuxAzNg6lSXjI4drYO6odBx7N1bYflYsvLzFQhhPtnLza3FoEF+5HU6gS6d9KVa27YdYNv2FbcC33QfJl/VGc7cEBxOiTyXaJqEs0JmaxC1Zlbd7Hvn966/aOneCOR/AYQcAJwIBhxY8eZx3fKpXjT66cifmubd+fqdeOr9pyyPeSRkyhl18ShuBZf10Odh2DHKg4go2yQSbHsM6tng34UQMc/yhRAXAfg71OIFsQvKEAGoC5prTsVSPrEcYwdbn7gt37E8km3jgwe1cCGIHNQiF8uOXtjo/fKWeLHqplVq23fDlcx+Xfph3RfrTM/RDT9NkjEbKoRQpM6H+/h/6U7WR7kbHqbY2O3FYmsNqyaguBV9facohe1b1klz+V19MeK2Z4C+q4DrvMDwp9UHDFmVte5Vuk6qEhJbv9Ef1A7sOtCWIZCfHvrUcv7D6x/GuOfGmQrVA8k3CIluXqJjCE7ZGfBV3AoWXbXI8rHIUEWbHDx10JSlqP1Mngs8Ka376FE/PvlkVszHP/74dhw9al9Gw9GjfkhZ/9pLqdZtEwKYNKkSI6e9gV69ZgPQMjBzwtP2UdwK/vGr2Vjnc+GBeQJVVWlK9ikrA/Ly1B8uL0+dJqJWS3ErpkYFd19+N9bcsgbTiqepM9oeAOCE2hVaYseJtbrldxxI7aJR+cTy+m1BLScRlEEEZTBmnVOncKa1pIzx+LChxlBERK1Rg8E2KeUuAL8H0BvAFiHEUiHEDCHEJCHEFUKIW4QQSwFsAVAE4P7wc9JGCHG3EEIKIR6LmieEEPcJIfYKIU4LIXxCiMGG53UWQrwghDga/v+CECL1QkctQNmQskgdp1xHbsxaZc3heO1x07yYJ9oxLPjlYMB5FkBQvR1Sn7n37ldq84KheT64UAsn6uBCHdp0fzPpfT5w6oBp3vtfv4/u7bub5ttxgGHV0GLT3k24/Pez8eKvZuhO1n2f+1Le3hu7Gx7G1iG3Q5PVsLJTdGBKIyDS9pkoG1KGPGceBATynHmN286wp4Cbrq4PtAH1w1JKngaGPg8AePMz/XvZ2Mn0uwO+m/T+R5t2yTTL+SGELJsynNPmnKSH5s4eYx6GYhWcsjvgq7gVU509h3DYflLiKfIA1eMtOzAX5KX2Z+fIER9CoXiZgUEcOeJLaRvG7UUTUS/bOYNWYcGcvsjPV3DppWvDnU/XJt2QoSGKAsydm8ZRdYqi1sd68EHWySIiAGqwSxv5sOS6JZFjo6VTlmLDjA2Y1GMa1LKgAhAhtdZwlDN1qV80Or/T+ZH7EhIO4YBTOJHjyLFc/tr+16a8zXi8JV5MK56Gc9qcg2nF0zLyeJGIKN0SKvIlpfw9gN9CHYh1A4CnALwBYCWAp8PzHAB+I6V8ID27qhJCjALwYwAfGB6aDeCXAH4KtcbctwDeEkJEt/F5EcAwAFcDuCp8/4V07m9TcggHBESDhcCb2prqNaZ5T1zbuORHRQEK77wBKP0tcPP4SLbZh/s/RCAUwKga4P/5N+JtlGIefoc3UYpftT+c9D7fcIm5y+SeI3twqk4/hLQgr8CWA4xYJ/rBz78DGcjVnaxbFaZtrEQ6Vw7rkfrw2OYwZdAU07zvD/h+Wuq1AWrwZvX01XhwwoNYPX11o7bTo2Oc13LrdGDzj3UZURpjMwRj99pkTR5gXQg/lovPvTjpbSluBU9e96S+jpphCK2r5sq0HMArPfWv0ff6f8/294fiVtBv2FfmLnZIPhtQU1DggcPhgppJYT0+uqAgtW2Yt2dRm1IAl19cEPndpdr5tKXwQ8F8zIUfmf1zEJF9tJEPxr9JilvBfTd7kJsjIATgcIYi3/WaVLOZAeDFD17UTXfI7YDSC0rx+DWPo2s7fXBPQOC1Xa+ltW5bxeYKLNu+DIfOHMKy7ct0dZSJiEiVcFRGSvkQgP4A5kGtyfYRgF3h+78H0F9KOT8dO6kRQuQDWAZgJoDDUfMFgFkAFkgpl0sp/wNgOoCOUAOBCA+BvQqAV0q5QUrpB3ArgOuEEPYVl2kmvmof6oJ1kJCoC9alrU5DMowp7j079kzqxPb+m64yFahtl9sOk/pOgqcacISA0diIu7AAY7AR14+9PfbKGlA+sdzU8fFU4BT2n9IPGS3qXJT0NqIpbsW6wYVhSKG4YC1mj059eFYi2UKJdNVsibThFlrWVJ4zzzQExG4Jd+Q1mD1mti4jdfaY2er7wBB00jKiNEfPHNVNG7vZJquxtR7jNaRIhFZHLVKH0PB+HzT827jPT1bZkDK4nC4ICLicrrS9Pzr3+8hU+3FEjxEpB/by8xUMGVKFCy6Yh/79n7RcZt++StuGktZv7yFdYwYBICf4FXbt+omtw1abk98PlJYC99yj3qal4ykRZR0t49fpMF8ESTWbGQD6dO6jmz5WewxVn1dh1spZ6N5OP+pCQiIog6gN1qbtfEDrUh5rmoiIGhFsAwAp5R4p5b1SyolSysFSykHh+/dJKfekayejVAD4f1LKfxnmXwCgEEBkrJVUC8yshdqwAQAUACcAbIh63noAJ6OWyVhd2nWJFDYPIbXOR3by1/hNWVRxs3ni8JZ4TY0Ejp49ippjNfAVqbVpJdRDHCEEcNA8NLMx/mvwfzW4zKjz7QtIDe9h0YPE0HL++6XdbcnA0ZpB9OzYM+YymVzsdumUpVg/Yz0emvBQo7PNmpLiVrDm5jV4aMJDWHPzGpRPLMc7M95B2ws36TOi2h7Q1TH7+sTXuvVEd69tSnYMzVXcCmaNCtcfM7zfRynWtWjs2KZvug8PTngQvum+tL0/Zg6baar9aMxKTJaWRdajhxft2w81Pf71109iy5bR2LbNnsYP2vZ69vyZbn4gsD+8re9kRcDN5wNqa4FgUL1NS8dTIsoqPh8QCKj1LINBANXjIo/lOnJtKVOwYOICXU1TKdWA2tngWVNplhxHDpzCCZfTlba6bW1y2+ima+OWNiAiap1a1njDOIQQPwZwIYB7LB7WIjDfGOZ/E/VYIYD9MqrYUfj+t1HLZKxkOyImo2JzBa584cpIyri/xo/56+Zbpqr7qn2mzLZUTjaNwa1vT36LtXvWYqMbuOMaoM4BBAWANm2Qaqu6pVOWIs8Zv6Npoo0GEhGrcHv0yXp7V3vbtuct8eKecVYfJ9XQ88wn8Jkk2WyzpmbcT8WtYNGPr68POl31M2DlI7o6ZmeDZ3XrsKoxmIyyIWUx678YpVKvzejgqYP19dvC7/fc3pvTWnuyKd4fxd2LTXXp0vG56t8/9rD8w4ffxI4dN9q2rR49vOjffwmcTuP3eAgff5x8NnGi/H5g/vz0ZZx5PIDLBTid6m2TdDwloozm8ajfGUIAEnW6YaQXnnOhLX9nHv/H+7qaprJmJAC1K/jV/a5WM+PD5WQAoOS8Eiy6alHa/sYZM9u37tuatiGrRESZKiOCbeFhng8BmCaljHfpxJgGIQzzrNIkjMto2/QKId4TQry3f3/q3SbTzTiMzK5hZUYVmytw64pb8eZnb+LWFbfixpduRGllKe5ZfY9lbQirDDurAvaJ6t81dmOFp4cD424BXrtpBOxqVZffJl+9Y+iQqLFqbJAsX7XPsoV7NKv6d6mIt/92DHug5HhLvJh2TV81yHq6a9whpQDQtX1X80qSoLgVrL15LYae23BAqLCjfdcoPEUetMlpAwfUgs+TL5qMNTevafGB0oZYDd9Jx+cqP1+BELGDpAcOvGzr9nr0sK6jd+rUR7Zux6gphngqivrnY9482/6MEFErEGkcYziMa+iibaLeeOu05bGAAw4cPHUQBXkFkFJCQiKwZzg2/W0C7liyNG0BMKuLYbf/M/0XXIiIMklGBNugDgHtCuA/QoiAECIAYByA28P3tYiB8eyvO+qz3fYB6B6u7wYgUuutG8wZcZBSVkgph0sph3fr1s3enyaD3fvCSl3Qadn2ZTgTOKOmsgfOmk4urYI5VgXsE9XQ0MZNvRw494FFtp0hFeUXWXZI1NiZnu8p8jTY3MKY0WTHNl1Ol2m+XcMeKHlLpyxVAzOGOmbGwstA6rXToiluBVtu24KOro5xl/vZyJ/Ffbyx26wqq8IDEx7AulvW4R/X/yPjA22AerEhOrM3nZ+rtm1jN6swdq+1g8PRxmJeO9u3E62phnimveMpEWWV6GGkMuTUXRSrDdozvPLqK9paHgvk5eTBU+TBkbNH1L83UcesgWdXYuHf1tmyfSPFrZguHm39xjq7zTgihoiotciUYNvLAIoBDI36/x6Av4bvfww1mHaF9gQhRBsAl6O+RpsfQAdA115MAdAe+jpuGclYy8w4bYc5z76MfY8vNQWdtJNJq1pxR84e0U2P6DEC5RPLk96HqYOmxn38knMvsfUk/dDpQzGL1Y/tPdbWbSluBSXnlehnGjLqzgbsDbZptatuK7kNY3uNxcCuA7Mmqygb9O/S31THLLpBiCYdwy3z8/JjPtavcz/bu4RmyrDfxjh46mCk8YmAwMxLZ6bt5xswIPZQUilP215PrVMnc73KgoKxtm7DKOOGeKZ7zCsRtQjRw0iFQ9+N1FhPLVlL//sOOG6eZDoW+OnIn0JxK3ht12vqgoZj1o/fO8+W7VspKigyzTNedJ/z9hzTiBgiotYiseI8zUxKeQSALmojhDgJ4FC48yiEEIsA/EYI8RHU4NtvoTZEeDG8jp1CiJUAloTrvwkASwCskFLuarIfJk2MtcM6telk6/r9NX78n799AASvC/8Bl+of9KgTf4dwmDLZnt78tG76k0OfpLQf3hIvfr7y5zgVOGX5+JHTRyznJ2tkz5H4RMssCsrI1UQBgQWlC2zdFqAWVN+0d5M6oV2dDLrU7U8vxXev6Wv7NhW3klUBjmwSeT+4N1oG2QB1WGLaX7+aUernvcgHuDfivE7pO3jPJp4iD/Jy8lAbrIXL6UprDTpVLoA6i/kSR474kJ9v3/ukV6/ZOHjwVSDcmAdwolevNHf9DQ/x9PnUk9sWnXmmjXmtrVUjgxyTSpTVtHEzxto08cqfNFb7Cz7A8Z7rdfMe3vAwJg+YjNOB0+oMwzHrzo5PomLzadsvkAHmcw2HcOiyt/01fixcv1C3zLLtyzC299i07A8RUUuTKZltiVgI4I8AHoea9XYegElSyuNRy0wDsA1q19JV4fs3NfF+poWuwDiAP/n/ZFudhjlvz8HoP4/GmZ5vxB3OJiDMmW1njsSdTobVsEfNsdpjKa8/2uBug+szi0qeBoY+DwD49ZhfpyXA4S3xYvaY8Amr4eqkc89ELJ2y1PZtUsuldY0d0WMEOrmsA+hBGUzLtm+45Ab1jsUw6j1HmqL5dObThsfOGz8PVWVVaQ2KHjniA2D9XhAiFwUFHtu3WV8nzon+/Z+wNZgXS8YM8WRbU6JWw+cD6urUYaShgEM3jNT3uc+27fQ5p49pXkiG4Kv2oVenXuoMQza87LkBd75+Z1pqt52pO6Obdndy6/7O6bLc3noIWPg1sOhTzF2YfO1mIqJMkrHBNimlR0p5Z9S0lFLeJ6U8T0rZRko5Tst6i1rmkJTyRillp/D/G8NZcxnPU+SB0+GMTNeF6iyLczdWxeaK+qtSDQxnC8ogZq2cpfuDnuvM1S2TSidSjVXauqahmmeNpaujtnU6sPnHEJWrMbl98kNhG1I+sVwNuBlqdY253CpjhbKdt8SLd3/8LlbeuNLy8e8N+F5atqu9D517JpqGUbfPta8rbrZrquGxsYNpAoWFM20PhB054oOMCvTW1dnXLCYrZNyYVyJKVpcuQCgEqDltTqBtfYfwHh172LYdpaf197inyFPfmMGQiQ4AwVDQlnMC03Yv8NRvc91d+PJDt+4cILLNtx4C1t8FnDoXOHIBDv3f+ahg+TYiagUyNthGeopbwfWDr9fN+3D/hymv95F3H9HPcG9UOyTGGNJ2JnAm8sd1zttzTAX950+cn/I+jepprhWkmXHpjJTXH01xK1h87WKI6vGRgIMI5aU9SaF8Yjlm/3AsMH0iMOF3cN58JRbc8v30bpRaNMWtYMOMDRh67lA4hRMupwvTiqelNduxfGI5+pfsNWW0/myUfc0RyE7mRggORxsUFto/fLWgwAOHwwXACYfDlZbMuYzGtqZErcbBg4DDAQACEEG1izjUER+R0Qo2iFeK4EzdmZgNvYQQaWnOU5BXoG7judVA1QMIPvuWriHDxi/D5wo7tXrLAlpUcPly23eHiKjFyYiabZSY3Qd366Zf3P4i7rjsjqQzKvw1fux4vxNQfZfuCplJ1FU06d4YGUr60o6XdIt1bdfVlhoNZUPKsGTzEl2XPwCY1GdSSs0XYvGWeIE7t+POdySCAYk8l2iSJIXyieWYPMAPX7UPnqKFrKtGkU6hTWnYiLPYOb008hkfMTLEWistkDqMVP+d2LHjCFx44aK0DO/Mz1cwZEgVjhzxoaDA0yRDSDOOojDIRtQKeDxATg5QWxcEHIFImZXLelxmeyOtwvaF2Hdyn27+wg0L4bnAg01/c9RnogcEsK0McG9MS0dqf40fm77apG4jmAdAAEEHXv1bAfzX+6G4FZzb/ly1vMvA5WpmW9TfqKnx+50REWUFZrZlEWN7cQmJym2VSa/vrmdfsbxCpmNxFW3L12owYMqgKbpF7co6U9wK1s9Yj6HnDkWeMw+FHQqx5LolWHXTKlvWb8U7uRiP/Z9cTCwVWLSo6c6fsrFDI2WW3Qd3N5jRSs2voMADIaLrWeamLdCmyc9X0Lv33CYNtLHBJxG1RGqDBP1p1cxhM23fzii3+Vh814FdapZZkU8N9kGq+7LlFqBmFEIImRoVpMJf40dpZSle2fWK6bEQgpFzj0jThivuBsYsANp9i07nHsKSJQJeXrMjolaAmW1Z5PCZw6Z5+07ss1iyYRWbK7B2rYiq1WTuPjqpzyS8uW6Y6SraU71+imXbl5nW2bezfZ00mzrDx+8HZs1S61yvWwcUFzNhgVqHNrltdNN21p8h++TnKxg61Id9+9STnMLCsqzLNmODTyJqiXw+IBAAIAUQzAF890J45qG4e7Ht25o9ejZe+egV3eiOAV0HwFPkgXD/BvLSZ4H3vACcQMgZOXZ/Zdcr8Nf4bbl466v2oTZYq+7DkEo1qBfMVUtNDKnEM1s2o1ObTvjy+Jf1T7ribpw/MHUh0wAAIABJREFU5XF8+YsvY6+YiCjLMLMti7R32VO03F/jxx2v32Eq0K+lxTuEI5JJVjR0j/4q2vszEHztURz/dDCO1x7XrXf5jswt0MDGctQa+Wv89TVXADiF09b6M2Sv/HwFAwYsxoABi7Mu0Abwe5iIWiaPR+2FonaEdgKfTYR8/i11hIjNFLeCJ697MtK8K9eRi9mjZ0NxK/j1mF+rwa+cs6ZjdwlpW5MET5EHLqcLTuFUL8LfPB4o/a16696IulAdln1gvug+7ZJptmyfiChTMLMti1zX/zrs2L9DN6+wQ2Gj1+Or9iEQCtR3H43qajT5osmRP+oAMPdHHty6Jvoqmku9v3W6qWPp0POGpvDTNS+tHkcopN6ysRy1Br5qHwLBQHPvBhGA+gafWmYbv4eJqKVQh5GGW4KGR4R89N65admWt8SL4u7F4Zq+nsgxefnEcvTtXIGH8mdiz7YiU73lFz54AXMvn5vy9hW3gqqyKviqfZi3dh5Ouzeayky0zWmrm+53Tr+01FUmImrJmNmWRY6dOWaad+l5lzZ6PbouplG1mjrmdsQ/rv+HLgXdW+LFwNL31KtoCIbnOtV08mpPg/uXSbT6smmoM0vUInmKPBBfjgbW3QXUjLL1yjhRY7HBJxG1RD5fuDlC5LQqCDjrcNHwb9K2zVg1fb0lXlT/z1/Q7/vLTQGwnQd2Ys7bc1Letr/Gj4XrF+Llj17G+R3Pt1ym5liNbnrE+SNS3i4RUaZhZluW05oVJMpf47estwYA57Q7x3L+jgXPQBxQ1I5EW25Ra0REpa5Hljuww/L5mcDnU4cuSane+nw80aNW4EsFjhdWI1gLwFmLnBnXwFPkae69olZM1+DT71e/jD0eW7+Q/TV+U8YIEVEsXQZuh3T0BWQu4AgClz4LDKnEtGtvabZ9en7y8xj959Gm+X/c8MeUMszmvD0noWYLZ4NnddPvfvlu0tskIspUDLZlkbIhZViyeYmuaGpjGyRMf3l6zMfuvvzumI916rsDx9y3q7Uiqj1A2wP1mW3hK2v7T+5v1L60JBy+RK2RzweEAjmABERIYEbB81DcvZt7t6i18/uBykrg2WfVquQ2dkvQuuzVBmvhcrpQVVbFgBsRxXWwywpg+qu6sisAcPDU95ptnxS3gt75vbHn6B7d/IAM4MoXrsSqm1bFfX70RQdALSvx4f4PLS/IFxUUofpIddz19encp1H7T0SUDRhsyyKKW8El516Cbd9si8w7dOZQws+/8aUbsfvQbsvHBnYdCG9J7D7df5j0B9y64tb6lPXnq9Qupc7aSO22AV0GJLwvLY2iAIsWAcuXA1OnMquNWget6HMoBOTmOFE2mYE2amZaS9IzZ+rH9GvdEmz4Yta67AVlELXBWviqfQy2EVFcniIP4L7bNGyzuTLBtUDZ1RdejSc3P1n/QM0ooNqDN2t8mHPeHBTkFVhm8Ppr/PA870FtsDbynEgQ0TgN4EzgDKYVT4s5MoaIqLVisC3L5DnzdNNn6s4k9Lx4w0e7teuGHXfEHwKqBeKeef8ZfPre/4eDQVekQKzWdjyTuxhWVAB33qkOIV23DiguZsCNWgch6odPb9/O9z01M60lqRZoE8LWdGOty56W2cZh00TUEMWtoG1OW5wOnI7Ma5fTrlkC9dHZuU6Hs/6BmlG6C+ELUQqHexMcDgd+ofwCBXkFOHL2CJ7e/HT9hXrDc3DVz4CVj5gupuc587B0ytK4wbapg6am+ScnImp5GGzLMjOHzcSmvZt004mIV/T8lR8m1rrcW+KFt8QL/8XA6FWngICM1G7Lc+ZlbHaA3w/ccYc6WgkAzp5lzTZqHXw+oK5OvR8MqgFnBpqpOfm7XAefOA2P419Qcv4NzJgBlJXZ9qaM7rLHmm1ElKju7bvrhmx2a9+tWfYjOjsXIWBsr7FY+8Va9cK34UJ4yL0RoVAodg226OcEAGz4tWkdcG/EpYVqM7Z2Oe1wKnDKclWfHv40HT8uEVGLxm6kWcZb4sW04mlol9MOhR0KE35erKv3kwdMbvTJhqIAzulXAhN+F7nqFZKhRq2jJfH51GF0GiFYs41aB48HcET9ldCagxA1B78fKJ1VjHtC96PUuRr+R98DFi+2Pfobq8sfEVEsxrrG8eocp5OWnesUTricLiyYuACT+kxSh306awFRV9/ErGZUpNu47r5Gew4CAJzAoT6AdAIiEFmHUzgjI1f+dNWfYu7XSzteSuNPTUTUMjGzLctUbK6IpHGfOnFKraMGxK23Fk+yQz9LRtRiU88FkWntqlcm8niAnBx15BKgDz4QZTNFAR5/vH4IdV4eA83UfLQRpMGQQK3Ihe9gMRgOI6KWQDvOXr5jOaYOmpr0cXeqrLJzV920Cp2/6owj00vr660B6hDRgEu9L8IriBoeCvdG9b7vXuCziWpGGwJAn7cBz/0oungfXpy6LnJhwlvixfIdy/HmZ2+a9mvKoCnp/tGJiFochg2yzPIdyxOaZ7RwgzmFvF/nfklf2Z980eS405lEUdSRSiJ8IBIKMbuHWg+vF1izBnjgAdsaPhIlResK7XRIuBx18HTZ3ty7REQU4S3xYtVNq5ot0Kaxys59fdrravDs8gXqbbUnHGjLAeBUM9ZkjjpMtNpTvzL3RsBzf31WXE4t4LkfOb3fw4tTXzSdJ6y6aRWmFU/TzZtWPA3lE8vT9vMSEbVUDLZlGasCpIkUJa36rMo07/kfPJ/0fniKPGib0xZO4UTbnLZNU2Ta7wfmz1dvbVZWpp7kCaFmuTG7h1oLv18NLns8DLRR81IUoGrRdsxz3Iuq4Hgos0am5fueiCjbKG5FP1qlyAc4QgAk1LQ2od4XofrMt7Chl50Bpk8EJvwOjpsn4bbJQ7H25rUxL8gvnbIUG2ZswEMTHsKGGRuwdMrS9PxQREQtHIeRZhlviRefHv40UuzUKZwo7l4c9zkVmytwvPa4bl5bZ9uU6tU0eZFpvx8oLVXHGLlcaUnB0ZrfabdE2a4JPlZEjaIcXAFFPgSEgkCtk91qiIgSVD6xHF8d+0otN+PeCFxzB/DPJ8LDQ6HWYrvmDvUxqKNSZo+eDcWtwH+NP3xM/4eEjukVt8K6l0TU6jHYloU+Pvhx5H5QBrFww0L84/p/mJbz16h/OCverzA9lt82P+X9aNI/tJFiPkH11uYTMJ9PXbWU9UXieX5H2S7NHyuixtPGkmoRYKs0Y6ZjEhFZWjplKc7vdL56UX7408C5/wG2lUHAgQ4jXkKX/h9jaGF9kE3D4BkRUeMx2JaF9h7bG3caUANtnuc9qAvWQcKcqjXq/FGmeS1aIidgLXf1RC2SxwPk5AYRkkBOLuDxOJt7l6i1UxQ1xTJWMI3pmEREcZVPLMfkAZOxcP1C7O2xFzN/4gzXmbu1uXeNiCirMNjWSlVuq0RtsDbm41f3u7oJ98YGDZ2A2bD6RYuA5cuBqVN57katRE8/ZNlc4NMxkH3XAz3nA+z/SM1NUWJ/CTMdkzKVlpHZpQtw8CAzMymtFLeCf/zQPOqFiIjsw2BbFtp5YKduets320zLvP3Z23HXcfDUQVv3qUnEOwFLkd8PzJqlnrutWwcUF/MYmLKfr9qH4PnvQPZYgzoIVG6r5DASalYNjhBlGjJlIi0j8+xZteW5wwHk5TEzk4iIKIOxG2kWOrf9ubrps8GzqNhcX5etYnMFPjn8ScznO4WzabqHZhCrZAmibOcp8kAIAQCQkHh267Pw17D7IzUPLR5xzz3qrWUjUi3Led48Biooc2gHGaGQOh0K8WCDiIgowzHYloUm9plomrd8x3IAaq2221bcFvO5uY5crLtlHbNXDLoM3A5HTh0cTslkCWpVZFT73bpQHXzVvubbGWrVEr7ooSjA3LkMtFHm0DIyHeHDcocDcDqBL76IEVUmIiKilo7BtixUNqQMTqEvZD510FQA6rAwq4YImqAMMtBm4K/xY9aHIxG8aTwcE+7Fohe38xyOWgVftU8XbGPWKzUnLR7hdHKEKGUZLSPzgQeAJUsArxcQAnjqqThpnERERNSSMdiWhRS3gl+O/qXlY54iDxxxXvb2ue3TtVsZy1ftQ22wFqGe6yG/8xAOdlnR3LtE1CQ8RR7k5eTBAQdyHDl47JrHGIynZsMRopTVtIxMrxfo1QsIBFi7goiIKIOxQUKWem3Xa7rpRRsXwVviheJW8L0B38PLu162fN7Dkx5uit3LKJ4iD1xOF2qDtXA5XczsoVZDcSuoKquCr9oHT5GHgTZqdmnsg0PUcrDRBxERUcZjsC1LnQ6c1k0fPnM4cv/QmUOWz5nUZxK8Jd607lfaNNiiLnkMOFBrprgVvueJiJqSomD7i4tw8I3l6HL1VBQzwkxERJRxGGzLUr3ye6H6SHVk+psT38Bf44fiVnCm7oxu2VxHLn6u/BzlE8ubeC9torWo064Ap2F8EQMORERE1BT8NX6UfjgLtefXwvXhOlSVFPMYhIiIKMOwZluWOqfNObppCYnKbZUAgII2BbrHxheNz9xAG9CIFnVERJTJ/H5g/nzWi6fsptWKDcogaoO17AJNRESUgTIi2CaEmCuE+LcQ4pgQYr8Q4jUhxMWGZYQQ4j4hxF4hxGkhhE8IMdiwTGchxAtCiKPh/y8IIfSRpyxR2KEw5mNbv9mqm/7s8Gfp3p30Yos6IqKspyUx33MPGzRSdtNqxTqFk7ViiYiIMlRGBNsAeAA8AWA0gAkAAgDeFkJEp2/NBvBLAD8FcBmAbwG8JYToGLXMiwCGAbgawFXh+y+ke+ebw6XnXaqbFhAoG1KGis0V+Pbkt7rHpgya0pS7Zj+2qCMiynpMYqbWQqsVO2/8PFSVVXEIKRERUQbKiJptUsoro6eFEDcBOApgDIDXhBACwCwAC6SUy8PLTIcacLsBwBIhxECoAbbvSCk3hJe5FcA6IcQAKeWuJvuBmsDBUwd10xIS27/djnt99+rmd3R1zOwhpBq2qCMiymps0EitCWvFEhERZbZMyWwz6gh137UWmxcAKATwpraAlPI0gLVQs+EAQAFwAsCGqPWsB3Ayapms4SnywCH0L+/v1/we+07s083Ly8lryt0iIiJKCpOYiYiIiChTZERmm4VHAGwFoFVs0QqUfWNY7hsA50cts19KKbUHpZRSCPFt1PMjhBBeAF4A6NWrl3173kQUt4Lh5w3Hpr2bIvO+Ov6VablhhcOacreIiIiSxiRmIiIiIsoEGZfZJoT4I4DvAJgqpQwaHpbGxQ3zjI9bLaMuKGWFlHK4lHJ4t27dUtnlZvPxwY8bXObL4182wZ4QEREREREREbUOGRVsE0L8CcCPAEyQUka30NTGRhoz1LqjPtttH4Du4fpu2voEgG4wZ8RlhUAo0OAyJ2tPNsGeEBERERERERG1DhkTbBNCPAK12cEEKeVHhoc/hxpMuyJq+TYALkd9jTY/gA5Qa7dpFADtoa/jljWG9TAMEa0ZBay7S70NM3YtJSIiIiIiIiKi5GVEzTYhxOMAbgIwGcBhIYSWwXZCSnkiXHttEYDfCCE+AvAxgN9CbYjwIgBIKXcKIVZC7Uz6Y6jDR5cAWJFtnUg1C0oXYPSfw70fakYBz60GgrmAsw64eTwcvTZh9ujZzbuTRERECfLX+OGr9sFT5GGnRiIiIiJqsTIi2Abg9vBtlWH+/QDuC99fCKAtgMcBdAbwLoBJUsrjUctPA/B/UN+19FUAd6Zhf1sExa2gbU5bnA6cBraVAcE8AAIIOoBtZVj8k1t4skJERBnBX+NHaWUpaoO1cDldqCqr4t8wIiIiImqRMiLYJqUUCSwjoQbe7ouzzCEAN9q2Yxkgx2H9Euc4cuEt+d9NvDdERETJ8VX7UBusRVAGURusha/ax2AbEREREbVIGVOzjZLzvQHfU+8MqQScZwEEAedZXHzF+826X0RERI3hKfLA5XTBKZxwOV3wFHmae5eIiIiIiCxlRGYbJW/plKXYfXA3NmEjcPN4oNoDccFaPHHrw829a0RERAlT3AoWDX4Xy984iKlXd4HiLm7uXSIiIiIisiTU0ZcUz/Dhw+V7773X3LuRkorNFXjm/WfQo1MPzB49m0NviIgoo/j9QGkpUFsLuFxAVRWg8E8ZERHZRAixWUo5vLn3g4iyAzPbWglviRfeEm9z7wYREVFSfD410BYMqrc+H4NtRERERNQysWYbERERtXgej5rR5nSqtx5Pc+8REREREZE1ZrYRERFRi6co6tDRysrm3hMiIiIioviY2UZEREQZ4/nngaeeUuu3+f3NvTdENvH7gfnz+aYmIiLKEsxsIyIioozAum2Uldj9g4iIKOsws42IiIgygsej1mwTQr1l3TbKClZRZCIiIspoDLYRERFRxhBCf0uU8dj9g4iIKOtwGCkRERFlBJ8PCAQAKdVbDiOlrKB1//D51EAb39REREQZj8E2IiIiyghaApBW2ooJQJQ1FIVBNiIioizCYBsRERFlBEUBFi0Cli8Hpk5lbIKIiIiIWiYG2yh7+P0cgkFElMX8fmDWLDWzbd06oLiYX/dERERE1PIw2EbZwe8HSkvrxxZVVfEMjIgoy1g1beRXPRERERG1NOxGStnB6gyMiIiyCps2EhEREVEmYGYbZQdWzSYiynqKAix6cTuWv3EQU6/uAkUpbu5dIiIiIiIyYbCNsgOrZhMRZT1/jR+zPixF7fm1WPehC8UlVVDc/L4nIiIiopaFw0gpO2hVs6uq1Fu/v7n3iIiIbOar9qE2WIugDKI2WAtfta+5d4mIiIiIyITBNsoOrNlGRJT1PEUeuJwuOIUTLqcLniJPc+8SEREREZEJh5FSdvB41IrZoZB6y5ptRERZR3ErqCqrgq/aB0+Rh0NIiYiIiKhFYrCNskcoBEip3hIRUVZS3AqDbERERETUonEYKWWHhQuBQEC9Hwio00RERERERERETYzBNsoOe/fGnyYiIiIiIiIiagIMtlF2mDkz/jQRERERERERURNgsI2yQ3Gx2hgBUG+Li5t3f4iIiIiIiIioVWKwjbJDZSUQDKr3g0F1moiIiIiIiIioiTHYRtlh377400RERERERERETYDBNsoOhYXxp4mIiIiIiIiImkCrDLYJIW4XQnwuhDgjhNgshLi8ufeJUlRWBrhcgBDqbVlZc+8REREREREREbVCOc29A01NCHE9gEcA3A7gnfDtG0KIQVLKL5p15yh5igL4fOp/j0edJiIiIiIiIiJqYq0xs+0XAJ6TUj4lpdwppfwpgK8B/KSZ94tSpSjA3LkMtBERZbMrr1SzmLX/I0fqH7/xRqBLF/U2GcbnFxXpt5ct/4uKzD/7yJHm5dq0SWxd48YBbjcwZ45+nYMGmZeP99oYX9+W+D83Fxg8GKioaPj3x//N+zoZXyMiIqImIqSUzb0PTUYI4QJwCsCPpJR/j5r/OICLpZTjrJ43fPhw+d577zXRXhIREZGlK68E3nzTPH/ECODdd9UgzrJl9fOnTQOWLk18/cbnt2sHnDqV/P62dL17A9XV6v2RI4FNm+xZ7+zZQHm5GmjbudN6GavXJtbr25ItWQJ4vfb+/she2mtE1AAhxGYp5fDm3g8iyg6tLdjWA8BXAMZJKddGzf8dgGlSygFR87wAvADQq1evkj179jT17hIREVG0du2A06fN83NygLo6NSPt0KH6+eecAxw8mPj6jc/PdkIAoZB6PzcXCATsWe+FFwK7dwNOZ/36jaxem1ivb0s2aRKwapW9vz+yl/YaETWAwTYislNrHEYKAMYIozDOk1JWSCmHSymHd+vWren2jIiIiKxdHqOf0bBh6u3VV+vnG6cbYly+XbvGPT/T9OpVf1/7HdphyhT1dsCA2MtYvTaxXt+WbOpU9dbO3x/ZS3uNiIiImlBrC7YdABAEUGiY3x3AN02/O0RERJSwVavULJVo2hBSQB2WOG2amjXV2CGkVs8/eVIdapmNooeQAurvcMQI83J5eYmta+xYoGfP+iGkALBjBzBwoHn5WK+N1evbEuXkqENko4cnxvr9UfPJyeEQUiIiajatahgpAAgh3gWwTUrpjZr3MYDlUsq5Vs9hzTYiIiIiIqLsxWGkRGSnnObegWbwRwAvCCE2AVgP4DYAPQA82ax7RUREREREREREGa/VBduklH8TQnQB8FsA5wH4D4BrpJTsgEBERERERERERClpdcE2AJBSPgHgiebeDyIiIiIiIiIiyi6trUECERERERERERFR2jDYRkREREREREREZBMG24iIiIiIiIiIiGzCYBsREREREREREZFNGGwjIiIiIiIiIiKyCYNtRERERERERERENmGwjYiIiIiIiIiIyCYMthEREREREREREdlESCmbex9aPCHEfgB7mns/WoiuAA40904QNTG+76k14vueWhu+56k14vu+Xm8pZbfm3gkiyg4MtlGjCCHek1IOb+79IGpKfN9Ta8T3PbU2fM9Ta8T3PRFRenAYKRERERERERERkU0YbCMiIiIiIiIiIrIJg23UWBXNvQNEzYDve2qN+L6n1obveWqN+L4nIkoD1mwjIiIiIiIiIiKyCTPbiIiIiIiIiIiIbMJgGxERERERERERkU0YbGuBhBBzhRD/FkIcE0LsF0K8JoS42LCMEELcJ4TYK4Q4LYTwCSEGG5b5jRBivRDipBDCNF5YCNFNCLEqvI6zQogaIcTjQoj8BPZxnBBisxDijBDiMyHEbYbHxwohXhVCfCWEkEKImxP82fOEEI8KIQ6E9/tVIUTPqMeHCCH+Et7X00KIXUKIXwsh+F7OcHzfx37fh5cpFUJsEEIcF0J8LYQoF0LkJLJ+apla+XveK4RYLYQ4En5eUYzlrhTi/2/v3oPtKss7jn9/QArlTrBp5JIBZbjLICA0yiWCVmjBUmCoKM5AR0AdWksFh6kdm+Ktwshl1AKl2owECkK1trTl1lBADW0JtbUtKdDCgJiQQCAYLuHi2z/edchis8/JOZyNZ++zv5+Zd1b2Wu96n7XOfnL2Pu9617uyOMmzTd1/HE/76l/DmvdJZja/55c25/RIkkuTbNuqs0HT7sNN7GVJFibZfn3tq38Na843+12R5H+bc1qZ5LtJ9uios02SK5OsbsqVSbYeT/uS1M/soOhP84A/Bd4JHA68BNyaZGarzqeATwK/A7wDWAHckmSLVp2NgW8DF48S52fAd4BjgF2BU4AjgCvGOrgkOwN/D/wAeDvwReArSY5vVdsc+E/gE8BzY7XX4WLgeOAk4BBgS+CGJBs22/cHVgIfBvYC/gj4DHDuBGKoP83DvO+a90n2aWLf3MT+APB+4E8mEEP9Zx7Dm/ObUvN5/hjxjwWuAa5s4s8FvjGBGOpP8xjOvN8O2L45t7cBJwOHAn/ZUW8RcCKwG/Vz4S3NeWhwzWM4cx7g7uY49gDeB4R67jNada4G9gOOAo5s/n3lBGJIUn8qpVj6vFA/4F4GjmleB1gGfLpV5xeBnwJndNn/hPpWjyvW7wLL1lPnS8D9Hev+HFg8Sv01wCnjiL0V8ALwoda6HalfHt43xn7nA0um+n2y9LaY9+vyHvgC8G8d+x1D/cK7xVS/V5belGHJ+Y59DgAKsFPH+g2Bh4HTpvp9sbyxZRjzvrXvrzW/67cco877m/8jm0z1e2XpTRnynN+nyefdmtd7NK/f1apzcLuOxWKxDGpxZNtg2II6CvHJ5vXOwGzqqAAASinPAXdQr5q9Lkm2A44Dbl9P1bnt2I2bgAM6rlRN1P7ADF59Xo8A9zL2eW3Jup+Npg/zft15bQw837Hfc8Amzf6aHoYl58djf2qn89ok9yRZnuTmJG9/g+Pq52+Y835LYC3wbLeNzcinDwH/XErp/AzQ4BrKnE+yGXAq9ULKQ63Ya6ij6kZ8H3iGSZy7JPUDO9sGwyXAD4HFzevZzfKxjnqPtbaNW+ocaM8Cj1Kvop26nl1mjxJ7I+BNE43f0e7LwONd2u56Xkn2ow5Pv3QScdWfzPt153UTcFCSDyfZqJm/5zPNtjdPIrb6y7Dk/Hi8pVl+ljqy89eBHwO3N39AavoYyrxv5qT6LHBFKeWljm1fSvIM8AQwBzi6V3HVF4Yq55N8PMkaaqfaUcARpZS1rdgrSymvzEHX/HsFr+PcJamf2NnW55JcSB1OfXwp5eWOzZ2To6bLuvE4izo/wrHUP3BemQsiyZpWuWw9sbut7yrJH3S0PWes6t3aTbIb8HfAxaWUvxpPXA0G8/6VtgtAKeVm4Gzgq9QRbvdR51eB2lGnAWfOv8bI95PPl1KuL6UsAU4HnqLO2alpYFjzvhnh87fUzpBPdWniAurcWb9K/R2/MEm61NOAGdKcv4qaz4dRv79cl2TTMWKPxH895y5JfcMn2fWxJBdRJ0J/dynl/1qbljfL2cAjrfWzeO2VqfUqpSxv2lya5AngziSfa25l27dV9elW/M6rTbOoE74+Mc6wlwHfar3+SdPuhtSraCs72r6jvXOS3YHbgGtKKT4cYRox71/V9it5X0q5sPnZvJl668lO1EmMHxxnbPWpIcz58VjWLP97ZEUp5aUk91NH+mjADWveJ9mcdRdLju52e2gp5XHqaOf7ktxL/TkcDNw5zvjqQ8Oa86WU1cBq4P4kd1G/wxxPfQjCcmBWkoyMbms6ln+J13HuktRP7GzrU0kuoX4gzyulLO3Y/CD1w+m9wL829TehPsXwnEmGHhlNsDFAKeWBLnUWU6+Wtb0XuLuU8uJ4gpRSVgGr2uuSLAFebNq6ulm3A3Xy1B+06u1JfVrXt0opZ40nngaDeT963jf7F5ovsElOon4pv2c8sdWfhjHnx2kJdS6r3YDvASTZAHgr9bZqDbBhzfvUJ0v+A3XUzpGllDUTPWYNpmHN+S7SlJF8Xkx9YMRc1n3nmQtsRsd3IEkaNHa29aEkX6PeJnMs8GSSkatPY768AAAGHklEQVRNa0opa0opJcnFwKeTLKUOyf5D6lwIV7famQPMpI6AIcnI1awHSilrkhwNbEv9o2YNsBf11oW7RvkwHnEZcGZzDJcD76LOm3ZSK/bmwC7Nyw2AOU38VaWUh7s1WkpZneTrwAVJVlCvpl0I/Adwa9PuXtSOttuAL7R+NiNX8jSgzPvR875p+xzgRuqT644DzgVO7HIbigbEsOZ8s99s6kiKXZtVe6bOYfVwKWVVKeXp5hanP07yY+pk2mcC2wALxzhm9blhzfumo+1m6kMRjgU2a24npdnvhSRzqbf/fY96y/RbqfO6PdSs0wAa4pzfhTqC7VbqyP0dqN9d1gI3AJRS7k1yI3B5ktOoHXGXAzeUUv5njGOWpP7Xy0ebWnpTqHMUdCvzW3UCzKfeavM89UlDe3e0s2CUduY1299DvaL0FPXJhvdRH/+9zTiO8TDqiJq11CtyH+3YPm+U2AvW0+4mwFeoHQ7PUuc02bG1ff5oP5+pft8skyvm/eh539RZ1Drmu4Cjpvo9s0yuDHnOj/a7/JRWnRnA+dQRH08D/wTsN9Xvm2VyZVjzfox92se8L/Vi4hOt2JcCO0z1+2Yx519Hzu9IHcm5AniBOhr/KmD3jnozqRdRnm7KQmDrqX7fLBaLZbIlpRQkSZIkSZIkTZ5PI5UkSZIkSZJ6xM42SZIkSZIkqUfsbJMkSZIkSZJ6xM42SZIkSZIkqUfsbJMkSZIkSZJ6xM42SZIkSZIkqUfsbJMkaYAk2SlJSbLgDYyxoImx0xsVQ5IkSZqu7GyTJEmSJEmSemSjqT4ASZI0IY8CewCrp/pAJEmSJL2WnW2SJA2QUsqLwNKpPg5JkiRJ3XkbqSRJA6TbnG3tOdaSnJHkR0meT/JYkj9LstUobb0nyZ1JnkmyKslfJ9l9PfEPSnJ9kuVJXkjySJLLk2zXUe+45pjuSjKjY9veSZ5N8pMksybx45AkSZL6jp1tkiRNH+c35d+Br1FvOT0N+E5nxSQnADcBBwDXAZcD2wKLgZ27NZ7kVOD7wFHAbcDFwN3AR4C7k8wZqVtK+XZzDAcBn2+1sSlwLbAxcHIpZcVkTliSJEnqN95GKknS9PErwNtKKQ8DJNkIWAS8O8mBpZR/adZvTu1c+xlwSCnl7pEGklwE/F5nw0l2bfZ5CDislPJoa9vhwC3AJcBvtnb7JPBO4Owki0opN1I74PYEziulLOrViUuSJEn9wpFtkiRNH+eNdLQBlFJeAv6ieXlgq95vADOBq9sdbY35dH/4wseAGcAn2h1tTZxFwN8AxyTZorV+LfBbwDPAN5OcDZwC3AGcN9GTkyRJkgaBI9skSZo+OjvOAB5pltu01u3XLG/vrFxKWZ3kh8BhHZvmNsvDkryjS5xZwIbArsCSVnv3JzkDuAq4AHgc+GAp5eX1nIskSZI0kOxskyRp+niqy7qXmuWGrXUjD0x4bJR2lndZt22zPGc9x7B5l3W3AE8DWwLXdY6MkyRJkqYTbyOVJGn4jNwm+sujbJ89xj5blVIyRnnVaLkkAb5J7Wh7HDg9yaG9OAlJkiSpH9nZJknS8LmnWXbeKkqSrYB9u+xzV7M8ZIKxzgGOpN5GejjwInB1kjdNsB1JkiRpINjZJknS8Pku8CTwwSQHdGybz7rbTNu+Su0ou6h5MumrJPmFJId0rDsI+BzwAPCxUsqPgLOA7YEFzag3SZIkaVpxzjZJkoZMKWVNktOBa4E7k1wLLAMOBvamPi300I59lib5beAbwH8luRG4j/qE0jnUEW8rgd0BkmwNXAMU4AOllJ827VyW5AjgBOD3gS+/wacrSZIk/Vw5sk2SpCFUSrmeenvnEuBE4KPAKupTRx8cZZ+FwP7UW0L3Ac4ETgZ2Aa4HPt6q/nVgJ+DcUsqSV7fER5oYX0xyYG/OSJIkSeoPKaVM9TFIkiRJkiRJ04Ij2yRJkiRJkqQesbNNkiRJkiRJ6hE72yRJkiRJkqQesbNNkiRJkiRJ6hE72yRJkiRJkqQesbNNkiRJkiRJ6hE72yRJkiRJkqQesbNNkiRJkiRJ6hE72yRJkiRJkqQesbNNkiRJkiRJ6pH/B05Hyj+20Fw/AAAAAElFTkSuQmCC\n", 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xR329AqtXm5CS8svNStQFBgbWnjx5shoAgoKCaqKiotq1rnmcp6dnXdNrFixY0H3gwIHlaWlpv5rb7r333jI/P79+y5Yt83jnnXcu7cw7f/68MiUl5Xh4eHi1ue3zzz+327Vrl27t2rVZs2fPLgaAuLi4cicnJ8PMmTN99+/frw0PD69+4403nH/77TdNamrqCXMMEyZMuBAQEBCYn5+vup7PhX5/OsV2VyIiIiIiIiLqxNLS7FFfr4DJBBgMCqSldboTU9vr6NGjmpycHM0DDzxQXF9fD/PL3t7eFBISUvndd9/ZNR7v6elZ1zhBBwC7du1yUKlUcurUqaWN54iNjS0DgD179tgDwHfffWfn4eFxWZLQysoKsbGxJTfjXunWwko6IiIiIiIiImpdVFQ5Vq82wWBQQKk0ISrqlj0tNS8vTwkAc+bM0c+ZM0fftL9r1651jd+7ubnVNx1TWFiorK+vFw4ODiHNrVFcXKwEgPz8fJWzs7Ohab+7u/sVbURM0hERERERERFR66KjK5GS8oslnknX0VxdXY0AsGjRorOjRo0qa9qv0WguO7lVCHHFSa5OTk4GjUYjv/zyyxPNreHt7V0PAO7u7vUnT560btqfn5/PfAxdgX8URERERERERNS26OjKWy05p1KpZHV19WWP+goODq7x9PSsO378uHbZsmXnrmXee++9t2zDhg0epaWlVrGxsS1WFQ4ePLhi27ZtzmlpabbmLa9GoxGfffaZ07WsS79vTNIRERERERER0e+Sn59fTWpqqsP27dvLnJ2dDd7e3vV6vb5+1apV2Q899JDf6NGjxYQJE0pcXV0NeXl5qv3799t5e3vXvfjii/mtzTtmzJjyMWPGlEyZMsXvscceyx88eHClQqHA6dOn1Z9//rnDypUrf+vfv3/trFmzitesWeMxceJEv+eee+6su7u7YePGja4VFRVWN+szoFsHD44gIiIiIiIiot+l119//YyNjY3pwQcf7BkREdEnKSnJFQAeeOCBC59//nlGVVWV4oknntCPGzfOf/Hixd0KCgpUd955Z0V75v70008z58+fn7tjxw7dpEmTek6dOrVHcnKym5+fX62Xl5cBAKytreUXX3zxS58+faoWLlzoPX36dL2Pj0/tU089lXsj75tuTULKK7ZWUxMDBgyQhw4dsnQYREREREREdAMIIX6QUg6wdBwd6ciRI1nBwcFFlo6DiC535MgRl+DgYH1zfaykIyIiIiIiIiIisjAm6YiIiIiIiIiIiCyMSToiIiIiIiIiIiILY5KOiIiIiIiIiIjIwpikIyIiIiIiIiIisjAm6YiIiIiIiIiIiCyMSToiIiIiIiIiIiILY5KOiIiIiIiIiIjIwpikIyIiIiIiIiIisjAm6YiIiIiIiIiIiCyMSToiIiIiIiIi6vTWrFnj7OPjE6RSqULt7e1vAwAvL69+8fHxevOYpKQkZyFEWEZGhtrc1nTM9Ro0aFDAoEGDAtozdu/evTZarTYkMzNTdb3xZGRkqOfOnet5/PhxddujLSslJcVeCBGWkpJif7XXzp0713PHjh1XXBcfH6/38vLq1zERXunMmTMqrVYb8tVXX9ncqDXaorTUwkRERERERERE7ZGVlaWaN2+ePiYmpjg5OblIq9WaAODDDz885ejoaLJ0fC2ZP39+twkTJhT7+vrWm9uuNeaTJ09qVq9e3fXuu+8u79u3b13HRtp5rF69uqvBYEBMTEx54/YlS5bknT9/Pv9Grevj41P/4IMPFi1YsKD7999/n3Gj1mkNk3RERERERERE1KkdO3ZMYzQakZCQUDxy5MgKc/sdd9xRbcm4WrNv3z6bgwcP2q9bty67cXtnitlgMEBKCZVK1fZgCwsMDKy90WvMnj27cMCAAYFfffWVzdChQ6tu9HpNcbsrEREREREREXVa8fHx+jFjxgQAQGxsrL8QIsy8XfRat46eOHFCHRMT46vT6YLVanVo7969+27atMmx6bjk5GSdr69voFqtDu3Zs2dgc2NasmHDBhd/f//qAQMG1DRub2mLblpamm1MTIyvnZ1diJubW/+EhITuVVVVAri4fXTs2LH+AHDffff5CyHCmm4nXblypUtAQEBfjUYTqtPpgu+//36f/Px8q8ZrCyHCHn/8ca9nnnnGw8vLq59Gown7z3/+ozVvT3333Xcd4+Pj9V26dLnNzs4uJCYmxvfcuXOXzVFSUqKYOnWqt5ubW3+1Wh2q1+uDXnrpJTeTqfXiwE8++aRLRERET1dX1/5arTakV69egYsXL3Y3GAyXxQcAa9eu7Wq+x7lz53oCzW93PXPmjOq+++7Tm79Hf3//vuvXr3dqPKY9n69ZWFhYTa9evaqTk5NdW72ZG4SVdERERERERETUptRU2KalwT4qCuXR0ai8WesuWbIkLzQ0tOq5557rnpiYmD1w4MAqDw8PQ9tXNu/UqVOq8PDwPs7OzoZXXnklx93d3fDBBx84JSQk+FlZWZ166KGHLgDAp59+aj99+vQekZGRFxITE38rKChQLly4sLvBYBC+vr5tVnWlp6c7REVFXWhvXA8//LDvfffdV/Lwww+f+vbbb+1WrVrlqdPpjKtXr84NDw+vTExMzF60aJH3yy+/nDN48OBKAAgJCakGgJkzZ3olJye7T5s2rSAxMfG3nJwc1SuvvOI1fPhw7eHDh08olf9L/2zdutW5e/futa+88kqOnZ2dydvbu760tFQJAE8//bT3nXfeWfbOO++czsjIsF62bJlXbGys6uDBg78AgNFoxPDhw3sdP37cZsGCBbnBwcHVO3fudHjxxRe7FxYWqtatW3e2lc9dExkZWT5r1qwCrVYr//Of/9i89tprnoWFhcr169efBYDU1NQT0dHRvePj44tnzJhRCAB6vb7Zrb1lZWWKiIiIgAsXLlg9++yzZ729veu2bNniPGvWLN+qqirFvHnzitr7+TYeN3jw4Irdu3c7tPd760hM0hERERERERFRq1JTYTtmDPzr66FYvRqmlBT8crMSdYGBgbUnT56sBoCgoKCaqKio61r3mWee8ZRSYu/evSc8PDyMABAfH18WHh6uWrp0qZc5SbdkyRIvX1/fmt27d5+ysrpYTNawfu+2knQ5OTnK3NxcdXBwcLu3TI4bN67EnDCKi4srP3TokO327dudVq9enevk5GQKCgqqAYDAwMDqxp9BRkaGeuPGjR5z5szJfe211/LM7X369KkZOXJk73/961+OU6ZMOd94ra+//voXOzs7aX5/5MgRAECvXr2qt23bltXQXObk5GSYOXOm72effWYfGxtb/uGHHzocPnzY7vXXX8964oknihviLquqqlIkJye7P/vss/ldu3ZtNoG6YMGCQvPvJpMJo0aNKq+rqxMbNmzwWLt27VkrKyuY78vT07Oure953bp1zmfOnNHs3LnzlzFjxpQDwP33318WHh6uWrZsmdeTTz5Z1Dg52drn23jekJCQqs2bN7tmZWWp9Hp9PW4ibnclIiIiIiIiolalpcG+vh4KkwkwGKBIS8NVn9rZWaSnpzsMHTr0grOzs7G+vh7mV3R0dFlGRoa2pKREYTAYcPToUZuxY8eWmhN0ADBs2LBKT0/PNg9tyM7OVgGAm5tbuyv+YmJiLkuk9e3btzovL6/Nk1xTUlK6mEwmTJs2raTx/QwdOrTSzs7O+PXXX9s1Hh8REVHWOEHXWHx8fEnj99OmTStVKBT49ttv7QDg66+/tlcoFPjLX/5y2bgpU6aU1NfXi6+++sq2pTjPnDmjmjRpko+np2c/tVodqlarw1asWOFVXl5udfbs2asuItu3b5+9m5tbvTlBZ/bggw8Wl5aWKg8fPqxt3N7ez9fNza0e+N93eDOxko6IiIiIiIiIWhUVhfLVq2EyGKBQKmGKikJ521d1TiUlJcrt27c7q9Vq5+b6CwoKlJWVlSaDwSDc3d2vqKRycXFps7qqurpaAQAajabdp7i6uroaG7/XaDSyrq5OtDS+cbwAEBQUFNRcf0lJyWW5Hw8Pjxbjb7qN2NraWnbp0sVw9uxZFQCUlpZadenSxaDVai9L8nl5edUDQHFxcbN5JqPRiNGjR/csKChQLVy4MDcwMLDGxsbGtG3bNse1a9d2NX9eV+P8+fNKV1fXK+7F09OzHgAKCwsve5Zeez9fGxsbCQBVVVU3vbCNSToiIiIiIiIialV0NCpTUvCLJZ5J19EcHR2NAwcOLF+0aNG55vp9fHzqVSqVVCqVMj8//4pqqqKiIpWXl1er1XTmCrqmCbIbwdnZ2QgAn3zyyUlnZ+crKveaVvMJIZqtogOAc+fOXRZvTU2NKCsrU5qTcDqdzlhWVqasqakR1tbWl+YxJ/FcXFyarRw8fvy45tixYzZvvPFG5syZMy9V4W3fvr3dB3E05ejoaDh9+rR10/bc3NyrrmJsrKioyOp6rr8enWK7qxDibiHEDiHEWSGEFEIkNOpTCSH+JoT4rxCiUgiRJ4R4Xwjh3WQOjRBirRCiqGHcDiFEtyZjvIUQOxv6i4QQSUKINktHiYiIiIiIiP7ooqNRmZiIc7dygg4AIiIiLvz88882oaGh1XfffXdV05dWq5VKpRL9+vWr2rlzp85o/F8B1p49e2xzc3PbzCP4+/vXaTQaefr0aU1HxW1tbW0CrqzwGj16dJlCoUBWVpa6ufvp3bt3m9tzzT7++OPLTkb9xz/+oTOZTLjjjjsqACAyMrLcZDLhH//4h67xuC1btjipVCoZGRnZ7N9GRUWFAgBUKtWlxF5tba1oup55THsq6+66667y/Px81ZdffnnZFtutW7c6OTk5GUJCQmpaurY1mZmZGpVKJXv37t3m4SAdrbNU0tkB+AnApoZXYzYAQgG8AuD/ADgAWAngcyFEfymlObO5BkAsgIkAigGsApAihAiTUhqFEFYAdjX03QXAGcA/AQgAj9/AeyMiIiIiIiKiTmL58uW5Q4YM6TN48ODejz32WEGPHj1qS0pKlEePHtVmZmZqPvrooywAeOGFF86OGzfOf/jw4T0fffTRwoKCAuXy5cs927Pd1draWvbv37/y0KFDLT6j7WoFBQXVWFlZyX/84x8uLi4uBmtra9mvX7+awMDA2hkzZpxbtGiRd0ZGhnVkZGS5Vqs1nTlzRp2amtrlL3/5S9HYsWPbtT355MmT2vHjx+snTpxYcuLECevExESvgQMHVsTGxpYDwIQJEy68+uqrFfPmzfMpLCxU9uvXryYlJcVh69atLrNmzTrX0qERISEhNZ6ennVLly71UiqVUKlUMikpyb25sX5+fjWpqakO27dvL3N2djZ4e3vXN3eAw6xZs4o3btzoPnHixJ6NT3fdv39/l1dfffVM40Mjrsb3339v269fv0rzttebqVNU0kkp/5+U8hkp5TYApiZ9F6SUw6WUW6WUGVLK/wB4DECfhheEEA4A/gxgvpRyt5TyMIApAPoDiG6YagSAQABTpJSHpZS7ASwA8BchRJebcZ9EREREREREZFm9evWqO3jw4PHAwMCqpUuXesXFxfnPnTvXe9++fXZDhw4tM4+Li4sr37BhQ+bp06etp06d6peUlOSxfPnynLZOdjWLj48v+e677+zLyso6JPfi4eFhTExMzP75559t7r333t4RERF9vv32W1sAWLdu3dmVK1dmHThwwG7atGk9Jk6c2HPNmjUejo6Oxr59+7a7ouxvf/tbtpQSCQkJPZYtW+Y1dOjQ85999tmv5n4rKyvs3r37ZHx8fPHatWs97r///p5paWkOL774Yk5SUtLZlua1traWH3300SlXV9f6mTNn6p966inv8PDw8ieffPKKLcevv/76GRsbG9ODDz7YMyIiok9SUpJrc3N26dLF9PXXX2fcddddZUuXLvWaNGlSz+PHj2vfeOONzHnz5hW1954bq6ioEAcOHOjS9ACNm0VIedMTg60SQlQAmC2lfLeVMYMBHADQXUr5mxBiGIA0AG5SysJG444B2CalXCyEWAIgXkoZ2KjfFUABgGFSyq9aWm/AgAHy0KFD13trRERERERE1AkJIX6QUg6wdBwd6ciRI1nBwcHXlKigjlFSUqLw9vYOXrFixZnGz2HrjFJSUuxy/8HRAAAgAElEQVTHjh3rv3379l/i4uJu2UNBrtdbb72le/LJJ/Vnzpz5r4uLi7HtK67ekSNHXIKDg/XN9XWKSrqr0fAMuZUAdkopf2to9gBgBND0H6D8hj7zmPwm/UUN13mAiIiIiIiIiKiDODk5mWbNmpW3Zs0aD5Op3Ye8kgWtXr3aY8aMGeduVIKuLZ3lmXTtIoRQAtgCwBFATHsuAdC4VLClssEr2oUQjwJ4FAC8vb2vuICIiIiIiIiIqDXPP/98vtFoFNnZ2armnqtGnUd2drbynnvuOb948eKmBV43zS2TpGtI0P0LQD8AkVLK4kbd5wBYAXABUNio3Q3AN43G3NFkWpeG6674AqSUyQCSgYvbXTvgFoiIiIiIiIjoD8TOzk6+9tpreZaOoy1jxowpl1L+YOk4LMnb29uwcuVKi35Xt8R2VyGECsBWXDwIYqiUsumDBX8AUA9geKNruuHiwRL7G5oOAOjT0G42HEBtw/VEREREREREREQW0Skq6YQQdgB6NrxVAPAWQtwGoARALoCPAAwEMBaAFEKYnyF3QUpZLaW8IIR4G8CrQogCAMUAVgH4L4DUhrFfAjgGYJMQ4ikAzgBeBfCWlPLS6S1EREREREREREQ3W2eppBsA4MeGlxbASw2/LwHQDUAsAE9crHjLa/R6oNEccwB8gosVd98CqAAwVkppBICGn6MBVDX0b20YP+/G3hoREREREREREVHrOkUlnZQyHRcPeWhJa33mOWoAPN7wamlMNoAxVxsfERERERERERHRjdRZKumIiIiIiIiIiIj+sJikIyIiIiIiIiIisjAm6YiIiIiIiIiIiCyMSToiIiIiIiIi6vTWrFnj7OPjE6RSqULt7e1vAwAvL69+8fHxevOYpKQkZyFEWEZGhtrc1nTM9Ro0aFDAoEGDAtozdu/evTZarTYkMzNTZW6bO3eu544dO+w7Kp6WdPR9N3Y1n0FTzX1HHemll15y8/f372s0Gm/E9DdUpzg4goiIiIiIiIioJVlZWap58+bpY2JiipOTk4u0Wq0JAD788MNTjo6OJkvH15L58+d3mzBhQrGvr2+9uW316tVdDQYDYmJiym/k2p39s7lRnnrqqcK1a9d2XbdunfNf//rXYkvHczWYpCMiIiIiIiKiTu3YsWMao9GIhISE4pEjR1aY2++4445qS8bVmn379tkcPHjQft26ddmWWL8zfzY3kp2dnRw/fnzx2rVrPW61JB23uxIRERERERFRpxUfH68fM2ZMAADExsb6CyHCzNs4r3VL54kTJ9QxMTG+Op0uWK1Wh/bu3bvvpk2bHJuOS05O1vn6+gaq1erQnj17BjY3piUbNmxw8ff3rx4wYECNuU0IEQYAa9eu7SqECBNChM2dO9fzhRdecFer1aG5ubmXFVOZTCZ069at39ixY30BICMjQy2ECFu+fLnrI4880s3JySlYq9WGDB06tGfT7aPNfTYnTpxQx8XF+bq4uASr1erQbt269Zs2bVp3c//XX39tM2rUqB7u7u79ra2tQ/V6fdDs2bO9KioqRHvvu7Hjx4+rIyMje2q12hCdThc8bdq07rW1tVfMlZycrBs8eLC/TqcLtrGxCenTp0/ftWvXOjce4+/v33f48OF+Ta9NSUmxF0KEffzxx13MbZMnTy759ddfrXfv3m17LXFbCivpiIiIiIiIiKhNpaWptqWlafY6XVS5ThddebPWXbJkSV5oaGjVc8891z0xMTF74MCBVR4eHoZrne/UqVOq8PDwPs7OzoZXXnklx93d3fDBBx84JSQk+FlZWZ166KGHLgDAp59+aj99+vQekZGRFxITE38rKChQLly4sLvBYBC+vr61ba2Tnp7uEBUVdaFxW2pq6ono6Oje8fHxxTNmzCgEAL1eX2dnZ2dasWKF1/r1651ffvnlfPP47du3dzl79qz6zTffLGo8z5o1a7r27du3av369Vn5+fnKl19+2WvkyJH+GRkZxzQajWwunhMnTqiHDBnSR6vVmhYuXHg2ICCg9syZM+rdu3dfSm5lZmaq+/fvX/2nP/2puEuXLsajR49qX3vtNc+srCxNSkrK6av5nGtqasTIkSP9a2trFcuXL892d3c3JCcnu/773//WNR17+vRpTVxcXKm/v/85hUIh09PT7efMmeNTXV2tWLBgQSEAPPzww4XPPfdc96ysLJVer7+0fXjjxo0uXl5edffdd1+ZuW3IkCFVdnZ2xl27djkMHz78pv2tXi8m6YiIiIiIiIioVaWlqbb//e8YfynrFTk5q039+6f8crMSdYGBgbUnT56sBoCgoKCaqKio61r3mWee8ZRSYu/evSc8PDyMABAfH18WHh6uWrp0qZc5SbdkyRIvX1/fmt27d5+ysrJCo/V7t5Wky8nJUebm5qqDg4OrGrebY/f09Kxreh+jR48u2bRpk+uSJUvyFYqLGx83btzoqtfra8aMGXPZ8+tsbW2NjePq06dPzciRI3uvX7/eec6cOZcl9MwWLVrkWVtbq/jxxx+PN05yPf7445e2hCYkJJwHcB64WMU3YsSIii5duhhnz57te+7cOSvz59Ueb7zxhvNvv/2mSU1NPWG+1wkTJlwICAgIzM/PVzUeu3z58nPm341GI0aPHl1+7tw51dtvv+1qTtI99thjxS+//HK3N954w+XVV1/NA4C8vDzlF198oZs3b16u+TMDACsrK/j7+1d///33t1QlHbe7EhEREREREVGrSkvT7KWsVwAmSGlQlJam3fDTSW+U9PR0h6FDh15wdnY21tfXw/yKjo4uy8jI0JaUlCgMBgOOHj1qM3bs2FJzIgwAhg0bVunp6VnX1hrZ2dkqAHBzc2t3xd/s2bMLcnJyNOaTX8+cOaPas2ePQ0JCQmHTsU3jGjFiRKW7u3v9d99912JSau/evQ7Dhg270DhB11RJSYlixowZXt27dw/SaDSharU6bNasWb5SShw7dsy6vfcCAN99952dh4fHZclIKysrxMbGljQde/ToUc3YsWN93dzc+qvV6jC1Wh22detWl6ysrEtr6nQ6U1xcXPF7773nYj65df369c5SSsyYMeOKxKSzs7OhoKDghpwge6Owko6IiIiIiIiIWqXTRZXn5Kw2SWlQCKE06XRRN/Rk0huppKREuX37dme1Wu3cXH9BQYGysrLSZDAYhLu7+xUJLRcXlxaTXGbV1dUKANBoNO0+XXXo0KFVgYGBVW+++aZrXFxc+bp161yUSiWmT59+xeEHLcWVl5fXYlLq/PnzVm0lGCdOnOi7f/9++wULFuSGhoZW2dvbm/bv32+7aNEib/M9tVd+fr7K2dn5iiSlu7v7ZW0XLlxQjBo1yt/a2tq0ePHi3/z9/Ws1Go1ct26d60cffeTSeOxf//rXgvfee8/1ww8/dHjggQcubNq0yXXEiBHnu3fvfsU61tbWppqammt6lp6lMElHRERERERERK3S6aIr+/dP+cUSz6TraI6OjsaBAweWL1q06Fxz/T4+PvUqlUoqlUrZdFsmABQVFam8vLxaTXaZK+hKSkquKu/yyCOPFMybN88nMzNT9d5777ncc889Je7u7ldsMW0prsDAwKqm7WY6nc6Ql5d3xXVmVVVVIi0tzXHu3Lm5zz//fIG5/ccff9RezT2Yubu71588efKK6rv8/PzLPpM9e/bY5ebmqj///POMxif3rlmz5ooE28CBA2vCwsIq3nrrLVetVmvKzs7WrF279kxz658/f16p0+mu+dmFlsDtrkRERERERETUJp0uurJHj8Rzt3KCDgAiIiIu/PzzzzahoaHVd999d1XTl1arlUqlEv369avauXOnzry1EgD27Nljm5ub2+YWSn9//zqNRiNPnz6tadqnUqlkS1VpjzzySImtra3p/vvv75GXl6eeOXPmFVtdAaBpXF9++aVtfn6+avDgwS1+N3fddVfZnj17HM+cOdNsoq66ulphNBqhUqkuO3hiy5YtLs2Nb8vgwYMrzp07p05LS7u0BddoNOKzzz5zajyusrJSAeCydQsLC612797d7Em6jz76aME333zjsHTpUk8fH5/amJiYZqs6c3Jy1H5+fjXN9XVWTNIRERERERER0R/G8uXLcysqKqwGDx7ce+3atc67du2y27x5s+OCBQu6TpgwQW8e98ILL5zNzMy0Hj58eM8PPvjAISkpyXny5Mk92rPd1draWvbv37/y0KFDVzwjzs/PryY1NdVh+/btXb755hubrKysS0kzOzs7OWHChKJDhw7Z9erVq7qlk0krKyutGsf10EMP+fn4+NTOnDnziq2xZomJiblqtdoUHh7ee+XKlS47d+60X79+vVNsbKwvADg7OxuDg4MrN2zY4L5u3TrnrVu3OowaNapHc1V77TFr1qzibt261U6cONEvKSnJeevWrQ7Dhw/vWVFRYdV43LBhwyrs7OyMTzzxhPcHH3zg8Pe//113xx13BLRUBfenP/3pvKOjo+Hw4cN2zT2vDwCKioqszpw5Y33XXXdVNNffWTFJR0RERERERER/GL169ao7ePDg8cDAwKqlS5d6xcXF+c+dO9d73759dkOHDi0zj4uLiyvfsGFD5unTp62nTp3ql5SU5LF8+fKctk52NYuPjy/57rvv7MvKyi7Lvbz++utnbGxsTA8++GDPiIiIPklJSa6N+x988MFSAJg2bVqzCSgAePLJJ/N69OhRM2PGDP3ChQu9AwMDqz7//PNfNBqNbOmagICAun379p0IDQ2tePnll73i4+N7LVu2zMvFxeVSMmzr1q2ng4KCqhYuXOg9Y8YMvZubm2HFihU57bnfpqytreUXX3zxS58+faoWLlzoPX36dL2Pj0/tU089ldt4nKenp+G999771Wg0ioSEBL+XXnrJa+rUqUXjx4+/4oAJANBoNHLEiBHn1Wq1nD59erMn2X700UcOKpVKTpo0qfRaYrcUIWWL3x81GDBggDx06JClwyAiIiIiIqIbQAjxg5RygKXj6EhHjhzJCg4ObjaBQTdHSUmJwtvbO3jFihVnZs6c2WzCqTmPP/6419tvv+3222+/HXFycrrs4ImMjAx17969+61cufLM3Llz/5Dfb319PfR6fb+BAwdWfPrpp5nNjbn77rt7OTk5GVrqt6QjR464BAcH65vr48ERREREREREREQdzMnJyTRr1qy8NWvWeEyfPr1EoWh9M+O3336rPXbsmPXbb7/tNmnSpMKmCbo/upKSEsUPP/yg3bx5s/O5c+fUCxYsaPbgj/3792sPHjxo/8MPPxy72TFeLybpiIiIiIiIiIhugOeffz7faDSK7OxslV6vb/VZdvfff3/P4uJi1Z133nnh1VdfzW1t7B/R/v37bceOHevv5ORkWLp0aXZ4eHh1c+Nyc3NVSUlJWUFBQe3altyZcLtrO3C7KxERERER0e8Xt7sS0c3S2nZXHhxBRERERERERERkYUzSERERERERERERWViHJ+mEEF2EEN4dPS8REREREREREdHvVbuSdEIIPyHEZ0KIC0KIYiHEFiGEbwvD5wDodEfcEhERERERERERdVZtJumEEG4A9gEYC8AegA7AJAA/CiFG39jwiIiIiIiIiIiIfv/aU0m3CIA7gI0AvAC4NrSpAHwihIi7ceERERERERERERH9/rUnSXcPgCNSyhlSyjwpZbGU8m8AIgGUAvhACDHmRgZJRERERERERET0e9aeJJ0PgD1NG6WU3wO4G0AxgI+EEPd0cGxERLe0AzkHkLg3EQdyDlg6FCIiIiIiIurk2pOkqwZgbK5DSvkL/ldR97EQIrrjQiMiunUdyDmAqE1ReP6r5xG1KYqJOiIiIiKi67RmzRpnHx+fIJVKFWpvb38bAHh5efWLj4/Xm8ckJSU5CyHCMjIy1Oa2pmOu16BBgwIGDRoU0J6xe/futdFqtSGZmZmqjojnxx9/tB48eLC/nZ1diBAibPPmzY5z5871FEKEXct8HS0pKcl5zZo1zpaOoy0ZGRlqIURYUlLSVcfa9Pv/9ttvtVqtNuTkyZPq1q5rD2U7xpwBENxSp5TypBAiCkA6gE8B7L/eoIiIbnXpWemoM9bBKI2oM9YhPSsdQ7oPsXRYRERERES3pKysLNW8efP0MTExxcnJyUVardYEAB9++OEpR0dHk6Xja8n8+fO7TZgwodjX17fe3HY9MT/55JPdcnJyNO++++6vOp3O2L9//5off/zRpuMivj5btmxxMRqNePLJJ4stHcvNcscdd1SHh4eXLVy40POTTz7Jup652lNJtw/A3UIIh5YGSCl/BhANoAZA1NUGIYS4WwixQwhxVgghhRAJTfqFEOJFIUSuEKJaCJEuhAhsMkYnhNgshLjQ8NoshHBsMqafEOLrhjnOCiFeEEKIq42XiKgtkfpIqK3UsBJWUFupEamPtHRIRERERES3rGPHjmmMRiMSEhKKR44cWXH33XdXARcTJIGBgbWWjq85+/btszl48KD9E088UdC4/XpiPnXqlHbQoEHl48ePL4uKiqp0dXVtducj3VyPPvpo0Y4dO5yysrJUbY9uWXuSdLsAaADMbG2QlPIoLibqzl9DHHYAfgLwV1zcXtvUAgBPAXgcwEAABQB2CyHsG415H0AoLh50Marh983mTiFEFwC7AeQ3zPEEgPkA5l5DvERErRrSfQjSpqZh6dClSJuaxio6IiIiIrrlpZaW2i46fdojtbTU9mauGx8frx8zZkwAAMTGxvoLIcLM20WvdevoiRMn1DExMb46nS5YrVaH9u7du++mTZscm45LTk7W+fr6BqrV6tCePXsGNjemJRs2bHDx9/evHjBgQE3j9pa26KalpdnGxMT42tnZhbi5ufVPSEjoXlVVJQAgJSXFXggRlpubq/7000+dhRBhLW1xbWkrp3mOlJSUxrkU/POf/3QMDg7urdVqQ+zt7W+75557ejTduunl5dUvNjbWNzk5WdejR49ArVYbEhQU1OeLL76wM48ZNGhQwPfff293+PBhO3N8rW0LNsezefNmx0mTJvk4ODjc1qVLl9v+/Oc/dzcYDPj6669twsLCArRabUjPnj0DP/744y5N51i/fr1TQEBAX41GE6rT6YLj4uJ8z5w5c1mirLy8XDF58mRvR0fH22xsbEKGDRvWMysrq9mtqbt27bIbMmSIv62tbYhWqw258847e33//ffWLd2D2bhx4y7Y2dmZ3nzzzeva6ttmkk5K+TkALYBX2zH2/wD4AehxNUFIKf+flPIZKeU2AJeVfDZUuj0JYLmU8mMp5U8A/gTAHsCkhjF9cDEx96iUcr+U8gCAxwCMEUKY/yAeAmAD4E9Syp+klB8D+BuAuaymI6IbYUj3IVh01yIm6IiIiIjolpdaWmo75r//9V+Rne015r//9b+ZibolS5bkvfzyyzkAkJiYmJ2amnpiyZIledc636lTp1Th4eF9fv75Z5tXXnkl51//+tepfv36VSUkJPi99957l3YRfvrpp/bTp0/v4evrW7tp06Zfn3jiiXMLFy7snpmZqWnPOunp6Q633357RXvjevjhh3179OhRu2XLllN/+tOfCjdv3uz27LPPdgWA8PDwytTU1BM6nc4QERFxITU19URqauqJq7/7y61YscI1ISHBz9/fv+bdd989vWrVqjMZGRnayMjIgNLS0styRt9//71dUlKSx/PPP5/7zjvvnDYajWL8+PE9i4qKrABgw4YNZ/r06VPl7+9fbY5vw4YNZ9qK4emnn+5uY2NjfPfdd09Pmzat4J133nH785//3H3atGm+U6ZMKdqyZcuvDg4OhsmTJ/vl5eVdemzba6+95jJr1izfXr161WzatOnX559//uw333zTJSIiIuDChQuXYp8yZYrP1q1bXR577LFzW7Zs+bVXr141CQkJvk3j+OCDDxxiY2MDbGxsjBs3bsx86623MisrK62ioqJ6nzp1qtUKOZVKhZCQkIrU1NQWd6G2R3ueSQcpZbvLMKWU53Ft1XQt8QXgAeDLRmtUCyG+ARAOYCOAIQAqcPnz8L4FUNkwJqNhzF4pZeNKvS8ALAWgB5DZgTETEQG4eIBEelY6IvWRTNYRERER0S0rrbTUvl5KhQmAQUpFWmmpfbROV3kz1g4MDKw9efJkNQAEBQXVREVFXde6zzzzjKeUEnv37j3h4eFhBID4+Piy8PBw1dKlS70eeuihCwCwZMkSL19f35rdu3efsrKyQqP1e/v6+raaJ8nJyVHm5uaqg4ODq9ob17hx40pWr16dCwBxcXHlhw4dst2+fbvT6tWrc52cnExRUVGVKpVKOjs7G673MwCACxcuKJYuXeo1fvz44o8++ijL3H733XdXBgUFBa1du9blhRdeuLRVt6KiwurIkSPHzVtsvby86iMiIvps27bNYfr06SVhYWE1dnZ2JqPRiKuJLzw8vPzvf//7bwBw3333le3evdth06ZNbp9//nnGyJEjKwCgW7du9YMHD+67bds2h8cff7zYYDAgMTHRa9CgQeUpKSmnzXMFBgbWjBo1KmDt2rUuzz33XMGRI0c0O3fudFq4cOHZZcuWnQOAcePGlVVUVCjef/9918ZxLFiwoPvAgQPL09LSfjW33XvvvWV+fn79li1b5vHOO+/ktHYf/fv3r3rzzTc9jEYjzH8vV6s9212bJYSwFUKECCHuutY52smj4Wd+k/b8Rn0eAAqllNLc2fB7QZMxzc3ReI1LhBCPCiEOCSEOFRYWXkf4RPRHxRNeiYg6wIEDQGLixZ9ERGQxUTpduUoIkxUApRCmKJ2u3NIxXav09HSHoUOHXnB2djbW19fD/IqOji7LyMjQlpSUKAwGA44ePWozduzY0sYJl2HDhlV6enrWtbVGdna2CgDc3NwM7Y0rJibmsoKnvn37Vufl5V33iaEt2bNnj11FRYXV5MmTixt/Dj169Kjz9fWt2bdv32XbYkNCQioaPwNv4MCB1QCQnZ19XTHec889Fxq/9/Pzq9FqtSZzgg4AgoODawAgJydHDQBHjhyxLikpUT7wwAMlja8dOXJkhaenZ93evXvtAWDv3r12JpMJkydPvmzcpEmTLnt/9OhRTU5OjuaBBx647LOwt7c3hYSEVH733Xd2aIOrq6uhrq5OFBQUtKsgrjlXfaEQohuA1wGMBWAFQJrnEULcCSAZwEwpZfq1BtUC2eS9aNLWtL89Y0QL7ZBSJuPivWDAgAHNzU1E1KpNRzahxlADCckTXomIrsWBA0BUFFBXB6jVQFoaMIT/jhIRWUK0TleZ0r//L2mlpfZROl35zaqiuxFKSkqU27dvd1ar1c0+P6ygoEBZWVlpMhgMwt3dvb5pv4uLyxVtTVVXVysAQKPRtPsU16aHQGg0GllXV3fDHs917tw5JQDExcX5N9fv4OBwWTyOjo6XvddqtRIAampqrrkADACcnJwuS2Sq1Wppb29/2VrW1tbmtQQAFBUVKQHA09Oz2e/n/PnzVgCQl5enAoBu3bpdtoanp+dl783baOfMmaOfM2eOvumcXbt2bTMxaz5xuLKy8pq/s6tK0gkhugI4CMAdwA4Abri4jdTsYEPbAwDSrzWoJs41/PQA0Li00A3/q4Q7B8BNCCHM1XQNz5lzbTKmacWcW8PPphV2RETX5UDOAbzzf+9ANvw3AKVCyRNeiYiuVnr6xQSd0XjxZ3o6k3RERBYUrdNV3srJOTNHR0fjwIEDyxctWnSuuX4fH596lUollUqlzM/Pv+JZZEVFRSovL69WkzbmCrqSkpJrrqq6VuZkUdMEn/nZcWaurq4GAEhKSsoKDg6+4hDPpkm6zsTFxcUA/C8J11hRUZGqX79+lQDQtWvXegD47bfflH379r30neXm5l72vZgTpIsWLTo7atSosqZzajSaNou3zN+1h4dHu6snm7raP5bFuJjYipZSpgshFqNRkk5KWS+E2AvgjmsNqBmZuJhgGw7gewAQQlgDuAsXT2cFgAO4eELsEPzvuXRDANg2en8AwN+EENZSSvPJKsMB5ALI6sB4iYiQnpUOg+niv80CAtNum8YqOiKiqxUZebGCzlxJFxlp6YiIiOh3ICIi4sIPP/xgFxoaWm1nZ9di8qVfv35VO3fu1K1cuTLXvOV1z549trm5ueq2knT+/v51Go1Gnj59ul2HTHSkbt26GdRqtfzpp5+0jdt37dp12cm0w4YNq7C1tTWdOnVK8/jjjxd3xNpqtdpUWlp6wxOTwcHBNc7OzoaPPvpIN2fOnCJz++7du21zc3PVM2fOzAeAu+66q0KhUGDLli1O5mfSAcD777/v1HQ+T0/PuuPHj2sbj7samZmZag8Pj7rW/qbacrUf3L0AdrSxlTUbFxNo7SaEsAPQs+GtAoC3EOI2ACVSymwhxBoAzwohTgD4BcBzuHhQxPsAIKX8WQjxOYCNQoi/4OI21o0AUqSUGQ3zvo+LScZ3hRAvA/AH8DSAlxo/y46IqCM42zjDJC9WtktIhHQNsXBERES3oCFDLm5xTU+/mKBjFR0REXWA5cuX5w4ZMqTP4MGDez/22GMFPXr0qC0pKVEePXpUm5mZqTEfovDCCy+cHTdunP/w4cN7Pvroo4UFBQXK5cuXe7Znu6u1tbXs379/5aFDh27aKbhmCoUCo0ePLtm6dauLv79/TZ8+fWp37tzpcODAgcueMefk5GRavHhxzqJFi3wKCwuV9957b5mjo6MxJydH9c0339hHRESUT58+vaSldZoTEBBQs3nzZte33npLFxAQUOvg4GAMDg5u92Gk7aVUKvH000+fnT9/vk9sbKzvlClTinNyctSvvPKKl4+PT+3s2bOLACA4OLh27NixJa+++qqnyWTC7bffXvXFF190+eqrry47hVWhUGDVqlXZDz30kN/o0aPFhAkTSlxdXQ15eXmq/fv323l7e9e9+OKLre7C/PHHH+0GDU1xV2UAACAASURBVBrU7tN8m72vqxzvDuBkG2PqcbGC7WoMAPBVo/cvNbz+CSABwAoAWgBvANDh4rbaEVLKxg+qfAhAEv53CuwOALPNnVLKC0KI4Q1zHAJQCmAlgFVXGSsRUZuKq4qhgAImmKCAAsVVHfIfpoiI/niGDGFyjoiIOlSvXr3qDh48ePyZZ57xXLp0qVdpaanS0dHR0KtXr+rJkydf+j/ucXFx5Rs2bMhMTEz0nDp1qp+3t3ft8uXLc9atW+fennXi4+NLXnrppW5lZWWKLl26tPvZdB0hOTk555FHHhENySkxevToktdeey174sSJPRuPmz9/fpG3t3f9qlWr3KdPn+5sMBiEm5tb3e23314xcODAdp9Ma7Z48eK8U6dOaZ588kl9VVWVYuDAgRX/+c9/Mtq+8urNmzevyMbGxvT66697TJo0qaeNjY0pMjLywuuvv/6bg4PDpc978+bNZ6ZPn27csGGDR1JSkhg8eHD5u+++e3rkyJG9G8/3wAMPXHB2dv7/7N17nFP1nfj/1yeZC3eQEQpIdPCGosjNApGCoWNR0e2i7na3VaHqOlS8lPbbYr1g7Y9tkdHtYuuNsV4Ya2+7uKxFUQSNIAYoI8xSUFBwIIgIDoKDwORyPr8/Tk4ml5NJMpPMhXk/ecxj5pyc5JxcSd55X7b/8pe/HHjXXXeVNjQ0OE499dTgqFGjvkocNJHoo48+Kty+fXvXBx544JOWXCeVTRKZUupT4E2t9fWR5Z8DD2itnTHbvAwM11oPacmBtScXX3yx3rhxY1sfhhCiA7EmuwbCAYqcRayavkrKXYUQQggh2imlVLXW+uK2Po5cqqmpqR0xYsTn6bcU+XLo0CHH6aefPqKiomL3rFmzsspIEx3LfffdN+D555/vt3v37i0FBU3nw9XU1Jw6YsSIUrvTsp3AsRb4tlIqcQADAEqpc4AriM+KE0KITsftcrNq+irmTZ4nATohhBBCCCE6ob59+xq33377pwsXLhxgGK2aSCda0bFjx9TTTz/d/5577tmXLkCXTrbnfhj4R+BtpdRsoBuAUqo7MAn4T8DALCMVQohOze1yS3BOCCGEEEKITmzu3LmfhcNhtWfPnsLS0tK0vexEx7N9+/biW2+99cCsWbNa3OMoqyCd1nq9UqoceApYFnOSNZ42BNystd7a0gMTQgghhBBCCCGE6Mh69OihH3nkkU/b+jhE/owaNerEqFGjmjURNlHWeXha6+eUUu8As4DxQAlwBFgHPBYzTVUIITo9n9+Ht9aLp9QjWXVCCCGEEEIIIVJqVrGs1vpD4Ec5PhYhhDip+Pw+PP9+D8GdEyg86x6898+XQJ0QQgghhGgthmEYyuFwZD4tUgiRV4ZhKMw2cbZa1tFOCCFEShV/XkPg2VchXETg7QBVI/8b920SpBNCCCGEEPmnlNp//Pjx3t27dz/e1scihDAdP368i1IqZWlss4J0SiknMBQ4BXDabaO1Xt2cyxZCiJOBz+/j5dePQLgIdAGENfu3ntfWhyWEEEIIITqJUCj0i9ra2sdKS0vp2rXrCcmoE6LtGIahjh8/3qW2trYoFAr9ItV2WQfplFJzMUtde6fZ1DZ4J4QQnUFVTRXGGZvBeR+ENTiDvBL4KT7/Q1LyKoQQQggh8m706NGvv/fee3fs3Lnz51rrAYCjrY9JiE7MUErtD4VCvxg9evTrqTbKKkinlJoD/AJzUMQLgB9zoqsQQohErnUwowxqPVDqJXTaery1XgnSCSGEEEKIVhEJBqQMCAgh2pdsM+luBT4BRmutD+bheIQQ4qQwauAo8w/XOvMHKHIW4yn1tN1BCSGEEEIIIYRot7JNd3UBSyVAJ4QQTas7VodCRZfHDhrLWzPekiw6IYQQQgghhBC2sg3SfYZMhBVCiLRKupWgaezNe8voWyRAJ4QQQgghhBAipWyDdH8BvqWUKs7HwQghxMkiNpNOoag7VtfGRySEEO2Lz+9j/pr5+Py+tj4UIYQQQoh2IdusuAeA8cB/K6Xu0lp/nIdjEkKIDi82k06jKelW0sZHJIQQ7YfP76OsqoxAOECRs4hV01dJtrEQQgghOr1sM+m2AqXAVOAjpdQhpdQum5+dOT9SIYToQOqO1eFQ5kus2nsJSyrPxSfJIkIIAYC31ksgHCCswwTCAby13rY+JCGEEEKINpdtJp0DCAF7YtYpm+3s1gkhRKfhKfVQ7CymoXY0xuIVrDS6suYFWLUK3JIsIoTo5DylHoqcRdFMOpl8LYQQQgiRZZBOa12ap+MQQoiTitvlZtX0VTz47w2sNLpihBWBAHi9EqQTQgjrNdJb68VT6pFSVyGEEEIIZFKrEELkjdvl5rort7Dq+SBKOykoBI/H2daHJYQQ7YLb5ZbgnBBCCCFEDAnSCSFEnvj8Pmb930TCN34daj0YZ74Lgx8C5EOpEEIIIYQQQoh4TQbplFLTI3/+j9a6PmY5La11VYuOTAghOriKtRWEdRhc68C1jiBms3TJHBFCCCGEEEIIkShdJt3zgAbWAfUxy01RkW0kSCeE6NT21e+LW3bgkOboQgghhBBCCCFspQvS3YwZcPs0snxTfg9HCCFOHreMvoUN+zaAfzzUTGf4gJEwxQ2utj4yIYQQQgghhBDtjdI6XWKcuPjii/XGjRvb+jCEEB3Q3c8t5eHyK9GhIkBRXAxvvSUTXoUQQggh2hOlVLXW+uK2Pg4hROfmaOsDEEKIk9mXG6ahQ8WYnQAgEACvt00PSQghhBBCCCFEOyRBOiGEyBOfD559Nn5dURF4PG1yOCIDldWVXP7C5VRWV7b1oQghhBBCCCE6mXTTXXc183K11vqsZp5XCCFOClVLdxMMubC+Dxk8GObOBQb7mL/Gi6fUI5Ne25HK6kpmLpsJwIpdKwAoH1PelockhBBCCCGE6ETSDY5wkH6aqx3VjPMIIcRJw+f38ezhe9COV8EoApzs26e464dhdPU9hE97hyJnEaumr5JAXTuxZNuSpGUJ0gkhhBBCCCFaS5PlrlrrUq31kOb8tNYVEEKI9shb6yV82jswowzOWoVSGsMwe9IFd04grMMEwgG8td62PlQRcd2w65pcFkIIIYQQQoh8apWedEqpi5RS01twfqdSap5S6mOl1InI739XShXEbKOUUg8qpfYppY4rpbxKqQsSLucUpdQLSqkjkZ8XlFJ9WnLdhBDCjqfUQ5GzCIdy4uy7h4JCcDrNnnSFZ63FqZwUOYvwlHra+lBFRPmYchZdvYgpZ05h0dWLJItOiFbg8/uYv2Y+Pr+vrQ9FCCGEEKLNKa2bU82a5U6U+jnwgNba2czz3wv8BJgBbAEuAhYDv9Zaz4tsczdwP/B9YDvwAPANYKjWuj6yzXLgdOBWzDLe3wG7tNb/0NT+L774Yr1x48bmHLoQohOrXLqFO/71PEJBJw6nwT/8y+fMmTWALUWVLNm2hOuGXSeBICFEp+Xz+yirKiMQDkj5vxCizSmlqrXWF7f1cQghOrd0Penai0uAv2qt/xpZrlVKvQyMAzOLDpgNPKS1XhJZNwM4AHwPWKSUOh+4AviG1vrdyDYzgTVKqaFa6+2teo2EECe9uveHEw5ptKEIGwb/66uh+9eX8FL9TwmEA6zZs4bh/YfLh9J2xOf34a2VoR5CtAZvrZdAOBBX/i/POyGEEEJ0Zq1S7poD7wCTlVLnASilhgHfBF6NnD4EGACssM6gtT4OrMYM8AG4gaPAuzGXuxb4KmYbIYTImZLzt4CzAQgBTvSuMl78fzdxonaU9KRrh6ysnrlvzaWsqkzK74TIM6stgJT/CyGEEEKYOkom3QKgJ7BNKRXGPO5faq2fiJw+IPL7s4TzfQacFrPNQR1T36u11kqpAzHnF0KInPD5fczeWoa+cRR458Kuy0AXQFijaifjcK2XD6XtjGT1CJF/idmqq6avkuxVIYQQQoiIjhKk+xdgOmbp6lZgJPCoUupjrfUzMdslNthTCevsGvAlbmOuVKocKAc4/fTTm3/kQohOyQr4aNe74PkF7J4EYQ0FQX7yvYvpc/Y8+VDazpR0K8GhHGi0BFCFyINUPejkdVAIIYQQwtRRgnQPA49orf8UWd6ilDoDuAd4BtgfWT8A8Mecrz+N2XX7gf5KKWVl00V62fUjOQMPrXUlUAnm4IjcXh0hOp/O1uvLU+rB6XASDofBtQ7HlT9m0N47+d53urDgpmnAtLY+RBGjsrqSO169g5ARwulwsvCKhZ3icSpEa0rMVq2qqepU/y8IIYQQQqTTUYJ03YBwwrowjT31PsYMwn0L+BuAUqoLMBH4aWQbH9ADszed1ZfODXQnvk+dECLHrABIWIcpdhZ3igl+bpebm0fezKLqRWj/OIzlv+YTo5jffuBk2qXgPrmvfofi8/u4/dXbCRkhAAxtUHesro2PSoiTj9WDLhAO4HQ4eW7zcwTDQRwOB49PfVymXQshhBCi0+sogyP+CvxMKXWVUqpUKXUN8GPgf8DsLQcsjGxzrVLqQuB5zEERf4hs8z7wGuak1/FKKTewCFgmk12FyB8rABI0ghjaoCHc0GmGJUwfMZ1CZyHUeiBchDacNAQ0Xm9bH5mI5a31YhhGdFmhpNRViDywetDNmzyPm0feTDAcxMAgZIS449U7ZFiLEEIIITq9jhKkuxP4b+AJ4H3gP4CngftitqkAfg08DmwEBgJTtNb1MdtcD9RgToF9PfL3jfk+eCE6s84cALGy6Sh9G5wBUEGcBSE8nrY+MhHLU+qhwNmYWG52QhBC5IPb5eaeifcwfcR0HI7Gt6FhHe40X+AIIYQQQqTSWkE6FflpFq11vdZ6ttb6DK11V631mVrre7XWJ2K20VrrB7XWA7XWXbTWl2qt/55wOYe01jdorXtFfm7QWh9uwfUSQqTR2QMg00dMp7j0PZhxGc6y/4/H/vSBlLq2M26Xm6nnTI0uh4wQFWsr2vCIhOi4fH4f89fMT5sV53a5eXzq4xQ6CnEoB8XO4k7zBY4QQgghRCpZBemUUs8qpb6dZpurlVLPxq6LBM86StaeECKH7AIgVTVVbXhErS+8ZyzU3Ig+MrhN9p/ph+bObOuBrXHLL+94WW4vkWTg2rUorzf6c8O2ba22r8travK2L8u46moK336bcdXVzTq/Nb117ltzKasqS/scKh9Tztvff5tvn/tthvcfzpYDW5q1XyGEEEKIk0W2gbPvAyPTbDMCmNGsoxFCnJw68XzkqmUfEnp2BWz8Acbfypn1nfPxtWLsJ9sPzZ1RZXUlHx76MG6doQ0pvRNxBq5dy/5gMG7diwcOUJqHJ7TdvlZ88QW9Vq/O+b4s46qr2VBfT0hrNtTX06UZzTMTp7dm8hxauvIzlv7uPDasdzBz2UzuXnl39gcvhBBCCHGSyEd2WzHJk1iFEJ2Uz+/jlQ9fiS4XOgqZPmJ6Gx5R6/H5fby3rheEC7Gq/o2Qs1UHRzTnQ3Nn88x7zyStcyqnlN51AK2ZJZoYNLPsbmjIeUZdqn3VG0azs9zS2VBfH7fcAAzbsCGry7CmtzqVkyJnUdrnkM8Hj/zgCnhzHixeBf7xPPLuI/JlghBCCCE6rYL0myRJmROjlCoGJgH7m31EQoiTSsW7FQSNxg+cV51zFW7Xyd+UzcpgaygaDc4rIGx+J1JUpCgpgfnzweMh7/3prA/NgXAgow/NndGgnoOS1rl6uTrF47Qjiz7HQg04HA4en/o45WPK87a/AYWFKYNnyw8darV9vXf0aE73ZVEkv8HbfuxYVpdhTW/11nrxlHrSPoe8XtChItAOCGtzErZrA95arzz/hBBCCNEppQ3SKaV2Jaz6kVLqJptNnUA/zEy6p3JwbEKIDs7n9/HyBy+39WG0iaqaKk6ETqAHr8Vx02UM/eSX9Czsicfdm9mzzyIQgKIiWLUqv4G6bD80d0ZzJsxh6falceu6FnZto6MRmfLWemkINWBgYBgGd7x6B8P7D8/bY/yrcOoigYt79szpvgIxE7ETje7RI6f7snzrlFNY8cUXceuGduuW031cXlPD24e/4HR9iMVnDcTjcdOl2MGJhjDaEUSVrqa4QAZICCGEEKLzyqTc1UHjdFYd83fiTxDYAiwAfpqPgxVCdCzeWi86ITdjQI8BbXQ0rcfn9/Hs5mej112h2BFcycb+d/HrNxfTENCEwxAI0Kqlrx1Na5Uyul1urh9+fdw6Vy+XDNto5zylHhyOxrcxISOUt3LuXqtXU58icKYAT58+OdtXyZo1HEoREBzbsyfrx4zJ2b5ivT5iBFNOOSW6fH63bmwbOzary/D5fUxePJn73ryPyYsnxz1/Lq+pYcUXX9BgaD7UpzDhb+/AYB+rVsHMn+xl2vzHmDntIlZNXyVfJgghhBCi00qbSae1LrX+VkoZwH9qrf+/fB6UEOLk4Cn1UOAoiJa7dpZ+dN5aLyEjZC743eiqNzBCBeC8B33ljygoCKEopKjILHnNJ6sk0Cp37SgfgFv7uC/od0Hc8opdK1j58UqKncUd5jZraz6/r1UzNt0uNz92/5iKtRUAaDQl3Urysi+7AF2hUhhaU+Rw5DRIZxegm3LKKbw+YkTO9pFKS/dRVVNFQ7gBgIZwA1U1VdHHwpojRwANSoHW6F7D8dZ6Kanrwe82/RfGGW9S3PBep/g/QgghhBAilWwHR0wGFufjQIQQJyelFGA24n9s6mOdIthR0q0EQ0c+1Ndeig4Xgi6AcCHOE1/jsT99wLx5+S91hY47OCL2uE+ETlBVU5XX/VkB5ViGNmgINXSY26wttdUU4T7FfXAo862MQzmoO1aXl/30dCS/XTq7a1fCwHHDYPZHH+VsX32dzqR1/oYGlNeL8nqzHuaQrRu2bYvuK9vJtfuPpm5JPLF3b8AM0AHw5RZK6q7mjn89j9DKBzCeX0FD7Wh5vgkhhBCiU8sqSKe1fltrvdtaVkr1Ukq5lFK9cn9oQoiOzlvrJWyEwT+e8Oqf8uRLm9v6kID8l1Eu/3B540KpF1UQwuHUFBbBj787mrqSZXhu8OU9QAdm8MnpcKJQOB0dZ2Kpp9QTDfBqNM9seibvgZ+rz7kap4oPkBgYecvOOplULfuQE2/9iPCer7dqMNhT6qHYWYxTOSl25q+X2ZeTJkUDdUVKMbZnT96PGaqwob4+Z1NX6yZOjAbqCjDLTmP39f6xY3kL1N2wbRsvHjgQXd7d0JBxoM7n9/Hy9vgepLu+aGxr/PqIEYztAhgNULeB4m1zWb6kH8FAQfRLDFXrYc+RPVJmLoQQQohOK+vprkopJ2bPuX8DhsSs/xj4HfCI1jqUsyMUQnRYnlIP2j8eFq+AcBGb3w5ww9ce5/d33d5mx9QaZZT76vc1LrjWMfRHs7ix9zOUnP8Bd/39XwnsMff91oy3WiWzUKHifncUYaOx7C9oBONK53Ip9jGRyEH+srMSVVZXsmTbEq4bdl1ep5Tmms8Hz/zoe+gA4LwP581TWy0Y7Ha5WXjFwujtls/n05eTJkX/7rZ6ddLp1fX1OdvXsosuwnv4MJ4+fSirqUk6Pdupq5mym1K7p6Eho/N6a70YxJcFv7HrDXx+X/R+mRb0Ub12LmEdJrh3Ai//qSQyUlaDMjAOu6j8Xy+La8qkzFwIIYQQnVJWQTqlVBHwGnAp5tsqP/ApMBAoBX4JXKGUmqK1Tv60I4Q4aWTSf8rtctNj39V8GS6KZEpo/vr6l3BXKx9sDLvyz1x/EPQM8bBhX2Omi+uCvXi5nBOHT9BQOwpqPTSUevMWdIpl9cfT6Ghj/Y7wwbeqpipp6Ei+xD4mkihaJeBUWV3JzGUzAbMfHtBhAnVVS3cTDJwWfY5PLazA7cpu4EBz+fw+Zr82m0A4wJo9a/Iy3bVy3z5m7dhBGLMUtW7iRCb27p00CXVgUVGL9+U7cgTPpk1Yb6C6OhwM796dDQkBwFxPXbVc2bdvXCYdwOnFxRmd1y7jVKOjrznmQAw3THgNlu9A1RRgGNZsMgO0E119C3rzjTR8f0qHea0SQgghhMilbHvS/RjwAK8A52utS7XW7shwiaHAX4GJke2EECepbPpPnTdmPzgDoILgDHJ00CttWsrkKfVQ5CzCqZwUOYvyEoDpUxzfRH7FrhWs2LWC1e8EYPEqeHMeLF7F/g+GpLiE3GmN69ta6gO5y1SKZd1GdpmGBaqgVQIFS7YtaXK5PVtXsCDuOX6o/0uttm/bnos+H8yfb/5uocp9+5gZCdCBOdShZM0as3SzZ8/odg7gLxdcYHsZmfIdOcIlMQE6MPvdTTv11BZPXc3U74cN4/r+/aPLZxQXU5thXf6L//ei7XpPqSd+Yq2jAK4ahh40FYdDYX7nq0A7o2Wvjt3f7NCvVUIIIYQQzZVtkO57wN+BaVrrD2NP0FrvBK4FtgLX5+bwhBDtkbfWS0O4gbAO0xBO3Vjf5/fxXuHjMKMMvvkAzChDD363TRuDu11uVk1fxbzJ81h4xUK8td6cBw1T9jCr9YCVVRgqhs035nS/dqxywLIhZSy8YmGHyUzp1SW51en6vevzsi/rMTHx9IlJpwWMAJXVlXnZb6yRA0c2udxe+fw+Nhc9Gfcc39Prz622/8Qg9NV1JVBWBnPnmr9bGKhbcvBg0jor2LR+zJho8MwAvrttW4v25T182Ha9p08fXh8xgjkuFw7MnnQD165t0b6a8vthw1h07rk4MXvSlaxZk9H5Pvj8A9v1S7cvjZ9YG4mFGxPqGDPagdl6MrJSmb07H5/1zx3mtUoIIYQQIpeyDdKdDSzXWht2J0bWLwfOaumBCSHar9jppYZO3VjfW+vFMAxwrYOJD4FrXZtnc1lluiXdSpj92mzuf/N+Jj0/KSkQ05LhEss/Wm5/QqkXZ4HGzBxxsPy/B+Yi2adJVjngyl0ruf3V21sl4JQL3o+9SevGDR6Xt/25XW5OhE7YntYaWW2x2ZcKlZSN2V5FA+4xz/HWbH0YG3RfNX0Vw9+vg0AAwmHzt9fbosu/rl+/pHXWUIcbtm2LK3nNZsiCHU+f5Pu8p8OBu3dv7t65kwq/P9rxbX8wmLdAXarswXSKnPblvs9teq5xYq318gfwTi881+yksFBFVzocmsd+U0j5tOEtug5CCCGEEB1VtoMjAkCPNNt0B4LNOxwhREdQd6wOBw4MjCYb63tKPRQ4C6IN+R3KwW+u/E2bZUj4/D4mL55MIBzAoRwY2kCjMQyDWa/MivazaslwicrqSpZ+sDRu3fXDr+fgVwe57urr2FRYyFOLNGhFMGTg9TryOuXVW+ulIdSAgYFhGNzx6h156duVa4N6Dkpa17Oop82WuRMw7FupHj5hn+GUS4cbGveh0R1moqyn1INCxfUPPL3X6a16DG6Xu/Hx7AGKiswAXVEReDxZXVZir83yQebjMLEnHdgPWdjd0MDAtWvZH2x8G3R9//78ftiw9Nejd2/eHTWKsk2bOJ6wr5dsMvr2B4OU+nzsjhnsMOWUU3h9xIhsrnKSVNmDqfZl3Waxj+FYTuXkwMSJkZLXIIQNWP4Bjv7302fCVUz9zk0s/X0/wIlhhNi008+wDZ/FTbQd27Mn68eMadH1EqI9yaSvrxBCiM4p20y6/wP+SSmV/NUyoJQ6FfgnIHkUmRDipOEp9VBcUIwDBw6HI2VAwe1yM37w+Lh1mUzKbEkWW1OqaqpoCDeg0YR1OC6wENZhqmqqgBR9rjJkl3XVs6gnr9/4OuVjyuk1dik4j4MKYqgTHB6w1OZScqekW0lcZlNYh9u03DhTcybMwZHwX9Qzm57JWz9Dn9/H5199bnvahn0bcpqBmPj49vl9/Me7/xG3Tar+Xu2N2+VOKhPu27Vv/ncc03eusrqSy1+43LyP3G5YtQrmzTN/ZxEBT9Vrs3zQIEIeD9rjiQbNwByyYCc2QAfw4oED3JBhKay7d2+O2ezrWpuMPiAuaAaw4osvuNxmGmw27LIHU+1r3DovZVVl3P/m/RwNHLU934CeAwComziRRYf7UbBgNY4jf6K49D0zq3rAe+AIgwqBM8ifPB/EBegANtTXM666ukXXS4j2Ipu+vkIIITqfbIN0jwH9gA1KqVuUUmcqpboqpYYopW4C1kdOfyzXByqEaBt2ATOrz5nT4cTQBrNfm237JrOyupLVu1dHl5sqjY3dX77evO4/uj+j7VoybOG6Ydc1ud/NBfH9uzYXPJnxZWfLKnW1SpMVimJncYdoyO52ufn2ed+OWxc0gnkJMFqPuU/qP0m5Ta5KXn1+H5c+fyn3vnkvlz5/aTSbInGy7Oo9qztMaXLfbgnBqnyXu/p80b5zocmX8tyTM1mxawUzl81sDNTdc09WATrIPjj/+2HD6OHI7G2UXdZdNhacdRalGU5ZXXPkSIv2VT5oENNOPTWjbatPhKOZuqkUOcwyWJ8PZn9vOMabv0BVvcnXw3dR8ec1vPLbKWA4QBkUXPUTjnS1v57vHbUPAraJHA4nadf7FHlRVVPF8dBxs69vKHVfXyGEEJ1TVkE6rfVfgIeAM4BK4EPgKPAR8DtgCPBwZDshRAd398q7+cZz3+DeN+9l4nMTuebP10SDZnXH6jC0gaGNlB9o7QIbmz7d1OQ+W5LFls6h401/UO7VpVc0aLLwioXRPlfZlKKUjynnnL7npDx95MCRcf277IJ6ueKt9cb1WdNo7hx3Z4corfH5fbyy45Wk9VsPbs35vqzHXGxmZaJclbxWvFtB0DAzrYJGkIp3K/CUeih0FCZt+8x7z+Rkn3mXcLMN6D4gv/vzehv7zgWDeGobT7K7zTLNzPWUeihwFKBQFDgKMgpmzzrttIwOOVXWXTbuOeOMjLab2Lt3i/c1x+XKqB9KXxVqMkAH4BniAcy7rSGgMcKKcFCxerVi6WtfEAwAFIB2MHjvD9GhUPKFaM1onfr52ap8Prj0UvS99xKcOIGlz93dOvucRanpMAAAIABJREFUPBnuu8/8LYG6Dsvn9/G7Tb+LLhuk//JSCCFE55JtJh1a63uBS4BngU3ArsjvZ4EJWuuf5fQIhRBtorK6koq1FdEsrLAOs/SDpdHsn0yyzewCUM9tfq7JD8styWJLZ8ehHU2e7v3YG83im/3a7Gb1ivH5fez8Ymf8StV42sJ1C6OrncrJ8P75a5DuKfWgVHxa0+ZPN8cdaz7KinOhqqYqGsyK9eKWF3N+vLHBmUJHIcomFWzDvg052e/m/Zvjlvd9uQ/ANkCYLqjcHvj8Pl796NXoslM5mT5ien536vGY/eacTsIFDryljScl9jK0+lDe9+Z9TF48Oe19aN0PTQVsYy046yyu79+/yW0y7UmXTvmgQcxxuZrcJhc96cAsu109ahT2oyBMDuA6tSPp+VLoKIwrVX903aP4/D5Kzt+C4TBL/XEGodRr/jgDQAi0k9of1YGzAGIDcobB2G3bWD91avsITlVUoINBFFAQ1hgVFfnPeq2qgoYG83ZpaDCXRYfkrfUSNhozpxUqozYgQgghOo+sg3QAWut1WutbtdYXa63Pify+VWvdDt49CSFyIVV5X9AIUlVTlTRV0S6YVT6mnEVXL2Jwz8GN5w8HedD7YMoPy1YpbdmQMhZeYQa0chFI8vl9actdB/Ua1OIsPm+tNxrYtFiZRd5aL8FwY+DJ0EZey1zcLjffvfC7cetGDhwJdOyeOKnKq1vCGiIC8NMJP7XdxupZ2FyV1ZXUHq6NW+cZ4sFb6yVkJGcPpZo22554a72EwjaZT/kU03dux5+fpPoMMwux0FHInAlz4jaN7UPZEG5o8j6sqqkiGA6aPSuNzHs3XtC9e8rTfjVkSE4CdJYFZ52V8rRcBegs7t69sR+lAn0LCnhn1ChGdS9Gb7wFnvbBn5aAfzzD+g2Ly65rCJvlfHUly3DMmBIt9ce1zvyZUQZnrQQVhoGRPVpfLmhN3/p61t9xR06m9ebE9u1xi+d+3oKsVylh7XQ8pR4KnY2Z006Hs0O0oBBCCNF6sgrSKaUeUEpNSrPNRKXUAy07LCFEOvnOgkpXhpnpZLLh/Ydz9blXR8v5DAxWfrwyZWDI6qO26uNV3Ln8TiYvnpyTQFJTH7j7dunLnAlzmHPJnKQsvmxv58SyldjMosQ355mW1DWXz+/jpfdfiltnZbXks6w4F3p16ZXytA37NuQ0sBibtRc0gnx54ktK+5Tm5LJj2QW+H133aMpSp0tLL835MeSap9SDI6Yvm0Y367GU9etZpO/c8GnlPDb1MaacOYXHpj6W9FqUGJhPFaj3+X08s+mZaKA2m+emp0+flKfd+/HHKK83p0MPeqbog7fiiy9yvq8Bhcll2ACHQiEu2bSJ23efBssq4ZNx8ME18Nzb7Kr5WtL2Jd1KzMfK6eujpf5RrnXg+YWZUfdpJHfPyqRTiiv/9jdwOps1rTcvhg4FwMd45vMz/tp9PLsO70p/vsSAXGUlXHop3H+/2WOxqUDd9Onm9Qfzthg1qoVXQrQVt8vN7PGzo8shI8SWA1va8IiEEEK0N9lm0j0IeNJsMwn4eXMORgiRmdbIghref7ht2R+YAZRM9m8dZ2V1ZVzpoqGNlM2SY4NHwXAwZ4Ekq4zWzqETh6JlqLHZgUDWt3Pdsbro7aZQ3Dr61rjAwdSzp+LAgUIllaLmmnVbxrKyifJZVtxSPr+PX/t+3eQ2+Q4sfueC78QtK1SLyzitLMZYDeEG6o7VMaBHch+3rwJftWh/kP9gvtvl5vGpj5sljsrRrMEkLXk98/l93Ln8Tt7Y9QZ3Lr8z7XlTlRAnllePO21cxqXu7t69eTdNaWgup5N+OWlSykBdrvf16YQJKQN1AKFTusHvNmDW9CswCnD6y3AqZ9x2dcfqcLvcnFtyrv0FWRl1z/0V6j8HpXEQKRW+6qpmTevNmzlzWKPGU8YqHmAe9+9dxefbz276sRcz7ISyMjNAd/vtEAyCYZglrE1lCbrd8NvfQmGhGcCcPVuy7zow78feuOUO039UCCFEq8ikL3BzLrPpLsJCiBaxy4LK9TAAb603ZV+mF2peyGj/1nHaNRZP1SzZCh4FwgGcDicKRcgItTiQ5Ha5+e2Vv+UHy35ge70C4QBVNVU8efWT0esyf838rG/nkm4lcX2tRg00Mx4qqyu549U7CBmh6OlWSV2+BjlYt+Xx0PGk06xy5UyyIVtbYs8eO7kMLNYH6uOWe3XpxZcnvoxbp9FsObClRbdTn2L7jKutB7fy2dHPktbvq9/X7H2BOfilYm0FYGZ0rrlpTV7u5/Ix5QzvPzyrx1JsJm5LXs+qaqqigWjrORx73o8Pfxy3/Tv+d/D5fUmXv+3gtrjlNXvW2G6Xirt3bxpisrwK336bUMKgg1xOJ/1yUmNRQ7739emECdG/u61ezXEj4fW89DjR6SGOEOXXncvmgjJW7FoR3WTrwa34/D521CX0BfWPh1qP2ZsuUv666LRFlI/5p/jt2kNwzuJ2M3PcVE6sK0JTAIaGWk/Tj9vYYScNDfDww+bfFqczfZZgXZ0Z0DOMxtLf9nS7iIwl9s5MXBZCCNG55SNINwb4PA+XK4SIKOlWgkM50Oi8ZUElBdBiPkx96lpHsbMYjKaDJU0FiQCWf7Sc8jHlcesSg0dAzgJJdcfqmmwIv2zHMlhm/j1q4Cj2HNlDgaMg7fWMtfyj5UnLw/sPZ9Yrswjr+MBTvnvRWLdlxdoKXt7+MhpNobMwmhHmdrnbVXDOYg280E1Mc7z2/Gtzcuw+v48//v2Pces2f7qZM085M2nbJduWJD1es3G4wX5C7ItbXrRdf+j4Ia750zUM6DGA6SOmZ3V9rcEvlrAO87NVP+Pt77+d3UFnKJvHkpU5FwgHKHIWsfCKhdHAfLavZ769qbOJfH4fNZ/VxK0ztEFVTVXSa0pi/z+NNvvX9RrGlM2bOao1AwoL4wJWTRndowcb6uODv84000kr9+1j1o4dhIG+Tid1Eyc2e18qT5NQJ/buzYovvojZkQIdgtPWQ899XH/bpyy46XbO+U18X8cXe32XF3ceB/dfYcv/gy+3mf+nLF4F4SKz1HXGZVw/9UzqjtVlFSBtC2O+25cPNgYgrCNDMN7GU/ofqc9gDTtpaDCDbDt3mhlxSpkBusceSx9wsy4jEGg/pb+iWa4850qWbl8atyyEEEJY0gbplFJvJqz6vlLKY7OpE3ABZwB/tDldCJEDVs+2kBFCKcWd4+7My4eZTZ9ualxI+jBVxk3TRnJ679ObDJ5ZQyDuf/N+Dh47mHS6NdkyNqsGkoNyubp+qXp/WfbW7+Wp6qfi1ikU/3jePzLnkjkZHcf2z7cnLVfVVCUF6KzLbg2v73wdjUYpxezxs9v1h1+ALQe2JA3fSLRi54omT8+U3aCPkQNHMm3oNJ5+7+m4+y1dn8a0+0oocUrnoy8+4qMvPgLMqchvzXgr4/vOrnzqb3v/ltX+M1VZXcmSbUu4bth1GQUxvbVejn88Amo9HC/1svzD5c3K6rx75d1JQbjYXoapyqGffu9pDG1Q5CyK3qa3jL6FDfs2xG2331HCJZsaXwf3B4MMXLs2o0Dd+jFj6LV6NfUxWWcNwLANG9g2dmzS9pX79jFzR2OW2aFwmJI1azIK1K0fM4aSNWs4FJOZFQRKfT5qc5xp9fqIEZT6fOxuaGhcWVBAj98fYcVZA3G7rgVg3OBx0ccu7v8FZzdAgbMYRj4Gm++AmukQKgacZrCr1sOLW+ZHy6ZTDSRqDy4Y9aVZnhuTBbh0+9LUx2sNO3nwQVi50gzUORxw2WXmukzuJ+syvF4zQCdZdB1W3Psrm2UhhBCdWyaZdJ6YvzVQGvlJZAB1wJ+BH7XwuIQQKXhrvTSEzGmFWmv+0/efTBs6Lb8fZmo9ZoBOF0Q/TC3/6I/Uzq5t8mxWQDHVlMpzSs6Jy6pxOpxoraPlrdkEJTJh9YtrKpsukUbz8vaXmXPJnPQbA182xJdJFhcUp9w2aATzWu4KkYBIJJOx1R4vLZRJf56Dxw7mJNvGU+rBqZxxwbg+xX1wu9w8cdUTceXRO7/Y2aJ9taSkyZqQmen17VLYJWmdXaC4pSqrK5m5bCZAtLwxXaBu6RufxQX9l1LGleds4Z6J92S175e2vZS0LvbxbXffQuPtYPVndLvc9vdtnxEkjjfdHwwmb5eC3czb7ceO2W675GDylxixQbe0bPpb7okNpOVQoc2+jlIc99jsWdSz8cSCHo1/KxUZCvEP8N7NmK2RNTjCUPoWYGY75quNQ654Sj3gujduAMZv1/2WBZctSN7Y5zMDayUlcOaZZl+5UMjMhss0QGdxuyU4J4QQQpzk0g6O0Fo7rB/MzsAPxq6L+SnQWn9Na/09rXXyu00hRE4kTlO0+prl2vQR0xubf5d6zQw6FYyU9njZfWQ3N7x0Q5OXYfWaShUUO/jVwaRBEUEjiPaPo8H7I2b89inb8zVXSbeSZmWvGdrI6DaurK7kk/pP4tYVOYqifekSOVV+y10hucQyZITa3STXRIkBplQDP2LLOVsidoBH7OCDF7e8GPfYrVhbwd0r7272fuZMyCzQC5jZq2t+Zv6OyOaxMuzUYcnr+ieva6nEibV2E2wrqyu5/IXLqayuBGDLhpKYoH8h1Hq4Z2V2ATp8Pl76i8L3NPzbxsbVia+HKQcVJLAL+A0IJU+CbWqIQqKJvXsnrRvarZvtttf165e0rq/TabOlvSv79k1ad3px6i8IWuJam2NNvF2iPf7846HBJly59jwwisB6PT7n1biAl0M52tUwm0RulxuHin8LHTACyRtaAyPuvx9mzoRFi8wA3T/8Q/sZhCGA5NepfErsg5q4LIQQonPLdrrrTcD/5uNAhBCZcbvc/Nj94+hyqgEMudjPE1c9wYDuAxon733zAfN35MPUf239rybf1MZOELULjo0cODJuG4dyNJbWvjmPD//zSS6f/2BOro+V1Wc3xCITWw9uTbvNo+sfTVp3y+hbqDtWhyPh5dahHDw29bG8Z4ps/nRz3LJSql1/+IXkAJNdwAlge9122/XZ8NZ6o73vFIqbRt4UvU8ShwlAywN1qQKOcWKeAyxeBf7xSQGBdBIDwwrFE1OfyOoyMpE4sTZx2cq0W7FrBTOXzeTyFy6n69nrG4P+jjAcOZ1DH57LNX+6htuW3ZZ+uqvPBxMncuHaDxn3CVQuiw/UeUo90QzdDz7/IOXFFDmLov0Z7XoQjupezKLTCinEzJ7LpicdmKWhU045JW7dsRTZceWDBrHo3HOxwnLZ9KQD+P2wYVzfv3/cuobEAQ85suCss5jjcsWtCyTs6+Cxg42P46u+CfUOMKAQYPOdsLUXTele2D3HR517Z/aJf8wM6TMkeSNrYIR1+2htDox45RXYsgXmz89sSqvPl/m2Imt3r7w77nUq34G69XvXN7kshBCic8vqXb/WerHWuib9lkKIfEqcPJmPfiZWUOvgsYNmgM21DiY+FJftEDACTb6ptQYXzJs8jzP6nJF0+jr/Ory1XhZesZB5k+dRNqQsvrQ2VMyKl77GyKdGpv/gnoa31puy7DYTL255MW3m4FeBr+KW+3bpS/mYcjylHooLiuMClel6ruWCz++jW2F85s5PLvlJuy0hsyRmFVzQ/wJzgEeCoacObfG+YoPEXQq6RIM2AIN7DbY9T8XaimZ9iPPWegkZdkWQxGfOxZWXm5lmhjb4xrPfyPh5kPia8I9D/zEv93uf4j7Rx7VCJU2wXbhuYdx1W7FrBYf6vWIG+8f8ztyo+lZYvIqlK/fzVPVTXPr8pU1fz6oqCIdRRPOwuO5987eBwZYDW8y2AOGGlFm8YweNxTujsZxyx6FIP7iYY13+4XJm/+VbGKuvpOvaqbx0erbfa0K/hAyz3Q0NlKYItJQPGkTI40F7PFkF6CynJWTOWT308uGsrl3jlq0eepahpw6NfxxPu4Qpf4ZfOH3w5VbokTDNuMeBuMXDDYeZvHhyi1/386nqmqq4L1/GDR6XvJE17MGR8NgJheCOO2DuXDPTrqngm5WNl8m2Ims+vy8pK9suIziXrh12bZPLQgghOres3nEqpb6tlPqzUupjpdRRpVS9UmqXUuqPSqmr8nWQkX0PVEotVkodVEqdUEptU0pdGnO6Uko9qJTap5Q6rpTyKqUuSLiMU5RSLyiljkR+XlBK9UnemxDt27pP1sUt22X8tJTV+y6swxn1cEvVR8zqDbXn8J6k097Z8w5z35rL7NdmU9KthDd2vWGW1jpCmC0wHbDpJmo2duUbz34jsyybFA43HM6qF11UzIf2F7e82GRwZtSA+OylSWdMAhqDlf26xZeJZdJ7rbl8fh+Tnp8UN0Gu0FHItKHTUm4/f838dvGh2C7L4ML+FyZtdyxg398rG7GB5MRG9eNPG5/yfM35EFfSrcQ+OJuYOdf186TycjADULNemZXRvhJfEw4dP5T18WbCU+qhS0GXaJAzMUtz70t3wDNrYdWv4Lm3G8t3Xeug9x4wCuKCkWD2aqyqqUq90/2NZajWM3rJ+Y0nP/PeM6lv64hbRt8Sva8rqyupPVybdD9s39w3Wopv9UjL1vJDybd7vnrFvWTT1y6bHnrZSNdDb84lc2zbJFhBcQa8F9nSvAenfOPUpMtr7m3eWtwuN98d/t3o8otbXrTPsp0xA779bXOKq0UpM1AXDpuZdl5v6h1Z2XiZbCsyUlldybinx3HNn69h1qvJr6n9uieXdOfStKHTmHT6JAb3HMycCXPsexkKIYTotDIK0imluiulXgH+B/hnzAmu3YDumEMk/gV4WSn1slKqa8oLaqZIIG0t5pfmVwHnA3cCsV+9zgH+X2T91yOnvaGUiulezB+A0cCVwBWRv1/I9fEKkU8+v4/N++NLGHcf2Z3z/ZR0K4krDT37lLOb3L5LQXKjektVTZVtmalGE9ZhGkINPLz2YTOI5loHo57DnEWjwHCamUQYmWXZpJDtZE3Atuxw/pr5KTe/8pwrm1w+cCw+WyQQtulhlCMVayuSsraCRpB/e/nfkm4/qzRw7ltzKasqa/NAnV2WgV2p5opdK/JalhSbVZeoOaW2dmXPQFLmXD81zLa8HGDzZ5szus6JWaMtySJtSlNBzrsf2kn9m7eBlfNmFMLanzae2SaIY2nyi4cBA5o8pi4FXag7VtfkNrEB/2jANeF+GHp0ZjTLsshZ1Kwy8fbWKy5X0vXQc7vc9Bu6M+5xvLX4d7hdbrwzvJwftrKSzVxI//bkyytwFLT70vz//SC+A8wTG2Jep6wMuKefhr/+1QyyxYqU2VNQYGbcpWJl4zmd5u+mthVpWSX4G/ZtYOkHS5PeT4EZcM3X/4PW/7dr/WupO16X8oszIYQQnVemmXSLMANbnwP/DlyGGSgbFvn7V5iTXa8Cctvp3TQH+FRrPV1rvUFr/bHWepXW+n0ws+iA2cBDWuslWuu/AzOAnsD3ItucjxmYK9dav6u19gEzgauVUi2vmRKdXms1Ha6qqUpqKu//0p/zN5SJ5XJ2mUwtZWW2GRh89MVHjSeMqIKCBtsP70EjaPvNdzr76vdltF2hI+ZDrU3Z4efHP095Xmt6LJilf7GBAruMkIz6kzVTYralZdvn25ICnbHDO9pD9sqCyxYwZ8Iczj7l7GiWgdvljmYmxmppWZLP72Py4snc9+Z9SeV1bpc75bAHKzCezfPeKnt2Kmd8+W5CsOrfb76MRbfdlFRebsnkOnuGeJpcbg0vVlmB+5h+lPUxE25T9LqEmPJTO9Ong1JokstdAfp27Zu2T6eBEc3Wi/bRi7kfCosUc64fy8IrFlI2pIyFVyxsVrlwe+sVlytWDz3rTWSRUiy76KK4bYqdxXFtEqzXRrfLzdCS+Lddx4PHGxci/79pf+pM1vYi8YuWuOXYDLhUATqAceOaHiDhdptDJubNk2ETOZDp/xlNZvO2QOz/tw2hBma/NrvZVQLtKQNeCCFE7qQN0imlRmIGuv4PuFBr/YDW+k2t9Xat9QeRv+8HLgT+DtyglLqoqctshmnA+kip7QGl1Gal1B2qcSTfEGAAsMI6g9b6OLAauCSyyg0cBd6Nudy1wFcx2wjRLInN0fMZqNv/wZCk7C5DG8x+bXZO36jZlcvZBUksa/asSXm9p4+Ybp9BlEoTH94BNu/fnFVG3d0r72Zv/d602xU7i3n7+2+z6OpFDO452DbT56vAVyn3W9KtJBp41Oi4QIFd4CtfgZPK6kr2H02eTGkJGsG44/GUeihwFKBQ7SZ7ZcFlC/jwrg/jyoCuH359zvdTVVMV7V3WEG5I+mDWVJbDuKfHZfW8d7vc0aDP41MfZ9HVi+hR2CPp8V5XsozyMeVMOXOK7eVkksWXrldcrjSVhXnW6bHZtZGgxKjGEu9uBd1se10C7D+6P/Xz2+2Gn/4UhX256ysfvsKTG59Me+wrd60EYnp8utbBFT+k/4V/57HfFMBgsy/nqo9Xtej1dVKf+Nu+LXvF5dLw7t0piLwNC2jN5M2b8R05ktF5D527EJwNQBicDfQZ+7J5wsZ/M8ui35xH6LnXqFr2YV6OPVcSJybHLXs88SWusWImSrN6NdydZhiN2w333CMBuhy4bth1bbp/q+TbgQMDgw37NvBU9VNZ92BsbxnwQgghcieTT83fw3wfPF1rndyEJEJrfQCYjvnF9vdyc3hRZwKzgF3A5cCjwEPA7ZHTrdqXhE7EfBZz2gDgoNaNX19G/j4Qs02UUqpcKbVRKbXxoE3vFSFiJX4zm8+mwwMO/ktSdhfAhn0bmPjcxJwFCO3K5R4qeyjl9hrNHa/eYftG0e1yUz6mPLsDSPHh3bJ69+qMrq9dU2g7XZxdeGvGW9Fj/cs//4WCMzYmBQs1monPTbS9nnXH6qJTOB3KEZdJt25v8vVIHACSKz/3/jztNrGBuC0HtkRLY5vVt6+V2A1Iecf/Tl4/nDSVVbhh34a45XQ9Bn1+H3ctv4s3dr3BXcvvYnj/4QzrF/lQH3m8O1wbovfN6ze+ToFKHpix+8jutENM0vWKy5WmsjAf+nkJzgIDs3TdgAkPwcW/i57ev3v/pMtLvOyUFiyARYtQU6bwyPRz+N3FjScFjSAf1qUP7uz8Ymf864d/PLz2KAe3XsTs2VC17ENOhE5EM16am2Fq17+trXrF5ZL38GGCMRlhAa3xHj4cXf7eRfFvBa3lyupKVhsL4PuToex+nDd9i6LSavP2f/VxsyxaF5j/z9VeSnv2xNQncCozEOfAwfjTxje+Hm3ZYvadS1RYCOedF7/ukUdkIEQ7kzghO1esFgGXnXlZ3DCphnB2rzHtLQNeCCFE7mQSpBsHvKe1/r90G0Ymv1YDua5RcESO4R6t9Sat9XPAb2gM0kUPIWFZJayz+/SZuI25odaVWuuLtdYX97PpvSJErMRvZkcOHMn8NfOprK7MeSlCr/M2pezjFNbhlIGybJ1Tck7csmeIB7fLzdhBY1OeJ2yEU75RnD5iejSAZckquy5WpBwqvOfraa9vpiUrXQq6xJWzuV1uVn9/NYOH7U0KFoa1/fX0lHoodprljMXO4rjASHFBch+qp997OudZl+my6CxbDmwBzMDRrFdmRQeEhMKhdvFm366Mx+56HQse45JnL6FkQQl3r7w76+fb9BHTKXIWoVAUOYuS+tDZBrcSys0tHx/+uMl92WXt3TL6lrhtfjIhfvrujy/5se1lpeuZ1FSvuFxqKgvT7YY1q538YI6faQ//B84pc6OnOZWzscw0hXQlq5SXw+uv8/CFh5NOOqXLKWmPXaOZuWwm730aGWIQKW/XhpNAALZt7BdXkn+4IXk/mbDr39ZWveJyydOnD4UxGWFFSuGJyRqcNnRatKy7wFEQzUpduG5h3OUM7jXYfB7UesBwEH1bpjTTpyVPBW9P3C43a25awzmnnBPtm+pZ7GHL0kqYNQsSy42Vgltugauvjl+vtQyEaCVNfpkS89qerq9lc1j/r4F9Rt/Wg1szvqz2mAEvhBAiN5K/ok92DvDXLC6zGviH5h1OSp8CiV2k3wd+GPnb+uQ2APDHbNOfxuy6/UB/pZSysuki5bL9SM7AEyIrVpbYkm1LGDlwJI+ue5SGsDnBT6HoUtAlZx+UNxc8CTMWmB9oSr1JmWZWAKkl+/L5ffzx73+MW2eVyw3qOcjuLID5QfY3G35DSbeSpMw5t8vNdy/8Li9ueTG67rvD45djjR00NilTCWgc5hAuAmeA4Iwyqmqqsrq+DhxJ+y6/ODnTz+1y4/+xH/ULlXSaXQDBCox4a80phumOyQqqDu8/PCePDZ/fx8NrH85o24XrFlI+ppyqmirCOmzerrUejFJv+uBIhsdSsbaCffX7uGX0LVllUlplPIFwgCJnUfS5M6BH6oEBh04comJtRdbPN6uRfar7zO1ys+jqRcxcNtNckfD4iy3HPnjsID6/L6v7Mva147ph1yXdTgsuW8DCdQtth4yke9y7Xe68BedihYyQGeA1krOG3G5wu88A5uDzT4wGza1g6NIPliadx5Lph+Tz+53Pwd3xGWSf1H+S4dHHZERa5e0GFBUVcLDff8dt16zhM5j923YeP06Fv/HtyUsX5r7Hp7UvgFk7dhDGDNDVTZyYl325e/fGO3IkVZFpu9MHDMDdu3f0dG+tF6t4QWsdDf5/8PkHcc+jPauDMPEjxl6yjQ1vByAEOAym3PUybvc/5eXYc+nxvz3Oh180Zm4GwgF2/s8zDE/MYFQKunQxeyomBuSUkoEQreSL41/Yn5Dw2n54wgrI4VPH6n8aCAcocBTgUI6kzPU/bPkDt3/99oxft3XkX8gIseXAllZ5vRdCCJF/maSx9Aayqff8HMh185u1QOJwh3MBa6Tlx5hBuG9ZJyqlumD+92r1oPMBPTB701ncmBNqY/vUCZERu0yfwycO88yGlp2wAAAgAElEQVR7z0QDdGC+iToeOs6D3gdzl1GXohTUgSMpg6s5KtZWYOjGDACHckQvM90Ahv1H99v25/L5fbz0/ksoFA7lYM6EOazfuz7l5QzqNYh3b343uSeYzTCHp6qfSnnb2pWs9Cruxe+v/X3ScIJUzul7TtK6VMHFLQe24K31RjPVLOf3O992+1RZedmyAltxAziasOPzHVzwxAXmgImEKbbL32pexlDssUx6fhJLty9lw74NWfdpTFXGk0lvQ+v59rOVP2vJVYhTPqacd29+l5FfG2n7+IvVVGn1qIGjcCpnUtZe+ZhyXr/x9ZSBzNnjZ9uuX7VrVbOuTy79bNXPzCAv5mP5Z6tS3+5ul5snr36SJ69+MhpAHDkgdTZdpplrdmX4dmXbaTN3I70Be15ewcI/bGHoyENxJzf1BUU6fQoKsPLZnBBXFppr5YMGEfJ40B5P3gJ0Fnfv3jw5dChPDh0aF6CDxt5bsdNxq2qqzPsm5nmkQwXc/sR/MXpsA8y4DMrmwk0ePNdGAl8+H8yf327LQf/09z8lrdsxfJA5idXidMLMmY2DHzwec6qrxTDM8liROykeN8dDx+23T3htX/Z6fU4PJzaTOmgE494nWjQ64/cD3lovobD5xUguqyiEEEK0vUyCdF2B5K/wUwsCXdJulZ3/BMYrpe5TSp2tlPpn4C7gcYj2llsI/Ewpda1S6kLgecxBEX+IbPM+8BqwSCk1Xinlxpxau0xrnb4LtxAxYhv2ehZ7uPS5S5m5bCYb9m2g7rh99seKXSuybgxst98Vu1akPL1bYTcuP/vyZl++ZfP+zXFlH/279Y9+Q5tYnpdKYkmTFXjRaBSKL0982WRAadOnm3C73PzPv/5P/IRNm2EOQMqgjF02jpU1ZzecwE7ZkLKkdX/b+7ekdU0NEHmo7KFo6ZcDB07lxKFyE1SFxts3U2HCbDu4zbyvEz6c7NtybouPJTGr6tH1j2Z8frsP92AGed65+Z2MLmP1ntXcvTJNM3aanu4ay+1y88RVT6R8/Fl21NlPJfX5zSEEWmucDie/vfK3GWc9LLhsAV2dXZPW+7/022zdut4/+H6Ty+k8MfWJlKe9tO2ljF4v3S63OeilCXMmzOGdm9+J6wFly7WO+nH3MXvrOM49Nf55cOU5V6Y9llQ8ffrgjJSGhoH5u3fHnX73zp04vV6U15uzoRK9Vq9Geb0Uv/123ECHu3fuZPC773Lppk0ZD3pIZ1x1NU6vl95r1lC5z/wix+1ys/A7b8Ck1zg+4VWuro15TUh4HoXPWGWWs7t8kS+gfGZGr88HZWUwd675u50F6nx+XzRIHWviv8wxs+V+8APzZ80aePLJxsEPbjecm/A6+0zTPS1FFnw+jIkT0ffeizHxG1Q92Tg99dLSFH0OEx6Tx05b3nrHGyPTLyc8pZ64wdm5+sJPCCFE22tmQ6jWpbX+G+aE1+9gTpD9JTAXiH13XwH8GjNwtxEYCEzRWsd+FXY9UIM5Bfb1yN835vv4xcknMdNn9Z7VGZ3PbnpkNirerUjZDwvgaPAoSz9YmtXkUzsnakfFT5Dd2xhMKB9TzqKrFzF20NgmP/C+//n7cceQGHjZ9nliBXu87kXdo38vuGwBi65eZC6kmPyaKisv8Q2vA0eTEzvtJPYpAzgePp6UHZYYiIrtfWP1uPvVN3/FOze/wxNXPcFlQy5j4RULc1KiYt2+dvp26Wt/Juux1PXzuA8nt1xzVouPJdFXga8yPn9T/dTcLje9inpldDmVG9Nn76Wb7hqrYm1F2snD55bYBzit1wwDA6111v2OZoyckbSuW1G3rC7jyBEfu3fP58iR3AU6zj/1fNvlTPdllRMn9qsE+OiLjzKeWng0eDTladZz3u1y860zv5Vyu1iBcCCpvNVucEk2AjFDFuoNIzp19e6dO6nw+7Fyl3Mx/bXX6tXUR/qhBbTmkkhAztrXJ4EAq48cZmIOAnXjqqvZUF+PAXwZDjNzxw4q9+3Dd+QIMz8JEo50VjkUDvNiz3+h2Fmc9DzSg98F1Zjt6CAyeMfrhUAAwmHzdzvr22b3etGzqKf5muV2m4G52OBcrMQg3aDmZ2qKeEdm34YKh1GAChucvuCp6KCpC/pdYH+mhMfk6RdmXjKfiUwHUfz573/OaLstB7bEVTw4lVP60gkhxEki0yDd95VSb2byAyR/ksgBrfUrWusRWusuWutztda/SZzUqrV+UGs9MLLNpVrrvydcxiGt9Q1a616Rnxu01vmrOWkldmWXIr+sgEhSkKqJAJolk6b+qWzf1Dc+eOYfb7vPoBFsUTCwy94r4jKruuy9Iu708jHlrL91PV0LkrN7YsUeQ2Lg5eBXTVfR/3DcD+OWy8eUNwbXbMp9G4wG2+fA5k83xy0bGFl/2+x2ubl++PVJ6297pfHbeZ/fl5RFlFge53a5uWfiPQDcWfkH3lh8MXdW/iEnz13r9j37lLPj1isU37ngO8lniC1xfe1RuOKH0Q8nw8ekDnhkIrHUF+CUrukb+Wfq4SmZ9d073HA4bZntyl0r45abCh5HS72bmDx8LHjM9rwtbfJtFyi2pkpm4sgRHzU1ZXz88Vw2b/awffttOQnWXX/R9dHXQady8tBlD8Xtq6amLO1+yseU885N73Bqt1OTTjsROpH2tczn93H4ROr/yg2M6GW8fuPrSYHFRNYXCYN65S5oYlfeak1dfSkP01/rEwcWRI4hfl+KsNZU7U49F8zn93Hbstu4bdltKV+n3jua/Hqx5OBB2+tcr+GtGW8x5cwpSc+jQ8cOUVwQGbxTEMkw9njMslGn0/zdAfq2PTLlkcw2nDPHnPQK5u85c5reXmQsvDN+uvOZh8xMs9uW3dZ0plrMYzI6eTsHKqsruePVOzLaNl1LEYs1gMJS0rVEetIJIcRJItMgXSngyfCnNDeHJjIRW3aZacaBaDkrIDJzzMzGlQl9vVIF6moP1zZ7v0OPzowJnhVBzfTGfT7/Fix7Irrf5zc/3+zHw+kX7YrLrDr9ol2221086OLklTFBw7/uiJ85YwWp3C43/bonTyEcO2gsU86cwqKrF9n255ozYU7KbDGwz2pI3E9zv22edMakpHWGNqI9yKK9lmKkKo+rWvYhgWdfRb/5CwLPvkrVsg9tt8uW2+XmzFPOjFv39UFfZ/qI6ckBncTeasdPjX44aWnJzJJtS5LWHT6e+fch6UpQy8eUmx/yE9kErOe9PS/l86CyujKp5Lqp4HEmpd7r9iYH7izW48OuX1o6bpebaefFZ4AePHaQG166IaPzHz7sxTACQBitA3z66aKMAmhNsUp4rcDjE1c9gdvljtuXYQQ4fNib9rLcLjc9inokrddontn0TJOvZdk+Xm+8qOkE+ltH38qq6au48uz452+mmTB2YqeeWrpGyl+vzcP0156O5Ld3nj59zH1pbc6010BYsWTpX2wvw+f34Vls9vx8qvqplOXgo3sk32/X9etne537Op24XW4e9DyYdNqeI3uSM2jdbrOP27x5jf3c2pHEx8T1w6/PfEiO2w1vvw2/+pX5u51dt47sb+eYmfjWK6231PxtYPDIu5kFUesDuelJZ01QDxrBjL7IDRrBjHq4fhWMz063K7sWQgjRMWUSpJvcjJ9v5uNgRbJUDdZF/rld7vg3cWkayls2f7aZrz3ytYz6ZSXq3vs4aCegQTtRRwfE7LMYNpZHA4Qnwie45NlLMv4QH+d0X1zZR9+h9m0bH7osoWF7QqBy//tDUu7iy4Yvk9b16dKnyQb61iTOX33zV5zR+4yk0+2yoD6siw+AnVtybrO+bY4tXY21dPvSlG+oU5U07t96XtxjZf/W87I+nlS2HYy/DQ4dP4Tb5WbMwDHxG6borZaLkpmRA5OHAez5ck/GQeNMSlCTjjFFkHxv/d6U5d929+nQksQZRY3Kx5TH90e0EdZh2315a72EjTD6/2fv3OOjqO7+/zm7yebCJeEeIgsBBAQNCUQjQwRXEgEVK4Xfr1qjQeBxQdBKrQZBW7UoErQtfbwgUYtEsO2vD4qVKiCRBSQLPAaIaBAQDAYBgUDCJeSyO+f3x+zsznWvs4GE8/blKzuXnTPLzsye8z3f7+cDCjcfnnZQ4Uh121oBUS2Sk20wmSzwiRhR8HyjN4B27FgxKirG4dix0A0+lCW8vrbMMJksSE62yd6n11bvjr012wmUGWxLs8mC0COqgae3Cn8BIZtUmoloS7Npltd6zyOpNzgrJytvJSAhlyhL4ZKSsExR3liaKdwnRf37o9Bq9XbIUmJjcTwnJ+y2AODc6NHeQJ2FEJQNGwYuKQlF/fvD7KgFTlqAiiTg8Uyc+mSI5jNMqXOp18fYkZWF7A4dYALQ0WzGsoEDYU9NBZeUhLJhwyDmW0udZjkrh7TkNNlxEmMTZRM5XjgOmDfvigxi1dTXeK8lEzHpl1LqIX424Io2x2htHOoZBx7C044HsK+7b5u0RNQfq/auCsnwSA+Zg/qKUqB0AbB8M/DVf+m+Z+HWhQGPO7b/WL/LDAaDwWi9xATagVK6uSVOhBEeYtllk7tJJrB+teOsdsJR5YAtzRbV9P/PDkqEhcWgh5tqCspLOXnxpDcDK5BpgYiz2okPdmwAkAPh1nUhLqkWDeYmwEUgxNzNvgChp4Ro1d5VOFhzEDse1ndSVbaz9chWwEq9x0hpN1NzX87KoTCn0OdoKQtUUph/VBsuiG0cOK0W2J88ZHLA8xNdIeeNmodur3TD6frT3m1bj2yFs9op+85TO6QKneMqG5DmwKBBKQHb0CK1YyqgU4WyunI1nrc9j3d3vyvMlgN+DSFSrv8OMN/gvVZSrv8OQHZY5yXFWe3ET+flOjpiZt304dOx89hO3wZRf8fz7yJ+1+EGMaWca1AHYHkqlBkbdT/a0mxIiEnwOfUprj3pPdDMN+Pp0qex+SH5z1l8jNrjKJA5QFFeEfYc36Nr4HKx+SJu+dst+HLal7LPKpa78m4+rHJXQLj2Y02x3msMCD4rLymJQ0ZGKfbvn4X6erEEnMfp02sQG9sFBw4IWcFnzwqfKzU1cDaQ3mcS26qtdSA2tos3EJiUxOHYsWLdtoZ0GxK0vqcUzsph69StGPm3kfivr4Cla4WnYbMJsE0FLLeMUukaDu0xVDBNUSA6WTurnXh719ve9RRUMDKIAHtqKtLbtYOjtha25GSZG2pR//4o6h+ZFqSSc6PV2b8AkLSpGWf+yEEIYVDgRmB+6XzV5Mj7X7+veq/edbsjK0tzPZeUhHqdEtV5t8zDjLW+bPTHRzyuud+VjC3NhjhzXGR9MNEco6lJKOldsgSoqRFKe6/AwGRrIPXu+9H4yWLEuoFmsy+TToqZmFXZZwRE9kxdXbk6+MxIHbwSJ1U2wGUBEAPwFPj0DaDHN5qyCUfqjqj6MkqUAeEOlg4RnSeDwWAwrhxahXEEQx9/AutXK0rnVX9aOpEiK/cMICivxaqvVwXdVklFCWjaF0CMJ/sppgmj7zkitHVjMWBu9GVFJZyWlVTsPLYzaDOJxdsWqwb+/sq8RFOHwV0Hq7KzmqzrMe79cTLNRPH7qXfJtbsGdBoQcmdYqQlIQVUZN3fE/1GWYXVH/B9DakNEK4tJCmflsPmhzZiZNRMzs2Zi05RNuvfjsOwG2bUyLLshrHNSolVyKyIafqS0S0G82ROc0tBWO1BzIGr3S7BBjoKMAq/mo8Vs0dRjE599mT08WXsBXFe3HNmi+lxamkPBmAOsf3A98tPz0Tm+s2bZLQ8es/4zS7XeTT2ZdBGUJSl1DlPahxZ09gXoBM6f34kDB2bL1lVVLQi6DFavhDcpicPFi9/iwIEZ+OGH+di9ezQOHZqLw4fnyfY7ftyXzaj1PYsEKjXlrBz+T+01WLoWMEPo3Fh44KltwJCu6u+50dWoeZxf3/BrcFbOl/0iIVLjCEAIWr36448YuXs3Yh0OrxNqtOi5bRuIw4EYhwNzDx0CANyQt0v4vYBb+JtRgppLNar7Q+lUTED89jGG7NwJs8OBITt36u4jRXwm+ZM4uNIxpA8mNcdobAQeffSKdbNtLUycWoT7HumKP4wBcqcA263qfdzUjbH9xsJETDDBhISYBNyUepNsHy1ZjlA5c+mM8CLhNISnEwVAAN6sW3EBwDf5qUOXxC6yDOJ3dr/DJG8YDAajjRBykI4QcishZD4h5HVCyGue1zp+5oyWQLM85CpGWQK8rHyZSq/PKLMN1ay5dTsG//JjLJzyC5RNK9PUMQuXExdOqAKBif0qsOyRqRj7mzW45rEHhfXjHxeMABRlf1uObNHVFJJy4Iw6wy1QmZc9y47K2ZUovG+0KlC54fAGPPPFM97vwFHlUA2QTTBhxS9XhPgvAiTFeTJRJDovSv2qmn3pIHw8QGNgovGo2ZcecjuAcJ8lx6k1lgBg4w8bvW32TuqNgowCv/djTX0NiHUHMGoRiHVHRGV0IsXlxXhrzR6V3o209NSeZcfxJ4/j0rOXNI0wAGHgEmhwoHf/FJcXY9z749AxvqOmqcGqr1cFde+JZc0vjXkJjin62XeclUP3dp46piCC5MrPVZBR4Lfs0R8rJ61Ezdwa3cyZPT/vkZVKlVSUwMW7AAAu3hW2scv8UfP9LotouasePvy0zlFdsqWmpqPYs+e2gIE6fyW8hw7NxcmT0kkIF6qrF8PlOiM7xoULu2Tt6DlGB3OPvGq5GyZAdoTME9rBv0Fdtcua//HNP+CsdkZk8OOPLlu3eg0jXIDXCTUa9Ny2zWtA4QawuLoacw8dwpDhdcBDtwG5zwp/PffKLctvkV2znRPkrtD+guxDdu7Evvp6obSwvj6kQJ0/iYPWQMR9MKk5hskkBOuuUDfb1oR55C1YNEo7QCdS+kMplt61FC+OeRGlBaU4WX9Stn1bdWQuy4CkT3WpK4Q70ZPBanL7rbj4eP/Hur+TzmonHvvsMdlEgot3YXGZ/99uBoPBYLQOgh6deIJzlQC+ALAAwCwAsz2vvyCEfMuCdYxwMdKhVum8SkFlWjpGmm18e+pb1boHhz7o7bAvyl2EWJO+APhP538KWpvuh9ofhBeS7Kf9p/d7Bzn/euIJYf2lrrraeHr6XlIGdpbrJonlX8FQlFcEU++dquws6XdgS7OBEPlA/BeDfhHWAKdzQmeVFlnzkSzZZ/y23RugpgaANIM3NaDLYLXzaLAMTRmqvYEKQZhgr6suiV1kGUiRltHN3TgXM5YuF8xDSl8U/noCdXqBxZWTVqIwpxDXdLgGPdv3lG3794F/+x0caH3O4vJizFg7AxsOb9AN8m35cQtufe9WXUMIaRvBlqvLSqT9uK4CwP4aubYiZ+Ww9K6lMBMzCAjizHF+s7m08HdvSDXvlHqJ/lxk/RFM9pGeu2pdXfADTkobAxo+iM9a0Q1V+m9RXf3nINtxedvxp9Pn15HRQ5+JwncnzelL7pSieQ0VjizUDAiKZdlaBj+hXhtaiAE6Kas13F2NQMsh9sNTp1CQUeCdJJDeKzzlMWPtDO9v0uTBcvkB5bKU/fX1fpcZfhDNMR5+GJgwQXB6bUVutlcqwWQZu6kbNfU13j6b0uDo9MXTOu8MDlnAP80hVELABZiagTtn+624oKD41b80nNkh9Dea3E0qIwojsn0ZDAaDcfkJKkhHCJkM4HMA1wE4DuDvAIoALPa8Pg5gMIDPCSGTonOqjLaK0Q61UufVOHOcagApZnK5qRuNrsawzTac1U6s2isvVyUgsoGqWAKZ0k6js+jpXC3+h7oMT6utip8rVOul2SCclcOyCcu0y/4kHTl/7pOA4KAqZkERECy9a2lIAbR+yf0014vfAWflkNNbLooeasmeyIAuAzQNO8ROcXF5MVadfdSbYUUKbkdNl7VhtQVAN/ss1iwEYhtcDXBTNxpcDX6vK6XYeCSZdMXlxUJQrKJAMA+BWfhbIQQU/AWRivKKcPSJoyqnRZ7yusFc6f3T4Grw7rdk+xLZfnolnc18s9cQQiuYF+rzwJ5l1/1elGhlT9mz7Ng6dSteGvOS3xJlPTgrh7SkNM1tjW5fxqjSNdafi2wgAmUfabmrHjo0F0IWR/AECtLplfoJbbn8vlfKxYvCZIctzQazSZ2BCQB/KvsTnNVOb7ampqA7x8FUKJSli4G6Z4ee1ryGOCuHUX1GqdaLzymp1iUAdEvsZki2emez+vNN1nB3NQIth9hJ3YTPoXImlwz0F29bjOLyYhRkFCDOHBdUAHtQYqLfZUYQLF8OrFkDNDcDOTnAlCmX+4xaNcEE1U2QT0J2Tewq236x+WJEfVJHlcN3fwFCXyT398DUW4Eb3/Hup5dBfPT8UX3zLw2zpHax7cI+13AwcoKdwWAwGD4CBukIIakAVkDocT8CoA+l9AFK6TxK6dOU0gcA9AYwA0AzgBLPexgtRGv/kYyGQy1n5bB0wlJsmrJJNYDsktgFPAR3Lx582FlMWkGMpLgk1UCOs3L48N4P5TsqOldPL//Yb1t6/yZKnTR7lh3LHpkqL/sDZG3t+So+4LViNgmZRbHmWKR3D6089KmcpzTXTxo8CZyVg7PaibLqMtm2QHpTepy6eMqvFpnX+dKTYUV6b4/IXEUvmDY1cyo6xneUZcf5y/wRxcbNxOzXYCIY9FxnAcAMc1CBhVBm36X3DwVF8a5iFJcX4/DZw/pvUgQBRLQy9sJ5HqyctFKWXZbRI0Nzv4FdBqrWGWEy86sbtLMdpM6YSm0jI7SO9NByV/3ppzdDPs7ZsxtQWenfHVqr1C8Uh1gAOHduh/dYWt8RIAR9Z/1nljdbc8baGdqBuqIifPbkRGzoD9gnAMXDqeY15Kx24ssjX8rWpSWleQO19w+9X7Zt6rCpIX0mPWpGjfIG6mIArxNqNDiek+MN1JkBFFqtXnMKbwBDxxX5yQ1PgrNy2DRlU1AB7MrsbAxOTIQJwODERFRmR26Ec1VRUiLo0QEAzwNbtgDFxUyXLgI4KxdQciSvX57suhYn3EQotJ8fwVL7/WD5/QWoMlh7tu+JbdO2oUuCdl901d5Vqt/JgowCkKoxqgnKljRfMXqCncFgMBg+gsmkmwMgEUA+pXQZper0CEopTyl9G0C+Z9/WZ9HVSnFWO2FbYcMzXzwD2wpbq/yR9FcyFSlaA0gjs5iU2G/UzmzxZrmJKLK/vt7ZWfN9IlqBxPax7TUHTfYsO8qe+zNG55cJHUGNTDN/mmP+dKaCQS/gIx5HPL6UcL+DyUMma2qRrT24Fs5qp8ot9smRT0aUDSO6WSo533Qejh8csnXKZSmclcNjNz+Gvsl98djNj0V0Tt5srYwSlRj873J+F9YxTTDpZiEovyueCiYJ0qwxGTpBAPG9yusr3OeBNLuM66X97/lq2asqbUojBhl6JcXSbCyleYGWmYFRiO6qffsuQEZGKZKSOPB8eOWHJ0+uCtpEQoTnL4a0f7duvgR8Wbm9Iri752e56YU3CK+g028K8cupCVh+k/415KhyeIPNIpkpmd57ceKgid6MYjMxY+KgiSF9Jn/UjBqFlNhYuADMOnAAaU4niMMBEoLpQrAcz8lBn7g4uAG8Wl3tbWvkoUYk5qzR/H0AgAtNFwCEprdWmZ0Nt83GAnRGwfNMly5CAkmO1DbIJ9O0sq3Dnch1Vjvxpw++0ry/xMw5MzFj9a9W62b2iignhTkrh6fyb5JNUJr6bg15UjUSojHBzmAwGAyBYIJ04wHsoJR+FGhHSukaADsA3BHpiTGCQ9SlEDW/Agm+X4m0tEOtLc3m7bQREG8HzG8ZlQYd4zvKlrNTs1GUV6S7vz3LjrJpZYL+l8oFdYPftrQCX8N7Dtfdn7Ny2Dx1s7CgkWm2Zv8a3c8pBqIICGJMMSEHTU9c1BZcP3HhhBBUTrOpxPrD7QTbs+xC1pRCi8zFu7wdRmlnONKBNmfl8F/D/ku1ftXeVT7NQA9NfJNqPxGxRPX7s997S8vCQVYGbd0uE4PPvLHB7/UoRXRTFfl1+q9170Ot78qvW6lOEEBEeX0Z8TxQfh4RZVDQqEGGLc0Gk8bP6an6U159r2Aca40kKYlDnz7zkJTEoaJiHKAISAGA1VqIDh2yER8/AEJelzaByl6lVFY+AErVWmjdu+ejU6ex6N49H2ZzZwAEJlMCrNZC9O/vu07vGODpPvgJ7ooog/AiwVxDtjSbytxEWnbvqHLIMmONHIAqDR2ONPoC3KGYLgRDmtPpPT6vaKs+Jgmm396smYncwdLBsHNgBEFBgWAaIYUQpksXIaLkiN71fPbSWdly4chCWf+EgIQ9ieiocsDd5wvZ/ZU98hLKppVh27RtWDhmIbZO3ep9PunpZALazq1FUydi4suvA2OeA6bkgvYqC9uQKByiOcHOYDAYVzvBBOn6ACgLuJePMgBpYZ0NI2SUARF/blBXI3M3zsWA/x6AuRvnesuC957c63VZdFM3fvPZbwTh/UBlVAr8ZUrpwVk5rP7ValX2V31KKeZunBtS2fKivEUB97mmwzW6rpf+yiSlg9NQ0dTfg/BvLQ50eSoPGESSzTjrplma67899S1m/WeW9zO4qduQDmxBRoFmR/pUvVxjTOlgK0X5b6/UcwsWVeBALOu17sCbdwVf3shZOcwZMce7vGrvKt17QJqJCuhr6YiY+36pW44MAGv2r9E8n0jcEkV3WK0MN2kZslGDDM7K4bpu12luW/X1Ku8+r93xGm7vdzteu+O1FnPjPnasGGfPqicBOnYcjf79i5CVtQMjRhyAzdaMTp3Gah7j55/fD6qtujqnwtFVIC5uAIYMWYmMjPUYMmQlRo2qgc3GY/ToelmADpA8C6psgMsT3HVZVMHdsf3G+nUEDXQNcVYOb971pq5pSJfELt7nFE/Dl0XQQsvQQYqRpgs/Nuo/hwCAtkvGNY9OUf0+vDL2FcPOgREkCkMlEAIsWSIYS9R7pvMAACAASURBVDDChrNyeOSmRzS31TXWqfZ9cuST3uVIjJ1saTbA6lT0v5zgrJzm84mzcnhrwlu+A0gyiaUTj1LuuC0ZGPUyYN0OCqpyt48mLT3BzmAwGFcTwQTpYgHop4SoaYYgf8JoAZQBEQra6izYH/jwAYx84QnMf+EcbvnjU4Z1MB748AFZttLIv43EM188g1n/mSXL/GlyN+HDSrlmnF4ZlRRlppS/zCkpnJUThO4V2V+vbntVt+xOmbWXn54fVIfoX//3X8ILDdfL8uPlmv/WkZa76gWxRFONpzc+rQr+BePcqIdegG/H0R2qDC+vy1oEcFYO9wy6J+B+g7qoy2ZEUjvKNaj21+wP67rX+3e7tvO1IXeYlUFnvXtA1NMzwQQzMeOe6+5Brw69dI/r7vWlb5Ay/nEh2CLJilq+e3lI5xksnJXTDLq9us1X8mrkIOPxm7VVHo6dPwZntRPOaid+89lv8Pnhz/Gbz37TYgOpU6e0v8d27dTlthkZ62G1FqrW19fv82Tj+Ucv485i6RTwvSLekvKE0xC6ElT4mxCZy6IW/kxDpM+VSLJptNAydJBipOlC77i4gNv/9cQTsNj+DFi3g4CgMKfQbwCUEQUcDoAqJsV4Hli9mmnSGUBRXhEKcwoRZ5bfD9d1VU+unGs4J1uO2DFV0v/ae9K/u7w9y47CnELNTGKtvpjy3PSCedEi0gk1BoPBYGgTTJDuOIBQRA6uBxD5SJgRFFolU8fOHbsMZxIeczfOxapPD3k7I/x7G3Bn0YKIjDCKy4tx89s3q5xXASGIqQzcmIgJk4bITYn1yqiknDh/wu+yP7RKL3jwumV3aw/IHUl3nwiu08hZOZRNK0O3RLVIvTSzTUqk2UV6Qaw+yX3AWTns/VndSd1zfI9qXbBola0BQL9O/fyWs0WCtyRPBxNMQkdbB2VJDaBvDuKP9yu0M5yUOjuBcFY78b/H/jeofUU9PRDhGlrz3RocOx/gmWPdLmTQrfurqnzxYlNo+mWhoPUd8OBlsgBGDTLsWXaM7afORKOgKKkoQUlFCRrdjV5nWyOyOuvqnDhy5OWQNeMAICVFu9xWyGxTB9nPni0N2Jbo0qqkZ8/pQZ8XZ+Ww5aEt6E6GAMQtnAtxA5fkrouZPTODPmag9pTfv7OuDuuaOgMdhUBmICOYUJEaOigx2nShiuPQRydQ1ycuDlUc5808XThmIbZN2xZ0mTzDQGw2QOt72rBB2MYCdRFTlFeETVM2ybQmtSoSlJN54U7u/epfHkMhSUZcMJpxRXlFMB/JVclEOI8GvgZMxMTKThkMBqMNEEyQbguA2wkh2rU8EgghgwGM87yH0QJwVk41EJ0+PPgB0eVCLD19bcdrKs2q2u8yMP+L+WEZYRSXF2PG2hnYeSx4TZ/7brjPO8t6badrg84iuNgsDy40uBpCOlclxPOf2WRWdbKUgYxQAhuclcOwFG33VK0yDiNMDbSCYaIgvEmpu4PInC45K4cca45q/aaqTcjtmytbp8xIDJdAWTW39L7F778bZ+Ww9K6liDXFwkRMAR1e9Ryc6xrqNPcf3HWw3/NTItXfEtFza3VWO/Fn559lJctKAX4lHSwddLXp6l31qs9llGM1Z+WEkm8F/96vdpU1gvUPrkdSXJJq/YkLJwzJ4pRSV+dERUUufvjh96ioyNUNnl26pP4erdZCJCX5u6+1AkjugG2JLq1SunfPR2pqaFlZnJXDQ7/sKymTbpKVSRMQXbOOSHHW1eG23RXYQgYC6X/xBuoimUjQIk7jOZjfvXtUTBcSzepJjPzu3VHFyUvtWEbMZYTjgNJSYOxYddlrU5Pg/sqIGM7KYevUrSo9OClnLp2RLX97UnvyIRDHLhxTZcSl1gWeAAaAbtdXqmQiLjZf1HR5FTWWTcSEN+96k93DDAaD0QYIJkj3OoQe+1pCiK4dnSdA9wmE+pQ3jDk9RjD079T/cp9CSDirnbhtxW2Y/8V8XHJd0jQ2AIQy1Fn/0dYb0+Ov2/8a8vn845t/YO7GufiL8y84XHsYr+14LeAA3lntxPmm87J1Q3sMDbpNrQxI6vlPq1RUGWTTC7rpoZcZqFXGYYSpgVbJ68YfNsJZ7UT3dt1V+x+sORhyG1I6J6jdcZv5ZlXG4dr9a1X7hUOgmeotP24J+O+W3j0d04dNh3243W+ppT8H5xu636Dan4AEpVcoxZZmU31f/Tr109xXy51X6cKpxEzMuvc5IHeuM8pxVUQroMqD97YZqmFMIG5Lu0217kzDGXy8/2PZumE9Q7uHldTWOsDzTQDc4Pkm3VJTqXMqAHTqNFalA6fEap2js0XeljKYqtXWkCErA30UTZKv3aeppQlEN1uk5OtaNLp5wEQAkwWwzgMQXHZ1KGhpxf395ElD2xDR0rhbFaW2GBHAccDzzwPx8epA3dq1QLExz6irnUABaaW+7MGzB70GQMHirHYKE1mKyaljewcGfC8AvPDgeNXzT8sNHRCehwQEZmLG7uO7mS41g8FgtAECBukopeUAXgHQD8AuQsgHhJDphJCxhJDbPa//DmC3Z58/U0q/iu5pM6QotaOC0VO7nIhlXwCEQX2VTdCq0hiM7fl5D8a9H1gLSSQcowM3dWPxP7agefPvwP+YjUuuSwFLD71lDBJCCYxwVg7J8Z5MEEWAo5lvVrWvLK8MVG6pxJ5lx7WdrlWtrzxVqVqnNDXwZzChB2fl8FTOU/KVVAjwSA0KROJj4kNuQ4peGavSvKG+2ThB9kCGCf7uQzEQVVxejHd2v+NXp0bp4CwGl5zVTnx1XP6oTUtKw7Zp20KeSeesHG5KvUm2Tq9kVhUcCcKFc2jKUBDrDt2gy/affK+NclwV6ZSgr4cmZt6GYhgTCK1r8Zufv1E9myLROKurc6Kh4UcQEgPADJPJguRkm+a+XbtOhO+n3oS0tOcDHr9//yIkJmpnY4ptaQVTw2lLD1uaTbhmFFqaqB4Bfstc7C1vH/ax/fGPC8eEj0AhVP12uQaDc943XKNNSyuOB3Bzebmh7QD6GndGusgyDELMqJsxA5CWRB89KqxjgbqoM6irWk928bbFsP7Zigc+fCCoLG/vxJNicmr6L4ObVLdn2TH6Fovq+fftKXlWn6PKARfvAgVFM9+MZeXLDJncYjAYDMblJZhMOlBK5wJ43rP/fQCKAXwGYJ3n9b0QMugWANAXYmJEBeUMv9Ez/kbjHZBLB/fr/urLrFFk5Gw4vCHowfPdg+4O/YQ0ggzKjpASpQYXAQk5MNItsZtm26LBghRpxlu4IuZa5bhbftyi6swpDTDiY8MLoE0cNFGmCRdrjoUtzSZ0PvuMlu07pJtukm5QaGUmAkBcjHwgnJlijI6Vo8qh0pRT4u8+dFQ50OhqBA8eLt6FR/7ziPcaV2YnKR2cxeWSihKVS25mSmbYpS62vjbZ8q4TuzQ7+ntP7pUHnHTKWEViTDFYlLsI26Ztw+gcC7qNe1cedAHw07mffOdhkOOqyPO25zXXn7h4QhWANmKCQytD7kyDvHxK6x4Plro6J/bsseH48bdAaSOSknKQkVGqW7564kQJ4C1H5j3LgcnOroTaA8rkbUu8ht3UjUZXIxxVjrDb0oKzcrjnOoW2ped5Sb94AY/cOzAqMl11CZ7APoFXmu97Ypyzq0iVjmPnrgsXDG+rMjtbc0rBSBdZhoFwHLB0KbB5M3CNolz/3dAnzRihMbCLItvNM5F6tLIXVu1dhflfzMet790aXCDMut07OZX/p+WwTwxe4ntRrnri9+Pv5BnZ4u+lOGkoTua1pHkEg8FgMIwnqCAdAFBK/whgAIRA3CYA3wHYD8DhWTeQUvocpUp7KkZLYPJ8lYECB9EgFP0oZ7UTe054tH2Ug/uKAt2MnN+u+21Q5xKWTpFGkOEf3/wDv/znL3U/k1LLLbVDquZ+/ugU30nRtgWosqmMLZzVTry9623vMgXV1JILxP1D79dcryw1/PrE17LtQ7qGF0CTdhIJCKZmTvUGkBblLkKcOQ4EBHHmON0gW7BwVk4z0Nc1sav33og1xfo1cwgFsWOsR+f4zn4zb2xpNpk2H095PPrpoyguL1ZlJykdnJXLsm0RGGMo7x09lzhVIEujjHV079GYmTUTM7NmYstDW8BZBXH6zVM34+RTJzGg8wDZIa7p6BuIGqGJKEVPqHvNd2uw68Qu2TojJjiCCaDfn35/2J/rxIkSUOoLpNfVbcHFi/qZmGfPbpQtnzu3XWdPNRZLT8UaHqdPrwEgPANFLUIePLokdlEdO5S2tCgcqbhfJc9L3mWGwxHR4TW5LjERABWcNj3dmfhzFcY3BCC7g9pAaHj76GQI3t5JnVFqpIssIwpwHHCTPMMZqaH3NRihIXM618kUb+abMemfk3Dz2zdrTiIXZBT4Jimt22Ee/Qpm/3J4xOd2ofmCt1/qrHbCUeXAkvFLMCNrBmJNsbq6xgwGg8FoXYQU0aGUHvEE4vIopddTSodQSnM9636I1kky9HFWOzH709m+wZJnsN9Sqe7OaidsL87DMy9cgO3FeQHbLfhIEoxRDu4B3Yycelc9bn775oDnoxe88luaqBFkEF0rtWZLndVOnG04K1v3h1v/EPDclEwfPh1IOA1QM4RBoVlYhjxwVlJRogrchZNJV5RXhD5JffzuU1JRIjMBMMEUdgBNmhEVHxMvOw5n5bBpyia8NOYlbJqyyRCh4wkDJ6jW7Tu9Dzx4mIkZr9/5umGCypyVw5LxS3S3KzMFtd7/BPeEbJ2Ld+GVba+g0d0oK/UsyCjwzpRbzBbvv6NsEAAhCBlJsFPr3gnK0VKSKSCWsQ7pNgRLJyzF0glLNf/Nr+92vWy5ydXkvc+M0ESU4i+jwMW7MLjrYGSnZmPZhGWGlDTqDY46WDqgc3xn5KfnY+Wk8HTa9Dh6VFuLUyiL/V62rrHxJ819tejRQx3YP3FiOZzVTqyuXO19rpqICTX1Nbh0Sd5WQ8ORoNvSgrNyyOiR4VsheVabYtyw2SI6vCaV2dlo33waoDxA3cCJDchtVBtiGMGOrCxZoC67QwfsyMqKSlvrMzIwVhKoM9pFlhElCgt9Za+xscIyI6rIJl39ZIqfuHgCO4/t9EolKCespRnnFDTk7Da9/Sf/v8myCb056+bgfNN5uHm3rq5xtDDK5InBYDAYcmIC7UAIiQOwFcB5AOMppc06+1kglMC2AzBKbz+GsWiJuIsZMC3h8FSy9iCa/vYp4LagaXMTSjL/B9wj+gL435+VDOLEwX2VzVfqumcK4KZyYXmPbt3ONAec451+P5eWEQIAZKRk+DL4lCjPQ1KKJ+rDSdsUNUCkhBM0s2fZsaq7FVvghnAruoBLXQO+z0zCnyW949o78Fb5W7J1m49s9r5WOlAGcin1B2flUFpQCkeVA7Y0m+o4YnaVUfjLonRTt+61ES563zkBCSpjT3m+FBSHzh4CBYWJmLylnpyVg2OKQ/Pf0URMcFM3TMQUcRBS6/N88PUHmDhoouy4WjqGsG733jdmYg4YLKyqrZItV56uhG2FDY4pDk2NzUiCZ4HulX2n98FETJgOY1yxOSuHtKQ0VNVVydZfaLoAEzHhw30fwlnt/znmj5SUAhw/Lr+HL12qwpEjLyM52SYre9UqN9XTmtOif/8i/PTT6+B5X1lkc3Mt3isdjWNn3d5rNc4ch9EpXdD8k7xUMyYmcjflpXctRc7fcoQBr+RZ/WT+zeC4iREfX4s/dTmLGWt9uqN3TFgWlXYARC0op8X6jIzAOzGuHJxOwOEAXn8dqKkBbDYhu44RVQpzCrFmv5Ax7J0YUPZLAZ+mcpoDT767GhcP/giatgnxaQsw7tpxMjmKcCQO9CbJjl84jln/mQUKCp7yaHA1YNXeVd7tzW51vzUaiLqkTe4mWMwWvwZYDAaDwQiNYDLp8gFkAfiTv8AbFepvXgGQ7XkPowWwpdlUJa7hlkOGw4lvr5PNMp749jrdfZ8ufVrtAmnd7hPG1cjIUZYaTHntLd3jA9oBhMKcQrx555v+P4j0PBQoZzOVenWRBM2G3HgKiPFk8cU0eTuAHeN9g1ulxtXvRv7O0I7QvtP78MCHDwAAzlw6E2Dv0AjkomYktjSbLLMs2ujdYxTUrxGEiJbuoTjz3i+5H5aMX+L9d9P6dyypKEEzLzySecpHHIS0pdkQY5LP2xw9fxS3rbhNNkuu5/pKQBBjisGbd70Z8Ps+XX9atU40xTBaY5OzcshP9/+TxFMeM9fONCwbILOnWvuQggr6be7GiPSCkpI4xMR0kx+b1uOHH55BRUUu6ur8f4b+/UNz/o2JkTsnUzSjoI8LrwyluKEjQV7fPJQWlCKp8TPVe/v0mR9SW1pwVg6397vdt8LzrE6+dl/Ex9bjs+8/87vMYEQdpxO47TZg/nxg1iygSxcWoGshOCuH9hZP2bnYL816B8hc4dtJ2jd9bxPOL/sE/MYXQJeXoqFqGI6dk+sW3z3w7pD7QXuO60wsQ5h4NBMzzMQMonQCJoEnp4xAS5eUwWAwGMYQTJBuEoDDlNJPA+1IKV0H4CCA/xvpiTGCg7NySO+h1lyKxDkwFFKu/05WKppy/Xe6+5aVUV3NucFdBwsaX8pgmaLU4OCuVL9ur8rBf0q7FBTlFYGzciibVobRvUfDYtLXEtPiix++kC3vOCovfUppnxJ2EKpgwgBYpt2pcryUds5q6mt8moMwhae750FL1B4AVu1dhbkb56rMJbTMJq5UOCuHN+96U1eX8fDZw4a25+8eC2RA4Kx24u/f/F13+/dnv8fMtTMxd+Nc2XvEshJntVMVOFBmQYYKZ+Xwxp1vqEpllCLUi/IWea9HwJc5+NKYl7DloS1BZb3p6SOW/lCK3cd3e49vJmZdTblQmH3T7ID7UFAs3rY44rYAQUtNr+SIp3zEkyjSzDYfFDzfiNpah3dNSkoBhCR3ADBj4MBlugYTerhc8tJ+AsBEgFgTMKyTCc/bngdn5VBfv1+2X2xsClJTjXFE3V8jP3YkxhvBoBxgK5cZjKhTUgI0ekxM3G4hUBcNpxSGJsNTFPpxe6YA5Q/7+q7KMlg+FoAZcMeBfvkkpg+fLpOpCEcP198EFQHB63e+jgW3LcCvb/i1bNuvb/h1i0yMfnvqW5UuKYPBYDCMIZgg3TAI5hDBsgWAMRaKjKBocjep1rXUj6UYZCJjnodl2p0omDBAc7/i8mK4DudoansM7joYlbMr4ZjiwMIxC7FswjKM7u3R9BJLDeACCAUSTmPD4Q2y4IUUS4w8ACd16RKF6x0POZAQkwATTDATMzJTMpGdmo0uCYp/M0/Wn+vHG2W6WDf3kmvjRTJY5KwcHM++jNH5ZbIsPmkmji3NhlizIAgsOqSGi7/A0qtlryI5Xh4AHNBF+/u8klE6nop8fvhzQ3VTbGk23UBMoOwvR5UDgTx2xKBRcXkxisuLcet7t+LZTc/CtsKGW9+7FUfq5HpfP9RGLguqdX0oAyKclYM9yy7TI0uOSw4pY7Iorwi9OvRSrT945iCWlS/zdvzd1C3TZwyXYI+hdG0OF01nUgmRZj0mJOjdlzxcLl+JVFISh8xMB/r2XYhhw7aGFTTTaotSofMQ978zMeu3dXBWOxEbK8/uS0wcqHpfuFxyXZItt7e0j+ogdPrw6X6XGYwWh+cRFacUhiaL8iQZx1q6dNK+qXIotf9upDfZ8dodr+H2frfjtTteC+t5Zc+y6wb3CCFI756OeaPm4WLTRdk25XI0KC4vlpXYApH/rjEYDAbDRzBBuq4Afg7hmD8DYNMpLcjAzurBUEtl0olBppeeaw/Hsy/rdkRWV67WdYGsnF3pPda8UfNgz7Jj/LXjhSCAdTsw/nHAxAPUBKz7K1A9Ast3L1e14ax2Yu/P8jJDLcdPUSvtxTEvYuvUrdg9Yzd2PLwD468d79tJUWa75F++7LmLzYoOUXNkHSItZ9JzDedkyy7eBQqq0sILFa3yaBGe8thWvU227mDNwYjaa2ne3fWu7rZwhJv9wVk53J+uzgjL7JEZMJvM+z189V/A+58Jf3V4+cuXMfvT2Wjmm8FTHs3uZm+ZqxQjAkxa1wcPXla+KwY6RVMQUTsvVC40X9BcLxXbbmkMDcZE8WMMHKhfvn/y5D9ly0lJHPr0mRdyBp2/tsTqqtty1mLP6nEYlf8lzikuSWWZbCTIyl2rR+BC6WMoXhO4pDxc7Fl2LJuwDGP7jTXMUITBCImCAiBGIj9gsSAqTikMTcTqi46Wjpp9V28ZbP+NANwQcoyp5y9QsuYI5qybg9IfSjFn3ZywJwiL8orQwaJ2geYpD0eVA85qJz7e/7Fs278P/DvqRg5afa1Is/kZDAaD4SOYIN0lAO1DOGZ7AK2nRq4NUJhTqHJ5bEn79aB1xzQ052SzlRJsaTbEx8QLC5e6CgE6GgO44oCKAs0yTEeVA9k/uvH0VmBEtX8Be61zPnXxlG8Hxczpvq96eDs9Sp0Qf7ohwaLs3EiXny592uvu6qZuQdsvTDgrh6V3LdXdrgw4er+DVkJ8rP75RuO+WDlppdx9EsCIXiN09vbBWTkkfv04sLYYODRO+CsN1Em0Gy82XQTPywWoY02xqmMqsyDDgbNyyOmdo1ovdsid1U7c+t6teKv8LTS5m3D3oLvDFouONwd3bemVaIdCQUaBbnBaJDEmscWCMZF+pqQkDklJ2g7Czc3GTtAkJXHo2lXboCE5WXhmurfbUVv/o2yby2WcvqXXDdgzeUK/eAGP3nddVKv/7Fl2rH9wPQvQMS4PHAds2QLMnCn8v2kT06RrYTgrh3UPrNPWSwaEv7YXBD1hiBOoPIjJDaRt9jq1R6pD+urYVzXX29JsQla+YkaIpzyedzwf1UBdasdU1bqU9ilRa4/BYDCuNoIJ0lUDuCmEY94I4MeAezEMw6vFFdTX2fI4q534/PDnwoJEc64wp1B3cC9mu83MminMWppcEGYpTcCuaTh/6HpZCaqz2om6Tevw0nsj0L70aSx8bwT+mnxfSMEDWYmiauZ0k1ev6uTFk7L3JcQmBN1GsEgNHPadkgukK5dDxZ5l95UTtzGGdFVnTorcNeCuqJTIzbpplmw52ACM+bt7Pa88aUn7PNefIosz8UQuYsy+jAqzyYzX73wd7WPlcyed4juFdf5SnNVOfHnkS9V6sUO+eNtibxYfBcW/9/877LaC7dAbUUIjBqf9GYskWhIjbkeKv89nhBFBYqLeta5dgh0JVqt2yZXZ7BmYuuORpIgbNzefUr8hTGxpNkGzVDJ54nbFsOo/RtuG44ClS4X/WYDussBZOWSmZOqbi4kBvBvfBkzNAChizLHoGN/RK70RqQ6pPcuOhBh1P3Pvyb34sU57uPX54c+RW5IbtUCdlu6q1PCMwWAwGJERTFTHAWAEIeTGQDsSQrIAjASwKcLzYoTI7uO7vTpOzXyzITpORlFSUaKa6UuKS0JRXpHf93FWDksnLEXhfaOBAaJvCQF4C1BRgBlrZ6C4vNib3XPskybc5S7Fc1iAu9ylOL8uLqTzlJY4XTv0tGrm9Nj5Yxj3/jjUu+Si7cN7Dtc5YvAoB/Rf/vilt3M1uOtg2TblcjholQFr0ZqMIwAhY0pPJy4KsQsA6iBSsEEl+wNip91zbwz2mE0osjiPfzMI0zKned/n4l347OBnqnJRI0o1ny592vsckTKwy0A4q51Yf2i9bL1YchMOWlqaWmg5NoeDPcuOrVO3Ijs1W3P7uP76hjTh4A3WKh2tYYwRQYcO2sFgnj+PY8eKNbeFS1ISB0LUg8TY2CYMGeJEXMcL6NJRLkWbkDDIsPY5KwfHFAcmju8EcywPk5kizkJY9R+DwYg6b96pLvnvk9THN9lp3Q4k/QjwJgBmuF0mrN1wXrZ/pJNNj938mGrdo58+ird3va3Z56GgEWfw+YOzcrgpVZ6/YURVCYPBYDAEggnSvQ5hFPkvQohudIAQch2Af0EQZ9AXzGEwADS71ZpaehTlFWH09doDvtWVq73ZPQ7Y0AQL3IhBE2Kxqu7akM9LLHFqb2mvmjkd0GUAtv64VfUeWZlsmCizr3jwXh0T6/lfyQb5I6yByylDbU+P1iaYzlk5uX6VhJR2V1YpRtHT/ZE9812g/3pggh248R1hgyKLs8m6XuZMS0Gx52d5Z3hw18GGlOUdOnNIc/2r217Fre/dqhLwB8I3TpGaushQBLWMDBRzVg5Lxi/R3OYtqTSImvoaVVak+JlsfW0RHbuuzonvv5+ju/3AgVmoqzMug6KuzglKfd89pYIuHSHA2LEluDn/M/TuXQhAzPiM8SwbB2fl8NGThdjqsODFBQSlpSy5iMFgRB/OyqkMHOaPmo/NUzcjPz1fWJFwGoAZgss2ReWFLbL9K09HNtlUlFfkawuC7IWbuuGmbl0dVzMxR1X6Rtk/DGSYxWAwGIzgCRiko5TuB/BHAH0A7CaErCSETCOEjCWE3E4ImUoIWQlgN4A0AC943hM1CCHzCSGUEPK6ZB0hhDxPCDlGCLlECHEQQq5XvK8TIeR9Qkid5//3CSGRCzldARRkFHh1qmJNsbpabJeD803nVet0B+g6LPrd9YC5EYBb+JvhyxTc8ZNg6pAZ54AFTTCjGRY0I777hrDP+XT9adW6Xcd3oXu77qr1RnRMtIw+dh7biVF/LMQHT06TDfIdPzgibu+zg4HL7drHtm+VekzSgJYIAYnaPVGQUYA4cxwICOLMcaG1M/xt4ME7fAE6wFc+k/UOkLkCALDhsPxaVjrD3j3o7rDPX0r+0HzN9Tx4TbOKzvGdwy4hLsxRl8toBbWMDhRzVk6lI2giJsMHM7Y0G1B1m6ajdXJcZD87tbUO8Ly/TEQ3amsdEbWhbE8KkXxtnYesx6K5/ZGUxGHYsC0eJ9ktYRtVBILjgHnzWICOwWC0HEV5RZpmMisnPIuZ5gAAIABJREFUrUTZtDKMTc2HIHtKAMILWsoSGpojn2y6puM13tcUFCZigpmYEWOK0dz/roF3RdymP+xZduSn56NzfGfkp+e3yv4ig8FgXKkEJWJGKf0jgGchFIzdD+BtAJ8BWAfgHc86E4BnKKUvRudUBQghIwA8DOBrxaZCAL8D8BgEDb2TAD4nhEhtkT4AMBzAHQDGe16/H83zbUlMxAQCElAgvaXZXLVZte7Nu0JLtuQ4IOXR+4HcZ4GHbvNmt3176lu4eBdGVAP/49yOjcjFAvwBG5CLJ9udDfuc7x+qdu08UnsE9c3yUtfkuGRDOiZ6AQL3D7eAumJlg3wtwd5QCcYJdHhq5GW8l4NJQyap1t0z6J6o6NEBQtBn05RNeGnMS9g0ZVNI7aR28PNd7pkClD8sy8ASUZpEKN2Aw2XiIG2DAD1u6HFD2G1xVg5vTXhLrhOnKPW1VI+LSsef6yX/jn4x8BeGXx+clcOA4T+pXQERfvahSHKyDSaTBULmhnYdd3JyZG2o29PQ3iTAqBuSvf92kTrJXik4ncDLLyOqxhQMBqN1oWcmw1k5PP+QDbExBIQAJjPvfdaLRJo9DQAffP2BbLl9bHvk9s3FG3e+ga6J8qAgAcEn+z+Jqi5dcXkxVu1dhTMNZ7Bq7yqZTjSDwWAwIiPoaA6ldCGAgQAWQNCc+w7Afs/rPwIYSCl9ORonKUIISQKwCsB0AGcl6wmAOQAWUUpXU0q/ATAFQAcIAUR4SnXHA7BTSssopU4AMwBMIIQYJ55zmXBUOdDsbgYFRbO7OWo6FOGgTMXv1aFXWAPiFx4crxLuTYxNxNj+Y2GrAkw8MBLb8TQWIQfbce/oWfoHC0BRXpHKQbPeVY9T9fLS1rROaWG3IYWzctrGH4rSR9J3CwpHRl5GFkx2UjAupVciYlmImKUVZ45TlaoYTdAOxwoKcwplGbCFOYXCdaAIVokZWCJ1DXWyZaU7cLiEqmXpz6gjGESdOK/OouJ6H3LjSb/vD5eCjAJYzBYQEFjMlqhdH50GfKfStsxOzY44IJiUxCEjoxR9+y7AwIFvae5z4kSJYSWvvvYWygwrCIAY90/Yv/8RQ8trLydOJ5CbC/z+98JfFqhjMBjBIGYYm03qyZNIs6cBoF+nfrLlc03nUPpDKeasm4PuifIqDwoKN3Wjyd0UtfGA6Pqut8xgMBiM8Akp5YpSeoRS+hylNI9Sej2ldIjn9fOU0iPROkkJxQD+h1L6hWJ9XwApALw1YVQQ0NkCwcgCADgAFwCUSd63DcBFyT6tli6JXbyC7zwic5IyEme1U5W15Td7yA/2LLvKYKGusQ7V56rhSBM0eymErhEhBKhRl5CGwq+u/1XAfUZcY1wg68ZUDW8WsfTRM8i/J7e7IRk/oklGrw69dPdpzSLAKyetxLZp27BwzMKQs9taEs7KYfNDm7FwzEJsfmgzivKK8OW0L5Fw7U55BlbCaZlO2/ELx2XHkboBtyRGlBBzVg5zRnj01RTX+whOW2vHiDYdUxx4acxLcExxRO36mD58ukrbUpkFGS5i1lpqqh3t2mWqth8//hZ27x6JigpjDDHE9nr1ely23uU65WnrljYRqHM4gKYmwO0W/jIHWQaDEQiHA3C5BL1OtxtA1a3ebbGmWEPkFBblLZJptlIqBOIa3Y0qCZkYUwzMxAyL2RI1Xbr42HjZcpNfCQYGg8FghMKVVRfpB0LIwwCuBfB7jc1i5OZnxfqfJdtSAJyiEjEnz+uTkn1aLeE6TIZDcXkxxr0/zpva7qx24uWtL2um1DuqHKpMukgGqcqg2MmLJ7HlyBZstwKz7wSaTYCbAIiPR6TWfysnrUSc2b9DbLAGDMGgJ2gvHeS3s7QzrD17lh2/v1XrdhLI7Kke+Lcmws1ua2mU58lZOSx5+F5fsGr848C6v8p02hrdjbJjaGkohkNBRoGuvo2SSPTolNTU1/j06TzXe2yf8qhqa7bE9ZHePV2luxeN+2rgQH35gLNnN6Cy8gHD2kpNtWPgwGUwm5XPcR4HDoSfvRws0S5FtdkAiwUwm4W/zEGWwWAEwmYTnhmEABTNsnLXaztfa8jvzBsf7ZJpttLqmwEILut3DLhDyMT3yN4AQFbPLCwZvyRqv3HKTPo9J/ZErbSWwWAwrjZaRZDOU466EEA+pdTfVI0y7YIo1mmlZSj3Edu0E0K+IoR8depU5O6d0UZZ7mZU+ZuS4vJizFg7AxsOb8CMtTPwwIcPILckF7/f9HtN7QutjD4tYf9gGdhV33DinRuBW6cCnzyYDaOs/5Lik4QXCsdJES3Dh3BxVDnUQvoKtPT9IsHf+RtRnsEID3uWHfl39heCs5e6+i19BYCu7bqqDxIGnJXDloe2ILNH4EBSSgfj5jZsaTbEx8TDBEEIe+J1E7H5oc1XfIA1EFplRtG4r5KSOBCiH1w9fXqNoe2lpmrrBNbXf2doO0paohSV44SfjwULDPsZYTAYVwFeQx1FNy7QZG+wfPb5Jc2+gAkm1NTXIDkuGZRSUFC4jtyInf8cg9nLVkYtcKY1iTbrP9GfqGEwGIyrgVYRpINQqtoVwDeEEBchxAXgVgCzPK/FSINy1Ngdvuy6EwC6e/TrAHi17LpBnYEHSmkxpfRGSumN3bp1M/bTtGKee3+dLFi1au8qNLgahJR7V6NqUKoVBNIS9g+WQCWYO3ub0OPFJYaNrNKS0jQdJ0WMLCOwpdkCmn4oM6iMaNNitqjWG1WewQiflZNWCgEdhU6bUpAaiFwbTgpn5bB75m50sHTwu9/jNz/ud3uobZYWlOLFMS9i69St+Ojej1p9gA4QJimkmcTRvK8SEvRNPJRuwEZgMsVrrEs0vB0pLVWKyhxkGQxGKEjLXSlvlk2mNbmNKQO94/YEzb5AXEwcbGk21DbWCr83kj6ra/k6LP7nVkPaV8JZOdWk056ftbPplBU4DAaDwfBPawnSrQGQDiBT8v9XAP7heX0AQhDudvENhJB4AKPg06BzAmgPIeAnwgFoB7lOXatEqdWmXDaCucvX4MQbK1XBKnEQqqWFV9tYK1vOTs1GUV5R2Ocwechkv9uH9hhq6OD+zKUzuiL+o/uMNrQtzsohq2eWfKUig6/RZWyQTtTmmpk1E6N7j8bgroPbTBZTW2Bgl4EqnTapcYpINMpCk+KSdLcN6DTAcNfV1lKeHAo19TVeQxgCgunDpkft8w0apF/ySuklw/XiOnZU63EmJ482tA0lrBSVwWBciUjLXYlJ7u6q1IsLl5W/mQ3TQ2NVfYHHbn4MnJXDJ/s/EXZU9FkPfNXTkPa1SEtOU61TTtbP3ThXVYHDYDAYDP8EJz50maGU1gKQRXsIIRcBnPE4uYIQsgTAM4SQ7yAE7Z6FYBTxgecY+wgh6wAs8+jbEQDLAKyllO5vsQ8TJZTaaB3jOxp6fGe1E//9z68B9wTPDz8VOgKSgIGJmFSZc++UvyNb/v7M9xGdhz3Ljt+u+y3qXfWa22sv1WquD5ebe92M78VMJjf1zl4SECzKXWRoW4AgNL/z2E5hQZwNdVuE9qfk4u47+xveJmfl2lRgpC3hvR6s2zWDc4BQPhn17696hHC/pzkA63b07Bi9Tn9bwpZmQ1xMHJrcTbCYLVHV2BOIBdCssZ6ittaBpCTjrpPevQtRU/NvwGNYBJjRu3eUXZQ9pagOhzAoZpluDAbjSkGs01Fq6PiTaQmVdn2/xvle22TrXi17FRMHTcQl1yVhhaLPuq/DWyguv2T4xBqgHmuYiEmWLe6sdmLxtsWyfVbtXYXRfUZH5XwYDAajrdBaMumCYTGAPwN4A0KWXU8AYyml5yX75AOogOACu97z+sEWPs+oIBNeB/AX518M06GYu3EuRv5tJBp6fea37I6AqDPpGmr9LoeDVnmmyLmmcxEfX8r13a73ZTJlvQNkrgAAPJXzVFQCI/YsOwpzPANdxWyo+UgeVk5aaXibjCsX0YU3OzUbHS3agXc3dUel7fuH3i+80Cj3PlLbEmberR+xjHfBbQtQWlAa1WBqba0DgPa1QEgskpNthrfp08EzY+DANw0NAurBSlEZDMaVhsMBNDcL5a68yyQrd3X84DCsnX6d+6nW8ZSHo8qB3h17CysU2fe0Vxke/fTRqGjTNTQ3yJatHa2y3zlZVt3nC4HFx4ElhzBvcfja1AwGg3E10GqDdJRSG6X0UckypZQ+TyntSSmNp5TeKmbZSfY5Qyl9gFLa0fP/A54svVaPLc0Gs8nsXW7mmzVFy0OluLzYNwsWoOzOTd2Ys26OrCMQa46V7ROJs6uIVnq9SCBNt1CR6cTtmQKUPwxSsgkT24VfshuIorwiIVCn0CLLGaWVIcNo69iz7Njx8A6se2Cd5vZfDPpFVNoVr0PzkTxVuXe7WONchts6LVXGqx+EI0hJmW54AK221gEqCRA3NxtnosNgMBitiS5dAJ4HhBw6M5Dgc1xP7ZBqWDtcL+3nuC3N5jOsUGS+A4CbdxsyJlC129fma3Pr0zj6rVU2BvC2+flCYNvTQH0PoLYvzvy/l1HM5OkYDAZDl1YbpGPI4awc7r3+Xtm6b099G/Fx/7rjr/IV1u2C46RO6V2Dq8H7ozx341yV0cHLeS9HfE4jeqm1kESmDZsW8fGlcFYOS+9aClJ1mzdQQfi4qAmWixTlFaHwvtHAlDxgzB9gfmgcFk29J7qNMq5oOCuHsmllyOyRCTMxw2K2ID89P6rZlUV5RRiYdUyVQfv4CONMIxhGojaIMJnikZJifJltcrINJpMFgBkmkyUqmXoMBoPRGqipAUwmACAAcQuu7BAqTLzVEQbgTzKhoblB1+iMEBIV06LkuGShjfc2AaUvwr38c5lRxfajnrHCPlFPmkCMJq5ebfjpMBgMRpuBBenaEAdrDsqWP9j7QUTp7c5qJyp3dZQZF2giMTegoN6S1w8rP5Tt1jWxqyEaFAUZBbLSXpGx/cZGZEqhhz3Ljrce/TViLRQmM0WchbSIYHlRXhHKnvsTFj7XEVv/sJjpxjG8zquuP7jQ+Gxji5Q/D89ulGXQZt/MMy2ZKxCh3FUepOvQIRsZGaVRKUNNSuKQkVGKvn0XRK0NBoPBaA3YbEBMDIQAncnllYO5KfUmww3GUtqpjeEWly0WstqkMimuOKBCCOpFw+HbWe3Ezp92Cm244wCYAXcc/v3PZO/Yo0e7HsLOg8WIHIX4OzXZvw8cg8FgXNWwIF0bQmnzTkFRUlES9vGeXv6x5oycDI1Zu93HdwMAJg2ZJNvVqCw3zsph27RtyOyRiThzHFLap2DZhGVY/+B6Q46vhX1iOl7/71jk5RIsWdJyekht0fGS0bo4WHMwYAYt4/KTnGwDIVK9zlhce+2SqAbPkpI49Okzr0UDdE4n8PLLwl8Gg8G4UhCMI+TDqunDpxvezgirui++//R+IastzSEECUGFc9k9FageAR68ysAhEpzVTuSW5OLj/R+rtvFwe8ceXjOL2+cDOYuAxJPo2OMMli0jsLO5PgaDwdClVbi7MoLjbMNZ1boTF06Edazi8mJs2UIkWlRqN9ex/cZiw9bhklk7AlQU4O3ej2HV3lWqY/bvZJwzqZhR1FI4ncCcOUBTE7B1K5CezoTLGVcH8bHxsmUj9XUYxpGUxCEz04ETJ4TBUUpKQZvLbnM6gdxc4TlssQhOr+w5zGAwLjcOB+ByAaAEcMcAjudAbAuQ3j3d8LYKRxbi4+8+BpVkTg/qOgi2NBuI9RnQYcuBr+wAzABv9vbdP97/MZzVTkMmfR1VDjS5m4RzyCgRgoHuWEESI6ME7+4uR8f4jjh6/qjvTbfPxzWT3sDRJ47qH5jBYDAYAFgmXZuincUYMXdntROzP52tMi4Q0/dNxOTNXEvLPCKftds1De5PXsP5Q9fjfNN52XFXV7ZeAQqHQxgYut3C32hr0jEYVwLOaqdPUwaAmZgN1ddhGEtSEodBg5Zi0KClbS5AB7DnMIPBuDKx2QCzGRActs3A4TzQFZ8LFSkGw1k5vDXhLa+pWawpFoUjC8FZOTyV85QQNItpVPXdKahh5hG2NBssZgvMxCxM3j90G5D7rPDXuh3NfDNWfa2erM8fmm9I+wwGg9HWYZl0bYgJAyeg8lSlbF1Ke7V2RSAcVQ64eJfPzVXiEjXxuonezgAAzPu1DTM2S2ftLMLrPVNUDrCZPTMj+HSXF1FvhOeFvy2hScdgXG4cVQ643K7LfRoMBgDhuWux+DLp2HOYwWBcKQjlrh69ZE8Fyndf9YhKW/YsO9K7p8NR5YAtzebtkxflFaF/p2IsTJqOIxVpModXAHj/6/cxb9S8iNvnrBxKC0rhqHJgwZYFuGTdrpLDSIhJkC0P6DwgKrrRDAaD0RZhmXRtiHMN51TrhvUcFvJxZK6wEi2qDrEd8NG9H8lS5e1ZdgzO/UqYtYPbs9YspL1X2QKeX2tC1N2Ngv4ug3FFYkuzgRwdKTOGMWomnsEIFY4TSlwXLGClrgwG48rB4QCamt3wDavcgLkZ1934c9Ta1NMstmfZUfWnv2PAPatVgbN9p/dh7sa5EbftrHZi8bbFWPPdGlzT4RrNfarPVcuWs6/JjrhdBoPBuFpgmXRtHNHEIVic1U5NPTkA6JzYWXN95aJ3QU5zgsPT7qmCBoYkxd673+lKzfe3BhwOocSKUuGvw8EGiIyrgKMcTO9vgrsJgLkJMdPuhC3NdrnPinEVw3GSZ6/TKTyMbTZDH8jOaqcqQ4XBYDD06DJ4L6ipP0BjAZMbGLYcyChB/l1TL9s5rZi4AiP/NlK1/s9lf44oo23uxrlBmVA0uhtlyzuO7gi7TQaDwbjaYEG6NkRBRgGWlS+TicmGahwxZc0U3W3zR83X3daxfyXOWWcJWhhVNiDhtC+TzjOTd+riqZDO5UqClVkxrkYcDoB3xQAUIDzBtOQV4Kx9LvdpMa52nE6gpARYvlxQazfQRUJ0LWxyN8FitqC0oJQF6hgMhl9quqwFpvxbJg8DADX1v7hs58RZOfRJ6oMjdUdk613UhXHvj8P6B9f7fb90sgIQ5C++PfWt5kR+WnIaqmqr/B6vX6d+IZ0/g8FgXM2wIF0bgrNyGNpjKCp+rvCuO9NwJuj3P/DhAzh45qDmtsFdB8Oepe+X/srYVzBj7Qxfav2KUsH11dzk1aYb1GVQ0OdypcFxwJIlwOrVwOTJLIuOcXUgimHzPBAbY0bBRBagY1xmRIvXhgaf9oDoImHAg1l0LXRTN5rcTXBUOViQjsFg+MWWZgOs81XlpZcr81wMsN1x7R14q/wt34bqEUCVDRuqHZjbcy6S45I1M4ad1U7YVtjQ5G7yvscbfFQuA2hwNSA/PV+3EofBYDAYocGCdG2MOHOcbLmhuSGo9/krc+2W2A2Vs/2XqooBvHd3vYtDX/0f1LgtXuFc0f69NbtCFhcDjz4qlLpu3Qqkp7NAHePqgBBfmffevey6Z1xmRItXMUBHiKHpzaJroZhJx8q7GQxGIDgrh4SYBFxyXfKuS4xJvCwBfmk2sNlk9m2oHiGbQF+MXJisO2EymfAE9wSS45JR21iLd8rf8U3wK96D8Y8D6/6qmoSPM8dh5aSVfoN0k4dMjvInZzAYjLYDC9K1MaYPn46dx3bKloPBnxj8x/cFZyFvz7LDnmWH8wZg5Pp6wEW92nRx5rhWm43gdAKzZwtVVQDQ2Mg06RhXBw4H0NwsvHa7hUA1C1AzLifOLhPgIJdgM30BLuZ/gWnTgIICwy5KqWsh06RjMBjB0r1dd1lpabd23S7LeUizgcEDo3uPxpYftwgT5ooJdN66HTzP62vMSd/jAlD2lOoYsG7HsBTBpC4xJhH1rnrNQx06eygaH5fBYDDaJMzdtY1hz7IjPz0fiTGJSGmfEvT79LIFJg6aGPIgheMA85RxwJg/eGfZeMqHdIwrCYdDKPcTIYRp0jGuDmw2wCT5lRBNUxiMy4HTCeTOScfv+ReQa94E52tfAUuXGh411nNNZDAYDD2Uus3+dJyjiZgNbCZmWMwWLMpbhLH9xgrlqeYmgDT7zN2qR3jd22WvRcT3wAXADJzpB1AzQFzeY5iJ2Vsp85fxf9E9rw8rP4zip2YwGIy2Bcuka2MUlxd7083rL9QLOnGAXz05f4RbopqV3YSdvRZ5l8VZttaIzQbExAgVVoA8aMFgtGU4DnjjDV+pd1wcC1AzLh9ipaubJ2gisXDUpIOF0RgMxpWA2M9eXbkak4dMDrvfHSla2cDrH1yPTj91Qu2UXJ+eHCCUsroswmviOYCkjBXW7cJrx3PA4Twhgw4uoN9GwPYC0m44gQ8mb/VOaNiz7FhduRobDm9QndekIZOi/dEZDAajzcDCDW2M1ZWrg1qnZHGZOtV9QKcBYWcSTLxuot/l1gTHCRVVxNOB4XmWTcS4erDbgc2bgRdfNMxAk8EIC9Fl22yisJiaYeuy93KfEoPBYHixZ9mx/sH1ly1AJ6KVDfxp/qdC0G3UIuFvlc0ToIsBYBYy5GiMUM5aZfMdzLodsL3gy8KLaQJsLyCmz1f4YPIHqnHC+gfXIz89X7YuPz0fRXlFUfu8DAaD0dZgQbo2hpYwazBiraWHS1XrVvxyRdjnYUuzISEmAWZiRkJMQsuIbzudwMsvC38NpqBAGBwSImTVsWwixtWC0ykEpW02FqBjXF44DihdshcLTM+h1H0buDk3R+V5z2AwGG0NzsrJq2PSHICJB0AhpNER4TXhfZl2HjJvagCm5AFj/gDTQ2Mxc2Imtjy0RXcif+WklSibVoaFYxaibFoZVk5aGZ0PxWAwGG0UVu7axrBn2XHo7CGvCKyZmJHePd3ve4rLi3G+6bxsXYI5ISI9nhYX33Y6gdxcoRbKYolKyo9oJij+ZTDaOi1wWzEYIcHVrAVHFwK8G2gyMxcfBoPBCJKivCL8dO4nQRbHuh24czbwnzc9ZawQtObunC1sg1AFUziyEJyVg/NOp6dP/0pQfXrOyjFdTwaDwQgTFqRrgxyoOeB97aZuLC5bjI/u/Ui1n7Na+MEt3lWs2paUkBTxebToD7RXrMgt/DV44OZwCIem1Ceez8aFjLZOlG8rBiN0xJpXMXKsldbM0j8ZDAZDk5WTVuKajtcIk/k3vgP0+AaoKACBCe2zP0SXgQeQmeILzomwoBuDwWC0HCxI1wY5du6Y32VACNDZVtjQ7G4GhTo1bMQ1I1TrrmiCGbhduYdnMK5IbDYgJtYNngIxsYDNZr7cp8S42uE4IaVTLwjH0j8ZDAbDL0V5RZg4aCIWb1uMY6nHMP0Rs0dHb8blPjUGg8FggAXprlpKKkrQ5G7S3X7HgDta8GwMINDAzYDDL1kCrF4NTJ7MxnyMq4ReTtCCecChHND+24BeLwPMT5NxueE4/YcwS/9ktFbEDNAuXYCaGpYJyogqnJXDR/epq2wYDAaDcflhQbo2yL7T+2TLFT9XqPbZeHij32PU1NcYek4tgr+BW4Q4ncCcOcKYb+tWID2d9Z0ZbR9HlQPua74ETd2MZhCUVJSwchfGZSVgJStLe2a0RsQM0MZGwULeZALi4lgmKIPBYDAYVyHM3bUN0qNdD9lyo7sRxeU+3bni8mJ8f/Z73febibll3FhbEVrJGQxGW8eWZgMhBABAQbF8z3I4q5mbJuPyIMYxfv974a+msauYVb1gAQtwMFoPYieD54VlnmedDQaDwWAwrlJYkK4NktcvT7VudeVqAIIW3cz/3969R9lV1Qke//5SSQXlTQwTHkkH2yXyiI8kEi+CXIiCcZRhiMtuBTIwrQWydBoaYclotzTaIuhgGAUJjA4NgdZWum1hhocGSiIUYMLA0MjLkTQRSAgJr0CgqGLPH+dccutSVakklTr3nvv9rLXXqXPOPmfvk7NTj9/dj+tPGfLaCeMmsPSkpfaWaTBpv/sZN/41xnUkO2eoraS65Yxfe/01uld0F1cZtbURf1hSqcDZZxugU+uo9QAdl/9aPm4cdHTA448PEY2WJEllZZCuhBa8ZwEdMXCC9/n7zwey4WuDLRRR05/6DdA16FnZw2kPzKH/hMMZd8TXWHjN/f7tp7bQvaJ7QJDOXrYqUi2O0dHhSFaVTK0H6De+AYsWQVcXRMDllw/TbVSSJJWRQboSqkytcMbBZwx6rjq9yrhhXvv2E7bfVtVqWd0ruunt7+X1vW8nHfJN1k66vugqSWOiOr3KxPETGcc4xo8bz/c/9n2D+CqMI1lVarUeoF1dMG0a9PU5x4YkSW3IhSNK6rqHrxuwv/DOhXTN6qIytcLR+x7Nzx/++aDXfefI74xF9VpKdXqVzo5Oevt76ezotCeR2kZlaoUlC5bQvaKb6vSqAToVbhuuDyQ1DxdAkSSpbRmkK6kNfRsG7D/7yrNvfL3ulXWDXnPk24+ka1bXNq3XNrPJJf+2nIEKtbPK1IptXpLGUqXC/dcsZO0N1zJp3nxmGJmWJKltGKQrqWk7T2PFcyve2F+9fjU9K3uoTK3wymuvDMg7YdwETq+czvkfPn+MazlKakv+1T5x3gbjoAxUSJKksdCzsoe5D5xG7169dD6wlCWzZvg7iCRJbcI56Upqt+12G7CfSFx535UA7LLdLgPOHT798NYN0MFmLPknSWplPT1w3nnOo69yq82F25/66e3vdVVtSZLaSEsE6SLi7Ij4bUS8EBFrIuK6iDiwIU9ExDkR8WREbIiI7og4oCHPrhFxVUQ8n6erImJgxKokpuwwZchz966+d8D+H579w7auzrblkn+SVHq1TtN//dcueKlyq82F2xEdzoUrSVKbaYkgHVAFLgEOBo4A+oBfRUR9d7GzgDOALwLvB54GfhkRO9bluQaYCcwDPpp/fdW2rnwR3rfH+wbsB8GC9yzgsuWX8fRLTw84d+z+x45l1UafS/5JUunZaVrtojYX7tcP/zpLFixxqKskSW3yGy7jAAAWOklEQVSkJeakSykdVb8fEScAzwMfBK6LiABOA76VUro2z/OfyAJ1nwEWRcR+ZIG5Q1JKd+R5TgaWRsS+KaWHx+yBxsDal9cO2E8k7n/6fr7W/bUBx3fs3LG1h7rWuOSfJJWaC16qnTgXriRJ7alVetI12pGs7rUlS/cBpgA31zKklDYAt5H1vgOoAOuBO+ruczvwUl2e0qhOrzIuBr7ec399LqvWrxpwbOL4iWNZLUmStoidpiVJklR2LdGTbhAXAfcCtRlpahOwrW7ItxrYqy7PmpRSqp1MKaWIeLru+jdERBfQBTBt2rTRq/kYqUytMHuP2dz95N1vHHvixSfelG/mlJljWS1JkraYnaYlSZJUZi3Xky4iLgQOAeanlPobTqfG7A3HGs8PlifLmNJlKaXZKaXZkydP3poqF+aRtY9sMs8fX/zjGNREkiRJkiRJw2mpIF1EfBf4NHBESql+SdLaGM7GHnG7s7F33Spg93z+utr9ApjMm3vglULf632bzPNS70tjUBNJkiRJkiQNp2WCdBFxEdkiEEeklB5qOP0YWRDuI3X5twMOZeMcdD3ADmRz09VUgO0ZOE9daczcs2Eo68oPwNIvZ9tc4yqwkiRJkiRJGnstMSddRFwMnAAcAzwbEbUec+tTSuvzueUWAl+JiIeAR4Cvki0UcQ1ASunBiLiRbKXXz5ENc10EXF+2lV1rvjX3Wxz8o3xNjJUfgCtuhf4J0PEanHg446bdzVkHn1VsJSVJGqGelT10r+imOr3qypeSJEkqnZYI0gGn5tslDcf/Fjgn//oC4C3AxcCuwF3AkSmlF+vyHwf8dzauAvsL4AvboL5NoTK1wlvGv4UNfRvgvgXQPxEI6B8H9y3gB58/yT9yJEktoWdlD3OvnEtvfy+dHZ0sWbDEn2GSJEkqlZYI0qWUYgR5ElnA7pxh8qwDjh+1irWA8eMGf8Xjx02ga9Znx7g2kiRtme4V3fT299Kf+unt76V7RbdBOkmSJJVKy8xJpy1z9L5HZ1+850roeBXoh45XOfAj9xRaL0mSNkd1epXOjk46ooPOjk6q06tFV0mSJEkaVS3Rk05bbvGxi3l07aPczZ1w4uGwokrscxuXnPydoqsmSdKIVaZWWHjAXVx7w1rmz5tEZeqMoqskSZIkjarIRolqOLNnz07Lli0ruhpb5bLll/HDe37InjvtyVkHn+UQIUlSS+npgblzobcXOjthyRKo+KNMkjRKImJ5Sml20fWQ1N7sSdcmumZ10TWrq+hqSJK0Rbq7swBdf3+27e42SCdJkqRycU46SZLU9KrVrAddR0e2rVaLrpEkSZI0uuxJJ0mSml6lkg1xvfLKomsiSZIkbRv2pJMkSS3j7/8eLr88m5+up6fo2kijpKcHzjvPRi1JUpuzJ50kSWoJzkunUnJVFEmSlLMnnSRJagnVajYnXUS2dV46lcJg0WdJktSWDNJJkqSWETFwK7U8V0WRJEk5h7tKkqSW0N0NfX2QUrZ1uKtKobYqSnd3FqCzUUuS1LYM0kmSpJZQ63BUm7rLDkcqjUrF4JwkSTJIJ0mSWkOlAgsXwrXXwvz5xjQkSZJULgbpVB49PQ4VkaQS6+mB007LetItXQozZvjtXpIkSeVhkE7l0NMDc+duHAO1ZIl/uUlSyQy2CKbf6iVJklQWru6qchjsLzdJUqm4CKYkSZLKzJ50KgdnE5ek0qtUYOE193PtDWuZP28SlcqMoqskSZIkjRqDdCoHZxOXpNLrWdnDaQ/MpXevXpY+0MmMWUuoTPX7vSRJksrB4a4qh9ps4kuWZNuenqJrJEkaZd0ruunt76U/9dPb30v3iu6iqyRJkiSNGoN0KgfnpJOk0qtOr9LZ0UlHdNDZ0Ul1erXoKkmSJEmjxuGuKodqNZtJ/PXXs61z0klS6VSmVliyYAndK7qpTq861FWSJEmlYpBO5fH665BStpUklVJlasXgnCRJkkrJ4a4qhwsugL6+7Ou+vmxfkiRJkiSpRRikUzk8+eTw+5IkSZIkSU3MIJ3K4S/+Yvh9SZIkSZKkJmaQTuUwY0a2YARk2xkziq2PJEmSJEnSZjBIp3K48kro78++7u/P9iVJkiRJklqEQTqVw6pVw+9LkiRJkiQ1MYN0KocpU4bflyRJkiRJamJtGaSLiFMj4rGIeCUilkfEoUXXSVtpwQLo7ISIbLtgQdE1kiRJkiRJGrHxRVdgrEXEnwEXAacCv8m3N0TE/imlxwutnLZcpQLd3VmqVrN9SZIkSZKkFtGOPen+CrgipXR5SunBlNIXgaeAzxdcL22tSgXOPtsAnSSV2VFHZb2ma2nOnIHnjz8eJk3Ktmptje+6WdKECXDAAXDZZQPrO2dO8XUzDXxPje9IkqQm11ZBuojoBGYBNzecuhk4eOxrJEmSRuyoo+Dmhh/hd9+9MVB3/PFw9dWwbl22NVDXugZ7182irw9+9zs4+eSNQaA5c7K2qObR1zfwHUmS1ALaKkgHvA3oAFY3HF8NDFhpICK6ImJZRCxbs2bNWNVPkiQNZenSwY/fc0+2veGGgccb99U6hnrXzebaa7NtrQ2q+dTekSRJLaDdgnQ1qWE/Go+llC5LKc1OKc2ePHny2NVMkiQN7tAh1nmaOTPbzps38HjjvlrHUO+62cyfn21rbVDNp/aOJElqAe0WpHsG6Keh1xywO2/uXSdJkprJTTfBkUcOPHbQQXDXXdnXixfDccfBbrtl28WLx76OGh2DvetmMX487L8/LFoEXV3Zsbvuytqimsf48QPfkSRJLSBSauxUVm4RcRdwX0qpq+7YI8C1KaWzB7tm9uzZadmyZWNVRUmSJEnSGIqI5Sml2UXXQ1J7G190BQpwIXBVRNwN3A6cAuwJXFporSRJkiRJktS22i5Il1L6SURMAr4K7AH8K/CxlNK/FVszSZIkSZIktau2C9IBpJQuAS4puh6SJEmSJEkStN/CEZIkSZIkSVLTMUgnSZIkSZIkFcwgnSRJkiRJklQwg3SSJEmSJElSwQzSSZIkSZIkSQUzSCdJkiRJkiQVzCCdJEmSJEmSVDCDdJIkSZIkSVLBIqVUdB2aXkSsAf6t6Ho0ibcBzxRdCWmM2e7Vjmz3aje2ebUj2/1Gf5JSmlx0JSS1N4N02iwRsSylNLvoekhjyXavdmS7V7uxzasd2e4lqbk43FWSJEmSJEkqmEE6SZIkSZIkqWAG6bS5Liu6AlIBbPdqR7Z7tRvbvNqR7V6Smohz0kmSJEmSJEkFsyedJEmSJEmSVDCDdJIkSZIkSVLBDNI1oYg4OyJ+GxEvRMSaiLguIg5syBMRcU5EPBkRGyKiOyIOaMjzlYi4PSJeiog3jWuOiMkRcVN+j1cjYmVEXBwRO4+gjodFxPKIeCUi/hARpzSc/1BE/CIinoiIFBEnjvDZJ0bE9yLimbzev4iIvevOvyci/iGv64aIeDgizowI23KLs90P3e7zPHMj4o6IeDEinoqI8yNi/Ejur+bU5m2+KyJujYjn8uumD5HvqIjoiYiX87xLRnJ/Na92bfcRsVv+ff6h/JlWRsQPImJSXZ5x+X0fz8t+KiIWR8Rem7q/mle7tvn8ussj4v/lz7QmIv4lIvZryLNrRFwVEc/n6aqI2GUk95ekMjKw0ZyqwCXAwcARQB/wq4jYrS7PWcAZwBeB9wNPA7+MiB3r8kwE/glYOEQ5rwP/DHwCeCdwIjAXuHy4ykXEPsD/Bu4A3gecB3wvIubXZdsB+FfgL4ENw92vwUJgPvBp4FBgJ+D6iOjIz88C1gAnAAcAXwP+BvjyZpSh5lTFdj9ou4+Id+dl35yX/efA0cC3NqMMNZ8q7dvm30rWns8ZpvxjgB8DV+XlV4AfbUYZak5V2rPd7wnslT/bDOB44EPAPzTkuwX4FLAv2c+Ft+fPodZVpT3bPMCyvB77AUcBQfbsE+ryXAPMBOYBH82/vmozypCkckkpmZo8kf1g7Ac+ke8H8BTwlbo8bwFeBE4e5PpPZq96RGX9F+CpTeQ5H3i04dj/AHqGyL8eOHEEZe8M9ALH1R2bSvZLx1HDXHcBsLzo92Qa3WS739jugW8C/6fhuk+Q/aK8Y9HvyjQ6qV3afMM1s4EETG843gE8Dnyu6Pdi2rapHdt93bUfy7/X7zRMnqPz/yPbFf2uTKOT2rzNvztvz/vm+/vl+x+sy3NIfR6TyWRqt2RPutawI1mvx2fz/X2AKWS9EABIKW0AbiP7lG6LRMSewLHArzeRtVJfdu4mYHbDJ2ObaxYwgYHPtRJ4kOGfayc2/tuoPGz3G59rIvBKw3UbgO3y61UO7dLmR2IWWbD61Yi4JyJWRcTNEfG+bVyuxl47t/udgFeBlwc7mfe0Og64K6XU+DNArast23xEbA+cRPYBzIq6steT9eKruR14ia14dklqZQbpWsNFwL1AT74/Jd+ubsi3uu7ciEU2x9vLwBNkn9qdtIlLpgxR9njgbZtbfsN9+4FnBrn3oM8VETPJutH/YCvKVXOy3W98rpuAORFxQkSMz+cn+pv83B5bUbaaS7u0+ZF4e779OllP0n8P/BH4df6Hp8qjLdt9PufW14HLU0p9DefOj4iXgLXANODjo1WumkJbtfmIODUi1pMF4+YBc1NKr9aVvSal9MYce/nXT7MFzy5JZWCQrslFxIVk3b7np5T6G043ThobgxwbidPJ5n84huwPozfmuoiI9XXp0k2UPdjxQUXEf22497Thsg9234jYF/hfwMKU0rUjKVetwXb/xr0TQErpZuBLwPfJetQ9QjZ/DGQBPrU42/yb1H4/+buU0s9SSsuBLuA5sjlJVQLt2u7zHkXXkQVRzhrkFt8mmxvsSLLv8YsjIgbJpxbTpm3+arL2fBjZ7y8/jYi3DlN2rfwteXZJanmuDNjEIuK7ZBPEH55S+kPdqVX5dgqwsu747rz5k7BNSimtyu/5UESsBZZGxDfyIXfvrcv6Ql35jZ9u7U42Ee7aERZ7KfCPdftP5vftIPvUbk3DvW+rvzgi3gXcCvw4peSiESViux9w7zfafUrpwvzfZg+yITLTySZ3fmyEZatJtWGbH4mn8u3vagdSSn0R8ShZzyK1uHZt9xGxAxs/ZPn4YMNYU0rPkPWufiQiHiT7dzgEWDrC8tWE2rXNp5SeB54HHo2IO8l+h5lPtjjEKmD3iIhab7o8ID2ZLXh2SSoDg3RNKiIuIvtBXk0pPdRw+jGyH2ofAX6b59+ObFXIM7ey6FrvhYkAKaXfD5Knh+zTuXofAZallF4bSSEppXXAuvpjEbEceC2/1zX5sb3JJpW9oy7f/mSrn/1jSun0kZSn1mC7H7rd59cn8l98I+LTZL/M3zOSstWc2rHNj9Bysrm69gV+AxAR44A/JRv+rRbWru0+spU6byDrJfTRlNL6za2zWlO7tvlBRJ5q7bmHbCGNCht/56kA29PwO5AktQuDdE0oIi4mG85zDPBsRNQ+3VqfUlqfUkoRsRD4SkQ8RNZ1/Ktkcz1cU3efacBuZD1uiIjap2e/Tymtj4iPA5PI/hhaDxxANsTiziF+iNdcCnwhr8Mi4INk88J9uq7sHYB35LvjgGl5+etSSo8PdtOU0vMR8UPg2xHxNNmndxcC/xf4VX7fA8gCdLcC36z7t6l9cqgWZbsfut3n9z4TuJFsJcBjgS8DnxpkuIxaRLu2+fy6KWQ9N96ZH9o/sjm6Hk8prUspvZAPxfrbiPgj2STjXwB2BRYPU2c1uXZt93mA7mayxSKOAbbPh72SX9cbERWyYYq/IRva/adk89atyI+pBbVxm38HWY+5X5GNFNib7HeXV4HrAVJKD0bEjcCiiPgcWQBvEXB9SunhYeosSeU1mkvFmkYnkc3BMFg6py5PAOeQDQl6hWzlpgMb7nPFEPep5uc/TPYJ1nNkK0U+QrYM+64jqONhZD14XiX7BPCUhvPVIcq+YhP33Q74Hlmg4mWyOVum1p0/Z6h/n6Lfm2nrku1+6Haf57mlrs53AvOKfmemrUtt3uaH+l5+Yl2eCcAFZD1MXgC6gZlFvzfT1qV2bffDXFNf5/eSfQi5tq7sHwB7F/3eTLb5LWjzU8l6jj4N9JL1/r8aeFdDvt3IPnx5IU+LgV2Kfm8mk8lUVIqUEpIkSZIkSZKK4+qukiRJkiRJUsEM0kmSJEmSJEkFM0gnSZIkSZIkFcwgnSRJkiRJklQwg3SSJEmSJElSwQzSSZIkSZIkSQUzSCdJUguJiOkRkSLiim1YxhV5GdO3VRmSJEmSBjJIJ0mSJEmSJBVsfNEVkCRJm+UJYD/g+aIrIkmSJGn0GKSTJKmFpJReAx4quh6SJEmSRpfDXSVJaiGDzUlXP4dcRJwcEfdHxCsRsToiLouInYe414cjYmlEvBQR6yLi5xHxrk2UPycifhYRqyKiNyJWRsSiiNizId+xeZ3ujIgJDecOjIiXI+LJiNh9K/45JEmSpNIwSCdJUnlckKf7gIvJhsZ+DvjnxowR8UngJmA28FNgETAJ6AH2GezmEXEScDswD7gVWAgsAz4LLIuIabW8KaV/yuswB/i7unu8FfgJMBE4PqX09NY8sCRJklQWDneVJKk8PgDMSCk9DhAR44FbgMMj4qCU0t358R3IgnKvA4emlJbVbhAR3wVOa7xxRLwzv2YFcFhK6Ym6c0cAvwQuAv5j3WVnAAcDX4qIW1JKN5IF7vYHzk0p3TJaDy5JkiS1OnvSSZJUHufWAnQAKaU+4H/muwfV5fsPwG7ANfUButw5DL4oxeeBCcBf1gfo8nJuAX4BfCIidqw7/irwZ8BLwJUR8SXgROA24NzNfThJkiSpzOxJJ0lSeTQG3ABW5ttd647NzLe/bsycUno+Iu4FDms4Vcm3h0XE+wcpZ3egA3gnsLzufo9GxMnA1cC3gWeAz6SU+jfxLJIkSVJbMUgnSVJ5PDfIsb5821F3rLaQxOoh7rNqkGOT8u2Zm6jDDoMc+yXwArAT8NPGnniSJEmSHO4qSVI7qg1n/XdDnJ8yzDU7p5RimDSgd15EBHAlWYDuGaArIj40Gg8hSZIklYlBOkmS2s89+bZxSCsRsTPw3kGuuTPfHrqZZZ0JfJRsuOsRwGvANRHxts28jyRJklRqBukkSWo//wI8C3wmImY3nDuHjcNh632fLMD23Xyl1wEiojMiDm04Ngf4BvB74PMppfuB04G9gCvyXnaSJEmScE46SZLaTkppfUR0AT8BlkbET4CngEOAA8lWX/1QwzUPRcR/Bn4EPBARNwKPkK34Oo2sh90a4F0AEbEL8GMgAX+eUnoxv8+lETEX+CTwV8B/28aPK0mSJLUEe9JJktSGUko/IxuGuhz4FHAKsI5sFdfHhrhmMTCLbOjqu4EvAMcD7wB+Bpxal/2HwHTgyyml5QPvxGfzMs6LiING54kkSZKk1hYppaLrIEmSJEmSJLU1e9JJkiRJkiRJBTNIJ0mSJEmSJBXMIJ0kSZIkSZJUMIN0kiRJkiRJUsEM0kmSJEmSJEkFM0gnSZIkSZIkFcwgnSRJkiRJklQwg3SSJEmSJElSwQzSSZIkSZIkSQUzSCdJkiRJkiQV7P8Drn8rDTJGecoAAAAASUVORK5CYII=\n", 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" ] @@ -798,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", @@ -830,7 +828,7 @@ " dtype='object')" ] }, - "execution_count": 78, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -847,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", @@ -859,3337 +857,39 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:810: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:819: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1403: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "ename": "TypeError", + "evalue": "catching classes that do not inherit from BaseException is not allowed", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 3103\u001b[0m return self._engine.get_value(s, k,\n\u001b[1;32m-> 3104\u001b[1;33m tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m 3105\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 0", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mabsolute_to_relative\u001b[1;34m(series, start_date, unit, decimals)\u001b[0m\n\u001b[0;32m 1662\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1663\u001b[1;33m \u001b[0mtime_delta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1664\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'IndexError'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 766\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 767\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 768\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 3109\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3110\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3111\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mIndexError\u001b[0m: index out of bounds", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_model('CODtot_line2',model_output_ontv_1['.sewer_1.COD'],\n\u001b[0;32m 2\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2013\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2013\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m22\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m only_checked=True,plot=True)\n\u001b[0m", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_model\u001b[1;34m(self, to_fill, to_use, arange, only_checked, unit, plot, clear)\u001b[0m\n\u001b[0;32m 892\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 893\u001b[0m indexes_to_replace['abs_indexes'] = absolute_to_relative(indexes_to_replace['indexes'],\n\u001b[1;32m--> 894\u001b[1;33m start_date=self.data.index[0],unit=unit)\n\u001b[0m\u001b[0;32m 895\u001b[0m \u001b[0mindexes_to_replace\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'time_index'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexes_to_replace\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'abs_indexes'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 896\u001b[0m \u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfind_nearest_time\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_values\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mabsolute_to_relative\u001b[1;34m(series, start_date, unit, decimals)\u001b[0m\n\u001b[0;32m 1662\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1663\u001b[0m \u001b[0mtime_delta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1664\u001b[1;33m \u001b[1;32mexcept\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'IndexError'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1665\u001b[0m raise IndexError('The passed series appears to be empty. To calculate ' + \\\n\u001b[0;32m 1666\u001b[0m 'a relative timeseries, an absolute timeseries is necessary.')\n", + "\u001b[1;31mTypeError\u001b[0m: catching classes that do not inherit from BaseException is not allowed" ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -4210,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.917107", @@ -4225,7 +925,7 @@ "(2.4506423271968965, 0.6721532140851265)" ] }, - "execution_count": 80, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -4244,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", @@ -4252,6 +952,15 @@ } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1380: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", + "You can access Timestamp as pandas.Timestamp\n", + " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -4273,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", @@ -4285,7 +994,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:453: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:462: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, @@ -4316,9 +1025,61 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 63, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -4330,10 +1091,10 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 83, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, @@ -4364,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.016129", @@ -4376,7 +1137,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:560: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:569: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, @@ -4409,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.731819", @@ -4421,7 +1182,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:954: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:963: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, @@ -4445,7 +1206,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", @@ -4487,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", @@ -4496,29 +1257,9 @@ "scrolled": false }, "outputs": [ - { - "ename": "TypeError", - "evalue": "float() argument must be a string or a number, not 'Timestamp'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalc_daily_average\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'CODtot_line2'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2013\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2013\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mcalc_daily_average\u001b[1;34m(self, column_name, arange, plot)\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtslib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTimestamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 229\u001b[0m ax.errorbar([pd.to_datetime(x) for x in to_return['day']],to_return['mean'],\n\u001b[1;32m--> 230\u001b[1;33m yerr=to_return['std'],fmt='o')\n\u001b[0m\u001b[0;32m 231\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 232\u001b[0m ax.errorbar(to_return['day'],to_return['mean'],\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1853\u001b[0m \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1854\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1855\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1857\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36merrorbar\u001b[1;34m(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)\u001b[0m\n\u001b[0;32m 3185\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_line\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3186\u001b[0m \u001b[0mdata_line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_line_style\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3187\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_line\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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\n", 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\n", "text/plain": [ "
" ] @@ -4540,7 +1281,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 446edfd0d..dbe8dbae3 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -814,9 +814,7 @@ def fill_missing_model(self,to_fill,to_use,arange,only_checked=True, # CHECKS ### self._plot = 'filled' - wn.warn('When making use of filling functions, please make sure to '+ \ - 'start filling small gaps and progressively move to larger gaps. This '+ \ - 'ensures the proper working of the package algorithms.') + wn.warn('When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.') # several checks on availability of the right columns in the necessary # dataframes/dictionaries From 8d639bc144bf59634ccab1d72d97486649995426 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Fri, 29 Jun 2018 14:19:05 +0200 Subject: [PATCH 13/42] Enhancement for fill_missing_interpolation are implemented and working --- Showcase_OnlineSensorBased.ipynb | 3438 +++++++++++++++++++++++++++++- 1 file changed, 3350 insertions(+), 88 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 88089d928..e8168f15e 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,10 +546,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ "4895" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -615,14 +615,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 38.215709508155975%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 38.45350418438349%. This value is also saved in self.filling_error.\n" ] } ], @@ -636,14 +636,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average deviation of imputed points from the original ones is 52.98648788031606%. This value is also saved in self.filling_error.\n" + "Average deviation of imputed points from the original ones is 55.3020444019021%. This value is also saved in self.filling_error.\n" ] } ], @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -706,7 +706,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -730,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", @@ -828,7 +828,7 @@ " dtype='object')" ] }, - "execution_count": 58, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -845,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", @@ -857,39 +857,3301 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:819: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:817: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " wn.warn('When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.')\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" ] }, { - "ename": "TypeError", - "evalue": "catching classes that do not inherit from BaseException is not allowed", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 3103\u001b[0m return self._engine.get_value(s, k,\n\u001b[1;32m-> 3104\u001b[1;33m tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m 3105\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 0", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mabsolute_to_relative\u001b[1;34m(series, start_date, unit, decimals)\u001b[0m\n\u001b[0;32m 1662\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1663\u001b[1;33m \u001b[0mtime_delta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1664\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'IndexError'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 766\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 767\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 768\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 3109\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3110\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3111\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mIndexError\u001b[0m: index out of bounds", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset.fill_missing_model('CODtot_line2',model_output_ontv_1['.sewer_1.COD'],\n\u001b[0;32m 2\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2013\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2013\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m22\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m only_checked=True,plot=True)\n\u001b[0m", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mfill_missing_model\u001b[1;34m(self, to_fill, to_use, arange, only_checked, unit, plot, clear)\u001b[0m\n\u001b[0;32m 892\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 893\u001b[0m indexes_to_replace['abs_indexes'] = absolute_to_relative(indexes_to_replace['indexes'],\n\u001b[1;32m--> 894\u001b[1;33m start_date=self.data.index[0],unit=unit)\n\u001b[0m\u001b[0;32m 895\u001b[0m \u001b[0mindexes_to_replace\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'time_index'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexes_to_replace\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'abs_indexes'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 896\u001b[0m \u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfind_nearest_time\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_values\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py\u001b[0m in \u001b[0;36mabsolute_to_relative\u001b[1;34m(series, start_date, unit, decimals)\u001b[0m\n\u001b[0;32m 1662\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1663\u001b[0m \u001b[0mtime_delta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1664\u001b[1;33m \u001b[1;32mexcept\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'IndexError'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1665\u001b[0m raise IndexError('The passed series appears to be empty. To calculate ' + \\\n\u001b[0;32m 1666\u001b[0m 'a relative timeseries, an absolute timeseries is necessary.')\n", - "\u001b[1;31mTypeError\u001b[0m: catching classes that do not inherit from BaseException is not allowed" + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", + "will be corrected to return the positional minimum in the future.\n", + "Use 'series.values.argmin' to get the position of the minimum now.\n", + " return (np.abs(df[column]-value)).argmin()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -910,7 +4172,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.917107", @@ -925,7 +4187,7 @@ "(2.4506423271968965, 0.6721532140851265)" ] }, - "execution_count": 60, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -944,7 +4206,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", @@ -953,19 +4215,19 @@ }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1380: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", - "You can access Timestamp as pandas.Timestamp\n", - " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" + "Best ratio (2.5328218826106403 ± 0.16586491872475548) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Best ratio (2.5328218826106403 ± 0.16586491872475548) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1380: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", + "You can access Timestamp as pandas.Timestamp\n", + " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" ] } ], @@ -982,7 +4244,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", @@ -1000,7 +4262,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1025,7 +4287,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1091,10 +4353,10 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 63, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, @@ -1125,7 +4387,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.016129", @@ -1143,7 +4405,7 @@ }, { "data": { - "image/png": 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1rnGcRqOpM79m4cKFXcPDw8tTU1PPGdsefvjhsu7duwfFx8e7b968+drOwitXrsiTk5NPDho0qNrY9s0339js3r3bYd26dRnPP/98EQBMmDCh3NHRUTdr1izvAwcOqAcNGlT93nvvOV26dEmVkpJyyhjDpEmTSv38/ALy8vIUt/NciG5Wu9gOTERERERERETtWGqqLerrZTAYAJ1OhtTUdnfibmsdP35clZWVpZo8eXJRfX09jB9bW1tDSEhI5U8//WRjOl6j0dSZJgABYPfu3Z0UCoX0xBNPlJjOMX78+DIA+P77720B4KeffrJxd3e/LglpYWGB8ePHF9+LeyUyxUpAIiIiIiIiImpeVFQ51qwxQKeTQS43ICrqvj1tNycnRw4Ac+fO9Zo7d66XeX/nzp3rTH93dXWtNx9TUFAgr6+vF506dQppbI2ioiI5AOTl5SmcnJx05v1ubm43tBHdbUwCEhEREREREVHzoqMrkZx8ui3eCXinubi46AFg8eLFl0eOHFlm3q9Sqa47+VcIccNJwI6OjjqVSiV99913pxpbw8PDox4A3Nzc6s+cOWNp3p+Xl8d8DN1z/KMjIiIiIiIiopZFR1feb8k/hUIhVVdXX/cqtODg4BqNRlN38uRJdXx8fO6tzPvwww+Xbdy40b2kpMRi/PjxTVZFDhgwoGLbtm1Oqamp1sYtwXq9Hl999ZXjraxLdDuYBCQiIiIiIiKiDql79+41KSkpnbZv317m5OSk8/DwqPfy8qpfvXr1xccff7z76NGjxaRJk4pdXFx0OTk5igMHDth4eHjULVmyJK+5eceMGVM+ZsyY4unTp3d/5pln8gYMGFApk8lw/vx55TfffNNp1apVl3r37l0bFxdXtHbtWvfHHnus+6uvvnrZzc1Nt2nTJpeKigqLe/UMiIx4MAgRERERERERdUjvvvtuppWVlWHKlCk+ERERvRITE10AYPLkyaXffPNNelVVlWz27NleEydO9H3jjTe65OfnKwYPHlzRmrl37NhxYcGCBdk7d+50mDp1qs8TTzzRLSkpybV79+61Wq1WBwCWlpbSt99+e7pXr15VixYt8nj22We9PD09a1988cXsu3nfRI0RknTD1nYy07dvX+nIkSNtHQYRERERERHdBUKIXyRJ6tvWcdxJx44dywgODi5s6ziI6N47duyYc3BwsJd5OysBiYiIiIiIiIiIOjgmAYmIiIiIiIiIiDo4JgGJiIiIiIiIiIg6OCYBiYiIiIiIiIiIOjgmAYmIiIiIiIiIiDo4JgGJiIiIiIiIiIg6OCYBiYiIiIiIiIiIOjgmAYmIiIiIiIiIiDo4JgGJiIiIiIiIiIg6OCYBiYiIiIiIiIiIOjgmAYmIiIiIiIio3Vu7dq2Tp6dnoEKhCLW1te0DAFqtNigmJsbLOCYxMdFJCBGWnp6uNLaZj7ld/fr18+vXr59fa8bu3bvXSq1Wh1y4cEFxu/Gkp6cr582bpzl58qSy5dFtKzk52VYIEZacnGx7s9fOmzdPs3Pnzhuui4mJ8dJqtUF3JsIbZWZmKtRqdcgPP/xgdbfWaGvytg6AiIiIiIiIiKg5GRkZivnz53uNGzeuKCkpqVCtVhsA4Isvvjhrb29vaOv4mrJgwYIukyZNKvL29q43tt1qzGfOnFGtWbOm85AhQ8r9/f3r7myk7ceaNWs663Q6jBs3rty0fenSpTlXrlzJu1vrenp61k+ZMqVw4cKFXQ8fPpx+t9ZpS0wCEhEREREREVG7duLECZVer8eMGTOKRowYUWFsf+CBB6rbMq7m7Nu3z+rQoUO269evv2ja3p5i1ul0kCQJCoWi5cFtLCAgoPZur/H8888X9O3bN+CHH36wGjp0aNXdXu9e43ZgIiIiIiIiImq3YmJivMaMGeMHAOPHj/cVQoQZt9Pe6tbaU6dOKceNG+ft4OAQrFQqQ3v27On/0Ucf2ZuPS0pKcvD29g5QKpWhPj4+AY2NacrGjRudfX19q/v27Vtj2t7UFubU1FTrcePGedvY2IS4urr2njFjRteqqioBXN1eO3bsWF8AeOSRR3yFEGHm221XrVrl7Ofn569SqUIdHByCH330Uc+8vDwL07WFEGEvvPCC9uWXX3bXarVBKpUq7Oeff1Ybt+/+/e9/t4+JifGys7PrY2NjEzJu3Djv3Nzc6+YoLi6WPfHEEx6urq69lUplqJeXV+Cbb77pajA0X9z45Zdf2kVERPi4uLj0VqvVIT169Ah444033HQ63XXxAcC6des6G+9x3rx5GqDx7cCZmZmKRx55xMv4Pfr6+vpv2LDB0XRMa56vUVhYWE2PHj2qk5KSXJq9mfsUKwGJiIiIiIiIqEUpKbBOTYVtVBTKo6NRea/WXbp0aU5oaGjVq6++2jUhIeFieHh4lbu7u67lKxt39uxZxaBBg3o5OTnpli9fnuXm5qb7/PPPHWfMmNHdwsLi7OOPP14KADt27LB99tlnu0VGRpYmJCRcys/Ply9atKirTqcT3t7eLValpaWldYqKiiptbVxPPvmk9yOPPFL85JNPnt2/f7/N6tWrNQ4ODvo1a9ZkDxo0qDIhIeHi4sWLPd56662sAQMGVAJASEhINQDMmjVLm5SU5DZz5sz8hISES1lZWYrly5drhw8frj569Ogpufx/6Z8tW7Y4de3atXb58uVZNjY2Bg8Pj/qSkhI5ALz00ksegwcPLtu8efP59PR0y/j4eO348eMVhw4dOg0Aer0ew4cP73Hy5EmrhQsXZgcHB1fv2rWr05IlS7oWFBQo1q9ff7mZ566KjIwsj4uLy1er1dLPP/9s9c4772gKCgrkGzZsuAwAKSkpp6Kjo3vGxMQUPffccwUA4OXl1ejW57KyMllERIRfaWmpxSuvvHLZw8Oj7pNPPnGKi4vzrqqqks2fP7+wtc/XdNyAAQMq9uzZ06m139v9hElAIiIiIiIiImpWSgqsx4yBb309ZGvWwJCcjNP3KhEYEBBQe+bMmWoACAwMrImKirqtdV9++WWNJEnYu3fvKXd3dz0AxMTElA0aNEixbNkyrTEJuHTpUq23t3fNnj17zlpYXC2Ga1i/Z0tJwKysLHl2drYyODi41VtKJ06cWGxMSE2YMKH8yJEj1tu3b3dcs2ZNtqOjoyEwMLAGAAICAqpNn0F6erpy06ZN7nPnzs1+5513coztvXr1qhkxYkTPf/zjH/bTp0+/YrrWjz/+eNrGxkYy/n7s2DEAQI8ePaq3bduW0dBc5ujoqJs1a5b3V199ZTt+/PjyL774otPRo0dt3n333YzZs2cXNcRdVlVVJUtKSnJ75ZVX8jp37txognbhwoUFxp8NBgNGjhxZXldXJzZu3Oi+bt26yxYWFjDel0ajqWvpe16/fr1TZmamateuXafHjBlTDgCPPvpo2aBBgxTx8fHaOXPmFJomP5t7vqbzhoSEVH388ccuGRkZCi8vr3p0INwOTERERERERETNSk2FbX09ZAYDoNNBlpqKmz71tb1IS0vrNHTo0FInJyd9fX09jJ/o6Oiy9PR0dXFxsUyn0+H48eNWY8eOLTEmAAFg2LBhlRqNpsVDOS5evKgAAFdX11ZXLI4bN+66RJ2/v391Tk5OiycBJycn2xkMBsycObPY9H6GDh1aaWNjo//xxx9tTMdHRESUmSYATcXExBSb/j5z5swSmUyG/fv32wDAjz/+aCuTyfDUU09dN2769OnF9fX14ocffrBuKs7MzEzF1KlTPTUaTZBSqQxVKpVhK1eu1JaXl1tcvnz5povU9u3bZ+vq6lpvTAAaTZkypaikpER+9OhRtWl7a5+vq6trPfC/77AjYSUgERERERERETUrKgrla9bAoNNBJpfDEBWF8pavap+Ki4vl27dvd1IqlU6N9efn58srKysNOp1OuLm53VAJ5uzs3GJ1WHV1tQwAVCpVq08BdnFx0Zv+rlKppLq6OtHUeNN4ASAwMDCwsf7i4uLrcj/u7u5Nxm++zdrS0lKys7PTXb58WQEAJSUlFnZ2djq1Wn1dElGr1dYDQFFRUaN5Jr1ej9GjR/vk5+crFi1alB0QEFBjZWVl2LZtm/26des6G5/Xzbhy5YrcxcXlhnvRaDT1AFBQUHDduwxb+3ytrKwkAKiqqupwhXNMAhIRERERERFRs6KjUZmcjNNt8U7AO83e3l4fHh5evnjx4tzG+j09PesVCoUkl8ulvLy8G6rBCgsLFVqtttlqQGMFoHkC7m5wcnLSA8CXX355xsnJ6YbKQ/NqRCFEo1WAAJCbm3tdvDU1NaKsrExuTPI5ODjoy8rK5DU1NcLS0vLaPMYkobOzc6OVjydPnlSdOHHC6r333rswa9asa1WE27dvb/VBK+bs7e1158+ftzRvz87OvukqTFOFhYUWt3N9e9YusppCiCFCiJ1CiMtCCEkIMcOkTyGE+LMQ4r9CiEohRI4Q4jMhhIfZHCohxDohRGHDuJ1CiC5mYzyEELsa+guFEIlCiBZLa4mIiIiIiIh+76KjUZmQgNz7OQEIABEREaW//fabVWhoaPWQIUOqzD9qtVqSy+UICgqq2rVrl4Ne/78Csu+//946Ozu7xTyCr69vnUqlks6fP6+6U3FbWloagBsr1EaPHl0mk8mQkZGhbOx+evbs2eL2ZaN//vOf152s+7e//c3BYDDggQceqACAyMjIcoPBgL/97W8OpuM++eQTR4VCIUVGRjb6t1FRUSEDAIVCcS1xWFtbK8zXM45pTWXggw8+WJ6Xl6f47rvvrtuCvGXLFkdHR0ddSEhITVPXNufChQsqhUIh9ezZs8XDX+437aUS0AbArwA+aviYsgIQCmA5gP8A6ARgFYBvhBC9JUkyZmbXAhgP4DEARQBWA0gWQoRJkqQXQlgA2N3Q9yAAJwAfAhAAXriL90ZERERERERE7cSKFSuyBw4c2GvAgAE9n3nmmfxu3brVFhcXy48fP66+cOGCauvWrRkA8Prrr1+eOHGi7/Dhw32efvrpgvz8fPmKFSs0rdkObGlpKfXu3bvyyJEjTb4j72YFBgbWWFhYSH/729+cnZ2ddZaWllJQUFBNQEBA7XPPPZe7ePFij/T0dMvIyMhytVptyMzMVKakpNg99dRThWPHjm3V9u0zZ86oY2NjvR577LHiU6dOWSYkJGjDw8Mrxo8fXw4AkyZNKn377bcr5s+f71lQUCAPCgqqSU5O7rRlyxbnuLi43KYOBQkJCanRaDR1y5Yt08rlcigUCikxMdGtsbHdu3evSUlJ6bR9+/YyJycnnYeHR31jB3TExcUVbdq0ye2xxx7zMT0d+MCBA3Zvv/12pumhIDfj8OHD1kFBQZXGbcEdSbuoBJQk6V+SJL0sSdI2AAazvlJJkoZLkrRFkqR0SZJ+BvAMgF4NHwghOgH4I4AFkiTtkSTpKIDpAHoDiG6Y6iEAAQCmS5J0VJKkPQAWAnhKCGF3L+6TiIiIiIiIiNpWjx496g4dOnQyICCgatmyZdoJEyb4zps3z2Pfvn02Q4cOLTOOmzBhQvnGjRsvnD9/3vKJJ57onpiY6L5ixYqslk4GNoqJiSn+6aefbMvKyu5I7sXd3V2fkJBw8bfffrN6+OGHe0ZERPTav3+/NQCsX7/+8qpVqzIOHjxoM3PmzG6PPfaYz9q1a93t7e31/v7+ra6I+/Of/3xRkiTMmDGjW3x8vHbo0KFXvvrqq3PGfgsLC+zZs+dMTExM0bp169wfffRRn9TU1E5LlizJSkxMvNzUvJaWltLWrVvPuri41M+aNcvrxRdf9Bg0aFD5nDlzbtiS/e6772ZaWVkZpkyZ4hMREdErMTHRpbE57ezsDD/++GP6gw8+WLZs2TLt1KlTfU6ePKl+7733LsyfP7+wtfdsqqKiQhw8eNDO/ICUjkJIUvtKbAohKgA8L0nS35sZMwDAQQBdJUm6JIQYBiAVgKskSQUm404A2CZJ0htCiKUAYiRJCjDpdwGQD2CYJEk/NLVe3759pSNHjtzurREREREREVE7JIT4RZKkvm0dx5107NixjODg4FtKhNCdUVxcLPPw8AheuXJlpul78Nqj5ORk27Fjx/pu37799IQJE+7bQ19u1/vvv+8wZ84cr8zMzP+uvisGAAAgAElEQVQ6OzvrW76ifTp27JhzcHCwl3l7u6gEvBkN7/BbBWCXJEmXGprdAegBmP8PXF5Dn3FMnll/YcN17iAiIiIiIiIiukMcHR0NcXFxOWvXrnU3GFp9SDC1oTVr1rg/99xzufdzArA57eWdgK0ihJAD+ASAPYBxrbkEgGmpY1Nljze0CyGeBvA0AHh4eNxwARERERERERFRc1577bU8vV4vLl68qGjsvXbUfly8eFE+atSoK2+88YZ5AVmHcd8kARsSgP8AEAQgUpKkIpPuXAAWAJwBFJi0uwL4fyZjHjCb1rnhuhu+YEmSkgAkAVe3A9+BWyAiIiIiIiKi3xEbGxvpnXfeyWnrOFoyZsyYckmSfmnrONqSh4eHbtWqVe3+u7od98V2YCGEAsAWXD3oY6gkSeYvjvwFQD2A4SbXdMHVg0MONDQdBNCrod1oOIDahuuJiIiIiIiIiIg6pHZRCSiEsAHg0/CrDICHEKIPgGIA2QC2AggHMBaAJIQwvsOvVJKkakmSSoUQHwB4WwiRD6AIwGoA/wWQ0jD2OwAnAHwkhHgRgBOAtwG8L0nStdN/iIiIiIiIiIiIOpr2UgnYF8C/Gz5qAG82/LwUQBcA4wFocLViL8fkM9lkjrkAvsTVisH9ACoAjJUkSQ8ADf+OBlDV0L+lYfz8u3trREREREREREREbatdVAJKkpSGq4d4NKW5PuMcNQBeaPg0NeYigDE3Gx8REREREREREdH9rL1UAhIREREREREREdFdwiQgERERERERERFRB8ckIBERERERERERUQfHJCAREREREREREVEHxyQgEREREREREbV7a9eudfL09AxUKBShtra2fQBAq9UGxcTEeBnHJCYmOgkhwtLT05XGNvMxt6tfv35+/fr182vN2L1791qp1eqQCxcuKO7U+gcOHFDPmzdPk5eXZ2HeJ4QImzdvnuZOrSWECJs9e/Ydm+9uM7//N99809XX19dfr9e3ZVjtBpOARERERERERNSuZWRkKObPn+8VFhZWkZycfHr37t2nAeCLL744u3Tp0py2jq8pCxYs6DJp0qQib2/v+js155EjR6zWrFnTuaCgQG7el5KSciouLq7gTq11v3vxxRcLiouLFevXr3dq61jagxv+YIiIiIiIiIiI2pMTJ06o9Ho9ZsyYUTRixIgKY/sDDzxQ3ZZxNWffvn1Whw4dsl2/fv3F5sYZDAbU1dUJS0tL6XbXjIqKqrzdOToSGxsbKTY2tmjdunXuf/rTn4raOp62xkpAIiIiIiIiImpRSUqJ9fnF591LUkqs7+W6MTExXmPGjPEDgPHjx/sKIcKM23tvdavvqVOnlOPGjfN2cHAIViqVoT179vT/6KOP7M3HJSUlOXh7ewcolcpQHx+fgMbGNGXjxo3Ovr6+1X379q0xbddqtUHjx4/3Xrt2rZNx7i+++KITAMydO1fj7+/fy9bWto+Dg0PwgAEDfFNTU68978TERKc//elPXgAQFBQUKIQIM93+3Nh24G3bttn16dOnp6WlZaitrW2f6Ojo7seOHVO19j4kSRKLFi1yd3Nz621paRnat29fvwMHDqhNx3z55Zd2ERERPi4uLr3VanVIjx49At544w03nU533Vx/+ctfHHv16uVvZWUVYmtr28fX19f/7bffdjYds3v3bpuBAwf6Wltbh6jV6pDBgwf3OHz4sKXpGJ1Oh9mzZ2uM6/Xr18/vyJEj140xmjZtWvG5c+cs9+zZc0//btsjVgISERERERERUbNKUkqs/zvmv75SvSTLWpNl6J3c+7RDtMM9qTpbunRpTmhoaNWrr77aNSEh4WJ4eHiVu7u7ruUrG3f27FnFoEGDejk5OemWL1+e5ebmpvv8888dZ8yY0d3CwuLs448/XgoAO3bssH322We7RUZGliYkJFzKz8+XL1q0qKtOpxPe3t61La2TlpbWKSoqqrSxvoMHD9qePHnS6qWXXspxd3ev9/HxqQOA7OxsRVxcXJ6Hh0d9RUWF7NNPP3UaOXKk3759+37r379/dWxsbOm5c+dyEhMTO2/evPm8h4dHHQB4eHg0ut1427ZtdpMnT+7Rv3//sg8++OBceXm5RXx8vCYyMrLn0aNHT7Zmm/LWrVudNBpN3TvvvHOxpqZGLF++XPvwww/7paenH3dzc9M3PFNVZGRkeVxcXL5arZZ+/vlnq3feeUdTUFAg37Bhw2UA+Pbbb21mzZrlPWPGjPyEhIQsg8EgTp48aXnlypVruanPP/+807Rp03wiIiKubNq06QIArFq1yj0qKqrn0aNHT/j4+NQDwIsvvqhZv3595//7v//LGzlyZNnPP/9s9cgjj/g0Fv/AgQOrbGxs9Lt37+40fPjw33WlJJOARERERERERNSsktQSW6leksEASDpJVpJaYnuvkoABAQG1Z86cqQaAwMDAmtvd8vryyy9rJEnC3r17T7m7u+sBICYmpmzQoEGKZcuWaY1JwKVLl2q9vb1r9uzZc9bC4uoZHA3r92wpCZiVlSXPzs5WBgcHVzXWX15ebnHkyJGTHh4e1yUzt2zZkmn8WafTITY2trRHjx6Bf/nLX5z79++fpdFodN27d68FgPDw8KrAwMBm41iyZIm2S5cutT/++OMZheLq2SSRkZEVgYGBgcuXL3f761//eqml51VbWyvS0tJO29nZGQBgyJAhlQEBAYHx8fFu7777bjYALFy48Np7CA0GA0aOHFleV1cnNm7c6L5u3brLFhYW2L9/v7Wtra1+8+bNWcaxEydOLDNda+HChV3Dw8PLU1NTzxnbHn744bLu3bsHxcfHu2/evDmroKDA4v3333ebMmVKQVJS0iXjPBYWFoiPj9eax29hYQFfX9/qw4cP/+4rAbkdmIiIiIiIiIia5RDlUC4UwgALQMiFwSHKobytY7pVaWlpnYYOHVrq5OSkr6+vh/ETHR1dlp6eri4uLpbpdDocP37cauzYsSXGBCAADBs2rFKj0dS1tMbFixcVAODq6tpoxWJwcHCleQIQuFp92L9/f197e/s+CoUiTKlUhmVmZqrOnj3b6FbX5pSVlclOnjxpNX78+GJjAhAAevbsWRcaGlp58OBBWwDQ6/UwfQ7mW3gjIyNLjQlAAPDz86sLDg6uPHz4sI2xLTMzUzF16lRPjUYTpFQqQ5VKZdjKlSu15eXlFpcvX5YDQP/+/SvLysosxo8f7/2Pf/yjU2Fh4XWnGx8/flyVlZWlmjx5cpFpPLa2toaQkJDKn376yQYAjhw5oq6urpZNmTKlxPT6P/zhD8VNPQsnJyddfn6+sqn+3wtWAhIRERERERFRsxyiHSp7J/c+XZJaYusQ5VB+r6oA74bi4mL59u3bnZRKZaMnxubn58srKysNOp1OuLm53bBd1tnZucUttNXV1TIAUKlUhsb6XV1db5hj3759VpMmTerx4IMPlq1bty5Dq9XWy+Vy6emnn/aqra296SKugoICC0mS0Llz5xvWcnV1rf/3v/9tDQALFizQrFmzprOxLzw8vOLnn39ONxl7Q7LSxcWl/vTp02rgahJx9OjRPvn5+YpFixZlBwQE1FhZWRm2bdtmv27dus7GZzF69OiKzZs3n9+wYYPr9OnTuwNAv379KtasWZPVv3//6pycHDkAzJ0712vu3Lle5mt27ty5DgAuXbqkAACNRnPdfXXp0qXJ78XS0tJQU1Mjmn1gvwNMAhIRERERERFRixyiHSrv5+Sfkb29vT48PLx88eLFuY31e3p61isUCkkul0t5eXkK8/7CwkKFVqttthrQmDgrLi5uNO8ixI35qC1btjjI5XLp66+/PqdSqa6dFFxWVmZhZ2enb+G2buDi4qIXQiA3N/eGe8jPz1c4ODjoAGD27NkFEyZMuGLs69Spk95s7A33UFBQoHB3d68HgJMnT6pOnDhh9d57712YNWvWtWq87du333CIysyZM0tmzpxZUlpaKvvXv/5l+9prr3UZO3Zsj5ycnP+6uLjoAWDx4sWXR44cWWZ+rfGZGJN92dnZCgDXDl0xJgcbc+XKFbnxfn/PmAQkIiIiIiIiot+NiIiI0l9++cUmNDS02sbGRmpqXFBQUNWuXbscVq1alW3cEvz9999bZ2dnK1tKAvr6+tapVCrp/PnzrT6Ft6qqSiaTySCTya7FtHPnTtucnBxlly5drq1nTIZVVVU1Wx1oZ2dn8Pf3r9q5c6fDqlWrsuXyqymg06dPK//9739bz5gxIx8AvLy86r28vJqsoktLS+tUVlYmM24JTk9PVx47dsw6Li4uFwAqKipkAKBQKK7FXVtbK/75z386NjVnp06dDI899ljpuXPnVK+99lrXvLw8eXBwcI1Go6k7efKkOj4+vtEELQCEh4dXq9Vqw+eff+4wbty4a9vSP/zwwybXy8rKUvbp0+e+T2DfLiYBiYiIiIiIiOh3Y8WKFdkDBw7sNWDAgJ7PPPNMfrdu3WqLi4vlx48fV1+4cEG1devWDAB4/fXXL0+cONF3+PDhPk8//XRBfn6+fMWKFZrWbAe2tLSUevfuXXnkyJFWH0bx8MMPl27evNk1NjbW+8knnyw8deqU5apVqzqbbx3u3bt3NQCsXbvW5cknnyxSKpVSv379qi0tLW9IaC5ZsuTy5MmTewwbNqzHs88+m19eXm6RkJCgsbGx0b/yyit5rYlLpVJJkZGRvnPnzs2tqakR8fHxGhsbG8PLL7+cBwAhISE1Go2mbtmyZVq5XA6FQiElJia6mc8zZ84cTX5+viIyMrKsa9eu9ZmZmcqkpCTXnj17Vms0Gh0ArF69+uLjjz/effTo0WLSpEnFLi4uupycHMWBAwdsPDw86pYsWZLn7Oysf+qpp/LWrVvX2dbW1jBy5MiyQ4cOWX366afOjcVfWFhokZmZafnCCy+06n47MiYBiYiIiIiIiOh3o0ePHnWHDh06+fLLL2uWLVumLSkpkdvb2+t69OhRPW3atCLjuAkTJpRv3LjxQkJCguaJJ57o7uHhUbtixYqs9evX35DgakxMTEzxm2++2cW0iq6F8WVvvfVW1oYNG9y+/fZbBx8fn+qkpKQL8fHxGtNxAwcOrJ43b172xx9/7LJlyxYXg8GAU6dOHffz87uhOjE2NrZsy5YtZ9566y3Nk08+2V2hUBj69etXvnr16kvNVf+ZmjRpUpG1tbV+/vz5HleuXJEHBgZWfvrpp+fd3Nz0wNWE59atW8++8MILHrNmzfKys7PTP/bYY4UeHh51L774oqdxngEDBlSuX7/e9ZVXXulaWloqd3R01A0ZMqR05cqV2cYxkydPLnVyckpfvnx559mzZ3vV1tbKnJ2d60NCQiqnTp16bavxqlWrsiVJwmeffeby4Ycfuvbu3btyx44dZ/v27RtgHv/WrVs7KRQKaerUqSXmfb83QpKarHylBn379pWOHDnS1mEQERERERHRXSCE+EWSpL5tHceddOzYsYzg4ODCto7j96y4uFjm4eERvHLlykzTd+XRvTVkyJAejo6Ouh07dlxo61julWPHjjkHBwd7mbff9OkyRERERERERETUPEdHR0NcXFzO2rVr3Q2GFgsB6S44cOCA+tChQ7ZvvfVWdsujOz5uByYiIiIiIiIiugtee+21PL1eLy5evKho7fZbunOys7MViYmJGYGBgbVtHUt7wCQgEREREREREdFdYGNjI73zzjs5bR3H71VsbGxZW8fQnnA7MBERERERERERUQd3x5OAQgg7IYTHnZ6XiIiIiIiIiIiIbk2rkoBCiO5CiK+EEKVCiCIhxCdCCO8mhs8F8Ls5cYWIiIiIiIiIiKi9azEJKIRwBbAPwFgAtgAcAEwF8G8hxOi7Gx4RERERERERERHdrtZUAi4G4AZgEwAtAJeGNgWAL4UQE+5eeERERERERERERHS7WpMEHAXgmCRJz0mSlCNJUpEkSX8GEAmgBMDnQogxdzNIIiIiIiIiIiIiunWtSQJ6AvjevFGSpMMAhgAoArBVCDHqDsdGRHRfO5h1EAl7E3Aw62Bbh0JERERERES/c61JAlYD0DfWIUnSafyvIvCfQojoOxcaEdH962DWQUR9FIXXfngNUR9FMRFIRERERHSb1q5d6+Tp6RmoUChCbW1t+wCAVqsNiomJ8TKOSUxMdBJChKWnpyuNbeZjble/fv38+vXr59easXv37rVSq9UhFy5cUNyp9e8VIUTYvHnzNDd7XWJiotPatWudGms3/27uhfT0dKUQImz16tXO93LdW2WMNzEx8doznDlzZtfIyEif251b3ooxmQCCm+qUJOmMECIKQBqAHQAO3G5QRET3u7SMNNTp66CX9KjT1yEtIw0Duw5s67CIiIiIiO5LGRkZivnz53uNGzeuKCkpqVCtVhsA4Isvvjhrb29vaOv4mrJgwYIukyZNKvL29q5v61julU8++cRZr9djzpw5RabtsbGxpQEBAac8PDx+N8/iTlmyZEmun59f0M6dO23HjRtXfqvztKYScB+AIUKITk0NkCTpNwDRAGoARN1sEEKIIUKInUKIy0IISQgxw6xfCCGWCCGyhRDVQog0IUSA2RgHIcTHQojShs/HQgh7szFBQogfG+a4LIR4XQghbjZeIqKWRHpFQmmhhIWwgNJCiUivyLYOiYiIiIjovnXixAmVXq/HjBkzikaMGFExZMiQKgB44IEHqgMCAmrbOr7G7Nu3z+rQoUO2s2fPzr9Xa1ZXVzeZ42iu717QaDS6qKioSrVaLbVlHPcjT0/P+mHDhl1ZtWqV2+3M05ok4G4AKgCzmhskSdJxXE0EXrmFOGwA/ArgT7i6/djcQgAvAngBQDiAfAB7hBC2JmM+AxCKqweZjGz4+WNjpxDCDsAeAHkNc8wGsADAvFuIl4ioWQO7DkTqE6lYNnQZUp9IZRUgEREREd33UlJSrBcvXuyekpJifS/XjYmJ8RozZowfAIwfP95XCBFm3N57q1t9T506pRw3bpy3g4NDsFKpDO3Zs6f/Rx99ZG8+LikpycHb2ztAqVSG+vj4BDQ2pikbN2509vX1re7bt2+Ned+qVauc/f39e1laWoba2dn1CQ8P99uzZ8+155qZmal45JFHvIzx+fr6+m/YsMHRdA7j9tqvv/7aZtSoUd1sbW37hISE9AKuPjM3N7feKSkp1iEhIT0tLS1DZ82a1cV0fT8/P3+VShXq4OAQ/Oijj3rm5eVZNHc/v/76q2rChAneWq02yNLSMrRLly5Bjz/+uEdBQcG16/r16+d3+PBhm6NHj9oIIcKEEGHGrdONbQeura0Vs2fP1mi12iCFQhGq1WqDZs+eramtrb2WsDRuj3377bed58yZo3Fxcelta2vbZ9iwYT7nzp1r9Tbruro68X//939dHB0dg9VqdcjQoUN9zLcmJyUlOQwYMMDXwcEh2MrKKqRXr17+69atu2Fr87Jly1y7desWYPz+AgMDe5n/bXz44Yf2wcHBPdVqdYitrW2fUaNGdTtz5sx165WXl8umTZvmYW9v38fKyipk2LBhPhkZGY1ul548eXLxvn37Op09e/aWt5a3uB1YkqRvhBBqNPFeQLOx/xFCdAfQZNVgE9f9C8C/AEAI8XfTvoZKvTkAVkiS9M+Gtj/gaiJwKoBNQoheuJr4GyxJ0oGGMc8A2CuE8JMkKR3A4wCsAPxBkqRqAL82XDdPCLFakiRmoonojhrYdSCTf0RERETUIaSkpFiPGTPGt76+XrZmzRpDcnLy6ejo6Mp7sfbSpUtzQkNDq1599dWuCQkJF8PDw6vc3d11tzrf2bNnFYMGDerl5OSkW758eZabm5vu888/d5wxY0Z3CwuLs48//ngpAOzYscP22Wef7RYZGVmakJBwKT8/X75o0aKuOp1OeHt7t1h9mJaW1ikqKqrUvP3pp5/u8v7777s9+uijha+++mq2TCbDwYMHrS9cuKAEUFlWViaLiIjwKy0ttXjllVcue3h41H3yySdOcXFx3lVVVbL58+cXms43c+bMbhMmTCh+5plnzul0umvJs4qKCosnnniiW1xcXF5wcPBlKysrAwDMmjVLm5SU5DZz5sz8hISES1lZWYrly5drhw8frj569OgpubzxVFFWVpZCq9XWxcbGZjk5OenOnDmjWr16defhw4db/ec//zkFABs3bsycPn26t16vFxs2bMgEAHt7+ybzSbGxsV5ff/214/PPP58zZMiQiv3791u/++67nS9cuKDatWvXBdOxa9eu7RwaGlrx3nvvZeTl5Slef/31LlOmTOl2+PDh9Ja+C+P1/v7+VRs2bMjIy8uTv/XWW9oRI0b4pqenn1CpVBIAnD9/XjVhwoQSX1/fXJlMJqWlpdnOnTvXs7q6WrZw4cKChnt0fPPNN7vOmTMnOyIioqKqqkp27NgxdVFR0bUHt3LlSpdFixZ5xMbGFr388ss5ZWVlsoSEBE1kZKTfr7/+esLBwcEAANOnT/fcvXu3w7x587L79+9f9e2339rNmDHDu7H4H3rooQqDwYDk5GQ7863WrdWadwJCkqRWl9ZKknQFt1YN2BRvAO4AvjNZo1oI8f8ADAKwCcBAABW4/n2E+wFUNoxJbxiztyEBaPQtgGUAvABc98dFRHQnHMw6iLSMNER6RTIhSERERET3rdTUVNv6+nqZwWCATqeTpaam2t6rJGBAQEDtmTNnqgEgMDCwJioq6rbWffnllzWSJGHv3r2n3N3d9QAQExNTNmjQIMWyZcu0xiTg0qVLtd7e3jV79uw5a2FxtditYf2eLSUBs7Ky5NnZ2crg4OAq0/Zff/1V9cEHH7j98Y9/zPvrX/96ydg+ZcqUa8nC9evXO2VmZqp27dp1esyYMeUA8Oijj5YNGjRIER8fr50zZ06haaJu9OjRJX/5y18uwUxVVZVs06ZNWdOmTbuWo0lPT1du2rTJfe7cudnvvPNOjrG9V69eNSNGjOj5j3/8w3769OmN5nRGjRpVMWrUqArj79HR0RV+fn61I0eO9Nu/f7/6gQceqA4LC6uxsbEx6PV6tPQ9HT582DI5Odlx7ty5OatXr84GgIkTJ5ZZWFjg7bff1hw6dCi3f//+13I4Go2mzjQxWFBQIF+2bFmXjIwMhZeXV4vvGbS2ttabfpfGe96wYYPT3LlzCwFgxYoVucbxer0eo0ePLs/NzVV88MEHLsYk4MGDB218fX2rTJ/f5MmTr31/paWlsmXLlmljY2OLtm7dmmFsHzJkSGVgYGDgunXrnF9//fX8Y8eOqXbt2uW4aNGiy/Hx8bnG+6+oqJB99tlnLubxd+7cWefm5lZ/6NAhGwC3lARszXbgRgkhrIUQIUKIB291jlZyb/g3z6w9z6TPHUCBaTVfw8/5ZmMam8N0jWuEEE8LIY4IIY4UFBTcRvhE9HvFE4KJiIiIqKOIiooqVygUBgsLC8jlckNUVNQtH07Q1tLS0joNHTq01MnJSV9fXw/jJzo6uiw9PV1dXFws0+l0OH78uNXYsWNLjEkjABg2bFilRqOpa2mNixcvKgDA1dX1uorFf/3rX3YGgwFxcXGFjV8J7Nu3z9bV1bXemAA0mjJlSlFJSYn86NGjatP22NjYksbmsbCwkKZMmXJdQi85OdnOYDBg5syZxab3PnTo0EobGxv9jz/+aNNUXDU1NeKll15y9/b2DrC0tAxVKpVhI0eO9AOAEydOWDZ1XVNSU1NtAeDJJ5+8LqH1xz/+sQgAUlJSTF8BhxEjRlx3L8HBwdUAcO7cOSVwNWlnek96/fUFiObf5UMPPVTp5uZW/9NPP13bhn38+HHV2LFjvV1dXXsrlcowpVIZtmXLFueMjIxr9xceHl556tQpqz/84Q9dd+zYYVteXn5dbu3777+3qaiosJg2bVqRaTzdunWr8/b2rtm3b58tAOzdu9fGYDBg2rRpxabXT5069brfTTk6Otbn5ubeve3A5oQQXQC8C2AsAAsAknEeIcRgAEkAZkmSlHarQTXBfLuuMGtrbDtvS2NEE+2QJCkJV+8Fffv25VZhIrppHx37CDW6GkiQeEIwEREREd3XoqOjK5OTk0+npqbaRkVFld+rKsC7obi4WL59+3YnpVJ5w7veACA/P19eWVlp0Ol0ws3N7YYKM2dn5xarzqqrq2UAoFKprju5uKioyAIAunXr1mQi8cqVK3IXF5cb1tBoNPUAYPoOPgDo0qVLo/E4OjrqzLf25ufnywEgMDAwsLFriouLm8wTvfDCC9q///3vrnPnzs0ZPHhwRadOnfSZmZnKP/zhD91rampuusjMuJb5acFdu3atb+i/7j4dHR2vy+oZt/Aan/Wjjz7q9eWXX177TidOnFj0z3/+M8P4e1PfZU5OjhK4WsE3cuRIX0tLS8Mbb7xxydfXt1alUknr16932bp1q7Pxmri4uKKamhrx0UcfuXzyySeucrlcioiIKF23bl2Wn59fXW5urhwAJkyY4NvYfXfq1EkPADk5OQoA6NKly3WJYo1G0+RWd0tLS+lWnrXRTSUBhRCdARwC4AZgJwBXXN1ma3SooW0ygLRbDcqMsRTTHUCWSbsr/lfJlwvAVQghjNWADe8SdDEbY17x59rwr3mFIBHRbTmYdRCb/7MZUsN/Y5DL5DwhmIiIiIjua9HR0ZX3c/LPyN7eXh8eHl6+ePHi3Mb6PT096xUKhSSXy6W8vLwbqq4KCwsVWq222WpAYwWgeVLN2dlZBwAZGRmK4ODgRrcU29vb686fP39DZV12dnaj1YVX0x83aqzdyclJDwBffvnlGScnpxuSTeZzm/rqq68cJ06cWLRy5cpr22B37drV7GEizXF0dNQBV981aHrCc1ZWlqIh1pt672N8fHz2n/70p2snMbu5uV13fVPfZUBAQBVwtYIvOztb+c0336SPGDHi2rbntWvXXvcgZTIZFixYULhgwYLCgoICix07dh0C1NMAACAASURBVNi9+uqrXSdNmtTtv//97ykXFxcdACQmJmYYqxVNGZOAnTt3rgeAS5cuyf39/a/9PWVnZzeZq7ty5YpFr169WqxEbcrNZg/fwNXEWbQkSRNx9bTdayRJqgewF8ADtxpQIy7gagJvuLFBCGEJ4EH87x2AB3H1hGHThORAANZmYx5suNZoOIBsABl3MF4iIqRlpEFnuPr/OQICM/vMZBUgEREREVE7EBERUfrbb79ZhYaGVg8ZMqTK/KNWqyW5XI6goKCqXbt2OZhuK/3++++ts7OzGz291ZSvr2+dSqWSzp8/rzJtf/jhh8tlMhnWr19/wzvfjB588MHyvLw8xXfffXfdKcxbtmxxdHR01IWEhNxw2nBrjR49ukwmkyEjI0PZ2L337NmzyQRTTU2NTC6XX7dTcvPmzTdUUyqVSkNrqtWio6PLAeCjjz5yMJvTEQCioqIqGruuKX5+fnWm9+Ln53fdvZh/l9999511Xl6eYsCAAZXA/2fvzuOirtY/gH/ODDMDCLIjgujgAu6EuI3rKKVmaiTtmlJdce+aNzVLs6LSiy3kkklZhtpts7xl11ywUdRRk4z8aeIGMgaKAir7LN/z+2MYnIFhn2HR5/16zQu/53tmzpkNmWfOcx6gqKhIBAASiaTiPl6/fl28d+/eaitC+/j4GGbMmJE/ceLEvPPnzzsBwOjRowvbtGkjXLhwQWbtMTYFf4cPH14oEomwdetWi6rPX375pae1sfR6Pa5evSrt1q1bg5//+qYDjwfwYy2pvpkwBujqjDHmAqBr+aEIQEfG2H0A8jjnmYyxeACvMsbOAjgHYBmMhUC+BADO+V+MsV9grBQ8A8Y0340AdpZXBkZ53xUANjPG3gIQDOBlAG9QZWBCiK15OXtB4MaV/xwcYe3DmnlGhBBCCCGEEABYtWpVlkKh6DF48ODuM2fOzOncuXNZXl6ew6lTp5zS09NlpmIOr7322t+TJ08OfuCBB7rGxMRcz8nJcVi1apV/XdKBHR0ded++fYtOnDhhEcjr1atXWXlRkHaFhYXiSZMm3RSLxfzYsWNtunfvXjpjxoz8uXPn5m7cuLHdU0891dW8OvCRI0farl69+nJ11XvrolevXmWzZ8++unTp0o5paWmOSqWywMnJSbh8+bJ03759bWfMmHFj4sSJVvd7HDly5K3vv//ea9WqVSXBwcFl3333nXtKSkqVPQRDQkJKt2zZ4vPJJ594hISElLm5uRmsrXrs379/6YQJE/Lee+89f71ez4YNG1Z0+PDhNvHx8e0nTJiQZ14UxBaKiorE5s9lbGxsQKdOncrmzJmTCxiDdy4uLoYXXnih47Jly7IKCwtFcXFx7T08PPSFhYUVKx6feuqpTi4uLoYhQ4YU+fn56f766y/H7du3ew0bNuw2AHh6egorVqzQLF26tNP169cdxo8ff9vd3d2g0WgkBw8edB05cmTBrFmz8kJDQ8smTpyYt3r1an9BEGCqDvzrr7+6WZv/b7/95lRSUiJSKpUN3o+zvq+cdgDO19JHB+MKvProD+BXs+M3yi9fAIgGEAfACcB6AB4wph2P4Zyb3/EpANbgThXhHwHMM53knN9ijD1QfhsnAOQDeA/A+/WcKyGE1Cq3OBciiCBAgAgi5BY3qHgTIYQQQgghxMa6deumPXbs2JlXXnnFPzY2NiA/P9/B3d1d361bt5KpU6dW/OEeGRlZsGHDhvSVK1f6T5s2rUvHjh3LVq1apVm3bl27uowTFRWV98Ybb3S4ffu2qG3bthV7AyYkJFzp2rVr2aeffuqzfft2LycnJyEkJKTkwQcfvA0Abdu2FQ4cOJD2z3/+s0NsbGxAUVGRWC6Xl65fvz59zpw51RaNqKt169b93aNHj5KEhATfL774wocxBj8/P+2wYcMKevbsWe0qs4SEBM2MGTPYO++8EwAASqXy1pYtWy4plcoe5v1WrFiRfeHCBdmCBQvkxcXFogEDBhQeP348zdptfvfddxmLFi0q+89//uP94Ycftvf19dXNmTPn6urVq7Ot9W+MBQsWZF+4cEE2e/ZseWlpqWjQoEEFH3/8caZpb0F/f3/9tm3bLi5ZsiQwOjq6i4+Pj3bmzJk5eXl5Dh988EF70+0MGTKkcMuWLd7bt2/3KiwsFPv4+OgmT56ct3r16r9NfRYtWnSjY8eOuvfff7/drFmzvPR6PfP19dUOGjSocMCAARUVo7ds2XJ51qxZhg0bNvitWbOGDR48uGDz5s2Xxo4d273y/Hfs2OHm7e2te+ihhxocBGT1WQTHGMsGsJ9zPqX8eAWA1zjnYrM+PwLowzkPauikWpr+/fvzEydONPc0CCGtiKkysNaghVQsRdK0JEoHJoQQQghpoRhjKZzz/s09D1tKTU3NCA0NrbYCLbG/vLw8UceOHUPj4uIu2yJ4R+5tXbp06TVhwoT8Dz/8MKu2vqmpqd6hoaHyyu313RPwMIBJjLHKBTYAAIyxbgDGwXJVHyGE3HMUgQokTUtC7KhYCgASQgghhBByD/L09BTmzp2bHR8f7ycIQu1XIKQaW7dudc/NzZUsX768UYVt65sOvBrAwwAOMMYWAHAGAMZYGwAjAHwAQIAxzZYQQu5pikAFBf8IIYQQQgi5hy1fvvyawWBgmZmZErlcXutegoRYU1xczBISEi55e3sbau9dvXoFATnnxxhjMQA+BrDT7NTt8p96AM9xzk83ZlKEEEIIIYQQQgghrZ2Liwt/9913bb6/Hbm3xMTE5NvidupdUoZz/jlj7BCAOQAGA/ACcAvAUQDrzKrxEkLIPU+tUUOVoYJSrqRVgYQQQgghhBBCmk2D6kpzzs8DeNHGcyGEkLuKWqOG8q2l0F0cCkmXpVAtW0mBQEIIIYQQQgghzaJBQUBCCCG1i/s6GdrP/gcYpNAe0CLxvu+gmE1BQEIIIYQQQgghTa9BQUDGmBhACAAPAGJrfTjnBxsxL0IIadXUGjV+3H0LMEgB7gAYOK6e7t7c0yKEEEIIIYQQco+qdxCQMbYcxlRgt1q6Wg0OEkLIvSAxNRFCpz8A8auAgQNiHX7WLoJas4pSggkhhBBCCCGENLl6BQEZY4sBvAFjIZAtADQwVgQmhBBSWeBRYHoEkKEE5CroA45BlaGiICAhhBBCCCGEkCYnqmf/GQD+BhDMOY/mnC/nnL9h7WKHuRJCSKsR1j7M+I/Ao8DwVUDgUUjFUijlymadFyGEEEIIIa1VfHy8V6dOnXpLJJJ+rq6u9wFAQEBAn6ioKLmpz5o1a7wYY+FpaWlSU1vlPo01cODAkIEDB4bUpW9ycrKzk5NTWHp6usTUtnDhQv8ff/zR1VbzqY6t77e5+jwGlVl7jmzpjTfe8A0ODu5pMBjscfOtWn3TgQMBfMI5v26PyRBCyN0itzgXDAwcHAAw0H8g4sfF0ypAQgghhBBCGiAjI0Py0ksvySdNmpSbkJBww8nJSQCAb7755oK7u7vQ3POrzqJFizo89thjuUFBQTpT2wcffNBer9dj0qRJBfYcu6U/Nvbyr3/96/ratWvbr1u3zuuf//xnbnPPpyWpbxDwWgOuQwgh9xwvZ6+KACAAPN/veQoAEkIIIYQQ0kCnT5+WGQwGREdH544dO7bQ1D506NCS5pxXTQ4dOuR87Ngx13Xr1mU2x/gt+bGxJxcXF/7oo4/mrl271o+CgJbqmw78DYAHGGMye0yGEELuFqaVgADAwJBbTP/3EEKIObVGjZXJK6HWqJt7KoQQQlq4qKgo+YQJE0IA4OGHHw5mjIWb0lwbmvJ69uxZ6aRJk4I8PDxCpVJpv+7du/dMTEx0r9wvISHBIygoqJdUKu3XtWvXXtb6VGfDhg3ewcHBJf379y81tTHGwgFg7dq17Rlj4Yyx8IULF/q/9tpr7aRSab+srCyLhVeCIKBDhw59Jk6cGAQAaWlpUsZY+KpVq3z+8Y9/dPD09Ax1cnIKGzVqVNfK6bXWHpuzZ89KIyMjg7y9vUOlUmm/Dh069Hn22WcDTecPHDjgPG7cuM7t2rXr6+jo2E8ul/eeN29eQGFhIavr/TZ35swZqVKp7Ork5BTm4eER+uyzzwaWlZVVua2EhASPwYMHB3t4eIQ6OzuH9ejRo+fatWu9zPsEBwf3fOCBB7pUvu7OnTtdGWPh27dvb2tqmzp1at7Fixcd9+7d26Yh875b1XdV32sABgP4jjH2Auc83Q5zIoSQVs98JSAHh5ezVy3XIISQe4dao0ZEYgS0Bi2kYimSpiXRamlCCGkF8vP3tcnPT3L18Igo8PC4v6ipxn3zzTez+/XrV7xs2bLAlStXZg4YMKDYz8+vwUVKL1y4IBkyZEgPLy8v/dtvv61p166d/quvvvKMjo7uIhaLL0yZMuUWAOzYscN11qxZnZVK5a2VK1deycnJcViyZEmgXq9nQUFBZbWNo1Kp3CIiIm6Zt+3bt+/s/fff3z0qKip39uzZ1wFALpdrXVxchLi4uICPPvrI66233rpm6v/DDz+0/fvvv6Uff/zxDfPbiY+Pb9+zZ8/ijz76KOPatWsOb731VsDYsWOD09LSTstkMg4rzp49K1UoFD2cnJyEJUuW/B0SElJ2+fJl6d69eyuCZ+np6dK+ffuWTJ8+Pbdt27aGU6dOOb377rv+GRkZsp07d16qz+NcWlrKxo4dG1xWViZatWpVZrt27fQJCQk+u3bt8qjc99KlS7LIyMj84ODgqyKRiKtUKtcXX3yxU0lJiWjx4sXXAeC55567vmzZssCMjAyJXC6vSK/euHGjd0BAgPaRRx65bWpTKBTFLi4uhp9//tntgQceaLLXaktX3yDgaQASAP4AxjPGbgG4aaUf55xXic4SQsi9Irc4FyImgsAFsCtDsD0hGH2iAQV9xiWEEKgyVNAatDBwA7QGLVVOJ4SQViA/f1+bP/+cEMy5TqTRfCD07bvzXFMFAnv16lV2/vz5EgDo3bt3aURERKPGfeWVV/w550hOTj7r5+dnAICoqKjbQ4YMkcTGxgaYgoBvvvlmQFBQUOnevXsviMVimI3fvbYgoEajccjKypKGhoYWm7eb5u7v76+tfD8eeuihvMTERJ8333zzmkhkTNzcuHGjj1wuL50wYYLF/oFt2rQxmM+rR48epWPHju3+0Ucfeb344osWAUOTpUuX+peVlYlOnjx5xjyINn/+/Iq0pejo6Jsoj/MIgoAxY8YUtm3b1jBv3rygq1evik2PV12sX7/e68qVK7J9+/adNd3Xxx577FZISEiva9euScz7rlq16qrp3waDAQ899FDB1atXJZs2bfIxBQFnzpyZ+9Zbb3VYv3699+rVq7MBIDs722H37t0eL730UpbpMQMAsViM4ODgkt9++41WApqpbzqwCIAeQGb55RYAZuVS39slhJC7ilKuhEwsg+jKUPAv9mLfppGIiADUlPVGCCFQypWQiqUQMzFVTieEkFYiPz/JlXOdCBDAuV6Un59k9+q29qJSqdxGjRp1y8vLy6DT6WC63H///bfT0tKc8vLyRHq9HqdOnXKeOHFivinQBgCjR48u8vf319Y2RmZmpgQAfH1967xicd68eTkajUZmqhx8+fJlyf79+92io6OrFGetPK8xY8YUtWvXTnf06NFqg17Jycluo0ePvmUeAKwsLy9PNHv27IDAwMDeMpmsn1QqDZ87d24Q5xynT592rOt9AYCjR4+6+Pn5WQQ7xWIxHn744bzKfU+dOiWbOHFikK+vb1+pVBoulUrDv/76a++MjIyKMT08PITIyMjcbdu2eZsq/3700UdenHPMnj27SuDTy8tLn5OTY5cKxK1VvVYCcs7ldpoHIYTcVRSBCiRNS8Lrb5Vhn+AEwcCg1QIqFa0GJIQQ0+9IVYYKSrmSVgESQkgr4OERUaDRfCBwrhcx5iB4eETYtbKtPeXl5Tn88MMPXlKp1OqePTk5OQ5FRUWCXq9n7dq1qxIw8/b2rjaIZlJSUiICAJlMVufqvKNGjSru1atX8ccff+wTGRlZsG7dOm8HBwfMmjWrygbj1c0rOzu72qDXzZs3xbUFMJ966qmgI0eOuC5evDirX79+xa6ursKRI0faLF26tKPpPtXVtWvXJF5eXlWCoO3atbNou3XrlmjcuHHBjo6OwooVK64EBweXyWQyvm7dOp9vv/3W27zvP//5z5xt27b5fPPNN25PPPHErcTERJ8xY8bcDAwMrDKOo6OjUFpa2qC9DO9WVOmXEELsRBGoQNSDp5C0WQfGxXCQAEqluPYrEkLIPUARqKDgHyGEtCIeHvcX9e2781xz7Aloa+7u7oYBAwYULF269Kq18506ddJJJBLu4ODAK6etAsCNGzckAQEBNQbTTCsA8/Ly6hV3+cc//pHz0ksvdUpPT5ds27bN+8EHH8xr165dlRTc6ubVq1ev4srtJh4eHvrs7Owq1zMpLi5mSUlJ7gsXLsxavnx5jqn95MmTTvW5Dybt2rXTnT9/vsrqwWvXrlk8Jvv373fJysqS/vLLL2nmlZ/j4+OrBPAGDBhQGh4eXvjJJ5/4ODk5CZmZmbK1a9detjb+zZs3HTw8PBq8d+TdiNJ2CSHETtQaNeb8GQbDMyPBRy2D8MxooAPlAxNCCCGEkNbJw+P+os6dV15tzQFAABg5cuStv/76y7lfv34lI0aMKK58cXJy4g4ODujTp0/xTz/95GFKPQWA/fv3t8nKyqo1xTQ4OFgrk8n4pUuXZJXPSSQSXt2qun/84x95bdq0ER5//PHO2dnZ0jlz5lRJBQaAyvPas2dPm2vXrkkGDx5c7XMzfPjw2/v373e/fPmy1UBgSUmJyGAwQCKRWBQW2bp1q7e1/rUZPHhw4dWrV6VJSUkVKcoGgwH//e9/Pc37FRUViQBYjHv9+nXx3r17rVZijomJyTl48KBbbGysf6dOncomTZpkdVWqRqORdunSpdTauXtVjRFpxti08n/+wDkvMDuuFec8sVEzI4SQVi7ucBwM3AAEHgUCj0IH0Ob3hBBCCCGENLNVq1ZlKRSKHoMHD+4+c+bMnM6dO5fl5eU5nDp1yik9PV327bffZgDAa6+99vfkyZODH3jgga4xMTHXc3JyHFatWuVfl3RgR0dH3rdv36ITJ05U2aOvS5cupfv27XP74Ycfbnt5eek7duyoM+3T5+Liwh977LEbmzZtatetW7eS6irbFhUVic3nFRsbG9CpU6eyOXPmVEkdNlm5cmXWr7/+6jZkyJDuCxcuzA4ODi7TaDSS3bt3u/33v/9N9/LyMoSGhhZt2LChXfv27XU+Pj76zz//3MvaqsO6mDt3bm58fLzfU0891WXZsmV/t2vXTr9x40afwsJCi/So0aNHF7q4uBheeOGFjsuWLcsqLCwUxcXFtffw8NBX7gsA06dPv/nKK6/of//9d5cVK1ZcsTb2jRs3xJcvX3acP3/+NWvn71W1rQTcDOBzAO0rHdd0MfUhhJB7WlZBlsWxCCLa/J4QQgghhJBm1q1bN+2xY8fO9OrVqzg2NjYgMjIyeOHChR0PHTrkMmrUqNumfpGRkQUbNmxIv3TpkuO0adO6rFmzxm/VqlWa2ioDm0RFReUdPXrU9fbt2xaxlw8//PCys7Oz8OSTT3YdOXJkjzVr1viYn3/yySfzAeDZZ5+1ugoQABYsWJDduXPn0tmzZ8uXLFnSsVevXsW//PLLOZlMxqu7TkhIiPbQoUNn+/XrV/jWW28FREVFdXvnnXcCvL29K1Jmv/7660u9e/cuXrJkScfZs2fLfX199XFxcZq63N/KHB0d+e7du8/16NGjeMmSJR1nzZol79SpU9m//vUviw9K/v7++m3btl00GAwsOjq6yxtvvBEwbdq0G48++miVAiIAIJPJ+JgxY25KpVI+a9Ysq5WQv/32WzeJRMKffvrp/IbM/W7FOK/29QHGWDQADuD78pWA0+t6w5zzLxo/vZahf//+/MSJE809DUJIK5OQkoCZO2cCmsFA6jSE+t2HDa8oqDAIIYQQQkgLwxhL4Zz3b+552FJqampGaGio1QAJaRp5eXmijh07hsbFxV2eM2eO1YCWNfPnzw/YtGmT75UrV1I9PT0tCoukpaVJu3fv3ue99967vHDhwnvy+dXpdJDL5X0GDBhQuGPHjnRrfUaMGNHN09NTX935u11qaqp3aGiovHJ7jenAnPPNlY7vmsAeIYTYW0x4DC7+6YvVbz0IrpciFQyj9gK//koVggkhhBBCCLnbeXp6CnPnzs2Oj4/3mzVrVp5IVHMy5uHDh51Onz7tuGnTJt+nn376euUA4L0uLy9PlJKS4rRlyxavq1evShcvXmy1sMuRI0ecjh075pqSknK6qefY0lF1YEIIsaPbxyPBzepRabWASkVBQEIIIYQQQu4Fy5cvv2YwGFhmZqbEtO9fdR5//PGuubm5kmHDht1avXp1Vk1970VHjhxpM3HixGBPT099bGxs5pAhQ0qs9cvKypKsWbMmo3fv3nVK276X1JgOTIwoHZgQ0hBqNaBUGgN/JjIZrQRsyRJSErD9zHZE9YxCTHhMc0+HEEIIIU2E0oEJIXeTBqUDM8YuNXA8zjnv0sDrEkLIXSFxx2Xo9IEw1WDq0AFYvhxABzVWJquglCupUnALUrGHI4A9l/YAAAUCCSGEEEIIIXeN2tKBRTAWBqkv1oDrEELIXUOtUeOzm0vBRf8DBCkAMbKyGF74pwE8ZSkMAYcgFUuRNC2JAoEtxPYz26scUxCQEEIIIYQQcreocVdKzrmccx7UkEtT3QFCCGmJVBkqGAIOAdMjgC5JYIxDEIypwbqLQ2HgBmgNWqgyVM09VVIuqmdUjceEEEIIIYQQ0prVXJrGRhhjfRlj0xpxfTFjLJYxls4YKy3/+RZjzMGsD2OMvc4Yy2KMlTDGVIyxXpVux4MxtoUxdqv8soUx5t6Y+0YIIdYo5UpIxVKImBhiz0w4SACxGJBKAUmXwxAzMaRiKZRyZXNPlZSLCY/BxgkbMabzGGycsJFWARLSBNQaNVYmr4Rao27uqRBCCCGE3PWaqjrwIwBeA5DYwOsvATAXwHQApwD0BfAFgDIAseV9FgP4F4BoAGnl4+1ljIVwzgvK+3wJoCOAB2FMc/4UwBYAExs4L0IIsUoRqEB8r2OYF9sdep0YEAuY+FQOFs/xwynp09h+RoaonlGUCtzCxITHUPCPkCai1qgRkRgBrUFL2yMQQgghhDSBpgoCNtYQAD9xzn8qP85gjP0IYBBgXAUIYAGAVZzz7eVt0wHkAHgawEbGWA8A4wAM45wfKe8zE0ByeaAwrUnvESHkrpf7Vx8Y9BxcYDAIAv6rTkWbAdvxfcEiaA1aJGcmo49vH/rQ24KoNWqoMqhoCyFNQZWhgtagtdgegd53hBBCCCH20yTpwDZwCMAoxlh3AGCM9QQwGsD/ys8HAfADsMd0Bc55CYCDMAYQAUABoBDAEbPbPQygyKwPIYTYjFePU4C4DIAegBj8UgS2/etZlGaE0Z6ALZBpVdLyX5cjIjGC0hMJsTPTtgm0PQIhhBBCSNNoLSsB/w3AFcAZxpgBxnm/zTn/qPy8X/nPa5Wudw1AgFmf65zzimrHnHPOGMsxuz4hhNiEWqPGgtMR4M+EAarlwKX7Ae4AGDhYxiiIAo/Rh94WhlYlEWJ/lVfbJk1LotW3hBBCCCFNpLWsBHwCwDQYU3v7lf97DmPs+Ur9eKVjVqmt8nlrfYyNjMUwxk4wxk5cv369wRMnhNybTAElHngEUL4BiLUA0wEOOrz0dH/Ejoql/a9aGC9nL4iYCCImogAtIXZgbbWtIlCBpcOX0u9CQgghdRIfH+/VqVOn3hKJpJ+rq+t9ABAQENAnKipKbuqzZs0aL8ZYeFpamtTUVrlPYw0cODBk4MCBIXXpm5yc7Ozk5BSWnp4uscV8Tp486Th48OBgFxeXMMZY+JYtW9wXLlzozxgLb8jt2dqaNWu84uPjvZp7HrVJS0uTMsbC16xZU++5Vn7+Dx8+7OTk5BR2/vx5aU3Xawlay0rA1QDe5Zx/VX58ijHWCcBSAJsAXC1v9wOgMbueL+6sDrwKwJcxxkyrAcv3EvRB1RWE4JwnAEgAgP79+1sLHhJC6uFe22tNKVdCLBLDYDAAgUchenAh/K/Mx9OPO+Lfz0YCiGzuKRIzCSkJmPe/edALeohFYsSPi78nXqeENKXKq20TUxPvqf8XCCGENE5GRobkpZdekk+aNCk3ISHhhpOTkwAA33zzzQV3d3ehuedXnUWLFnV47LHHcoOCgnSmtsbMecGCBR00Go1s8+bNFz08PAx9+/YtPXnypLPtZtw4W7du9TYYDFiwYEFuc8+lqQwdOrRkyJAht5csWeL//fffZzT3fGrSWoKAzgAMldoMuLOSMR3GIN8DAH4DAMaYI4DhABaV91EDcIFxb0DTvoAKAG1guU8gIcTGTAEWAzdAJpbdEyvgFIEKPHffc9iYshFcMwjCrvfxtyDD2rNiRI4EFHf33W9V1Bo15v5vLvSCHgAgcAG5xffM3yyENBnTHoBagxZikRif//E5dAYdRCIR1o9fT5W5CSGE1Oj06dMyg8GA6Ojo3LFjxxaa2ocOHVrSnPOqyaFDh5yPHTvmum7dukzz9sbM+cKFC04DBw4sePTRR283fobEVmJiYm5MmTKlS0ZGxt9yuVxX+zWaR2tJB/4JwMuMsYcYY3LG2CMAFgL4ATDu7QcgvrzPZMZYbwCbYSwE8mV5n78A/AJjpeDBjDEFgI0AdlJlYELsxxRg0Qk6CFxAmaHsnimGMS10GiRiCZChBAxScEGMMi2HStXcMyPmVBkqCMKdL2IZGKUCE2IHpj0AY0fF4rn7noPOoIMAAXpBj3n/m0fFeAghpBXYl5/fZumlS3778vPbNOW4UVFR8gkTJoQAwMMPPxzMGAs3pdM2vc2vHgAAIABJREFUNLX27Nmz0kmTJgV5eHiESqXSft27d++ZmJjoXrlfQkKCR1BQUC+pVNqva9euvaz1qc6GDRu8g4ODS/r3719q3l5dCnNSUlKbSZMmBbm4uIT5+vr2jY6ODiwuLmYAsHPnTlfGWHhWVpZ0x44dXoyx8OpSgKtLdTXdxs6dO13N27/44gv30NDQ7k5OTmGurq73Pfjgg50rp7YGBAT0efjhh4MSEhI8Onfu3MvJySmsd+/ePXbv3u1i6jNw4MCQ3377zeX33393Mc2vprRp03y2bNni/vTTT3dyc3O7r23btvc9//zzgXq9HgcOHHAODw8PcXJyCuvatWuv7du3t618Gx999JFnSEhIT5lM1s/DwyM0MjIy6PLlyxLzPgUFBaKpU6d2dHd3v8/Z2Tls9OjRXTMyMqym7v78888uCoUiuE2bNmFOTk5hw4YN6/bbb785VncfTCZPnnzLxcVF+Pjjj1t0KnRrCQLOB/AdgI8A/AXgPQCfAHjVrE8cgPcBrAdwAkB7AGM45wVmfaYASIWxivDu8n8/Y+/JE3Ivu5cDLKbVgJAfqNgTUOygh1LZ3DMj5pRyJRzEdxbGG3eKIITYg2kPwGmh0yAS3fkz1MAN98wXRIQQ0lrty89vM+HPP4PjMjMDJvz5Z3BTBgLffPPN7LfeeksDACtXrszct2/f2TfffDO7obd34cIFyZAhQ3r89ddfzm+//bbmP//5z4U+ffoUR0dHd9m2bZubqd+OHTtcZ82a1TkoKKgsMTHx4gsvvHB1yZIlgenp6bK6jKNSqdwGDRpUWHtPo+eeey6oc+fOZVu3br0wffr061u2bPF99dVX2wPAkCFDivbt23fWw8NDP3LkyFv79u07u2/fvrP1v/eW4uLifKKjo7sEBweXbt68+dL7779/OS0tzUmpVIbk5+dbxIx+++03lzVr1vgtX74867PPPrtkMBjYo48+2vXGjRtiANiwYcPlHj16FAcHB5eY5rdhw4bLtc3h5ZdfDnR2djZs3rz50rPPPpvz2Wef+T7//POBzz77bNAzzzxzY+vWrRfd3Nz0U6dO7ZKdnV3xh/u7777rPXfu3KBu3bqVJiYmXly+fPnfBw8ebDty5MiQW7duVcz9mWee6fT11197z5w58+rWrVsvduvWrTQ6Ojqo8jy++uort4cffjjE2dnZsHHjxvRPPvkkvaioSBwREdH9woULksr9zUkkEoSFhRXu27fPraZ+za2p0oFZ+aVBygN5C8ov1fXhAF4vv1TXJw/A1IbOgxBSf6YAi9agBXDvBVimhU7D53+MQtn0+yG+HIF1cx+HQtGnuadFzCgCFRjfbTx2nN0BANALesQdjsMPT/7QzDMjpPWp6/6vikAF1o9fb7FVxL3yBREhhLRWSfn5rjrORQIAPeeipPx81/s9PIqaYuxevXqVnT9/vgQAevfuXRoREdGocV955RV/zjmSk5PP+vn5GQAgKirq9pAhQySxsbEBU6ZMuQUAb775ZkBQUFDp3r17L4jFYpiN3z0oKKispjE0Go1DVlaWNDQ0tLiu85o8eXLeBx98kAUAkZGRBSdOnGjzww8/eH7wwQdZnp6eQkRERJFEIuFeXl76xj4GAHDr1i1RbGxswKOPPpr77bffZpjaR4wYUdS7d+/ea9eu9X7ttddyTO2FhYXi1NTUMz4+PgYACAgI0I0cObLHd9995zZr1qy88PDwUhcXF8FgMKA+8xsyZEjBp59+egUAHnnkkdt79+51S0xM9P3ll1/STKnfHTp00A0ePLjnd9995zZ//vxcvV6PlStXBgwcOLBg586dl0y31atXr9Jx48aFrF271nvZsmU5qampsp9++slzyZIlf7/zzjtXAWDy5Mm3CwsLRV9++aWP+TwWL14cOGDAgIKkpKSLprbx48ff7tKlS5933nnH77PPPjOvQVFF3759iz/++GM/g8EA0+ulpanXSkDG2GeMsUm19JnAGPvMvI1z/jrnvLWsOiSE2JApwGKiF/RITE1sxhk1PUPmQCD1GfBbHZplfLVGjZXJKynVrganc05bHP947kd6vEgV7Q8fBlOpKi5Tz5xpsrHGpqbabSyTQSkpkBw4gEEpKQ26vrXqvzWJCY/BgegDmBQ8CX18++BUzqkGjUsIIaRpRHh4FEgYE8QAHBgTIjw8Cmq9UgulUqncRo0adcvLy8ug0+lgutx///2309LSnPLy8kR6vR6nTp1ynjhxYr55QGf06NFF/v7+2trGyMzMlACAr6+vvq7zmjRp0k3z4549e5ZkZ2fbreLs/v37XQoLC8VTp07NNX8cOnfurA0KCio9dOiQRdpwWFhYoSkACAADBgwoAYDMzMxGzfHBBx+8ZX7cpUuXUicnJ8F878fQ0NBSANBoNFIASE1NdczLy3N44okn8syvO3bs2EJ/f39tcnKyKwAkJye7CIKAqVOnWvR7+umnLY5PnTol02g0sieeeMLisXB1dRXCwsKKjh496oJa+Pj46LVaLcvJyWmx9TfqO7FoABkAfqyhTyiA6QCea9iUCCF3nXu4vnbizvPQf7YHMMggAJjzuAF9DjRdYRDTh3KtQQupWHpPFGWpr4SUBJzPO2/RJnABqgwVPVakQvvDh3FVZ7nH87acHBy6dQsZNn5DWxtrT34+2h48iNsjRth0LJNBKSk4XmD8LHe8oACOKhVK67l3QeXqv3V5D+3Ydw07tnUH5FdxPGsmLuZfxL/v/3dD7wYhhBA7ut/Do2hn377nkvLzXSM8PAqaahWgPeTl5Tn88MMPXlKp1Or+bTk5OQ5FRUWCXq9n7dq1q1Lkwdvbu9bCDyUlJSIAkMlkda4CbB5gK78u12q1dkulunr1qgMAREZGBls77+bmZjEfd3d3i2MnJycOAKWlpY1a9OXp6WkRKJVKpdzV1dViLEdHR9NYDABu3LjhAAD+/v5Wn5+bN2+KASA7O1sCAB06dLAYw9/f3+LYlGb84osvyl988UV55dts3759rYFfU8XqoqKiFpv+Zo/opAxVK/kSQu5Rao0aP5//ueJYIpJgWui0ZpxR01Fr1Pj9aFvAIIFpRwRBL4ZK1XRBwIZ8KL/XbPp9U5U2MRNTamIrUNfUU1uoHJQzuVxWhqlnzmBrz552H6tAEDAoJQXHwq3uAd4opgCgSRmAnseP48zAgXW+DfPqv1KxtNb3kFoNvDtrHKCbYNw3dXoE3j3yLiJDIun3FCGEtFD3e3gUtebgn4m7u7thwIABBUuXLr1q7XynTp10EomEOzg48GvXrlXZC+7GjRuSgICAGoNCphWAeXl5Tb4qzBSMqhxANO3dZ+Lj46MHgDVr1mSEhoZWqVhcOQjYknh7e+uBO0E+czdu3JD06dOnCADat2+vA4ArV6449OzZs+I5y8rKsnheTAHYpUuX/j1u3LgqlZdlMlmtS1tMz7Wfn1+dV382tYa8GKu944wxGYARAKy+kQgh9564I3HQCXc+0D7U7aF74sOdaQVembQfIB4HGIxfjkmlDF5ewMqVgFJp/2BgfT+U34v8Xf2rtAW2DbwnXqetWcV7TF8GkUiE9ePXIyY8xm7j+Ukk1QbnduXlWW23x1i/F9Z5b/F6Yaj6B15acZ23MAJwp/pvXQOzKhXA9VKAiwADN1ZSDzxOX1YQQgixu5EjR95KSUlx6devX4mLi0u1MY4+ffoU//TTTx7vvfdelikleP/+/W2ysrKktQUBg4ODtTKZjF+6dKlORURsqUOHDnqpVMr/7//+z8m8/eeff7aobDx69OjCNm3aCBcuXJDNnz8/1xZjS6VSIT8/3+6Bz9DQ0FIvLy/9t99+6/Hiiy/eMLXv3bu3TVZWlnTOnDnXAGD48OGFIpEIW7du9TTtCQgAX375pWfl2/P399eeOXPGybxffaSnp0v9/Py0Nb2mmlutTwxj7FKlphcZY89a6SoG4APjSsCPbTA3Qkgrp9ao8ePZmnYPuHslpiaiVF8K3uEwRM/ej5C/34arxBVKhRsWLOgCrRaQSoGkJPsGAuv7ofxetHjoYuxI22HR5iRxqqY3aSlUGSqU6csgQIAgCJj3v3no49vHbq/xIkP1X4T3d3Wt9lxDaIXqs4b6udS6HU2DPODhgT35+RZtIc7ONh1jbGoqDtzMR0eehy+6tIdSqYCjTITSMgO4SAcmPwiZAxUIIYQQYn+rVq3KUigUPQYPHtx95syZOZ07dy7Ly8tzOHXqlFN6errMVCTjtdde+3vy5MnBDzzwQNeYmJjrOTk5DqtWrfKvSzqwo6Mj79u3b9GJEyearIqyiUgkwkMPPZT39ddfewcHB5f26NGj7KeffnJTq9UWf7R4enoKK1as0CxdurTT9evXHcaPH3/b3d3doNFoJAcPHnQdOXJkwaxZs+r1bWdISEjpli1bfD755BOPkJCQMjc3N0NoaGiNRVQawsHBAS+//PLfixYt6vTwww8HPfPMM7kajUb69ttvB3Tq1Kls3rx5NwAgNDS0bOLEiXmrV6/2FwQBgwYNKt69e3fbX3/91aKKr0gkwvvvv585ZcqULg899BB77LHH8nx8fPTZ2dmSI0eOuHTs2FH7+uuvX6tpTidPnnQZOHCgfb6xtZG65G2LcKe6Lzf7d+WLDsApAP8GsMgekyWEtC6qDBV4pbUlfi5+zTSbpqPWqPHZH59V3HcGhnO6fTjh+wLe3/8FyrQcBgOg1RpXwhDrmqqgiSJQgSl9pli0BbYNpGIqLZxSroRIdOfPGL2ghypDZZex2h48iIJqAnMMgNLd3eq5hvBKTkZeNQHHga6udkkFBoDdoaEY4+FRcdzD2bleqcCA8T076otReHX/qxj1xSiL98/Y1FTsyc9HmcBxnntg6G+HgA5qJCUBM1+6gsiV6zAzsi/tW0oIIaRJdOvWTXvs2LEzvXr1Ko6NjQ2IjIwMXrhwYcdDhw65jBo1qiIVNDIysmDDhg3ply5dcpw2bVqXNWvW+K1atUpTW2Vgk6ioqLyjR4+63r59u8kLpSYkJGjGjh17c/Xq1f7R0dGdS0tL2bvvvptZud+iRYtubNu27cLFixcdZ82aFfToo492W7lypb9er2cDBgyoX1oAgBUrVmQrFIrbCxYskI8cObLHjBkz5Da5Q1a89NJLN9avX59+9uxZp6effrrrG2+80WHYsGG3Dxw4kObm5lbxx9uWLVsuP/744zc2bNjgN2XKlC7nzp1z3Lx5c+UFb3jiiSdu/fLLL2nFxcWiF154QT558uTgFStWdMjJyZEMGzasxuDehQsXJGlpaU5PPvmkbVNEbIxxXvdViowxAcDrnPM37Tellqd///78xIkTzT0NQlodtUaNkZtHVqQDS0QSHIg+cNd/wFuZvBLLfl0GgQuARgFR4n4IegdArAV78EU47FkHQS9pkpWArbUwSFPPe2XySryy/xWLNhETQSaWtZrHrLk15f58Jkv2LUHc4biK440TNtolJZhZidZLGIPAOaQiEZJCQ6Fwc6t6RRuNNcbDA7tDQ21y+/Y0e+dsfJxyJxlkVvgsbJiwAQDgfPAgSgQDAAZwDhjK8I7sJLxyJ2DuR99C6LQfMvnv9H4jhDQbxlgK57x/c8/DllJTUzNCQ0Nv1N6T2EteXp6oY8eOoXFxcZfnzJnTooNDpHFeffVVv82bN/tcvnz5lIND8xcHTk1N9Q4NDZVXbq9vNHoUgC9sMiNCyD2BMeNetGImxrrx6+6JD3dezl7GACAAZIwEN0gA7gAYJBCXtsO6r84iNtb+AUDAemGQ1sB83qX6UiSmJtp1PKVcCQeR5X/WAhdQpi9rNY9ZczIFbZf/uhwRiRFNtoLSXeYOETP+KSNiIuQW22QrmypcRVX/XOrq5AQDgBJBwIILF2w2lqdYXKVNU1YGplKBqVToefy4zcayZuqZMxVjydX1ex6vFla/fc5wNzdUBAAB4PYpeOVOwLwnu0O/7zUIm/egLKMfvd8IIYTcVTw9PYW5c+dmx8fH+wk1bPdBWrfi4mL2ySef+C5dujSrJQQAa1KvICDn/ADn/LLpmDHWljEWyBhra/upEUJaO1WGCgbBAGgGw3BwETZ8/0dzTwmA/dNMd53fdedArgJz0EMk5pBIgYVP9UOu104op6qbpEKwUq6EWCQGA4NY1Hoq3irlyooAMgfHppOb7B5YmtBtAsTMMgAjQICXs5ddx70bJO48j9JfX4Qhc0CTBpuVciVkYhnETAyZ2H57yd0eMaIiEChlDANdXfGXWdGM4wUFGJSSYpOxcocPrwgEOsCYlms+1l/FxXYLBE49cwbbcnIqji+XldU5EKjWqPFjmuUesJfy72TZ7A4NxUBHAEIZkHscsjPLsWu7D3Rah4ovSViGEpm3MikNnxBCyF1l+fLl1yZNmpSfmZlZpYotuTukpaXJZsyYkTNnzhz7fCNtQ/UOUTLGxDDu+fcPAEFm7ekAPgXwLue8xZZDJoQ0HaVcCa4ZDHyxBzBI8ccBLaa2W4+tL8xttjk1RZppVkHWnYPAowh5cQ6ecdsErx5n8cL/PQltpnHsX6f/2iQrIxmYxc/WwiDc2RdNJ+iQmJpol8fL/DVRmQj2W11WWUJKAraf2Y6onlF2rXJra2o1sOnFp8G1AMSvQvzc+CYLNisCFYgfF1/xuNnz/XR7xIiKfzsfPFjlfEpBgc3G2tm3L1Q3b0Lp7o6I1NQq5+tbtbeurFU5ziyr2z7eqgwVBFiucNh7aS/UGnXF8xKpUyPl8HIYuAG6K0Px41de5SWJOcAECDcDkfBfFb5IjaC0YEIIIXcNFxcX/u6772Y39zyI/YSFhZWGhYU1qKJwU6tXEJAxJgXwC4CRMP7ZpgGQDaA9ADmAtwGMY4yN4ZzXWC6bENK61WX/L0WgAi5ZE3DbIC1f6cHx0+7bwAtNPFkz1tJjbf1BUxmkxPGsOyt1AntdgQpjUXqzFGUZYUCGEmVyld2CWuZUGSroBT04eEXhhNbwwToxNbFKURl7MX9NVMHQJAGthJQEzNw5EwCw59IeAGg1gcDEHZeh0wZUvMfHS+KgCKxfQYmGUmvUWPDLAmgNWiRnJtulOnBCVhbmnDsHA4ypurnDh2O4m1uVSrrtpdJGj6W+dQvKkydh+gPKSSRCnzZtcLxSgNHWVXtNHvT0tFgJCAAdZbI6XdfailkOXvE7x1jwRAEM/QXYdQ4s1QGCYKo9JwBcDJ7yPPgfz6Asekyr+V1FCCGEENKa1HdPwIUAlAB+BtCDcy7nnCs453IAIQB+AjC8vB8h5C5Vn/2/uodfBcRagOkAsQ6F/j83a6qXUq6EVCyFmIkhFUvtEuBxl1lWCt1zaQ/2XNqDg4e0wBdJwP5Y4IskXD0bVM0t2E5T3N+mUqC13Uorc6bHyNpKSQfm0CSBiO1nttd43JIddfi3xXs8z/f7Jhvb6p6XajWwcqXxZyMlZGVhZnkAEADyDAZ4JScbU1tdXSv6iQB806tXo8ZS37qFIWYBQMC432Ckt3ejq/bW1daePTHF17fiuJNMhow67luw7c9tVtuVcqVlxWORA/BQT3D/8RCJGIzfKTOAiyvSgkWXR7fq31WEENKCCIIgtK5UEEJIo5UXALa6oqK+QcCnAfwfgEjO+flKg1wEMBnAaQBT6j9NQkhrocpQocxQBgM3oMxQfeEEtUaN3yXrgekRwOjXgOkR4B2ONOvG74pABZKmJSF2VCzix8VDlaGyeVCy2j3kMpSAaVWkXgb88YxNx7XGlC4ZERSB+HHxrWZlTVvHqlvNHrtyzC5jmV4TwzsOr3JOK2iRkJJgl3HN3df+vhqPWyq1Ro0/pBss3uOZbb9usvErB7kn5HoBERHA8uXGn40MBG6/fr1KmymYdSw8vCI4JwB46syZRo2lunnTarvS3R27Q0OxODAQIhj3BGx/+HCjxqrJ1p49sTE4GGIY9wT0Sk6u0/XO3jhrtX1H2o47AUAApli7MDQX4f1EMG79Wd7IjHunrp/zWKv5XUUIIS0ZY+xqSUmJY3PPgxDStIqKipzLt+yror5BwK4AdnHOrZa1KW/fBaBLPW+XENKKmFe/FXj1hRNUGSoIggAEHgWGrwICjzb7ajRTGrOXsxcW/LIAy/Yvw4jNI6oEehpTPGTXhV3WT8hVEDtwGL+UEWHXd+1tsVipRqZ0yX2X9mHu/+Y2SUDLFlTpqiptgzoMstt4ikAFSvWlVs81xao889WjDKzKatKWqiKgb/Yeb8qtJ82D+knTktDnr1xAqwUMBuNPlapRtx/l41OlzVS0Y+qZMxYpwfUpomGN0r3qc+4qEkHh5oYlFy8iTqOp2HHvqk5nt0BgdasfayMVW0+H/vzk53cqHpt+/QHAobZQPnIREgmraBSJONatkSAmsk+j7gMhhBAjvV7/RkZGhrSoqMiJVgQScnfjnEOr1TrcuHHDPSMjw0Gn08VZ61ffwiBaAC619GkDQFfP2yWEtCK5xbkQQQQBQo2FE5RyJRzEDhUFF0RMhDUPrmm2FR5qjRqjvhgFrUELERNB4AI4OARBwJyf51TsJ9aY4iEJKQnYcXaHRduUPlNwveg6oiZE4aREgo83coAz6PQCVCqRXasEqzJUKNOXQYAAQRAw73/z7LJvmq35u/pXaXOVulrpaTtawfpWtjdLra/QsqWbZXfG4OCtpiKxUq4EA7PYv7Fj245NOgdFoOLO61kJQCo1BgClUkCprNdtVd7rNMbf+DqsvCcgYL2IxuWyMrQ/fBhXdXf+DJri64utPXvWfj/c3HAkLAwRJ0+ipNJY31tZkXhVp4NcrcZls8IdYzw8sDs0tD53uYrqVj9WN5bpMTN/DZsTMzFyhg8vTwnWAQYB2HUWIt9lcB/6EMY//ix2bPUBIIYg6HHyogY9j1+zqIg80NUVx8LDG3W/CGlJ6rKvMiG20K9fv92///77vIsXL67gnPuh/ouACCGth8AYy+ecH9Tr9SvDw8PPW+tU3yDgnwAeZYy9zjmv8lciY8wbwKMAqpayI4TcNZRyJWQOMpTpyyASiaoNWCgCFRjcYTAOXr5TSbMulVbt9cdxYmoiygzGD7GVi0AYuKGiUEdjiodYWzXmKnXF1slbAQBL/twBfDoGMEggMB1u+u0BENm4O1YDL2cv48qs8hiNgRtaxYb7i4cuxo9pP1pUG910chOmhU6zW4XgG0U3rJ47nnUcCSkJNivUUfn1rdao8d6R9yz6bPtzW6soDKIIVGB4x+E4mHnnPe7p5Gn/gdVq4yo/pRIJ0lN3qiorYoCkpIpz9YmwVxf8j/H3rwgGmrNWRAOARQAQQEWfugYCi60ELif7+CBOo6nSfrlS5d49+fkYm5raqEBglI9PlaIn1Y016KgKp5LGV3zRYI2fqx8AIHf4cCTsOIW5H30LodN+yOS/QylfjeN+vwOi+wHOAbEOXynP4maxZTGS4wUFGJSSQoFAcldozBeNhDREv379dgPY3dzzIIS0DPX9JmAdAB8AxxljzzPGOjPGnBhjQYyxZwEcKz+/ztYTJYQ0D2tpsaZ95sQiMQQuYMEvC6ymzSakJFgEAGtKHTYfr65FR+rramHdqrY3pphGVM+oGsf9w8Fy/7Q/HDbU+bbry5QKbErdZmCQiWWtYsN9RaACk7pPsmjTCTq77Cdpes39XfB3tX1slRKs1qgxcvNIvLL/FYzcPLIiIFg5KH0w82CrSd32dK4U9LN3spFaXbHvn37USHy+YSb2XNqDmTtnGh8zhQJYurReAUCgmiIjNdjasydcRHX7M8raqsH6+HeXLpDXsUpv8q1bjRorxt8fkd7edeqbUmqoMQAIAFKRMU1YrQYWPN0Hwv43wBL3Y4DhBcR9nYyf144BBBHABDg89BJuOVm/n78XFtb/zhDSAiWmJqJEX2LcV1lf/b7KhBBCiD3UKwjIOf8GwCoAnQAkADgPoBDABQCfAggCsLq8HyGklVuybwmGfT4Mr+x/BcM/H45Hvn6kIiiXW5wLgQsQuFDtB2ZrgZOT2SdrHLO+H8TrI6+k5g/ibR3bVgRl4sfFV+wzVp9v6GPCY9DNs1u15+9rf5/F/mnWgoa2ospQWexzx8Exf9D8VrHiQK1R4+dzP1dpP339tM3HMr3muPUCWgBslxIcdyQOOsG4Ukwn6BB3JA5KuRISkaRK302/b7LJmHZX6WHza+Nn3/FUqjv7/ul0UGbcOWXtMavr/p5KuRIOIgcwMDiIHOoULJ8TEFCnKT/o2fjVkUs7dapTv+Fubo0ea3FgYJ1SRTyZvsYAIAAog5QAjE9bmZZDMDAYdAwHDzLs+CUfOi0AOABchA5X/gmu11u9nX4ute1G07QSUhIwdsvYVhOsJy2DWqPGpyc/rTgWUPuXo4QQQogt1XtPAM75KwCGAPgMwEkAl8p/fgZgKOf8ZZvOkBDSLBJSEhB3OK5iFZmBG7Dj7I6K1Ut1WS1nLcD1+R+f1/hhvDGr8GpzLu9cjedV6aqKVYgLflnQoHRktUaNi/kXLRvZnXPxR+MrmsVMjD6+9tsAXylXgjHLZVl/ZP9hMdeGFj+xt8TUxIpgmbltp7bZfL7mwR+JSAJmZSnb8azjNhn3j6t/WBxn3c4CAKsByNqC1i2BWqPG/y78r+JYzMSYFjrNvoMqlcb9/sRiGBxEUMnvnKq8l6RpH9BX97+KUV+MqvU5ND0PNQWEzf27SxdM8fWtsU9d9wSsTYy/PxYHBtbYxxZ7AgLGtOSDYWGwXurDSAQgip2r8n6RiCQQmf15+eHRD6HWqOHV4xQEUQnAdIBYB8hVxotYC0APcDEyXswFxA7G1GAzLW1PwISUBMzcWWkFKiF1oMpQwSDcWfnNwOq0TQohhBBiKw3aGJRzfpRzPoNz3p9z3q385wzOecv7JEkIaZDq0h91gq5i7zzzqpzWgmUx4THYOGEjOrh2uHN9gw6vq16v9sO4KdU4IigC8eOMATPQXp7vAAAgAElEQVRbBKrUGnWt6cD+bf0bvQpRlaGqCJyamFZGqTJU0BnuBLYELtg1DUgRqMBTvZ+yaLuv/X0A7Jt2bW/VpZ83hqlIDAAsGrrIap/E1MRGjZGQkoCMmxkWbcogJVQZKuiFqqufqqtW3JKoMlTQG6yv3LIbhcK4719sLM59vQEpnYyrKCUiCRYPXWzR1bQPKAdHmaGsxucwMTUROoMOHBwGwVDn92avNm2qPfdOUJBNAoAm/+7SpdpztgoAmijc3GC9VA7g6eCAQ2FhCGsjAz/xPPCJGvhqO6AZjJ4+PS1WB5YZjOmOuV47IZo+pmIrBAQeNV6mRwBd9gHMALQvH9HsywtPB4cWFQAEqq44bTWrdkmzU8qVkIjvrPwWi8StYosOQgghd496BQEZY68xxkbU0mc4Y+y1xk2LEFIbe6/iqi1Nta7FO/r49sGE4AkV6Y4CBOxL31dt4Mm0j11SehLm75qPUV+MskmgqqYP9J6Onlg8dDEWD1lcZRVifR/nymk95iujKv/xX9eUw4ZSa9T4/q/vLdpMq3LsmXZtC20d21Z77njWcZsGLs1XHeoEHW6X3obcXW6T2zZnLbD+4dEPq00FGykfafM52JpSroTIbF88Dt6g11K9f5+V7/vXJzIG68avw5jOY7Bu/Loqv4sqB/6r+yJArVFj08lNFYHg+rw3le7u1Z57JT0dTKXCoJSUOt1WXbhWsw/hnvx8m4/lJ6mapg4AeXo9hpw8ibmXA4CdCcDfg4CzjwCfH8Cl1HZV+ns5exlfKx2PVWyFUCHwKKB8w7giMLt87aHZSkBbpFLbWuX9Qy/dvNRMMyGtjSJQgQWDF1Qc6wU9TuWcasYZEUIIudfUdyXg6wCUtfQZAWBFQyZDCKmbpljF1ce3j9W0SMAYoKnL+KZ5JqQkWKR2ClyodjNs8+CUzqCzWaDKlGZsTV5pXkWarvnqRgD1fpxzi3MrHjcGhhn9ZlgEJsZ3HQ8RRGBgVVJ1bc30WJozrYayZ9p1Y6k1aryvfr/GPvYOXD7e63GLYwbW6DRX0ypMc2WGMuQW58LPpeo+ekXaokaNB9j/ywJFoALrx683poAyUYMKzzTm95lao8b8XfOx99JezN81v9brVpdiXTn9fFDAoDpvBaBwc8ORWlJnTdVtbeH2iBHVBgJtPVb20KHVBgIBQO/hDHx6HMY9DxggOECsiYCYiS365RbnQhGoQLBXsPUbMq0I/PwnoOAGwDhEsF0qta1VTt+8UXyjVa2mJs1Lla6yOKaVpIQQQppSg9KBa+EA1LJLNCGkUZpiFZcqQ1XtvlhbUrfUaXzTPK1tHF/dZtjmwSmJWGKzQJUiUIG1D66tNrCpNWgr0pyXDl8KRaCiQY+zl7OXxb5iYe3DABhTQUduHon/pv0XAoR6pxw2RE2Bz7qkczeXynsmWWPLwGWBtsDiuK1jW9wuvW3RxsEbvVrDXWZ9xdjp66dxrfBalfasgqxGjbdk3xIM+WxIRWEfewUpYsJjcCD6AN4a9VadX0vmwcnG/D5LTE2sKOpieg+bS7+ZbnF8SHPI6uNw5voZi+PkzOR6PV4KNzeUKZXg5RcHKwF+W1a3vT1iRJONlT10aMVYTtaCj/ISGKvDcECkR0xUMCKCIiy6nL5+GmqNGudyK+3LqhkMJL9s/FleMGljQB64cjQMSmWLDAACgIeTR5W2lraamrRclfcurXxMCCGE2FNdir/VVziAG3a4XUJIOS9nL4iYCBzcbqu4qgToNIOBDCUgVyE78ChkYhkg1ByMMQWhSvQlVs/vurALMeExFm2m4JQp1RhAndKO6yK3OLfGDf93ntsJ7DT+O6x9GDJvZcJB5FDr/TS368KuKsd9fPtgzs9zYOCWgS177wVkeizjDsfhx7QfwcEhEUsqVrQpAhUtKvhnYipownn1z9XkHpNtMne1Ro3//N9/LNr+yP4DnT06V+m7/cz2Kq/X+rhZZr3C8LZT26y255Xk4ZGvHoGfix+mhU6r1/01FfYxMXADXk56GQeiD9Rv0nVUn9eSaeWf1qCFVCxF/Lh4SMXSiuP6vCfUV6oP1Kk1aqReS7VoE7iAxNTEKr9TKu+/yMGNAcW2PTHmjz9QyDn8JBJkDx1ap3n1c3HB8QLL4LK4htczACRkZWHOuXMwAPAUi5E7fHiDx2K1jNVQw93csCc/32wgBnA9EHAMcM3ClNnZ+Pezc9FtjeW+mtvaPoVtF0sAxU/AqX8Bt88Y/0/5IgkwSI2pwNPvx5TxnZFbnAu1Rt0ifzeZPBP6jMX7i4G1qNXUpGV7sNuD2JG2w+KYEEIIaSq1BgEZY/srNUUzxpRWuooBBALoBOA/Vs4TQmzAtGeeXtCDMYb5g+bb5cPSyeyTdw6qfFiLwLOR96GjW8cag3OmIh/L9i/D9eLrVc6bKqOa7y8IVA362er+Vbf3msmVgiv4OOVjizYGhoe7P4zFQxbXaR5pN9KqHCemJlYJAJpuuynsvrgbHByMMSwYvKBFf7gGgFM5p6oUV6lsz8U9NhnLWiGX+9rfh8iQSHzy+ycWz1tt+2TWOlalFLDaXMi/gAv5FwAYq2r/Ov3XOj931tLLfrvyW73Gr6uElARsP7MdUT2j6hQkVWWoUJIeCmQoUSJXYdf5XRaB/7rexyX7llQJ8pnvJVndyqxPfv8EAhcgFUsrHtPn+z2P41nHLfpdFXlhyMk7vwev6nRof/hwnQKBx8LD0fbgQRQIZgUyAPQ8fhxnBg6s0j8hKwszz91ZJZdnMMArOblOgcBj4eHwSk5GnuHOa1UHQK5WI0Nh2/f67tD/Z+/d46Oo7v//19nNjXBJIIAhuhBAQFBMMAoOCKwGuXj5FPH7aatoEGnDxRu1GkRrSz+2QqJtsSqQWG9RrG1/ULR4AYksYBigRBIjICAQWIRICCRcctnszvn9MTuzczmzO3tJRJ0nDx7ZmTk7Z3bnsjOv836/X1nI5HkcaW0NzIyLQ5e3G7F+YB9wjmkAgFGXjZKPXXDvAfZkAASwJwLZLwGVDwJVeYA3EYAd8FGgxomV1YvltPKLLUJZiTaql4Jizb41F+32WlxcqO6vGNMWFhYWFhbtiZlIQKfiNQWQ6f+vRQBQD+AfAH4V5XZZWFgY4KpxodUrul1SSvEX/i+YOmRq+z581DhFAZDGyQ9rH339d9TMrwn6NkmwNHI5HZQ2SBUVZLfZQSmFV/CqHtBjhVSvL1g0oBYKivf3vY+C0QWhGwM426pOI02MSzRs2ya0wVXjatd956pxyZGYHXa8RImZ+kh1TXUxiRZyZjphJ3aV2JeamArOwWHZrcswZ+0c+Xg5eOZgVH1Fk/IlOaya/bxJ8Um6eSwhOlpKKkowe+1sAMD6Q6IwG0oIXPPJt6pBhTXIxZRB1Vg4dmFYfa/es1o3T3l8s/YtEPgepPqYnINj79vULGjtcWvb2vTtDGB5Ju9ramK2XVWnHyRRinohYaQEH1UKdTEkntHXeSSqjs2uCV0DC+O6BF4T4jf9uB34/H6IVWkoYPMBmRsBiNGaUlr4xXqdYkX9vbjtRRROKOz4jbGwsLCwsLCwCIOQNQEppTbpP8TKz4uU8xT/4yill1BK76aU6u9mLSwsYoLWjbO96srlZeUFirtnusQIQNIG2NuATBeONB7BPavvCboOqdaXkehWd6FOZwTSJrSBukeh1fUrzHhxBfN9kZKWnBZR9J1ABVPfcUlFic41MsGWINcF1GIn7ZsODOhTUL2C96KvXaUVsIzqGirT8aJBadCiNLZYWb1SdewWlRdhwYYFEfdTMMackAxAXSvNTzjHyrCe+lpqw3rHvr6a1vGY5YBcUlGCSW9NQklFCQCgekeaYlAhHqhxYuGG8ARA8DxW/5OAfwX4xc7AbO310NCIQgNLUEz36p2Eg5lkaBmbkqKbNyQ5mdn2zl69dPN62O2MlmxYDrp9E40HIKJhGmNbtd+LXGPRfT3QypBDy68AhARAuh4P+lDlGGwjtos6vZZzcLAR9S20R/AYtLb4PqC9TrUn2jq02mkLCwsLC4v2JFxjkJkA3muPDbGwsDAH5+DwKPeoPG1ksBGLfpbdugzpndMDzo03/Vb8639Y+9fufwW9aVaafLDEt+w+2ao2NmILpB5/+gwO/GU5Ji1eFJPPI0UlskxKzLC7bnfINi9sf0E3b9Y1s1DfVA+b5nJrIza8dMtL7R7pUnmiUjVNyMVfu0orYLEELQDYV7+POT8cXDUuufYgAcHM7JnyPtGaRQDRC4FGgqYKxTmAN8sA9/U6wSEUWuGZgGDZLcvCWocZtI7H2mkpUnD9ofWYvXY2Jr01CZ0u3x4YVLD5gMa+OH1gMO549w7MXTs3tCEHzwNjx+Kq8gMY9Q1QslYtBDoznXKE8VenvjJcTYI9Qa6PyaoBOaJzIoovjUc8xOi/cGoCAmLq7MTuagOJJoPovvyMDBQPHgxJ9gunJiAAvD1sGKb37q2a1yq0j0db4cCBKHA4VPM8mr7qmuoCx/GtNwHnbIAAxANA5UPA7m4IRuf4zjHe6tgzIFV9zPRP7f8dbYlFtCzYsEB1nWpvIXD7se1Bpy0sLCwsLNqTsJ4qKKVvUkqrQre0sLBoT7TOpe1RT0YSzeqa6kQBz+/cqIzW8AieoDfNSgfafqn9dMu3ubfBVePC0slL8cyNz4iOksrUY28i1q++BNkrsqN2NnXVuAzTks2wsnplyMjHC54LqukeST2Qn5MPZ6YTiXGJKiE0VM27WMC7eSTHqyOPHhv92EWbYiehjYq4sveVokGLhiE9h0Tdl1KETopLkkUhALis22XM9xSVF0X0kOiqccErsJJEoY78U6Xfi5FyAhVww2s3mD4PtNeEnwz5Sbvs99TEVPm4JiC6WmlLty1Vfbb1h9bjdK8PxMGEnL+JjSp+CbxZhjUbarGiYgXGvzE++OcsLQV8PhDIcWS4c6/4V4CA6pPVYtkEX6thFPLIjJFwzQikm+4/7a/Hp9jWjw58hPn/vBnC5inoVH4LVvcNd9wU6KWJkDvS2opMnv3Z8jMy4PW78IYjAEpcqon8k2oYtgcDO3VSTUs1DCWG9ByiPo6njsbEfwC/t/PA2d1AF40bdpeTqsmG1gbc+OaN7eZoHQtK7yhVDe6MumzUd7g1FpHCu3ldVDkrojmWTBs2Lei0hYWFhYVFexLWHS0h5H8IIf8ghBwmhJwnhJwjhBwihPydEHJre22kv+8+hJA3CSF1hJAWQsgeQsh4xXJCCFlECDlOCGkmhLgIIVdq1tGdEPIWIaTR//8tQkiqvjcLi4ubbd9sU02zIpaiRao96KM+UzX0jOq4SbW5jjYc1S377OhneHrj05j/8XykJafhk0OfiKnHNi/EEqQ2YNdMVO3shBteu8FclJABDa0NYdUClFGIAiurVwYVf0akq6OvxvUbByAghvZKVqfRmal9Fym8m8e4N8apHAjjbfGYOmSqYfvFWxZfFA/drCiJq3pfpWvX5GHXVwsHpVCtNSK4/tLrDd8XyUNiWnIaW/zVRv51OqVLvwdEgWveB/NM9aW9JpxuPh329prBmelEUlySLKJqo0yPrX4QeLUcKHsWeH1TIL3ZsQ1IOQoIcSqxExBrZZZWlRp3WhtI05XO6FVDA4tf/fxV4+/az6xrZsn7uqSiBDUNNbr9sK+yh1yqQKpRFy4fndZ/7+1Vq281o65gODUMwyFUDcOC0QXMMhKS6I70z/0txT048YaeuvVF+p13FJyDw13D75KnV1avjCpK2KLjKKkowahXRuGOf9yBeR/qr6m9OutT3mPJ1CFTMa7vOFzW9TIUjCmwaklaWFhYWHQopkRAQkhnQsgHAP4N4H8hOgAnA+gM0STkZwDeJ4S8TwjpZLiiCPELdeUQB/1vBTAUwEMAlEPHBQB+7Z9/nX/ZJ4QQRXVqvAPgGgBTAEz2v34r1ttrYdGe8G4elbXqFM8jjUdi3k9acpoqdfby7pcHbZ8UpzcikCitKmWm4VJQ+KgPrd5WPFf+nCjSObYBI16H6DVEAMEuRkJBMBclZEC4zqwAmGmZi7csNmw+ZdCUoNMnm9TRLh5f+9WQKiov0kWdtQlt+MX7v9B9f1Lq5NMbn0Zuae53LgSyoiRYqazrD61v17QtZVSglkhSkVlp4QB0kX+9yDBm+j0AVH5baeoza6Neo4mCDUYwEXXBkoM49+lcQIrZE+KB8scDb2aIRBJBBzbS04NuU1JcEuqb6oO2UQ4oyIKuZj8MOT9bjhJNsCdElEZ/sdXqixWhahhyDg69hhxUHce7E/8GzsHBNcOFoT4pqlqM5XTv068vzhZ30ZcueO8rdYWcZTtin3JvEVukEgU7ju/Amq/W6O6nAFHQba/fQen3ttxdjvrmesOBOQsLCwsLi/bCbCRgMUTh7BSAPwCYAFGIG+Z//SxEZ+BbAcS2kr9IAYATlNI8SukOSulhSmkZpXQvIEYBApgPYAmldBWl9EsAMwB0BXC3v81QiMJfPqV0K6WUBzAbwG2EkOhzyix+9HRUUenSqlKdaYD7rDvmN6zadEJWJFa0SJF5AgR8febrwIKsUiCulSkOtAltzJH7UBw/d9xUu3ib4qGZkZZ5qvmU4Xsl92FATI1UChGsiBZT9eEiRBstKrHn1B6dkKo0Z7kYom8KJxSiYEwBLu9+uRwlwTk4ObJSSbRpW7ybx41v3oinPn1Kl37IOThDMw9JeA/nvJfSwu3Erk5v1ohhf7h/AornztSl30uY+czO/s6g0x3BylJpYEBRD/ScwiHZoNYooEjPZZGXBxACCn06MAD06NQjZJ1UAYIcbSjXMVTsh/gEgoLpI7F08lLk9s/F0slLI0qnvthq9cUKqYahdBOZQAjWXn21qk2iPVFVRkK6NnIODkPS1LddzW3NgQn/7xt1G0fiXixoB3Lac2DHIjaY/c0IGo0cBcrf21ZvK+Z/PD/iLIeLKYLfwsLCwuL7Q0gRkBCSDVFI+wLAVZTS31JKP6WU7qOUfuV//RsAVwH4EsA9hJCrg60zAqYC2O5PRT5JCKkkhDxIApaO/QGkA1gvvYFS2gxgM4DR/lkcgPMAtirWWw7ggqKNhUVEaIvft6cQWPtVf110mkAFzP94fkxvBFnphCwRRmLL0S2GnzsvK48dAWVEEHEAACprK8OKCFywYQGOnTsWsl2iPRGb7tuE4tuKcVnXy5iRShc8Fwz7TUtOk4VNCqoSIljCWnsJMyUVJag9r3c2lWgT2lTb48x0Is4WBwJy0UTfFE4oxIGHD6jSpKYPnx7zfkqrSuXaca2+Vt2DX7AojVGvjArrvOccnCwqvXzLyyi+rRhd4rvojvf6tLXIz8nHxAETmesxE4UYqlZfrAgWRTqwrzI62J+4OyKQAp8cl8ysNQoAtedrjc9vjgMefxwE7HTgDw58gOU7l4fc9g2HNgBQ1Fh1bAMmP4LeV32Jl/4aB1wm1kUtO1wW1fV1XKr6u/8ua/XFkuGdOyPOfxvmoRQ3VlaCb2w09d7Tg5cC9lYAPsDeitSR74sLdv5CTBv/9Bl4X/8YpWsPAAAa+UYcWXwEjby59XcUWsft9nDgtogtdw678zvtX0qJt8EGAQJ2HN+BFRUrwq6BebFF8FtYWFhYfH8w81R+N8T77DxKqb4IjB9K6UkAeRAH5u+OzebJDAAwD8AhAJMAvABgCYAH/Mul3CBNpWl8q1iWDqCOSjaQ4jZTiGnDutwiQkg+IWQnIWRnHaP2jYWFEu3IcnsWlU6v+5kuOg0AdhzfgbGvj42ZAMlKJ1ySu8SwPQXFgx8+yLwR5Rwc8nPyw9sAA3FAYvORzaY+L6voN4skexI2ztgob+s///efiOu3UydGUlCMfX0s83PWN9XLLq42YlNFAm47pv8cWoOXWPE71+9CtlEKfdUnq+XU4YjqJnYQLAOcz9yftevDT7CoyB3Hd6imQ9V45N08Hv7oYXxy6BM8/NHDGN57OIb18osG/uPd5tgh75t1965DHNEbohxpPBLSpCZUrb5YESyKdMnv0mCPEyCm9gvAmCXAtX+Tl/fu3Fu3Pu26DSksBIqLQSZOxPN5g/C3awOL2oQ2HKg/EHLbD545qL5+uK8HPn4Bdbuvxvz5QOnaA2jxtsgRO5FGyLLq531XtfpiiauhAW2BWyp4KIWroUGevvtq9a2gNF1SUYLNQiFw341A7m9gn3kzEjIrxO//w5fFtHEaJ/7O1YxHI9+IqtwqHH76MKpyqy4qIXDZLctgJ2IatA02XH/p9ZYY8wNB67AeK6QSChMGTFCZhbX6wrvGXGwR/BYWFhYW3x/MiICjAHxOKf0iVEO/c3AFgFjncNj827CQUrqLUvo6gL8iIALKm6CZJpp5rKdbbRuxIaUllNJrKaXX9mLUvrGwUKIdWc7uk43FWxajpKIk5qka3a7YZVhHy0d9hkJcuAxKG6SadvZ3gnNwGJkx0vA9PsFneCOal5UnC2QSYUUHKvGni/mOXhfy85pN6UmKS1Kl+3EODpvv24zLhh3TiZE+yv6czkwnEu1iumeiPVElvCTG6euAvfL5KzGPGg0VBShRfbIagChMzftgnmwA4/V5L4qHCVaaE+tzNbU1YfRro5FWmIYFGxaEfb7lZeUhwZ4AAoIEe4KuDiBTPNOk40scbjgctC9W1OGsa2ap2jw2Ru3e/OjoR5nrClWzKlitvlgSLIqU44Atm+2YU+DG1Of+BPvEp+VldmIPpOEaECqlF/n5wLp1eO6qBt2i7kndQ247BcXstbPx+Qm/SYU//Z8Kdng8wJ6dvVQlCxpa9f2YgVU/77uq1RdLnKmpiCcBESOBEDgVUY9Th0yV097jbHFyVO3SbUtV67ms22XieVDjBAQb5NsyQpE3tR8aXA0QPALgAwSPgAZXZPuhPeAcHLbM3IJB3QfJdWudbzotIfAiJuhgjeLaHqquaCRIv2sAOyJxd91u0+u6GCP4LSwsLCy+H+hDDPQMAvCfMNZZAeD2yDbHkBMAtFXC9wJ4xP9aejJMB+BWtOmNQHRgLYDehBAiRQP604l7QR9BaGERFlKU26o9q5DdJxsvbHsBrT7RAZKAICkuKWYP4pVxy4EZheIDU6ZLFyknCVTR9MW7efz9y7+r5knphBldM1hvASA+KP91x1+Rlpymi/zjHBzuuuourKxeKc+7a7h6WsnIjJG6SCsAAbMOXwJg96BtRi5Kq0rD+rw22HR951+rj1TkHBzcj7pBfk90y1gChSS8uGpEF8xQ2ySJtsN7D4/JscG7eTxX/pyptku3LUV+Tj5Kq0rhoz7xe61xQsh0hRZfTG5LUXkRjp87jlnXzAorElRKc/L4PEiwJ8jnTnoXY0OI0y2nUVReFPb5JhkVGO0zzsGh+LZizF47W5yhOf6U6ep1TXXg3XxY+1J57bhz2J2676lwQiGWblvKrDUW6rjnHFy7iX9KvIJXFJA1RjSAKARyXD8ABeDdY2VRXhJb13y1RvceCbMP4UN7DUXdEXUE3DfnvjG59YqITin9XwASEuJQ1+v/U7WLyFwIYv28g83NKHIHbk9WXxX7GqtSXwAwb/9++CAKgPVjx7ZLX1xKClzZ2Sj1uzXnpaeDS0mRl7tqXJCSLyil8uDCV6e+Up1HRze3AWO/xsjRe7BjkwfwArAJmPjw++C4/4dGpMKWYIPgEWBLsCHV2T6p7ZHy8n9fxoEzgchTj8+jOjd5nofL5YLT6QQA5muOa//z1ELkTPMZ9gLNtb1hzHoghqeOVH/W4/MgzhYHG7HpIu/fqX4HD1z3gOnrNvX/8wpeVJ+s7pDrvYWFhYXF9x8zImAKgHDyYU8BiPUdWjkArXnHYACSJephiCLfzQD+CwCEkCSIP9+SFSEPoAvE2oBSXUAOosOxsk6ghYUpeDevEw4aWhrw6uevygIgIN6kNXubsci1CIuci2Jzk+bYxkyTtcGmi0CLhKLyIgg0UFDeRmzyOkMZbNSer5UFE6Wgwbt5rN67GgQEhBA8NvoxrN6z2nA9Gd0ysHXyVhSVF2HNPoVQoDLroECNEysqliAvK4/53bJSeroldsPb097Gpd0uxeo9qzFt2DRV7Tktg3oMwoHT6vTCldUrmcJW9clquGpEIU25PSyhAoiNaAsEhLNmb3PoxgD2n9qPK5ddKZqTaB5+PrriJeTnRLct494YJ4tCkshiVghkpTlxDg55WXkoqShhOk1LSOfbExuewKaZmyL/EAryc/IxvPdwzPtgHiq3OHXHn/JcLCovwr9//m/mekb0GQE7sUOgAuLt8bIQlp+TH/S7mX/9fGZKe9mhsqg+Vyx4ouwJUUSGeCw/UfYENt3H/t5ZomR2ejbTnROA6ci7JblLMPo1dWlfVlq7VIPLEH9txq7Hb8fz+bfjo5bT2KsovxhsACQUqXFxsAPwAbBDTKVVCmaxJD8jQxYD2xsuJcXwc0i1zyQx35npRGlVqbhvFNdx6qV4YNm/8IuHW7FjxgSgZjyQuQnOaeJ4cgqXgqyyLDS4GpDqTEUK1z7fW6S8++W7hst4nkdubi48Hg/sdjsIIfB6varXCQkJKCsrs4TAWMPzgMsFOJ3iaIQfw99Izb3F2nXnUDgzdpsjRYIDYskCFhTU9P2Aq8YFr0/8jY31gKKFhYWFxQ8bM7l4nQCEY3fWBiApZKvw+AuA6wkhTxFCLieE/C+AhwG8DMi1/ZYCeIIQMo0QchWANyAagbzjb7MXwMcAigkh1xNCOIiux2sppaGrrFtYKFAWZHa+6cT418dj9trZ2HF8B+qb2dEr6w+tD7vwM6vf9YfWGy5Pjk/GpMsnRbx+icraSlVaTO/k3vKNpTZ90Qhtypck7FBQEBCcbTmrdgTWsOvELnAODv/++b/VDq0Msw4AeGLDE8z1sKKJpKg/lvkEi9z+ubp5/z32X928YAYxS3KXyKlxNthgJ3bYSGxEWyDw/ZrFBzG+BmUAACAASURBVB/21O0R97XGBfl49eCot0UbFfbC9hdMv18SD+zELosHgCgifXb/Z6bWsfnoZizYsCBku2DuwEo4B4dlty4zPP4k9tezXW15t2gyQSmF3WbHi1NeNP2wVjihEJ3snXTz3WfdjNYdy966vUGnQ7HslmWGy1bvWW3qesk5ONHIJwgFYwrw2f2fqWpwMXFsw7lRT2H+7lEY3FN9HkwZNCXkthjhTE2F3Z866wOw+MgR1fIFBw/C7nKBuFwxMw3ptnkziMuFxE2bVIYdCw4exGVbt2L8rl2mjTxCMaqiAnaXCylbtqDkuDhQxDk4LP3pJ8C4j9E85kPcVqO4JmjOI1+/MjHd38H7yy/wckRyI9940QqAvJuXRXAlksDvcrng8Xjg8/nQ1taG1tZW+bU03+PxwOVydfCW/8DheQhjx4I++SSEsTegdHnAfXd85nj2ezTHZNOlH3Xc9iowO/jhzHSqjNeNypRYWFhYWFhoibAgV8dCKf0vRIfgn0J0IP4jgKcBKJ8eigD8GaIwuBNAHwATKaXnFG2mA6iC6CK8zv/63vbefosfHtpIpc1HN5t6H8t9NByKthYZ1iMDgPNt57HmqzVhOeeyaKkZoXYgPhYQK/Jz8lF8WzFGZowM+kC999Re1TZohZ09p7QZ/mo6J3SWXxdOKETxbcXihIFz8PZj25nr0d5Q22AL6vjKQlsnDgCafc26en5aoUtZe0iqMfjsTc/is/s/w7Jbl2FC/wlYOnlpTEbupe+XRY+kHuw3ScdSp1Oqh59ZdwyMelu0XPBcMP3+YPXsOAeHbgndTK2nZGfoeouh3IGVFJUXhXSuHpzGFlCla4YAAZTSsOtNzcieoZuXnJAc1joaG3kcObIYjY2xq1c2tOdQ5rTZvqR0a229UAD4+szXpl0vz7edN1wmnfOcg8PNA24OuS5ATOnUpv+yjGnCwaMw0TgnCLJr74KDB1HkdssxirFwD+62eTPOCYLc72i/4Cf19Y3Hg82NDRgbAyFwVEUFdpw7BwHAWZ8Ps/fvR8nx4+AbGzH7mzb4/Eknp30+rOz6MyTaE3XnEb1sK0ACdWJtEI2VGvlGVN5YicNPHUbljZUXlSkIwK452zWhq3zNcjqdSEhIgM1mg8KXDoQQORowLi5OTg+2iA2N8+eC+HwgAIhPQN/CFbKR2JW9rmS/SXNM9r3KfEkBM5g1GvnHl/8w1a76ZLUqY8NO7FZdQAsLCwsLU5gVAe8jhHxq5j8A/ZNKDKCUfkApzaKUJlFKB1NK/6p1+qWULqKU9vG3GU8p/VKzjtOU0nsopd38/++hlF48FaYjhFVA36J9kQQXnQgWRKCTMGPaYMS+XT3U4pz7emafbUJbVGJj0rHJqsiwpGOTVcvzc/Kx/Zfb0SlOH52kRLkNWmGn7kLwKgOPjHpENZ2fkx8Q7xjOwa1CK/McqDyhTjUUIIQ9Ws45OEwfPl03f+4HgegC3s3roqC06YOcg8PCsQsBAA+VvINP3rwWD5W8E5NzV/p+L+9+uWo+AcFPr/yp/g1SCvCnzwAfvwBMfkR++BmeYyyomEEyHVHSvVNoowazPDfRXN3DhtaGkMYrGw5tUE0HE6flVPggztVNbU3M90ZbxJ0lREuupGZobORRVZWLw4efRmWlE/v2zY2JGDj96unyddBO7FgyYYmqr6qq3JD95Ofk47OZn6Fnck/dshZvS8hrGe/m0dBi/FMuQJDXse7edTrhUos0UJHRLXYptUrXXAnJtXd1O7gHSwKgdhvUfRH4KEXpEWPfN97NY+7auZi7dq7hderz8/rrxaq6OuZnPkeBjTM2YuKAibrz6HTTaSTG+Y2V4sQI6drSWtBWClCAtlLUlkb++9lRPD/xefk1x3EoKyvDtddeq2ojCAK8Xr8jO714Hdm/r/gOqst3DDgtRsrNXTs3eKSd4piUndtjQElFCR788EFTbUOVXJGQDEYk0jqlWanAFhYWFhamMCsCZgJwmvyfGZtNszCDMi3VbMSERfRIgsvsnNmBmUpRRRLoGNQ01ETc75DzsxXiXAJQlRfo842NwNplcr9vVL4R8fHQ9+pDqsiwvlcfYra7NuNa/UyFKPmf/WpPIUkE4xwcenXWu1iOzBiJiQMmovi2YmaNtIIxBYbRbgA7KkPbT6Sj5eP6jdPNE6gg12qTa10pMEofLF17AJ7XPgT99PfwvPYhStceYLYLF87BYUD3Aap512Vch7ysPL1gpEkBRnNP+eEn2pSiVXtW6eY1NJsfbwmVopufky+KCFoYgvgzm54xPA9KKkp0KenBxGkzqfDbjumFQQnp+GDVqwsF5+Aw9Qp1BGtdUx3uWX2Pqfc3NLggCB4APlDqwYkTxaYEumBIKc6SsLns1mXgHJyqL0HwoKHBFXJdnINDl4QuuvkUFK/uejXotSzc4/Xeq4MnAPzyml+iLK8MUy5Xn79mI3lYKF1zJTr504OntYN7cFeb/vbOmZoq9kVFUQ0UgI9g1Zp/MtfBu3k433RiRcUKrKhYYZguf00X/X67s1cv5mfuYbeDc3BY5FykW3a08WiHOFrHEu0xMX34dOZv186dO5nvp5TC4/GgtDTyQTsLPf8dJGYSSFdaV6b4V4CA57c+z3yPlnOec6EbmYB385j3wTyxDqCJgeI2oS3k4BUAXGhTR9ez0tItLCwsLCxYmBEBb4zg/03tsbEWelgF9C06Bs7BqW8StaJKjZP5vspvK3HJ85eYqlempXNKM0DtEMMi7CDn0xV9JgI782UBssXXgtGvjTYtEqjoy6vSYnoMYZfNXDJhiXqGRgit3dvfsIuzrWd181KTUrHu3nWGJgmSk+uzNz2Lfin9dMtZUVwH6tUC2+C0wRE9XCpTe5Ws2bfG8IbdKOWzdvcVqmOldvcVYW+PEXvq1N/B6ebT4BwccvponD4MatvFIqUou0+2bt7Rs0dNi9JmUnR122ggwh87d8wwPZ61T4ekaT2oAuTn5KvrUzLwUR+zL1eNCz7BBwoKnxBZ7aaC0fq+WYIri9RUJ2y2BASKSFEIQqss0B0/XoKqqkk4fjz0w6eEUYpzoC87bLYEpKY6Ve8z6qtvt77MfkJFNjsznSqR+3o38MQW8S8gRsMqIymdmU5m+rG8HSl9wTk4VfovAQk7hVsJl5KC4sHqVPGybPE8KRw4EAUOh3xDlh4fjxNjxkTcFwCcHTdOFgITCMHWESPApaSgcOBA2F0NwMkEoCoFeCQbdf8ZxryGaeuMGt1jbM/JwciuXWED0M1uR/HgwcjPyACXkoKtI0ZAihdXOhVzDg6ZqZmq9STHJ6sGigAgPS8dJIEABCAJBOl5xg7h3wX1TfXysWQjNmaqqcvlgsCIzFTyyiuvgOd58DyPxYsXg+etAd1oONgnEQLEq50AYG/vwDJlCm0wVlavNCXGhaK0qlQU6KTfqLJngNc3ATt/YfieZ7c8G3K9EwdODDptYWFhYWFhREh3YEppbOwVLdoFlvueBdu5tz346ICicLQkqvgo0zBAyckLJ+UIslCmFBK8m8c729cDGAPx1PUiMaUBLXYP4CUQNX17QID0p1itrF6JA/UHsP2X7Jp5rH62HNkCOKi8jvTOc5htOQeHgjEFAedSjbue/ajeUEPqY/8pvYHCncPuDLl9ksPowrEL0eu5XjjVdEpetuXIFvBuXrXPM7pmiDffNU4g04UhQyJ7iMzolgEYZOms2rMKi5yL8OquV2XXv2CGH+lXfgXYr5KPlfQrvwIwMqLtUsK7eXxzTl3HSIoMnHXNLNmlF0Cg/pH/e5H2daQiqZKzLXqBV6BCTFyQJZyZTnSK6xRwemS4RkufqU1oY7rWJsXpPaxCmT8UTihE5YlKQ4OeC20XcMNrN+Cz+z9TfVYpHVjwCRGlAwPisR9vi1c5S5qNKkxJ4ZCVVYZ9++ahqUlKkRdw6tQaxMenYf9+Mar5zBnxc2VkhHZyNvpMUl8NDS7Ex6fJQmNKCofjx0sM+xrWa5jp+qpKOAeHLTO3YPRro/GLncDyteLVsM0GOGcCCTeM1dWVvPqSq5muxJITOu/m8crnr8jzKahsVBEp+RkZGN65M1wNDXCmpqpcdQsHDkThwOhqcWo5O04fvQwAKRvbcPr/OIgSCQWuBZ4se1I3+PLWF2/p3mt03G7PYduJcykpaDKod7fwhoWykzwAPHL9I7o2KVwKsl3ZF60xiDPTiUR7YtB7sLS00MeNz+dDUVER1q1bh5aWFhBC8Nhjj6GwUH1/wPM8XC4XnE6n5SYchIzb70brf4oQ7wPa7IFIQCV2YtdFzxEQ1TV11Z5Vpl3tjZBLwNQ4AW8CgDhAoMCHLwOXfMksK3Gk8YjuXkaLVnDumtA1qu20sLCwsPjx8L0wBrEwJlgB/R8rWufeYLWMokWVDhvCMIDFyi9Wmu6rtKoUNPNTIM4fvRXnwbifHBH7urYEsLcGoro6nVKlnOw4vsO0WUhReZFOWAiWBieZdgztOVQXXeZxrMOktyapalZK+6fJq66dNqj7oLBvtrU1GSmoLmJoStL/qSLEpiT9X1h9SLCisJRwDg6b7tuEOTlzMCdnDjbO2Gh4Po4Y2aI6VkaMbIlom7SwUpIlJEOX9M7pSLL7xS9Gbbv99fvb7XwxK6LkZeXJNTcT7AnMenjStS/7En/UYQjX3s1HNus+F6vmkxnzh3X3rsP04dPRI6kHMy1ZgIB5H8zTzfdRfyRgFGlb2jqT6V3CE7UDAqDIuXM7sH//A6p5NTXPmE4TNkpxTknhcOHCbuzfPxuHDz+JXbvG4eDBBTh0aKGq3YkTgWhM1n6WCJWKyzk4/L+GS7F8LWCHeHOTIACPlwPDeur3c6u3lbmeu666C5yDC0TvKIjWGAQQRbHnjx7F6F27EO9yyU667UWf8nIQlwtxLhcWHDwIALhqwufi7wV84t+sUtQ31+vOD63TNQEJeo8xbMcO2F0uDNuxw7CNEumaFKwEBCAKgf0W9rvoBEDA3D1YfX09bIr07OzsbNjt+nqe+/fvR0tLCyilEAQBRUVFKCkJRKLxPI/c3Fw8/fTTyM3NtaIFgzB1ZiF+PrcnfnsTkDsD2ObQt/FRHyYOmAgbscEGGzrFdcJ1Gdep2rDKloTL6ebT4otOpyBenSgAAgh2w4wRAIHBVQPSktNUEdB/2/U3qySQhYWFhYUpwhYBCSHjCSFPEkJeIoS86H89vj02zsIc2vSZHzvaFOniimJdvcRYmanoRv0d2zD0jvfw7Iz/wdb7tzLryEVK7flandCYPKAKxXNnYuLDa3DpQ/eK8yc/Iho9aNIiNx/ZbFjTScn+0/oIvVBpcPk5+djzwB4U/HycTghdf2g9nvr0KXkfuGpcugdwG2x48443w/xGgJRE/0Ohos6Otn5Y/d7hIEISQONgo0mo3zs87H4A8TxLTdTXuAKADYc3yH32TemLvKy8oOdjfVM9iGM7MHYJiGN7VGmGEiUVJVixplJXb0iZmpufk48Tj51A82+amUYngPhgFOrhw+j8KakowaS3JqFbUjemacXKL1aaOvektO8/3vRHuGYYRw9yDg69O/vzvEyI8NrPlZeVFzQtNBhvT3sb9QvqDSOjKr+tVKWSlVaVwiuIRgBewRuxcc+TY58MOi3Bcuc9dOgJg7V6VVMezzFUVt4YUggMluJ88OACnDypHOTwwu0ugtd7WrWO8+c/V/Vj5Dhu5hx5PuF22ADVGrJr2eLikJ7stO93v3wXvJuPysApGGlbtsiGIF5AdtJtD/qUl8sGIz4ARW43Fhw8iGHXNAL33Qjk/kb86z9Xbnj9BtUx26OT2lU8mIg/bMcO7G1qElMvm5rCEgKDlYD4PhDqHszpdCIxMRF2ux2dOnXCsmXL8Mtf/lLXbvDgwSBEffyvWhVI9y8tLUVLSwt8Ph88Hg9cLldMP8cPDfvoG7BkLFsAlCg7XIblty7HH276A8ryynCy6aRqebk7OpduQHFP1dwT4pnoj8C1+YJmjLy37z3D30nezeOhjx5SDVR4BS+Ktgb/7bawsLCwsADCEAH94t8eAJ8CeAbAPAAP+F9/SgjZbYmBFpESS4djrXMvBVXVMoqlmcruut26efdefa/8QLAkdwnibcYF3r85943p2oCHGw6LLxTRW/tO7ZMfov716KPi/OaehrUJjeqrKRncQ123SkqPM0PhhELY+u7QRZcp94Ez06l70PmfIf8TkYjdo1MPXS24tiM5qs+4u/PLoLYWgLRBsLUgbajeudYsV6dfzV5ARZHH7HGVlpymiqCKNs1wwYYFmL38ddEcpuwP4l+/EGgkXL497W0UjCnApV0vRZ8ufVTL3t//ftCHD9bnLKkowey1s7H+0HpDEXHz0c0Y/8Z4Q8MPZR9m0/lVKeRBXHsBYF+9urYl5+Cw/NblsBM7CAgS7YlBo9FYBDs3lDUHtfUqg7kQB8NM9JSRO29jo/kHWkpbQxp6SNdayU1X+V243X822Y9X7idYncSgjp5++k0V950yJjG1ezrzGCoYXcAUHKW0dZaBU7jHBgtJAFSyiuEOHAtYDsOr6+qQl5UnD0IozxWBCpi9drb8m3TnUHV5Bu20kn1NTUGnf8xIDsHPPPMMysrKwHEc8vLykJAQMLmy2WxIT0/HhAkTVO/t5TeN4Xker776quwkHBcXB6dBmrWFiJkoaR/1ob6pXr5n0xpYnbpwyuCd5lANKGS6xEwOeAFbG3DLA0EzRigofvqvnzKXlVaVivU6NUYjsYhWtrCwsLD44WNKBCSE3AngEwBXADgB4O8ACgEU+V+fADAUwCeEkGnts6kWP1Ri7XCsdO5NtCfqHlClSDQf9aHV2xqxmQrv5rGyWp3OS0BUD8JSimh6Z8bNqP/mrehdfZoiq6+qb6t085XRLJyDQ/Ftxey0SMWNYjD3UkB04JWiuAgIlt+6PCyBbkDqAOZ8aR9wDg5j+qqL3oeb0igxKG0Q05BFuukuqSjByjMPyhFiJO9m1KetjagvAIbRc/F2Ueht8bbAR31o8bYEPa60xeSjiQQsqSgRRbeqPNEcBnbxb5UoWAQTqQonFOLYo8d0Tp0CFQzFYuX50+Jtkdst3bZU1c4o5bVNaJMNP1hiYbjXg/ycfMP9ooUV/ZWfk48tM7fgjzf9MWgKtxGcg0NmSiZzWasvEPGqdR0O5kIcilDRUyx33oMHF0CMQjFPKBHQKBVS7Msb9L1KLlwQB1OcmU7YbfoIUgD409Y/gXfzcrQps2A/x8FWIKbtS0Lgb64+xTyGOAeHsf3G6uZL1yllrVEA6JXcKybR9j0YaaB3MtyBYwHLYXhaL/Fz6JztFUJCUXkRSipKkJeVh0R7oimBfEhyctDpHzscx2HhwoVyHT+O4/Diiy/KacGCIGDFihX45JNPVO/75z//CZ7nUVRUhDaFqDtlyhSrJmAIzIj2NqgHOXsm91Qtv9B2Iap7UleNK3B+AeK9SO7TwMzxwLV/k9sZRUAfO3fM2NyNYYbVOb5zxNsaCbEcwLewsLCw6DhCioCEkAwAb0K8o58LoB+l9B5K6UJK6ROU0nsA9AUwG0AbgFL/eyw6iO/7j3B7OBxzDg7Lb1uOjTM26h5Q05LTIEB0hxMgRByFxRJJUhJTdA+KnIPD6p+tVjfU3Lw98fp7Qfsy+k60deryc/JRPHemOi0SUPVVuTMp5LFit4mRUfH2eAzvHV767ONjHmfOnzZ0GjgHB97NY6t7q2pZqHpfRtRdqAtaC052TvVHiJG+26IyzzES62Zmz0S3pG6q6L5gkUtSMXk7sQc1EDGDkWsxANhhNyVchBM9oDx/KChKPi9BSUUJDp05ZPwmjcggwYo4jOR68Pa0t1XRcVmXZDHbDU4brJsXCxOhn17FjtZQOqtqa0vFotaUESx33m++WRb2es6cWY89e4K7i7NSIcNxGAaAs2e3y+ti7SNAFJXnfTBPjjadvXY2WwgsLMRHj03F+oFA/m1AyTWUeQzxbh6fHflMNS8zJVMWgu+++m7VspkjZob1mYyoHztWFgLjANlJtz04MWaMLATaARQ4HLL5iCyQGLhqP7b+MXAODhtnbDQlkO8ZORJDk5NhAzA0ORl7RkZvdPR9RenwG8ztt76+Xo7sk9BO+3w+lJaW4j//+Y9qfnr6xeWSfDHCObiQJVkmDJigOq6lAT0JCvb1wywNXw9Vn1+ALgK3T5c+KL+/HGmd2PeiK6tX6n4n87LyQGpu0g2Assx12otYD+BbWFhYWHQcZiIB5wNIBjCdUlpMqT68g1IqUEpfATDd37bjfoV+5PBuHs43nXjq06fgfNP5vfwRDpZSFi2sB9RYRmFpyb+WHZkjR+lJaKLXvtjRg/k+CZZQ2SW+C/OhLD8nH1t/92eMm75VvNFkRMoFq/kWrM6XGYwEJWk90vqVRLoP7hx2J7MW3NoDa8G7eZ3b8GOjH4sqmkdyQ9VyznMOrsMu1TzttBLOweGhUQ+hf2p/PDTqoai2SY42yyrVFfv/9ZhfR7ROG2yGURTafSVQ0QRDGfWmwkBkkN6rPb4ivR4oo+O4y9jf5/Nbn9fVBo3FQ4xRyrUymkxrTsEyq4gVkjtv//7PICurDCkpHAQhsvTMkydXmjYJkRCEC2G179UrkECgKkegEY8rv1Wbmsgiv4buDxfgjpmd8Pp1xseQq8Yli9kS2enZ8rk4dchUOSLaTuyYOmRqWJ8pGPVjxyI9Ph5eAPP270cmz4O4XCBhmGqY5cSYMeiXmAgfgOfdbrmv0QdbkTxmDfP3AQDOe84DCK/m8J6RI+FzOn/0AqBk3uF0OjF+/Hg89dRTcDqdOiEwLS0NgiAYrElMEU5MTASgFgftdjvy8qJPTf8xEKokS0OLerCOFS0e6UAx7+bxp3d2Ms8vKfLPTuxY9dNVhpHJEtpBZ87B4fHp16kGQG39t4Q9aBsN7TGAb2FhYWHRMZgRAScD2E4p/XeohpTSNQC2A5gS7YZZmEOqCyLVXAtV0P9ipKMdjp2ZTvmmkIDIN3hB08wYdEvqppoemTEShRMKDdvn5+Rj6/1bxfprOhfd9UH7Yglr1/S5xrA95+CwaeYmcYIRKbdm3xrDzykJXQQEcba4sEXZ2gvsgvq152tF0TrTqTNjiPQmOz8nX4z60tSC8wpe+YZUebMd7YM85+DwixG/0M1fWb0yULPRj0fw6NpJSCm8X5/5Wk69iwRVmrhjm6rYf/a1LUGPRyWSG6/EXcPvMjwPWfsqqNutgcggoT2+YnE90H4eCa3oGKuHGGemEzbGz2ldU51cX82M43EsSUnh0K/fQqSkcKiqmgRALzY4HAXo2nUkkpIGQYxLYxMqLVjJnj33gFJ9Lbrevaeje/eJ6N17Ouz2HgAIbLZOcDgKMHBg4DidMsh/+xBEPJbQivwSZo4hZ6ZTZ16jLEvgqnGpIntj+YCrNew40hoQ0MMx1TBDJs/L6xc0fTXFpcD2q1HMSOquCV1jtg0/JlwuFzwej2ze0dbWBkopPB4PSkvVQo7WNVjLhAkTUFZWhry8PCQmJsJmsyEuLg7Lli2zUoFNIpVkMTqezzSfUU0XjC5Q3Z8QkIgHKV01Lvj6fao6v0aObsbW+7ei/P5yPHvTs9gyc4t8fTKqUwqwnX8LZ07F1MUvATf9DpiRC3rZ1ogNpyKhPQfwLSwsLCzaFzMiYD8AW0O2CrAVQGZEW2MRNlrBJZib2I+RBRsWYNBfB2HBhgVy2nT1yWrZpdNHfXj4o4dFY4VQaWYagkV6GcE5OKz66Spd9FpTehkWbFgQVlr3kglLQra5tOulhq6pwdJIlQ+/4cKsfwjxu5YepAWqFiSiicacd9085vzddbsx74N58mfwUV9MbpDzsvKYN+p1Teoab1oHZCXa715bT88sOmFCSnt2bMeyW82nf3IODvOvny9Pr6xeaXgOKCNpAeNaRhL2/p8ZpmsDwJp9a5jbE43jueQuzIrQU6Zpx+ohhnNwuKLXFcxlK79YKbd5ccqLuHnAzXhxyosd5uZ+/HgJzpzRDzJ06zYOAwcWIidnO66/fj+czjZ07z6RuY5vv33LVF+NjbzGEVgkMXEQhg17G1lZ6zBs2NsYO7YeTqeAceOaVAIgoLgW1DgBr1889iboxOOJAyYGdZQNdQxxDg7Lbl1maAqTlpwmX6cEGnnZCBYsww4lsTTVONpqfB0CANo5FZc+OEP3+/DcxOditg0/JpxOJxISEpjiXm1tra6t5BqsbW+z2XDnnXfC5XKhuroaM2bMQH5+PjZv3oz8/O+vk/J3AefgMPe6ucxlja2NuraPjX5Mno7GuMuZ6QQcvOb+iwfn4JjXJ87BYcVtKwIrUERCKwc2lUy5MRUYuxhwbAMFxau7Xu2wZ4COHsC3sLCwsIgdxkP/AeIBGIe06GmDWH7GogPQCi4UFEVbi/Dvn4UM3LxouGf1PVj54UGgxglb/8fx2W+fi8nNxD2r75GNO4rKi1BUXgQCAhuxqSKXPD4PVu9R1+xbtWdV0AdMQB/pFSzySwnn4DB9+HSsxEpVXZjny58HIWKUkPaGSht1OH34dFPf0b/+918Y/dposR+NC13FiQrwbl63HlY6cDj7Iy8rD8UVxToBUTJNeWLDE7plZpw/jTASELcf266LUJNd+qKAc3D4yZCfMMUrJUPS9GlFEhndMoDjgel99fuY+yIURt/b5T0uD3tdWlHb6ByQ6hm2eltBCMHtQ27Hzm924ti5Y8z1+i77THz4qXECnU4FxBz/8fj6rtdNRyyGA+fg4Mx06vbT8+XPY+qQqfKDWFleWdQ1AQHgkVGPYPba2br5x88dlx/KHv7oYXh8Hmw6sgnDew/vkIemujp2ymznzvp05KysdTh4cAHcbnVEeVPTXlRVTUJW1rqgfRlFDCYkdDe3sQhEIns7nYJ4K0HFv52ic+lkkZ+Tj+G9hzP3v/K6Ek00EIv0+PigQmAseSNFXwAAIABJREFUTTX6Jiaqov9Yy//+6KNwvumEx+cBAcHjYx4P+ftnwUZyAl60aBHWr1eL79o6flJbl8uFhoYGFBUFzru77roLDz30EDwe8b6CEIL4+HgrDThCpN+YF7a9oCpdcUVP/eDN2ZazqumoHXcV91/VJzsFbZqfk4+DZw6i6N3NYgS0L0EcRJuRC1eNCwvHLgy6bZJY2FGCnPQ7amFhYWHx/cJMJOAJAOEUmbgSQPRP2hamYKWUHT97nNHy4mTBhgWiAOhP+xLeWI9bCp+JyuikpKIEo14ZpXPuBUSRVCsM2YgN04apTa2N0syU1J6rDTodDFZqigDBMC1x7X61o+2uWnM3pZyDw9b7t6JXst6EQBmZpyTa6ChJJNPSL7UfOAeH6m+rdcsqT1Tq5pmFldYHAAO6Dwia7hcNcsqiATbYUDCmwHC5NuUIMDZ/CcZbVewILW2do1Dwbh7/Pf5fU22leoYg4jG05qs1OH4uxDXHsU2MAPz4BV165wVPePXjwoG1DwQIqrIJ0UYdSuTn5GPiAH0kHQVFaVUpSqtK0eprlZ2RYxGV2tjI48iRxWHX7AOA9HS2mCBG5umjO8+cKQvZl+Tyq6VPn1mmt4tzcNh832b0JsMA4hO3hfiAZrVrZ3afbNPrDNWfdv/zjY342NMD6CYKpaGMfsJFadihJdamGjUch37+unJa+iUmoobj5MjZZ296FuX3l7eLKP9jguM4LFq0CAkJgZIERgKe5BpcWFiI4uJiTJw4EcXFxejatassAAIwTCm2ME/hhEJsnLFRVeuTlVGhHSyMdPDwp//yG0YpIvrM1OwrnFAI+5FcXRkN/ljo67yN2Ky0XAsLCwuLkJgRATcDuJkQws51UkAIGQpgkv89Fh0A5+B0D7qzrjH/wPVdIaXmvrj9RV3NsIavsvDkp09GZHRSUlGC2WtnY8dx8zWVfn7Vz1E4oRAFYwpweffLUTCmwFQUxIU2tXjR4m0Ja1u1EP8/u82uu4nTCiXhCCecg8OIdLb7LivNJRamFSyxTSr4z0qTisYplXNwGOMYo5u/sWYjcvvnquZpIyojJVRU0A19bwj6vXEODstvXY54WzxsxBbSIdjIAbyxpZHZfmjPoUG3T4uy/pmEkdsv7+bxZ/7PqpRurcGClq4JXQ1rAzZ5m3SfK1aO55yDE1PiNby/T+9KHAvW3bsOKYkpuvm152tjEoWqpLGRR1VVLg4ffhpVVbmG4lxzs34/OhwFSEkJdl6zBCpfyL4kl18lvXtPR0ZGeFFlnIPDfXf0V6SRe1Rp5ATE0IwlWvjGRty4qwqbyWBg+F9kITCagQoWiYzr4PTevdvFVCPZrh8kmd67N2o4dSpiLMRwCxGO4+ByuTBnzhzMmTMHmzZtClnHLz8/H+vWrQua7qtNKbYID87BYcvMLbp6fEpON59WTe8+yR7cCMXx88d1tU0zGkMPMANAryv36MpoXGi7wHQJlmpc24gNy25dZp3DFhYWFhYhMSMCvgTxiWAtIcTQztAvAP4HYv7Oy7HZPAszDOw+8LvehLDg3TxufPNGPPnpk2j2NjONKwAxTXfeB+x6b0a8sO2FsLfn3S/fxYINC/AX/i841HAIL25/MaRAwLt5nPOcU827+pKrTffJiuCk/n+sGmtaEc9I1DPCKLKRleYSC9MKVt28DYc3gHfz6N25t679gfoDYfehpEcnvbtym9Cmi5hcu2+trl0khBpp33x0c8jvbXjv4Zg1Yhbyr8kPWk8nmAP4Vb2v0rUnIKbqRSpxZjp1+2tA9wHMtix3Z62LqxY7sRue54Da+TBWjr0SLMFWgCD3Ga4hUChuzLxRN+90y2m8t+891bwRfcI7h7U0NLggCB4APgiCxzAVV+m8CwDdu0/U1eHT4nDMN1ii7ksr1rL6Gjbs7VAfhUnq5XuZtUyB9o12Kf2iAa0+AbARwJYAOMT0OzPR4eHAqtX395MnY9qHBKvG4Mp26ssiAMdxWL58OZYvXx62kceIEezrgzal2CJ8Qgne2vq+B84ckA2ezMK7eXGgTDP4dbx6cMj3AsDv752su/5pja0kbMQmDiATO3ad2GXVBbewsLCwCElIEZBSWgHgOQADAHxOCHmHEDKLEDKREHKz//XfAezyt/kzpXRn+262hZJVe1YFnb7YkNLiAIiiQY0TmPwI82Gv8ttKTHprkul1R2Jk4aM+FL27GW2bfg3h6Eg0e5tDpmbKaR4KwhFeOAeH1CR/JItGQGkT2nT9a9NPQ6WjasnPycfl3S/Xzd9Tt0c3T2taEcxAxAjOweHxMY+rZ1JRQFIaUEgkxSWF3YcSozRfrTlHU1vsCu6HMsQIdh5KQldJRQn+tutvqD6pT5GW0DqAS+IV7+ax84T6UpuZkony+8vDjgTgHByuy7hONc8opVgnvphwcb06/WoQx3ZDUWfbN4HXsXLslejeybgenRQ5HI4hUChYx+KX336puzZFU2OusZFHS8tREBIHwA6bLQGpqU5m2549pyLwU29DZuaikOsfOLAQycnsaFKpL5ZYG0lfRjgzneIxo3D9BgC4r4eweQGqK7pEvO5gvHv+uPgRKMSs6LRLMXTMWzGvkdeXkaIrABhVURHTfgDjGoOxdCG2iC319fUgRP0bY7fbrZqAHcCQnvp6vkXlRXD82YF7Vt9jKkpdHtjSDH7NusPcoH1+Tj7G3ZCgu/7trlNHJbpqXPAKXlBQtAltKK4ojsngmYWFhYXFDxszkYCglC4AsMjf/ucASgB8BOBj/+ufQYwAfAaAcSEsi3ZBG6EQ64iFWCM/8CvFg49fCEQGaSKK1h9ab/rh/PYht4e/QQwRQ3ujpUVbA42AhC289EruxexbMtBQoozYi7RIPStdefPRzbqbRa3BSVJ8ZALd1CFTVTX54u3xcGY6xZvbfuNUbYf1MgwyNgUrshIAEuPUD9rZ6bGpI+aqcelq+mkJdh66alxo9bZCgACv4MXcD+bKx7g2ukrrAC5Nl1aV6lyWs9OzI04FcvZ3qqY/r/2c+SBRfbJaLWgZpPlKxNnisCR3CcrvL8e4MQnoNelVnUnNN2e/CWxHjBx7JRY5FzHn116o1QncsRhAYUX4nW5Rp5exznGzNDbyqKx04sSJFaC0FSkpY5CVVWaY3ltbWwrI6dqCfzo0I0fugd7jyyb3JR3DPupDq7cVrhpXxH2x4BwcfnKFprao/3pJP/095v5sMPh2eM5t7OQfOCCQSyN+TWLnDCxRYxAZ9vn58zHva8/Ikcwhi1i6EP/Y4HkeixcvBt8eByFE5+B4Rd1Iu92OZcuWhR1RaBE+g9M00Xr+gdpjey7DyuqVePLTJzH+jfHmhDbHNnnwa/qfXkf+VPMl1pfk6geW3/tKHVEu/V5Kg5LSYGG0g2cWFhYWFj9sTImAAEAp/T8AgyAKfRsBfAVgHwCXf95gSunvKKXhh2JZRI3NvytDCRPtQTj1u3g3j8paf20lrXhQlWcYUfSrj39lalsiqhPFEDHe/fJd3PGPOww/k7aWXkbXjLC77Z7UXdN3AlDj1BmX8G4er3z+ijxNQZm1/EJx99V3M+drUzG/qP1CtXxYz8gEOuVNKAHBzOyZskC1JHcJEu2JICBItCcainhm4RwcU0jsmdxTPjfibfFBzTrCQbrxNqJHUo+gkUPOTKeqNqJABTz44YMoqSjRRVdpHcC106plURifaM8dyWVQi04oY6T5jus7DnNy5mBOzhxsvm+z7CC4aeYmnHz8JAb1GKRaxaXdAnX7YlGTUolRIfY1X63B57Wfq+bFYgDFjEB/9/C7I/5ctbWloDQg1Dc2bsaFC8aRpGfObFBNnz27zaClnoSEPpo5Ak6dEt2W05LT5FqQAgSkJafp1h1OXywKRmvOV8X1UvDa4XJFtXomVyQnA6AA9f8HkHS2KvYdARjZVW8QdU2X9olwvLm7PiI2li7EPyZ4nkdubi6efvpp5ObmtosQqK0puGXLlqC1Ai1ih+uwKzBhEOneJrRh2j+mYdQro5iD1HlZeYFBUMc22Mc9hwfuuCbqbTvfdl6+L+XdPFw1LiydvBSzc2Yj3hZvWFfawsLCwsJCSViKEaX0iF/om0ApvZJSOoxSmuufd7i9NtLCGN7N44EPHwg8jPnFhI5KBeDdPJx/WIinfn8ezj8sDNlv3r8VYo9WPAAMI4qavE0Y9cqokNtjJI4FTd1kiBiS6ylrtJd38zjTckY177fjfxty27TMumYW0OkUQO0QHzrt4jTUwlxpValOGIwkErBwQiH6pfQL2qa0qlRl8mCDLWKBThnRlRSXpFoP5+CwccZG/PGmP2LjjI0xKWR92+DbdPP2ntoLAQLsxI6XbnkpZgWzOQeHpZOXGi7XRjqy3v8o96hqnlfw4rny59Dqa1WlwuZl5ckj/Qn2BPl7VD1kQBQ5oxFTWeeOKUdURaSDlOY7rNcwLL9tOZbftpz5nV/Z60rVtMfrkc+zWNSkVBIsIsIreDG051CMzBiJ4tuKY5LyafTw1TWhK3ok9cD04dPx9rTI6uQZcewYuxaqmDb8tWpea+s3zLYsLrlEP3BQW/s6eDePVXtWyddVG7Ghvqkezc3qvlpajpjuiwXn4JB1SVZghuJabYvzwemMavVM9owciS5tpwAqANQH1K5Hbqve8CQWbM/JUQmBI7t2xfacnHbpa11WFiYqhMBYuxD/mHC5XPB4PPD5fPB4PHC1hxqN6GoKWkSOalA3SKR77YVa7Di+Qy4loR0QV0bMU9Cwo/OM2t/5zztVA4bzP56Pc55z8Ak+w7rS7UWsTLwsLCwsLDqWuFANCCGJALYAOAdgMqW0zaBdAsQU4c4Axhq1s4gtrCL9UgRPRziEla49AM9rHwK+BHg2eVCa/f+Bm2tscPD1GcVDoiQe1DgDqcCVMwAfVRsH+OsG7sh0gZ/MB/1cLKMLAMhKzwpEIGrRbociVVGqz6fsU6rBoiQSUS4/Jx8rezuwGT6Ip6IXaO4Z8n12Evko75TLp2BFxQrVvE1HNsmvtQ6moVxug8E5OJTllcFV44Iz06lbjxQdFiuCRYH6qM/w2IgUo31OQExFHGq3l4Li4JmDoKCwEZucCss5OLhmuJjfo43Y4KM+2IgtapGT9Xne+eIdTB0yVbVeVh1JOLbJ542d2EOKkTUNNarpPaf2wPmmE64ZLmaN02jEuVDnyt5Te2EjNsxCbFzVOQeHzJRM1DTWqOaf95yHjdiweu9q8O7g17FgpKfn4cQJ9Tnc3FyDI0cWIzXVqUoLZqXjGtX6YzFwYCG++eYlCEIgbbStrQFvlI3D8TM++VhNtCdiXHoa2r5Rp7LGxUXvxr381uUY89oY8YFaca1+bPoocNzUqNfP4k9pZzB7baDu65TbitulHwDtJvqxWJeVFbqRRUicTifsdjsEQYDdboezPdRoi++MgjEFWLNPjHiWBx6096VAoKZ1pguPvboKFw4cBc3ciKTMZzDp8kmqch2RlIAwGoQ7cf4E5n0wDxQUAhXQ4m3ByuqV8vI2n/6+tT2Q6sJ6fB4k2BOCGpxZWFhYWFxcmIkEnA4gB8Cfggl7VMxPeg7ASP97LDoAZ6ZTlwIcabpoJNTuvkI1Slq7+wrDtk+UPaF3EXVsCxQ+ZkQUaVMxZry4wnD9AFugKBhTgGW3LAv+QZTboUE7GqutFxiNKDfs2jogzh+FGOeRbzC7JQUenrU1xn49+tcxvdHae2ov7ll9DwDgdPPpEK3DI5QLXyxxZjpVkXHtjdE5RkGDGn1IsOpOSpEDA1IHYOnkpfL3xvoeS6tK0SaIl2SBClGLnM5MJ+Js6nGhY+eO4cY3b1SN8hu5BhMQxNnisOzWZSH396mmU7p5kulJrGuccg4O04cH/0kSqIA5a+fELJohu4++9iQFFevn+VqjqteUksIhLq6Xet20CYcPP4Wqqlw0Ngb/DAMHhuccHRendt6maENePy+eu5riqm4EE/pPQFleGVJaP9K9t1+/J8PqiwXn4HDzgJsDM/zX6tTL90a9biM++vqjoNMWFpRS+b/FDwvOwaFLgj8tX7ovzfkbkP1moJHy3vSNjThX/B8IG34P+noZWmpG4PhZdd3o2wffHvZ9UOUJg4FriAObdmKHndh1BjIgoQe/YgGrLqyFhYWFxfcDMyLgNACHKKUfhmpIKf0YwAEA/xvthlmYg3NwGH6JvuZVNM6T4ZB+5VeqVNr0K78ybLt1KzWs+Te051CxxppWjNOkYhz4PCOoW7BWXEjvnI7CCYXgHBy23r8V4/qOQ4LNuJYbi08Pf6qa3n5MnRqW3iU9YpEr77ZBSLj/Fp1jqvLmr76pPlDzEbbI6h76YZkWAMDK6pVYsGGBzjyEZSZyscI5OCy7dZlhXcxDZw7FtL9g51gogwnezePvX/7dcPnXZ77GnLVzsGDDAtV7pLQb3s3rhAltFGe4cA4OL9/ysi6VSFtkfMmEJfLxCAQiH/940x+x+b7NpqL2jOpTlh0uw64Tu+T124ndsKZfODxw3QMh21BQFJUXRd0XINayM0rJEqgQ9SCNMjIvAIUgtKKhwSXPSU/PgxikDwB2DB5cbGggYoTXqy59QADYCBBvA0Z0t2GRcxE4B4empn2qdvHx6cjIiE0Ns3316nVHY6xiBu0DvHba4sdNaWkp2trEAZi2tjaUlkZugGNxcXJNuqZ+X+UMoOKXgXtXbZqwEA/ADvgSQT97DLOumaUq4xFJPeJgA2AEBC/d8hKeufEZ3HXVXapld111V4cMvO6u262rC2thYWFh8f3AjAg4AqL5h1k2A4iNBaeFKTw+j25eR/0YSyIWuWkREu6/BXm3DWK2K6kogffQGGZtlaE9h2LPA3vgmuHCszc9i+LbijGur7+mmpSKAS9AKNDpFNYfWq8SR5QkxKkFPqXLm2RM4LrPhU5xnWCDDXZiR3Z6NkZmjERaJ8135o9a9B69VlWXbNRl6tqE0TyMcg4Ort8sxrjpW1VRiMpIImemE/F2seCz5LAbKcGEq+e3Po/UJLXAOCiNvT8vZrSOuRKfHPokpnVrnJlOQ6EnVPSaq8YVMoJEEqVKKkpQUlGC8W+Mx282/gbON50Y/8Z4HGlU11s73BB9WVbW8aEVXDgHh/ycfFU9uNTE1LAiPgsnFOKyrpfp5h84fQDFFcXyg4WP+lT1MSPF7Dq0rt+RwnS2VRBt1GanTkbnpQCvN5BClpLCITvbhf79n8WIEVsiEuVYfVEq3jwk/ncO5v2qEbybR3y8OjoxOXmw7n2R0uxtVk13SejSrg+5s66ZFXTa4sdNbW1t0GmL7z9LJigipll1AZX3ptpHqX23Y7gnHy9OeRE3D7gZL055MaLrVX5OvqF4SAjB8N7DsXDsQlzwXFAt0063ByUVJaoUZCD63zULCwsLi47DjAjYE8C3YazzWwDWcFAHMriH/mGroyIBJRHrj7/rAtdvFhve6Kzas8rQRXTPA3vkdS0cuxD5OfmYfPlkUWRwbAMmPwLYBIDagI9fANzX4/Vdr+v64N08qr9Vp2GyHGOlWnV/uOkP2DJzC3bN3oXtv9yOyZdPDjTSpCEv/Vcg+u9Cm+aGqy26Gy6Ws+3ZlrOqaa/gBQXV1SIMF1b6uIRABZS7y1XzDtQfiKq/jubVz181XBZJYe5gcA4Odw/XR7RlX5IdMhpO3g87fwG89ZH414DFny3GAx8+gDahDQIV0OZrk9OAlcRCwGIdHwIEVXqzJKRKpi9S7cJwOd92njlfWUy9o4mp2NOOH2PwYOPyBidP/kM1nZLCoV+/hWFHAAbrS8o+u3HMWlSumoSx0z/DWc0hqU0jjgZVOrD7epwvewgla0Kn3EdKfk4+im8rxsQBE2NmGGPxwyE9PT3otMX3Hyl7pFtCN+a9q5wmPHADAB/EGGnq/wuUrjmC+R/PR9nhMsz/eH7EA5CFEwrRNUHvIi5QAa4aF3g3j/f2vada9v7+99vdqIN1rxVtNoKFhYWFRcdhRgRsBtAljHV2AfD9ySH8AVAwpkDnEtoR9UAkTNd9Y9T8U422KnBmOpEUlyRONPcUBUAaB3gTgao8Zpqqq8aFkUd9eGILcL07uEEBa5vrLtQFGmhGfvfuvES+qdLWaQlWt8Us2psn5fQTZU/I7sA+6hNrK0YI5+Cw/Nblhsu1gqa8D74nJMUbb297nBdvT3tb7V4K4PrLrjdoHYBzcEj+4hFgbQlwcJL4VykEKmpnXvBcgCCoC4zH2+J169RGcUYC5+Awpu8Y3Xzphp938xj/xnisqFgBj8+D24fcHnEx8CS7uWPLKIU9HPKy8gzFb4nkuOQOE3ui/UwpKRxSUtgO1G1tsR0ASknh0LMn24AjNVW8Zvq25aOh6ahqmdcbu/qispu0f3CGfvp7PPjzK8C343Nufk4+1t27zhIALXTk5eUhMTERhBAkJiYiLy9yV3aLixfOweHjez5m16sGxL/O34v1nCEN0AogNh+QuQmtvtaY1IF9fuLzzPnOTKeYVaAZcRKogEWuRe0qBGZ0y9DNS+9iieEWFhYW3xfMiIBuANeFsc5rARwN2coiZsi10Eztzo6Hd/P45NAn4oSi5l/BmAJD8UCK1puTM0ccdbV5IY6y2oDP78e5g1eqUnR5N4/GjR///+3deXxU9b3/8dcnk0xYZBMXRINxg7ogCBQ4WnU0FJday4X76IbG7Rqttb/a24ra1mpLXcDeFu+tValeK6L39rbUWr1V1JRRKoNcUCl1X4rGBWURXFiSTL6/P85MMltClplMZub9fDzmMXOWOd/vkEMy53O+38+H634zlT3qr+T630zl5qFf7VJwImkKZ9qd32Wt+cI++PSDpPf1r+jf6TY6K7FAx4sbkxPgpy53Vd3Eurbp1kXmiL3SR37GfeGwL+RkCuEln70kabmzAZ7AS1+JvYoNq3oxdv6ljEIdsKGG8kBbwY5AWYBfnv5L9qhIvjczrN+wbvU/UaQhwl/f/Gva+vgX/vlPzW8dhehw/OnlP3W7rc5eMGRjilE8+N1R4ZgBwQE9bidRR58vG4UmBgxo71zPPEW9J6qqMk9JCwRiF77RfgxJiUs3NW1Mf0M3hapDfs7YhJsz0eZywuGsNSHSaZ7nsWzZMq677jqWLVuG56kiarHyqjzGjxjffvG4eIBw0q+hrAlwlAcqGNxvcGtqkp7mga2bWEf/8vTvmes+WMdb2zJfbj32xmPULKrJWSAwU97bxIJ2IiLSt3UmahQGpprZpN3taGYTgWOBZT3sl3TRs+8925pHq6mlKSt5tLJl0dpFaXcqh1QOYd60eR2+z6vyuPWMW5nz1RPgsHhdGoOWIKyt5aKHLmLhmoWto5PefbCRL0TruYa5fCFaz8ePVHapn4lTwA49elPand93P36XU+45he3NyUn5J+w3oZ0jdl5qwOCvb/219cvb4XsdnrQtdbk7Mk2TzqSQCoOAP+KrvTx9OYiNAOlBqs4GrerOil8UxP5vHB4rJpIyCvW9v4/h/PHnt76vuaWZh199OG06bTamsl5Zf2Xr75FEo4ePJtIQYenrS5PWx6ckdUemXKaZZKr43R11E+tYft5yJo+cnHH7KYe0X3CoO1qDwakV0clOoYlBgzIHm1taPubddxdm3NZdQ4Z4mKVfhFZUNHLEEREqB3/C8MHJqYD79x+Ttfa9Ko/wOWFmnDqMQEULZQFHZdAIhbLWhEiXeJ7HVVddpQBgCfjV6ekpEQ4ccmDbzdSqlTDkLWgpAwJEm8t46NGPk/bv6c2sb035Vtq6S/98Kb9+5tcZv/M4XI9HIHbEq/L47Mjk8SHZmBUjIiK9ozNBwF/iX6X+zszajT6Y2WeA3+Enx2g/YZEI0BRNz2nWnnnT5nHCkZkvKJe8sKR1dFKYEI0EiVJOIxXcu+3QLvcrPgVsj+AeaXd+Dxt+GMvfWp72nqRpxN2UOnqshZbWPDJVH385KYgwtWr300272l57Ci0hvlflJecPSzBiYN+aqjLvykOYfPGdcMhSOKMOJt3hb0gZhdpYtTSpsrHD8dz7yV+2D9/r8KxMW3x9y+sZ1//sqZ9x4m9OTCvQAN0vjJNYtCdJStAsm4For8pjwakLMm5rnXKaJZu3b04b1Rn/TKGDQj069rZtEV577bJ2t7/yyiVs25a9ESDbtkVwru1n75yfF9AMpk9fxJTZDzNq1BwgPmK1PLacPV6Vx/3fm8PycJCfzjXq60HxFxHJNa/KSyvQ8f3jv88T5z3B7LGz/RX9NwEB/Crtjhc+eTJp/xc29exm1rxp89rawk8LEnVRoi7abh7dgAVymhoo9fvh7gqiiYhI37HbIKBz7mXgJ8CBwLNmttjMzjez6Wb2eTM7z8wWA88C1cCPY+/JGTP7vpk5M/tlwjozs2vN7F0z22FmYTM7MuV9w8zsHjPbFnvcY2Y9T6TVB9SOq23NE1ZRVtFuLrx8+Ljx47R17QYA2nHjd4+EwC4g6j+Paxvp+PQ7ftGO8ZVhgjQSoIkgTfTb59Fu93nT9k1p65557xn2GbhP2vpsfPHJVMhl1burOP4nc7jve+cnBRHC/wj3uL2HX939dMQ9KvYoyHxYiQGzOMNy9n+idlwtlYFKDKMyUNm1dib8Gs4+rS0ACG3TiybeAePvBuDRN5LP5dTKwl8c88Vu9z/R7KNnZ1zfQkvGYiR79tuz21Os5xyXPp0oU9As24For8pLy+NYZmVZv1gKVYdg/UkZK6IPrezZn52tW8O0tHQ0kjLK1q3hHrWR2l4iS/ix7XnEUm684hCGDPE45pgnY5WIn+x2IZLd8Ty46ioFAEWk98ybNi9jsaDFMxez4vwVTB85Gz/trIG1+LmsE+xs6vnNrP0H79/62uEoszICFqC8rDzj/l8Y/YUet9mRuol1zB47mz377cnssbML8vuiiEip6lQSOefcT4Af4k+o+zrwa+Bh4BHgjti6MuAHzrmf5qa3eh8XAAAgAElEQVSrPjObClwI/C1l0xzgu8C38HMYfgA8ZmaJZbXuAyYApwGnxl7fk8v+9qYyK8Ow3SbA721PrH8ibd2vvtC1waKeByMu/TrU/BDOPal1dN7zG5+nuaWZqQ3w+8hKHqeGufyIR6nhewM/7Hafv350etXXN7e+yfam5KnAQyuHZuWLT3sBiOg/PodrrkgKImRKyNxVnakkO2Fkz6c558PMI2amrfvSmC/lJB8g+EGlZecs47qTr2PZOcu61M7IQR38LJ87B9ZcmDSCLC61CEhqNenumjEmcwGI9hy171Hdbsur8rjtjNuS8/SlTIUONpySkwsL74Dkn9GZo8/M+vnhVXkcNuGd9KqSdH/0ZNzQoSHKyoL4I08yz3MfOrRnbaS3lyH3qcHxRw1t/bfraSXiviISgRtuIKeFR0SksLRXLMir8rj23BAV5YYZlAVaWn/Xx/V09DfAfX+7L2l5j4o9qDmohltOv4W9BiQHHQ3jwZcfzGlewIVrFnLvunvZsnML9667NylPt4iI9G2djhY5564HRgNz8XP+vQS8HHv9E2C0c+6GXHQyzsyGAPcCFwAfJqw34DLgRufcEufc34FzgEH4AUpiU5lPBeqccyuccxHgIuAMM8te8qI8Ca8P0xRtwuFoijblLA9Id6ROVThg0AHduuD+8dmnpiVmHlAxgOmHTCe0Hspa4FhWciU3chwr+coJl7R/sN2YN21eWgXW7c3b2bg9eepv9bDqbreRyKvyMhd2SZkaagc9yZxjez7NrjOjqzpT5bYvik+biY8yqwxUpk3lybZOV8hOMee4OUkjeOccN8c/D1KCYfERZHHbdm5LWk6tLt1dXc0l2lEhls6I5+lrzXOZcr4fMemDDt/fXbXjagkGghhGMBDM2fkx7LCX0nKLTh45uccBxyFDPMaNq+egg+YyevRtGffZsGFR1qYEt7V3fVJBEgPKo+/w8svfyOr043yKRKCmBq6+2n9WIFBEOiM+QjpQln5zpqejvwEOHnZw0vJHjR9R/496LnvkMvYZkDxLxeGIuiiN0cacXQ/c+cydHS6LiEjf1aUhY865N51z1zjnpjnnjnTOHRF7fa1z7s1cdTLBQuD3zrm/pKw/CBgBtM6Zc34CoyfxC5UAeMAnwIqE9z0FfJqwT8EaPmB4a0L/FnpWiSybIg2RtFFnHY5+6kDdxLq0Ahrbdm2j4aMGwtV+TmaH/9XLzGBz+hTbrvjykV/e7T5T989eoGzSyAy1d+JTQ2NBhC/V7JOVEUvxIigHDDqg3X0KOcnz4pmLeer8p7j+5Ou7PDqvN3lVHk+c+wTXn3w9T5z7BPOmzeOv5/+V/oeuSh5B1n9TUp689z55L+k4idWke1M2plh7VR6XTY3lt0s536d6mXMdZaPN8Dlhrjv5OsLnhHN2flww4YK03KKpozi7Kz7qbuTIOgYOHJ+2/b33buPZZ49l7drsFDyJt3fAAd9OWt/cvDHW1ueKIhAYDkNjI0Sj/rMqEIvI7oTD0Nzs50uNRoH1J7ZuqyiryEq6iRun3ZiUM9c5P9C3K7orLcVOeVk5AQsQDARzlhewX0W/pOXGDlNUiIhIX9K35o12wMwuBA4Frs6wOR4Zej9l/fsJ20YAG11CMq3Y6w8S9ilY3a1Q2h0L1yzklHtOaR36H2mIcMPyGzJOOQivD6eNBOzJRXBq0O2DTz/gyTefZGUVfPN0aCqDqAH9+tHT0pGLZy6mMtBxheHOFtjojPYKFiQGEQYGB2atvbqJdVx9Yqb/Tr7x+6UHFgpJd0fn9bbUfnpVHgsu/EpbMOzUb8MjNyflydsV3ZV0jEw5LLujdlxtu/mFUvUkH2Cqzds3t+UHjJ3vFQeuyWlu0944P8buMzYt72Eu/l+NHt1+eoUPP3yUF144K2ttjRxZx+jRtxMIpP4eb+GVV7o/+rqzcj1VNxSCYBACAf9ZFYhFZHdCIf93hhk4mpKmAx+656FZ+Ttzy/3PJOXMdQ1TAGhxLZx22Gn+TIJYWiCAiftNZMGpC3L2Ny51JsBzG57L2dRjERHJroIIAsam614PzHbOdXSrKXXYiKWsyzSsJHWfeJt1ZrbazFZv3Njz6q+5ljodMFvTA1MtXLOQix66iEffeJSLHrqIs/5wFjWLarh62dUZc49kGpGYqXBDZ43eq/2CIndMghPPgwfPnky2SkcO6TfEf5FSsTQuU0GP7gqvD6cXSkiRKb9iT3TU/2xMX5HuqZtYx+zTD/GDvzv26nBqMMBeA/dKP0g3eFUeT577JOP33X2gasSg7N07CVWH6FfejzL8ROczPjODJ859os8HcHcn0zSsXPy/GjLEw6z94O2mTX/MansjR2bO07h9+0tZbSdVb0zV9Tz/z8fcuVn7MyIiJaC1YFLK17jd3UzurIcf25Hxu0AZZWzevpmhlUNxzuFwNL85iVW/PZlv3r44Z4G5TDfpLvnf3N8IEhGRniuIICD+VN69gL+bWbOZNQMnApfEXscjGalXpfvQNjpwA7BPLH8g0JpLcG/SRxDinFvonJvknJu09957Z/fTFLBr7nkkKRh277p72dm805+S0Lwr7aI3U5ApU+GGztrdFNVVo8rY96cLsnblVj2kOmPF0rhsTrMIVYd2W9QldQRYNtoMBoJp67M1fUW6b/HMxX7AKCVPXmrCceh5br5EXpXHsxc/y6DgoA73+/aUb3e4vatt1tfW89OTf8ry85Zz/1fuL/gAIPg3QRJHQufy/1X//u0XaUmtJp0NZWX9MqwbkPV2EvXWVF1VIBaRrkicDuxaAkk36xqj2Zkme9rn+2f8LlBZXkmoOsTWXVv9vzcJ31mb73qE+b9dnpX2U3lVXtpNrefezzwaMHUGkYiI5FehBAH/CIwFxic8VgP/HXv9Cn6Q7/PxN5hZP+B42nIARoA98AOKcR4wkOQ8gQUpNVde6nI2XHHXH9lwy+K0YFj8IjdTLsKtu7YmLU8eOZl50+Z1uw+zjpjV4faj9z06q8GDLTu2tFuk4YQDT8hqW16Vx8T9JiavTBmBuKs5u0HAeG60iydezAmjTuDwvQ4vmlFYxWD08NFpefISC+PE5WLa7JDKIe1uO2zYYVmv2lso07e7YvP2za0FfwzjgmMuyNnnGzOm/SnBzu3Ier6+wYPT86EOHXpCVttIpam6ItIXJU4HtrLk6sCp+fq6a/H/+yZl505P+y7wrSnfwqvyePDlB/0dU76zvrJ6v6y0n0n10Oq0damDAa54/Iq0GUQiIpJfnUv+lGfOua1AUjTJzD4FtsQqAWNmC4AfmNlL+EHBH+IXArkvdowXzewR4PZYfkEDbgcecs693GsfJkdSc9MN7jc4q8ePNET499/+DaJnxL5YOP+LRkJAoszK0kb+3bHmjqTl17a81qN+1E2s4zuPfIftzdszbt+6Y2vG9d015YApvBYfiRV1rXdfDePGmhuz2hb4hQRWvbvKX4jfzY0G/fbPqeGLpx+S9Ta9Kq+oAi/FpPV8qFqZMfgH/vTSnP/8Gqb6/9+rw1C1kv0G5+6iopiEqkNUllfSGG0kGAjmNMehrwJoyrDesXVrmCFDsneejBo1h82b/wSxglQQYNSoHFfhjk3VDYf9i26N1BORviI+zyg1x1BHaWy6auBBf+PjA55KWvezFT9jxpgZ7Gje4a9I+c764qDbWLhmR9Zv3EH6tUaZlSWNdo80RJj/1Pykfe5ddy8nHHhCTvojIiKdUygjATtjPvBz4Bb8UYL7AdOdcx8n7DMbWItfRXhp7PXZvdzPnEhKrA/8IvKLrOUBueLxKzj2P49l5wEPdzgt0bD0kYA7t3a43B2Zpq/GfdT4UY+Pn+jIvY9sG4k18Q4YfzcAlx93eU4CL3UT65hzXOxCOuVubuDNaSyeuTjrbUrfFa/iPHnkZAYHMwf2oy6ak7a/fvTX/RcZpsO/ubU3isEXvvg057knzaW+tj6nwdqtW8NA5nPBrIKhQ0NZb7MtD2GA0aN/ldUgY3s0VVdE+ppwGJqa/OnALc1lSdOBw/8IZ62dg/c8OG1di2shvD7MqMGj/BUpswfcASu49M+X5iQ34M6mnUnLVYOrkv7OJY0KfOx6mP8eLHidq+Z3Pze4iIj0XMEGAZ1zIefcpQnLzjl3rXNuP+dcP+fcifFRggn7bHHOneWcGxx7nBUbZVjwQtUhAmWB1uWmlqaMSem7auGahW138XYzLTHqolz2yGVJXzQqAhVJ+/SkMnBcpukHcbvLqddVSXn6njsH1lyILVrGjIHdn9K8O/OmzfMDgSm54I47PtMIHyl2dRPrePrCp3nkrEcybj9zzJk5aTd+HgbenJY2HX5gRfaqVBe73prm3H6Qzxgx4oKsB+i2bg3jEgLQTU3ZK5IkIlJIhg+HlhbwxwAGoP+m1m0jB43MWjveAZl/j4eqQ20FSVJG7gNEW6JZuSZIa/egUFuby6/k7eerkq4BWtt87Hp46krYvi9sPYgt/3MDC5UeUEQkbwo2CCjJvCqPrxz5laR1z298vsfHvfnpm5NXVK30K5a2MzVxZ/PO1j/6Vzx+RVohixum3dDjPk09ID0XVdz5x5zf4+Mn8qo8bv3Crdj6k1oDIdZSmbOE9HHzps1jzldPgHOmwck/InDuKdx43pdy26j0aV6Vx4rzVzB+3/EELEAwEGT22Nk5HR06b9o8Rk98N20E8LenZq8oiGRTegGQsrJ+jBiR/WnIQ4eGKCsLAgHKyoI5GWkoIlIINm+GsjIAA4vCjr3wl6xtdkcWdJRSYmfTznYL2ZlZTopSDa0c6rfxm2VQ/1Oidz2WVIhk5duxa4UX4/m8jXi0csmSrHdHREQ6SUHAIvLq5leTlu9bd1+Phv9HGiK88MzgpMIUGSUUr3C41inBf3jhD0m77TVgr6zkAKkdV5s09Tlu+sHTe1R0pD11E+u47dKvURF0lAUclUHrlYT086bNY8U1/8b11wxm+Y/mK2+ftFbubf5RM7t+uKtXpodPmLwraQTw5CktyuXTB/nTgZODgIMGTWbcuPqcTNMdMsRj3Lh6Djpobs7aEBEpBKEQlJfjBwDLmlvT5Xx25GezXkBuxMD0wn/zV8z3R+UlppFproS1ftAwFxXiIw0RVr2zym8jWgkEIFrJn347tPXaY9+B+/o7Hx6P+Dnif6dmdVznT0REckhBwCLSGG1MWnY4Fq1d1O3jXXnXAxnvKCbJcNfx2feeBWDmETOTds3WKD2vyuOp859i/L7jqQxUMmKPEdx+xu0sPXtpVo6fSd2Msfzy3yuYVmMsWNB7+aiKsWKqFJZXN7+62xHAkn9Dh4YwS8yXWsGhhy7IaXBuyBCPAw+8qlcDgJEI3HCD/ywi0lf4hUGSL6sumHBB1tuZWpX+XfzlTS/7o/Kqw34QEuf35dnzoGEqLbSkFejoiUhDhJpFNTzw8gNp21qItl57tBYr+fz34bgbYcAHDN53C7ffbtTpXqKISN4URHVg6ZwPd36Ytm7DJxu6dayFaxby5JOWkAssvRrw9IOn8+jyCQl3HQ3W1vLrUd/i3nX3ph3zkGHZq2wbHxHVWyIRuOwyaGyE5cth7FglppfS0K+iX9JyNvMbSfYMGeIxfnyYDRv8i68RI2qLbnReJAI1Nf7v4WDQrxSs38Mikm/hMDQ3A84gWg7ha7DQXMbuMzbrbc05dg4PvPQALmHk95i9xhCqDmFVP8AdcxesrgMC0BJo/e7+wMsPEGmIZOWmcnh9mMZoo9+HcYv8YGO0wk8ZMm4Rdz67hsH9BvP2x2+3venz32f/mbfw9r++3f6BRUSkV2gkYBEZGMxOsv5IQ4Rv/vmbaYUp4tMbyqysdeRd9fg3k+86PnM+0Qf/g49fP5KPGz9OOu6SFwo3AUg47F94RqP+c65zAor0BZGGSFtOHyBggazmN5LsGjLEY8yYWxkz5taiCwCCfg+LSN8UCkEgAH6F9gC8MQ1392P+jJos86o8bjvjttaidRVlFcw5dg5elcflx13uB+XKd6V9d3e4rBUHCVWHCAaCBCzgDw449ySo+aH/XLWSppYm7v1b+mCA2UfPzkr7IiLSMxoJWETOGH0GL2x8IWndiD3Sc4fsTnh9mOaW5rZqwAlVxmZ8Zkbrlw2Aq74W4qInEu86Bv3Xz52TVkF4/H7je/Dp8iue76WlxX/ujZyAIvkWXh+mOdqc726IAP7v3WCwbSSgfg+LSF/hTweO5auOzaB5afW+OWmrbmIdY/cZS3h9mFB1qPU7+bxp8zhk2EKuH3IBb66tTqoQDHDP3+7hquOv6nH7XpVHfW094fVh5j45lx1VK9PShfQv75+0fNieh+Ukb7eIiHSdRgIWkY92fpS27pj9junycZKqCifkAhtUMYj7v3J/0lSCuol1HF6z2r/rSDS2NuBPC1gf2m3/Ckk8r3IO8iuL9Emh6hD29rFJhX+yNZJApKs8z58CPHeupgKLSN8RDkNjU5S2y6ooBJr4zKT3c9Zmezmj6ybWsf7f/ovDvrQkLTD34qYXueLxK3rcdqQhwvyn5vPHl/7I/oP2z7hPw0cNScuT95/c43ZFRCQ7NBKwyMWLdHRWpCGSMZ8fwJ4D9sy4/oUb78Q2eX6FsGfP83OQJExBaN1v0wsZ318IwmF/Cppz/nM4rAtQKQFve5Tds4xoIxBopPz80wlVh/LdKylhnpfwuzcS8X8Zh0JZ/YUcaYikjbAREWnP8MPX4coOAVcBZVE45i4Yt4jZXzgvb326e8bdHPufx6at//mKn/doRN4Vj1/RqSIju6K7kpaffvvpbrcpIiLZpSBgEakdV8vta25PShbc1cIg5/zxnHa3ff/477e7bfAhL/BR1SV+LpL1Iei/qW0kYOxO5MZPN3apL32JpqFJKQqHoaW5HBxYi3H+0Lvxqg7Md7ek1EUisGgR3HWXn40/i1VC4lUvG6ONBANB6mvrFQgUkQ5tHv4QnPOnpPQ5AJu3n5m3PnlVHgcOOZA3t72ZtL7ZNXPKPaew9OylHb4/8WYI+OlBnt/4fMaBAtVDq1m/dX2Hxzt42MFd6r+IiOSOgoBFxKvyOHrfo1n7/trWdVt2bun0+8/6w1m8uuXVjNsO3+tw6ibWtfvem6bfxEUPXdQ29eDuer9qcKCxNTfgmOFjOt2XvsbzYMECWLIEZs3SKEApDfFk5y0tUFEeoHaGAoCSZ/ESwTt3tuVmiFcJycIv5njVy6iL0hhtJLw+rCCgiHQoVB2Cqu+nTb/N18j5eADvtENP47Y1t7VtaJgK60M82hDmiv2uYGjl0IwjniMNEUJ3h2iMNra+pzW4mboM7Gzeyeyxs9udSSQiIn2LgoBFpjJQmbS8s2lnp97X0TTgvQfszQvf7HgqbzxAeOczd/L66n9mczTYmhiZ9SGoWlnQVUUXLoRLL/WnAi9fDmPHKhAopcGsbRr8unU67yXP4iWC4wFAs6wOz45XvYyPBNT0dxHZHa/Ko395f3Y072hdN6B8QF5uICSOZg6UBdo2NExNukE/nxrKqlZRVlbGv3r/ytDKoWzdtZU71tzRNoAg5T2c+m145Oa0m/yVgUoWz1zcYRBw1hGzcvzJRUSksxQELDIXTLiAVe+uSlrujI6S/T/w1Qc6dYy6iXXUTawjchQcu3Q7NLvW3ICVgcqCHU0RicA3v+nPOgPYtUs5AaU0hMPQ1OS/jkb9QLgC4JJPkeFnELYdhMr+glf+f3D++VBbm7WTMrHqpXICikhn7TNwn6Spt3sP3Dsv/UgczUwLnDDqBJ5860n/hnzKDfqWqpW0tLS0n+Mv8T3NwIrL045B1UqOGeEXIRxQPoDtzdszHur1D1/PxccVEZFuUHXgIlM3sY7ZY2czoHwAI/YY0en3tTfaYcaYGV2+CPI8CJxzCpz8o9a7hC2upUvH6EvCYX86ZJyZcgJKaQiFoCzhr0S8KI5IPkQiUHPZWK5u+TE1gWVE/mM13Hpr1qPS7VXdFBFpT2re7I7yaOdSfDRzwAIEA0FunHYj0w+e7k/fDTSCNbUV72uYCsuv9J8TX8fF30MzEIAtB4MLgDW3HiNggdaZPr849Rft9usPL/whh59aRES6QiMBi8zCNQtbh+Nv/2S7n6cPOszn15HuTuGdOLmRVQfc2Locv0tYiEIhKC/3Z6BBclBEpJh5HtxyS9tU+MpKBcAlf+IzgaMtRqNVEN48FoXpRKQviH/PXvLCEmYdMavb37t7KtNo5qVnL2XYO8PYek5NWz4/8Kf6Ngf91xY7QMI0X6pW+q/D18Ab0/wRgDTDwY9D6MdUH7WB+2Ytb71hUjexjiUvLOHRNx5N69fMI2bm+qOLiEgnKZxRZJa8sKRT61LNX5E+FeCwYYd1eyTEjM/M6HC5kHieP+PMYl+QWlo0GkpKR10dPPEE/PSnWSvAKtIt8SrtgTJHsKyJ0PB1+e6SiEiruol1LD17ad4CgHGZRjP/efaf/aDe8Tf6z+tDsQBgORDwR/i5cn+67/pQ28GqVkLox22jCMsbIfRjyg9czX2z7ku7Tlh69lJmj52dtG722NnMmzYvZ59XRES6RkHAIpMp8W5nkvHWv1Gftu7uf7q72/0IVYfoX96fgAXoX96/d5KrRyJwww3+c5bV1voXn2b+qECNhpJSEYn4Qe9QSAFAyS/Pg/oF65hbdg310ZPwLpuSk9/3IiLFxqvykmf3VIehrAVw+MMAzX9tLW0jBWPGf3YnnDMNTv4RZedO5+IZ43ny3CfbHSiweOZiVpy/gutPvp4V569g8czFuflQIiLSLZoOXGTqJtbx+oevtyb5DViAsfuM7fA9C9cs5OPGj5PW9Q/071E+pF5Prh6JQE2NP1csGMzJkKV4Mcr4s0ix64X/ViJd4m1+CM9dDy1RaAyoSpOISCfNmzaPdz56x08bVLUSTv8m/O+vYtN88XP9nf5Nfxv+LJ45x87Bq/KInB6Jfae/qVPf6b0qT3lVRUT6KAUBi9Arm19pfR11UeavmM/9X7k/bb9Ig/8HfeEzC9O2Dek/pMf96NUvAK3JoqL+c5YvDMNh/9DOtRVH0HWnFLsc/7cS6br4nOB4ZDrTsGwNXxURyWjxzMXsP3h/f7DApDtg37/D2lqMMvaY/AeGj36F8SPagn9xCuqJiBQPBQGL0LsfvdvhMvgBwNDdIZqiTTjSh7ZN3X9q2ro+rTMXhn338CJ9UigE5RVRWhyUV0AoFMh3l6TUeZ4/JLW9IJ+Gr4qIdGjetHnMGDOD+U/N592R73LBNwKxPIYX5btrIiLSCxQELFGL1i6iMdrY7vbTDjutF3uTBbu7MMzC4RcsgCVLYNYsXVNKiTgggqu9Cl4/DnfIU3DADaB6rJJvntf+L2ENX5UCF4lECIfDhEIhPJ27kiNelcf9X02fJSQiIsVPQcAi9OKmF5OW176/Nm2fx994vMNjbN6+Oat96hUdXRj2UCQCl13mX1MuXw5jx+q6UopfeH2Y6P5/xY18giaMRWsXaTqQ5NVuZ/pq2LYUsEgkQk1NDY2NjQSDQerr6xUIFBERkaxSdeAitO/AfZOWd0V3sXBNW96/hWsW8tqHr7X7/oAFeqeabwHJNLhEpNiFqkOYGQAOx13P3UWkQdVYJT/iM32vvtp/zlgYOD4qfO5cTQWWghMOh2lsbCQajbJz505qa2tZuDA9b7OIiIhIdykIWISmHTwtbd2SF5YAfi7Aix+6uN33VpRVsPy85Rrtk2L44esoK2+iLOA0uERKiksoh93U0kR4fTh/nZGS1umbMZ4HV12lAKAUnFAoRDAYxMxwzvHaa69x0UUXKRAoIiIiWaMgYBGqHVdLwJIT+M86YhbgT+/LVAgkLuqiCgCmiDREuOz5KUTPPomyk69hwX3rdG0pJSG8PpwUBNQoYcmn+EzfQEAzfaU4eZ5HfX09hxxySNL6JUuW5KlHIiIiUmwUBCxCXpXHd4/9bsZtoeoQZR382AdWDMxVtwpWeH2YxmgjLQc8hfvc9Wwe/lC+uyTSK0LVISrLKymjjPKycn55+i91k0DyRjN9pRR4nsfll1+etG7WrFl56o2IiIgUGxUGKVIPvvxg0vKClQuom1iHV+Vx5pgz+ePLf8z4vp9N/1lvdK+ghKpDBANBGqONBANBjYSSkuFVedTX1hNeHyZUHVIAUPIuh/WfRPqMuro6wB8BOGvWrNZlERERkZ5SELBI7WjekbT84c4PW19v2bkl43umHzyduokF+kVztyUju0+BECllXpWnc15EpJeNPW0smw/fzNjqsfnuioiIiBQRBQGL1Kgho1i/dX3r8vufvE+kIYJX5bGzaWfSvhVlFXzH+w7zps3r5V5mSbxkZGOjnygqB/PEFAgRERGR3hBpiFCzqKZ1BkJ9bb2+g4iIiEhWKCdgkdqz355Jyw7HorWLABjab2jStpOqTyrcACB0oWSkiIgUskgEbrjBfxYpVvFcxFEXpTHaqKrsIiIikjUFEQQ0s6vM7P/M7CMz22hmD5rZUSn7mJlda2bvmtkOMwub2ZEp+wwzs3vMbFvscY+ZJUfEisSIPUa0u+25959LWn7jwzdy3Z3cUslIEZGiFx/0ffXV/rMCgVKs4rmIAxZQLmIRERHJqoIIAgIh4FfAscDJQDPwuJklDnebA3wX+BbwWeAD4DEzG5Swz33ABOA04NTY63ty3fl8OGa/Y5KWDaN2XC0L1yzkg08/SNo284iZvdm17FPJSBGRoqdB31Iq4rmI5540V1OBRUREJKsKIiegc+6UxGUzOxvYBhwHPGhmBlwG3OicWxLb5xz8QODXgdvN7HD8wN/nnHMrYvtcBCw3szHOuZd77QP1gs3bNyctOxzrPljHNeFrktYPCg4q7KnAcSoZKSJS1OKDvuPpXzXoW4qZchGLiIhILhTKSMBUg/D7Hi95exAwAng0voNzbgfwJP7oQQAP+ARYkXCcp4BPE/YpGqHqEIDZ3LkAABclSURBVGWW/OP9yRM/YcMnG5LWVZZX9ma3REREukWDvkVEREREeqYgRgJmcDPwHBDPCBRPgPd+yn7vA/sn7LPROefiG51zzsw+SHh/KzOrA+oARo0alb2e9xKvymPSfpNY9e6q1nXvfPxO2n4TRkzozW6JiIh0mwZ9i4iIiIh0X8GNBDSznwOfA2Y556Ipm13q7inrUrdn2sff0bmFzrlJzrlJe++9d0+6nDevbH5lt/u8/fHbvdATERERERERERHJp4IKAprZL4CvASc75xJL2sbnuKaO6NuHttGBG4B9YvkD48czYG/SRxAWheaW5t3u82njp73QExERERERERERyaeCCQKa2c34RT5Ods69lLL5H/hBvs8n7N8POJ62HIARYA/83IBxHjCQ5DyBRWPCyJSpvg1TYfmV/nNMahVhEREREREREREpPgWRE9DMbgHOBmYAH5pZfMTfJ865T2K5/RYAPzCzl4BXgB/iFwK5D8A596KZPYJfKfhC/GnAtwMPFVtl4Lgba27k2P+M1TxpmAq/WQbRCgg0wbknUTZqFXOOnZPfToqIiHRSpCFCeH2YUHVIlVNFRERERLqoIIKAwCWx5/qU9T8Gro29ng/0B24BhgFPA9Odcx8n7D8b+Hfaqgj/Cbg0B/3tE7wqj/7l/dnRvAPW1kK0EjCIlsHaWm79xnm6iBIRkYIQaYhQs6iGxmgjwUCQ+tp6/Q0TEREREemCgggCOuesE/s4/IDgtR3sswU4K2sdKwDlZZl/xOVlFdRN/Jde7o2IiEj3hNeHaYw2EnVRGqONhNeHFQQUEREREemCgskJKN1z5pgz/RfjFkFgFxCFwC6O+vwzee2XiIhIV4SqQwQDQQIWIBgIEqoO5btLIiIiIiIFpSBGAkr3LZ65mFc3v8oqVsK5J8H6EHbQk/zqop/lu2siIiKd5lV5LDjyaZY8vJlZpw3Hqxqb7y6JiIiIiBQU82fRSkcmTZrkVq9ene9u9MjCNQu585k7GTl4JHOOnaMpVCIiUlAiEaipgcZGCAahvh48/SkTEZEsMbM1zrlJ+e6HiEguaSRgiaibWEfdxLp8d0NERKRbwmE/ABiN+s/hsIKAIiIiIiJdoZyAIiIi0ueFQv4IwEDAfw6F8t0jEREREZHCopGAIiIi0ud5nj8FeNGifPdEJPsikQjhcJhQKISnIa4iIiKSIwoCioiISMG4+25/OvDddysvoBSHSCRCTU0NjY2NBINB6uvrFQgUERGRnNB0YBERESkImfICihS6cDhMY2Mj0WiUxsZGwjqxRUREJEcUBBQREZGCEAr5OQHN/GflBZRiEAqFCAaDBAIBgsEgIZ3YIiIikiOaDiwiIiIFwyz5WaTQeZ5HfX29cgKKiIhIzikIKCIiIgUhHIbmZnDOfw6HlRNQioPneQr+iYiISM5pOrCIiIgUhFAIgkF/KnAwqOnAIiIiIiJdoZGAIiIiUhA8DxYsgCVLYNYsjQIUEREREekKBQGleEQi/tywUEhXhiIiRSgSgcsu8ysDL18OY8fq172IiIiISGcpCCjFIRKBmhr/yjAYhPp6XRmKiBSZcNj/NR+N+s/KCSgiIiIi0nnKCSjFIdOVoYiIFBXlBBQRERER6T6NBJTiEL8yjI8E1JWhiEjR8TxYcN86ljy8mVmnDcfzxua7SyIiIiIiBUNBQCkOyhYvIlL0Ig0RLnu+hsb9G1n+fJCxE+vxqvT7XkRERESkMzQdWIpDPFt8fb3/HInku0ciIpJl4fVhGqONRF2Uxmgj4fXhfHdJRERERKRgKAgoxUE5AUVEil6oOkQwECRgAYKBIKHqUL67JCIiIiJSMDQdWIpDKORnim9p8Z+VE1BEpOh4VR71tfWE14cJVYc0FVhEREREpAsUBJTi0dICzvnPIiJSlLwqT8E/EREREZFu0HRgKQ7z50Nzs/+6udlfFhERERERERERQEFAKRbvvtvxsoiIiIiIiIhICVMQUIrDBRd0vCwiIiIiIiIiUsIUBJTiMHasXxAE/OexY/PbHxERERERERGRPkRBQCkOixZBNOq/jkb9ZRERERERERERARQElGKxYUPHyyIiIiIiIiIiJUxBQCkOI0Z0vCwiIiIiIiIiUsJKMghoZpeY2T/MbKeZrTGz4/PdJ+mh2loIBsHMf66tzXePRERERERERET6jJILAprZV4CbgeuBY4AVwMNmNiqvHZOe8TwIh+G66/xnz8t3j0RERKRAnHLKKZhZpx4VFRUceeSRLFy4MOkYU6ZM6fQxevtRXV2dn39YERER6VPMOZfvPvQqM3sa+Jtz7sKEda8Cv3fOXZXpPZMmTXKrV6/urS6KiIhIe045BR59tG158mR4+um25bPOgocfhtNOg8WLe79/kjVrT1nLh49+mPN2LudyVtO973m33347dXV1TJkyhVWrVmW5Z9l1ILA+353oTeXlcMstUFeX755IgTCzNc65Sfnuh4hILpXUSEAzCwITgUdTNj0KHNv7PRIREZFOSw0AAqxaBVOm+K/POgvuvRe2bPGfzzqr9/soWdFbAUCAdazr9nuXLFkCwDPPPJOt7uTMW/nuQG9rboaLLoKUEZsiIiKlrKSCgMBeQAB4P2X9+0BSJQkzqzOz1Wa2euPGjb3VPxEREWnP8uWZ18cDMA8/nLw+dVkKxrbl23qtrbGM7fZ7Z82aBcCECROy1Z2cKdm8N7FArYiIiJReEDAudQ60pa5zzi10zk1yzk3ae++9e69nIiIiktnx7dTxigdgTjsteX3qshSMIccP6bW2buImJtH5GYDl5eUcccQRrVOBAZ5++mkmT56cqy72WMlNBU4UC9SKiIgIlOe7A71sExAlZdQfsA/powNFRESkL1m6tOOcgPEcgMoJWPDGLR3Xq1OCb+ImAIZNH8a4peO6dYynE3NT9hVXXAE33QQllgMcUE5AERGRDEq1MMha51xdwrpXgCUqDCIiIiIiIlJ6VBhEREpBqY0EBPg5cI+ZrQKeAi4GRgK35bVXIiIiIiIiIiIiOVJyQUDn3G/NbDjwQ2A/4O/A6c65N/PbMxERERERERERkdwouSAggHPuV8Cv8t0PERERERERERGR3lCq1YFFRERERERERERKhoKAIiIiIiIiIiIiRU5BQBERERERERERkSKnIKCIiIiIiIiIiEiRUxBQRERERERERESkyCkIKCIiIiIiIiIiUuQUBBQRERERERERESlyCgKKiIiIiIiIiIgUOXPO5bsPfZ6ZbQTezHc/+oi9gE357oRIL9N5L6VI572UGp3zUop03rc50Dm3d747ISKSSwoCSpeY2Wrn3KR890OkN+m8l1Kk815Kjc55KUU670VESoumA4uIiIiIiIiIiBQ5BQFFRERERERERESKnIKA0lUL890BkTzQeS+lSOe9lBqd81KKdN6LiJQQ5QQUEREREREREREpchoJKCIiIiIiIiIiUuQUBBQRERERERERESlyCgL2QWZ2lZn9n5l9ZGYbzexBMzsqZR8zs2vN7F0z22FmYTM7MmWfH5jZU2b2qZmlzfs2s73NbGnsGLvMrMHMbjGzIZ3o44lmtsbMdprZG2Z2ccr2E8zsT2b2jpk5Mzu3k5+90sz+w8w2xfr9JzM7IGH7ODP7r1hfd5jZy2Z2uZnpXC5wOu/bP+9j+9SY2Qoz+9jM3jOzeWZW3pnjS99U4ud8nZktM7OtsfdVt7PfKWYWMbPtsX3rO3N86btK9bw3sz1jv+dfin2mBjO71cyGJ+xTFjvuW7G23zOzxWa2/+6OL31XqZ7zsff92sxej32mjWb2gJkdnrLPMDO7x8y2xR73mNnQzhxfRES6ToGTvikE/Ao4FjgZaAYeN7M9E/aZA3wX+BbwWeAD4DEzG5SwTyXwB2BBO+20APcDXwRGA+cCNcCvO+qcmR0E/BlYARwD3AD8h5nNSthtD+DvwLeBHR0dL8UCYBbwNeB4YDDwkJkFYtsnAhuBs4EjgWuAHwFXdqEN6ZtC6LzPeN6b2dGxth+Ntf1V4Ezgxi60IX1PiNI95wfgn8/XdtD+DOC/gXti7XvAf3ahDembQpTmeT8S2D/22cYCZwEnAP+Vst9fgC8DY/D/Lhwc+xxSuEKU5jkPsDrWj8OBUwDD/+wVCfvcB0wATgNOjb2+pwttiIhIVzjn9OjjD/w/vFHgi7FlA94DfpCwT3/gY+CiDO//Z/9H3am2/h/w3m72mQe8mrLuDiDSzv6fAOd2ou0hQCMwO2FdFf6XmlM6eN98YE2+f056ZPeh877tvAeuB55Ned8X8b+ID8r3z0qP7DxK5ZxPec8kwAHVKesDwFvAhfn+ueiR20cpnvcJ7z099rt+cAf7nBn7P9Iv3z8rPbLzKPFz/ujY+Twmtnx4bPm4hH0+l7iPHnrooYce2X1oJGBhGIQ/avPD2PJBwAj8URQAOOd2AE/i32XsFjMbCcwEntjNrl5i2zFLgUkpd/a6aiJQQfLnagBepOPPNZi2fxspHjrv2z5XJbAz5X07gH6x90txKJVzvjMm4gfDd5nZM2a2wcweNbNjctyu9L5SPu8HA7uA7Zk2xkaKzQaeds6l/g2QwlWS57yZDQTOw7/Bsz6h7U/wRyHGPQV8Sg8+u4iItE9BwMJwM/AcEIktj4g9v5+y3/sJ2zrN/Bx724F38O86nrebt4xop+1yYK+utp9y3CiwKcOxM34uM5uAP83g1h60K32Tzvu2z7UUmGJmZ5tZeSw/1I9i2/brQdvSt5TKOd8ZB8ee5+KPhP0C8DbwROzCVopHSZ73sZxnc4FfO+eaU7bNM7NPgc3AKOCMbLUrfUJJnfNmdomZfYIf7DsNqHHO7Upoe6NzrjXHYez1B3Tjs4uIyO4pCNjHmdnP8YfFz3LORVM2pyYFtgzrOuM7+Pk3ZuBfeLXmGjGzTxIet+2m7UzrMzKz76cce1RHu2c6rpmNAf4XWOCcW9KZdqUw6LxvPbYDcM49CnwP+CX+iMBX8PP3gB9AlAKncz5N/PvJdc653zvn1gB1wFb8nLBSBEr1vI+NiHoQP0gzJ8MhbsLPzTYd/3f8YjOzDPtJgSnRc/5e/PP5RPzvL78zswEdtB1vvzufXUREdkOVJfswM/sFfgGAk5xzbyRs2hB7HgE0JKzfh/Q7ebvlnNsQO+ZLZrYZWG5mP41NSRyfsOtHCe2n3p3bBz/R8eZONnsb8D8Jy+/GjhvAv+u4MeXYTya+2cw+AywD/ts5p6IgRUTnfdKxW89759zPY/82++FPIarGT979j062LX1UCZ7znfFe7PmF+ArnXLOZvYo/MkoKXKme92a2B203cc7INM3XObcJf3T4K2b2Iv6/w+eA5Z1sX/qgUj3nnXPbgG3Aq2a2Ev87zCz84h8bgH3MzOKjAWMB773pxmcXEZHdUxCwjzKzm/G/KISccy+lbP4H/h/NzwP/F9u/H35V0ct72HR89EUlgHPutQz7RPDvLib6PLDaOdfUmUacc1uALYnrzGwN0BQ71n2xdQfgJw1ekbDfEfjV8/7HOfedzrQnhUHnffvnfez9jtgXazP7Gv7FwjOdaVv6plI85ztpDX6utDHAXwHMrAw4BH96vBSwUj3vza/0+jD+KKdTnXOfdLXPUphK9ZzPwGKP+PkcwS+U4tH2nccDBpLyHUhERLJDQcA+yMxuwZ/uNAP40Mzid+c+cc594pxzZrYA+IGZvYQ/tP6H+Lk27ks4zihgT/wRQ5hZ/O7fa865T8zsDGA4/sXWJ8CR+FNQVrbzJSHuNuDSWB9uB47Dz8v3tYS29wAOjS2WAaNi7W9xzr2V6aDOuW1mdidwk5l9gH/38efA34DHY8c9Ej8AuAy4PuHfJn7nUwqUzvv2z/vYsS8HHsGvJDkTuBL4cobpRFIgSvWcj71vBP7Ik9GxVUeYnyPtLefcFufcR7Gpaj82s7fxk8hfCgwDFnfQZ+njSvW8jwUAH8UvBjIDGBibFkzsfY1m5uFP4/wr/tT3Q/DzBq6PrZMCVMLn/KH4I/4ex5/pcAD+d5ddwEMAzrkXzewR4HYzuxA/QHg78JBz7uUO+iwiIt2VzVLDemTngZ8DI9Pj2oR9DLgWf8rUTvzKX0elHOc37RwnFNs+Df8O3Fb8SqOvAPOAYZ3o44n4I5B24d/BvDhle6idtn+zm+P2A/4DPxCyHT9nTlXC9mvb+/fJ989Nj549dN63f97H9vlLQp9XAqfl+2emR88eJX7Ot/e7/NyEfSqA+fgjZD4CwsCEfP/c9OjZo1TP+w7ek9jn8fg3OTcntH0rcEC+f2566JzvxjlfhT/y9QOgEX/2wr3AZ1L22xP/5s5HscdiYGi+f2566KGHHsX6MOccIiIiIiIiIiIiUrxUHVhERERERERERKTIKQgoIiIiIiIiIiJS5BQEFBERERERERERKXIKAoqIiIiIiIiIiBQ5BQFFRERERERERESKnIKAIiIiIiIiIiIiRU5BQBERkQJiZtVm5szsNzls4zexNqpz1YaIiIiIiPQuBQFFRERERERERESKXHm+OyAiIiJd8g5wOLAt3x0REREREZHCoSCgiIhIAXHONQEv5bsfIiIiIiJSWDQdWEREpIBkygmYmMPPzC4ys3VmttPM3jezhWY2pJ1jTTOz5Wb2qZltMbM/mtlndtP+FDP7vZltMLNGM2sws9vNbGTKfjNjfVppZhUp244ys+1m9q6Z7dODfw4REREREekkBQFFRESKx/zYYy1wC/7U4QuB+1N3NLN/BpYCk4DfAbcDw4EIcFCmg5vZecBTwGnAMmABsBr4F2C1mY2K7+uc+0OsD1OA6xKOMQD4LVAJnOWc+6AnH1hERERERDpH04FFRESKx1RgrHPuLQAzKwf+ApxkZpOdc6ti6/fAD/q1AMc751bHD2BmvwAuSz2wmY2OvWc9cKJz7p2EbScDjwE3A/+U8LbvAscC3zOzvzjnHsEPDB4B/MQ595dsfXAREREREemYRgKKiIgUj5/EA4AAzrlm4K7Y4uSE/b4E7AnclxgAjLmWzEVHvgFUAN9ODADG2vkL8Cfgi2Y2KGH9LuArwKfAIjP7HnAu8CTwk65+OBERERER6T6NBBQRESkeqQE9gIbY87CEdRNiz0+k7uyc22ZmzwEnpmzyYs8nmtlnM7SzDxAARgNrEo73qpldBNwL3ARsAr7unIvu5rOIiIiIiEgWKQgoIiJSPLZmWNccew4krIsXCnm/neNsyLBueOz58t30YY8M6x4DPgIGA79LHUkoIiIiIiK5p+nAIiIipSc+3XffdraP6OA9Q5xz1sEjaXShmRmwCD8AuAmoM7MTsvEhRERERESk8xQEFBERKT3PxJ5Tp/xiZkOA8RneszL2fHwX27ocOBV/OvDJQBNwn5nt1cXjiIiIiIhIDygIKCIiUnoeAD4Evm5mk1K2XUvbdOFEv8QP4P0iVik4iZkFzez4lHVTgJ8CrwHfcM6tA74D7A/8JjZKUEREREREeoFyAoqIiJQY59wnZlYH/BZYbma/Bd4DPgcchV+994SU97xkZucD/wk8b2aPAK/gVwwehT9CcCPwGQAzGwr8N+CArzrnPo4d5zYzqwH+GfhX4N9y/HFFRERERASNBBQRESlJzrnf40/TXQN8GbgY2IJfBfgf7bxnMTARf2rv0cClwFnAocDvgUsSdr8TqAaudM6tST4S/xJr4wYzm5ydTyQiIiIiIh0x51y++yAiIiIiIiIiIiI5pJGAIiIiIiIiIiIiRU5BQBERERERERERkSKnIKCIiIiIiIiIiEiRUxBQRERERERERESkyCkIKCIiIiIiIiIiUuQUBBQRERERERERESlyCgKKiIiIiIiIiIgUOQUBRUREREREREREipyCgCIiIiIiIiIiIkVOQUAREREREREREZEi9/8B2RvbH0MEOdEAAAAASUVORK5CYII=\n", + "image/png": 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\n", 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" ] @@ -1170,7 +4432,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.731819", @@ -1182,13 +4444,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:963: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:961: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1206,7 +4468,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", @@ -1216,7 +4478,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1248,7 +4510,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", @@ -1281,7 +4543,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", From 9e675cbaf2688fbe29afb06a71ac8e9118d8d352 Mon Sep 17 00:00:00 2001 From: jorasinghr <40352266+jorasinghr@users.noreply.github.com> Date: Wed, 4 Jul 2018 12:16:44 +0200 Subject: [PATCH 14/42] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 0b53e9f9c..435d9c653 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,3 +5,4 @@ scipy==1.1.0 matplotlib==2.2.2 statsmodels==0.9.0 xlrd==1.1.0 +seaborn From f6efc69e0968c7e379285e9c40b91c27795c8055 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 5 Jul 2018 11:42:42 +0200 Subject: [PATCH 15/42] Small changes to calc_ratio --- wwdata/Class_HydroData.py | 50 +++++++++++++++++++++++++++------------ 1 file changed, 35 insertions(+), 15 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 417af8ac4..566952d6e 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1320,26 +1320,46 @@ def calc_ratio(self,data_1,data_2,arange,only_checked=False): raise IndexError('Index out of bounds. Check whether the values of ' + \ '"arange" are within the index range of the data.') - if only_checked == True: - #create new pd.Dataframes for original values in range, - #merge only rows in which both values are original + # original: + """ + if only_checked is True: + + # create new pd.Dataframes for original values in range, + # merge only rows in which both values are original + data_1_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]=='original'].values, \ + index=self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]=='original'].index) + data_2_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_2][self.meta_valid[data_2]=='original'].values, \ + index=self.data[data_2][arange[0]:arange[1]][self.meta_valid[data_2]=='original'].index) + ratio_data = pd.merge(data_1_checked,data_2_checked,left_index=True, right_index=True, how = 'inner') + ratio_data.columns = data_1, data_2 + + mean = (ratio_data[data_1] / ratio_data[data_2]).replace(np.inf, np.nan).mean() + std = (ratio_data[data_1] / ratio_data[data_2]).replace(np.inf, np.nan).std() + """ + + if only_checked is True: try: - data_1_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]=='original'].values, - index=self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]=='original'].index) - data_2_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_2][self.meta_valid[data_2]=='original'].values, \ - index=self.data[data_2][arange[0]:arange[1]][self.meta_valid[data_2]=='original'].index) - ratio_data = pd.merge(data_1_checked,data_2_checked,left_index=True, right_index=True, how = 'inner') - ratio_data.columns = data_1,data_2 - except KeyError: + # if self.meta_valid[data_1] and self.meta_valid[data_2] in globals(): + # type(self.meta_valid[data_1]) is str: - wn.warn('only_checked cannot be fulfilled') + # create new pd.Dataframes for original values in range, + # merge only rows in which both values are original + data_1_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_1]\ + [self.meta_valid[data_1] == 'original'].values, + index=self.data[arange[0]:arange[1]][data_1][self.meta_valid[data_1]== 'original'].index) + data_2_checked = pd.DataFrame(self.data[arange[0]:arange[1]][data_2]\ + [self.meta_valid[data_2] == 'original'].values, + index=self.data[data_2][arange[0]:arange[1]][self.meta_valid[data_2] == 'original'].index) + ratio_data = pd.merge(data_1_checked, data_2_checked,left_index=True, right_index=True, how='inner') + ratio_data.columns = data_1, data_2 + mean = (ratio_data[data_1] / ratio_data[data_2]).replace(np.inf, np.nan).mean() + std = (ratio_data[data_1] / ratio_data[data_2]).replace(np.inf, np.nan).std() - mean = (ratio_data[data_1]/ratio_data[data_2])\ - .replace(np.inf,np.nan).mean() - std = (ratio_data[data_1]/ratio_data[data_2])\ - .replace(np.inf,np.nan).std() + except KeyError: + # else: + raise KeyError('only_checked cannot be fulfilled for the self.meta_valid DataFrame') else: mean = (self.data[arange[0]:arange[1]][data_1]/self.data[arange[0]:arange[1]][data_2])\ From ba3cf9198c64a0d621f95c82eed65565ed5158b3 Mon Sep 17 00:00:00 2001 From: jorasinghr <40352266+jorasinghr@users.noreply.github.com> Date: Fri, 6 Jul 2018 11:04:01 +0200 Subject: [PATCH 16/42] Update README.rst --- README.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/README.rst b/README.rst index 092dc5f92..31d6a5de1 100644 --- a/README.rst +++ b/README.rst @@ -46,6 +46,7 @@ Examples -------- For the workflow with code and more specific examples, check out the Showcase Jupyter Notebook(s) included as documentation of the package. +MyBinder can be used to view the code and the specific examples. Go to https://mybinder.org/ and use the GitHub URL, https://github.com/UGentBiomath/wwdata. Specify the branch(master, develop etc.). Generate a new MyBinder link if the branch get modified. Example link: https://hub.mybinder.org/user/ugentbiomath-wwdata-faxuev5l/tree Credits From 05c07353f3c9257bfbd671b244ea6c157a8c033f Mon Sep 17 00:00:00 2001 From: jorasinghr <40352266+jorasinghr@users.noreply.github.com> Date: Fri, 6 Jul 2018 11:04:59 +0200 Subject: [PATCH 17/42] Update README.rst --- README.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/README.rst b/README.rst index 31d6a5de1..7fd9b5761 100644 --- a/README.rst +++ b/README.rst @@ -46,6 +46,7 @@ Examples -------- For the workflow with code and more specific examples, check out the Showcase Jupyter Notebook(s) included as documentation of the package. + MyBinder can be used to view the code and the specific examples. Go to https://mybinder.org/ and use the GitHub URL, https://github.com/UGentBiomath/wwdata. Specify the branch(master, develop etc.). Generate a new MyBinder link if the branch get modified. Example link: https://hub.mybinder.org/user/ugentbiomath-wwdata-faxuev5l/tree From b89b849981356c36da12483ba32f55ccc590c438 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Fri, 6 Jul 2018 12:41:30 +0200 Subject: [PATCH 18/42] New function detect_drift, not finished #303 --- wwdata/Class_HydroData.py | 42 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 566952d6e..586e0938c 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1544,6 +1544,48 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return slope,intercept,r_sq + def detect_drift(self, arange, max_slope, period=None): + # data input or using self.data? + """ + This function calculates the slope of the data in a certain given + period by for example fitting a line through it and compare it with + the maximum expected slope. + + Parameters + ---------- + arange : 2-element array of ints + the range in which to apply the function + max_slope : int + the maximum slope a signal is expected to have over a certain period + period : + the period over which a certain slope is allowed + + Returns + ---------- + information about the drift + """ + from scipy import signal + + if period is None or period is arange: + detrended_values = signal.detrend(self.data[arange[0]:arange[1]]) + line_segment = self.data[arange[0]:arange[1]] - detrended_values[:] + slope = line_segment[-1] - line_segment[0] / (arange[1]-arange[0]) + + if slope > max_slope[0]: + print('The actual slope is larger than the specified max slope') + + plt.plot(signal.detrend(self.data[arange[0]:arange[1]]), 'r', + self.data[arange[0]:arange[1]], 'g', + self.data[arange[0]:arange[1]] - signal.detrend(self.data + [arange[0]:arange[1]]), 'y') + + + else: + pass + + return None + + #============================================================================== # DAILY PROFILE CALCULATION #============================================================================== From fc1d464bcceaf37fc3b2fcff1f4c51247f9a9a4a Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Fri, 6 Jul 2018 16:51:08 +0200 Subject: [PATCH 19/42] added some functonality to detect_drift #303 --- wwdata/Class_HydroData.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 586e0938c..51013c40e 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1544,15 +1544,17 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return slope,intercept,r_sq - def detect_drift(self, arange, max_slope, period=None): + def detect_drift(self, data_name, arange, max_slope, period=None): # data input or using self.data? """ This function calculates the slope of the data in a certain given - period by for example fitting a line through it and compare it with - the maximum expected slope. + period by fitting a line through it and compare it with the maximum + expected slope. Parameters ---------- + data : str + name of the column containing the data to detect drift arange : 2-element array of ints the range in which to apply the function max_slope : int @@ -1565,20 +1567,18 @@ def detect_drift(self, arange, max_slope, period=None): information about the drift """ from scipy import signal + series = self.data[data_name][arange[0]:arange[1]].copy() if period is None or period is arange: - detrended_values = signal.detrend(self.data[arange[0]:arange[1]]) - line_segment = self.data[arange[0]:arange[1]] - detrended_values[:] - slope = line_segment[-1] - line_segment[0] / (arange[1]-arange[0]) - if slope > max_slope[0]: + detrended_values = signal.detrend(series[:]) + line_segment = series[:] - detrended_values[:] + slope = (int(line_segment[-1]) - int(line_segment[0])) / len(series) + print(slope) + if slope > max_slope: print('The actual slope is larger than the specified max slope') - plt.plot(signal.detrend(self.data[arange[0]:arange[1]]), 'r', - self.data[arange[0]:arange[1]], 'g', - self.data[arange[0]:arange[1]] - signal.detrend(self.data - [arange[0]:arange[1]]), 'y') - + #plt.plot(detrended_values, 'r', series[:], 'g', line_segment, 'y') else: pass From c5498cc160d376d5cc84875953e6496ecd8a7e5a Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Tue, 10 Jul 2018 12:55:31 +0200 Subject: [PATCH 20/42] detect_drift works now with NaN and inf values #303 --- wwdata/Class_HydroData.py | 47 +++++++++++++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 9 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 51013c40e..c6e73a7ae 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1451,6 +1451,9 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, default to 'False' if a value in one column is filtered, the corresponding value in the second column also gets excluded! + plot : bool + if true, a plot is made, comparing the original data with the calculated + prediction Returns ------- @@ -1544,7 +1547,7 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return slope,intercept,r_sq - def detect_drift(self, data_name, arange, max_slope, period=None): + def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=False): # data input or using self.data? """ This function calculates the slope of the data in a certain given @@ -1559,8 +1562,10 @@ def detect_drift(self, data_name, arange, max_slope, period=None): the range in which to apply the function max_slope : int the maximum slope a signal is expected to have over a certain period - period : + period : int the period over which a certain slope is allowed + plot : bool + if true, a plot is made, ....... Returns ---------- @@ -1569,20 +1574,44 @@ def detect_drift(self, data_name, arange, max_slope, period=None): from scipy import signal series = self.data[data_name][arange[0]:arange[1]].copy() + #removes NaNs and infs from the dataset + index = 0 + nan_values = [] + for value in series: + try: + signal.detrend([value]) + except ValueError: + nan_values.append(index) + index += 1 + series = series.drop(index=series[nan_values].index) + + if max_slope is None: + print('Please specify a maximum slope') + return KeyError + if period is None or period is arange: + detrended_values = signal.detrend(series) + line_segment = series - detrended_values[:] + slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) - detrended_values = signal.detrend(series[:]) - line_segment = series[:] - detrended_values[:] - slope = (int(line_segment[-1]) - int(line_segment[0])) / len(series) - print(slope) if slope > max_slope: - print('The actual slope is larger than the specified max slope') - - #plt.plot(detrended_values, 'r', series[:], 'g', line_segment, 'y') + print('Based on the specified maximum slope, a drift was' + ' detected with a slope higher than the maximum one. \n' + 'Slope detected: {}, maximum slope: {}'.format(slope, max_slope)) else: + if type(period) is int: + for n in range(len(series)-period): + pass + pass + else: + print('period must be an integer') + return ValueError pass + if plot is True: + print(plt.plot(detrended_values, 'r', line_segment, 'y', series[:], 'g')) + return None From 1db6ca00887fb242287e1066a78cb789786928af Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 12 Jul 2018 15:11:47 +0200 Subject: [PATCH 21/42] detect_drift function works with a specified period, but needs improvement. There's no plotting yet #303 --- wwdata/Class_HydroData.py | 53 +++++++++++++++++++++++++++++++-------- 1 file changed, 42 insertions(+), 11 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index c6e73a7ae..26799ace2 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1563,7 +1563,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals max_slope : int the maximum slope a signal is expected to have over a certain period period : int - the period over which a certain slope is allowed + the period, in days, which a certain slope is allowed plot : bool if true, a plot is made, ....... @@ -1586,10 +1586,9 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals series = series.drop(index=series[nan_values].index) if max_slope is None: - print('Please specify a maximum slope') - return KeyError + return KeyError('Please specify a maximum slope') - if period is None or period is arange: + if period is None or period is arange[1].day - arange[0].day + 1: detrended_values = signal.detrend(series) line_segment = series - detrended_values[:] slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) @@ -1599,18 +1598,50 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals ' detected with a slope higher than the maximum one. \n' 'Slope detected: {}, maximum slope: {}'.format(slope, max_slope)) + else: + print('No drift detected.') + + if plot is True: + plt.plot(detrended_values[:], 'r', line_segment, 'y', series[:], 'g') + else: if type(period) is int: - for n in range(len(series)-period): + start_index = 0 + end_index = 0 + new_index = end_index + while series.index.day[new_index] + period <= series.index.day[len(series)-1]: + checked = False + while series.index.day[end_index] < (series.index.day[start_index] + period): + if series.index.day[end_index] == (series.index.day[start_index] + 1): + if checked == False: + new_index = end_index + checked = True + if end_index == len(series)-1: + break + end_index += 1 + + detrended_values = signal.detrend(series[start_index:(end_index-1)]) + line_segment = series[start_index:(end_index-1)] - detrended_values[:] + slope = (int(line_segment[-1]) - int(line_segment[0])) / ( + arange[1].day - arange[0].day + 1) + + if slope > max_slope: + print('Based on the specified maximum slope, a drift was' + ' detected with a slope higher than the maximum one.\n' + 'Slope detected: {}, maximum slope: {}, period(in days):' + '{}-{}'.format(slope, max_slope, series.index.day[start_index], + series.index.day[end_index-1])) + + start_index = new_index + end_index = new_index + if plot is True: pass - pass else: - print('period must be an integer') - return ValueError - pass + return ValueError('period must be an integer') + - if plot is True: - print(plt.plot(detrended_values, 'r', line_segment, 'y', series[:], 'g')) + #if plot is True: + # print(plt.plot(detrended_values, 'r', line_segment, 'y', series[:], 'g')) return None From 7cf3be171bc8cc0c03dc7452f54540d44674c128 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Fri, 13 Jul 2018 14:53:18 +0200 Subject: [PATCH 22/42] added plotting to detect_drift function. the whole function need to be tested with data containing drift #303 --- wwdata/Class_HydroData.py | 46 +++++++++++++++++++++++++++++---------- 1 file changed, 34 insertions(+), 12 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 26799ace2..fbade47ee 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1548,7 +1548,6 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return slope,intercept,r_sq def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=False): - # data input or using self.data? """ This function calculates the slope of the data in a certain given period by fitting a line through it and compare it with the maximum @@ -1570,7 +1569,10 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals Returns ---------- information about the drift + + !!Doesn't check the last day mentioned in the arange!! """ + from scipy import signal series = self.data[data_name][arange[0]:arange[1]].copy() @@ -1601,24 +1603,39 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals else: print('No drift detected.') + # detrend_values=pd.DataFrame(detrended_values, index=series.index) --> dataframe of detrended values if plot is True: - plt.plot(detrended_values[:], 'r', line_segment, 'y', series[:], 'g') + fig = plt.figure(figsize=(16, 6)) + ax = fig.add_subplot(111) + ax.plot(line_segment, 'b-',label='slope') + ax.plot(series, 'g--', label='original data') + ax.plot(series.index, detrended_values, 'r', label='detrended values') + ax.plot(series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data + ax.legend(fontsize=16) + ax.set_xlabel(self.timename, fontsize=20) + ax.set_ylabel(data_name, fontsize=20) + ax.tick_params(labelsize=15) else: if type(period) is int: start_index = 0 end_index = 0 new_index = end_index + if plot is True: + fig = plt.figure(figsize=(16,6)) + ax = fig.add_subplot(111) + ax.plot(series, 'g--', label='original data') + while series.index.day[new_index] + period <= series.index.day[len(series)-1]: checked = False while series.index.day[end_index] < (series.index.day[start_index] + period): if series.index.day[end_index] == (series.index.day[start_index] + 1): - if checked == False: + if checked is False: new_index = end_index checked = True + end_index += 1 if end_index == len(series)-1: break - end_index += 1 detrended_values = signal.detrend(series[start_index:(end_index-1)]) line_segment = series[start_index:(end_index-1)] - detrended_values[:] @@ -1626,11 +1643,17 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals arange[1].day - arange[0].day + 1) if slope > max_slope: - print('Based on the specified maximum slope, a drift was' - ' detected with a slope higher than the maximum one.\n' - 'Slope detected: {}, maximum slope: {}, period(in days):' - '{}-{}'.format(slope, max_slope, series.index.day[start_index], - series.index.day[end_index-1])) + print('Drift detected in period {} to {}. \n' + 'Slope detected: {}, maximum slope: {}'.format + (series.index.day[start_index], series.index.day + [end_index-1],slope, max_slope)) + + if plot is True: + detrended_values = pd.DataFrame(detrended_values, + index=series.index[start_index:(end_index-1)]) + ax.plot(line_segment, 'b-', label='slope') + ax.plot(detrended_values, 'r--', label='detrended values') + ax.plot(series[start_index:(end_index-1)]-(line_segment-line_segment[0]), 'm-') #bad visualisation start_index = new_index end_index = new_index @@ -1639,12 +1662,11 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals else: return ValueError('period must be an integer') - #if plot is True: # print(plt.plot(detrended_values, 'r', line_segment, 'y', series[:], 'g')) - return None - + def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): + pass #============================================================================== # DAILY PROFILE CALCULATION From 2f38580de5ab2d99b3a8b2dd8f44d3ff57d5f9b0 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Fri, 13 Jul 2018 15:07:25 +0200 Subject: [PATCH 23/42] new function remove_drift #303 --- wwdata/Class_HydroData.py | 30 ++++++++++++++++++++++++------ 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index fbade47ee..0881772f7 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1564,7 +1564,8 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals period : int the period, in days, which a certain slope is allowed plot : bool - if true, a plot is made, ....... + if true, a plot is made of the orginial data, detrended data and + slope Returns ---------- @@ -1657,15 +1658,32 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals start_index = new_index end_index = new_index - if plot is True: - pass else: return ValueError('period must be an integer') - #if plot is True: - # print(plt.plot(detrended_values, 'r', line_segment, 'y', series[:], 'g')) - def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): + """ + This function calculates the slope of the data in a certain given + period by fitting a line through it and compare it with the maximum + expected slope. + + Parameters + ---------- + data : str + name of the column containing the data to remove drift + arange : 2-element array of ints + the range in which to apply the function + max_slope : int + the maximum slope a signal is expected to have over a certain period + period : int + the period, in days, which a certain slope is allowed + plot : bool + if true, a plot is made, ... + + Returns + ---------- + the fixed dataset without drift + """ pass #============================================================================== From 8cf5a9031991021038602038054413561588d534 Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Wed, 25 Jul 2018 11:20:01 +0200 Subject: [PATCH 24/42] check fix #79; small update of readme --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 7fd9b5761..49a902335 100644 --- a/README.rst +++ b/README.rst @@ -47,7 +47,7 @@ Examples For the workflow with code and more specific examples, check out the Showcase Jupyter Notebook(s) included as documentation of the package. -MyBinder can be used to view the code and the specific examples. Go to https://mybinder.org/ and use the GitHub URL, https://github.com/UGentBiomath/wwdata. Specify the branch(master, develop etc.). Generate a new MyBinder link if the branch get modified. Example link: https://hub.mybinder.org/user/ugentbiomath-wwdata-faxuev5l/tree +MyBinder can be used to view the code and the specific examples. Go to https://mybinder.org/ and use the GitHub URL, https://github.com/UGentBiomath/wwdata. Specify the branch (master, develop etc.). Generate a new MyBinder link if the branch gets modified. Credits From 655b12886cf42ce7a594da976580948291518c60 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Thu, 26 Jul 2018 13:39:32 +0200 Subject: [PATCH 25/42] improved the detect_drift function, it works fully with plot. Remove_drift works but needs improvement #303 --- wwdata/Class_HydroData.py | 201 ++++++++++++++++++++++++++++---------- 1 file changed, 148 insertions(+), 53 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 0881772f7..c1e770b72 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1555,7 +1555,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals Parameters ---------- - data : str + data_name : str name of the column containing the data to detect drift arange : 2-element array of ints the range in which to apply the function @@ -1573,7 +1573,6 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals !!Doesn't check the last day mentioned in the arange!! """ - from scipy import signal series = self.data[data_name][arange[0]:arange[1]].copy() @@ -1596,70 +1595,119 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals line_segment = series - detrended_values[:] slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) - if slope > max_slope: + if slope > max_slope or slope < -max_slope: print('Based on the specified maximum slope, a drift was' ' detected with a slope higher than the maximum one. \n' - 'Slope detected: {}, maximum slope: {}'.format(slope, max_slope)) + 'Slope detected: {}, maximum slope:+/- {}'.format(slope, max_slope)) + self.line_segment = line_segment else: + plot = False print('No drift detected.') - # detrend_values=pd.DataFrame(detrended_values, index=series.index) --> dataframe of detrended values if plot is True: fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) - ax.plot(line_segment, 'b-',label='slope') ax.plot(series, 'g--', label='original data') - ax.plot(series.index, detrended_values, 'r', label='detrended values') - ax.plot(series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data + if slope > max_slope and slope < -max_slope: + ax.plot(line_segment, 'b-',label='slope') + ax.plot(series.index, detrended_values, 'r', label='detrended values') + ax.plot(series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data ax.legend(fontsize=16) ax.set_xlabel(self.timename, fontsize=20) ax.set_ylabel(data_name, fontsize=20) ax.tick_params(labelsize=15) + ax.legend(loc='upper right', shadow=True) else: - if type(period) is int: - start_index = 0 - end_index = 0 - new_index = end_index - if plot is True: - fig = plt.figure(figsize=(16,6)) - ax = fig.add_subplot(111) - ax.plot(series, 'g--', label='original data') - - while series.index.day[new_index] + period <= series.index.day[len(series)-1]: - checked = False - while series.index.day[end_index] < (series.index.day[start_index] + period): - if series.index.day[end_index] == (series.index.day[start_index] + 1): - if checked is False: - new_index = end_index - checked = True - end_index += 1 - if end_index == len(series)-1: - break - - detrended_values = signal.detrend(series[start_index:(end_index-1)]) - line_segment = series[start_index:(end_index-1)] - detrended_values[:] - slope = (int(line_segment[-1]) - int(line_segment[0])) / ( - arange[1].day - arange[0].day + 1) - - if slope > max_slope: - print('Drift detected in period {} to {}. \n' - 'Slope detected: {}, maximum slope: {}'.format - (series.index.day[start_index], series.index.day - [end_index-1],slope, max_slope)) - - if plot is True: - detrended_values = pd.DataFrame(detrended_values, - index=series.index[start_index:(end_index-1)]) - ax.plot(line_segment, 'b-', label='slope') - ax.plot(detrended_values, 'r--', label='detrended values') - ax.plot(series[start_index:(end_index-1)]-(line_segment-line_segment[0]), 'm-') #bad visualisation - - start_index = new_index - end_index = new_index - else: - return ValueError('period must be an integer') + if type(period) is not int: + return ValueError('the period must be a integer') + + start_index = 0 + end_index = 0 + new_index = end_index + n = 0 + m = 0 + list_value = [] + + while series.index.day[new_index] + period <= series.index.day[len(series)-1]: + checked = False + while series.index.day[end_index] < (series.index.day[start_index] + period): + if series.index.day[end_index] == (series.index.day[start_index] + 1): + if checked is False: + new_index = end_index + checked = True + end_index += 1 + if end_index == len(series)-1: + break + + detrended_values = signal.detrend(series[start_index:(end_index-1)]) + line_segment = series[start_index:(end_index-1)] - detrended_values[:] + slope = (int(line_segment[-1]) - int(line_segment[0])) / ( + arange[1].day - arange[0].day + 1) + + if slope > max_slope: + n += 1 + print('Drift detected in period {} to {}, slope: {}'.format + (series.index.day[start_index], series.index.day + [end_index-1], slope)) + if n == 1: + start_value = series.index[start_index] + end_value = series.index[end_index] + else: + if n > 0: + list_value.append([start_value, end_value]) + n = 0 + + if -max_slope > slope: + m += 1 + print('Drift detected in period {} to {}, slope: {}'.format + (series.index.day[start_index], series.index.day + [end_index - 1], slope)) + if m == 1: + start_value = series.index[start_index] + end_value = series.index[end_index] + else: + if m > 0: + list_value.append([start_value, end_value]) + m = 0 + + if series.index.day[end_index] == series.index.day[-1] and (n > 0 or m > 0): + list_value.append([start_value, end_value]) + start_index = new_index + end_index = new_index + + if len(list_value) == 0: + plot = False + print('No drift detected') + + if plot is True: + detrended_values = pd.DataFrame() + fig = plt.figure(figsize=(16, 6)) + ax = fig.add_subplot(111) + ax.plot(series, 'g--', label='original data') + for l in range(len(list_value)-1): + if list_value[l][1] > list_value[l+1][0]: + ind = len(series[:list_value[l][1]]) + list_value[l+1][0] = series.index[ind] + + for value in list_value: + detrend = signal.detrend(series[value[0]:value[1]]) + df = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrended_values.append(df) + line_segment = series[value[0]:value[1]] - detrend[:] + ax.plot(line_segment, 'b-', label='slope') + ax.plot(df, 'r--', label='detrended values') + + if line_segment[0] < line_segment[-1]: + #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[0]), 'm-', label='without drift(?)') + series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[0]) + else: + #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift(?)') + series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[-1]) + #ax.plot(series, 'k--') + self.list_value = list_value + def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): """ @@ -1669,7 +1717,7 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): Parameters ---------- - data : str + data_name : str name of the column containing the data to remove drift arange : 2-element array of ints the range in which to apply the function @@ -1678,13 +1726,60 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): period : int the period, in days, which a certain slope is allowed plot : bool - if true, a plot is made, ... + if true, a plot is made... Returns ---------- the fixed dataset without drift """ - pass + from scipy import signal + org_dat = self.data[data_name][arange[0]:arange[1]].copy()#for plotting + + self.detect_drift(data_name=data_name, arange=arange, max_slope= + max_slope, period=period, plot=False) + series = self.data[data_name][arange[0]:arange[1]].copy() + + if period is None or period is arange[1].day - arange[0].day + 1: + new_data = series - self.line_segment + self.line_segment[0] + self.data[data_name].update(new_data) + if plot is True: + fig = plt.figure(figsize=(16, 6)) + ax = fig.add_subplot(111) + ax.plot(series, 'm--', label='original data') + ax.plot(self.data[data_name], 'r--', label='new data') + ax.legend(loc='upper right', shadow=True) + + else: + for n in range(len(self.list_value)-1): + if self.list_value[n][1] > self.list_value[n+1][0]: + ind = len(series[:self.list_value[n][1]]) + self.list_value[n+1][0] = series.index[ind] + + detrended_values = pd.DataFrame() + for value in self.list_value: + detrend = signal.detrend(series[value[0]:value[1]]) + df = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrended_values.append(df) + line_segment = series[value[0]:value[1]] - detrend[:] + if line_segment[0] < line_segment[-1]: + series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[0]) + else: + series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[-1]) + self.data[data_name].update(series) + + if plot is True: + plt.figure(1, figsize=(16, 6)) + plt.subplot(211) + plt.plot(org_dat, 'k--', label='original data') + plt.subplot(212) + plt.plot(self.data[data_name][arange[0]:arange[1]], 'g--', label='new data') + plt.show() + + #ax.plot(series, ) + #ab = fig.add_subplot(212) + #ab.plot(self.data[data_name], ) + #ax.legend(loc='upper right', shadow=True) + #ab.legend(loc='upper right', shadow=True) #============================================================================== # DAILY PROFILE CALCULATION From d2e864a55fccd1b5914f7e3bccc662e919a392bd Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Mon, 30 Jul 2018 17:01:16 +0200 Subject: [PATCH 26/42] Applied a new way of fitting the data without drift, and restructured detect_drift and remove_drift --- wwdata/Class_HydroData.py | 129 ++++++++++++++++++++++++++++++-------- 1 file changed, 103 insertions(+), 26 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index c1e770b72..859583eec 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1545,7 +1545,7 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return fig, ax - return slope,intercept,r_sq + return slope, intercept, r_sq def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=False): """ @@ -1656,7 +1656,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals end_value = series.index[end_index] else: if n > 0: - list_value.append([start_value, end_value]) + list_value.append([start_value, end_value, 'n']) n = 0 if -max_slope > slope: @@ -1669,11 +1669,14 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals end_value = series.index[end_index] else: if m > 0: - list_value.append([start_value, end_value]) + list_value.append([start_value, end_value, 'm']) m = 0 - if series.index.day[end_index] == series.index.day[-1] and (n > 0 or m > 0): - list_value.append([start_value, end_value]) + if series.index.day[end_index] == series.index.day[-1] and n > 0: + list_value.append([start_value, end_value, 'n']) + if series.index.day[end_index] == series.index.day[-1] and m > 0: + list_value.append([start_value, end_value, 'm']) + start_index = new_index end_index = new_index @@ -1681,39 +1684,81 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals plot = False print('No drift detected') + for l in range(len(list_value) - 1): + if list_value[l][1] > list_value[l + 1][0]: + ind = len(series[:list_value[l][1]]) + list_value[l + 1][0] = series.index[ind] + if plot is True: detrended_values = pd.DataFrame() fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) ax.plot(series, 'g--', label='original data') - for l in range(len(list_value)-1): - if list_value[l][1] > list_value[l+1][0]: - ind = len(series[:list_value[l][1]]) - list_value[l+1][0] = series.index[ind] for value in list_value: detrend = signal.detrend(series[value[0]:value[1]]) - df = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) - detrended_values.append(df) - line_segment = series[value[0]:value[1]] - detrend[:] - ax.plot(line_segment, 'b-', label='slope') - ax.plot(df, 'r--', label='detrended values') - - if line_segment[0] < line_segment[-1]: - #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[0]), 'm-', label='without drift(?)') - series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[0]) - else: - #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift(?)') - series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[-1]) + df1 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrended_values.append(df1) + line_segment1 = series[value[0]:value[1]] - detrend[:] + ax.plot(line_segment1, 'm--') + + ax.plot(df1) + """ + detrend = signal.detrend(series[value[0]:value[1]], type='constant') + df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrended_values.append(df2) + + b = df2.iloc[-2][0] + a = line_segment1[0] + slope = (b - a) / len(df2) + f = [a] + s = df2 + s[:] = a + for val in range(len(df2)-1): + a += slope + f.append(a) + + ds = pd.DataFrame(f, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + ds = ds[:] + s[:] + ds = ds / 2 + ds = ds.squeeze() #from dataframe to series + + if value[2] == 'n': + #ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') + series[value[0]:value[1]] = series[value[0]:value[1]] - line_segment1 + ds #series[value[0]:value[1]] - (line_segment1 - line_segment1[0]) + elif value[2]=='m': + #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') + series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment1-line_segment1[-1]) #ax.plot(series, 'k--') + """ self.list_value = list_value + def drift_analysis(self, data_name, arange1, arange2=None, plot=False): + """ + This function analyse the data before and after a given slope. It + gives out useful information about the data that can be used to fix the drift. + + Parameters + ---------- + data_name : str + name of the column containing the data to analyse + arange1 : 2-element array of ints + the range in which to apply the function + arange2 : 2-element array of ints + the range in which to apply the function + plot : bool + if true, a plot is made.... + + Returns + ---------- + information about the drift + """ + pass def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): """ This function calculates the slope of the data in a certain given - period by fitting a line through it and compare it with the maximum - expected slope. + period by fitting a line through it and removes the drift. Parameters ---------- @@ -1735,8 +1780,7 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): from scipy import signal org_dat = self.data[data_name][arange[0]:arange[1]].copy()#for plotting - self.detect_drift(data_name=data_name, arange=arange, max_slope= - max_slope, period=period, plot=False) + self.detect_drift(data_name=data_name, arange=arange, max_slope=max_slope, period=period, plot=False) series = self.data[data_name][arange[0]:arange[1]].copy() if period is None or period is arange[1].day - arange[0].day + 1: @@ -1750,13 +1794,45 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): ax.legend(loc='upper right', shadow=True) else: + """ for n in range(len(self.list_value)-1): if self.list_value[n][1] > self.list_value[n+1][0]: ind = len(series[:self.list_value[n][1]]) self.list_value[n+1][0] = series.index[ind] + """ - detrended_values = pd.DataFrame() for value in self.list_value: + detrend = signal.detrend(series[value[0]:value[1]]) + df1 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) + line_segment1 = series[value[0]:value[1]] - detrend[:] + + if value[2] == 'n': + detrend = signal.detrend(series[value[0]:value[1]], type='constant') + df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) + + b = df2.iloc[-2][0] + a = line_segment1[0] + slope = (b - a) / len(df2) + f = [a] + s = df2 + s[:] = a + for val in range(len(df2) - 1): + a += slope + f.append(a) + + ds = pd.DataFrame(f, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) + ds = ds[:] + s[:] + ds = ds / 2 + ds = ds.squeeze() # from dataframe to series + + # ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') + series[value[0]:value[1]] = series[value[0]:value[1]]-line_segment1+ds + + elif value[2] == 'm': + # ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') + series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) + + """ detrend = signal.detrend(series[value[0]:value[1]]) df = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) detrended_values.append(df) @@ -1765,6 +1841,7 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[0]) else: series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[-1]) + """ self.data[data_name].update(series) if plot is True: From 2fee8b9e2b483d0d5ab85aaa6da9bdb52c1c2b55 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Tue, 31 Jul 2018 14:21:46 +0200 Subject: [PATCH 27/42] modified remove_drift function and how it fix the drift. This is now also shown in the showcase #303 --- ...howcase_OnlineSensorBased-checkpoint.ipynb | 1280 +++++ Showcase_OnlineSensorBased.ipynb | 4343 ++++------------- wwdata/Class_HydroData.py | 30 +- wwdata/Class_OnlineSensorBased.py | 4 +- 4 files changed, 2145 insertions(+), 3512 deletions(-) create mode 100644 .ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb diff --git a/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb new file mode 100644 index 000000000..3a624c825 --- /dev/null +++ b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb @@ -0,0 +1,1280 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook serves as a showcase for the functions written in the ``wwdata`` package, more specifically the OnlineSensorBased subclass. For additional information on the functions, the user is encouraged to use the provided docstrings. They can be accessed by entering a function name and hitting shift+tab between the function brackets.\n", + "\n", + "All information and documentation on the ``wwdata`` package, including how to install it, can also be found online at https://ugentbiomath.github.io/wwdata-docs/.\n", + "\n", + "An elaborate explanation on the functionalities of the package is accepted for publication in *Environmental Modelling and Software* and will soon be available on [ResearchGate](https://www.researchgate.net/project/Data-analysis-and-gap-filling)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading the necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:55.404080", + "start_time": "2017-05-09T11:54:53.499498+02:00" + } + }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pandas'", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mos\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlistdir\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mscipy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" + ], + "output_type": "error" + } + ], + "source": [ + "import sys\n", + "import os\n", + "from os import listdir\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import numpy as np\n", + "import datetime as dt\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "# seaborn is not a required package, it just prettifies the figures\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now for the actual package..." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import wwdata as ww" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check what version you have installed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.2.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ww.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "pd.read_excel" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:55.587365", + "start_time": "2017-05-09T11:54:55.406913+02:00" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Time', 'TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", + " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2', 'TSS_line1',\n", + " 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns', 'Turb_ns',\n", + " 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es', 'Temp_es', 'NH4_infl',\n", + " 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter',\n", + " 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST',\n", + " 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT',\n", + " 'Cond_line1', 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", + " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", + " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1', 'Flow_line2',\n", + " 'Flow_line3', 'Flow_total'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "measurements = pd.read_csv('./data/201301.txt',sep='\\t',skiprows=0)\n", + "measurements.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create Class object and format data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:55.669059", + "start_time": "2017-05-09T11:54:55.589786+02:00" + } + }, + "outputs": [], + "source": [ + "dataset = ww.OnlineSensorBased(data=measurements,\n", + " timedata_column='Time',\n", + " data_type='WWTP')\n", + "dataset.set_tag('January 2013')\n", + "dataset.replace('Bad','NaN',inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the values in the column containing time data to the pandas datetime format." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:55.780731", + "start_time": "2017-05-09T11:54:55.671616+02:00" + } + }, + "outputs": [], + "source": [ + "dataset.to_datetime(time_column=dataset.timename,time_format='%d-%m-%y %H:%M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "use the time-column as index" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:55.788079", + "start_time": "2017-05-09T11:54:55.783330+02:00" + } + }, + "outputs": [], + "source": [ + "dataset.set_index('Time',key_is_time=True,drop=True,inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the absolute timestamps to relative values. This can be important when data is to be used for modeling purposes later on, and needs to be written to text files." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:55.793662", + "start_time": "2017-05-09T11:54:55.790638+02:00" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "#dataset.absolute_to_relative(time_data='index',unit='d')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Drop any duplicates that might be present in the index" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:55.812335", + "start_time": "2017-05-09T11:54:55.796021+02:00" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "dataset.drop_index_duplicates()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert all or the selected columns to float type." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:56.047638", + "start_time": "2017-05-09T11:54:55.815534+02:00" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "dataset.to_float(columns='all')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:56.758532", + "start_time": "2017-05-09T11:54:56.050129+02:00" + } + }, + "outputs": [ + { + "data": { + "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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m+wytsx7THCf1Qjtcq20bjLk1GDIMwHTcYIW+ZWWRaU/4SNKlVVuFWWZbG6SGODIAiCsEVIY6MF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\nX78ugYGB8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2'],'.g')\n", + "ax.set_ylabel('Total COD [mg/L]',fontsize=18);ax.set_xlabel('')\n", + "ax.tick_params(labelsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filter data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Selecting data happens through tagging, so no original data is lost. When applying filter algorithms such as ``tag_doubles``, ``moving_slope_filter`` etc., a new pandas dataframe is created (``dataset.meta_valid``, see also below figure) that contains these tags. It is also based on this new dataframe that the plotting of selected and not selected datapoints in different colours happens.\n", + "\n", + "![validation](./figs/packagestructure_validation.png)\n", + "\n", + "The written output of the filter functions tells the user how many data points were tagged based on that specific function. When the plotting argument is set to true, the plot shows the aggregated results of the filter functions used up until that point." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Maxima\n", + "Tag the data points that are higher then a certain percentile" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:57.347519", + "start_time": "2017-05-09T11:54:56.761091+02:00" + } + }, + "outputs": [ + { + "data": { + "image/png": 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89dZbWLp0KRQKRZMAV6vVQhAEeHl5QaFQAGgaBNfV1cGzPj+5uW0YX3s6Y2I+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Pjw4fJ82xUemUyGIU21q/LMKriQMD9zLHxhCQBu+JExrL2hvtrkNd79clWpTQ2Y8Nd2W9xWrHpz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.get_highs('Flow_total',0.95,arange=['2013/1/1','2013/1/31'],method='percentile',plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NaN values\n", + "Tag all NaN (Not a Number) values as 'filtered'." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:57.358210", + "start_time": "2017-05-09T11:54:57.350077+02:00" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47 values detected and tagged as filtered by function NaN tagging\n" + ] + } + ], + "source": [ + "dataset.tag_nan('CODtot_line2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensor failure\n", + "Tag all datapoints that are part of a constant (within a given bound) signal." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:57.391744", + "start_time": "2017-05-09T11:54:57.361076+02:00" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2464 values detected and tagged as filtered by function double value tagging\n" + ] + } + ], + "source": [ + "dataset.tag_doubles('CODtot_line2',bound=0.05,plot=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Noise \n", + "Tag all data points for which the slope as compared with the previous point is too high to be realistic (i.e. the data point is noisy)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:58.312987", + "start_time": "2017-05-09T11:54:57.394331+02:00" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "199 values detected and tagged as filtered by function moving slope filter\n" + ] + } + ], + "source": [ + "dataset.moving_slope_filter('index','CODtot_line2',72000,arange=['2013/1/1','2013/1/31'],\n", + " time_unit='d',inplace=False,plot=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tag all data points that are more than a specified percentage away from the calculated moving average. This function makes use of the ``simple_moving_average`` function, also written as part of this package." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:58.360928", + "start_time": "2017-05-09T11:54:58.315777+02:00" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2810 values detected and tagged as filtered by function moving average filter\n" + ] + } + ], + "source": [ + "dataset.moving_average_filter(data_name='CODtot_line2',window=12,cutoff_frac=0.20,\n", + " arange=['2013/1/1','2013/1/31'],plot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:59.889452", + "start_time": "2017-05-09T11:54:58.363535+02:00" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "65.77546296296296% datapoints are left over from the original 8640.0\n" + ] + }, + { + "data": { + "image/png": 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Lly/H6NGjtcddtmxZsx6/Zs0aAMB3332HTp06AQCio6MxYcIEw785NdhDhIiIiIiMSxQh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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dataset.plot_analysed('CODtot_line2')\n", + "ax.legend(bbox_to_anchor=(1.15,1.0),fontsize=18)\n", + "ax.set_ylabel('Total COD [mg/L]',fontsize=18);ax.set_xlabel('')\n", + "ax.tick_params(labelsize=14)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", + " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2',\n", + " 'TSS_line1', 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns',\n", + " 'Turb_ns', 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es',\n", + " 'Temp_es', 'NH4_infl', 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz',\n", + " 'PO4_mixinggutter', 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST',\n", + " 'CODsol_efflPST', 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT',\n", + " 'CODsol_efflRBT', 'Cond_line1', 'Turb_line1', 'Cond_line2',\n", + " 'Turb_line2', 'Cond_line3', 'Turb_line3', 'NH4_efflPST',\n", + " 'PO4_efflPST', 'PO4_sandtrap', 'NH4_splittingworks',\n", + " 'PO4_splittingworks', 'Flow_line1', 'Flow_line2', 'Flow_line3',\n", + " 'Flow_total'], dtype=object)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of a package-specific filtering, data points can also be filtered and replaced by other filtering algorithms, such as the Savitsky-Golay filter as illustrated below. The disadvantage of this is that no tags are added to the ``meta_valid`` DataFrame and that original data are replaced (when the ``inplace`` option is set to ``True``)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:54:59.895406", + "start_time": "2017-05-09T11:54:59.892052+02:00" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7HyQ/P59vvlnK9evXaNasOVZWVowZM5b333+X+vW96NSpC7t2befs2TPY2Oi/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\nMnjwYHbt2sWSJUsICwsDlHd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1/Tl4bb/O/W3LmWZe08lAWQghTCDCp73WD9HU3BRSciRH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\nfiH7OtAjCNG+Hewn+gBg8P9b2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GMNqUhXq8lYLvOg71uDsI88uyab1WOQSvAylmZ6kZhX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\n5rjy4otaYJeUdObfZdnZ4OIC7u7lr2/a1JviYgNdu5b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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.savgol('TSS_line3',plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Check the reliability of the filling algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to be able to make a choice and apply the best method to fill gaps in the data, the ``wwdata`` package provides the option to check for the reliability of each filling algorithm. This is represented in the below figure.\n", + "\n", + "![validation](./figs/packagestructure_reliability.png)\n", + "\n", + "In wording, the workflow of the ``check_filling_error`` is as follows:\n", + "* Randomly (!) create large or small artificial gaps in the data within the given ``test_data_range``. \n", + "* Fill the created gaps with a chosen filling function (see [further in this notebook](#Fill-data) for illustrations of those).\n", + "* Compare the original data points with the filled data points and calculate the deviation between them.\n", + "* Iterate for a given number of times, to average out the random creation of the gaps.\n", + "\n", + "Before applying this, it is wise to check the total number of points within ``test_data_range`` and then determine the number of gaps to create. Take into account that the length of the gaps is sampled from a uniform distribution between 0 and the maximum length of a gap given as an argument. \n", + "For example: creating two large gaps of 50 datapoints in a dataset containing 100 datapoints would mean a theoretical average of 50% data recovery (2*(50/2) = 50 data points are left out of the 100; the 2 gaps can however still overlap)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4895" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dataset.data['2013/1/1':'2013/1/17'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average deviation of imputed points from the original ones is 39.46857910106997%. This value is also saved in self.filling_error.\n" + ] + } + ], + "source": [ + "dataset.check_filling_error(100,'CODtot_line2','fill_missing_standard',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + " nr_small_gaps=70,max_size_small_gaps=12,\n", + " nr_large_gaps=3,max_size_large_gaps=800,\n", + " to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + " only_checked=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average deviation of imputed points from the original ones is 54.261283673154466%. This value is also saved in self.filling_error.\n" + ] + } + ], + "source": [ + "dataset.check_filling_error(100,'CODtot_line2','fill_missing_daybefore',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + " nr_small_gaps=70,max_size_small_gaps=12,\n", + " nr_large_gaps=3,max_size_large_gaps=800,\n", + " to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + " range_to_replace=[0,10],only_checked=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fill data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Filling data can be done using a range of functions implemented in the package. Again, a new pandas dataframe is created (``dataset.meta_filled``, see also below figure), starting from the ``dataset.meta_valid`` dataframe, and updated with tags indicating what filling method was used to obtain a certain point.\n", + "\n", + "![validation](./figs/packagestructure_filling.png)\n", + "\n", + "Using the ``only_checked`` argument, implemented in most filling functions, the user can always choose whether only data points tagged as ``filtered`` will be filled, or all data points within a certain range.\n", + "\n", + "When using the plotting argument to plot the analysed data, the user will see a plot based on the latest function that was used; if this was a filter function, the data will be plotted based on the ``dataset.meta_valid`` dataframe, if it was a filling function, the tags in ``dataset.meta_filled`` will be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpolation\n", + "Fill missing data points by interpolation, if number of consecutive missing points is lower than a specified number." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:01.060520", + "start_time": "2017-05-09T11:54:59.898063+02:00" + }, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:324: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:367: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", + " 'filling of gaps in the data during rain events.')\n" + ] + }, + { + "data": { + "image/png": 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d++67j2+++YaWLVua2rt06cJLL73EoEGDrltDUlJ6+d5UNdawobPeD7E66vdi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HAAAg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\nu9DfRrwGBJ4CfC5wK3wuAIGncG/vUV17YIQQQkgbK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],\n", + " plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Average daily profile\n", + "Fill missing datapoints by using an average daily profile. The ``fill_missing_standard`` function requires the running of the ``calc_daily_profile`` function, also developed for this package, first. This creates a dataframe (``dataset.daily_profile``) containing the average daily profile calculated within a defined time period (e.g. selecting only non-peak days for example)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:01.103135", + "start_time": "2017-05-09T11:55:01.063627+02:00" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py:1593: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", + " 'representative average day and/or high standard deviations.')\n" + ] + } + ], + "source": [ + "dataset.calc_daily_profile('CODtot_line2',[dt.datetime(2013,1,1),dt.datetime(2013,1,8)],\n", + " quantile=0.9,clear=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:01.844129", + "start_time": "2017-05-09T11:55:01.105608+02:00" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:675: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" + ] + }, + { + "data": { + "image/png": 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Bl156ib59+142hrS0nBt7UzcxLy93PQ+xOxr3Ym805sUeadyLvdGYt+bl5W7r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vTs2RNpaS0LVBMcoyO/eFzhPqlftVMqlIITsgjvKFGybL7+8wte29tDl/MmojRF4//6v2TUdTBz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\ndjO6q+tLFb3zAIkEuB3FKVvEgr5shte2L9jjb3Vhj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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_standard('CODtot_line2',[dt.datetime(2013,1,14),dt.datetime(2013,1,17)],\n", + " only_checked=True,clear=False,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model output\n", + "Fill gaps using a model output. This assumes that the user has good reason to trust that the model predictions are sound and can indeed be used to replace missing data where needed." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:02.248297", + "start_time": "2017-05-09T11:55:01.847864+02:00" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "data": { + "text/plain": [ + "Index(['.sewer_1.COD', '.sewer_1.CODs', '.sewer_1.NH4', '.sewer_1.PO4',\n", + " '.sewer_1.Q_DWF_UB', '.sewer_1.Q_in', '.sewer_1.TSS'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_output_ontv_1 = pd.read_csv('./data/model_output.txt',\n", + " sep='\\t')\n", + "units_model = model_output_ontv_1.ix[0]\n", + "model_output_ontv_1 = model_output_ontv_1.drop(0,inplace=False).reset_index(drop=True)\n", + "model_output_ontv_1 = model_output_ontv_1.astype(float)\n", + "model_output_ontv_1.set_index('#.t',drop=True,inplace=True)\n", + "model_output_ontv_1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:03.902986", + "start_time": "2017-05-09T11:55:02.251053+02:00" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:811: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" + ] + }, + { + "data": { + "image/png": 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xMf32PlQ5Vru2q96HVDjq91LRqM9LRaR+LxWN+ry12rVdbR2ClFMxMTEMGzaM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\nW4fZs2cjPDwcubm5+j+9e/eGj48P5s+fD5VKhVWrViEtLQ3jxo0DACQmJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_model('CODtot_line2',model_output_ontv_1['.sewer_1.COD'],\n", + " [dt.datetime(2013,1,18),dt.datetime(2013,1,22)],\n", + " only_checked=True,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ratio or correlation\n", + "Constant ratios or correlations between data can be used to filled missing points. The user can calculate and compare ratios and correlations (currently only linear) between selected measurements, and fill data using these.\n", + "\n", + "*nb: in the examples below, data filling based on ratios or correlation is obviously not a very good choice. Both methods are included here for completeness of method showcasing.*" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:03.917107", + "start_time": "2017-05-09T11:55:03.905461+02:00" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.450642327196896, 0.672153214085126)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.calc_ratio('CODtot_line2','CODsol_line2',\n", + " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find the 'best' ratio (i.e. the one with the lowest relative standard deviation ($\\sigma/\\mu$)), the ratio obtained in different periods can be compared and the best one used during possible further replacements." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:03.978297", + "start_time": "2017-05-09T11:55:03.919697+02:00" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best ratio (2.53282188261064 ± 0.16586491872475553) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" + ] + } + ], + "source": [ + "avg,std = dataset.compare_ratio('CODtot_line2','CODsol_line2',2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the average obtained from the ``compare_ratio`` function to fill in missing values. (*in this case, as mentioned before, this does clearly not work, since zero-values are replaced with zero-values. This only showcases the function and its arguments*)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:04.632959", + "start_time": "2017-05-09T11:55:03.980745+02:00" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:454: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" + ] + }, + { + "data": { + "image/png": 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UXaxmTVe9D6lwNO+lotGcl4pI814qGs15SzVrulo7BLlLJSYmMmzYML7//nsM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\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_ratio('CODtot_line2',\n", + " 'CODsol_line2',avg,\n", + " [dt.datetime(2013,1,22),dt.datetime(2013,1,23)],\n", + " only_checked=True,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of a ratio, a correlation can be sought. In case of a zero intercept, this of course gives a result in the same range if the same data is used. To have a good impression on how useful the calculated correlation is, a prediction interval is plotted as well when ``plot`` is set to ``True``." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slope: 0.405512924986 intercept: 0 R2: 0.973774656376\n" + ] + }, + { + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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pyGQyLl36By+vWTRvbkrnzl24dOkfrWCkUinR0Xmy3427eTOPhIR4LCzuP+1N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obE5mFXN4J6A6r9DAbTQrTaN+jU7Rz2SinbEf0z0eoVKspNRA1Od63Mmjh276VApFcI7Gxm7dq9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\n7rv06NGD1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.get_correlation('CODtot_line2',\n", + " 'CODsol_line2',\n", + " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", + " zero_intercept=True,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the previously made assessment, use the correlation function to fill gaps in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:06.016129", + "start_time": "2017-05-09T11:55:05.261370+02:00" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:561: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" + ] + }, + { + "data": { + "image/png": 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YPHiwrUP5xwwbNox69eoxceJEW4dyy9h8aWpxnD9/nuHDh+Pu7s7TTz8N5B39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IpEFVu7zUFJDc6r+lODY1Yw4rm0BXB39MAI/ziLvvPPWT+hts180CvcPQK7HjyA1Lw9eP3kEv3A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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_correlation('CODtot_line2',\n", + " 'CODsol_line2',\n", + " [dt.datetime(2013,1,23),dt.datetime(2013,1,25)],\n", + " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", + " only_checked=True,clear=False,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data from previous day\n", + "Under the assumption that \"The best prediction for tomorrows weather is todays weather\", one can also replace missing data by making use of (one of) the previous days." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:06.731819", + "start_time": "2017-05-09T11:55:06.018568+02:00" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:955: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" + ] + }, + { + "data": { + "image/png": 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1VqlUqRIAr7zyCt7e3gXK69ate9V3TSYTQUFBPPXUUwXKqlatisViASAlJcWqLD/5J1Jc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_daybefore('CODtot_line2',\n", + " [dt.datetime(2013,1,25),dt.datetime(2013,1,27)],\n", + " range_to_replace=[0,10],plot=True,\n", + " only_checked=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:07.431337", + "start_time": "2017-05-09T11:55:06.734413+02:00" + } + }, + "outputs": [ + { + "data": { + "image/png": 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2Y7id75mXl3dH3/9Bc98kygpPogBYsWLFLZ/39vYudm1voUGDBhVZUywiIiIi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qXIk+VhhP8o8rq4JgLcGgAAn6/Gxy9Pph3LHC6zzwKlNSS9V1hdgOEHWr7+owUk8mR5+Pnvn/BX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\npBR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dataset.plot_analysed('CODtot_line2')\n", + "ax.legend(bbox_to_anchor=(1.3,1.0),fontsize=18)\n", + "ax.set_ylabel('Total COD [mg/L]',fontsize=18);ax.set_xlabel('')\n", + "ax.tick_params(labelsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the daily average of a certain data series" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:07.830400", + "start_time": "2017-05-09T11:55:07.433945+02:00" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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KwocOHdL999+v4uJiDRo0SD179lRISIhOnz6trVu36sMPP9TYsWP17rvvKjw8\nvLFqBgAADmKkAAA825DETjYzf35p7+iCatxHvYLw4sWLVVJSomXLlql///422+68804NGzZMDz74\noJYtW6a5c+c2aKEAAKD+GCkAAM9WPbvnlQ2ZqjprqENwoIYkdmTWz3k0q8/OW7Zs0TXXXFMjBFfr\n37+/rr32WqWlpTVIcQAA4OIMSexUR7v7jxRULwJWeLpUj72arvTMXFeXBAA2kif2dsoCggmRobo0\nsLlCglroyTHxhGAH1CsI//TTT+ed8hweHq4TJ05cVFEAAKBhJESGavywKHk1s0iSOgQHavywKLf/\nI6l6EbCqs+fWKqleBIwwDABwRL2CcFhYmHbu3Gl3n507dyokJOSiigIAAA3HE0cK7C0CBgDA+dQr\nCF9//fXavXu3Fi1aVGNbRUWFnn/+ee3evVuDBg1qsALNiKleAADYxyJgAICLUa/FsiZOnKjPP/9c\nKSkpWrt2rXr27KmWLVsqNzdX33zzjXJzc3XFFVdowoQJjVWvx+N5jwAAnB+LgAEALka9RoQDAwP1\n9ttv69Zbb1VhYaHWr1+vN954Q59++qlOnTql2267TW+++aZatmzZWPV6PKZ6AQBwfp68CBgAoPHV\na0RYki699FI988wzeuKJJ/TDDz+oqKhIAQEBuuKKK+Tr69sYNZoKU70AADg/HhcCALgY9RoRvvfe\ne7V27VpJko+Pj7p27aqrr75aV111lTUEr1y5UjfccEPDV2oS7dr419rOVC8AAGxVLwLWupWfxywC\nBgBwDrsjwqWlpaqsrJQkGYahrVu3KjY2VkVFRbXuX15erq+++krHjh1r+EpNYkhiJ5t7hH9pZ6oX\nAAAAADQEu0H4vffe09y5c23aXn75Zb388st2D9qjR4+Lr8ykmOoFAAAAoL6SJ/ZWcHBL5ef/7OpS\n3ILdIPy73/1O27ZtU2FhoSQpIyNDYWFhat++fY19LRaLfHx8FBISwqrRFykhMlTvbjwgSXpyTLyL\nqwEAAAAAz2I3CDdr1kwLFiywvo6IiNBtt92mhx56qNELAwAAAACgMdRr1eh9+/Y1Vh0AAAAAADhF\nvYJwQUGBduzYofz8fBUVFcnf31/h4eGKjo7WZZdd1lg1AgAAAADQYBwKwjt27NALL7ygjIyMWrc3\na9ZMvXv31sMPP6zu3bs3aIEAAAAAADSk8wbhf/3rX3riiSdUWVmpdu3a6eqrr1ZoaKh8fX1VXFys\nH3/8UbtVxFGlAAAgAElEQVR27dKXX36pLVu26IknntCIESOcUTsAAAAAAPVmNwh//fXXmjNnjgID\nAzVnzhzdeOONte5XVVWlDz/8UHPnztXjjz+uqKgoRURENErBAAAAAABcjGb2Nq5cuVIWi0Wvvvpq\nnSFYkry8vDRkyBC99tprMgxDq1atavBCAQAAAABoCHaD8I4dO9SnTx+H7/uNiIjQb3/7W23btq1B\nigMAAAAAoKHZDcKFhYXq3LlzvQ7YtWtX5ebmXlRRAAAAAAA0FrtBuKysTAEBAfU6oL+/v8rKyi6q\nKAAAAMBdTE/ZrOkpm11dBoB6sBuEDcOo9wEtFssFFwMAAAAAQGOzG4QBAAAAAPA0532O8NatW7V4\n8WKHD5ienn5RBQEAAAAA0JgcCsJbt26t10GZHg0AAAAAaKrsBuF58+Y5qw4AAAAAAJzCbhC+9dZb\nnVUHAAAAAABOcd6p0f+rvLxcOTk5OnnypC677DKFhobK19e3MWoDAAAAAKDBORyEv/jiC7311ltK\nS0tTZWWltd3Ly0t9+/bV3XffraSkpMaoEQAAAACABnPeIFxRUaFZs2Zp/fr1MgxDfn5+Cg8P1yWX\nXKKSkhJlZ2dr48aN2rRpk26++WY9/fTTjBADAAAAAJqs8wbhp556SuvWrVOXLl00depU9e/fX82b\nN7dur6qq0ldffaUFCxZow4YNat68uebOnduoRQMAAAAAcKGa2du4Y8cOvfPOO+rdu7fWrl2r66+/\n3iYES+emRvfv31/vvPOOBgwYoPfee08ZGRmNWjQAAAAAABfKbhB+44031KJFCz333HPy8fGxeyBv\nb2/NmzdPgYGBeueddxq0SAAAAAAAGordIPztt98qKSlJQUFBDh0sKChI/fv3165duxwuoKCgQI88\n8oj69u2ruLg4jRkzRt9//711e1pamoYPH67o6GgNHTpUmzZtsnl/YWGhHn74YcXFxSkxMVHJyck2\ni3kBAAAAAPBrdoNwTk6OwsPD63XADh06KC8vz6F9z549q4ceekiHDh1SSkqK3n77bQUGBuoPf/iD\nTp48qaysLE2YMEE33HCD1qxZo4EDB2rSpEnav3+/9RiTJ09WQUGBVq1apfnz52v16tVatGhRvWpu\nipIn9lbyxN6uLgMAAAAAPI7dIOzv769Tp07V64CnTp1yeAR537592rlzp5555hlFR0fryiuvVHJy\nss6cOaNNmzYpNTVVMTExmjBhgnWxrtjYWKWmpkqSdu7cqe3bt2v+/PmKiIjQgAEDNGPGDK1cuVLl\n5eX1qhsAAAAAYA52g3DXrl2Vlpams2fPOnSwqqoqffnll+rcubND+4eFhWnZsmW64oorrG0Wi0WS\n9NNPPykjI0Px8fE270lISLAuxpWRkaH27dvbjFrHx8eruLhYe/fudagGAAAAAIC52A3CN910k44d\nO6bly5c7dLCXXnpJx48f1+233+7Q/kFBQUpKSlKzZr+UsXLlSpWWlqpv377KyclRaGiozXtCQkKU\nk5MjScrNzVVISEiN7ZJ0/Phxh2oAAAAAAJiL3ecI33777Vq1apVefPFFlZSUaNy4cQoICKixX1FR\nkRYtWqTU1FT16NFDgwcPvqBiPvvsMz3//PO677771KVLF5WWlsrX19dmH19fX5WVlUmSSkpKajzO\nycfHRxaLxbpPXYKC/OXt7XVBdXqq4OCWri4BcCr6PFzNy+vcLChn9EVnnsuZuC73Opcz8TV0L3wN\nGw5fQ8fYDcJeXl5atmyZRo8erWXLlik1NVVXX321rrjiCgUGBqq0tFSHDh3S1q1bVVxcrM6dOysl\nJcVmhNdRq1ev1uzZs3XTTTdp+vTpkqTmzZuroqLCZr/y8nK1aNFCkuTn51fjXuCKigoZhiF/f3+7\n5zt58ky9a/RkwcEtlZ//s6vLAJyGPo+moKrKkCSn9MWqKkNeXhaP6/fO/Bo6k7P7hrPO5UzO7POe\n+jV0Jr6GDYO/b2zZ+1DAbhCWpHbt2mnNmjVasGCB3nvvPaWlpSktLc1mn1atWmncuHF66KGHaozQ\nOmLJkiVasGCBRo0apVmzZlnvEw4LC6uxAnVeXp51unTbtm1rPE6pev//nVINAAAAAIDkQBCWpMDA\nQM2aNUt/+tOftGvXLh08eFBFRUVq1aqVLr/8csXHx8vHx+eCCli+fLkWLFigKVOmaNKkSTbbevbs\nqW3bttm0paenKy4uzrr973//u44fP66wsDDr9oCAAEVERFxQPQAAAAAAz+ZQEK7WokULJSYmKjEx\nsUFOvm/fPr3wwgsaMWKE7rzzTuXn51u3BQQEaNSoURoxYoQWLlyoIUOGaMOGDdq9e7fmzJkjSYqN\njVVMTIymTZum2bNnq6CgQMnJybrvvvtq3FsMAAAAAIB0nlWjf+3gwYM6efJkrdsWLlxofaRRffz7\n3/9WVVWV3nvvPfXt29fmv9dff11XXXWVFi9erI8++ki33HKLPv/8cy1dulRdunSRdO5RS4sXL1br\n1q01cuRI/fWvf9Udd9xRY2QZAADAXaRn5upUUZkKT5fqsVfTlZ6Z6+qSAMDjnHdEuLy8XI888og+\n+ugjPfPMM7rllltstufn5yslJUVLlizRtddeq2effVaBgYEOnfyPf/yj/vjHP9rdJykpSUlJSXVu\nDw4O1ksvveTQ+QAAAJqy9MxcLVu/x/r6aH6x9XVCJOufAEBDsTsiXFVVpbFjx+o///mP2rZtq6Cg\noBr7tGjRQn/+8591+eWX67PPPtODDz4owzAarWAAAABP9cGWQ3W0Zzu1DgDwdHaD8Ntvv62tW7dq\n2LBh+vjjjzVgwIAa+wQGBmrs2LFat26dBg4cqO3bt+vdd99ttIIBAAA81bGC2h/veLyw2MmVAIBn\nsxuE33//fbVr105PP/20vL3tz6L28/PTs88+q6CgIK1du7ZBiwQAADCDdm38a20Pax3g5EoAwLPZ\nDcL79+9X3759HX40UmBgoPr06aPvvvuuQYoDAAAwkyGJnepo7+jcQgDAw9kd5q2qqlLLli3rdcDQ\n0FBVVlZeVFEAAABmVL0g1isbMlV11lCH4EANSezIQlkA0MDsjgiHhYXp8OHD9Trg4cOHFRrKL2sA\nAIALkRAZqksDm6t1Kz89OSaeEHwBqh9BlXeyhEdQAaiV3SDcq1cvffHFF8rPz3foYPn5+dq4caOu\nuuqqBikOAAAAqI/qR1BVnT33FJPqR1ARhgH8mt0gfPfdd6u8vFxTpkxRUVGR3QMVFRVp8uTJqqio\n0N13392gRQIA4GmqR6wKT5cyYgU0IB5BBcARdu8RjoyM1IMPPqglS5bohhtu0MiRI9WnTx9dccUV\nCggI0E8//aTDhw8rLS1Nb7zxhk6cOKERI0aod+/ezqofAAC3Uz1iVa16xEoS02CBi8QjqNxT8kTy\nA5zL/jORJE2ZMkU+Pj5KSUnRwoULtXDhwhr7GIYhHx8fjRs3TtOmTWuUQgEA8BT2RqwIwsDFadfG\nX0fza4ZeHkEF4NfOG4QtFosmTpyom266SWvWrNGXX36p3NxcnT59WpdeeqnCw8PVr18/3XzzzQoP\nD3dGzQAAuDVGrIDGMySxk82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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.calc_daily_average('CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,2,1)],plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the proportional concentration of different flows coming together." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:07.842239", + "start_time": "2017-05-09T11:55:07.833046+02:00" + } + }, + "outputs": [], + "source": [ + "dataset.calc_total_proportional('Flow_total',\n", + " ['Flow_line1','Flow_line2','Flow_line3'],\n", + " ['TSS_line1','TSS_line2','TSS_line3'],\n", + " 'TSS_prop')" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "latex_envs": { + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1.0, + "eqLabelWithNumbers": true, + "eqNumInitial": 0.0 + }, + "nav_menu": {}, + "toc": { + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": { + "height": "282px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": "3", + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": true, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index e8168f15e..3cacb7368 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,10 +546,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ "4895" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -615,17 +615,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average deviation of imputed points from the original ones is 38.45350418438349%. This value is also saved in self.filling_error.\n" - ] - } - ], + "outputs": [], "source": [ "dataset.check_filling_error(100,'CODtot_line2','fill_missing_standard',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", " nr_small_gaps=70,max_size_small_gaps=12,\n", @@ -636,17 +628,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average deviation of imputed points from the original ones is 55.3020444019021%. This value is also saved in self.filling_error.\n" - ] - } - ], + "outputs": [], "source": [ "dataset.check_filling_error(100,'CODtot_line2','fill_missing_daybefore',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", " nr_small_gaps=70,max_size_small_gaps=12,\n", @@ -685,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -706,7 +690,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -717,7 +701,7 @@ ], "source": [ "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n", - " order=2, plot=True)" + " order=3, plot=True)" ] }, { @@ -730,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", @@ -742,7 +726,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1599: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1959: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", " 'representative average day and/or high standard deviations.')\n" ] } @@ -754,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", @@ -772,7 +756,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -796,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", @@ -828,7 +812,7 @@ " dtype='object')" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -845,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", @@ -862,3290 +846,265 @@ "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", "will be corrected to return the positional minimum in the future.\n", "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", " return (np.abs(df[column]-value)).argmin()\n" ] }, { - "name": "stderr", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_model('CODtot_line2',model_output_ontv_1['.sewer_1.COD'],\n", + " [dt.datetime(2013,1,18),dt.datetime(2013,1,22)],\n", + " only_checked=True,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ratio or correlation\n", + "Constant ratios or correlations between data can be used to filled missing points. The user can calculate and compare ratios and correlations (currently only linear) between selected measurements, and fill data using these.\n", + "\n", + "*nb: in the examples below, data filling based on ratios or correlation is obviously not a very good choice. Both methods are included here for completeness of method showcasing.*" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:03.917107", + "start_time": "2017-05-09T11:55:03.905461+02:00" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.4506423271968965, 0.6721532140851265)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.calc_ratio('CODtot_line2','CODsol_line2',\n", + " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find the 'best' ratio (i.e. the one with the lowest relative standard deviation ($\\sigma/\\mu$)), the ratio obtained in different periods can be compared and the best one used during possible further replacements." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:03.978297", + "start_time": "2017-05-09T11:55:03.919697+02:00" + } + }, + "outputs": [ + { + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "Best ratio (2.5328218826106403 ± 0.16586491872475548) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1400: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", + "You can access Timestamp as pandas.Timestamp\n", + " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" ] - }, + } + ], + "source": [ + "avg,std = dataset.compare_ratio('CODtot_line2','CODsol_line2',2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the average obtained from the ``compare_ratio`` function to fill in missing values. (*in this case, as mentioned before, this does clearly not work, since zero-values are replaced with zero-values. This only showcases the function and its arguments*)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:04.632959", + "start_time": "2017-05-09T11:55:03.980745+02:00" + } + }, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:462: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_ratio('CODtot_line2',\n", + " 'CODsol_line2',avg,\n", + " [dt.datetime(2013,1,22),dt.datetime(2013,1,23)],\n", + " only_checked=True,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of a ratio, a correlation can be sought. In case of a zero intercept, this of course gives a result in the same range if the same data is used. To have a good impression on how useful the calculated correlation is, a prediction interval is plotted as well when ``plot`` is set to ``True``." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slope: 0.4055129249855649 intercept: 0 R2: 0.9737746563763395\n" ] }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.get_correlation('CODtot_line2', 'CODsol_line2', [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", + " zero_intercept=True, plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the previously made assessment, use the correlation function to fill gaps in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:06.016129", + "start_time": "2017-05-09T11:55:05.261370+02:00" + }, + "scrolled": true + }, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:569: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.fill_missing_correlation('CODtot_line2',\n", + " 'CODsol_line2',\n", + " [dt.datetime(2013,1,23),dt.datetime(2013,1,25)],\n", + " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", + " only_checked=True,clear=False,plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data from previous day\n", + "Under the assumption that \"The best prediction for tomorrows weather is todays weather\", one can also replace missing data by making use of (one of) the previous days." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:06.731819", + "start_time": "2017-05-09T11:55:06.018568+02:00" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:961: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -4155,114 +1114,331 @@ } ], "source": [ - "dataset.fill_missing_model('CODtot_line2',model_output_ontv_1['.sewer_1.COD'],\n", - " [dt.datetime(2013,1,18),dt.datetime(2013,1,22)],\n", - " only_checked=True,plot=True)" + "dataset.fill_missing_daybefore('CODtot_line2',\n", + " [dt.datetime(2013,1,25),dt.datetime(2013,1,27)],\n", + " range_to_replace=[0,10],plot=True,\n", + " only_checked=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "ExecuteTime": { + "end_time": "2017-05-09T09:55:07.431337", + "start_time": "2017-05-09T11:55:06.734413+02:00" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dataset.plot_analysed('CODtot_line2')\n", + "ax.legend(bbox_to_anchor=(1.3,1.0),fontsize=18)\n", + "ax.set_ylabel('Total COD [mg/L]',fontsize=18);ax.set_xlabel('')\n", + "ax.tick_params(labelsize=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Ratio or correlation\n", - "Constant ratios or correlations between data can be used to filled missing points. The user can calculate and compare ratios and correlations (currently only linear) between selected measurements, and fill data using these.\n", - "\n", - "*nb: in the examples below, data filling based on ratios or correlation is obviously not a very good choice. Both methods are included here for completeness of method showcasing.*" + "# Calculations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the daily average of a certain data series" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 35, "metadata": { "ExecuteTime": { - "end_time": "2017-05-09T09:55:03.917107", - "start_time": "2017-05-09T11:55:03.905461+02:00" + "end_time": "2017-05-09T09:55:07.830400", + "start_time": "2017-05-09T11:55:07.433945+02:00" }, - "scrolled": false + "scrolled": true }, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "(2.4506423271968965, 0.6721532140851265)" + "
" ] }, - "execution_count": 29, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "dataset.calc_ratio('CODtot_line2','CODsol_line2',\n", - " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)])" + "dataset.calc_daily_average('CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,2,1)],plot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To find the 'best' ratio (i.e. the one with the lowest relative standard deviation ($\\sigma/\\mu$)), the ratio obtained in different periods can be compared and the best one used during possible further replacements." + "Calculate the proportional concentration of different flows coming together." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 36, "metadata": { "ExecuteTime": { - "end_time": "2017-05-09T09:55:03.978297", - "start_time": "2017-05-09T11:55:03.919697+02:00" + "end_time": "2017-05-09T09:55:07.842239", + "start_time": "2017-05-09T11:55:07.833046+02:00" } }, + "outputs": [], + "source": [ + "dataset.calc_total_proportional('Flow_total',\n", + " ['Flow_line1','Flow_line2','Flow_line3'],\n", + " ['TSS_line1','TSS_line2','TSS_line3'],\n", + " 'TSS_prop')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data with drift\n", + "Finding and replacing a dataset with drift." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best ratio (2.5328218826106403 ± 0.16586491872475548) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" + "No drift detected.\n" + ] + } + ], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n", + " plot=True, period=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy import signal\n", + "data = dataset.data['CODtot_line2'][:].copy()\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "fig, ax = plt.subplots(figsize=(18,4))\n", + "\n", + "ax.plot(data['2013/1/1':'2013/1/14'],'k--', label='original data' )\n", + "\n", + "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']+= line10\n", + "\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/14'],'g--', label='data with drift')\n", + "ax.legend(loc='upper right', shadow=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drift detected in period 5 to 6, slope: 55.642857142857146\n", + "Drift detected in period 6 to 7, slope: 56.714285714285715\n", + "Drift detected in period 7 to 8, slope: 48.5\n", + "Drift detected in period 8 to 9, slope: -112.21428571428571\n" ] }, { - "name": "stderr", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", + " plot=True, period=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1380: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", - "You can access Timestamp as pandas.Timestamp\n", - " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" + "Drift detected in period 5 to 6, slope: 55.642857142857146\n", + "Drift detected in period 6 to 7, slope: 56.714285714285715\n", + "Drift detected in period 7 to 8, slope: 48.5\n", + "Drift detected in period 8 to 9, slope: -112.21428571428571\n" ] + }, + { + "data": { + "image/png": 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A/kFWcabTAGZkt9RmApgJ4AyAxwD+yd6WMVaAo0ePwtvbmy9QGdMTvr6+0nOxCjCQVcFXtHDhQgiCILWovk6bNm2wdOlSAIBCoYCnpyeUSiUAYNOmTZoK+43Hn7G6ISkpCZs2bYK3N18eMsbyV5wqxPnNXr7zNdv/BuC3fNa7AHAp6nEZe9OZmprCyMiIL64Y0xPPnj2DiYkJ0tPTce/ePWm9IAjYtWsXoqOj8eTJEwDA3LlzYWdnJ23z7bffIjY2Fo6OjgAAHx8f+Pv7AwD8/PxQtWpVqFQqvPXWW3B2dpbGwzLN4RZYeXl7e+Obb75B9+7dcfv2bbnDYYzpIE1UIWaMadGIESNw9uxZZGZmSvMzMsZ017lz52BhYYHff/8dRARfX18cO3YM4eHhsLW1Vdu2QYMGasvr168HACxatAjW1tZ4+vQpAGD+/Plo2bKltF1YWJiWz+LN061bN3z99df8OcsYYzqOE1jGdNzZs2cBZBWA4QsrxnRbZmYmwsPDMWnSJHz00UcAgEOHDsHd3R3169fPs31oaCiIKE8PizZt2kjPu3btiuXLl2s3cIZBgwZh0KBBcofBsnGvI8ZYQTiBZUxPcLc2xnSfkZERwsPD1SoOF1ZJPyUlBVWqVAGQVV145MiR0ms1atSAh4cHX8yXAaVSCZVKxUM2GGNMx+lPfXvGGGNMT+ScPqZhw4YFbte7d2+kpaVJyz179sTevXtx4cIF+Pj4ICwsjJOpMrJmzRpUqFABqampcofCwDdtGWMF4wSWMT3BX+aM6T43NzeMGjUKr169ktaZm5vDyckJmzdvRsWKFXHy5EkQEYgI7u7uMDMzA5BV/MnKygrVq1dH//790bZtW1SsWFGuU3lj8WetvFq1aoWRI0fit9/y1AFljDEAnMAypvMGDhwodwiMsSLy8/PDsWPHpGluRGPGjMHXX3+N1NRUaWxsbsePH0dgYCAaNWpUFqGyXHgeWN1gZmaGo0eP8ncfY6xAnMAypuMOHz6Mly9fokKFCnKHwli5kZKSgnXr1iEjI0Oj+xW7A1eqVKnQbQMDA2Fubo5jx44ByKpe3L59e7W5Y1nZ4a7auiE+Ph6LFy/GnTt35A6FMaajOIFlTMeJxWD44ooxzVm8eDG+++47/Pzzz2oFl0pLHD9ZlK6/1apVg5+fHy5cuIBHjx7h4sWL+OCDDzQWCysZboGV15MnT/DHH39gxowZcofCGNNRnMAypuOsra3RpEkTKBQKuUNhrNwYOnQoAOCPP/7AqlWrNLZfsQXWxMSk0G1r1aqFmjVrYtOmTejYsSOUSiUGDBigsVhY8fTo0QMLFiyAsbGx3KG80fhmLWOsMDyNDmM6zt3dHQCQkZHBF1aMaUinTp2k561atdLYfk1NTdG6desiX4SvXLkS06ZNw4IFC/DWW29JiTUre3379kXfvn3lDuONxy3gjLHCcAssYzqOv8wZ07xnz55Jz+vVq6ex/c6bNw9Pnz4t8vZTp05Famoqli9fjjlz5sDQ0FBjsbDiSUtLQ0xMjEa7lJeVxMREtG3bFo6OjnKHojHcEssYKwgnsIzpCU5kGdMcJycn6fmOHTsA/Pd/7OnTp9i7dy9UKhUuXryI3r174/Dhw7h//z5SU1MRFBSEsLAwJCUlYceOHUhISEBkZCQ2bNiA8+fPF7swFE+Voxu2bt0KMzMzJCYmyh1KsRERnjx5gujoaLlD0Rj+zmOMFYS7EDOm4wRBkOaMZIxphjhWFQB2796NIUOGYMyYMdi/fz8WLFiA4OBgTJkyBZmZmQCAa9eu5dmHiYkJ0tPTMW3atDyv8f9X/aWPv7vY2FgAwF9//QU7OzuZoykdCwsLzJgxA2PHjpU7FMaYjuIWWMZ03PDhw+UOgTGdlpqais2bNyMjIwMhISHIzMyEr68vvL29kZmZibVr12Lt2rVqiUlaWhqqVq0qLQcHBwMAbGxspOdi8rp27dp8j5uenq6tU2Iy0Od5YMVuz8nJyTJHUno1atTApk2b0KtXL7lDYYzpKG6BZUzHHThwAKmpqTA1NZU7FMZ0Rnx8PH755RfY2trit99+w99//42ZM2fm2c7a2hr//vsvAMDIyAhEhDlz5qBu3bpo3LgxbGxskJGRgYsXLwIAGjVqhICAAHTr1g0JCQlwcnJCt27d8O233wLISm4EQUDLli0REBAgHcfc3By+vr7SMZn+4TGXuiE2NhbfffcdJk+ejH79+skdDmNMB3ECy5iOi4uLQ1xcHGrWrCl3KIzpDBcXF6ll9XXE5BUATp48iXPnzgEAXr16hVGjRmHu3LkAssbBnjp1Cnfv3sXFixcxfPjwfAsqiUnO1atXERcXB4VCgZCQEPTt2xdVqlTR1OkxGeljC6yBQVaHuhYtWsgcSen5+vrC0dER3t7euH37ttzhMMZ0EHchZkzHDR8+HG3btkVqaqrcoTCmE8LCwjBu3Di1dfPmzQMADBgwACEhIVi9ejVWrlyJDRs2gIigVCoxefJkafszZ85gw4YN0vLUqVNBRKhRowZGjhxZaDXg+vXro23btujUqROGDBnCyWs5YGVlhWXLlqFSpUpyh1Js4hRro0ePljkSxhjTPm6BZUzHeXh4AMgab6ePF1aMadrVq1cBAL1794aJiQlmzJiB4cOHY8WKFVILae5CNgYGBhgzZgwaN26M3r17c3dRlkf37t3RvXt3ucMokYoVK+Kzzz4rFy2wjDFWGG6BZUzH6WN3Nsa06ebNmwCAmjVrws3NTWoxLSwpNTAwQJ8+fTh5ZflKTEzEy5cvoVQq5Q6l2KpVq4br16/Dx8dH7lAYY0zrOIFlTE9wIstYFnHcK8+fyjRp9+7daNq0qTQljS4YMGAAKlSogEePHsHb2xuhoaEFbhsSEoL4+PgyjI4xxuTBCSxjeoITWMayii8BQKdOnbB161aZo2HlkS591p4/fx4KhQLr169Hhw4d0LBhw3y3E6d+2rdvX1mGpxVt2rTBsmXLYG9vL3cojDEdxQksYzqOJ3NnBQkODsbPP/+sUxfc2rZx40YAwPbt22FmZiZzNKw80eV5YMWxrX379s33dXEeWIVCUWYxaUu1atWwZMkSvP3223KHwhjTUZzAMqbj9u3bB4VCwdPosDxGjx6NpUuXwsvLS+5QyoRKpcLq1asBAF27dpU5Glbe6OLYaDGmwMBAmJubF9gCq4tJd0lFRUXhww8/hKurq9yhMMZ0FFchZkzHvXz5EtHR0ejWrZvcoTAdk5KSAgB6WXSmuBITE3HhwgVs3rwZycnJMDLiry+mHbqUDN68eRM9e/bEsWPHEBUVVeAYVzHmDh06lGV4WhEQEICzZ88iNjYWQ4YMkTscxpgO4hZYxnSctbU1LC0tkZiYKHcoTMeYm5sD+G8OyPJsy5YtGDFiBCIjIzFr1iy5w2HlUO/evbFhwwaYmprKHYrE0dERQFarJPDfGPDcxASW54FljL0JipzACoKwSxCEV4IgPMqxrpYgCOcEQfDN/rdm9npBEISNgiD4CYLgJQhC1xzvmZS9va8gCJM0ezqMlT8PHz4EAKSlpckcCdM1+/btQ2JiItq3by93KFrl6uqKhQsXonbt2pg/f77c4bByqlOnTpg1axaqVKkidygAspLS3IXKCqqJULVqVUyZMgUtW7Ysi9AYY0xWxWmBdQQwONe6hQDOE5E5gPPZywAwBIB59uNLAFuBrIQXwE8AegLoAeAnMelljL2eLnVrY7qhQoUKMDU1hYFB+e5Mc/LkSQCAi4tLuT9XJp+YmBh4e3vrRCEkIso3joIKl7311ltwdnbGmTNntB0aY4zJrshXAkR0BUBMrtXWAHZnP98NYESO9Xsoy00ANQRBqA/gQwDniCiGiGIBnEPepJgxlgMnrqwgtra2EAQBAQEBcoeiVV5eXujduze6d+8udyisHDt06BA6dOggddeV05o1a2BiYpJn/aZNm1C7du183xMfH4/U1FRth8YYY7IrbRWMekQUBgBEFCYIQt3s9Q0BBOXYLjh7XUHrGWOF4ESW5Xbv3j0AQGxsrMyRaNfq1au5Cz0rM7rwWevh4VHga9HR0XnWeXt7IyMjA4cOHcLy5cu1GZrWtW/fHjt37kSvXr3kDoUxpqO01Rcrv1r09Jr1eXcgCF8KguAhCIJHZGSkRoNjTJ9Mnz5d7hCYjtLFaT807dy5cyAi9OvXT+5QWDmnS/PADh06tFjbi/PAlgdVqlTBlClTYGFhIXcojDEdVdoENiK7azCy/xXL4wUDaJxju0YAQl+zPg8iciAiSyKyrFOnTinDZEx/bdu2DUSEevXqyR0K01G6cMGtLYMGDcK7774rdxjsDaBLCWxxhwXoQsyaEhERgU6dOuHw4cNyh8IY01GlTWBPABArCU8C8G+O9ROzqxG/AyA+u6vxGQCDBEGomV28aVD2OsZYAZ48eYIrV67IHQbTQbp0wa0NGzZsAAA0bty4kC0ZKz1d6tGwdOnSYm0vtsD26NFDG+GUqYCAADx8+BArV66UOxTGmI4qzjQ6TgBuALAQBCFYEARbAMsBDBQEwRfAwOxlAHABEADAD8B2AF8DABHFAPgVwJ3sxy/Z6xhjBRgxYgT69etX7sc5suJ7++235Q5Ba4gIc+bMAQAcOHBA5mjYm6BPnz7YuXMnatbU/ckRct+0EpfHjBkjRzgaVV5vyDHGNKfIRZyIKP/Jx4AP8tmWAMwoYD+7AOwq6nEZe9P5+voCAFJSUvTiwoqVHQcHB2zfvl3uMLQiJCQEDg4OiIiIgJWVldzhsDeAhYWFToy7VKlUEAThtYnc+vXr8e2330rLderUwezZs9GiRYuyCFGrOIFljBWGJ9RjTMfxlzkriC51edQkKysrNG7cGI8fP8b3338vdzjsDfHq1SvcvHkT6enpssaRnJxc4Oe+sbExgKyppXJq1KgR9u7di507d2o9PsYYkxsnsIzpCU5kWW6fffYZBEHAs2fP5A4F/v7+2L59O3x9feHv71+s97q4uMDFxQVhYWG4ffs2bty4AQCYMGGCNkJlLF8nT57Eu+++i4iICFnjSEhIKPA1hUIBAHnmRFYqlUhOTkZmZqZWYysL/F3HGCtMaeeBZYyVEf5SZ7m9fPkSABAaGorWrVtr7TjR0dEwMzMDkPV3OHv2bCiVStjb22PQoEGws7PDF198gbCwMOk9ycnJqFSpEgRBgFKpxI0bN5CUlASlUglPT08sWLAAxsbGICJ89NFHAIAKFSogIyMDAPDee++ha9euWjsnxgoi92dt5cqV8ccff2DRokUFbiMmsiIPDw+kp6fD1dVV2+FpXbdu3eDq6or27dvLHQpjTEdxCyxjOm7BggUAAAMD/u/K1IldiJVKJQBg//79sLe3L9U+ExMTkZKSgrCwMMybNw+CIKB27dqIiIjA4cOHYWBggD///BMPHjyAUqmEq6srBgwYoJa8AllzOQ4bNgzfffcdBg8ejD59+mDIkCEYNmwYfvzxR5iYmMDAwACGhobSe8TkdefOnbh48WKpzoOx4tKVLvk1a9bEwoULkZycXGAvhNOnT6sty510a1KlSpUwePBgrj7OGCsQXxEzpuP++OMPEBEaNmwodyhMR6lUKqSnp2PChAn46quvYGpqirVr16ptEx4eLnXN/fHHH+Hu7o6MjAysWLECkZGRAIDHjx+jWrVqaNOmDY4ePYrVq1dL769RowYmTpwoLa9atQpLliyBra1tgXG5uLhg3bp1cHNzK9b5WFpaFmt7xjRJ7mQwMTERz58/h5GREbp06ZLvNpMmTVJbljtmTQoKCsJbb72FPXv2yB0KY0xHcQLLmI7z9PTMc7edsZyUSiU8PDyk5eTkZNjZ2QHIuhgGgJ49e8LKygppaWn49ddf0bdvXxw7dgwLFy5E3bp1YWhoiHbt2gHIuoDs3r07Pv30U7Ro0QKRkZEwMTGBq6srvvrqK6SmpuLdd9/FsmXLsGPHDqSnp2Pnzp148eIF3n//fWn6m6ioqEJjHzp0KLp164bly5eDiEBE6NSpk6Z/RIwVSldaYM+cOYMWLVrg6dOn+N///pfvNrl75IjzwL7//vtaj0/b/P39ERERgU2bNskdCmNMR3ECy5iO++STTzBkyBC8evVrdS7SAAAgAElEQVRK7lCYjhEvVg0MDODn56f2mpmZGRYvXoxq1aohODhYGi8rJpdAVhEoUc2aNWFjYwMAGD9+PDp06IBDhw7B398ftWvXBgD069cPW7duRcWKFdWOVaFCBUyZMgVNmjTB+fPnsXbtWqSnp0vjZgHA2toarVu3RmJiImJiYjBs2DCEhITg1KlT8PDwkLrKMyaX9957D//88w/q1KkjaxxiEadq1aqhcuXK+W7j7u6utiy2wO7YsQM9e/bUboBaVp5akxl7HZVKxX/vJcRFnBjTcWLikZKSInMkTNf8/vvv+P333wH813p08eJF2NjYIDAwEH/88QcA4NSpUwCA6tWr448//oC9vT1Gjx4NAwMD+Pj4ICQkBFZWVqhcuTJ27Sr9NN2CIKBChQoAgDt37iA4OBgjRoxQ28bZ2bnUx2FMk5o1a4ZmzZrJHYZaAgtkjSdPTk5W2yY4OFhtOed40du3b2s5Qu3iC3r2pli7di0WLlyIuLg4mJqayh2OXuEWWMa0zMnJCQEBASV+P3+Zs4IoFAqp8FHTpk0BAH379sWJEydgYGCAZs2awc3NDUOGDEHFihVx6tQp1KxZE0SEAwcOAADatm2LAQMGFNjSU1qWlpZ5klfGdFFYWBjOnTsn+83C3Alsy5YtpdfERLVWrVpq7xH//4vELsX6iL/z2JsiPj4eSqWSixaWALfAMqZl48aNQ6tWreDr61uq/fCXOstt0qRJcHJyQq1atXD69GkYGhrCwMAAHTt2RFpaGoyNjaVtU1JSdGaMH2O6yM3NDRMnToSfn59a0ljWEhISUKVKFalCd3p6uvRaUFAQAGDXrl2oVKmSNE5UvJElyszMlHpBMMZ0y7Vr13D27FksW7YMAHDhwgV8/PHHMkelXziBZUzLTExMMGrUqFLvhxNYlptYPTgmJgbr1q3D33//Lb2WM3kFdKdADWO6Tu7P2lGjRsHCwkJanjVrFmbMmJFnO29vb+l57jGxCoVCbxPY3r17w8vLiyvvs3Krd+/eastc46T4uAsxY1pmaGhYqu5c4jhGIyO+38TU5ZyeZtWqVTJGwpj+05WbPFZWVpg2bZq03L59+3y3u3TpkvQ8d9ItdxJeGiYmJujYsWOebtJFsXv3bhw7dkwLUTGmGTlnDBBFRETIEIl+4wSWMS1SqVRISUnBzp07S7yPBQsWgIjQpEkTDUbG9JlKpcLGjRul5StXrnBrBWMaInfyd+XKFURHR0vL/fr1Q9euXV/7npwxt27dWq8Lwvj5+cHQ0BBbt24t9nsnT56skR5PjGlLfg0aoaGhMkSi3ziBZUyLxA+q2NjYEu/j2rVrOHr0qKZCYuXAjRs3MHv2bMyZMwdHjhxBnz595A6JMb2nCy2wt27dQr9+/bBo0SK19YVN7ZMzgbW3t9dKbGXF398fKpUKf/31l9yhMKZxPXr0QHBwMK5duwaVSoUZM2ZI87WL4uLikJmZKVOE+oETWMa0SExgc1+MFMfnn3+OTz75BCEhIZoKi+mxH374QRo/895773FrA2Ma0r9/f7i6uqJBgwayHP/atWsYMGAAgKyWxJw+//zz175XrFwMZJ1HaGgoUlJSsHbtWjx48EDjsWpTaVrAbW1tZfv9seLp0qULBEGAr6/vG5WsPX78GMnJybCysoIgCFi/fj08PT0xYsQIBAcHY8qUKahZsyZsbW3lDlWncQLLmBYplUoA/02HUBLh4eEAeB5YlnVDRJw/tVOnTrC2tpY5IsbKjwYNGmDw4MGoUqVKmR87NTUVvXv3RlJSEszNzWFlZaX2+pQpU7B37161dfPnz5ee566anJaWhlevXsHOzg7r1q3TXuBaUJoEtnXr1mjWrBlu3ryZZ+5cpjtCQkKkGyutW7fG7NmzZY6o7MyaNQuTJk2Slo2MjNCrVy/8+++/aNy4sdTzYM+ePdy1+DU4gWVMi8QW2JMnT5Z4H3KPx2K6w8DAAJ6envD09MT9+/flDoexciUoKAhHjx7N052vLOQc75mamprvNuPHj4enp6e0PHToUOn522+/rbatu7s70tLSAAA3b97UZKhaV9LvPGdnZxw4cAArVqzAu+++i5kzZ2o4MqYpua+JnJ2dERISgqioqHKftEVGRqoNCSCiAqdZbNiwISZNmgQ/P7+yCk9vcALLmBaJY6o00YWLE9k32/nz57FlyxYAWRerujBej7Hy5Nq1a7IM1yAiODg4SMtmZmb5bicIglqiKlaoBwAfHx+1bY8fP67hKHXfgwcPcO/ePakA1oULF4r0voCAALi6umozNJaLu7s7atasCVdXV9jY2CAoKAiNGjVCnTp10LBhQwiCgLCwMABAWFgYAgMD5Q1YQ1QqFZ4/f65WdFEQBPj4+ODo0aNYvnw5fvzxR/zzzz/S63v27IG5uTlsbGzkCFln8bwcjGlR5cqVUb9+fQwbNqzU++IE9s22fPly+Pr6YvLkyahcubLc4TBWbpX1Z21ERASePn2KjRs3Ytq0aUWeMu3MmTNISkqCqakpJk2aBDMzMyl5O3LkCJ49e6bNsLVm0KBBiIyMLHYl5adPnwL4b7zwy5cvi/Q+CwsLZGZm8ndsGUlPT8ehQ4dgY2ODwYMHo0WLFvkW7Dpz5gwmT54sjWkuD7+fXr16ISEhAe+++67a+rZt26Jt27Zq6+Lj47Ft2za0adMG1tbWcHR05MJmOXALLGNaZmBgII2FLYmtW7fC2NhYbyelZ6UXFBSEy5cv49NPP+XklTEtEXs1lOWFckJCAurXrw8AsLS0RMWKFYs15/f3338PIGsaDktLSwBA165dYWCgv5d3RkZGqF27NipWrFis90VFRQEAMjIyACDPmOGCvEkFhOQWHR2NihUrIiMjAx988AGArDGwQUFBiI2NxZAhQ6RtbWxscO/ePblC1QobGxtUr14dAwcOLHTbatWqYf78+Rg+fLi07urVq1CpVOUimS8t/f2EY0wPxMXFISQkBPv37y/xPqZPn46MjAw0b95cg5ExffL3339DoVDgq6++kjsUxsqtsu6WT0RSsaa2bdvmaZUpyJEjR6Tn8fHxCAkJQWhoKLp37w4HBwc8ePAA69evR4sWLQBA74q9eXt7QxAErFmzpljvi4uLQ9OmTaXl8ePH5zvnZm6dO3cudoysZGrXri09z9kdvlGjRqhRowYOHjyIW7duSZX2xfmPb9y4gWXLlpVtsBoWGBiIzp07IyoqSrppVVRilfE+ffrA0NAQBgYG+O2335CWlgYigqOjo1Tw803BCSxjWiTeCU5PTy/xPs6dO1eqBPhNU5qfta46cuQILC0t0apVK7lDYazcEqsPX758GYIgQBAEeHh4aO14f/75J7y9vTFq1Kg8Y1hfJ+fUWY6Ojli7di2ArIvcadOmwdjYGKGhoTA2NkZMTAyWLl2q8di1yd/fHwBw8ODB124XEhICLy8vODo6AgC6d++uNk7QwMAA9erVK/R3OG7cOHz55ZelC5oVycqVKwFkTfWU3/dZ1apV0aNHD5w4cQLNmjUDAGzatAmnTp3CDz/8gF69euH58+dlGbLG7N27F++88450XVgcVatWxapVq9TWff/99xgyZAh8fX1hY2OD+vXrS+OG3whEpPOPbt26EWP6KCwsjADQxo0bS7yPOnXqEAAKCAjQYGTl0759+wgAeXl5yR2KxigUCho0aFCp/oYYY4WLi4ujK1eu0Pvvv08ACABZWVmRg4MDTZw4kdLS0kilUmnseACoV69eJXrvpk2bpBgB0MCBAykwMJCIiGrWrEkzZ86kiIgI+vnnn8nHx0djMZeFf//9lwCQpaVlntfu3LlDEydOpI0bN6qdPwDpPF+8eEGHDx9Wey0pKanA4wUGBtLJkycpIyMjz2tXrlzRu5+frkpPTydTU9NifZelpaUREdGFCxek3+VPP/1Ex48fz3f7jIyM1/6u5fT+++9T69atS7WPiIgIiomJIScnJ+nnUaNGDfrggw+kZWdnZ5o+fTqdOnVK7fMqMjKSLl++XNrT0DoAHlSE3FD25LQoD05gmb4KCQkhAGRvb1/ifRgYGBAA8vb21mBk+kelUlFYWNhrt7G2tiYAtGvXLmldcnIy+fr60sWLF2ns2LFUv3598vLyokqVKtGIESPy7CMlJYWUSqVGYk5MTKS7d++Wej8pKSmUmZmpgYgYY4VJS0ujH3/8kWbOnElBQUF5EqWHDx+W+hgqlYoA0MyZM0u8j+DgYCmm8PBwaX2DBg0IAK1fv54AkK2tbanjLUvHjx+XzuvatWtqr+X+XeR8/P3339J2KpWKbG1tpdcEQaD09HRSKBT06NEjUqlU5O/vT35+fvT7778TAIqKiiKirJuGAQEBlJycrLb/1NTUMv05aMrDhw/p66+/pvv378saR0BAAAGgnTt3luj9AwcOJADUp08ftd+Lq6srHTlyRHodAB04cCDP+9PT0+nx48d0/Phxjd6IKgqVSkXVq1enr7/+WmP79PPzo3bt2tGff/5JCQkJZGxsTACoZ8+e0s/Bzc2NiIiePn0qNYa8fPlSYzFoAyewMggMDKSUlBS5w2A65OXLl6W6y070XwL76NEjDUamf9auXUsA6OnTp2rr3d3daf369UREtG7dOgJAH3zwAU2bNo1q1qwpXSTm/MKrW7eu9LxLly4EgM6ePUsKhaLUF5U5jRs3jgBQZGRkid4/Z84cWrRokUZiYYwVX0ZGBjVp0kTt86NWrVp05coVevz4MZ0/f75E+x0+fDgBoB9++KFU8QUEBKjdsCMisrKyIgDUu3dvAkAWFhalOkZZO3bsmNrPW/To0SNpnYmJCU2bNo3S0tLIzc2N3Nzc8tx4DA8Pp4YNG0rvERNVADR9+nT67rvv1I4TEhJCoaGh0vI///yj9vqrV6/K+kdRahEREVL8PXv2lDWWy5cvEwA6c+ZMid6vUCgoMjKSUlNTqX379tJ51atXL8/NjFq1apGfnx8plUpydHSkbdu2qb0+YcIEOnr0qIbPMC+VSkWHDx+mn376iQDQ1q1btXashIQEun//PvXq1Us6zy+++IJu3rwpLa9bt05rx9cUTmDLmHg39aOPPpI7FKZDwsPDCQBVrVq1xPsQE9iC7vrfv3+fFApFifevL8S7q66urkSU1bq5efPmPBc6VatWzfNl5u3tLT1/8uQJhYWF0eDBg6ly5crUpk2bfO/mOzs7U0xMDKlUKnJycqL58+fTkCFD6NmzZ5ScnEzXr18na2tr2rBhAx09epTS09OJiCgpKYkePHhACoWC2rZtK+2ruOzt7QkATZkyRXM/RMZYsalUKoqMjHxt619h3VRzE99TWK+SklixYoWUuAKgNm3aaPwY2pQ7gVWpVOTq6kq7d+8udgu4mECcOHGCunbtKu3zww8/pA8//FDtOBUrVpQ+s8XHJ598Ij3Xt2E8KpWKZs2apdYKLWcjyw8//EAAyNfXV2P79PLyoo8//pgAUP/+/enBgwdST6xq1aoV+n8WAAUFBRER0aVLl2jt2rX08uXLfLuTl4SHh4d0HGNjY4qLi9PIfguiUqnozz//pPv379OuXbsoOTmZmjdvLl2H6sO1YpkmsAACATwEcF88MIBaAM4B8M3+t2b2egHARgB+ALwAdC1s//qQwMbFxeW5W8gYEVH79u3pk08+KfH7X5fAPnnyhADQwoUL1dY7OTlRmzZtNNYVVheICezp06eJ6L8LwCpVqkjP4+Li6Nq1a3T27Fm6c+cOTZ06VUoeo6Oj6cmTJ3n2q1KpaO/evdI+Vq9eLT3/7bff8v3CMzY2piFDhqits7e3l8Y8i4m22BJy8uTJIp9nUlKS2nFDQkI08wNkjJXK9evXadWqVQVeCHfu3Jm++uqrQvdz584dunTpkla7dHbt2pVq1KihNwlsenq6lDRER0eTp6enWmua+PzSpUslPkZKSgo5OjpKNxpUKhXt2bOHli1bRgDI1NSUWrRoQatXr6bTp09LLXQnT54kADR69OgSd38tjL+/f4luZogJen7XB/fu3SMAtH37dnry5AmdP39e+jn++uuv+SYzSqVSuhmraWPGjKF69eppfL8pKSl07949afnVq1c0YMAAOnLkiHS+7dq1IxcXF/L19aX4+Hjy8/OTXqtcubJa67wmby6J47nt7e2lRLmsxcTEUGhoqCzHLgk5EtjaudatBLAw+/lCACuynw8F4JqdyL4D4FZh+9eHBDYwMJAT2CIQW8LySyTKq44dO9LIkSNL/P4DBw5QkyZNyM/PL89rd+/eJQDk6Oiotn7s2LEE/Dempzzw8PCgyZMnU3BwsFpBB3t7ezp58iTdvHmzVPuPj4+n2NhYIsrqliwIAv3yyy9SK4Z4ISXeUIiNjZXGOAOgjIwMsrS0lJZjYmLI2to63y6GYWFh9PDhQ3JxcaH27dtTYmIixcbGUr9+/QiAVJChKBfDjLGyFRkZSZ999lm+SWydOnUKfb+4rTaHhXzxxRfScfQhgRVvRIrXCDVr1qSIiAgaNGiQdB7Tpk3T2vHFYkH5SUxMVPsda4O479wtpO7u7hQREZHve65fvy69b+7cudLYRrFewuLFi8nIyEjq+pyzoQUALVu2jARBoAcPHhBRVvIqvmZpaUmdO3em7777TmPneOzYMXJwcNDY/gpz584dOnLkSIGvi93P3333Xem869evLz23sbEpdQzbt28nAPTixYtS7+tNoQsJ7FMA9bOf1wfwNPu5PYCx+W1X0EMfElgioo0bN5KTk5PcYei0JUuWEABaunQpRUdHyx2O5MCBA2RpaUnXr1/X6OB+sWiBtr70rl69SoD6mJKMjAzav38/AaDbt28Xa3+zZs2iBg0akIeHx2u3CwwMpNmzZ5d5gqxUKmnFihW0f/9+GjduHM2aNeu1Fx6aEh8fX2D3QKVSKd0FB0CNGjXKs434JdalSxcKCwujDh06qF1IDB06VG15z549OltJkTGWJSkpiSZPnqz2f3fgwIG0dOlSSkxMzPc94lh+AJScnKy12NasWSMdp0OHDlo7TmnZ29tLXYPzuxFw48YN2rNnD926dUvWOD/99NMySWCPHTtGN27coOPHj0sJZ0FjmDt37iy9r3bt2tK24t/hyJEjqV27dmrvuX37Ni1atIhMTU2lFlogq0t1fpWdK1asSKdPny5Xvblyy8jIoBUrVtDu3bspPT1d6nbdu3dv+vzzzykqKooePnxI8fHxxd632LLL9XGKrqwT2OcAPAHcBfBl9rq4XNvEZv97EkDvHOvPA7B83f71IYG9d+8enTx5khwcHGjt2rVyh6Oz9uzZQwCoR48eBIDu3LlTJsdVKBTk7u6utu78+fN09erVfLuJPn78mFQqFZ06dUrqmhIVFUVKpbJYlQgfP35c6i+9Y8eO0Y4dO/J9zdXVlQDQzz//TCqVim7duqV2Hg0bNpRa9QDQiRMnaPPmzdK0APHx8ZSQkCB1lWnWrBkBoDlz5lC3bt3ol19+yfe44o2Isvxbd3NzUzs3bXVzKqkrV64QABowYECe13799Ve1C9z8Wm46duxIo0aNKnB6AMaY7nn8+LFaUprzkZaWRpmZmZSSkkIhISFqn8XaLggUHR1Nzs7ONGnSJAK0N34zPj6eHBwcSpTgiLGJDysrKzIyMiLoaC+tnLFquiJxp06dCAA5ODhIQ08EQVA75oYNG+j333+nmTNnUmhoKF2+fJkGDBhAqamp9OrVK7Vthw0bRkBWt/bXyVmx9p9//qF169bR/fv3acuWLQRkFTxs0KCBRlojvb29S5QEljWlUklJSUl09uxZtZ9p27Ztaffu3VIdjqLYsmULvfPOO1qMtvwp6wS2Qfa/dQE8AND3NQnsqXwS2G757PNLAB4APJo0aaLVH5YmiH/gvXr1InNzc7nD0Vn+/v4EgAwNDQlAgYmZJqSnp1NycjJFRkbS6NGjCQD9+OOPRET07bff5nvBIT4WLVpEX3/9tbR86NAhArIGwRsaGtK9e/do69athX5pi92hDh48WOLzqF69upRU55b7wqldu3Zqyy1btszT2ic+fHx8qG/fvtLy0aNHpediIaQOHTpI45JWrVpFrVq1otTUVGl6gq1bt+Y7R55KpdJoS3bOaRUAaLRbk6Zcu3aNgP/G6OakUqmku9utWrWiqlWrkpmZGbm4uFBoaKjsrQuMsZJLTU197fdJ7kdZTeHx7NkzatmyJQGgy5cvU2ZmZqHTsalUqmJ1bRYL8+Q3bcnriBXfgawhMOLPJC0tTWMFdDTtxYsXUswXLlzQ6L7Fcb7Lly8v8t9R7u+N8PBwWrNmDUVERFBKSgoBWWNdCzunNWvW5OlNFRwcTNWrV6cDBw5I3btnz55d4vNLSkoiIKvbsr7I+Tea+3H27FnKyMigtLQ0rlOhYWWawJJ64vkzgLl4g7oQ//3339IftdidqHbt2tSjR48yn2tK18XGxtLcuXOlqnHF/dIrznFyTn2Qs5Lg4sWLafDgwdKypaUlzZgxQ2ppXb16NY0cOVLtwyrnWBMANGPGDOn58ePHydjYmKZMmZKny66Xl5eUAJeUOOYyZ5EC8YNVnCZGfEyfPp0aNmxIK1eupLCwMIqLi5MuSJYuXarWpdnOzk6aNyznI+cYEHE5Z6Kbc0yS2LL45Zdf0q+//koqlUoqvjFt2jSNzIEaGxsrHa958+bk5eVV6n1qg9j6/bqCTeI8hIyx8sXd3Z3eeeedQpOOrl27lllMn3/+uXTcOXPm0Lx58wgA+fv7E1FWS9PcuXPVElaxO29+N+LyI97cXb58eZHjSk9Pp6ZNm0rfQ/pEnHon91jO5OTkEvcoK6jC9a5du6SxzB9//DFNmjRJ+n5u0KBBodeXMTExpZo/XNx/zoJax44dK9G+xBbdspi6RpOcnJyKdDPBzs6O/vzzT3rx4gUdPnyY/Pz86Ndff6V9+/bJfQp6p8wSWABVAFTN8fw6gMEAVkG9iNPK7OcfQb2I0+3CjqHrCWxUVBRZWFjQli1baOXKlWp/1AkJCXKHp1PEFkOxTL4m72KmpKTQzJkzadKkSbRz506134OzszPZ2trSW2+9RW5ubpSRkUHBwcEF7svJyYmmTp2q1k1o9+7d1KlTJwoKCqL4+PgCP8i6d+9OAGjq1KlS66eVlVWJz0tsrfb09JTWiXe9kX03f82aNUXuepqcnEw7duyguLg4OnPmDM2ePZt8fHzo9u3bZGFhQUFBQdKFDgDatm2bdLHxzjvv0KNHj2jv3r106dIl2rFjh7Rd3759ycbGJs/PY9GiRaW6kfPNN98U6U6y3MSCWs2aNZM7FMaYDOLi4sjT01Oqapv7YWNjU6qEorjee++9fOP49ddfqU2bNmpTiN28eVOtp8uCBQto1qxZRUrKatSoQTNmzCjwdYVCQdu2baPY2FhaunSp2k1QffTnn39Kxflu375Nnp6e9NVXXxFQsilifHx8CMgacynODwxkFQZUqVR5bnqmpKSU+ZjKGzduUL169ej06dPS937//v1p0aJF5OvrSzt37iSlUkm+vr55ikoSEY0aNYoAzXe91jaVSkXXr1+n6OjoPD3BivrQZk/D8qgsE9gWyOo2/ACAN4Al2evNkNU92Df731rZ6wUAmwH4I2vqndeOfyU9SGBzio6OliYPB7RbZVAfiV/sERER0odzaYWHh5Ojo6NaJbl//vmHgoKCaM+ePTRw4ECt3Ejw9/en5s2b0+3btyktLU2assDc3DzfD7ErV65QeHh4scuZi+NgxAQ2JiaGjI2NqWfPngUWCilLf/zxBwGgc+fO0YEDB6TzdXd3l56fOnWq2PuNioqi4OBg8vb2pi+//FLni0iIFYnlniyeMSa/devW5fkOKOuidznrO9StW1d63qVLl0IvupcvX04mJibSTdKCJCYmUpcuXWjo0KEFbrNp0yYCIO0vZ9Ksj4KDgwnIGjIm9kISf74TJ04s1r7S09Olm9Ti2MpTp05pZX5gTTh9+vRr/27Ec0lMTKSIiAgKDAyUpvv75ptv5A6/VJRKpdr1nZmZmVQ863WPyMhIuUPXK2WWwJbFQ58SWNG9e/fI2tq60Gqub5rFixcTkDXfWrVq1Sg8PLzU+xQ/JMTutIsXL5al67ZCoaC0tDRSqVQ0ffp0mjFjBgUEBNCIESPyfKAVdT4wcUJ6ANKdXrH1tTiFBLQtZzXNnMWVfv75ZwL+m1Dc3Nxc6r72OlOmTJHOW9cT15zq169PU6dOlTsMxpgOOHjwIDVs2JBOnTpV7IrwmpBzWpSQkBBavHgxPXjwgJydnaX1zZs3V/tuatmyJQ0YMIDS0tLUbgi3bNmSVq5cmaf4VMOGDal69eqvHVu7fv36PN+BV69e1fbpa03On03OR69evQjIGrL022+/Ffj+zZs3U+XKlSk1NVUaZgRoZt5RbRNnPijskbM3olhfI3chTX2VlJSkNu+uvb09LV68WBrulbPRQhcaGfQNJ7A6gMe/5jVnzhy1/+Bnz54t8b6USiX98ssv0r48PT11ssu22D2ocuXKUqxvv/222jYJCQl5poO5ceOGtH21atXoxx9/lO5iynExVFILFy6kVatW5Ungc3fhvnTpEp06dUrqiis+ymKaHE3IzMwkAwMDmjx5styhMMYYEWXNFfq6bq1KpZLCwsIoICCAbGxs1LoM/+9//yMAebpEd+/enZRKJSUnJ5MgCLR06VJKS0sjOzs7taEuoqCgIAL+q6qriRvXcsuvhT33QxCEPAV+bt++LRVmBED79u2jGzdu5FsMUVf5+vqSUqkkR0dHevbsGalUKsrMzFTrBSe2ugOgevXq6dU1S0mpVCr68MMPqUGDBjzvaylwAqsDVCoVXbp0SWvl63VJTEwM2dnZUVRUFAUEBNDdu3fzHaMxffp0tQ/40kxq7eHhIe3HxcWlNOGXGbGL7aZNm2jnzp3k7Ows3SXv1KkT2dnZ0bmzQXMAACAASURBVMuXLykqKko6N/FOn1hc43VFgnSVWEgq95isTZs2STd6cq5fsmQJffXVV3qTvBJlVfwEQDVq1JA7FMYY0wiVSkVKpZKmTZsmTbliZ2dH8+fPlz6vnZ2d6a+//pKWV61aRWFhYRQZGUnTp0+X5iW3tLSkRYsWyX1KGqFQKOijjz6iyZMn06VLl6Rzz1kwEsgqtiTKWcVYfJSXnwdRVsukhYWFNJXc06dPaf78+XTp0iWZI2P6hBNYHRAVFUVGRkblakxcWloahYWFkaurKymVSlKpVNS1a1fpw7hjx45qH85ixbro6GhKTU2VKhaWJoFNS0uTqtv++++/dOPGDY2eY1kQJ8oGsuYNzf2lduHCBQKy5gz95JNPaNy4cVI1PF2cH684cs/nCoDWrFkjPTc3N6ekpCS5wyw2scJz//795Q6FMca0QpzXVpzLvV+/fpSenk7h4eHSOgD01VdfqRUbBKA21KS8SUhIkG7ai0Uqgaw57wMCAujq1atq4yWTk5PJzc1N74oaMaZtnMDqCPHD6vz583TmzJkij33UFbGxsXT69Gnq1q0bzZ49W20A++zZs6XpZoyNjalOnTp5kpMFCxbQ1KlTpeWDBw+qdSctSQLbuHFjAqDXXVJytrDm/MIXnw8bNkyqVimuE+94P3v2TOboS0elUtHcuXOlolfiw9HRUa/Gu+b28uVLArjiIGOs/CtobN+VK1fom2++oWfPntHKlStpzJgxUiL3JlGpVLR06VJycXGRvuMmTJhALVq04KnUGHsNTmB1hJjgiXOStmzZUu6QiuTgwYMUGhpKVlZWakmGvb292h3Ew4cP0+LFi/NNPNLT0yk1NZX69++vtg9vb29pehmxtSo5OZkuXrwodTX18/OjlJQUUqlUamOJxfnI6tWrp/djjGNjY2njxo108+ZNWrduHaWnp5OXlxfduXOHYmNjpe3En9uGDRto4sSJ5WL8kMjBwUFtLJA+E6sQb9u2Te5QGGOMySz3kKlbt27JHRJjOo8TWB2hUCjo2rVrah9iuph4ubq60rJly+jChQtqc7SJk2h/9NFHUsGluLi4ElXLO3PmDA0fPpyOHTtGGRkZ1Lx5c2lsjPgQKwmLj82bN1Pt2rWl440fP56MjY31olqfppTnL799+/ZJ53fgwAG5wymViIgIAkC1a9eWOxTGGGMya926tdr1DGOscEVNYI3AtMrIyAjvvvsuOnfujAcPHuDZs2do0aIFzp49C3Nzc1lj27JlCxQKBVxcXHD27FkAwODBg6XnALBnzx7s2bNH7X3Vq1dH9erVi328QYMGYdCgQdKyl5cXTExMMH78eGmds7MzxowZg+DgYACAm5sboqKiYG9vjz59+mD//v2wtbXFW2+9VezjM91TqVIl6bmRkX5/HJmamgIALC0tZY6EMcaY3J49eyY9t7OzkzESxsof/b5i1BOCIOD+/fsAgFGjRiEwMBDr1q3Dli1bZInn5cuXaNq0aZ71TZs2xaBBg3Do0CF4eXmhUaNGWo1DvOD38/NDZmYmLCwsAADPnz9HaGgo0tLSYG5uDgMDA/z888/4999/sW/fPlhbW2s1Ll31119/oU+fPvDz80Pjxo3lDkcjRo0ahbt376Jbt256n8CKyXjPnj1ljoQxxpjc2rRpgydPngAAPv74Y5mjYax8MZA7gDeNj48PAMDR0RHx8fFldlyVSgU/Pz+MHTtWLXldtmwZUlJSkJmZicDAQHz77bcwNTWFlZUVmjRpUiaxtWzZUkpegayWuCZNmqB169YQBAGtW7cGAERHR2P8+PFS4vum2L17Nzp27IiqVasiIyMDBgbl679t27Zt4e3tjf79+8sdSqlkZmYCABITE2WOhDHGmNwUCoX03MTERMZIGCt/yteVsB64fv06pk2bhoYNG6JGjRoa2adSqcT69euRlJSU78VzeHg4DA0NYW5ujgMHDmDQoEHw8vICEWHJkiWoVKkSDA0NNRKLNly5cgXPnz+HjY2N3KHIYuLEifDy8kLLli0BoFwlsI8fP4atrS2ICNWqVZM7nFLJyMgAAFy+fFnmSBhjjMlt8ODB0nNOYBnTrPJzJawnatWqBQcHB3z66acAgPXr15d4XxkZGVAoFPjf//6Hb7/9FlWrVkW1atVw5coVAMCtW7ewbt061K1bFy1atJDe5+TkhI4dO5buRMpQvXr10KxZM7nDkM3QoUPRt29fqFQqAOUrgf0/e/cdHlXRBXD4N0lIAgQCCQm9914EBFEQkS4CKiIiUlQEAf0EVBRRwA4qFlABRXoTUEroIEWUTgKhB0JLgFBCSCVtvj+2kCUJpG/JeZ9nn+zenXv3ZG/KnjszZ27cuMGiRYt47733zPOe7VXhwoVZvXo169ats3YoQgghrGzEiBHm+5LACpGz7HvSmR0bPXo0X375JW+//TZNmjShdevWmT5GsWLFKFu2LEFBQRbb27Rpw+DBg5kxYwYABw4cICgoiIULF9K5c2e8vLxy5HsQecOUEJnmJDtSAmv6p+7n58fZs2dzfd51bnvqqaesHYIQQggbUKBAAfN9+dwlRM5ynE/Cdsbb25sDBw4A0LFjRwICAgzrGmWCj48PQUFB+Pr6pnrOlLyCofqdUoq+ffvKH1E7VqNGDYYNG2ZRudfeubq6mu/bexEnIYQQwsT0P23KlCmycoIQOUw+MVpRkyZN+P333yldujTdu3dn1qxZPPHEExnaV2vNnTt3AEPxmNjYWMDQo3X16lXi4uIoUqQI3t7euRa/yBsuLi4kJibSqlUr2rdvb+1wclTKBDbl1WohhBDCnpkSWEe66CyErZAeWCsbMGAAjRs35vz587z55pvExMSYn4uLizMXhknp1KlTODk5cfXqVcqUKYO/vz/u7u64u7ujlKJUqVJUqlRJklcHsXPnTsAw5zk+Pj7TPfW2zJHWgRVCCCFMTFNkhgwZYuVIhHA8ksDaAF9fX6pWrcrRo0dp3LgxV65cITExkYIFC9KkSRNu375NcHAwjRo1YsqUKRZLzhw5csRh1gQVaTNViP76669xc3MjOjrayhHlnEqVKrFs2TJAElghhBCOw9vbmzp16vDss89aOxQhHI4ksDZi9erV5jmta9asoU6dOgAcPXqUWrVqUaVKFQICAszJyyOPPEJ8fLzMac0HfH19GThwIKVLlwYcq4gTQOfOnTl//rzFhRkhhBDC3t26dSvHlkwUQtzlWJ+E7Vjt2rUJCwsjPDycV199lfnz5wMwbdo0ihcvbm43duxYtNbs2rVL5gzmExUrVmTWrFk0aNAAcKwENjo6mt69e3PgwAGL+bBCCCGEPUtISCA0NJT169dbOxQhHI6M2bMxRYsWBaB58+bmuY5vvPEGt2/fJiQkBKWUNcMTVuSI68AqpVizZg0HDx6kZ8+e1g5HCCGEyBGm/9XdunWzciRCOB5JYO1E0aJFzcmtyF9OnjxJ3bp1qVWrFuBYCayp1zU0NNTKkQghhBA5x9nZmcjISKlCLEQukARWCBvn5OREUlISzZs3p1u3bg6VwJoKVFWqVMm6gQghhBA5zMPDw9ohCOGQJIEVwsaZqvO2adOG/v37WzmanKWUYs+ePdSoUcPaoQghhBBCCDvgOF05QjgoUy/lgAEDuHnzppWjyXnNmzeXKo1CCCGEECJDJIEVwsalXB/V29vbipEIIYQQQghhXZLACmHjZA6NEEIIIYQQBlZLYJVSnZRSJ5VSQUqpMdaKQwhbV7RoUfMcUVn7VwghhBBC5GdWSWCVUs7ANKAzUAfoo5SqY41YhLAHK1euBAwLowshhBBCCJFfWasKcXMgSGt9FkAptRjoDhyzUjxC2LRatWoxevRoTpw4Ye1QhBBCCCGEsBprJbBlgYspHl8CHrZSLELYhcmTJ1s7BCGEEEIIIazKWnNgVRrbtEUDpQYrpfYrpfZfu3Ytj8ISQgghhBBCCGGrrJXAXgLKp3hcDghN2UBrPUNr3VRr3dTHxydPgxNCCCGEEEIIYXuslcDuA6orpSorpVyBF4BVVopFCCGEEEIIIYQdsMocWK11olJqOLABcAZmaa2PWiMWIYQQQgghhBD2wVpFnNBarwXWWuv1hRBCCCGEEELYF6W1fnArK1NKXQPOWzsOkaYSwHVrByHylJzz/EfOef4j5zz/kXOe/8g5z39s/ZxX1Fo/sPiRXSSwwnYppfZrrZtaOw6Rd+Sc5z9yzvMfOef5j5zz/EfOef7jKOfcWkWchBBCCCGEEEKITJEEVgghhBBCCCGEXZAEVmTXDGsHIPKcnPP8R855/iPnPP+Rc57/yDnPfxzinMscWCGEEEIIIYQQdkF6YIUQQgghhBBC2AVJYB2MUqq8UupvpdRxpdRRpdRbxu1eSqlNSqnTxq/FjdtrKaX+U0rdUUqNTnEcd6XUXqVUgPE4E+7zmv2Nxz2tlOqfYvtnSqmLSqmoB8T8kFLqiFIqSCn1g1JKGbdPVkqdUEodVkr9qZQqlt33xxE50jlP8fxopZRWSpXI6vviyBztnCulRiilThpjmJSd98ZROdI5V0o1UkrtVkr5K6X2K6WaZ/f9cUR2es7TbKeUclNKLTH+LOxRSlXK2rvi2BzsnI9USh1Ths9wW5RSFbP6vjgyRzrnKZ5/Thk+w+VetWOttdwc6AaUBpoY7xcBTgF1gEnAGOP2McBXxvu+QDPgM2B0iuMowMN4vwCwB2iRxut5AWeNX4sb7xc3PtfCGE/UA2LeC7Q0vuY6oLNxewfAxXj/K1PMcnPcc258rjywAcPazyWs/f7a4s2RzjnQFtgMuJlitfb7a4s3BzvnG1Pc7wJss/b7a4s3Oz3nabYD3gB+Md5/AVhi7ffXFm8Ods7bAoWM94fKOXf8c57ie9gB7Aaa5tb7Jj2wDkZrfVlrfdB4PxI4DpQFugNzjM3mAD2MbcK01vuAhHuOo7XWpisrBYy3tCZMdwQ2aa1vaq3DgU1AJ+MxdmutL98vXqVUaaCo1vo/bfjJn5sito1a60Rj091AuQy+DfmKI51zoynAu+m8tsDhzvlQ4Eut9R1TrBl8G/IVBzvnGihqvO8JhGbgLch37O2cP6BdypiXAe1MPfLiLkc651rrv7XWMcaH8hkuHY50zo0+wZB8xz3oONkhCawDMw7RaYzhKkxJ0w+b8atvBvZ3Vkr5A2EYftj3pNGsLHAxxeNLxm0ZVda4z4P2H4ThCr64D3s/50qpp4EQrXVAJo6Xr9n7OQdqAI8ZhxVuV0o1y8Rx8yUHOOf/AyYrpS4CXwPvZ+K4+ZKdnPP7MR/beGE6AvDOoWM7JAc45ym9gnyGeyB7P+dKqcZAea31mpw43v1IAuuglFIewHLgf1rr21k5htY6SWvdCMNVs+ZKqXppvVRau2biZR64v1JqLJAILMjEcfMdez/nSqlCwFjgo0wcK1+z93Nu/OqCYRhTC+AdYKn0zKTPQc75UOBtrXV54G3gt0wcN9+xo3N+P7l5bIfjIOfc8AJKvQQ0BSbn5HEdjb2fc6WUE4YRdKOye6yMkATWASmlCmD4JVigtV5h3HzVOKTLNLQrw8P0tNa3gG1AJ6XUw8pQeMPf2Ft2CcOcRZNy3Gc4mOnqkPE20bh/ymElFvsbJ5c/BfQ1DkMTaXCQc14VqAwEKKXOGbcfVEqVymjc+YmDnHOMz60wDn/aCyQDUrwrDQ50zvsDpvj/AKSIUzrs7Jzfj/nYSikXDEPHb2Y07vzEgc45SqknMVyYflobp4mI1BzknBcB6gHbjJ/hWgCrVG4VctI2MIFZbjl3w3BlZS7w3T3bJ2M5GXzSPc+Px3IyuA9QzHi/ILATeCqN1/MCgjH0oBQ33ve6p82DJoPvM/6gmwp9dDFu7wQcA3ys/b7a8s2Rzvk9bc4hRZwc/pwDQ4CJxvs1MAxtUtZ+j23t5mDn/DjwuPF+O+CAtd9fW7zZ4zlPrx0wDMsiTkut/f7a4s3Bznlj4AxQ3drvqy3fHOmc3/PcNnKxiJPVT5zccviEwqMYhgIcBvyNty4Y5ppsAU4bv3oZ25fCcDXmNnDLeL8o0AA4ZDxOIPDRfV5zEBBkvA1MsX2S8XjJxq/j09m/qfE1zgBTMX54NR7vYorv4xdrv7+2eHOkc35Pm3NIAuvw5xxwBeYbnzsIPGHt99cWbw52zh8FDgABGOZ6PWTt99cWb3Z6ztNsB7hj6G0PwlCduoq1319bvDnYOd8MXE3xfayy9vtrizdHOuf3tNlGLiawpn8mQgghhBBCCCGETZM5sEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IIksEIIIYQQQggh7IKLtQPIiBIlSuhKlSpZOwwhhBBCCCGEELngwIED17XWPg9qZxcJbKVKldi/f7+1wxBCCCGEEEIIkQuUUucz0i7DQ4iVUrOUUmFKqcAU28YrpUKUUv7GW5cUz72vlApSSp1USnVMsb2TcVuQUmpMRl9fCCGEEEIIIUT+lpk5sLOBTmlsn6K1bmS8rQVQStUBXgDqGvf5SSnlrJRyBqYBnYE6QB9jWyGEEEIIIYQQ4r4yPIRYa71DKVUpg827A4u11neAYKVUENDc+FyQ1vosgFJqsbHtsQxHLIQQQgghhBAiX8qJKsTDlVKHjUOMixu3lQUupmhzybgtve1CiHSERYex8cxGa4chhBBCCCGE1WU3gf0ZqAo0Ai4D3xi3qzTa6vtsT0UpNVgptV8ptf/atWvZDFMI+9V2Tls6zu9Isk62dihCCCGEEEJYVbYSWK31Va11ktY6GZjJ3WHCl4DyKZqWA0Lvsz2tY8/QWjfVWjf18XlgNWUhHFajUo0AUGle/xFCCCGEECL/yFYCq5QqneJhT8BUoXgV8IJSyk0pVRmoDuwF9gHVlVKVlVKuGAo9rcpODEI4umrFqwGglCSwQgghhBAif8vMMjqLgP+AmkqpS0qpV4BJSqkjSqnDQFvgbQCt9VFgKYbiTOuBYcae2kRgOLABOA4sNbYVQqTjxI0TADKEWAghhMO7HnOd7ou7s+nMJmuHIoSwUZmpQtwnjc2/3af9Z8BnaWxfC6zN6OsKkd9tO7cNMCSwTion6q4JIYQQtun8rfOsOrmKyDuRtK/a3trhCCFskHwaFsLGjWg+AgCt06x3JoQQQjiMJJ0EQFR8lJUjEULYKklghbBxpuJNOu2C3UIIIYQQQuQbksAKYeNmHpwJSA+sEEIIxyf/64QQDyIJrBA27nzEeWuHIIQQQuSJAs4FACjiVsTKkQghbJUksELYuJcavATc/acuhBBCOKpqXtV4pvYzjG452tqhCCFsVIarEAshrKNckXIUcCogFYiFEEI4vKJuRVn+/HJrhyGEsGHyiVgIGxd4LZCE5AQSkxOtHYoQQgiRq0Juh9BubjtZB1YIkS7pgRXCxh2+ehiA+KR4XJzkV1YIIURqJ66foHKxyri5uFk7lGwJjQxla/BW4pPiZR1YIUSapAdWCBs3rNkwQCozCiGESFtEXAS1p9Xm852fWzuUbDMtGXcn8Y6VIxFC2CpJYIWwcbIOrBBCiPtJ0kkAeBfytnIkQgiR+ySBFcLGTdk9BZAeWCGEEGlL1snA3QueQgjhyCSBFcLGXY66bO0QhBBC2LCo+CgAZh6caeVIss/dxR0AT3dPK0cihLBVksAKYeP6NegHQKEChawciRBCCFtkGqFz+85tK0eSfTW8a9C/YX9Gthhp7VCEEDZKSpoKYeN8Cvng4eqBs5OztUMRQghhg0yVhyt4VrByJNnn7uLO7B6zrR2GEMKGSQ+sEDYu4GoAUfFRUpFRCCFEmrwLGoo3da7W2cqRZN+Zm2doPrM5W85usXYoQggbJQmsEDbu0u1LAMQkxFg5EiGEELZIKUPxJlMxJ3t2I/YG+0L3MXbrWGuHIoSwUZLACmHjhjYdau0QhBBCWIHfKT/mBcwj8k7kfdtdjboKgP9V/7wIK084QjIuhMgdMgdWCDsh68AKIUT+8tSipwDoVK0T6/quS7edKdnrUq1LnsQlhBDWJD2wQti4T3d+Csg6sEIIkV/tC9lHnWl1GLom7RE55nVglawDK4RwfJLACmHjbsTcsHYIQgghrMDN2VBduG/9vhy/fpw9IXvSbGcaoTNp16Q8iy23FC5QGACvgl5WjkQIYaskgRXCxvVraFgHtph7MStHIoQQIi+V8igFQDWvagAcunIozXamHti4xLi8CSwX1SpRi2HNhvF2i7etHYoQwkbJHFghbJynmyfF3YvLOrBCCJHPdK/ZnfC4cFpXbH3fdh6uHoBjrAPr7OTM1C5TrR2GEMKGZbgHVik1SykVppQKTLHNSym1SSl12vi1uHG7Ukr9oJQKUkodVko1SbFPf2P700qp/jn77QjheA5cPkB4XLgsoyOEEPmI1pof9v7AvMPz+GHPD/dtW8qjFM7KmVblW+VRdLknMCyQKt9XYfPZzdYORQhhozIzhHg20OmebWOALVrr6sAW42OAzkB1420w8DMYEl7gY+BhoDnwsSnpFUKkzZS4RsVHWTkSIYQQeSHkdghf7frK/HiW/6wH7uOknByiWn1MQgzBt4L5YMsH1g5FCGGjMpzAaq13ADfv2dwdmGO8PwfokWL7XG2wGyimlCoNdAQ2aa1vaq3DgU2kToqFECm81uQ1QKoQCyFEftF9cXfe3/J+ms/V/aluqm0nrp8gITmB8xHnczs0IYSwuuwWcSqptb4MYPzqa9xeFriYot0l47b0tgsh0qEwLIvgCFfWhRBCPFhh18LpPnfs2rFU2xKTEwHoWatnrsWUV+RirRDiQXKrCnFaC5Hp+2xPfQClBiul9iul9l+7di1HgxPCnozZYhiZL//UhRAifyhRqESm2pvXgU3zY5Z9kYu1QogHyW4Ce9U4NBjj1zDj9ktA+RTtygGh99meitZ6hta6qda6qY+PTzbDFMJ+Rd6JtHYIQggh8tDuS7sz1d50gfO9ze/lRjh5yrRkXGaTeCFE/pHdBHYVYKok3B9YmWL7y8ZqxC2ACOMQ4w1AB6VUcWPxpg7GbUKIdPRr2A+Fwrew74MbCyGEsHsjmo+47/OmIcMmph5YR+i9rFWiFu8/+r6sAyuESFeG14FVSi0CHgdKKKUuYagm/CWwVCn1CnAB6GVsvhboAgQBMcBAAK31TaXUJ8A+Y7uJWut7C0MJIVIo6FIQ38K+sg6sEELkE49WeBSAOT3moLWmcvHKtJndxvz82C1j+ar93SrFXgW9AChftDyO4PN2n1s7BCGEDctwAqu17pPOU+3SaKuBYekcZxbw4HrwQggA9oXu42r0VW7fuU1Rt6LWDkfYkGSdTERcBMULympkQjiK2IRY/E75AbA4cDGHrhxKlZi6OFl+fKtYrCKebp40LNkwz+LMLftC9tFmdhtW9F5Bp2qyUIUQIrXcKuIkhMgh7i7uANy+c9vKkQhb8/nOz/Ga5MWVqCvWDkUIkUOuRF3hy11fArAuaB1Xoq6wL3SfRZvGpRun2s9R1oG9k3SH2MRYxv09ztqhCCFslCSwQti4gY0GAlKFWKS2/PhyAEIj06yFJ4SwQ7fibgEwuMngdNvcjLWcfbXn0h7C48KJSYjJ1djygiNVVBZC5A5JYIWwcbIOrEhPDe8aAHi4elg5EiFETjElsH3q96Fj1Y5ptlkcuNjisSnp61WnV1rN7YpcrBVCPIgksELYuDfWvgHIP3WR2iPlHgHAp5AsNSaEozAlsMXci/FT15/SbPN4pcctHpsS2NkBs3l60dO5Gl9uk4u1QogHkQRWCBuXkJRg7RCEjXq96etcf+c6nu6e1g5FCJFDUiawVYpXYVCjQanaxCfFWzw2JX2LAxez+tTq3A8yF5kuyPkUlgtzwrFtDd5KmW/KcOjyIWuHYnckgRXCxr3U4CW8C3pT3tMxlkcQOeevE39RYnIJTt04Ze1QhBA55Jnaz3B4yGHKFikLwNFrR1O1OXz1sMVjUw+sI6jrW5dJT05iVMtR1g5FiFxVyqMUUfFR7A/db+1Q7E6Gl9ERQmTN5F2TebLKk2lWjcwIFycXChUohJOS603C0s7zOwGIS4yzciRCiJzi6e5Jfff65sd7QvakahMWHWbx2JTsAlQqVinXYssr77R6x9ohCJHrqhSvQmR8ZKrfZ/Fg8olYiFz27uZ3aTKjSZb33xOyh4u3L3Ij5kYORiUcgemDrQwztz1aa5m3LrJk45mN/Hbwt/u22ROyh35/9jM/rupV1Xz/3K1zJCUn5Vp8uW37ue2oCYo1p9ZYOxQhcs1Hf39Ewc8KAnA56rKVo7E/ksAKkcsUinGts76eXZkiZQCIuBORUyEJB6GULDNhq37Z/wtOE524EXODT7Z/wpLAJdYOSdiJhUcW8smOT8yP5/aYm2Y7/yv+5vv3JqyxibG5E1weSEg2XJCbsH1CpveNT4qXC3rCpmmt2XF+h8XvuCSwmScJrBC5SGuNRmdr+G+/Bv0e3Ejka1K10/acvnnafP+jbR/xwvIXrBiNsCfhceEWhdk6Vkt7KZ3AsEDz/a3BWy2es+ckLjsjF6r/WJ2eS3rmYDRC5KyZB2fSZnYbi23lipSzUjT2SxJYIXKRqbDGkqNZ730xXVmX4YjiXqa5bqU8Slk3EJFKEdciALz050tWjkTYkytRV1h1chVVilcxb/Mt7PvA/e69iGWqUjxt7zQuR9pX7052LsjFJcaZRy0J2zZ9/3RzHYf85Ni1YxaPjww9wvedv7dSNPZLElghcpHpH3FEXNaH/76y6hWLYwlhUsu7Fk7KiQqeYyXArwAAIABJREFUFawdikjh+LXjTNwxEYD1QeutHI3jSExOtHYIuerI1SO0ndMWgD71+lg8N6DRgPvueyfxjsXjhOQELkZcZPi64by7+d0cjTO3ZfVibUxCDGHRYQ5RxCo/GOI3hNazW7Pq5CouRly0djh5JuJOBOWKlmN93/XEjY2jnm894pPiOXH9BABLjy5FTVAsPLLQypHaNklghchFLk4ueBX04tnaz2b5GFJ9WKTnozYfce2daw7/wT43XIu+xjC/Ydy+czvHj33w8sEcP2Z+t/3cdgp8UoA9l1JX5HUUD//6MCeun+CdR97h+brPWzz329O/3bdn8d5lOBKSEijgXACAR8o9kvPB5qKyRQ0VlU3rwWbU+VvnARi7dSwnrp9IldQL29R9cXd+3v+ztcPIM1eirlDKoxQdq3XEzcWNtafX4vapG7Wn1abaD9Xovaw3AH1X9LVypLZNPhkLkcuclTNJOusVIfvU70PlYpWp5lUtB6MSjuCfC//gPcmbfy78Y+1Q7M7IjSP5af9PbD67OcePnbK4TkoxCTE5/lom52+dz7MLGVprrkVfy/XXiUmIYX3Qev69+C/uLu4AnLxxMseOn6yTmRcwzyYq9kbERZgLL6U1YsdJOVHao7TFthblWpjvh0SGWDxXxK2I+ftycbKvFRPr+dZj+lPTea/Ve5naL+XPRu1ptXl3k331POc36/veHZ0yJ2AOZ8PPmm+O7GrUVUoWLml+XN/37pJZZ8LPWLRVExRD1gzJs9jsiSSwQuSiyDuRXIu5lu2ri1JtVqRl1clVQOoKpPnBhYgLdJjXgcCwQC5EXODNdW9y+85tFhxewMTtEx+4f4mCJQBoV7ldjsf2x7E/aFSqEZ5uhkI87zxiWNMyNiF3KsNejrxMpe8r8cGWD3Ll+PeadWgWvl/7cvjq4Vx9nV8P/krnBZ1pNauVeVjozdibFm2+2PkF9X6ql6XfgVmHZvHyXy8zbd+0nAg3y5KSkyj2VTHzaJtWFVql2W7nwJ2Evxdufvx1+6/N95+p/YxF2yWBS4iKjwKw+veXFYMfGkybSm0e3DAF0/dr8t+l/zK036HLh/j14K+Zei2RdVprhvkNI+BqAMufX07/hv0JjQyl6g9VzbdTN06Zh5IfvnqY3Zd2WznqnNOnXh/aV2lvflzeszx7X93LL11/4dO2nzKu9Timdp5q7rSYfmA6k3dNZtpe+/s9zk2SwAqRi7LT82qy+9JuzoaftbtCHCL3mT6g5cTPmT1JSEqg8fTGbDq7ifo/16fidxX5ce+PeE/y5qU/X+LjbR+TlJxkLqKWFmcnZwBGbhjJfxcz9kE3I27G3uR8xHmervE0QW8Gce2da1T3qg4YErJ/L/6b44vWF3YtDNwt3JOeV1a+woagDdl+vQ1nDMc4fu14to+VHq01Rd2Kmh8vPboUwGKuXGxCLPsv7+fotaMU/6o4y44ty9RrXIm6Ahh6RKxpwZEFgKFHeN9r+9KtPF+wQEGKuRczP95xfof5fpfqXQgbfffnKjYx1lw3IS4xLjfCzjVrT69FTVD8deKvTO33UoOXWNPn7tqx83rOy9B+TWY04bXVr2XqtUTWRMVH4TTRiZ/2/0RYdBjP1H6GT5/4NFW7mlNrMmjVIILDg2n4S0Na/tbSCtHmLK01I9aOoEetHrzV4i2L55qVbcbrTV9nbOuxTGw7kWHNhxEwJIA5PeYA8O7mdxm+bjjR8dHWCN0mSQIrRC4y9Qp83ynrFebq+tQFZB1YkZqpZz4pOYkLERdQExRT/pti5ahyn99pv1Q9cXC3yM/HbT6m++LuNJ3RlKTkJGITYlMVCTH1hs7yn5WjPVTxSfG82vhVOlbrSIlCJShRqIQ5wRyzZQytZrWi5Ncl6buiL0PWDMlwz4LWmk1nNqVZ4MZU8fj7Pd8TGhma5v6JyYnM8p/FmlNr0nw+MwoVKATcvQiQG7ad28Ybfm8wptUYJjw+gR0XDMnat7u/NS8Z8+/Ff1lxfAUAkfGR9PqjF8HhwYBxCTPje7X93HY+3fEpNafW5Nytc9y+c5sbMTfMcyS9Cnrl6vDu+5m0axL9/+oPQOjIUJqWafrAETcfPGroaf/t0G/mOdwBVwJwc3EjYZxh+ZzYhFhzcbcu1bvkVvi5wnTh6bOdn2V63+IFi5vvf/PfNxmaB9umYuZ6ekXWFfmiiPm+qReyXNFyJH2UROK4RKZ2nmp+frb/bKr8cLcad3pTM+zF3pC9TN03leHrhmeofaEChXi54ct80vbuerHjt41n5oGZFhev8itJYIXIRaZ/xM4q6x/0Xqgn60eK+0vSSRy5egQwzO1ce3ptmgmeozAlJvV86/FK41d4od4LhI0OY8ZTM/Au6E3Hqh3xO+3HoSuHcPnEhc4LOlPhuwpcun3JfIwX679ovp+ThdJKeZRi5tMzeaT83cI5aS2DsvDIQqYfmM7iwMXpfn8pLQ5cTIf5Hfjt0G8W2+cfno/TxLvxPzrrUYuhyglJCQTdDDLPq8yJufQjmo/gicpPUKtErWwfKz1h0WHEJsbSt0Ff2ldpb9G72m5uO2b7z+ZCxAUAKnpWND/31KKnqPdTPZwmOlHy65KsOL6Cx+c8zri/x3Hqxikqf18Zzy89KTG5BJ/uNPT8jN40msKfF6bW1Fq0ndOWHed3MH3/dF5f/bq5lzY3HLp8CDdnNwA29dtE6SKlH7CHwRvN3gAM8+U6zOuA26duNJreiPHbxuPi5IKLkwuxibEUdCkIYNGTbQ8yWoU4KTmJKf9NMZ+jl1a8xKIji8zPzzw4k+BbwQ88TttKhsrP6Y3YiI6PNl8YETnn4XIPm+87KSecnZwZ1nwYieMSGfvYWABqeNegV51egGFK1r6QfXa7pKApAZ/cfnKm9vuw9Yes6bOGUh6laFCyAYPXDKbN7DbsD92P1poBfw1ATVAWP/v5gSSwQuQi09DOqfumPqBl+kzzeuz1j7bIPab1X6t7VbfoQeq6sCvek7ytFVauStbJxCfFo1AsfGYhvz79K4ueXYRPYR9ee+g1rr97ncalGzOl492eaFMV8PJTyjPgrwGM3TKWur51zc/POzyPW3G3WHp0qfl9zOrvW1JyUqp9yxQpk24hne/3WI7OeHLukzSe3thi296QvSw/vhyAnRcM6yZGxUfx/B/P0+9Py+GmwbeCKfVNKTad2YSaoHD91JUf9/xIs5nNAPjfhv/x6Y5P2XZuG32W98lSr8ZDZR5iy8tbqOdbL9P73s/lyMvm8/vCcsOFuwqeFcxLEfkU8mFo06EADFw5kM3BhgJcJ4afIPoDw9C6Y9eOcfTaUQCuxVzjzxN/MvHxB8+JBkMRoG3nttF5QWeG+A1hxsEZlP6mNGtOrWH+4fk5Pte8yYwmfLXrK/THmierPJnh/UxVegH2hOwxDx1vXbE1AAVdChKbEGu+iHXqxqkcjDr3mYY+K1L3RCclJ7Hz/E4CwwIp8EkBRm4cSelvSjNw5UDWB63ndvxt9MeaLS9vAQzFnHwn338d3bO3DEWD0uutfXLekxY9gSJrTH8XS3mUIvmjZIvh8Ck5Oznz6ROf8u+gf/F/3Z+lvZYS9X4UN2Nv0vzX5jhNdOLcrXN5GHnO2B2ymyKuRSyKNmVU1xpduTzqMi/Ue8Gc0Deb2Yxui7oxJ8AwzPjLXV9S/+f6bD+33erTIvKCJLBC5CLT1fVbcbeyfIzBqwcDsg5sRqw4voLx28Zz4voJiyQiOj7aPLRSa01EXASTdk1ijv8ca4WaI8oVKUeZImW4EHGBgSsH8lHrjyyenxcwz/w+HA07yukbpwF4b9N7bDu3La/DzbbE5ER+3vczLyx/AY2mpEfJNNu5u7jzvxb/4/Koyxx94yjDmg+jednmgKHa5ef/fE7xr4rzZ+8/zfucu3WO3st6M+CvAXy49UOcJjqRmJyI1jpTxZd+3PsjzhOdLX7na5eoTcjIEPPQz3ulvPiwJXgLAVcDAMNw5BsxN3j414fNCazfKT+01jy79Fn+OPaHeb93HnmH0yNO4+7iTkXPiuZ5lQAty7fk5YYvmx+P+3scbee0ZXHgYhpPb0zXhV1RExSf7jD0SAaHBzNw5cB0h9WuOL6CHot75OjcysNXD1Pm2zJU/r6yRVXtom5FGf/4eL7r+B1/vfAXP3X9yZzEmtZJdHdxp1CBQhwfdhy/F/2I+SCGm+/eZM+re3ix3ouMbT0W/bHm2BvHaFmuJWMfG0vSR0nsGLCDJc8twc3ZzfyhEGBq56nM7znf/HjZsWX0+7MfLp+4oCaoHEkITb+X3Wt2z9L+KXudXZxc2PDSBvOxPm/3OSdunLDL33G4+94E3QyyGDUB4PKJC61nt6b+z/Ut/ifO9p/Njdgb5vNo+n0Hw4WM3st6m497b0/rxjMbgdRD4pOSk3hn4zvmYf4JSQk58e3lqbjEOE5ez7nK3dmRkJzAl+2+ZOEzCzNUmLJl+ZYULGAYRVDYtTB7Qu4uobXu9DrCosNo8HMDCn9emKTkJBYdWYSaoGj9e2v2heyzOFayTubrf7/mRsyNnP2mMmHjmY10rt45W0U5CzgXYGmvpYxvM55mZZpx8fZFXJ1dCRwayDcdviEwLJDH5zzOsLXDLPaLio/iVtwtdpzf4TDzaJU99Oo0bdpU79+//8ENrexm7E2KuBYxr70mBBiKETQp3YRFz2ZteIf7p+7cSbrD8WHHc3XInq1bcXwFzy59lpCRIeb1EGMTYnlz3ZuMazOOUh6lcPvUzdy+pndNNry0gZ/2/cSkfycB8N8r/xGfFE+b2XfnPAUODeTZpc+y8NmFNCrViJa/teTVxq/y2kO2X9RjQ9AGftr/k7ka8eo+q7l95zbrgtYx//B8OlfrTPea3fEq6MXzywzrSvZr0I95h+dRqVgl9r22j7FbxjLm0TFULl7Zmt9KhnRe0NncG/fPwH/SrdaaFq01J2+cZNOZTby5/k0A9r66F093T5YeXcq4v8cBMK71OD7Z8Umq/Ve9sIqGpRpSrmg5tNbpzv/8YucXfLD1A+58eAdXZ9c02yTrZJYELmHx0cWsOrmKc2+do2IxQ0LiM9mH52o/R8SdCBYF3v2b8VqT13iyypNUKlaJZmWamYcN/9L1F15v+rrF9xl8Kxh3F3eq/VCNbzt+y5CmQ9Ba021RN/xO+1GrRC1cnV3TrCJcq0Qt3nr4LYb6GZLEN5q+QWHXwuwN2cu41uNoW7kt/f7sx8IjC1nTZw1da3R94Hv/ILEJsVyJumLu5arvW5+vO3xtHjJ3r/ikeJYELiH4VjBaaz5+/OMsv3ZcYhxR8VGUKFSCh2Y8xMHLB/m9++8MaDSAO4l3+Pfiv9TxqUOpb0pZ7JcwLsHcq56sk4lNiDXPdX6QhKQEXD81/Gx8+NiHfPJE6p+3BwkOD2bjmY34FvalS/UuuLnc/dt3Lfoavl/f7XX8s/ef9KjVI9OvYS1Hrh6hwS93z7v+2PA5VWtt/rmv61OX9S+tZ9u5bfgW9qXrwq5MfHwi7z/2vnm/qPgoxm8bzzf/fQPA1pe38sTcJ8zPnxp+inO3ztFhfgcA5vSYw4YzG5jbYy47zu+waAtw490beBX0yp1vOpc8vehpVp9azfLnlxMWHcaQpva7JMv5W+eZuncqhV0L83Gbjzl+/Th1fzKMpKnrU9c88gJgdvfZ1PapTbMyzVBKsfP8TlrPNoxQuPXeLbYEb8GnkA+PVXws1+Pec2kPNUvU5JPtn1DDu4bF3+vsOnfrHBU9K5qT4pf/fJl5hw3Fy66/c506P9VJVTTw564/2/TPgVLqgNa66YPa5cjiYEqpc0AkkAQkaq2bKqW8gCVAJeAc8LzWOlwZ3uXvgS5ADDBAa233q75rrfGe5M2L9V9kwTMLHryDyDeclFO2hp51r9WdgCsB+Tp5Bfhl/y+A4cONKYF9bfVrLDiygF8PpV4C4eSNk4zaOMrccwWG+YHvtrq7NuBvT/9GvZ8NwyAfmvEQrz/0OntD9rI3ZC+VilWifdX2nA0/y/Zz27kSdYVi7sUY0GiA+aqwNW0I2kCnBZ3Mj0e1HMVTNZ4CDGX6BzQcQFG3ovRd0ZfTN0+b25n+ub3Z/E18JvsAMOPgDFa+sJIPtnyAq7MrAxsNZFDjQWwJ3kLHqh0JjQy1eoIbHR/NpjObAENSlZnkFQwFr2qVqEWtErUY8fAIi+dSFnFJb+7w04ufNt/3cPUgYkxEmnNnYxJicFJOFHBK/0Kmk3KiT/0+eLh6sOrkKsKiw6hYrCIJSQnciLnBjgs7OHbtmMU+P3T+wbwW6oWICzQo2YBP2n5CtxrdUn2fVYobEsGYsTEW29e8aFnAKSk5ibDoMAo4F2D+4fm8veFt3F3ceaXxK+YE9qf9P1GiUAmux1xn+7zthmTJOLIkOiGahKSEbF20/eqfrxizZQyjWo5ixlMzGLxmMKNajqJD1Q7p7uPq7Eq/hmlX6s0sdxd38/taw7sGBy/f/Tji5uJG28qG+ZFVi1e1WKNx+NrhTD8wnf89/D++2/MdAJfevoRXQa8H/n1IOZzcNJ81syoXr5zuB+Ggm0Hpvp49qF+yPoufXWweRq615r3N77EocBGHXj9EUnISD5V5CDBUHgbMxatS8nD14It2X5CYnMiRsCP4nfazeH7mwZlM/vfufERTMa06Jerw8ba7F0Wmdp7K7/6/P7DKt63RWrP61GrAUBDr4OWDuDi58GqTV60Sz6xDs3ii8hPmJbEyq2KxikzucPd81fSuyaynZzFo1SCOXjvKX73/oseSHqkuVLQq34oPW39ofjx+23jz7ywYElpPd0/Wnl7L4auHeab2M3i6eaY7wiczDl89TIvfDOs1R38QbS6Al1PufS9ndZ+Fp5sn3Wp2Y/7h+RbJ68gWI7kZd5O+9fvmaAzWkiM9sMYEtqnW+nqKbZOAm1rrL5VSY4DiWuv3lFJdgBEYEtiHge+11g+ndVwTe+iBvX3nNp5fGtb8M10tFPZDa82RsCNpXu3PjuDwYHOvQlZ/Lnov683hq4c5Piz1khWm3997h6QsP7ackRtHcvbNs5mqFHog9AAnrp+gbwPb+wM3be80hq8bzs6BO2lRrgVXo65Sbko5wPABLTE5kUlPTuKhMg9xPeY6bs5uJCQnEBEXwVM1niI+KZ7LUZdpXrY5IbdDKOZejMKuhZntP5stwVuYf3g+DUo24GbsTfOwtaARQVT70bLoTZuKbehUrRNDmg5Jdw5PbtoXso/rMdfpsvBuZdG1L66lc/XOaba/EnWFLgu6EHwrmAODD7Dm1BoUihfqvWDRSzPpyUm8u/lucu/3oh9dF97tXUvZ870/dD9li5TNcNGZ7IpLjMPN2Y0twVvwLuhN49KNH7xTJmmtuRx1mfVB67kQcYH3Wr0HwKErh2hRrgUVv6toMZxxcJPBFCxQkGHNhvHDnh/4vN3nFHErwqgNo5hxcAaR70c+8DX3XNpDi99a4PeiH12qdyE0MpSy396d3+j3oh+HLh+iV91e1PCukePf872SkpNwUk4opUjWyRy7dozQyFCeqPwE47aO48tdXwJQuVhli+I4me0NN0nZozal4xT+1+J/HL9mGGlijbWvr0RdYdDKQYxsOTLVnNTIO5FExkfy0oqX+Pvc3+keo13ldmx+efN9X+eX/b+QlJxE1xpds/xh/n4i70RS9Mu7hZvq+dbjyNAjOf46OSnkdggRdyKoWrwqF29fZPel3anmdwPEfBCTpQuIgWGBVPCswPzD83muznP4FvYlWSczde9UbsbeZML2CYa5w4mxzOkxhyKuRbgVd4sBjQaglOJa9DW2ndvGc3Wey5WfzX8v/ounm6fF3PyMSEhK4Nv/vqVmiZqpetlNf1+mdZnGI+UfMc+tH9BoAA1LNuS1Jq+lGjUQFR/F2fCzLDyykA9bf4iHq0f2vrEUXlj2AleirrBtwLYcOyYYft4DrgbwaIVHzduGrhnKLwcMF70blmzIsueX8e6md3nz4TeZGzCX3/1/N7c98+YZqv5Q1fxYodBoTo84ne2id3+d+IueS3oCsGPAjjzp8U0pNiGWvSF7KelR0m46QTLaA5ubCexJ4HGt9WWlVGlgm9a6plJquvH+onvbpXd8e0hgATw+96B4weJcfPvigxsLm/LTvp8YtnYY2wdsNxfCyAlBN4Oo/mN1ChUoZC4yklkVv6vIhYgLFsMMTfyv+NN8ZnP+7P2nxVA+U7J3dfTVNCugpsfjcw+iE6IJfy8cFycXCroUzNWlMjJja/BW2s1tx7b+2/jw7w/N8+SWPLeE0h6l8S7kTR2fOlk+fkxCDMk6GQ9XDwKuBNDrj16MeXQMq0+tTnM9wsChgVyJusKms5t455F38C6U+0WTLkRcoOJ3FZn4+ES8Cnrx9X9f83X7r3m2zrNZPubN2JsE3QyiavGq/O7/O+9seodhzYZR3as6Y7eOJTrB8HObOC6RrcFbzcPtAHYO3MmeS3voULUD7i7uJCQnZOscpOflP18mMCyQg69bd7BORFwEW4K30KBkA6r/WD3V8992+Jbj14+z+tRqLo968LrN526do/XvrZnWZRrdanYjPimeo2FH8TvtR78G/VL9vltbYFggQ9YMYdfFXame2zVoF7VL1LZYxuR+zt06x28Hf2NR4CLOhJ/h2jvXKFGoRE6HnOMSkxNJSk7ixRUvmpfwSal33d7M7Tk33eHj52+dp9L3lYC7PT+5Ybb/bHae38ks/1nUKlErzQugtiJZJ+M80fB/JuXFkTJFyjC65WhGbhwJwMTHJzKuzbhciSExOfG+PdVjNo/hq11fMeHxCYx9bGyO/19UEwxJccjIEFafXM2cgDn8+8q/fPPvN7St3JYmpZuk2icsOoySX9/tJfyr9190rNaRfy78Q/mi5Zl3eB5Tdk8hdGQonu6e/LL/F4b6DWXi4xM5cPkAK0+uBGBZr2U8W+dZouOj8fjCMmGNGxtnMTw9Ox77/TFcnFz4u3/6F4By0pazhkJe7aq0s9gekxDD9ZjrlCtajitRVyjuXpxCnxt6RjtW7Whe57pXnV4s7bU0WzGY3vO1L66lfdX2djcawhryOoENBsIBDUzXWs9QSt3SWhdL0SZca11cKbUG+FJr/Y9x+xbgPa11uhmqvSSwBT4pQGJyYo7+wjuas+FneXfTuwxvPhz/K/68+fCbObqERVZcj7lO5wWd2R9q+BmL+SCG0zdPW/TGZnWY3MnrJ6k1rRYLnllgsWxHZvRd0ZeFRxYSMCQgVQ/xn8f/5Jmlz9C1elfWvLiGxORE5vjP4dytc3y681Pq+tTlj15/sDdkLy3KtWBuwFzaVGqT7vA8t0/diE+K5/fuvzNw5UCGNxvOj11+TNVuy9ktjNw4knV915l75XKbaQ6siZNyYuvLW2ldsXWu99ak/Dv5zX/fMGH7BC6+fZFha4eZC8mseH4FPWv3tNjP/4o/8w/PZ3L7ydmO8U7iHRr+0pCTN04S/FZwrvTcpCUiLsL8Ibv+z/UJDAsE4Pm6z7P0aOp/7j90+oG6vnV5ovITqZ7LqppTa1K7RG3+eiH1hQRrKf1N6XSXWPEq6MWNdzNeLOTUjVOsPrmaN5q9YRPD0+8nJiGGYWuHMdt/dprPp3WhLS2tZrXi34v/suqFVbi7uNO+avscjjTzFgcuZsOZDfze/fcHNzYasXbEfavMpxy5oLVm0KpBzPafTcOSDTn0+qFc/dsVlxhHwc8MP08p5+zaCtMIpV51elkUJDNJ/igZpRSRdyKJio/KsxEfaTElmGCYejKo8aBcOf7MbjM5c/MM3+7+lugPoinwSQEUiiNDj1C6SGmSkpPwdPfkrxN/Udy9OB3md6BWiVqcuH6CLtW7UKFoBXPP4x+9/qBbjW4Wn0cTkgw/B29veNtcAb1K8SrsfmU3D//6MMG3gulavat5uPWwZsNYH7Se7QO2W1S/zooq31ehZfmWNjnN7p8L/1DaozRVvaqy8sRKeizpQaVilXi5wcu4OrsSHhdO1+pdebTCoyilMvy7tPP8TpYeXcq3Hb+V+jgZlNEENqcyh1Za6yZAZ2CYUup+XVhp/bVOlUUrpQYrpfYrpfZfu3Yth8LMPS1/a0liciJP13yaz3Z+xvT9060dkk1ae3oty48vp9uibry94W1Wn1ydJ69rWgMRDFd7o+KjSEpOYuOZjfhM9jEnr2Do5XpizhP8sv8X+izvw7lb56j2YzVa/NrCXFl0bsDcDJUpNy2jk511YE1LgNwrODyYZ5Y+Axgql/qd8uPnfT/z6upXzesbHr12lHVB6xiwcgC1ptXi838+p/9f/RmzeQz+V/x5a91bzPafTdMZTVl3eh0tyhnmapy4fgIwLP+z+exmtNZci75GTEIM0fHRPL34aQq6FDSvM5jbrkZd5fU1lvO9ro6+SptKbfJkqKFSynwb/choIt+PpJh7Meb0mGPusX9m6TPsDdnLhiDD1duk5CQ+3Poh3/z3TY7MnRq9cTQnb5xkeLPheZa8AhY9RAFDAjj0+iEixkSw+NnFfPbEZ5QvWt6i/Zvr36Td3HY5tuzTgsMLOHXjFA+Xve9Mkzy39eWtrO+7Ps0LOO0qt0tjj7Ql62RqTq3J6E2jKfR5IQ5dPpSTYea4QgUK8Xv334n/MJ69r+5N9Xyl7yuxNXjrfY8xeddk/r34LwCLjy62ieQVDMP9ZvvPtlhz9kF+6PwDieMS0y3S99RCw/SFHed34PKJC7P9Z/Nw2YfxH+Kf63+7lgQuMd8/Gnb0Pi3z3tnws+bpNeFx4Xi4erB9wHaOvXGM15q8RvyH8eb3p4hbEasmrwATHp9gvv/KqldydF3YlFWRQyNDWX58OfFJ8cwLMNQr0Gjq/VwP70ne+H7ti9unbvRe1pvv9nxHwJAAjg87zqnhp5jx1AyLeb5D/Yam+hkr4FwApRTfdfqO4LeCWd93PXN7zKXx9MbZPgNrAAAgAElEQVQE3wpmVMtRrHlxDYufNaxPPW3fNM6En6HclHLsupB65EVGaa0JjQyljEfeXPDOrEcrPEpVL8Mw4u61unP9nevMeGoGE3dM5MO/Df/HOy3oRI8lPajxY8anczxW8TF+7PKjJK+5IMerECulxgNRwGvkkyHEZ26eMc+T29xvM0/OM8ydkbmwqU3cPpGPt31Mda/qnL55mqNvHM3xIYdxiXHEJcZRzL0YicmJVP2hKhciLtCzVk/C48LNSwt0rd4V38K+5rkQM7vNxMPVg07VOlH8q7vD4MY+NpbPdn5mfvxi/Rc5GnaUgKsBvN3ibT5u8zETtk9gYKOB1POth0abe5UDwwKp/3N9anjX4OTwrJWyn75/OkP8huD/uj8NSzU0bz989TANf2lo0bZK8SqcDTesafdK41eo7lWdd1u9S7GvinH7zu00j1/RsyLnI84DcGToEer61GXF8RU898dzqdqObzOegKsB/HniT4vtl0ddpmThkiw9upQOVTsw//B8ynuWp3vN7tn+kKa15sUVL7I4cDHP1H6Gxyo8Rv+G/TM8VDEvbD+3nS4Lu+Dp5kmHqh2o7lWdD/82FI2o7lWdUyOyt+zGiesneGjGQ/So1cPmrl5Hx0fz36X/uBJ1xWLOmquzK993+j7b1Q5NPRN7Xt1jsTSGrYi8E0liciJrTq3h7Q1vcyP2BsfeOEZtn9oZ2j8pOYk+y/uYe6BS9tjZizrT6nD8uuUQ1QJOBQh+KzjNXpsXl79orrD81ZNfWRRWs6bnlj7H8uPLmdtjbpaKRKXspbuf3Pi/l5ZmM5uZL86aejPv9d/F/2hQsoF5LuTZ8LN0X9ydTf02mdeZvp+gm0HcSbxDHZ86mfpb/9mOz8x/I+1p1JrpHGd13ndawmPD8ZqU+erGZ988m6q4XnR8NIVdC7P65GqcnZzpUr1LOnvfZZoLfDHiImMeHYN3IW/z3NoBjQYwcuNIFh5ZSD3fehwecjhL/9NDI0N55LdHeP/R93O0Cm9u6/dnP+Yfnp9qez3fengX9GZuz7m8suoVnqv9HL3q9jJXqdZaExkfaRiFV6IWRdyK5HXodivPqhArpQoDTlrrSOP9DsBEYBXQH/jS+HWlcZdVwHCl1GIMRZwi7pe82oPLUYbwi7oVZfv57ebtj856lC0vb7GbP8x5wdQTaVo/ML15QlmhtWZr8FYGrBzApduX2PjSRoJvBXMh4gJgKE6Qcl28hOQERjQfgU8hH8a1GWdRrODVxq8yy38WH7f5mP4N+/NJ20/ouaQnK0+upGetnjxS7hGGrxvOlN1TCLgawNbgrUzZPcX8PT1R+QnGtBpj7okIjw3P8vdlqgaacs27ZceWkZSchP5Ymws1NCvTjJUvrEQphU8hH4s5OhFjItBao5TiaNhRei/rjbOTM83LNOer9l9R6btKRMZHMmjlIPa+tpdO1TrRumJrRrccbVF99fFKj6PR/HniT4Y3G24eOlf6m9L8M/AfVp5caa4cCeBb2Jclzy3h8UqPZ/n7n7ZvGosDF9O1eleWP7/8wTtYQZtKbbj13i0aT2/MwcsH6degH14Fvbh95zaDHxpM3xV9md9zfpaTeZ9CPkzpOMWiWq6tKOxamCerPInWmuZlm+N3yo+RG0cSnxTPUL+hDPUbysgWI/nyyS8p4FyAg5cPUsO7RoaKg0TFR7HvtX1sDd5qk8krYP5g0q9hPx6r+Bh/HP0jU8UynJ2cWdprKRciLhCXGGd3ySsYeuY3nNlAyO0QhvgZLlgkJCewP3Q/vx78lfHbx3Pp7UuULVqW/aH7zcmrqXiVrcnq72nQiCB2XtjJ8uPLWXNqTZptcqIwTEalHPkTEhnCqI2jqOFVgxEPj+DXg79Sx6cOPZf0pKJnRY4PO86Z8DM0n9mc2MRYFh1ZxKtNXiU6Ifq+iexX/3zFr4d+5b1W7/Hlk1+m2+7Q5UM0KNkAv9N+bDm7hR/2/sCExycwoNEAu/qMFDY6jMj4SE5cP0HI7RBcnV2Jio+ivGf5LA/RvhFrmG4wpeMUFhxZYL7osH3AdvxO+VHDuwYvN3yZO0l3cFJObD+3HaVUmpXhTRciutXsluq59DgpJ958+E2LbQWcC/Deo4ZCdgueWUCjko1YfWo1J2+cZPu57QzxG8K3Hb6lcenGtCrfiqj4KIoXLE5EXATBt4JpVKqRxfHKFCnD0KZD71td3BZ93+l7CjgVoG2ltrz81921tE1TaSp+Z5gusfnsZob4DSFiTATvbHyHGQdnmNt+1PojJrSdgMhZ2e6BVUpVAUzdMS7AQq31Z0opb2ApUAG4APTSWt80LqMzFeiEYRmdgfeb/wq23wOrteZG7A1KFCrBqRunaDazGQMbDeT7Pd8TNCLIPCxBwOc7P2fs1rHmx0ueW8LzdZ/P1jFv37nNjvM7qOZVjdrT7vZ6vNr4Vdxd3Jm6byp/9/+b1hVbExUfRUxCDOGx4fftIUlMTiQ+Kd6i5HliciKJyYnmJRc2ndlEh/kdGN9mPBU8KzBolWFOzL0VOgFaV2zN9gHbOXPzDCUKlchU4Q5TYaXAoYF4uHpwPeY67ee1p0yRMhwZeiTHhqH9c+EfKnpWpLxn+VTP3Yy9SWhkKPV866V6rvvi7qw6uYrJ7ScTFh1mXpZgdvfZDFg5AID9r+03L3uQUXGJccQnxeN/xZ/ey3oTMCQgUwWp8prpQoJ3QW+uv2uoZ6e1ZvqB6Qz1G5pqvc6Myu4yJXktKTmJXRd34eLkQqtZ6fdQHB5ymBreNXBzcWP7OcOFvzaV2hjqCCTGUeQLQ2KY1d4wYR234m5ZjGC5n839NqcqsGJNph7Y+T3nZ6sSu9aaH/f+SB2fOrSfd3d4tGl92bwyasMovt39bZb2/erJr/Ap5MOYLWO4Ojr96TJPzHmCv8/9Tc9aPVnRO3VRKzBccO31Ry/zGtQmv3b7lVeavJKl+Kzp2LVj1P2pLr3r9jaszzzvSTxcPdjw0gZ2XdjFO63eyfCxknUyJ6+fZNCqQXzR7gvaVGzDqI2jeL7u8+YpPbZAa01gWCCnbpxKNTrLSTmRrJOp5lWN0MhQYhJiiB0bS3xSPPFJ8UTHR3M+4nyOFsi0hpa/teRCxAVCI0MBKFe0nEVl+vScHH4yT6rIO4o864HVWp8FGqax/QaQ6j+TNmTMw7L7urZEKWWunljDuwYRYyLYfWk33+/5nlUnV/F2y7etHKHtiEmIsXic3rDWzDAtX2T6Y9+0TFN2DNhhLoaScvJ8UbeiFHUr+sChUS5OLqmupt67rX3V9hbDxJ+u+TThceFU8KxAs5nNUCjKFS2H32k/dpzfQfOZzdkXug+AC/+7kGaieK9v/v3GXAW2rm9dSkwqYb5a+2PnH3N0DlXKEvT38irole4C7st6LcP/ij/NyjZDa03vur1pWKohLk4ubDizgUWBi2g6syndanTjctRlFjyz4L5/zJN1MlV/qMq5W+cAiP8wPkMVXW2Fad4zGP42DGo8iLfWv8UQvyEsObqEX576hZKFS3L7zm3Ke5YnKTkp3YqWp26coubUmvzY+UeGNx+eV99Ctjg7OZs/qMSOjeXb/761uGhl0mVhF8oWKcsj5R8xj14wLd3jU8jH3C5ldW1h+4q5F8PTzZOIOxH3bVfBs4LN9arn1JIhSimLHq2XGrzEG03foGX5ljly/Iz64skvuB57nWrFq/HRto9SPV/KoxQ+hXw4EnZ3iZ0CTgVISE6glEcp87qo606vo1zRcuy6uItuNbpZDAs3rTm7NXgrV6Ouprl2pun/fsr//z93/TnHCyHllcrFDD2fS44uYclRwzzjqPgo8wW71x56Ld0l1pKSk3h68dMMbjIYgB5LevD6Q6/z3yv/mdt82zFrFx1yk1KK+iXrm0ccpmSaw5ty/eE+y/swvNlw85Q6INOrItiafwb+g5Ny4krUFXZf2k3P2j3RWhMSGUL5Kak/z01uP5nn6z5PBc8KVojW8dlWSToH0qJcCyoVq8SSo0skgU2huHtxmpZpyrQu03j4/+3deXgV5fn/8fdNyMKWQljD4sJiFJGyRBCtuyhYBb+iohZFKPoFtVVRrNYFaqutoKCAXdBa9ScUUQQRcEPlpxXZRMACsohQAoggUEC2hDzfP86cYyJZgCxzZs7ndV1zZc7MnJk780DOuefZnutc5nk0o0kOwKDsQczuO/uw5kiVVXtVt3rd2FQqSwYuiW3/YssXdH6uM3n5ebFtvV/rzZxfzom9Xv3damqn1aZ+jR++uK/buY573run0DX6tevHE58+wZAzhxzzqMblLTkpmdObnA5EPuQK1rRO6DWB9NT02FPZhZsWkjU2iym9p3BJi0vId/lUT67Oxt0beWv1W2zes5muzbsWKtdD7hDJxH8NZHHNx1KSUvhnr3/Sa1IvPlz3Ife/f/9h029sG7LtsGl41mxfQ9bYLADqVqv4KXoqQlrVNH579m+Zs2FOocFFAHJ25ZCzK4d5G+fFtkXnnd26NzJw39u/eLvYBycSvzYO3ojDkflkJnsO7im075YOt9CxcUdu7nCzL3O9luSxCx9jX96+cp2+KPehXJIsyZffNSUphReveBGAjo07MmruKCZfMxnDqJlSk0PuEIbx1Y6vOJB3gMc/eZxB2YNomdGSfJfPrdm38ueFf+bSCZfSM6snb6x8I9alZeTFIxnQYQA5u3K4/KTLeXPVmzR6slGR87SaN3bn5BWRLiA7frPDlzm0y0u15Go8esGjRT6YA/jpX3/Kf/77H1pmtGRir4mFPhPHfTaOmatnMnP1zNi28uxKVdEubnExc385l/aZ7Xn2s2c5sc6JZNbMJKteFtnjsmN94ad+OZWeWT1j73vw7AcDnbwCsQfNmbUyYzMOmEUqKtxQx/cHv+fsf5zNhl0bmD9gfpFNvKX8lPsgThUh3psQFyc6kfLsvrM594T467tWXg7kHeDFJS8yau4oXv6fl3lpyUvku3xu7njzYdO+REWb4EzsNZHebXof9TWdc2z5fgsZ1TKo9mg1Pun/SVw1tynOrLWzGD1vNIO7DGby8smkVk1l6LlDSf9TOhnVMtg2ZBtf7/yalKQUaqXUYuK/J8b6lC0duJSGNRuSUS0j7qZDOBIHDx1kyLtDGD1/dKHtwy8aTu202twyPfJEOsmSmDtgLn9f9HfGXDomML/rkm+W0O5v7bi7y908cfETh+3/8OsPqVOtDsM/GR7rAxj1606/5rifHMfIuSPZtHsT8wfMp9Nzkdqpsd3Hclun4DdayXf5bP1+Kw1qNODtNW9z6YTD+z5Gm2T1yOrB2O5jj6iVgsSv/Xn72bZ3GzWSa/Dox49yVrOzDptqSuLb6yteJ/dQLofcIaavmh7729UjqwdXt76aG6bcwAc3fsCY+WMi07dd8Afufe9ezmx2Jku2LGFv7l76tevH3e/eDcAp9U5h+W3L/fyVykW+y+f2mbdTr3o9ep/amzZ/aUPbhm2pWqUqizYXnq862lIrOq0eEHsgcNcZd/HkxU/G3cOcY7E/bz97c/eybuc65myYQ69TejF91XQ6ZHY46i5EkrgqdR7YihbUBHZf7j6GvDeErXu38sylzwRikvaSfH/we56e9zQj5oygSa0mzLpxFtv2buO0v5wWO+amdjcVmh/w5g43M+7ycYz6dBS5+bkMzB7I22vepvdrkaT1+R7P0699v6OKY8HGBXR6rhPdWnZj0lWT2Ju7t8hmS/Fs5KcjYx/o1ZOrH9a0GuCPF/6R+352X2zUw8FnDGbk3JF8eduXZNXLqtR4y9Og6YNY/9/1vLXmLQAeu+AxGtZsyC+nRfpC/arTr3i629OB+0Bf9u0y2vylDZOumsTVp15d7HG5h3K5+c2bGZg9kI27NnLVq1dxVrOz+GTDD1MU3NH5Dp6e9zRtGrThi0FfFHuuIFuwcQFtG7Zl295tPPzhw3Rr2Y0rT7mS0fNG07ddX9W8SqV7eenLTF4xmSm9p5R+cAJbv3M9jWo2IiUphQlfTOD6067nwKEDrNu5jqpVqtJqTKvYsS3qtKDXKb0YPmc4o7uNZkCHAXE/13FZPDP/GW5/q3B3DzfUcd3k62hcszGz18+milVh/oD5bNy9kSa1mgTus06kIimBjRNrd6ylxejIIE4HHzwYmMFYnHPku3we+/gxHp79MK0yWtH71N6x+UUh0jSq07Od+Pybz+nfrj/nn3g+PbJ6MGbeGHbu38kTnz7Bucefy6wbZ5H8+x9+7yRLivUTfOPaN+iR1eOw6xdnX+4+qj8WGVjpF6f9gpGXjAxks5R8l8+NU25k/Bc/TIliGK3qtmLVd5EpV6b2nkrPk3syfdV0Lv/n5dzZ+U6emvcUq25fRau6rYo7dWBMXzWdWWtn0bhWY7o270qDGg3KPFG6nzbv3syFL13IQ+c8xHWnXXfE79u2dxvf7PmG9TvXk5KUwvWvX8/6O9cXGkBMRCreNa9ew6vLX+W1q1+jV+ui59+W0i3ctJA5G+ZQt1pdzjruLDbv3sx3+77jspMu8zu0ShGdI7bPlD6s2b6G7i278+KSSFPurUO2Br4yQ6QiVdogTlKyaGd/gJQ/pHDNqdcwsdfEuHvitj9vP6lJqdz1zl2s3bGWN1e9WWj/6u2rWb5tOQ+d8xArtq2gYY2GVLEqPHL+I7Su35rmdZrHjn3gnEi/kGHnDWN/3n7y8vN4vsfzsVF6+7TtE/tjPnP1TIbOHsqWPVtIrZrKxF4T6Tu1Lyu/W8lLV7zECbVPYOqXUxlx8QiqWBXGfRYZmvyR8x7hoXMfqoxbUyGqWBVevvJlRncfTe202uw+sLvYkYnX74zM0RpN+qNzzAbdZSddxvZ92+k7tS+/4TdMuHLCUSV+8SazViZbvt/CnA1zjur3qFe9HvWq14uN8Lx1yNaKClFEShD9G1twvAI5etmNs8lu/MP3zxNqn+BfMD6I9n38acOfsuq7VbHvO5vv3qzkVaScKIGtYGbGd/d+R93hkUFYJi2bRL92/ejWspvPkf1Qm1kzpSZ7Du7hmUuf4el5Txc6pk2DNozoOoIzmp5B9eTqhw02UNIT1RopNWJzkvVr34+b2t3Eyu9W0qRWE/q07cOMVTPYsGsDi79ZHHtPbn4uK79bCcCNU2+M9RNpn9meq1tfTc6uHLIbZwc6eS0o2kyypGl1os2Rol+qihuxNohSk34YcCsorRNKknsoN1TlI5KI4u0BswRT71N7s33fdl5Z9grVk6uXOvuBiBw5JbCVIKNaBl8M+oKUpBSyxmbRfXx31v56rS8jlC3ctJDu47tzZ+c7Oa1hpO9qdJTIM5udyfTrpjP+i/Fc3fpqrjj5inL9IDczTq4XGcDgouYXcVHzi9ibu5d2jdqx+8Bu/nDBH0hOSmZO/zkMnT2Ui5pfRJ+2fXhj5RvcMOUG3lj5BgPaD+CWjreUW0xBEn14EJYaWKDQiNFBGaypODm7cth9cDcvLH6Bp7o95Xc4InKMoqPmipTFki1LYtPszLh+RilHi8jRCPY3xgCJNg80DIcjZ1cONVNqFpo2pSLl5eexP28/pz8bme7kwQ8fZPp107nylCsZ2HEgbRu2pWHNhrRr1K5S512snlydB895sNC2Ls268O4N78ZeN67VmE27N9GlaRcuaXlJpcUWb/q3709mzcxATz/wY4VqYKsEuwY26Am4SKLLSNPAYVJ+/vWff8XW06qm+RiJSPjoG1cl2zh4Iz+f8HPOeeEcAA49fKjMNWr78/bz2/d/y+Aug2ma3rTQvkP5h5j65VSuevUqhpw5JLb9nT7v0LV510pNVo/VJ/0/YfeB3bEa40TVtmHbYqclCqqCNbBBb0IcTWAd8T8wnogcbth5w3A4zd8o5eLV5a/G1pXAipQvJbCVLLNWJsPOG0bPiZEJnm+aehMvXvFimZrqvrD4BUbNHcWouaMA6NayGy3qtGBE1xGxEXsBNuzawIQrJ/Dzk35Oemp62X6RSpRoA0AU56P1H9E0vSnH/+T40PSz7NK0C3P6z2FuzlxOqXeK3+GUSTSB7X3q0c9rLCL+a5LehOd6POd3GBISnZt0Zt7GeYASWJHypgTWBz2yerDithXcOuNW2jRow8FDBwvVRB2p5VuXM2beGP762V8LbX97zdsAPLfohw/iz//3c9o1ale2wMVXY+aP4bXlr4VqGP5qydXo0qwLXZp18TuUMosmsCfVPcnnSETkWLy05CVeWPwCs26cFaqxBsQf/+j5D1r/uTWpSak0rtXY73BEQkV/oX1ycr2T+aDvB9x71r1s27uNrv+vK99+/+1RnWPg9IGx5PXMZmcy7NxhvHndm7Su3xqAB85+gBnXz2Dnb3YqeQ2wj276CIADeQeAcA3itG3vNu6bdR/jl45nb+5ev8Mpk9SkVO464y46ZHbwOxQROQZvrnqTD9d9yOTlk/0ORULgJ2k/oU5aHZ69/NlAtXoTCQLVwMaBpqMi/Vb/tvBvRzQ9jHOOaydfy8f/+RiAYecOY+h5Q2P7E2Wy8EQRrdk7eOggEK4Edvu+7Tz+yeMALKi3oNDcgUGTnJTMyEtG+h2GiBwj59R/XcpP41qNWXHbCmqm1PQ7FJHQCc834QCb3Xc2AA/PfpjxS8fHtl/5ypU8NTcyHce+3H0MfmcwizYvImtsFpOWTQLg3T7vFkpeJXyi/V1z83Mjry0c/V8hXKMQi0g4aB5YKS/dxnfjusnX+R2GSOioBjYOnHvCuUy7dho9JvbgrOPOYtm3y2jzl8i0O1O+nMLjnzzON3u+ASCzZiart68GYOnApQk/Mm8iSE9Np1OTTtRIrgGEqwa2YN/vWqm1fIxERCRCNbFSHvbn7WfxN4tZt3Od36GIhI4S2DhxedbluKGRD81pK6fFtj90zkP8/qPfx14P7jKYwV0Gc/DQQaolV6v0OKXynVzvZOYNmMeizYu4pMUlxzTgV7wqWAPbvE5zHyMRkUTXqGYjQDWwUr527t/pdwgioaMENg5dftLl7PjNDlKTUkmrmsb9P7ufLd9vYcueLbHmpNWqKHlNNB0yO4RugKAwJeMiEmz3/ew+kqskk1U3y+9QJARSklIA+HWnX/sciUj4WBCaymRnZ7uFCxf6HYaIL9btXEfPiT25o/MddMzsSNuGbUNTQ+Cc44OvPyDf5dO1RVe/wxERESk3+/P2k5KUEqquPyIVycw+c86VOqKn/keJxLm8/DyWblnKH//1R9r9LVzTIZkZFza/UMmriPju+c+fp82f27Avd5/foUhIpFVNU/IqUgH0v0okzkWn0VmzfQ2g/lkiIhVhxuoZLNu6jBmrZ/gdioiIlEAJrEiciyawAIaSVxGRiqC/ryIiwaAEViTOFUxgHfHfZ11EJIj091VEJBh8S2DNrJuZrTSzNWZ2n19xiMS7tKppfocgIiIiIhIXfJlGx8ySgGeArkAOsMDMpjnnlvsRj0g8q51WmwMPHuCaV6/hghMv8DscEZFQapbeDECD7oiIxDm/5oHtBKxxzq0FMLOJQE9ACaxIEVKSUph67VS/wxARCa17zryH9NR02jRo43coIiJSAr8S2CbAhgKvc4DOPsUiIiIiCa5pelMeOf8Rv8MQEZFS+NVOpqih/gqNnmBmt5jZQjNbuHXr1koKS0RERBLRuM/GkfF4Btv3bfc7FBERKYFfCWwO0KzA66bApoIHOOfGOeeynXPZ9evXr9TgREREJLG8+9W77Ni/g/fXvu93KCIiUgK/EtgFQCszO9HMUoBrgWk+xSIiIiIJzkzzwIqIBIEvfWCdc3lmdjvwDpAEPO+cW+ZHLCIiIiLOaR5YEZEg8GsQJ5xzM4GZfl1fREREREREgkWTnYmIiEjCO7H2iQBUreLbs30RETkC+istIiIiCe+OM+6gfo36tM9s73coIiJSAiWwIiIikvCapjfl3rPu9TsMEREphZoQi4iISMJ7Zv4z2O+MTbs3lX6wiIj4RgmsiIiIJLwP1n0AwJwNc3yORERESqIEVkRERBKeoXlgRUSCQAmsiIiIJDyH5oEVEQkCJbAiIiIiIiISCEpgRUREJOG1rNMSgNSkVJ8jERGRkmgaHREREUl4v+r8K06ofQIdG3f0OxQRESmBElgRERFJeE3TmzLo9EF+hyEiIqVQE2IRERFJeE/PfRr7nfHV9q/8DkVEREqgBFZEREQS3sf/+RiARZsX+RyJiIiURAmsiIiIJDwzzQMrIhIESmBFREQk4TmneWBFRIJACayIiIiIRzWxIiLxTQmsiIiIJLysulkAVE+u7nMkIiJSEk2jIyIiIgnvtk63cUr9Uzi98el+hyIiIiVQAisiIiIJr3GtxvRp28fvMEREpBRqQiwiIiIJ74k5T2C/M5ZvXe53KCIiUgIlsCIiIpLwPs35FEAJrIhInCtTAmtmw8xso5kt9pZLC+y738zWmNlKM7ukwPZu3rY1ZnZfWa4vIiIiUh4MjT4sIhIE5dEHdpRz7omCG8ysNXAtcCrQGJhlZid5u58BugI5wAIzm+ac0+NOERERERERKVFFDeLUE5jonDsAfG1ma4BO3r41zrm1AGY20TtWCayIiIiIiIiUqDz6wN5uZkvN7Hkzq+NtawJsKHBMjretuO0iIiIivmldvzUAtVJq+RyJiIiUpNQaWDObBTQqYtcDwF+A3wPO+/kk0B+K7EjiKDphdsVc9xbgFoDjjjuutDBFREREjtmtp99Kx8yOdGrSqfSDRUTEN6UmsM65i47kRGb2LDDde5kDNCuwuymwyVsvbvuPrzsOGAeQnZ1dZJIrIiIiUh7qVa9H1xZdSaua5ncoIiJSgrKOQpxZ4OX/AP/21qcB15pZqpmdCLQC5gMLgFZmdqKZpRAZ6GlaWWIQERERKaun5j5FjcdqsPibxX6HIiIiJSjrIE7DzawdkWbA64D/BXDOLTOzSUQGZ8oDbnPOHQIws9uBd4Ak4C2BjHgAAAeZSURBVHnn3LIyxiAiIiJSJnNz5gLw1fav6JDZwedoRESkOGVKYJ1zN5Sw71Hg0SK2zwRmluW6IiIiIuXJTPPAiogEQXmMQiwiIiISaM5puA0RkSBQAisiIiLiUU2siEh8UwIrIiIiCa9NgzYAZFTL8DkSEREpSVkHcRIREREJvFtPv5Vzjj9H88CKiMQ51cCKiIhIwktPTefU+qdqHlgRkTinBFZEREQS3tj5Y2n0ZCMWblrodygiIlICJbAiIiKS8KLzwG747wafIxERkZIogRUREZGEp9GHRUSCQYM4iYiISML704V/Yse+HXRv1d3vUEREpARKYEVERCThtchowawbZ/kdhoiIlEJNiEVERERERCQQlMCKiIiIiIhIICiBFRERERERkUBQAisiIiIiIiKBoARWREREREREAsGcc37HUCoz2wqs9zsOKVI9YJvfQUilUpknHpV54lGZJx6VeeJRmSeeeC/z451z9Us7KBAJrMQvM1vonMv2Ow6pPCrzxKMyTzwq88SjMk88KvPEE5YyVxNiERERERERCQQlsCIiIiIiIhIISmClrMb5HYBUOpV54lGZJx6VeeJRmScelXniCUWZqw+siIiIiIiIBIJqYEVERERERCQQlMCGjJk1M7MPzWyFmS0zszu87Rlm9p6ZrfZ+1vG2n2xmn5rZATO7p8B50sxsvpkt8c7zuxKu2dc772oz61tg+6NmtsHM9pQSc0cz+8LM1pjZaDMzb/sIM/vSzJaa2RQzq13W+xNGYSrzAvvvMTNnZvWO9b6EWdjK3Mx+ZWYrvRiGl+XehFWYytzM2pnZXDNbbGYLzaxTWe9PGAW0zIs8zsxSzewV79/CPDM74djuSriFrMwHm9lyi3yHe9/Mjj/W+xJmYSrzAvuvssh3uIob7dg5pyVEC5AJdPDWawGrgNbAcOA+b/t9wOPeegPgdOBR4J4C5zGgpreeDMwDzijiehnAWu9nHW+9jrfvDC+ePaXEPB/o4l3zLaC7t/1ioKq3/ng0Zi3hLXNvXzPgHSJzP9fz+/7G4xKmMgfOB2YBqdFY/b6/8biErMzfLbB+KTDb7/sbj0tAy7zI44Bbgb9669cCr/h9f+NxCVmZnw9U99YHqczDX+YFfoePgLlAdkXdN9XAhoxzbrNzbpG3vhtYATQBegIveoe9CFzhHfOtc24BkPuj8zjnXPTJSrK3FNVh+hLgPefcdufcDuA9oJt3jrnOuc0lxWtmmUC6c+5TF/mX/1KB2N51zuV5h84Fmh7hbUgoYSpzzyjg3mKuLYSuzAcBf3LOHYjGeoS3IaGErMwdkO6t/wTYdAS3IOEErcxLOa5gzK8BF0Zr5OUHYSpz59yHzrm93kt9hytGmMrc83siyff+0s5TFkpgQ8xrotOeyFOYhtF/bN7PBkfw/iQzWwx8S+Qf+7wiDmsCbCjwOsfbdqSaeO8p7f39iTzBlxIEvczNrAew0Tm35CjOl9CCXubAScDZXrPC/29mpx/FeRNSCMr8TmCEmW0AngDuP4rzJqSAlHlJYuf2Hkz/F6hbTucOpRCUeUG/RN/hShX0Mjez9kAz59z08jhfSZTAhpSZ1QQmA3c653Ydyzmcc4ecc+2IPDXrZGZtirpUUW89isuU+n4zewDIA8YfxXkTTtDL3MyqAw8ADx/FuRJa0Mvc+1mVSDOmM4AhwCTVzBQvJGU+CLjLOdcMuAv4+1GcN+EEqMxLUpHnDp2QlHnkAmZ9gGxgRHmeN2yCXuZmVoVIC7q7y3quI6EENoTMLJnIf4LxzrnXvc1bvCZd0aZdR9xMzzm3E5gNdDOzzhYZeGOxV1uWQ6TPYlRTSmgOFn065C2PeO8v2Kyk0Pu9zuWXAb/wmqFJEUJS5i2AE4ElZrbO277IzBodadyJJCRljrfvda/503wgH9DgXUUIUZn3BaLxvwpoEKdiBKzMSxI7t5lVJdJ0fPuRxp1IQlTmmNlFRB5M93BeNxE5XEjKvBbQBpjtfYc7A5hmFTWQk4uDDsxaym8h8mTlJeCpH20fQeHO4MN/tH8YhTuD1wdqe+vVgI+By4q4XgbwNZEalDreesaPjimtM/gC7x96dKCPS73t3YDlQH2/72s8L2Eq8x8dsw4N4hT6MgcGAo946ycRadpkft/jeFtCVuYrgPO89QuBz/y+v/G4BLHMizsOuI3CgzhN8vv+xuMSsjJvD3wFtPL7vsbzEqYy/9G+2VTgIE6+F5yWci5Q+BmRpgBLgcXecimRvibvA6u9nxne8Y2IPI3ZBez01tOBtsDn3nn+DTxcwjX7A2u8pV+B7cO98+V7P4cV8/5s7xpfAWPxvrx659tQ4Pf4q9/3Nx6XMJX5j45ZhxLY0Jc5kAK87O1bBFzg9/2NxyVkZf4z4DNgCZG+Xh39vr/xuAS0zIs8DkgjUtu+hsjo1M39vr/xuISszGcBWwr8HtP8vr/xuISpzH90zGwqMIGNfpiIiIiIiIiIxDX1gRUREREREZFAUAIrIiIiIiIigaAEVkRERERERAJBCayIiIiIiIgEghJYERERERERCQQlsCIiIiIiIhIISmBFREREREQkEJTAioiIiIiISCD8H9IFDaosYFqjAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "avg,std = dataset.compare_ratio('CODtot_line2','CODsol_line2',2)" + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, period=2, \n", + " plot=True)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 41, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Use the average obtained from the ``compare_ratio`` function to fill in missing values. (*in this case, as mentioned before, this does clearly not work, since zero-values are replaced with zero-values. This only showcases the function and its arguments*)." + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/5':'2013/1/9'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 42, "metadata": { - "ExecuteTime": { - "end_time": "2017-05-09T09:55:04.632959", - "start_time": "2017-05-09T11:55:03.980745+02:00" - } + "scrolled": true }, "outputs": [ { - "name": "stderr", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.data['CODtot_line2']['2013/1/9':'2013/1/12']+= line10.values[::-1]\n", + "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']+= line10\n", + "\n", + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/5':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:462: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" + "text": [ + "Drift detected in period 5 to 6, slope: 51.6\n", + "Drift detected in period 6 to 7, slope: 52.53333333333333\n", + "Drift detected in period 7 to 8, slope: 44.86666666666667\n", + "Drift detected in period 9 to 10, slope: -48.266666666666666\n", + "Drift detected in period 10 to 11, slope: -41.06666666666667\n", + "Drift detected in period 11 to 12, slope: -34.6\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -4272,99 +1448,63 @@ } ], "source": [ - "dataset.fill_missing_ratio('CODtot_line2',\n", - " 'CODsol_line2',avg,\n", - " [dt.datetime(2013,1,22),dt.datetime(2013,1,23)],\n", - " only_checked=True,plot=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of a ratio, a correlation can be sought. In case of a zero intercept, this of course gives a result in the same range if the same data is used. To have a good impression on how useful the calculated correlation is, a prediction interval is plotted as well when ``plot`` is set to ``True``." + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,15)], max_slope=30, \n", + " plot=True, period=2)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 44, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\importlib\\_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", - " return f(*args, **kwds)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "slope: 0.4055129249855649 intercept: 0 R2: 0.9737746563763395\n" + "Drift detected in period 5 to 6, slope: 55.285714285714285\n", + "Drift detected in period 6 to 7, slope: 56.285714285714285\n", + "Drift detected in period 7 to 8, slope: 48.07142857142857\n", + "Drift detected in period 9 to 10, slope: -51.714285714285715\n", + "Drift detected in period 10 to 11, slope: -44.0\n", + "Drift detected in period 11 to 12, slope: -37.07142857142857\n" ] }, { "data": { + "image/png": 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\n", "text/plain": [ - "(
,\n", - " )" + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=35, period=2, \n", + " plot=True, drift_type='B')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" ] }, - "execution_count": 32, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -4372,40 +1512,68 @@ } ], "source": [ - "dataset.get_correlation('CODtot_line2',\n", - " 'CODsol_line2',\n", - " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", - " zero_intercept=True,plot=True)" + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/5':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 46, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "After the previously made assessment, use the correlation function to fill gaps in the dataset." + "fig, ax = plt.subplots(figsize=(18,4))\n", + "\n", + "ax.plot(data['2013/1/1':'2013/1/14'],'k--', label='original data' )\n", + "\n", + "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", + "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] += line10\n", + "\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/14'],'g--', label='data with drift')\n", + "ax.legend(loc='upper right', shadow=True)\n" ] }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "ExecuteTime": { - "end_time": "2017-05-09T09:55:06.016129", - "start_time": "2017-05-09T11:55:05.261370+02:00" - } - }, + "execution_count": 47, + "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:569: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" + "Drift detected in period 4 to 7, slope: 90.5\n", + "Drift detected in period 5 to 8, slope: 103.42857142857143\n", + "Drift detected in period 7 to 10, slope: -98.71428571428571\n", + "Drift detected in period 8 to 11, slope: -99.28571428571429\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -4415,44 +1583,32 @@ } ], "source": [ - "dataset.fill_missing_correlation('CODtot_line2',\n", - " 'CODsol_line2',\n", - " [dt.datetime(2013,1,23),dt.datetime(2013,1,25)],\n", - " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", - " only_checked=True,clear=False,plot=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data from previous day\n", - "Under the assumption that \"The best prediction for tomorrows weather is todays weather\", one can also replace missing data by making use of (one of) the previous days." + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, \n", + " plot=True, period=4)" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 48, "metadata": { - "ExecuteTime": { - "end_time": "2017-05-09T09:55:06.731819", - "start_time": "2017-05-09T11:55:06.018568+02:00" - } + "scrolled": true }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:961: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" + "Drift detected in period 4 to 7, slope: 90.5\n", + "Drift detected in period 5 to 8, slope: 103.42857142857143\n", + "Drift detected in period 7 to 10, slope: -98.71428571428571\n", + "Drift detected in period 8 to 11, slope: -99.28571428571429\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -4460,27 +1616,30 @@ } ], "source": [ - "dataset.fill_missing_daybefore('CODtot_line2',\n", - " [dt.datetime(2013,1,25),dt.datetime(2013,1,27)],\n", - " range_to_replace=[0,10],plot=True,\n", - " only_checked=False)" + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, period=4, \n", + " plot=True, drift_type='A')" ] }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "ExecuteTime": { - "end_time": "2017-05-09T09:55:07.431337", - "start_time": "2017-05-09T11:55:06.734413+02:00" - } - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -4488,42 +1647,65 @@ } ], "source": [ - "fig, ax = dataset.plot_analysed('CODtot_line2')\n", - "ax.legend(bbox_to_anchor=(1.3,1.0),fontsize=18)\n", - "ax.set_ylabel('Total COD [mg/L]',fontsize=18);ax.set_xlabel('')\n", - "ax.tick_params(labelsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Calculations" + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/15'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/4':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 50, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Calculate the daily average of a certain data series" + "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", + "fig, ax = plt.subplots(figsize=(18,4))\n", + "\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])#, type='constant')\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']+= line10\n", + "\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/15'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/4':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)\n", + "\n", + "asd = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']-line10" ] }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "ExecuteTime": { - "end_time": "2017-05-09T09:55:07.830400", - "start_time": "2017-05-09T11:55:07.433945+02:00" - }, - "scrolled": false - }, + "execution_count": 51, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -4531,31 +1713,176 @@ } ], "source": [ - "dataset.calc_daily_average('CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,2,1)],plot=True)" + "fig, ax = plt.subplots(figsize=(18,10))\n", + "ax.plot(asd, 'm--')\n", + "\n", + "\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'], type='constant')\n", + "df = pd.DataFrame(detrended_values, index = data.index[len(data[:'2013/1/4']):len(data[:'2013/1/8'])])\n", + "\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "#ax.plot(line_segment)\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/4':'2013/1/9'],'g--', label='data with drift')\n", + "#ax.plot(df, label='detrended drift')\n", + "\n", + "detrended_values1 = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])\n", + "df1 = pd.DataFrame(detrended_values1, index = data.index[len(data[:'2013/1/4']):len(data[:'2013/1/8'])])\n", + "line_segment1 = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values1[:]\n", + "ax.plot(line_segment1, 'c--')\n", + "\n", + "b = df.iloc[-1][0]\n", + "a = line_segment1[0]\n", + "slope = (b-a)/len(df)\n", + "f=[a]\n", + "s = df\n", + "s[:] = a\n", + "ax.plot(s)\n", + "for val in range(len(df)):\n", + " a+=slope\n", + " f.append(a)\n", + "\n", + "ds = pd.DataFrame(f, index = data.index[len(data[:'2013/1/4']):len(data[:'2013/1/8'])+1])\n", + "\n", + "ax.plot(ds, 'k--', label='Slope')\n", + "ax.plot((s+ds)/2, 'r*')\n", + "#ax.plot(df1, 'k--', label='detrended drift org')\n", + "\n", + "ax.plot(((s+ds)/2)+df1, 'k--')\n", + "#ax.plot(df1+ds, 'r--')\n", + "\n", + "ax.legend(loc='upper right', shadow=True)\n", + "\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 52, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Calculate the proportional concentration of different flows coming together." + "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "\n", + "\n", + "dataset.data['CODtot_line2']['2013/1/9':'2013/1/12']+= line10.values[::-1]\n", + "fig, ax = plt.subplots(figsize=(18,6))\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/3':'2013/1/15'], 'g--', label='data with drift')\n", + "asd = dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'] - line10.values[::-1]\n", + "ax.plot(asd, label='original data')\n", + "ax.legend(loc='upper right')" ] }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "ExecuteTime": { - "end_time": "2017-05-09T09:55:07.842239", - "start_time": "2017-05-09T11:55:07.833046+02:00" + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "749.1368773481072 513.0807872140786\n", + "-0.20490980046356652\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } - }, - "outputs": [], + ], "source": [ - "dataset.calc_total_proportional('Flow_total',\n", - " ['Flow_line1','Flow_line2','Flow_line3'],\n", - " ['TSS_line1','TSS_line2','TSS_line3'],\n", - " 'TSS_prop')" + "fig, ax = plt.subplots(figsize=(18,10))\n", + "ax.plot(asd, 'm--')\n", + "\n", + "\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'], type='constant')\n", + "df = pd.DataFrame(detrended_values, index = data.index[len(data[:'2013/1/8']):len(data[:'2013/1/12'])])\n", + "\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "#ax.plot(line_segment)\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/7':'2013/1/15'],'g--', label='data with drift')\n", + "#ax.plot(df, label='detrended drift')\n", + "\n", + "detrended_values1 = signal.detrend(dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'])\n", + "df1 = pd.DataFrame(detrended_values1, index = data.index[len(data[:'2013/1/8']):len(data[:'2013/1/12'])])\n", + "line_segment1 = dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'] - detrended_values1[:]\n", + "ax.plot(line_segment1, 'c--', label='slope')\n", + "#ax.plot(df1)\n", + "\n", + "b = df.iloc[0][0]\n", + "\n", + "a = line_segment1[-1]\n", + "print(b,a)\n", + "slope = (a-b)/len(df)\n", + "print(slope)\n", + "f=[a]\n", + "s = df\n", + "s[:] = b\n", + "ax.plot(s, label='Slope1')\n", + "for val in range(len(df)-1):\n", + " a+=slope\n", + " f.append(a)\n", + "\n", + "\n", + "#print(f)\n", + "ds = pd.DataFrame(f, index = data.index[len(data[:'2013/1/8']):len(data[:'2013/1/12'])])\n", + "\n", + "\n", + "\n", + "ax.plot(ds, 'C1', label='Slope2')\n", + "ax.plot((s+ds)/2, 'r*')\n", + "#print(df1)#(s+ds)/2, ((s+ds)/2)+df1)\n", + "#ax.plot(df1, 'k--', label='detrended drift org')\n", + "\n", + "ax.plot(df1+((s+ds)/2), 'b--', label='fixed drift')\n", + "\n", + "#ax.plot(df1+ds, 'r--')\n", + "\n", + "ax.legend(loc='upper right', shadow=True)" ] } ], diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 859583eec..67849e3e8 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1755,7 +1755,7 @@ def drift_analysis(self, data_name, arange1, arange2=None, plot=False): """ pass - def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): + def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, drift_type=None): """ This function calculates the slope of the data in a certain given period by fitting a line through it and removes the drift. @@ -1829,8 +1829,34 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False): series[value[0]:value[1]] = series[value[0]:value[1]]-line_segment1+ds elif value[2] == 'm': + if drift_type == 'A' or drift_type is None: + series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) + #if value[1].day - value[0].day == 1: + elif drift_type == 'B': + + #else: + detrend = signal.detrend(series[value[0]:value[1]], type='constant') + df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + + b = df2.iloc[1][0] + a = line_segment1[-1] + slope = (a-b) / len(df2) + f = [a] + s = df2 + s[:] = b + for val in range(len(df2) - 1): + a += slope + f.append(a) + + ds = pd.DataFrame(f, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + ds = ds[:] + s[:] + ds = ds / 2 + ds = ds.squeeze() # from dataframe to series + #ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') + series[value[0]:value[1]] = series[value[0]:value[1]] - line_segment1 + ds + # ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') - series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) + #series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) """ detrend = signal.detrend(series[value[0]:value[1]]) diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index dbe8dbae3..d1e3fea6f 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -1375,8 +1375,8 @@ def check_filling_error(self,nr_iterations,data_name,filling_function, # turn warnings on again wn.filterwarnings("always") raise ValueError("Checking of the filling function could not "+\ - "be executed. Check docstring of the filling "+\ - "function to provide appropriate arguments.") + "be executed. Check docstring of the filling "+\ + "function to provide appropriate arguments.") filling_errors = filling_errors.append(pd.Series([iter_error])) From b336c41194330bc817e87b24badd0e534d63eebf Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Sat, 25 Aug 2018 17:45:42 +0200 Subject: [PATCH 28/42] added comments to detect_drift and remove_drift to easier understand the functions. also made some minor changes #303 --- wwdata/Class_HydroData.py | 158 +++++++++++++++++++++++--------------- 1 file changed, 97 insertions(+), 61 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 67849e3e8..980f7d132 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1576,7 +1576,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals from scipy import signal series = self.data[data_name][arange[0]:arange[1]].copy() - #removes NaNs and infs from the dataset + #removes NaNs and infs from the dataset and other values that signal.detrend can't analyse index = 0 nan_values = [] for value in series: @@ -1590,9 +1590,14 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals if max_slope is None: return KeyError('Please specify a maximum slope') + """ + if the period is not specified or the period is the same as the length, it goes through this if-loop. + it is faster than the else-loop. the loop calculate the slope by using signal.detrend and compare it + to the max_slope. + """ if period is None or period is arange[1].day - arange[0].day + 1: detrended_values = signal.detrend(series) - line_segment = series - detrended_values[:] + line_segment = series - detrended_values[:] #constructs a straight line of the dataset slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) if slope > max_slope or slope < -max_slope: @@ -1609,10 +1614,10 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) ax.plot(series, 'g--', label='original data') - if slope > max_slope and slope < -max_slope: - ax.plot(line_segment, 'b-',label='slope') - ax.plot(series.index, detrended_values, 'r', label='detrended values') - ax.plot(series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data + #if slope > max_slope and slope < -max_slope: + ax.plot(line_segment, 'b-',label='slope') + ax.plot(series.index, detrended_values, 'r', label='detrended values') + ax.plot(series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data ax.legend(fontsize=16) ax.set_xlabel(self.timename, fontsize=20) ax.set_ylabel(data_name, fontsize=20) @@ -1623,6 +1628,9 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals if type(period) is not int: return ValueError('the period must be a integer') + if period < 0.5: + return ValueError('period must be larger than 0.5') + start_index = 0 end_index = 0 new_index = end_index @@ -1630,60 +1638,80 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals m = 0 list_value = [] - while series.index.day[new_index] + period <= series.index.day[len(series)-1]: - checked = False - while series.index.day[end_index] < (series.index.day[start_index] + period): - if series.index.day[end_index] == (series.index.day[start_index] + 1): - if checked is False: - new_index = end_index - checked = True - end_index += 1 - if end_index == len(series)-1: - break - - detrended_values = signal.detrend(series[start_index:(end_index-1)]) - line_segment = series[start_index:(end_index-1)] - detrended_values[:] - slope = (int(line_segment[-1]) - int(line_segment[0])) / ( - arange[1].day - arange[0].day + 1) - - if slope > max_slope: - n += 1 - print('Drift detected in period {} to {}, slope: {}'.format - (series.index.day[start_index], series.index.day - [end_index-1], slope)) - if n == 1: - start_value = series.index[start_index] - end_value = series.index[end_index] - else: - if n > 0: + if period == 0.5: #Need a solution + print('Not yet possible with period = 0.5') + pass + + elif period == 1: + pass + + else: + """ + the first while-loop makes sure that the calculations of the last period is right and that + it don't overextend. + the second while-loop finds the indexes for the right period length(could be improved). + """ + while series.index.day[new_index] + period <= series.index.day[len(series)-1]: + checked = False + while series.index.day[end_index] < (series.index.day[start_index] + period): + if series.index.day[end_index] == (series.index.day[start_index] + 1): + if checked is False: + new_index = end_index + checked = True + end_index += 1 + if end_index == len(series)-1: + break + + detrended_values = signal.detrend(series[start_index:(end_index-1)]) + line_segment = series[start_index:(end_index-1)] - detrended_values[:] + slope = (int(line_segment[-1]) - int(line_segment[0])) / ( + arange[1].day - arange[0].day + 1) + + """ + n and m is for separating the positive and negative slope. There are different methods + used for positive and negative slopes. + list_value stores the indexes where the slope was bigger than the max_slope. + """ + if slope > max_slope: + n += 1 + print('Drift detected in period {} to {}, slope: {}'.format + (series.index.day[start_index], series.index.day + [end_index-1], slope)) + if n == 1: + start_value = series.index[start_index] + end_value = series.index[end_index] + else: + if n > 0: + list_value.append([start_value, end_value, 'n']) + n = 0 + + if -max_slope > slope: + m += 1 + print('Drift detected in period {} to {}, slope: {}'.format + (series.index.day[start_index], series.index.day + [end_index - 1], slope)) + if m == 1: + start_value = series.index[start_index] + end_value = series.index[end_index] + else: + if m > 0: + list_value.append([start_value, end_value, 'm']) + m = 0 + + #combines the indexes where the slope was larger than the max_slope in a longer period + if series.index.day[end_index] == series.index.day[-1] and n > 0: list_value.append([start_value, end_value, 'n']) - n = 0 - - if -max_slope > slope: - m += 1 - print('Drift detected in period {} to {}, slope: {}'.format - (series.index.day[start_index], series.index.day - [end_index - 1], slope)) - if m == 1: - start_value = series.index[start_index] - end_value = series.index[end_index] - else: - if m > 0: + if series.index.day[end_index] == series.index.day[-1] and m > 0: list_value.append([start_value, end_value, 'm']) - m = 0 - if series.index.day[end_index] == series.index.day[-1] and n > 0: - list_value.append([start_value, end_value, 'n']) - if series.index.day[end_index] == series.index.day[-1] and m > 0: - list_value.append([start_value, end_value, 'm']) - - start_index = new_index - end_index = new_index + start_index = new_index + end_index = new_index if len(list_value) == 0: plot = False print('No drift detected') + #Makes sure that list_value don't have two values in the same index for l in range(len(list_value) - 1): if list_value[l][1] > list_value[l + 1][0]: ind = len(series[:list_value[l][1]]) @@ -1701,7 +1729,6 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals detrended_values.append(df1) line_segment1 = series[value[0]:value[1]] - detrend[:] ax.plot(line_segment1, 'm--') - ax.plot(df1) """ detrend = signal.detrend(series[value[0]:value[1]], type='constant') @@ -1751,11 +1778,11 @@ def drift_analysis(self, data_name, arange1, arange2=None, plot=False): Returns ---------- - information about the drift + information about the drift(highest and lowest point(s), mean, etc.) """ pass - def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, drift_type=None): + def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, drift_type='A'):#need to tag replaced values. """ This function calculates the slope of the data in a certain given period by fitting a line through it and removes the drift. @@ -1772,7 +1799,10 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr the period, in days, which a certain slope is allowed plot : bool if true, a plot is made... - + drift_type : str + separates the different type of drifts when the slope is negative. + 'A' is drift with no continuity in the data. 'B' is drift which looks + like a mountain(with continuity) Returns ---------- the fixed dataset without drift @@ -1793,6 +1823,13 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr ax.plot(self.data[data_name], 'r--', label='new data') ax.legend(loc='upper right', shadow=True) + if period == 0.5: # Need a solution + print('Not yet possible with period = 0.5') + pass + + elif period == 1: + pass + else: """ for n in range(len(self.list_value)-1): @@ -1803,11 +1840,12 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr for value in self.list_value: detrend = signal.detrend(series[value[0]:value[1]]) - df1 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) + #df1 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) line_segment1 = series[value[0]:value[1]] - detrend[:] + #method shown in Showcase_OnlineSensorBased if value[2] == 'n': - detrend = signal.detrend(series[value[0]:value[1]], type='constant') + detrend = signal.detrend(series[value[0]:value[1]], type='constant') df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) b = df2.iloc[-2][0] @@ -1829,12 +1867,10 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr series[value[0]:value[1]] = series[value[0]:value[1]]-line_segment1+ds elif value[2] == 'm': - if drift_type == 'A' or drift_type is None: + if drift_type == 'A': series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) #if value[1].day - value[0].day == 1: elif drift_type == 'B': - - #else: detrend = signal.detrend(series[value[0]:value[1]], type='constant') df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) From 40ece5f2e92f2e882cf08e30703632f5e04d5f1d Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Sat, 25 Aug 2018 21:36:24 +0200 Subject: [PATCH 29/42] detect_drift works partly with a period of 1 day. needs improvement #303 --- wwdata/Class_HydroData.py | 56 +++++++++++++++++++++++++++++++++------ 1 file changed, 48 insertions(+), 8 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 980f7d132..843e99c3a 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1599,7 +1599,6 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals detrended_values = signal.detrend(series) line_segment = series - detrended_values[:] #constructs a straight line of the dataset slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) - if slope > max_slope or slope < -max_slope: print('Based on the specified maximum slope, a drift was' ' detected with a slope higher than the maximum one. \n' @@ -1643,7 +1642,51 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals pass elif period == 1: - pass + count = 0 + day_list = [] + for value in series.index.day[:-1]: + count += 1 + if value < series.index.day[count]: + end_index = count - 1 + day_list.append([start_index, end_index]) + start_index = count + + for value in range(len(day_list)): + start_index = day_list[value][0] + end_index = day_list[value][1] + detrended_values = signal.detrend(series[start_index:end_index]) + line_segment = series[start_index:end_index] - detrended_values[:] + slope = (int(line_segment[-1]) - int(line_segment[0])) / 1 + + if slope > max_slope: + n += 1 + print('Drift detected in day {} with slope: {}'.format + (series.index.day[start_index], slope)) + if n == 1: + start_value = series.index[start_index] + end_value = series.index[end_index] + else: + if n > 0: + list_value.append([start_value, end_value, 'n']) + n = 0 + + if -max_slope > slope: + m += 1 + print('Drift detected in day {} with slope: {}'.format + (series.index.day[start_index], slope)) + if m == 1: + start_value = series.index[start_index] + end_value = series.index[end_index] + else: + if m > 0: + list_value.append([start_value, end_value, 'm']) + m = 0 + + # combines the indexes where the slope was larger than the max_slope in a longer period + if series.index.day[end_index] == series.index.day[-1] and n > 0: + list_value.append([start_value, end_value, 'n']) + if series.index.day[end_index] == series.index.day[-1] and m > 0: + list_value.append([start_value, end_value, 'm']) else: """ @@ -1661,12 +1704,9 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals end_index += 1 if end_index == len(series)-1: break - detrended_values = signal.detrend(series[start_index:(end_index-1)]) line_segment = series[start_index:(end_index-1)] - detrended_values[:] - slope = (int(line_segment[-1]) - int(line_segment[0])) / ( - arange[1].day - arange[0].day + 1) - + slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) """ n and m is for separating the positive and negative slope. There are different methods used for positive and negative slopes. @@ -1715,7 +1755,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals for l in range(len(list_value) - 1): if list_value[l][1] > list_value[l + 1][0]: ind = len(series[:list_value[l][1]]) - list_value[l + 1][0] = series.index[ind] + list_value[l + 1][0] = series.index[ind-1] if plot is True: detrended_values = pd.DataFrame() @@ -1845,7 +1885,7 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr #method shown in Showcase_OnlineSensorBased if value[2] == 'n': - detrend = signal.detrend(series[value[0]:value[1]], type='constant') + detrend = signal.detrend(series[value[0]:value[1]], type='constant') df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) b = df2.iloc[-2][0] From 39306d5b08a1dd7857d774b86e881a452dd01935 Mon Sep 17 00:00:00 2001 From: Jora Singh Randhawa Date: Mon, 27 Aug 2018 08:51:02 +0200 Subject: [PATCH 30/42] works now with a period equal 1 day #303 --- Showcase_OnlineSensorBased.ipynb | 324 ++++++++++++++++--------------- wwdata/Class_HydroData.py | 12 +- 2 files changed, 177 insertions(+), 159 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 3cacb7368..167c02242 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 94, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'0.2.0'" ] }, - "execution_count": 3, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 97, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,7 +120,7 @@ " dtype='object')" ] }, - "execution_count": 4, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 98, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 99, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 100, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 101, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 102, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 103, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 104, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 105, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 106, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 107, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 108, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 109, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 110, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" ] }, - "execution_count": 18, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 112, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,10 +546,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ "4895" ] }, - "execution_count": 20, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -615,28 +615,28 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ - "dataset.check_filling_error(100,'CODtot_line2','fill_missing_standard',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " nr_small_gaps=70,max_size_small_gaps=12,\n", - " nr_large_gaps=3,max_size_large_gaps=800,\n", - " to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " only_checked=True)" + "#dataset.check_filling_error(100,'CODtot_line2','fill_missing_standard',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# nr_small_gaps=70,max_size_small_gaps=12,\n", + "# nr_large_gaps=3,max_size_large_gaps=800,\n", + "# to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# only_checked=True)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ - "dataset.check_filling_error(100,'CODtot_line2','fill_missing_daybefore',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " nr_small_gaps=70,max_size_small_gaps=12,\n", - " nr_large_gaps=3,max_size_large_gaps=800,\n", - " to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " range_to_replace=[0,10],only_checked=True)" + "#dataset.check_filling_error(100,'CODtot_line2','fill_missing_daybefore',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# nr_small_gaps=70,max_size_small_gaps=12,\n", + "# nr_large_gaps=3,max_size_large_gaps=800,\n", + "# to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# range_to_replace=[0,10],only_checked=True)" ] }, { @@ -669,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 116, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -714,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 117, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", @@ -726,7 +726,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1959: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", + "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:2033: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", " 'representative average day and/or high standard deviations.')\n" ] } @@ -738,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 118, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", @@ -780,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 119, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", @@ -812,7 +812,7 @@ " dtype='object')" ] }, - "execution_count": 26, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -829,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 120, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", @@ -878,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 121, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.917107", @@ -893,7 +893,7 @@ "(2.4506423271968965, 0.6721532140851265)" ] }, - "execution_count": 28, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -912,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 122, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", @@ -926,15 +926,6 @@ "text": [ "Best ratio (2.5328218826106403 ± 0.16586491872475548) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:1400: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", - "You can access Timestamp as pandas.Timestamp\n", - " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" - ] } ], "source": [ @@ -950,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 123, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", @@ -993,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -1007,10 +998,10 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 31, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" }, @@ -1039,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 125, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.016129", @@ -1085,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 126, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.731819", @@ -1122,7 +1113,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 127, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", @@ -1164,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 128, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", @@ -1197,7 +1188,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 129, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", @@ -1222,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -1245,16 +1236,16 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 38, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" }, @@ -1288,22 +1279,23 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Drift detected in period 5 to 6, slope: 55.642857142857146\n", - "Drift detected in period 6 to 7, slope: 56.714285714285715\n", - "Drift detected in period 7 to 8, slope: 48.5\n", - "Drift detected in period 8 to 9, slope: -112.21428571428571\n" + "Drift detected in period 4 to 6, slope: 60.142857142857146\n", + "Drift detected in period 5 to 7, slope: 81.5\n", + "Drift detected in period 6 to 8, slope: 77.14285714285714\n", + "Drift detected in period 7 to 9, slope: 70.85714285714286\n", + "Drift detected in period 8 to 10, slope: 110.92857142857143\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1314,27 +1306,59 @@ ], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", - " plot=True, period=2)" + " plot=True, period=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drift detected in day 5 with slope: 454.0\n", + "Drift detected in day 6 with slope: 504.0\n", + "Drift detected in day 7 with slope: 359.0\n", + "Drift detected in day 8 with slope: 479.0\n", + "Drift detected in day 10 with slope: 258.0\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=180, \n", + " plot=True, period=1)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 134, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Drift detected in period 5 to 6, slope: 55.642857142857146\n", - "Drift detected in period 6 to 7, slope: 56.714285714285715\n", - "Drift detected in period 7 to 8, slope: 48.5\n", - "Drift detected in period 8 to 9, slope: -112.21428571428571\n" + "Drift detected in day 5 with slope: 454.0\n", + "Drift detected in day 6 with slope: 504.0\n", + "Drift detected in day 7 with slope: 359.0\n", + "Drift detected in day 8 with slope: 479.0\n" ] }, { "data": { - "image/png": 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\n", 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5enoSFBREt27d7B2KUkplK02aNOHIkSPMmDHDTFztbfz48Xz22We0bNkST09PJk+eTNWqVenZs6e5z4ABA6hZsybffvst3377rdk3NHfu3Hz99f/Xj7i6ulKkSJEMv4c79e3bl2PHjjFx4kT8/f2pXr06s2fPZt++fQwfPpw6derYO8QMs2jRIt555x38/f0pVaqUvcNRWUhqa16LAjeIb4//BVBMRN42DGMGsEdEFiXsNwdYR/zAUB4i8k5CeU+goYgMvsf1+gH9AEqXLl3fz8/vUe5RKZUN6OAOSimV9lasWEGnTp1wdXUlLi6OuLg4c1t6ft6uWLGCAwcOMGHCBJtyEeH48ePUqlULAAcHBywWCxaLBQAfHx/+/PNPWrVqZTYhVlnPnDlzeOedd/Dz86N06dL2DkfZWbrXvIrItSQX+xlYk7B6CUj6+KQkEJCwfK/y5M4/G5gN8c2GUxOjUkoppZS6v48//hiIn6/Uyckpw647fvx4Dh8+zFdffWU2G71w4QLlypUD4K233qJ48eLmVDSOjo4AVKlSJVP0xVWPRh9Iq9RKVSNzwzCKJVntABxPWP4L6GoYhothGOWAisB+4ABQ0TCMcoZhOBM/qNNfqQ9bKfW4qVy5ss3AHUoppR5d4pQwED+4UVIPmpc0NWJiYoiLi6Nv374A9OvXj9DQUAAmTZpk7jd79mzGjx9PgQIF0jwGZX+avKrUemDyahjGEmAPUNkwjEuGYfQBJhmGccwwjKNAC+BDABE5ASwHTgIbgPdExCIiccAgwBM4BSxP2FcppVJkzJgxTJ061d5hKKVUtlK2bFmb9aZNm5rLiaPjJjV37lwMw8DDw4NLly499PXKly+Pk5MTAwcOBOKbj37//fcAHD58mPz587N8+fIMrQVWGU+TV5VaD2w2LCLJjY4y5z77jwfGJ1O+jvj+r0op9dCKFCmiP2aUUioNLVy4kFmzZnHz5k22bNnCl19+yeLFi80+iP/99x9169ZlxYoVVK5cmaVLlzJ+fPxPvI0bN/Lbb7/xySefpPh6AQEBXL582VyfOnUqTk5OFC9eHICZM2cSHh5Os2bN0vAuVWZUr149vvjiC/Lnz2/vUFQWk6IBm+xJp8pRSgG0aNGCkydPcu3atQfvrJRS6oHq1avHxYsXuX79us38pr/++iu9e/cGYMOGDbz44ot3Hevi4kJERITZFzWR1WrFarVy/fp1ihcvTlBQEEOHDgXi52Ndv349OXPmpHfv3vpAUikFZNxUOUoplWG2bdtm7xCUUirbuHHjBocPH2bEiBE2iStgTjkDMGLEiGSPf+211+5KXAGOHz9O7dq1AbBYLGzZsoX58+cDMH/+fG0mqgCIiIjg5s2bFCtWTB9iqIeiswIrpZRSSj1mDh8+DMALL7xw17ak/WC9vb2TPX7x4sVMnDjxrvIrV66Yy7169cLJycmcY/Wll156lJBVNrJq1SrKlCnDhQsX7B2KymI0eVVKKaWUesx4eXkBULdu3bu21ahRAxFh5MiRNuW+vr7cvn2bN998k8KFCyfbIiZpMrJo0SK8vb25fv064eHh/P3332l6DyrrurO2X6mU0uRVKaWUUuoxU69ePb788ksKFix4z30+++wzFi5caK6XKlUKV1dX5s+fT/369bl58+Zdx+zevRvDMHB3d+eLL75gzJgxAOTOnTvtb0JledqMXD0s7fOqlMoSqlWrRlBQkL3DUEqpbKFVq1a0atXqvvs4OzvzxhtvEBMTw/r16236uBYsWJCzZ8/a7B8YGMiSJUto2bIlmzZtSpe4VfagU+Wo1NLkVSmVJYwbN47Y2Fh7h6GUUtnCtWvXyJEjB4UKFXrgvm+//TZvv/22TVmpUqVYsWIFkZGR5MqVC4ACBQqwevVqKleunC4xq+xDk1eVWpq8KqWyhFy5cnH16lV7h6GUUmkiNjaWEydO4OjoSK5cuahQoUKGXr9nz56EhYWxZ8+eVB3fsmVL1q5dy7lz59i/fz83b97k/fffp23btmkcqcqO6tWrx5QpU3jiiSfsHYrKYnSeV6VUlvDss8+yb98+oqOj7R2KUko9svPnz9skrBn9e6xJkya4ubmxcePGVB0vIhiGwXfffcewYcMAaNasGStXrrSZakcppR7kYeZ51QGblFJZws6dO4mJibF3GEoplSYmT55s1+uHhYXh5uaW6uOTGy02MDCQPHnyPEpY6jEREhLCiRMniIqKsncomcaHH37I4sWL7R1GpqfJq1JKKaVUBouIiLBZT4ua1//++y/F53nU5DXRhx9+SFxcHDdv3uTAgQO4uLg88jlV9rdx40Zq1Khx16Bfj7MpU6bw/fff2zuMTE+TV6WUUkqpDBYeHm4ujxs3jgMHDpjrSRPQ8+fPM3v2bAAsFguBgYEsX76cpUuX8vTTT7N//36CgoLYt28flStXZsqUKWZi/O+//7J69WqaNGmCj48PV69e5cKFC8TGxuLn50eRIkUe+T4Mw8DR0ZGCBQumSTKsHg86z+vdypQpQ7Vq1ewdRqanyatSSimlVCrcvn2bzz//PFXTeCUmmFWrViU8PJxGjRpx5MgRTp8+jYODA//88w8AXbt2pX///mzdupUcOXLwxBNP0KVLF65cucLevXtp1KgRhQoV4q+//gJg6NChuLm5Ubx4cZo1a0aHDh3Ys2cP3333HVWqVKFcuXJ0794dQAdXUnaX2cfeyUh+fn54eXnZO4xMT5NXpVSWUKNGDapWrWrvMJRSj6Hg4GDCwsJsykSEv//+m7Fjx9KsWTPi4uLMbUFBQQQFBbFp0yb++OOPZM+ZmLwWKlTI7P/60ksv8e+//wIwZswYdu3aZdbIPv/88+axPXv2ZNCgQdStW9dc//zzz/nll1/Mfa5cuWJzvVmzZhESEgJgxpR4vFIZTafKSd7x48ftHUKmp8mrUipLGD9+PBMnTrR3GEqpx8iVK1cYOXIkBQsWNJvzvf/++5QoUYJSpUqZzXlPnjxJ06ZNmTBhAs2bN6dQoULUqlWLLVu20LlzZ95880369+/Pb7/9hmEYPP/887z77rssW7aMDRs20KVLFwCGDBnCtWvXANi9ezenT58GYNKkScyePZuRI0dy9uxZ5s+fj5OTE15eXogICxYswNHRkT59+mC1WhERjhw5QuXKlfnnn39o2rTpXUlCkyZNKFiwYEa9lUrZ0ORVpZbO86qUyjJ8fHzsHYJS6jGydOlSJkyYAMClS5f44YcfuHDhAgEBAQBMnz6dTz75hLfffptWrVqxb98+tm/fDkDevHmpWbMmAAsXLgQwk92tW7fyxx9/mMnj0qVLWbp0KQCenp7kz5+fpUuX4uHhQbdu3ciVK1eKY05MCmrVqmUmv8888wyhoaGEh4fzxBNP8Oqrr9p9tGP1eKtfvz5z5syhZMmS9g4lU+ncubO9Q8j0dJ5XpVSW8Mwzz7B79259SquUyhDDhw9n8uTJGIbB+++/z9SpUwH4888/ad++PbVr1+bw4cM2A89YrVZ69eqFu7s7Q4YMAeKnBPn000+pX78+devWpU6dOnzyySd8/vnnODs72+XelFKZT758+Xj77bcfyxGHH2aeV01elVJZgjYxUkplpBw5cmCxWHjxxReZNWsWa9asoXnz5lSrVg0RQURwcNDeV0qlRnBwMGfOnKFmzZrkzp3b3uFkCoZh8NRTTz2WrcweJnnVT12llFJKqTvky5cPiB/tt3Tp0gwcONDs92oYhiauSj2CnTt38vTTT5tN21W8vHnz2juETE8/eZVSSiml7hAdHQ3ED2yklEpb2XGe19mzZ/P000/blG3bts1mJPL7qVWrFqVLl06P0LIVTV6VUkoppRJYLBZ69uxJREQEXbp0oWLFivYOSalsKzt1Berfvz979+6lWrVqHD16FC8vL1q0aMGUKVNSdPzRo0fRrpIPpqMNK6WyhJo1a5rN+JRSKj1ERUUxdepUFi1aRJ06dcyRhpVSaSs7jmPx1FNPcfbsWU6dOkVERIQ5mnjx4sVTfI5Lly6lV3jZhiavSqksYfLkybi4uNg7DKVUNhUaGmrzgGz37t3kzJnTjhEplX1lx+S1Z8+ejB07FgBfX1+KFCkCxI9CrtKONhtWSmUJERERbN261d5hKGV3TZs2zZb9xewtMDDQXF62bJkmrkqlo3r16rF8+XIqVKhg71DSTI0aNczliIgIczCqxLmfU2LQoEFpHld2ozWvSqksYeLEiezfv59x48bZOxSl7GrXrl1AfI2FJrGPLiYmhoCAAMqXL5+taoGUysyKFStG586d7R1Gmlq2bJm5HBgYyO+//w7Ez/X8IImfPYULF06f4LKRFNW8GoYx1zCM64ZhHE9SVtAwjE2GYfgk/FkgodwwDGOaYRhnDcM4ahhGvSTH9ErY38cwjF5pfztKqexq//799g5BqUzFYrHYO4RsoV+/flSvXj3Fg6oopR7dzZs32bRpE7du3bJ3KGkmNjbWXB49ejSbNm0CoGHDhg88NrFp8cKFC9MnuGwkpc2GfwVevKNsBPCPiFQE/klYB2gDVEx49QN+hPhkFxgLNAIaAmMTE16llFJKPZyUTr+g7i0yMpIlS5ZQpUoV2rZta+9wlHpsHDx4kNatW3Py5El7h/LILBYLVquVVatWmWX9+vUzl+vVq5fcYTYcHBzInz8/rq6u6RJjdpKi5FVEdgBBdxS3B+YnLM8HXk1SvkDi7QXyG4ZRDPAANolIkIgEA5u4OyFWSimlVApo8vrodu3aRUxMDF9++SWVK1e2dzhKPTay04BNDRo0oFKlSuZ6njx5bKbY2rZtG40aNcLb2/ue5zAMg1q1apmDPKl7e5Q+r0VF5AqAiFwxDOOJhPISwMUk+11KKLtXuVJKKaVSqEyZMvj5+ekIlmlgy5Yt5MiRg2bNmtk7FKUeK9mpv/6dU+GEhYUxb948c/2LL74A7t/3NTY2lh07duDm5pY+QWYj6TFgU3L/GuU+5XefwDD6Ed/kmNKlS6ddZEqpLKtmzZqUL1/e3mEoZXcXLlywdwjZRq5cuXj11Vf1B6NSdpIdal5z587N0aNHbcqSaw59v9Yy0dHRAISHh6dtcNnQo0yVcy2hOTAJf15PKL8ElEqyX0kg4D7ldxGR2SLiLiLuWn2ulAKYPn06o0ePtncYSqlsQEQYOXIkbdq0MUcEVUplnOzSbPj48eMsX778rgH0SpUqdde+90tetSVNyj1K8voXkDhicC/gzyTlbyaMOtwYCEloXuwJtDYMo0DCQE2tE8qUUuqBrl+/zqJFi+wdhlJ2V7duXQzDsJmXVKVceHg4TZs2ZcKECfftg6aUSj916tRh3bp1VK9e3d6hPJJ7zeGadOThRClJXsePH582gWVjKZ0qZwmwB6hsGMYlwzD6ABOAVoZh+ACtEtYB1gHngbPAz8BAABEJAr4ADiS8Pk8oU0qpB/r666+ZNm2avcNQyu4SE67IyEg7R5I1zZs3j927dwPw4os6bqRS9lC4cGHatGlDwYIF7R3KI7ly5Uqy5VevXr2rzNHR8Z7nSUxetQvDgxmZvbre3d1dDh48aO8wlFJ2ll2aGCn1qBL/L5w9e5YKFSrYOZqs5dy5czRt2pScOXOydetWypQpY++QlHosBQYG8u+//9K0adMsPcJunz59WL9+Pd988w09evR44P73+g1z48YNihQpQvny5Tl37lxah5npGYZxSETcU7LvozQbVkoppZSd6FQ5KWO1Wjly5AiGYVC/fn0+//xz5s+fr4mrUnZ04sQJOnbsyPHjx+0dyiNJTDrvHGjpp59+MpdPnz6Nk5PTfc9ToEABatasiYODpmYPou+QUkoplQVlleQ1MDCQyZMn2/QBCw8PJy4uju+++45r165x9epVDh8+nCbXW7lyJZs3bwbA19eXZcuWUadOHSB+qoq3335bp8ZRys6yS2uqcuXKUa9ePfr3729TXrZsWXPZy8sr2T6wSTk6OlKuXDltNpwC6TFVjlJKKaXSSf369Tl06NADn+RnpLCwMPLkyXNX+dChQ/n+++8BWLp0KePHj2fv3r2MGzeOunXrcvjwYYYNG2bub7FYiIiIwM3NzWYeSF9fXxwcHIiIiODYsWO4u7uzZMkSRo5xFgVDAAAgAElEQVQciWEYnDhxgpo1axITE8Nrr70GxNe4Jk6v1adPH+bMmcO0adPu2+9MKZUxsss8r1OmTAHia1f37t1rlnfv3j3Z5XsJCwvjr7/+SvsAsyFNXpVSWULlypWpXbu2vcNQyu7sNQ5EWFgYhmGQM2dOrl+/zvTp02nVqhXNmjVjyJAhhIaG0r9/f6KiovDz86NFixZm4gqwZ88eVqxYwbhx4wCSrWndtWsXHh4eiAjff/893bp148cff2TkyJHkyJEDi8WCiPDkk09y9epVLl++bDbPy5cvHyEhIea5GjVqZC737t2bX375Jb3eGqVUKmX1mtdE27dvx8/Pj0qVKgEQFPRwY9Im/exS96fNhpVSWcKCBQsYO3asvcNQKlOaOnUqK1asAOCNN96gVatW99z34MGD+Pv7A/HTOfTv3x8fHx8gvuZzxowZXLx40dx/+fLlTJgwgWLFivH2228zevRoihcvztdff83zzz/PmDFjcHV1xdPTk9atW/PKK68wePBgQkJCGDBgAH/99Rf+/v44OzvTtWtX2rRpQ+XKlTl9+jRFixalaNGi1KxZE4AOHTpw+/ZtoqKiGDVqFIsXL2bkyJFAfDPpxB+6iSN5Ju1XduePvx49elCmTBly586Ns7PzI72/Sqm0lV2aDbdq1Ypx48bh7OxMxYoVU32epPO8ZvX3JN2JSKZ+1a9fX5RSaunSpfLGG2/YOwyl7K5ixYoCyOHDh80ywHzlypVLAImLixMRkZCQEImNjbXZ18XFRUREdu3aJYA888wzIiLy448/2pzL19fXZv3999+XmjVrCiALFy6Ul19+WRYtWiQiIr6+vjJ06FBZvHixbNy4MUX3EhERIbdv3xYRkbFjx0qJEiWkSJEiMmXKFAkICDCvO2bMGHM5d+7cUrt2bXnuuefEzc1NABk6dKjs2LFDGjRoIIsWLZJJkyaJ1Wp99DdbKZUubt26JTt37pTg4GB7h/JIChUqJAMHDjTX//33X5vPzDtf95L4WTtv3jx59913pXnz5hkRfqYBHJQU5oY6VY5SKkuoUaMGJ06c0CeS6rGXWGOxbds2nnvuOfz9/ZMdOXfcuHG8/vrrVK1alY8++ojJkyebI2MCbNq0ievXr5vTO9y6dYu4uDgKFy5snuPPP/+kcuXKvPzyy+TOnZvZs2fj6upK0aJFeeKJJ9L83iwWC1ar1ezPm3ivYWFhvPHGG1itVubOnWsTo4hkm/5zSqmsxdXVlSFDhjBx4kQAYmJicHFxuef+9/oNc/78eSpUqECjRo0IDAwkZ86cWX4k5ofxMFPlaJ9XpVSWcOLECXuHoFSmkjja8FNPPUWuXLkYPHiw+QMKwN/fn3Xr1gHwzTff0LNnT8qVK0fevHkJDQ0lNjaWSpUqUbRoUUqWLMm///5Lu3btCA8PZ9u2bTRq1MhMEv/7778MuSdHR0ebAZX27dtHQEAAbm5urF69OtljNHFVKusJDAxk/fr1tGzZkhIlStg7nFQJDg4mOjraZp7aHDlSl1olNhvet28fEP+5rpKnyatSSimVBUVFReHv709sbCwffPABX3/9NT4+PnTp0oUWLVpQpEgR/Pz88Pb2ZtGiRfTp04eVK1dy69Ytrly5QvHixYH/7z+aKHfu3LRr184et3SXhg0b2jsEpVQ6OHv2LL169WL9+vVZNnk9efIkANWqVTPLHBwcaNy4sc3Iw6GhoVSqVOmuz9qkypUrh4eHB56enkD8+6OSpwM2KaXSxNy5c2nZsqW9w1Aq20t8yt+zZ0+qVq0KQKdOnTAMgxUrVvD666+b+5QpU4YFCxYQEBDASy+9xIULFzAMw0xclVLKHrLDgE0ODg60adPG/BxOVL9+fZv1s2fPmonr0KFDGTp06F3ncnR0JF++fOkXbDaifV6VUmli9OjRTJw40WzKmNaywxedUmnhueeeY8eOHeZ67969mTt3rh0jUkqph7Nv3z4aN27M2rVradu2rb3DSVNJW7bcqUCBAuTKlYtLly7ZlF+7do0nn3zSpuxx+r3zMH1eteZVKZUmdu7cicViITY2Nl3OX6ZMGd588810ObdSWYW3tzc7duxg/PjxxMTEsGXLFmbNmmXvsJRS6qFk577qbm5u99wWHBzM5cuX7yq/fv26zfqYMWPSPK7sQvu8KqXSxM6dO4H4kfYSRwpNS3///fd9vxCUys5EhFmzZuHt7Q2Ai4sLTk5OtGjRws6RKaVU6mXl2sVu3bpx/vx5c5ClRHny5OH333+nc+fOKT5X0nleAcqWLZsWIWZLWvOqlEpTFoslXc57/Phx3n///Sz9RadUasXGxrJ//36zlrVPnz52jkgppVKvRo0aeHt706xZM3uHkio//vgjS5cu5emnn052e9OmTW3WH5TI3pm8/vrrr+n2eyqr0+RVKZUmSpYsCZBufV7Hjh3LmjVrNHlVj5Xff/+d1q1b4+vry3fffcf333/P6tWryZ8/v71DU0qpVMuVKxe1a9cmb9689g7lofj5+fHdd98xatQoAPr27Zvsfnny5KFYsWLmeoECBe573jtHIt6xYwcxMTHm+q+//spXX32V2rCzFU1elVJp4pNPPgHSr+bVx8cHyNpNjJR6GMHBwbz11lts2rSJ9u3bkzdvXj744APat29v79CUUuqRBAYGMmPGDHx9fe0dykMZO3Ysw4YN49atW4wZM4bq1asnu1/u3LkJCAhgxowZAHh5eZnbEn8vJXXo0KG7yqKjo83ljz76iNGjRz9q+NmCJq9KqTTh6OgIpF/ymkiTV/U4sFqtdO/enejoaDw9PVmzZg0ODvqVrZTKHi5evMigQYM4evSovUN5KInTkAHUqVPngfsPGDCAAwcO0KBBAyC+L2u3bt3u2u/TTz+lXLlyNmUbN240l2/evJnakLMd/SZUSqWJgIAAnnnmGZ544ol0vY4mr+pxsH79ejZs2MDrr79O69ateeqpp+wdklJKpZmsNv3d4cOHyZ8/PydPnjTLOnTo8MDjHBwccHd3NweyvHDhAsOHD09230qVKtmsp9fsDVmdJq9KqTRx+fJl/Pz80r12KKt80Sn1KNq1a8eBAwdYtGiRvUNRSqk0l9WS11mzZhESEkKuXLnYvn07hw8ffqjj69WrZy5v3LjxrhGKX3vtNV566SVzvU+fPsnW0CpNXpVSaWT//v1cunQp3Zq2PPnkk/Tt2xdnZ+d0Ob9SmUFISAjLli0DwN3dXZsKK6Wypawyz2tUVBQtWrRg1qxZuLi48Msvv/Dss8+mqMlwUjly2M5O2rhxY3M5JCSElStXcvv2bZ577jkAbt26xZo1ax79BrIh/VZUSqWJEydOAPGDzKSHXbt28fnnn6fLuZXKLH755Re6detGQECAvUNRSql0JyJERETYO4x7MgyDqlWrAnDq1Cny5cuXqvN07tyZoUOHJrtt6tSpAJQvX56VK1cyfvx4du7cyd9//23u06BBA9q0aZOqa2c3mrwqpdJUek2Vs2/fPrp27Woz+p5S2YnFYmHhwoXUr1+f4sWL2zscpZRKN1WqVOHcuXM4Ozvj5uaGYRg4ODgQGBho79CA+ETVMAyWLl3KjBkzEJG7BlR6GM7OzkyaNMmmn+yKFSvw8fFh7NixANSsWZOCBQsyatQo8uXLR3h4uLnvokWLmD59eupvKBvR5FUplSYSn0ym12jDI0aMYPv27TppdxYRERFBZGSkvcPIMiwWC23btuXIkSMMGjTI3uEopVS6cnFxoXz58jbf6SLCli1baNeuHb169WLChAl2+86fNGkSAGvWrEmzJs6Ojo68/PLLNGnSBIBOnTpRqVIlxo0bR69evWwG5nNzc7NJXr29vc2YHneavCql0sQXX3wBpF/y6u/vD2SdwR2ymytXrjzUez9v3jxy587N3r17Wblypc22iIgIRMT8t3LixAlcXV3p27cvXl5eREZG0rJlS7p27Yq3tzfHjx+/6/xWqzVDk2Or1cq///6bbuefOnUqGzdu5JtvvuHNN99Mt+sopVRm0r59e6Kiopg9ezbbt2/n6NGjrFu3jgULFjBy5Eg6duzI8uXL2bt3LyKSYb8Bjh07BsDMmTPT9Ly9e/dm165deHp6mmXPPfccv/76q80YB/ny5WPNmjX06NEDEWHYsGHMnj07TWPJshL/IWTWV/369SUr8PHxkdjYWHuHoZTdrFq1SgDx8vJKl/MDAkhYWFi6nF/d28mTJwWQadOmJbt99uzZkiNHDlm9erWIiFy4cMH8+0p8eXh4iNVqlblz55pl/fr1E6vVKo0bN7bZ96OPPrJZb9WqlUycOFGmT58uVqtVRETGjx8vgAQFBT30/Vgsloc+5qeffhJAVq1a9dDHpkRwcLC0bt1aoqOj0+X8SimVFcTFxYm/v794enpKrly5bL4LnJ2dpXfv3hIVFZVu17969aq88847kidPHqlcuXK6XUdE5MqVKzJt2rRkv5MGDx5s3ndgYKC5nF0BByWFuaHWvKaBgIAAKlasyIgRI+wdilJ2s3btWjp27EjdunXT9TqiNa8Zzs/PD4Bz587ZlAcFBbFx40b69etHXFwcr776Kv3796ds2bJ3ncPT0xMRsRl0q0yZMlitVqpWrcpXX33FH3/8QY8ePejXrx+ffvopL7/8Ms888wzjx4/nk08+YfDgwTg4OGAYBqtXrwagYMGCbN26FYiv9ffz8+Py5cscOnSI/fv3YxgGI0eOxGq1cunSJTp27IijoyP169fn+++/JyYmhq1btxISEnLXvV24cMHsY12/fn0A81ppwWq1MmLECI4fP07+/Pnx9PTU0bSVUo81R0dHSpUqRevWrQkPD6ddu3bmtpiYGObNm8eQIUOYOXMmc+fONbcFBwdTvHhx87shtbZt28bOnTvZsmULu3fvfqRzPciTTz5pfq/d6csvv+SPP/4A4ueGVUmkNMu91wu4ABwDvEnImoGCwCbAJ+HPAgnlBjANOAscBeo96PxZoeZ1//79AkiNGjXsHYp6jMXExEhMTIzdru/h4SENGzZMt/OT8NQxPDw8Rfsn1tAlx8PDQ7799tu0Ci3b27JliwCydetWs+zEiRMCyKhRo6Rdu3YCyIgRI2TkyJHSvHlzOXTokISGhsrChQvl8uXLEhQUJFarVaZOnSoTJkyQa9eumefy8vK679+X1WoVLy8v6du3rwBSoUIF8fb2lqFDh5r/LgICAqR58+Z31fgmvkJCQsw4ATEMQ5o2bSqhoaECSNWqVWX16tUSGxsr169ftzm2fPnysnbtWnn22WelTJkycuvWrUd+T5cvX25zDa1xVUqpuwUFBYmnp6dMnjzZ/LycNGnSXZ/x3bt3F0DatGkjEyZMkC5dujz0taKjo6VatWqpbtWT1o4ePSqATYul1LQcygp4iJrXtEpeC99RNgkYkbA8ApiYsNwWWJ+QxDYG9j3o/Fkhef37778FkNq1a9s7FPUYK168uJQrV85u1y9RooQA4uPjky7nL1CggAwePDhF+xYrVkyGDRuW7La4uLg0b35z/vx5uz44SG+rV68WQA4ePCgiIps2bTLfw7lz59rsm57NuUREAgMDbb68T506JXXq1JEVK1bIvHnz5JlnnjFjy5EjhwDy7rvviojIuHHjZNSoURIXFyexsbHmg5Bx48aZx7Rr107i4uKkSZMmZlnJkiUlODhY1q5dK4A8+eSTySbb//33n1y/fl1EREaOHCm9evUytwUHB8uUKVOkSpUq5vuZ+Nq7d286vmNKKZU9nDlzRo4dOyaenp73fFCZ2IUpNd/xs2fPNo+93wPVjGK1WqVEiRLSqVOnbP+gMzMkr2eAYgnLxYAzCcuzgG7J7XevV1ZIXgMDA+Xzzz+Xbdu2yfjx4+0djrqPiRMnykcffWTvMNKcxWIRQJo2bSqnTp2SuLi4DI8h8YN1+/bt6XL+K1eupKjG6/jx4wJIxYoV79oWFRUl/v7+AsisWbMkIiJCAgMD79rv2LFjcvz48RTFFRkZKYAMGzbMTIaio6MlIiIiRcdnBStWrBBAXnjhBfHz85PChQsLIAMHDrR3aPcUFBQkFy9eTPH/hT///NP8N/y///3PLL/zHD/99JMcPnxYvL29pWPHjtK5c2f5999/JTw83Dx+9OjR5vKoUaPuqmUFZObMmXLgwIE0v2+llHoc+Pv7S3h4uAQHB98zkb1y5Yps3rxZXn31VZk5c+Y9HzJPmTJFRo8eLVar1ay9zSwWLFgga9eulSJFioiHh0e6VRDYW0Ynr76AF3AI6JdQduuOfYIT/lwDNE1S/g/gfr/zZ4Xkde7cueLv7y9bt26V3Llzy+XLl+0dkrqHsmXLCpDutUPpbefOneLt7S0HDx6UXbt2Sd68eQWQ/Pnzmx/atWvXlsjISBERCQ8Plz179oiXl5dERUXJqFGjzFq0RI/6lDHxuv/8888jnedeFi9eLPXq1ZOQkJB77nPgwAFxdHQUQPLmzWuW79q1S/r37y9VqlQx43zuuedsvuRu3rwpfn5+8vHHH5u1ditXrpTbt2/fdZ2LFy/K7du3xWKxmMlyYgJ7+/ZtMQxDvvzyywfe0/nz5+Wrr76SS5cupe5NyUAFChQQQMqVKyf79++XhQsX2uUhSXqyWCzSu3dvKVas2AP3TVoze+fT/jtfic2dR48eLZ6ennLhwoUMuBullHo8bNu2TX7//XcpU6bMPT+HE18XLlyQ8PBwsVqtcvPmTYmKijK37dy5U8LDwzPlAKxJB0LMjjI6eS2e8OcTwBHg2fskr2uTSV7rJ3POfsBB4GDp0qXT9c16VIcOHTL/MR04cEAAWbp0qb3DUveQ+Hd16dIl+fXXX2Xx4sX2DsmUWKt469YtOX/+vLRp00amTZsm06ZNk9OnT8vixYvN5pIffPCBzYfxZ599ZrNevHhxAWTw4MFy6tQpcx2wqSGaMmWKnDhxQq5duyYlS5aUnDlzyrp16yQ2NtZsVpnS/hWJP+Y3btyYLu/PE088YSaZ99K6dWub9+H48ePy4osvmut169aVcePGSeXKlW32c3d3l3Xr1t1VM5a4/Oabb8o777wj4eHh8uqrr5rl7733ntStW1cAqVOnjhiGYZ6nU6dOEhkZKVarVfz8/CQ0NFSGDBkiP//8s8TGxsrUqVNtavo6der0wOaj9kgWBwwYIMOHDzdjXb58eYbHkJF69eolKfneWbVqlSxcuFAAGT58uFy9elU8PDzk3XfflUWLFonVapXg4GDZtGmTnD9/Pk36ySqllLq3Fi1a3PX9fefrlVdesXmAPWDAAHNb9+7d7X0LyZo+fbrNPSQ2HbZYLHL06FE7R5c2MjR5Fduk8zPgIx6TZsOnT5+2+ccUGxtrLr/zzjtSsWLFdPshr1In8e8nsRO8PZ9gxcXFyS+//CL79u2Tt956S8qWLWsTIyCdO3cWV1dXm7I9e/bI8uXLzdpWQG7duiVr1641a08vXrwoQ4YMEX9/f8mTJ4+53+jRo0UkfpCxpE8od+7caXONUqVKSZcuXcz19u3bm1OEbNmyxXxqmdTu3bsFkPXr16fL+5UYy40bN+7aZrVaxcfHRzZt2iQ///yzvPbaawLI6tWrpVGjRgJIw4YNbZo0X7hwQSIjI83k/Pbt2zJp0iQZP368LFiwQKxWq7i7u9u8L4kPqBJfSfuhnD59WhwcHGy2lytXTmbMmGFT9vrrryc7lUzia8aMGXLx4kWZOHGiNGjQQJ5++mmJiYmRc+fOCSBz5syRLVu2pHsia7VazVpoNzc3mTRpkvzwww/pek17SzpQU0pFRESY/4YyQx8ppZR6XN28eVP+/fdfERE5fPjwPb9nk3v99ttvmfYz/Ntvv7WJdcmSJSIiZhcVPz8/m/1jY2Nl8ODBcubMGXuEmyoZlrwCuYE8SZZ3Ay8Ck7EdsGlSwnI7bAds2v+ga2Tm5DUuLk5efPFF+fTTT80mmHf+Z+jRo4edo1RJJf69bNu2zW7Ja2BgoMycOVOWLl1q829l4MCBEhERYa5PmDBBwsLCpEOHDvLuu+/KCy+8IHnz5pU///zTPJfVar1v85bDhw/LG2+8ISdPnrxrm6+vr7z66qsycuRIiYqKMvsIVqhQQSC++W/S+AYMGCDffPONTdmNGzdk+PDh0qtXL6lUqVKGJK/J9VH9+OOPJX/+/HLlyhWz7L///pO1a9dKYGCgnDhxIlXXvHLliqxfv14iIiJk27Ztcu3aNXn//ffl0qVL4uPjI1arVWbMmGHW4K9fv146depk1uz27t1bTp48Kb169RJAmjdvLseOHZO4uDjZvXu3zJgxQ1atWiXe3t5mIuzs7CwbNmyweZ+HDBlizmsKSO7cueXgwYOyf/9+EUmfpGn+/PkCSL58+bJEs+a0cP78ebs/1FJKKfXowsLCBJAGDRpIsWLFzN8oyb0S5yjPrLZv327G6uTkJEuWLDG7LNWrV0/CwsJk0aJF0qVLF9mwYYO88cYb5v5eXl5Zop9sRiav5YlvKnwEOAGMTigvRHyTYJ+EPwsmlBvADOAc8dPr3Le/q2Ty5DU569evlx49esibb74pgEyfPt3eIakkEv8zr1y5MsN/pMbFxcnmzZslX758AvEjjE6bNk2qVq0qH374oZmA7N+/X3x9fTMsrjtZLBab2sgTJ05IcHCwxMXFybx582w+8BO/HJK+IiMjZf/+/bJ582Y5d+5cmsSUOIgCYI7mmigxyW7Tpk2mfWqaUoGBgXLmzBk5deqUbNiwQb799lv5+OOPZciQIeLl5SXVq1eXChUqyEsvvSS///67QHyT5tKlS9/Vhzk1wsLC5MyZM2K1WqVu3bpSunTpxyZxFYlvsaDJq1JKZQ8Wi0WsVqvs3LnTrIVM/IwfOXKk+YA6s/92SPpba9q0aeLr6ys//PCDAHLkyBGbUfbv9Upu/I7MJMOS14x4ZbXkVWVuvr6+ZtPLjPqRGhMTI99//73ZBBOQt956K92vm55CQkIkMjJSoqOjZcmSJbJq1SqpXr26eHp6SsWKFc37bN++vcyZM0f27duX6mutXbvW5pxBQUFy69Yt2bRpk1StWtWsibwzqc1Okqtd37t3r80X0/Lly2XDhg2yadOmVF2jY8eO5rmOHj0qgwYNeuwGFrp27ZrZykAppVT2U6xYMXFxcbF3GA+ta9euAsivv/4q4eHh5uCUQUFBUqhQIXF2dpZ3331Xjh07Jv/73/9k8+bNsn79evN7PWnLtMzoYZLXHKh0FRgYyPHjx2nRooW9Q1FAiRIlOHz4MGXLlqVPnz7pfr0NGzbw22+/sWjRIrPM09OT1q1bp/u101PevHnN5a5duwLw6quvEhQUhKurq7ntyJEj/Pnnn+ZyrVq1bM4jIty8eZPChQsne52tW7fSrl072rZty/nz5xk6dCgFChTggw8+YNWqVdSvX5+iRYvyww8/UKRIkbS+zUwjR467P6obNmzI119/jbe3N9988w0Wi4VKlSoRExPDu+++y2effUZwcDCVK1e+69iQkBD++OMPmjdvzu7du7FYLKxcudLmetOnT0/Xe8qMnJycAKhSpYqdI1FKKZUefHx8EluPZik///wznTp1omPHjlgsFlq1akXTpk0pUKAAly5dsvntVaNGDXP55s2bODk5kSdPHnuEnS6MzP4X6O7uLgcPHrR3GKnWsGFDvLy88Pb2tvnHpFIvKioKFxcXAK5du4ZhGMTExHDt2jXc3d3veVxgYCBdu3bl448/5sUXX2Tq1KkEBQUxbty4dIlz+/btNG/eHIB69eqxatUqSpcunS7XyqxWrVrFkCFDuHjxIgDh4eH4+fkRHBxMzZo1+eOPP+jTpw8nTpxg48aN1KhRgxdeeAGAL774gjFjxgCwceNGAgICeOGFFyhRogSDBw/mt99+IygoyG73ltlERUWxaNEi+vbte9e2kydPmgnZH3/8webNm5k9e7a5fcKECZQqVYq///6befPm2XwJPk7Cw8PJkycPL7zwAps2bbJ3OEoppdRjwTCMQyJy7x/xSTikdzCPuxUrVpA/f36aN29OSEiIvcPJ1AIDA4H42rjz58+zceNGYmNjze1//fUXOXLkIGfOnDRo0IABAwZQrFgxnnzySUqXLs3SpUvN4zdt2kRkZKTN+W/dusWWLVv4559/eO+99xgyZEi6Ja4iwkcffQTAjBkz2Lt372OXuAJ06NABf39/Ro0axbhx49i/fz/Vq1enadOm9O3bl2XLlgGwbNkyPvzwQ1q1akXlypUJCAjg5MmTQPz75+DgwFtvvUXJkiUJDAzEYrHg6Ohoz1vLdFxdXXnnnXc4e/YsAD/88IO5rVq1ajg4ODBjxgxef/116tWrR506dShZsiSNGjWiW7dudO/enSVLljy2iSuAm5sbzz77rM3njlJKKaUyD01e01mpUqXo168fN2/e5KuvvrJ3OJmOl5cXn3/+OYZhUKpUKW7cuEHXrl2pUKECHh4eVKhQgdjYWHr37k379u2xWCz079+fV155hZ49e1KpUiXzXM2aNeP06dOUK1eO1q1bkzt3bipVqsSBAwcAiIiIAODQoUPMnDmTrl278uuvv6ZZ8xGLxULLli1p1aoVoaGh/PPPP5w8eZKBAweazREfV+PHj2fMmDHMnz/fLHvxxRfZuHEjEF8zPWjQIAD+++8/vv76a5YsWUJUVBQDBw7E2dnZPC4uLk6T1/uoUKECUVFRvPfee2zdupVnn33WbJGwZs0aIL5Z7OHDh7l48eJj+2DlXmJjYx/7/69KKaVUZqV9XjOAh4cHX3/9NTVq1KB///7Mnj2bBQsW0LNnT3uHliHCwsLIkycPAQEBbN++nZw5c1K6dGlKlSpF//79SWwW3qBBA3LmzElYWJh57JgxY3BycuLq1asMGjSIMWPG2PRtPHPmjM21rly5Qp06dfDz8+P5558nNjaWOXPmsGfPHoYMGQLAJ0O08P0AACAASURBVJ98wp49e1i2bJlZ89ejRw+cnJyIjY3l0KFDPPXUU8TExJA/f35y5cqFr68vZcqUwcHh3s97Zs+ezZYtWyhfvjzOzs7kzJnTpm+ogh9//JFhw4axY8cOevToQWhoKFWqVKFSpUo0a9aMjh07UrFiRZ544gkAs3l44p8QX6utyev9Jb5fzZs3Z/v27QQHB1OwYEHzQU3S91PZ2rNnj71DUEoppdQ9aJ/XDBIcHMyqVavMQYLatWtn1oJkF7du3SJ//vzm+saNG1m+fDlz5sxhxIgRNGjQgNdee83c7uXlxaRJk2jevDnnzp1j0qRJ5rarV6/i4uJCgQIFgPi+aG5ubqmOzdPTk4ULF2IYBgsWLOC3336zeXhQokQJ+vXrx59//omXl5dZ/uabbxIREcGKFSsA6NixI9OnT2fJkiW8/vrrlCpVCoCzZ8/y9NNPU7p0afbs2WNTU6genbe3N3Xr1gXg4sWLzJ49mz179mi/xBSKiooiZ86cNG7cmL179+Ll5WW+n8qWYRgAWXJAD6WUUiorepg+r1rzmkEKFChA165dGTduHP7+/jz33HPmj6Tly5fTuXNnO0f4cOLi4nB0dDTvwcPDw2wCOnDgQD788EM6dOhg9judMGGCOeoswOjRo6lVqxZLlixJ9vxPPvmkzfqjJK6J8Xl4eJjrb7zxBuXKlcPFxYWWLVty+fJlmjRpQpv/Y+8+w6Oq1j6M3ysJJRA6oQWQKoI06SooiKBgQREURLGdg1iOihUERVRQbMh5VUSPYEcQRUFEugJKC733GiCBhJJe1/thCglJIMAkMxn+P65c7Fm7zDMryWSevVq3bjz77LPExcWxdu1aihYtyo4dO9znzZ07l59++onnn3+eyMhInn/+eay1NGnSBIDx48crcc0HmevUWsvrr7/uxWgKn2LFimGMcY+718+oiIiIFEZqefWSVatWucehDR48mLfeesvLEeVdRkYGgYGB3HTTTVx11VWMHDmSnj17upPToKAgDhw4wKlTp/jll1+4/PLL6dSpE2XKlGHBggV06NChUIwpc42tjI6Opnjx4kRERLiT1Pr167snxjl8+DBz5szhqquucu8Xz0pISKBkyZIAREVF+fWyOPklJCSEvn370q9fP1q3bu2uT8nKGMMdd9zBtGnTvB2KiIjIJeF8Wl6VvHpR9+7dmTVrFsuWLePyyy8nKCjIp9dhSk9PJyAggOXLl3P11Ve7y//66y/Kly/PunXr6NevHwkJCZQoUcKLkea/QYMG8eGHH9KkSROmT59OrVq1vB2S3wsMDOTll1/mjTfeYNiwYezcudM9w7Scm7XW3VNCcle5cmXuvPNOPv30U2+HIiIicknQUjmFxJQpU/jxxx957rnnKF++PA0bNiywcVbr1q1zj+M8efLkWZ937969dOrUiaCgIO677z73UhpDhgwhJiaG6667jsaNG9OvXz8Av09cAd577z22bt3K+vXrlbgWgKSkJCZMmMC9994LOCbqWr9+vZejKlyMMWzfvp1JkyaRmJjo7XB8VlRUFIcPH/Z2GCIiIpIDtbx6mbWWunXrsmfPHgCmTZvGHXfckS/PEx8fT0hICNu3b6dBgwaAY1mSyy+/nGLFipGQkEBAQADWWjZs2MCMGTPo3LlzllZWgJiYGMqWLatWHCkwMTExVKhQAYDdu3fz3HPPsWPHDjZs2ODlyAqPYcOGMXXqVLZt28bRo0epWLGit0PySYsWLSIsLIy6det6OxQREZFLgiZsKkSMMfz999/8888/vP7666Snp+fL86xcuZIePXowZ84cZs+e7S7/5JNPAEhOTubpp5/miiuucK+3CfDYY4/xyy+/kJqaSocOHViwYIF7BmCRgpJ5gqHk5GQtlXMBpk+f7l5aShM25e66667zdggiIiKSC3Ub9gFVq1blrrvuYs2aNXTt2pWtW7fy0EMPsXnzZqKiojzyHElJSRw5coSmTZsydOhQXn75ZXf5gAEDuOyyy5g0aRJHjx51n1O6dGnKly9Pjx496NWrF5UrV6Zv374eiUfkfGSe4Cs1NVXJ6wUIDg52byt5FRERkcJILa8+JCAggFKlSjFx4kS+/PJLvvzyS5o0aeKRsX2uJTIA7rrrLkaOHMmLL75ISEgIgYGBZGRkEBsbS6lSpbjtttto2bLlRT+niKdkTrZSU1O54oorqFy5shcjKnwyJ6+FYbZvERERkTMpefVBo0ePpkyZMgwdOpQNGzawb98+LrvssmzHxcfHU6xYMYKCcv82Hjx4kBo1argfP/XUU+5lecqUKeMuDwgIcD9W4iq+xhjDzJkzueWWW0hNTeW9997zdkiFjit5DQwMVKu1iIiIFErqNuyjXn75ZZYvX86iRYuoWbMmTz/9NPPnz+fo0aPEx8eTkZFBSEgI//rXv9znZGRkMGTIEIwxfPfdd+zbt4969eq594eHhzN27NhLYjZg8T9hYWH06dNHY64vUIUKFahfvz7Lly/3digiIiIiF0SzDRcCu3btonXr1hw/fhyAatWqMWDAAF577TUARo0axTPPPMPVV1/NunXrALjmmmt4/fXXeeGFF+jduzeDBw/W7MDiNx588EGMMUycONHboYiIiIjIRdA6r36mbt26HDhwgBdffJF69eoRHR1NYmIizzzzDOBopd24cSO9e/cG4JdffuHvv/+mc+fOrF692t0aK+Ivdu3axb59+7wdRqFirWXQoEHMnz/f26GIiIiIXBCNeS0kSpYsyejRoxk9ejTWWncyOnDgQEJCQggLC6Nly5Z07tyZtm3bejlaEc8LDw+nU6dOTJ06lfT0dIoXL+7tkAqVsWPH8uGHHxIWFkbnzp29HY6IiIjIeVPyWghlbkVt0KCBezsgIIB27dp5IySRfGeMIS4uTuu8XqA5c+YAjqW5RERERAojdRsWkULBtbyL1nm9MK6lha655hovRyIiIiJyYZS8ikih4EpeU1JSaNu2rZZ0Ok9jxoxh+fLl1K5d29uhiIiIiFwQdRsWkULBlbzee++9HD9+nLJly3o5osKlbNmytGnTxtthiIiIiFwwtbyKSKGQOVlNSEjwYiQiIiIi4g1KXkWkUKhYsSL3338/AGFhYTz55JNejkhEREREClKBJ6/GmJuNMduMMTuNMYML+vlFpPDas2ePe3vv3r3eC0REREREClyBJq/GmEDgY6Ab0Ajoa4xpVJAxiEjhFBcXx5IlS9yP9+3b58VoRERERKSgFXTLaxtgp7V2t7U2BfgB6FHAMYhIIRQSEsLKlSs5cOAAl19+OU888YS3QxIRERGRAlTQsw2HAQcyPT4ItC3gGESkkGrVqhUA27Zt83IkIiIiIlLQCrrl1eRQZrMdZMwAY0y4MSb86NGjBRCWiIiIiIiI+LKCTl4PAjUyPa4OHDrzIGvtZ9baVtbaVqGhoQUWnIiIiIiIiPimgk5eVwL1jTG1jTFFgT7A9AKOQURERERERAqZAh3zaq1NM8Y8CcwGAoEJ1tpNBRmDiIiIiIiIFD4FPWET1trfgd8L+nlFRERERESk8DLWZpsvyacYY44CvrygY0XgmLeDuASonguG6rlgqJ4Ljuq6YKieC4bquWConguO6rpg+Ho9X2atzdNERz6fvPo6Y0y4tbaVt+Pwd6rngqF6Lhiq54Kjui4YqueCoXouGKrngqO6Lhj+VM8FPWGTiIiIiIiIyHlT8ioiIiIiIiI+T8nrxfvM2wFcIlTPBUP1XDBUzwVHdV0wVM8FQ/VcMFTPBUd1XTD8pp415lVERERERER8nlpeRURERERExOf5XfJqjKlhjFlojNlijNlkjHnaWV7eGDPXGLPD+X85Z/kVxpilxphkY8zzma5T3BizwhizznmdEWd5zgec191hjHkgU/lIY8wBY0zcOWJuaYzZYIzZaYz5rzHGOMt7O587wxjjUzOE+Vk9v2uM2WqMWW+MmWaMKXux9eMpflbPbzjreK0xZo4xptrF1o+n+FM9Z9r/vDHGGmMqXmi95Ad/qmtjzGvGmAjnz/RaY0z3i60fT/Gnenbu+48xZpszhncupm48yZ/q2RgzOdPP8l5jzNqLrR9P8bN6bm6MWeas53BjTJuLrR9P8bN6buaMbYMxZoYxpvTF1o8nFdK6zvE4Y0wx5/vHTmPMcmNMrQurlTyy1vrVF1AVaOHcLgVsBxoB7wCDneWDgdHO7UpAa2Ak8Hym6xggxLldBFgOtMvh+coDu53/l3Nul3Pua+eMJ+4cMa8ArnY+5yygm7O8IdAA+BNo5e269eN67goEObdHu2L2hS8/q+fSmY55CvjU2/Xrj/Xs3FcDmI1jjeyK3q5ff61r4LXMMfnSl5/VcydgHlDMFau369cf6/mMY94HXvV2/fpjPQNzMm13B/70dv36aT2vBK53bj8MvOHt+vWDus7xOOBxnJ/pgD7A5PysO79rebXWHrbWrnZuxwJbgDCgB/CV87CvgDucx0RZa1cCqWdcx1prXXcWiji/chogfBMw11obY609DswFbnZeY5m19vDZ4jXGVMXxoX6pdXzXv84U2xZr7bbzqoAC4mf1PMdam+Y8dBlQPY/VkO/8rJ5PZTq0ZC7P7xX+VM9OY4AXc3lur/LDuvZJflbPjwFvW2uTXbHmsRrynZ/Vs+sYA9wNTMpDFRQIP6tnC7haAcsAh/JQBQXCz+q5AbDIuT0XuCsPVVBgCltdn+O4zDFPBTq7WsDzg98lr5k5m62vwnEXorKrwp3/V8rD+YHObjNROL7hy3M4LAw4kOnxQWdZXoU5z7nQ873Oz+r5YRx37nyOP9Szq8sJ0A949TyuW2AKez0bY24HIqy1687jel5R2Ova6Unj6A4/wdW9y9f4QT1fDnRwdkf7yxjT+jyuW2D8oJ5dOgCR1tod53HdAuMH9fwM8K7zb+F7wJDzuG6B8YN63gjc7tzujaNHkk8qJHV9Nu5rW0dj0EmggoeunY3fJq/GmBDgJ+AZm7XFJ8+stenW2uY4WuLaGGMa5/RUOZ16Hk9zsed7lT/VszFmKJAGfHce1y0Q/lLP1tqh1toaOOr4yfO4boEo7PVsjCkBDMVHbwxkVtjr2vn/OKAu0Bw4jKOrpU/xk3oOwtHNrR3wAjAlP+/qXwg/qWeXvvhQq2tmflLPjwGDnH8LBwFfnMd1C4Sf1PPDwBPGmFU4uuWmnMd1C0whquuzKdBcxi+TV2NMERw/CN9Za392Fkc6uxe4uhnkuduRtfYEjnGnNxtj2prTExrcjuPORea7OdU5SxcQ190R59frzvMzd1M96/m+xJ/q2Tlw/Vagn7Pric/wp3rO5Ht8rAuPn9RzXaA2sM4Ys9dZvtoYUyWvcRcEP6lrrLWRzg8NGcDngM9MvAL+U8/OfT87u8etADIAn5mIzI/qGWNMENATmJzXeAuKH9XzA4Ar/h/R+0Z+vT9vtdZ2tda2xHEzZldeYy4ohayuz8Z9bed7SBkgJq9xnzfrA4OWPfmFI/v/GvjwjPJ3yToA+p0z9r9G1gHQoUBZ53YwsBi4NYfnKw/swXFXuJxzu/wZx5xrAPRKHHeUXYPNu5+x/098b8Imv6lnHH3+NwOh3q5XP6/n+pmO+Q8w1dv164/1fMYxe/G9CZv8pq6BqpmOGQT84O369dN6Hgi87ty+HEf3NOPtOva3enbuuxn4y9v16s/1jGNsY0fndmdglbfr10/ruZLz/wDna3rY2/Vb2Os6t+OAJ8g6YdOUfK07b3/z8uGHoT2Opur1wFrnV3ccfa/nAzuc/5d3Hl8Fxx2DU8AJ53ZpoCmwxnmdjZxl1j0cXRN2Or8eylT+jvN6Gc7/X8vl/FbO59gFfITzjzJwp/O8ZCASmO3t+vXTet6J48OQ63X40iy4/lTPPznL1wMzgDBv168/1vMZx+zF95JXv6lr4BtggzOG6WRKZr395Wf1XBT41rlvNXCDt+vXH+vZue9LYKC369Wf69n5WlYB63CMcWzp7fr103p+GscMvtuBt/GRG16FvK5zPA4ojqMXwU4csz/Xyc+6c32DRURERERERHyWX455FREREREREf+i5FVERERERER8npJXERERERER8XlKXkVERERERMTnKXkVERERERERn6fkVURERERERHyeklcRERERERHxeUpeRURERERExOcpeRURERERERGfp+RVREREREREfJ6SVxEREREREfF5Sl5FRERERETE5yl5FREREREREZ+n5FVERERERER8XpC3AziXihUr2lq1ank7DBEREREREfGwVatWHbPWhublWJ9PXmvVqkV4eLi3wxAREREREREPM8bsy+ux5+w2bIyZYIyJMsZszFT2mjEmwhiz1vnVPdO+IcaYncaYbcaYmzKV3+ws22mMGXw+L0hEREREREQubXkZ8/olcHMO5WOstc2dX78DGGMaAX2AK53nfGKMCTTGBAIfA92ARkBf57EiIiIiIiIi53TObsPW2kXGmFp5vF4P4AdrbTKwxxizE2jj3LfTWrsbwBjzg/PYzecdsYiIiIiIiFxyLma24SeNMeud3YrLOcvCgAOZjjnoLMutXEQkT7Ye28qqQ6u8HYaIiIiIeMmFJq/jgLpAc+Aw8L6z3ORwrD1LeY6MMQOMMeHGmPCjR49eYIgi4k8aftyQVp+38nYYIiIiIuIlF5S8WmsjrbXp1toM4HNOdw0+CNTIdGh14NBZynO7/mfW2lbW2lahoXmaNVlERERERET82AUlr8aYqpke3gm4ZiKeDvQxxhQzxtQG6gMrgJVAfWNMbWNMURyTOk2/8LBFRERERETkUnLOCZuMMZOAjkBFY8xBYDjQ0RjTHEfX373AowDW2k3GmCk4JmJKA56w1qY7r/MkMBsIBCZYazd5/NWIiN+Kej6KdMfbiYiIiIhcgoy1uQ499QmtWrWy4eHh3g5DREREREREPMwYs8pam6eJTS5mtmERkQLT8cuOXPbhZd4OQ0RERES85JzdhkVEfMFf+/7ydggiIiIi4kVqeRURERERERGfp+RVREREREREfJ6SVxEREREREfF5Sl5FRERERETE52nCJhEpFJKGJmHx7aW9RERERCT/KHkVkUKhWFAxb4cgIiIiIl6kbsMiUig0HdeUkqNKejsMEREREfEStbyKSKGwIWqDt0MQERERES9Sy6uIiIiIiIj4PCWvIiIiIiIi4vOUvIqIiIiIiIjPU/IqIiIiIiIiPu+cyasxZoIxJsoYszFT2bvGmK3GmPXGmGnGmLLO8lrGmERjzFrn16eZzmlpjNlgjNlpjPmvMcbkz0sSEX9kh1vscK3zKiIiIv7nq7VfsfTAUm+H4fPy0vL6JXDzGWVzgcbW2qbAdmBIpn27rLXNnV8DM5WPAwYA9Z1fZ15TRERERETkkvPgrw9yz9R7vB2Gzztn8mqtXQTEnFE2x1qb5ny4DKh+tmsYY6oCpa21S621FvgauOPCQhaRS1H1D6pjRqjDhoiIiPifmmVqckPtG7wdhs/zxJjXh4FZmR7XNsasMcb8ZYzp4CwLAw5mOuags0xEJE8iYiO8HYKIiIhIvjiRdIL41Hhvh+Hzgi7mZGPMUCAN+M5ZdBioaa2NNsa0BH4xxlwJ5NRckuvgNWPMABxdjKlZs+bFhCgiIiIiIuLTTiWfYurmqd4Ow+ddcMurMeYB4Fagn7MrMNbaZGtttHN7FbALuBxHS2vmrsXVgUO5Xdta+5m1tpW1tlVoaOiFhigiIiIiIiJ+4oKSV2PMzcBLwO3W2oRM5aHGmEDndh0cEzPtttYeBmKNMe2cswz3B3696OhFRERERET8QNe6Xb0dgs87Z7dhY8wkoCNQ0RhzEBiOY3bhYsBc54o3y5wzC18HvG6MSQPSgYHWWtdkT4/hmLk4GMcY2czjZEVERERERC5J5YPLU798fW+H4fPOmbxaa/vmUPxFLsf+BPyUy75woPF5RSci4qQ1XkVERMRfxSTG8Ne+v7wdhs/zxGzDIiIiIiIichEOnjp47oMucRc127CISEEJHhlMUlqSWmBFRETE7zSs2JDGldRJ9VzU8ioihUJSWpK3QxARERHJFwdPHSQmMebcB17ilLyKiIiIiIh4UWxKLPP3zPd2GD5PyauIiIiIiMhFOBJ3hL/3/+3tMPyeklcREREREZGL0PKzlrSf2N79ODktmUd+fYRDsYfyfI2+jXNa5EUyU/IqIiIiIiJyEVxJarNPm7H12FZ+2/4bE9ZO4Mnfn8zT+UUCinBZmcvyM0S/oORVRAoFO9xqpmERERHxSa2rtQZgfeR61h1ZR4USFQCoU65Ons5PzUhlxvYZ+Rafv1DyKiIiIiIichF6Nuzp3j6RdILqpasD0LxK8zxfIyI2wuNx+RslryJSKJgRBjPCeDsMERERkWz6N+tPk0pNADDGEGACKBJQBEPePru0rtaadtXb5WeIfkHJq4iIiIiIyEX4et3XbIja4H4cnRBNakYqZYuXPee51lo2H91MVHxUfoboF5S8ioiIiIiIXISX57/s3g40ge5ENsCcO92yWOJT41l9eHW+xecvlLyKiIiIiIhcBItjUskKwRUYs2wMj0x/BIBF+xad+1yrCSnzSsmriIiIiIiIB2x5YgvpNt39eOfxnWyI3HDWBDXDZgDwnzb/yff4Crs8Ja/GmAnGmChjzMZMZeWNMXONMTuc/5dzlhtjzH+NMTuNMeuNMS0ynfOA8/gdxpgHPP9yREREREREvKPmhzVJy0hzP566eSpNP23KxqiNuZ7jarWtElIl3+Mr7PLa8volcPMZZYOB+dba+sB852OAbkB959cAYBw4kl1gONAWaAMMdyW8IiLnonVeRURExFe51nNNSkviroZ3udd9dTmVfCrXc10tr9+u/zb/AvQTeUperbWLgJgzinsAXzm3vwLuyFT+tXVYBpQ1xlQFbgLmWmtjrLXHgblkT4hFREREREQKhSX7lzBrxyx6XnF6ndcGFRow896ZWY7L3JX4TEUCigBwOO5w/gTpR4Iu4tzK1trDANbaw8aYSs7yMOBApuMOOstyKxcROSfXGq9qfRURERFf0WFiBwB+uvsnqmyowpG4I3y57kveWPQGBsOL175Ii6otaFa5Wa7XCAwIpGOtjqRn5J7gisPFJK+5yWklXnuW8uwXMGYAji7H1KxZ03ORiYiIiIiIeEDmSZjumnKXezvzDMN3XnEnbau3Pet10jPSWX5wOVVLVfV8kH7mYmYbjnR2B8b5v2tV3YNAjUzHVQcOnaU8G2vtZ9baVtbaVqGhoRcRooiIiIiIiOftObHnnMd8Ev4JHb/sSMSpiFyPiU+NJzEtkd3Hd3syPL90McnrdMA1Y/ADwK+Zyvs7Zx1uB5x0di+eDXQ1xpRzTtTU1VkmIiIiIiJSqCzcs/Ccx3y97mv+2vcX6yLX5XqM1nnNu7wulTMJWAo0MMYcNMY8ArwNdDHG7AC6OB8D/A7sBnYCnwOPA1hrY4A3gJXOr9edZSIiIiIiIoVKdGJ0trLl/1runnk4s7ONZ3XNNvx6x9c9F5yfytOYV2tt31x2dc7hWAs8kct1JgAT8hydiIiIiIiID2pYsSEda3Wkz5V9GDhzIABt/9eWDjU7ZOsCfLbZhl3Ja5niZfIvWD9xMd2GRUQKjNZ5FREREV9yW4PbWPjAQh5t9WiW8u71u3NL/VuylJ2t5dU657D9eOXHng/Szyh5FREREREROU/WWjYf3cyn4Z8SNySOfk36AVAhuAKnkk9lObbXj71yvU6poqUAiIyLzL9g/UR+LJUjIuJxWudVREREfEn377vzx84/AHhs5mPu8lf/fJUjcUcAGNZhGG8ufvOs1ykWVIxbL7/1rDMSi4NaXkVERAqRKZum0OOHHt4OQ0TkkpeYmsi1Na7NVu5KXAEebP7gOa+TnJbMvN3zOHDqgCfD80tKXkVERAqRlRErmbd7nrfDEBG55CWmJRJSNCTHBNZl2MJh57xOdGI0SWlJHEs45snw/JKSVxERkUIk3aYTYPTnW0TE2xJTEwkuEsz8/vPZ9PimHI/5YeMP57yOa7ZhOTf99RMRESlEPl/9OXEpcd4OQ0TkkpeYlkjxoOIUCypGo9BGhBQN4aHmD533dRwrjcKnt3zq6RD9jiZsEpFCoVhgMZLTk70dhojXuRLXDJuhFlgRES96oNkD1Cpby/04LiWOiWsnnvd1XC2vRQKLeCo0v6XkVUQKhaRhSd4OQcSnpGekExCo5FVExFuGXZd1PGvEsxGEfRCW47Efdfso1+u41nkduXgkv277lYhTEYQPCPdcoH5Ef/VEREQKkZ4NewKOsa8iIuI9p5JPkZaR5n5csUTFXI99ctaTue6rVLISpYuV5mj8UaZvm86qw6s8Gqc/UfIqIoWCGWHca72KXMrahrUFHC2vIiLiHRk2g7Jvl2XEnyPcZTkN5Rh5w8hzXqtEkRJ0q9eNqqWqejRGf6TkVUREpBCJTY71dggiIpe8I3FHsFiqlarmLgs0gdmOe6L1E+e8VmxyLL9s/YXt0dspFliMdtXbeTRWf6LkVUREpBA5GHuQGqVrULJoSW+HIiJyydp7Yi8Al5W9zF1mTPYeYo/+9qh7e8zSMYwPH5/tmEOxh9yTUianJ7P0kaUejtZ/KHkVEREpRDJsBoEB2e/ui4hIwTkUewiAsFJZJ2gqHlQ8y+PJmya7tz9d9Snfb/w+27VcEza5HIk74qkw/c4FJ6/GmAbGmLWZvk4ZY54xxrxmjInIVN490zlDjDE7jTHbjDE3eeYliIgvGL1kNCVHldQ4PJF89sPGH9h7Yi/RCdHeDkVE5JJ1IukEAOWDy2cp3/LEllzPCQ4KpmzxstnKXUvluFR9X2Nfc3PByau1dpu1trm1tjnQEkgApjl3j3Hts9b+DmCMaQT0Aa4EbgY+MSaHjuEiUij9d8V/SUhNyPYG7CmZx5SIXMpS0lMAKfUaHwAAIABJREFUtO6xeMyGyA2sO7IOgHm752FGGHbG7PRyVCK+rUXVFrx2/WvZZhg+24zD6yLXMX3b9Gzl1tocjpaceKrbcGdgl7V231mO6QH8YK1NttbuAXYCbTz0/CLiZa7uM/mVvEY8G4Edrjd3ERf1chBPafppU5qPbw7A1mNbAcfspyKSuxZVWzC843CCiwRnKQ8pGsLBQQezHT+sw7BsZS759dnJH3kqee0DTMr0+EljzHpjzARjTDlnWRhwINMxB51lIuJHtPakSP5yzVyp3zXJD7uP7wbg162/ejkSEd92OPZwrmNTQ4qGZCt7c/GbuV6rTrk61C9f32Ox+bOLTl6NMUWB24EfnUXjgLpAc+Aw8L7r0BxOz7EZxRgzwBgTbowJP3r06MWGKCIFKL/uHrrWeVXXGrnUta7WGtCdevGcB5o9QM0yNXnjrzcYs2wMAI///riXoxLxbTd8fQO9pvTKcV9CaoJ7++3Ob/PCNS+c9Voli5bkusuuy1KW+fNOhs1QbxsnT7S8dgNWW2sjAay1kdbadGttBvA5p7sGHwRqZDqvOnAopwtaaz+z1ray1rYKDQ31QIgikt8aV2oM5LzGmSedOSOfyKUmIjaCooFFKRZYzNuhiB84lnCMqZunsv/kfl7981VvhyNSKLw490W2HttK70a9c9wfERsBQMuqLXmp/UsEB53uWnxl6JXZjo9OiOaLNV8A0CasDXc1vCvL550W41sQ9EaQJ19CoeWJ5LUvmboMG2MyT491J7DRuT0d6GOMKWaMqQ3UB1Z44PlFxAf8u8W/ubnezdnGfniaWl7lUrfs4DIahTYirLRG3sjF+2zVZ8SnxgNwV8O7vByNSOHw0YqPALitwW057m9auSnd6nVjSPsh3P3j3aw6vMq9b9/J7FMEbTq6yb29ImIFU++eSoA5naati1znqdALvYtKXo0xJYAuwM+Zit8xxmwwxqwHOgGDAKy1m4ApwGbgD+AJazVgR8Rf/KfNf5h570wllyL5LMNm5HsPB7k0JKclM3TBUADql6/PQ80f8nJEkp8Oxx72dgh+ITktmcS0RF669iXqlKuT4zFFA4vye7/f6fVjL37c/CP7T+537xvQYkC24xfsWZDl8Z7je7IMDalRusaZp1yyLip5tdYmWGsrWGtPZiq731rbxFrb1Fp7u7X2cKZ9I621da21Day1sy7muUXEt4wLH0fg64EcTcjfcerqNiyXut93/M6qw6vYcjT3tQTF9207to0GHzVwz+7rlRiit7m3d8Ts4I1Fb1AlpIrX4pH8s+rQKqp9UI3v1n/n7VAKrX8O/MM9U++h+MjiANxxxR15PrdP4z7u7eJBxbPtP3gq6+zEdf5bh+OJx92PG4U2ok2YFmkBz802LCKXuBF/jQDybxKZxpUaY5z/RC5lrhs4SWlJXo5ELsbY5WPZHr2dmMQYr8WwMcoxsuu7no6EZnnE8izLeagnjf+oWsoxqu940vEc98elxHEi6URBhlTo9J/Wnymbprgf1y1X95zn/OuqfzH8+uFZJmwatWRUtuNymrXYNXkaQPXS1SldrPT5huyXlLyKiEdExUcB+bf25IbHNpAxPIPAAHWXFAEtlVMQrLUcjj3Mon2LWHtkrUevXaJICUoWKUlkXCQjF4306LXzat7ueQQFBNGr0ekZU7vV70a54uX4T5v/YIxuFhZmP23+ibeXvA1A5ZKVCTSBRMZF5njsU7OeotzochxLOFaQIRYqZ37+CC157kllP7/9c17r+BpFAotQr3y9XI87HJe9S/eS/Uvc26kZqeyI3nEe0fovJa8i4lH51fKamp6qaeJFgLc6vwXk340iOW3a1mlU+6Aa1395PUMXDCU5LZmu33Rl0gbHPJVH44+yPnI9aRlp533txNREUtJT6DmlJ8MWDjv3CR6WYTOYsmkKbcLaUDSwKG92epP/6/Z/1ClXh9sb3M7yiOXEJscWeFxnOhp/lJURK322p8GRuCM888czrD682tuhZPHwrw/T68deDJk/hAybwYI9C0i36WyP2Z7j8V+u/RKAMUvH8OrCV5m+bbrHYpm+bTorI1Z67HrekJyWzPbo7Vx/2fX8fPfPLHxg4Xmd//pfr7MzZmeu++fdP49VA1YRWsKREHet25V5/edleX7XxGqXOiWvIuJR+ZW8Fn2zKEFvBJGclpwv1xcpLK6qchWQteX1cOxh6oyt49Xxk/5mZ8xOxq8a7348Z9ccjicdZ+7uue5JjiaunUizT5tx4OSB875+UloSqRmpALSq1sozQZ+HABPAkeePMKPvDACGXjeUJ9s8CUD7mu1ZEbGCxfsXF3hcZ5q+bTpt/teGscvGntd5s3bMyrLWZn45mXSSscvH+tTv3puL3mTi2omAYyWAABPAigjHAh/rjqxzdxfPzNUqGFwkmHf/eZfF+/L+vc+wGbT+vDXTtkzLcX+PH3owaPag830ZPiMlPYVv1n9D40qNefvGt7mz4Z10rNXxvK6x5djZ5ygoF1yOFlVb8MXtjuVyRt4wkqCA00vjTN40Wa3iTkpeRcQjrqh4BQClipXK1+fRhE1yqdsQtYEyxcpQrng5d9k1E65hz4k9jFk65ixnyvnoM7UPc3bNcT9Oy0jjio+u4Jb6t7DnxB6uGn8VL817iWKBxZi7ey4zt8/k4xUf53mc6A21b3Bvlyqav++buQkpGkL54PLZyl0Tytzy/S1sitpE+wntGbN0DAv3LOTT8E8ZMm8IZoQpkNbGlYccLXaD5w9m4Z68tXZtjNpI9++785/f/5OfoQG4E4o9x/fk+3Pl1ajFo2hepTnv3PgOn9zyCYB7bPW26G00Gdck2zmvXPcKM/rO4NmrnyUpLYn3lr6X5+dLTE0k/FA4z815Ltdj/j7wt3t7Z8zOQtMFNj4lnorvVOTfM/5Nh5odaFe93QVd58wb78sOLnNvn0w6ydD5Q1l3ZB1twtowvc903lj0BuNWjruo2P2VVrsVEY945KpH2HJ0S44fhDxJE4jIpe7nLT/TOqw1DUMbusv2ntgLZB+TJRfuhto3sDFqI5HPR1J2dFnA8T7XMLQhM3fMdI+B/VeLf/Hob4+6z7u53s0cTzpOxKkIOtfpTEjRkByv369pPz5e+TFLDy5l4d6FpKSnUDSwaP6/MKfftv/Gon2LePOGN7M9b+b38THLxvD3gb+zJB8uP2/5mRZVW1zQ8x9LOEZMYgyXV7j8rMelpKe4t5PT89bzxjVjcsUSFS8otvOx54Qjad18bHO+P1deWGuxWG6sfSPPX/M81395Pddfdj0xSTHUKF2DqPgoktOTiU6IpkKJCu7z7m92P+AYJ+vy1dqv6N+s/znHPhcLKkaVkCp0rt05x3jOVP//6gMQOyQ2198PX5CWkUbtsbWJTXF0n3/7xrcv+Frvd32febvnEVY6jK3HtnL1F1djhzvqZtXhVYxaMoqW1VrSrEozbmtwG4/+9iiVSlTyyOvwN0peRcQjnm77NAmpCaRnpOsDtEg+OnOd18xjXz9f/TlPtnmS/Sf3c3O9m70Rnl/Yc3wPKyJWkJyeTGBAIBmvZpCcnkzxoOJYa+lcuzP7T+5nxF8jeLrt02w6uok/9/7JHVfcwYztM9xdJCf3mkyXOl0oF1wu23MkpiZStnhZ9+P4lHiKBhdc8jp/93w+X/05o28cnW1fpZKnPzR/seaLXK9RIbhCrvvOpfmnzYmIjXB/gD9TbHIsPaf05FDsIXfZ2OVjaRTaiJplap712uWDy2MwBXIzwJWc+cpM+PGp8SSlJRFaMhRjDMFFgnlz8ZuElQqjYomKPNT8IV5f9Do9p/Rk2j3TKB9cnsTURDYf3cwVFa+g14+nJ+968NcHeW/pe/zz8D+k23R2xuxk89HN9G/Wn7HLxpKQmsA/B//ht+2/ARCdGE18SjyP/vYol5W5jLm757pbzgFmbp+ZpctyqbdKUalkJVYNWMXeE3v5e//fvHDtCwQY73cMTctIY8GeBRxNOMrtDW7nl3t+uagJzGqXq82pIadITkt2L7UDjjHdnb92JP2Z14wNLhJMYlqi+3GF4AoUCyp2wc/vT7z/0yEifuHnLT9TdnRZtkfnPBmEp6jbsFzqlkcsZ/au2fy5908AXln4CgBVQ6rSqlorrvzkSrp9103LXlyEm769ib/2/QXAj5t+xBjj7kprjKF2udpcX+t6FjywgPoV6jPz3pkMv34473Z5l8mbJruvM+KvEZR/pzzjVo7L0oII0O27bszaeXrJ+7iUuAJ4ZafFp8ZTuljpHD+Qu5LX9jXbn/UamVvuMlsRsYJTyafOeW6ZYmVy3JdhM7hz8p3M2z2PzUdPt2j+sfMP5uyag7WWx2c+zh87/wAcXTLf++c9vln3DdZa5u6ai8Wy9+Tes8bgCa6/SZnHJ3qTazZh18Q/N9W9CYCI2Agqh1Sm95W9CS0RSlpGGleNd4yfXx+5nlaft2LBngVsenwTmx7fxJD2QwBHF+zSb5em3OhyTFwzkQd+eYB6/63HM7OfYeiCoe7EFWDRvkW8OPdFvtvwHaOWjMqSuALcOulWBs8fnKUsKj6Kk0kn6TCxA4PnD+bl+S/nT8Wcp7HLxnLTtzfx2vWvXXTimlnm9+XlB5fz9B9Pux9nXnonOChr8lq9dHWvjI33RUpeRcQjBs4cCOTf8h3XX3Y9VUOqZmlxErkUuVp4XB+C3lrimH14Qo8JRJyKcB/n6sq65vCaLB+Yvt/wPUsPLC2ocAul564+PXava92u5zy+RJESvNbxNeqVr0enWp0Yd8s4dvxnBx91+wiAx39/nI5fdmTWjlmMXTYWa6179tzgoGDA8cH/RNIJktKSeGvxW/k+2VBCagLBRYJz3Fe9dHWShiax+KHFFAs83drTu1Fv9/Yf/f6gf7P+2c5NSU+h7f/acuPXN+Z47SX7l7Dm8BqKBxWndVjrbN1KZ+2YReDrgczfMx+A97o4xl6GFA1h7v1zuaX+LZQdXZZx4ePo9l03Ri8ZTfGRxXlh7gtsi97GoNmDuPk7R6+DlRErmbFtBrN3zuZU8in+t/p/9Jzck7SMtGw3Ey5UiSIlgKw/M95wIukE48PH03hcY4oFFqN5leYAdKnTBYA+jfsw+77ZNK7UmKgXorjt8tvYf3I/+07sc3d9rl2uNo1CG9EotBGjOo8i/dX0LOuTtq3elhqla7Dr+C4A5t4/l/UD19O1bleqlarGF7d/wSfhjjG2g9oNcifAZ7aAN6l0esztuFvGZbkJ8vOWn5m5fSZdv+nKC3NeIDU91dNVlSeuCZbua3qfR5eM+mjFR+7tdl+0Y9LGSXzX8zv2PL0ny5whwUWCSUw9nby2qNoiy+NLmW/cJhKRQs/14Ti/Zhv+88E/8+W6kj82H93MTd/eRJc6Xfjgpg+ydI90iUuJo2hgUYoGFmXWjlnEpsRy95V3A44ZRnfF7OLx1o8TYAIoElikoF+CzwotGUpUfBSRcZHuJPT1jq/TpU4XDpw6Pettcloyyw8up90X7RjQYgDjb3PMnNvv536Elggl6oUor8RfGLg++D9/9fOElQ47r3NHdR7l3q5Xvh5rH11L8/HNWXpwKd2/7w7Am4vf5FjCMXo06MGIjiNoPr459027j8vKXMYHN33AywteJjoxmve6vseENRN4Ye4LHHnuiEd/DxLTEt2J85kCTIC7i2KDig1YH7kegMHtB3NFxSt4Y9Eb7nHWZ4pOiAagdLHSOe7vMLFDlsclR5Vkw2MbqFu+LrN2zHLX0Q21b6BZ5WY8d81zPNPuGfdwlE9WfuJu1X2y9ZPulrywUmEMu24Yj8983H3tbdHbuP2H27PFMHHNRAb8NoBXrnuFwe0HcyzhWJauyJFxkRhjsnSfzo0rCS7I8couv2z9hXHh4/i+5/dUfPf0+N4F/RdwVVVHq2qTyk2Y2ntqlgnCwHEjYsj8IdQaW8tdVqtsrSzHBJgA3unyDoEmkAolKtC/WX/6NennHq/sGnc/+77Z7nNWD1jN8ojlDGw1kPSMdO5tci/xKfH8b/X/aF+zPcYYejToQXCRYCLjIqlaqipBAUGsH7iexLREigQU4fovryc2JZa5u+fyz8F/GNphKCsjVvL56s85+OxBD9dizooEFKFCcAXqlq977oPPQ043TSqVrJSt7sNKhXEk7ghvLnqTYdcNI7REKJM2TvJoLIWV8fXJT1q1amXDw8O9HYaInIMZ4bgzuXrAavcfTU+KTogmKCAo125ukn9ik2O58Zsb+eL2L2hcqXG2/YdiDxGdEE2Tyqfvpvf9qS8/bPwBgKEdhjKo3SAqlKjAnuN7GP33aPo16Uf377vTKLQRSx9ZytOznuajlY470hN7TOShXx9yX6tjrY782PtHyhUv5/4Au+zgMqZsmsL7Xd+/5H4eNkRuoOmnTXnkqkfc4xE3Pb6JRqGN+HHTj1xV9SrKFS9H2eJluXXSrfyx8w8qBFfg2IvHOJl0krKjy/LOje/wwrWOFpXohGhS0lOoWqqqN1+W16VnpFPnv3XYf3K/e8mKJpWaeGQMf2p6Kt+s/4ZHpj+SpfzH3j/Sq1EvZu+czSPTHyEiNoKTg09y+f9dzs31bubLO76k5KiSJKQmMLX3VIoEFqFF1RZsPrrZMa6xRChX17j6gmK6c/KdHIo9xPJ/LT/rcRGnIrhn6j38c+AfkoclE2ACGLl4JEv2L6Fe+Xru2WxdXD+fU3pNofeVvcmwGfy85We61OnC3VPvzjKDs0vNMjUZ2mEoV1W5ijb/a+Oos1dSc+yKm5KewttL3uaG2jfQvmZ71h5Zy5uL3uTTWz+lYomKnEg6wYGTB/hpy08EBQS5u9W7BJpA2tds7+4WDo4uvzP6zuCzVZ8xuddkir7pSETtcIu1lpfmvUSgCeStG9/KFk98Sjy3TbqNR1s+yj2N7zlrXXpSWkYaRd5w3Mx48ZoXmbZ1GjtidnBtjWtZ9NCiPI0bbflZyywzRuc2/rig7TuxjyX7l3DftPuy7Tv07KF8fa+y1rIjZgcNPmpAvfL12PEfz86KnJKewpG4I9QsU5NDsYf4cNmHjLxhZI43pkJGhRCfGk/ckDiemvUUE9ZO8JnvkacZY1ZZa/PUL1rJq4h4hCt5Df93OC2rtcy36/v67IT+aMa2Gdz+w+30btSbKb2nZNv/7OxnGbNsDA0rNmTRQ4t45+93ePefd937H2z+IMsPLnd3wwoKCCItI829v1ejXnx+2+fc+v2t7hlN5/ef757EonmV5qw9spYapWuw8fGNHDh5gMbjHEl0fv28+aojcUeo+n7WD273Nb2Pb+78Jtux8SnxjFo8ih82/cDu47tZ8+gabvjqBo4nHScoIIhyxctxNOEoALc3uJ1v7vyG5LRkQkuGFshr8TWupMsl49UMj94YCT8UztIDSwkrHUbXul3ZELkhS+L5594/6fRVpyzndKzV0T222eXGOjey5vAaohMdLZwHBx0879ZhF2ttnl5jZFwku4/vzhJvh4kdKBpYlPn952c51vU6vr7ja/o07sNv23+j55SeWY5Z8+gaElMTubrG1YwPH+8edvLtnd8SUjSEfSf38VTbpy7oNZ3pUOwhIuMiiYqPIiktie71u2OxTNsyjT4/9cly7OUVLufj7h/T5Zsu7njKB5d3twbvf2Y/NcrUIDE1MUuX6yJvFOGFa17I0uqen2Zsm8HHKz/m2auf5aZvHWNaE17OvRt4btIz0vl+w/fun58zW2e9LfxQONdOuDZba2XzKs35pPsnWX4ek9KSiIqPokbpGhf1e7tk/xI6TOxAWKkw/t3i3wzvOPyCr3WxXJ979jy9h9pjawO+c4PB084neVW3YQ84lXyKP3b+Qfua7alWqpq3wxHxirrl6rLr+K58b73Jyw23TVGbqFOuznn/IZecubrOPdj8wSzlSWlJFA8q7u5ut+XYFkLfPZ343N/0fr5Z/w39m/YnyAS5k9e0jDQGtBjAkA5DeHvJ2/y4+Uf2n9zPkoeX8Nv235i0cRKdanVi9YDVxKXEUT64PI3HNebAqQOUebsMU3qdTqAPnDpAy2otWbxvMd9v+J6X2r9EgAlgzeE1rDmyhrZhbQkuEnzeC8r7oh3RO7K0knzU7SMaV2qca8tbyaIlGdl5JE+3e5o5u+YQUjSE40nHAcdSIgdPne5+N33bdMq87Zg8Z+zNY3l+zvM83vpxrrvsOpbsX8JlZS5jQMsB7t8pay3RidGUDy7vEzODekLNMjUZfeNoYhJjuL3B7R5v0W9VrVWWCVfO/L51qNmBYR2G8ebiN91lrt+92y6/jRnbZwCO37vLyl7mTl7Hr3J0B5+7ey51y9Xl257f5jmmvL7GyiGVqRxSOUtZ6WKliYrP3vW8ZJGSAPT/pT8nkk7w574/3fsGtRvEQ80fytJL49FWj3JXo7sYHz6eng17evx9u1qpajl+Nrun8T0kpiXSvmZ7Hvr1IZbsX8L9Te+nUWgjnm77ND9v+Zn7pt3Hu11O34h7bOZjVAmpwjfrv2HmvTO5sc6NrIhYQVpG2jknqPKEtIw04lLieGT6IySmJTKj7wyGXz+cEX+N4J8D/9C5Tvalas4mMCDQvUSOL2pVrRWD2g1i9N+jmXnvTG75/hYA1h5ZyzUTruHK0CupVbYWH3X/yJ3cZe5VciFc6/UueGDBOZdxyk+ZP+uc2aX4UqeWVw9YfXg1LT9ryc93/8ydDe/0djgiXvHW4reIS4ljZOeR+XJ91x3Ik4NP5jqWChxdXEu/XZq7r7ybyb0m53jM0PlDaR3WmjuuuCNfYvU3C/cs5Iavb2DhAwvdSeDeE3upPbY2xYOK8/zVz3My+SSrD6/mu57f8e36b3myzZOUKlaKfSf2Ubuc40PFqeRTLD2wlGZVmrnXYTwfby56k1cWvsI7N75Dw9CG/LTlJ55o/QS/bv3V/YG/Xvl6HEs4lm2m3cPPHaZKSBWiE6JZH7meTrU75fQUPisqPooOEzvQv2l/hi0cBpz/HXhrLQv2LKBNWBtKFSvFnuN7OJ50nM1HN/PVuq/Yf3I/u2J20a1+tywziLav2Z4l+5cAjslzhv85nPjUeIKDgnmv63s88fsTAKwasCrHNT+ttcSlxGWZjMSX/Lb9N0YuHskHXT+gXfV2Xu+Gnp6RTnxqPNujt9OwYkMOxR6ibvm6DJgxgDZhbejZsCchRUN4/5/3ufXyW3ls5mMsPXh6Aq4tT2yhaGDRLMtu5GTwvMHULFOTx1s/ftbjctNnah/WHFnDtie3ZdsXfiic1p+3BhzLfwxsOZAX573I2kfX0qxKswt6vvwWFR+VZYxrQmoCkzdOpm+TviSkJjA+fDz3N7ufnzb/xDOznwHgt76/sS16G8/NeY6Hmj/E6BtHE58an2/JxluL3+LlBY7ZeF+45gXe6fIOyWnJfLD0A55q+xQli5bMl+f1pvSMdGJTYt3zJryy4JUsN3jO1KtRL/o27svGqI28ev2r5/18Y5aO4dk5zxLzYkyOS1wVpL/2/kWZ4mVoXqW5+zNQ+qvpfnPDMLMC7TZsjNkLxALpQJq1tpUxpjwwGagF7AXuttYeN46/CGOB7kAC8KC1dnVO13UpDMnr3F1z6fptV5pVbsbagWu9HY6cxa6YXeyM2clN9W7ydigeN2HNBIICgnKc/bEgJKclczThKKElQvNlLbK8Jq+JqYmUGFWCm+vdzKx+s3I8xnUtf+1+42mubsOD2g3ig5s+IDY5llcXvsqHyz8E4N8t/s1nt31WILFEnHIs9+AaC5eankrl9ypzPOk4oSVC+f6u77HW0vXbrDPEtg1rS78m/XjqD0dXxDZhbfi+5/cen4zD0w7HHua1P18jMj6SX7f9yuz7ZrMhcgPd6nejUWijfHnOQ7GH+GrtV2yI2kCN0jXoXr87Hb/qCECxwGIkpycDMPKGkRiM+8N0iSIlmH3f7GzLq3y/4Xv6/dzPZxOXev+tx67ju2hepTmrB6z2evJ6Pqy1LI9YzoQ1E1iwZwEBJoAdMY4xekPaD2HRvkVUL12d3cd3M+muSVl+3uv9t57j9+Cu7y/ouQfMGMCv234l8vnILOXRCdEEBgRSbrTjg7+rW3NscqzP3sA4X/2n9eeb9d9wQ+0bqF++PuNXjefh5g/zx64/OBR7iDn3zaFL3S4ef957f7qXSRsnUb10dfY+vfeSXVM9LiUOay2l387+WaBTrU4s3LsQuLC/8S/Pf5l3/3mXlGEpPvFe8OrCVylZpCSv/vkqrau1ZvFDi30iLk/zRrfhTtbaY5keDwbmW2vfNsYMdj5+CegG1Hd+tQXGOf8v1Fxdd9ZFrvNyJHIuV35yJcnpyX6ZtLgmAvFW8rp4/2K6fNOFxQ8tPufagBfjXDfcgosEUyG4ArXL1s5xf36spfjT5p94f+n7LHl4iV/eEXVN0uSqu5GLR7Ly0EqGtB/CW0ve4onWTxRYLGeO7SsSWIStT26lYomKWer+h7t+4Nqa17IhcgMnkk5Qu1xtPl/1uXv/iogV/LrtV569+lkybIbPft+KBBbhs9Wnbwx0rt05T0u3XIxqpaoxpMOQLGVJQ5N4e8nbNAptxLzd8+jTuA+danfCWkuTyk2w1vLGojc4FHuI+JR4unzThaUHl1KtVDWuqHgFAF2/7crBQQd9auboyLhIdh3fxVud3+K5q58rdB8KjTG0q96OdtXbAY71Ojt/3ZljCcfcSyi5DJw5kHG3jCM9I50qIVWITYmlVNELTybbhrUlJjGG1PRU9/c0MTWRuv+tS7vq7dj3zD6ql67u/t3yl8QV4Ms7vqRHgx78tOUnd7ftCWsnuPd3/bYr3/X8jnub3OuR53P93TsUewhwdDG/VBNXwD3vxeKHFvPPgX94ad5L7n0L9y6kTLEynEw+eV7XtNbyysJX3L83vvJe8OfeP/n7wN9ULFGRK0Ov9Jm4vCm//lr3AL5ybn8F3JGszYLAAAAgAElEQVSp/GvrsAwoa4wp9NMbHks4nbd3/667FyORc+lxRQ8aVGgAOFpsvLV+WH7ae2JvnsaFetptk24DHF188sMdV9xB8yrNz7kcwankU0QnRnM47nCO+w/HOsq/uuMr4lLisnUvPV+nkk/R68deLD24lB3ROziZdBIzwvDNuuwT6BRWtcvVJqRoiLur4ui/R1M+uDyjOo/CDrdeb02rVLJStuTznsb3UL10dbrV70bfJn1pV70dX/T4AjvcEjckjn3P7OPZq59lV8wuKrxTgc9Wnb3lOMNmsCJiRX6+jGx2xuykXPFyjLtlHE0rN2Xe/fO89oG1WFAxhnccTu8rezP+tvHubtfGGG69/FZua3Aby/+1nLuvvJu1R9a6u7Eeij1E93rdaV2tNVHxUayPXE96RjpmhOGJmU9gRhimbp7qldcEsPLQSgCurXGtTyXVF6pp5aYcfeEo19S4Jtu+ebvnUf//6nPFx1dw07c3ERUfdVHdIh9p8QhT756apd4W7FnAyeSTDGw1kJplavrsTaGLFWACuKvRXXSr142ggCD+d9v/sh3T7+d+/Lb9N6ZunupePuhCNfqkEXX/W5cNURsAxyRt4hjS8OK1L2YrdyWuS/YvIWBEAGaE4Y2/3sh1nVRrLa8ufJWRix3Dnn6464f8C/o8Na/SnAybQVR8FPtP7ffY+sSFmSfeVSwwxxizyhgzwFlW2Vp7GMD5v2sQQRhwINO5B51lhdqyg8vc2667kOKbTiSdcI+baDyuMe2+aOfliC5MfEq8+0341YWvurvBAtQeW5tyo8vx+MzH+ff0f2d7o7PWMnvn7ItO2s6UlJYE5N86r9PumcaaR9ecdTKPI3FHuG7idQD8vf/vLPsmb5xMyKgQ0jLSeLzV48zaOYtSb5Wi3OhyTNk0hfHh47nx6xv5e//frD68mlGLRxGfEn/OuDJPejN181R2H98NwDv/vJPn13Yy6fzuEBe0yLhI4lLi2Ba9jefnPg84ugoXViWLlqRmmZpYaxm5eCQnkk4waPagLDciz/TZqs9o+7+2/LHzjwKJ8f1/3qf+/9VnwIwBDGw1kHUD1533ZCwFzdUi0LRyU17v+Dpf3P4FM++dyaCrB/HzPT9jMBxLOMaLcx0fNj8Jdyyx8n8r/s9rMaemp3Jl6JU5jtUtzCb3msyfD/yZ6/7lEY7lcXJb5zWvMmwGc3bN4ZetvzB311x+3PwjIUVDuLnezRd13cLi/mb3kzQ0iUdaPMK0e6Zl23/bpNvo/WNvHp7+8AVdPzY5lsOxh9l6bCt7TuwhJjGG97q8R/f6aijJbP8z+9n25DY61My6hnCHiR2wOG7mv/rnq5QYVSLb+3xqeiofLP3APY425sWYAl3y6FxGdBzh3v5j5x/uz1qXMk8kr9daa1vg6BL8hDHmurMcm1Nbd7YmImPMAGNMuDEm/OjRox4IMf8cSzjGdxu+o3GlxqQMS2HpI0vzpVuieMacXXNYHrGcmMQYyhYvS2gJ310S4mh8zj/7KyJWEPJWCJ2+6sTGqI28segNABY9uAg73DLyhpGcTD7JuPBxbIjaQKAJZNzKcXT7rhvWWvr+1Jebv7uZGmNqEJ0QzZRNUxizdAwfLP2AX7b+ctFxp9v8aXndGbOT3cd3nzU5HjxvsLv7/h/3/cGGyA0M/G0gW45u4eHpDxOfGk+jTxrx5g1vutcgBcdNp4EzBzJ/z3zaT2xPz8k9GbpgKCFvhfDDxh9ITE1kyf4lPPjLg+w/uZ92/2vHnZPv5J8D/xBxKgJwzLD54fIPiYh1PG5SqQmbj27O8n3MnAwfij1Em8/bYEYYyo4uy/HE/2/vvuOyKvsHjn8uNriQISCQ4ECN3NsyR5EzV+mTmfmkZqXmesy0YVmPlQ1HWprmytypOXLheqxfuXAkLhTURFRQSRSQdZ/fH/cQFE0E7sX3/XrdLw7nnPucL1/x5lznus73Si7SfBWVnWd3Un+W/sL+4MWDAAysP5COYR0tGVaRUErR67FefNLmE9Ky0mg8uzE5uhw2ntrIpZuXWHF0BdvPbAcwDdU9nnS82OOKS45jVOQoGgc2LlTVTEsp41qG91u+T796/ehQrQMOyoGgskGcH3Ge1qGtWRK9JM/+Ph4+1J5RmyEbhpg1zvPXz9MkqAnRg6LtrtBNUNkgWoa0JOO9jPvuZ6wU/rC++v0r2v7Ylm7LuvHMj8+w4PACej3WCzcnt0Id15YYR0N0rdGV/+v3fzg73N2Dv/bkWt5Y/0aBe83qz6pPxUm3KyX7ePjQM7xn4QK2Q8HlggnzDmPXK7v4sdv9q20fvqS/RtA0jdfXv47Lf12YtHsSAE2Dmlq8SNOdyruXp1NYJ9P3Fb+qaJHRddak0M+8apqWYPiaqJRaDTQGLiulAjRNu2gYFmyspR4PBOd6exCQkM8xZwGzQF+wqbAxFqe1J9cC+uc8nB2duZBygaDJQUxrP40Xa72Ip5un3Q6bsWXGHvJ/GoJqLtdvXWdr3FbqB9TnfMp5NpzawMT/m0i4bzg6Tcfzjz7P3gt7WfLcEsbt0FfP23Nhj6lsPECLSvo7ju+0eIfAMoH8e82/md5hOpN3T+atSP0F8KBfBrHsqL4C783MmwzeMNj0PegbJc2Dm3Pp5iVq++nnO3zQeQCNiqvntdq0agBcHa2fnuNOmTmZ1PWvy4ZTG9DQyMzJpMEs/fyfq0+szhOXi6MLMzrOoGlQU6p7V8fd2R0vdy9eXPkiOVoOMzrOMM3r9+u5X3kr8i1TD2tNn5qmXotd53aZhud93f5r+q/tz4pjKwB9T3T4t+EApL+bzr4L+3hy/pOUdytP9KBopu6eahqyCOD1uRdtQtuwsfdGq/m9BHhp1Uum56xah7ZmUfdFFo6oaEVUiSCiSgSJqYnMPzyfL37/gg92fsDQxkP58o8v9ftUjuA/zf6Dj4cPR5OOFntM8w7OA2BFjxWFblxYE+PzyodeP8TNzJtULFMR9wnurDq+CoAzf58hMTWRdlXb0b1md9MomeLyyBR9bu2xBoKRi6MLSW8lkZmTyelrp/lm3zesObGGjJwMmgU145V6rxTq+E9Wut1fUd27OievnuT5R58vbNg2q3lwc1LGphAyJYRuNboxM2qmadvMqJnMjJrJ7Gdn07tW7weaEuj0tdOAfv7VJoFNGNdyXIm6MfAwetfuTVDZIFORuTvVD6hvKrhllHAjgadCnzJb4cGCeq/Fe6YK8KlZqVy4cYGgskEkpiby3f7vGNtirKmIYUlQqGrDSqlSgIOmaTcMy5HAR8BTwNVcBZu8NE0brZTqCAxBX224CfC1pmmN73cOa642vDt+N83m6Odp291/N02CmpiGb75S9xXmHZrHtPbTGNLYvHeTxb3NOziPfmv7cWDgAVNvkqUvXHSajoiFEWw/s52aPjVNc2HC7blTjaa1n8aGUxuoUKoCFUpVwK+UH280egOFuusP4d+3/mbT6U30WtmLUM9QWoW0YliTYUTGRdKtRjdWn1iNk4MTYd5hdFzckcrlK3PkjSOM2DSCWQdmUbl8Zd5r8R791vbD3cmdBV0X0CO8B1vjttI0qKmpYIJR8ORg4lPiOTPsTLFME2D8v3XlrSt4e3jn2aZpGm1/bMuvf/1K6jupOCgHnl3yrOnDvp5/PSa1nUSTwCY4Ozo/0Id8dGI0G09t5K3H32LT6U20X9QegDUvrOHI5SOm6UqMro+5TsfFHVnRYwXLjy5n2KZhpm0XRl4gcNLtJyRaVmrJgq4LCJkaAkC4bzhHk47i6ebJkTeOMHX3VFwcXXi1wav4lfIjS5fFzP0z6RTWiVDPUHbH76ZFpRbF/sdqa9xWIhZG8HHrjxlQfwAezh73rfRsyzRN41b2Ldotaseuc7uIfiOapLQkWi+4PaVOUNkgyrqW5eig4mvA7jq3i5bzW9K5emfWvLCm2M5jLTac2sA3+75hw6kNedY/WelJ1vVaV2y/b8bpn0BfjKo4KqRbq2NJxwj/NpzRzUczMWJioY93/dZ1snXZeHt4k3AjQea7B1MRK2Ol7fw8yLVH9enVibkaw9gnxvLJU58UdZh2LT4lnnkH59G9ZneeX/E8J66cAGBH3x15PteNYobEUM27mrnDfGC5Hw/7sOWHfNDqA8ZuHcv0fdNJGJlg8wXRzDZVjlKqMmAc5O8ELNY0bYJSyhtYDjwC/AX00DTtmmGqnOlAO/RT5byiadp9W6bW3HjN0eXQeWln3mr+lmnuw9y/XAC9a/Uu0IThonhti9vG0wufZmffnaa7cuZuvOo0HTFXY/Dx8MHTzRPnj/VDjKp5VaN1SGtuZN5gSfQSDr9+GCcHJ0ZtGUXP8J74evjSvlr7AvXkn7xykh8O/8D41uPv29A5cvkIVbyq4OHswZbYLbT9UT+VUNOgpnme6R7SaAjT900H9EVyzg0/x+/nf+fs32f5YOcH9Kvbj/Gtx+d7jsIy/t9KeisJHw+fPNu+3fctgzcMzvMHPisni/iUePxL+xfJpPcZ2RnEJscSWCaQcm7luJBygVvZ+ove9Kx0qnlXIy0rDQ9nDwA+++0z3J3c6VevH2Vcy9B9WXdCPUPpGNaRR8o9QlWvqkQnRqNQ+Jf2Z9e5XfiW8mXlsZWmKWiCywbzWoPX7mooAwxtPJSp7acW+ue6n1lRs3h3+7ucHXbW7oZV5sc4Zzfc/lw4mniUW9m32HF2B9m6bMZuG8u2l7fRJrRNscSQlJrEzyd+5l+P/ctubxTcydiYys/V0VeJS45j1fFVlHEpQ796/Tifcp6GFR/oGueems9pzh/xf/Bjtx/pXTv/xoU923thL/UD6peo3hpLmXtwLsM2DbvrkbLM9zI5lnSMAesG0DqkNROfnkh0YjS1/GqZ9gmaFMSFGxd4psozbH5ps7lDtxt/3/rbNHXTnRZ2W0hE5Qj8SvuZOaoHdzPzJmU+zds4PTDwAE/Me4IGAQ3Y9couziSfIepiFFW9qrLw8EKcHJyYtncak9pO4vWGr1so8gdn1nlei5s1N17zMztqNh/t+ojAMoHsubCH3/v9TrPgZpYOSxi0+7Edm2M3s67XOlN1XHM3XkdHjuaL378AYHiT4UzZM4XHgx83zd2laRrZumyLVb7MysnC/yt/HqvwGCt7riQ+JZ6A0gH4f+WPh7MHvh6+nLt+DtDf/fvwfx+a3juo4SA+e/qzYrkDeK/G65W0K4RMCaGmb032Dthr02Xk07LSCJ0aSmJqIou7L+bw5cP8df2vu54RBAgoHcD5EeeJTY5l+5ntvNbgtSL72Tef3szG0xt5/8n3Sc9OJ6hsUJEc19oduXyE2jMNw+Xz+VzIysni0s1LBJcLvmtbUbiRcYNSLqVK7KMm8SnxJKYmotN0NJrdyLS+jl8d07PslctXJi45Dg9nD/rW6cvUdlML9FmZkpFCcnoyy48uZ9GRRfz6yq8232MhbINO0+WpaHt+xHkazW7EpZuXgNvDrvcO2Iu7szuvrHmF/Qn669/2VduzofeGex5b/LPpe6fz5sY371qf+V6m1VcaT0xNxO9LfePa18OXpLQkIipHEBkXyYQ2E1gXs47d8btxVI751h15uvLTRPaJNHfYBWKJeV6FwasNXuXVBrZbhdOeaZrG5lj9nUsH5UCf2n347a/fzHLun479xOANg/mu03dsPL3RtL5z9c58+cyXeaa/UEpZ9IPU2dGZq6Nvl/U3NhRPv3maK2lXqFy+Mj/++SOtQlpxPeM6TWObkpSaRGxyLN/u/5YjiUcI8w6jQUADVh5fyduPv13oydpPXjlpWjbecJtzYA5pWWkcSzpGalYq41uNt+mGK4CHswezn53NI+Ueoa5/XXrV6kVGdga9HutFy5CWlHUtS7Yum59P/MzGUxsZ/7/xpoJd6VnpjGg24qHPfTXtKtGJ0UzdM5XVJ/QDat5q/laJabgC/zgNjbOjM8Hlgrl88zLbzmwrsjkcQf/v12h2I8a3Gm9VlS7NKahskOn3TftA4/sD3xMZF8nyo8tN+xireadlpTFj/wxerf8qZV3L4uzofM/ng3WajqtpV9FpOvy/8gfg1JunbLIYlrBdDsqBal63h6UGT857E+zkVf3fucbf3/00XVGMHirphjQekqfxuuvfu9gSu8XqG66gb7A+V/M5Vh5fyftPvk/3mt2Ze3AukXGR/BH/h2mEXA2fGvnWZdgat5XE1EQqlKpw1zZbJD2vxSgpNYl1MevoUK0D/qX9LR1OiZeRnYHbBDc+afMJY1uMZcDaAWw6vYn4kfH//OZC0Gk6HD/SXxQ7KAey38/mve3v0b9+fyqXr1ys5zYXTdO4mXkTr8+9yNZl3729EL3bCw8v5OWfXwb0w5i39tlKti6bSlMq0TGsI4/6PMr2s9vZ8tKWEjVp++lrp01FrADmdp5LSkYKYd5htK/WvkDHMhaay21lz5V0r9m9SGK1FTFXY6g+vTrfP/s9/ev3v+d+nZd0Zl2M/nnMlT1X0ia0TZ7e0pn7Z9LikRaEV8h/KOydsnKyCJwUSFJaEit6rCjRBW/udOejOHfqV7cfcw/NBeDrdl/TKqQVx5KOcfLqSbrV6EZ6djoz9s9g/qH5ed63+l+r6Vqjaz5HFKL4GAsg5q6j8E/61O7DkMZDaBx43xIx4gHEp8TzwY4PCCwbyEetP7J0OAVyLf0a8w7Oo0uNLlT1qsqAtQP45dQv7Bmwh/e2v8fABgOpUr4K30V9x4D6AyjlXIouS7swruU4Zu6fyeS2k4tt1FBRkGHDViIqIYqGsxuy9LmlJfZOujW5fus6nhM9mfTMJEY0G2G6KCrOYcNX0q5Q3q08Th/rBznYewGvmKsxvLnxTfxK+fFhqw+p8nUVAH7s9iN/Xf+Lt594u0BDItOz0qnwZQXTs0IHBh6gXkA9IhZGsDVuq6loQUm178I+ohOj+ddj/yIyNpKuy/QX4//0maPTdJz7+xyh5UP58/Kf1JlZx7Strn9dprWfxhOPPFHs8VubuOQ4qnxdhQVdF/BynZfvud/56+dNlWqNWlZqycbeG4lNjqXWDP0za+8/+f4DXSANWDuAOQfn4F/an7+G/2UTPQHmkl/jtVNYJ4Y3Gc7TC58u8PFeeOwFXnzsRTqFdbL5kRrCdmmaRsfFHU0jsfIb7lnerTztq7W3uwrvomikZ6WzO343rUPvLj5li6TxaiWyddn4f+mPi6MLZ4adKVHVDItbZk4mzg7OBbr4uHzzsmnI2PfPfs+wTcN4veHrfPnMl8US47X0a3h/7s1PPX4ixDOEcm7lqOpVtVjOZa1+++s39ifsZ8Rm/XDW+V3m07duX9P2uOQ4Rm0ZxcJuC01VgI2NW03TiE6Mpv2i9ox5YgzrY9bzRsM36BTWiS5Lu/DLqV84N/ycXU0lUhjpWelM3TOVsdvGArDv1X28tv41VvVcxf6E/bQObW2aYmjq7qkM3zwcL3cvWoe0ZuXxlbg4upAwMuGuSs4lyc3Mm3RY1IHq3tWZ3Xn2ffddH7OeAxcP0LVGV1Pj393JHW8Pb9O0SqCvQp1f4SVN00/nlJ6dTvtF7cnMyWTPgD1SQOcOUQlRXLhxgSaBTYhNjuXgxYP0r98fNyc3082Ge3F3cic9Ox0Ab3dv9g/cXyyV0IV4GKmZ+nnHZ3WaRduqbU03apoENuHnF36WEXuiRClI47VkVoUwEycHJ7565isu3rzIgsMLLB2OXUjNTGXdyXW4/teVj3d9zIqjK/j0109NU6Lcz63sW6blyLhIUwO4uEz+YzIACw4voEHFBiWu4QrwxCNPMKzJMNPF+7qYdXz666c8PvdxtsZtZeTmkaw+sZrVJ1bj87kPg38ZTHpWOsnpyfRa2YujSUf5a8RfDGo0iM2xm+m6rCtJaUnU8atDTZ+a0nDNxd3ZnTFPjOGLCH0xsIiFERy4eICQqSE8v+J5Hv3mUdKy0jiTfIZdf+0C9DdYRj8+mq/bfU3UwKgS3XAFKO1SGh8PH9M8vvfTKawT41qOo7ZfbTLeywAgPTvd1HD9d91/AzBw3UCyddmsOr6KPqv70GFRB44nHaf78u64TXBj6MahNAhowOaXNkvDNR8NKjagc/XO+JX2o3lwcwY3Hmya59L42EVgmUA+fepTulTvQstKLU3vNfZ6Vy5fmQltJkjDVViVUi6lODf8HG2r6qv7J45KZHLbyfzQ7QdpuApxH9LzWsw0TaPJ903Yl7CPv9/+m3Ju5SwdktW6mXnTNHdoji6H7We20zq0temCbsmRJby46naBlDvnRF39r9XEp8Rz6eYlHvV9lM7VO+eZi1TTNDJyMui/tj+bT2/marq+KFFRDxtec2IN/db241r6NVqHtGZ73+1FenxbdP76eabvnU73mt1pOqfpXdudHZzJ0mUB0LBiQ1OFRV8PXy6PukyWLgvX/+pHLiSMTCCgTID5grcxt7JvMfiXwabnAHN7uc7L/HD4BwLL6Oec3dB7A7X9aps7RKuVlZNFwFcBODk4cWnUpQK99+DFg6w8vpJz18/RNLApgxsPpv539Tl46SBh3mHEXI3J930lZT7X4nL91nUcHRxNn/Vn/z7LlN1T6BTWiadCn+Lw5cPU9qtdYis4CyGELZBqw1ZEKcWwJsN4afVLeE70JG5oHI+Ue4TkW8l3zVVZEuk0HQk3Eth1bleeibw/f/pzRm8djbe7Ny0qteDtx99m1YlVAExpO4WavjVpFtSMPqv7EHUxCjcnN0q7lGbUllFk5Oh7QZ4Ne5a1vdYyZfcURmweQdTAKOoH1KdZUDMWH1mcJ4aivLBpE9qGWhVq4V/an287fltkx7VlweWCmRgxEYBHfR/lWNIxxj05jo92fUQ9/3ocvHQQgG86fMPgDYMBqOpVlaiBUSil2BN/uydMw7pvuFmam5Mbc7rMoUd4DzRNw8fDh51ndzJ662ge830M0D973aVGF7mgv0NKRorpplZB1QuoR72AennWrXlhDZtjNzP/0HxOXzvN0ueWkp6dzitrXqFWhVr83v9307zA4uHceUM4xDOEKe2mmL6v61/X3CEJIYQoRtLzaiY9V/RkxbEVhHqG0ia0DXMOziHtnbQSW/78QsoF2i9qz5HEI5RxKYO7szuJqYmAvretWXAz1p5ca9r/0GuHOPv3WRpWbEhg2cB7HvfElRO0WdCGizcv4lfKj5g3Y3jjlzdMjdVej/ViaJOhNJtze+7dW+/eKpLnkY8lHdNPXdL6Y8K8wwp9PHt16NIhFh9ZzIetPmT8zvF0r9mdcm7lOHDxAC/WepHYa7Ek30qmtl9tXBxdAPjj/B80n9scgAsjL7Dy2EqiLkYxv+t8C/4ktsP4fOWOsztov6i9zD99D8aiblC8hdwupFzAr7SfDBMWQgghkIJNVuns32cJnRqaZ11JmBIhW5fNptOb6FitI+ti1jHh1wlseHEDvl/4mnrQPn3qU3qG9yQrJ4scLYcaPjX46dhP+Hj4UNa1LImpiXSo1uGBz6lpGlm6LFNBJ03T6LqsK2tPrsVBOZAyJoVPfv2EX079wuHLh009tLHXYsnSZTHpj0m8WOtFLt28RMKNBHw8fEyVR6funsq2M9uY22XuXT3nPVb04OcTPxM3NM6qy5Hbov0J+2k0uxEA8SPiGbdjHFvitnB+xHkLR2YbcnQ5bI7dzMkrJxm5ZSR7B+ylUWAjS4dldVIzUyn9qX74aXE2XoUQQghxmwwbtkIhniFEDYyiwawGpnXLji6zq8ZrZk6mqacMYN3JdYzdNpajSUdZ8twSeq3sBcA7294hsk8kW2K30DKkJe2rtr+ranDP8J4PHYdSKk8cSinWvLCGU1dPkZKRQimXUkx4agLDmw6nwpcViEuOY33Mep5d8iwA5VzLMbLZSFovuF1+/JdTv7D86HLT92f/Ppun8Rp7LZa1J9fSv15/abgWA2MPVSnnUpRzK4eOoh3qbe9ytBw6Lu5IgwD9549MxZI/4zzBn7T5xMKRCCGEECI/cvVnRnX963Lr3Vv8p9l/iBoYxfLnl/PmhjfZdW6XpUMrtAMXD+D6X1eqfF0F3y98mXtwLp2XduZo0lFA3yD8psM3AMw6MIs6/nWYGDGRDtU6mG2uvWre1WhQ8fbNA99SvujG6YgeFM2NjBum9cueX0Z17+qMbDrStM7YcPUv7U/MkBg2ntrIiqMrABi/czxVp1XFUTkyqvkos/wsJY2xKvS8LvMo7VKaHF2ONF4LwJi/MO8wJj0zyVSwSeRlvEmSrcu2cCRCCCGEyI/0vJqRg3LA1cnVNK/ovIPzmL5vOtP3TbfZIWq743fzXdR3PB78OKCftxP0F8l/vv4nsw/MpkFAA/0cZigqlqlI+6rtrWbOW2PDuWd4Typ5ViLcN9xUAOTziM+ZGDGRK2lXKONShpirMaaCLPMPz+dM8hmm7Z3G6w1fp4ZPDeZ1mVcip8MxB//S/rQOaU3CjQSycrKKvMiWvVNK4eroSnDZYEY0G2HpcKyWo3Kkjl+dPPO0CiGEEMJ6SOPVgoZvHg7Agq62NwdsVEIUL61+iRNXTgBQtXxV4obGcTPzJrHJsTQPbo6DcuDr9l/neV/XGl0tEe4/cnRwpHlw87vWAab51nJXEnV1dEVD49e/fuXziM85NuiY2XqQSyJvD292nN3BjrM76FazG/6l/U1zPIoH4+LowuXUyxxLOkZVr6p5htYLPaUUzo7OnE+RZ6mFEEIIayRdFxb09uNv46AcaPFIC67fuk56VrqlQ/pHu+N38+ySZ2k4uyEnrpzA3cmd8a3GM7bFWELLh1LLrxZda3S1+16xCW0mEFQ2iJghMTQNaioN12KWexinpml8+cyXRPaJtGBEtsfVyZVlR5cR/m04F29ctHQ4Vmt/wn5OXztt6TCEEEIIkY+HbmEopYKVUjuUUseVUkeVUsMM6z9USl1QSh0yvDrkes9YpdRppdRJpVTbovgBbItygrcAABBVSURBVNk7Ld4hdmgsjb9vjOdET55e+LTZzp2cnsyZ5DMPtG+2LpsdZ3aQkpFCsznNWB+zHoAtL20h7d00xrUcZ/eN1Tt1q9mN8yPOU827mqVDKRGSUpNMyzLP68NZ1XMVgxvp59CVgk33d+raKUuHIIQQQoh8FKbFkQ38R9O0mkBTYLBS6lHDtsmaptU1vDYAGLa9AIQD7YBvlVKOhTi/XQguG8yVtCsA/H7+d66lXzPLeTsv7UzlryuTkZ3By6tfZk/8nrv2ydHlMHzTcJw/dqbbsm48t/w5AJ4KfYpjg44RUSXCLLEKkbuxlaPL4f3t79NvTT8LRmR7WlRqQainfroumV9UCCGEELbooa9gNE27CFw0LN9QSh0H7lfCsguwVNO0DOCMUuo00Bj442FjsAeODo4sf3457+14j5U9V+Ll7lUs57mVfYvtZ7bToVoHvtn7Db/99RsALea1YF/CPhb+uZBro6+h03T6ysFOruxP2M/UPVMB/cXuoIaDuJFxg/UvrsfNya1Y4hQiP7kbWzlaDtFJ0abiYOLBbDq9ie1ntwO3qw8LIYQQQtiSIrn9rpQKAeoBe4DHgSFKqZeB/eh7Z5PRN2x353pbPPdv7JYYPcJ70CO8BwBLo5cy8f8msrv/7iKtyDtm6xim7pnKzI4zGbJxiGn9voR9puXhm4fzw+EfAH1l5DDvMABODjlpWu5Ws1uRxSTEg8rd2PIr5SfVhh/CmK1jOHz5MCA9r/dToVQFutfobukwhBBCCJGPQl/9KaVKAyuB4ZqmpQAzgCpAXfQ9s18Zd83n7fk+vKaUGqiU2q+U2p+UlJTfLnZr74W9HLp0iE5LOqFpRfdsn7EAyaTdk/KsH9RwELfevUXP8J55podwUA6E+4azs+9OU8NVCEsxNrYmtJlAObdyMs/rQ3B1cqVimYp8/+z3uDu7Wzocq+WoHGWeVyGEEMJKFer2u1LKGX3DdZGmaasANE27nGv7bGC94dt4IDjX24OAhPyOq2naLGAWQMOGDUtUdZavnvmKAxcPsDVuKzP2z2BQo0FFctzSLqUBaFSxEamZqaz61yrCfcNxc3LD0cGRRd0Xcf3Wdcq5lWNP/B4ef+TxIjmvEEXB2dGZCW0m4Onmyc3Mm+g0HY7yyHyBuDi6EOYdRv/6/S0dilVb8twSfEv5WjoMIYQQQuTjoRuvSj83yBzguKZpk3KtDzA8DwvQDYg2LK8FFiulJgEVgWrA3oc9v71SSrG973ZCpoQweMNgnqv5HH6l/Qp1zNTMVL585ktGNhtJuG84pVxK3bWPk4MT3h7eANJwFVbHQTlQ2682zy55lsaBjalSvgo+Hj6WDsumuDq6cvLqSfZd2EejwEaWDsdqtQxpaekQhBBCCHEPhel5fRzoAxxRSh0yrHsH6KWUqot+SPBZ4DUATdOOKqWWA8fQVyoerGlaTiHOb7cclANLn19Kt2XdePnnl2ke1JwxT4wp8DOwKRkpxF6Lpf6s+rQJbUNkn0gZailslrGnNVuXzbQO0ywcje1xdXIlPiWe5nObk/V+lqXDEUIIIYQosMJUG/6N/J9j3XCf90wAJjzsOUuS5sHN+fP1P/H/yp8tsVtYeXwlm1/aTECZAAAysjNIy0qjvHv5PO+bc2AOA9YN4I/+f9BndR/Ts66HLh2SIjfCpjk66BuvOTq55/UwJredjINyYFvcNkuHIoQQQgjxUKQlY8X8Svsxv8t8AI4kHqHbsm7oNB03M28SNDkIr8+9+GDHByTcSKDj4o4cvnSYAesGAPDy6pcZ0khfVbhvnb4kvZUkFUaFTTP+/mbrshm4biAD1w20cES2Jcw7DIXCw9nD0qEIIYQQQjwUac1Yub51+9K3bl/2XdhHiGcIS6OX0ntVb9P2j3Z9xEe7PgKgT+0+ANQPqE9kn0i83L0Y2GAgLo4u0uMqbJ5x2HCOlsPJqyfld7qAdp3bxbqYdbxY60VLhyKEEEII8VCk8WojjAVW2oS2YViTYXi6efLziZ85fPkw4b7hTGgzgbKuZfF082Rx98V4uXsByJQYwm486vsoy55fRrhvODm6HJyc5OOrIH7880cAKpWrZOFIhBBCCCEejlz92Rj/0v5MaTcFgA9bfXjX9mujr6EvBC2EffEt5UvP8J4AMlXOQ3BxdAFgYAMZbi2EEEII2yTj7uyMNFyFvbqRcYOtcVtJTE2U4mMPwc3JjVLOpQjxDLF0KEIIIYQQD0Wu/oQQNiE2OZaIhRH8fv536vnXo7ZfbUuHZFOiE6NJzUolLSvN0qEIIYQQQjwUGTYshLAJued5ndFphoWjsT2tQlqxOXazpcMQQgghhHho0vMqhLAJxqlyZJ7XhzPmiTFkvJchU+UIIYQQwmZJ41UIYRMcHW73vHZe0plBvwyycES2x1i0SQghhBDCFsmwYSGETcg9z2tccpypJ1YIIYQQQpQMcvUnhLAJAWUCqB9Qn74/9wWgpm9NC0ckhBBCCCHMSYYNCyFsgoezBxXLVDR9L1PlCCGEEEKULHL1J4SwCRnZGayPWW/6XqfpLBiNEEIIIYQwN2m8CiFswo3MG3m+r+BRwUKRCCGEEEIISzD7M69KqXbAVMAR+F7TtM/MHYMQwvaUcSkDwNDGQ2kZ0pKIyhEWjkgIIYQQQpiTWRuvSilH4BsgAogH9iml1mqadsyccQghbI+rkyu6cTqUUpYORQghhBBCWIC5hw03Bk5rmhanaVomsBToYuYYhBA2ShquQgghhBAll7kbr4HA+VzfxxvWCSGEEEIIIYQQ92Tuxmt+3SbaXTspNVAptV8ptT8pKckMYQkhhBBCCCGEsGbmbrzGA8G5vg8CEu7cSdO0WZqmNdQ0raGvr6/ZghNCCCGEEEIIYZ3M3XjdB1RTSoUqpVyAF4C1Zo5BCCGEEEIIIYSNMWu1YU3TspVSQ4DN6KfKmatp2lFzxiCEEEIIIYQQwvYoTbvrkVOropRKAs5ZOo778AGuWDqIEkDybB6SZ/OQPJuP5No8JM/mIXk2D8mz+UiuzcPa81xJ07QHelbU6huv1k4ptV/TtIaWjsPeSZ7NQ/JsHpJn85Fcm4fk2Twkz+YheTYfybV52FOezf3MqxBCCCGEEEIIUWDSeBVCCCGEEEIIYfWk8Vp4sywdQAkheTYPybN5SJ7NR3JtHpJn85A8m4fk2Xwk1+ZhN3mWZ16FEEIIIYQQQlg96XkVQgghhBBCCGH17K7xqpQKVkrtUEodV0odVUoNM6z3UkpFKqVOGb6WN6yvoZT6QymVoZQales4bkqpvUqpw4bjjL/POfsajntKKdU31/oJSqnzSqmb/xBzA6XUEaXUaaXU10opZVjfw3BunVLKqiqE2Vmev1BKnVBK/amUWq2U8ixsfoqKneX5Y0OODymltiilKhY2P0XFnvKca/sopZSmlPJ52LwUB3vKtVLqQ6XUBcPv9CGlVIfC5qeo2FOeDdveVEqdNMTweWFyU5TsKc9KqWW5fpfPKqUOFTY/RcXO8lxXKbXbkOf9SqnGhc1PUbGzPNcxxHZEKbVOKVW2sPkpSjaa63z3U0q5Gj4/Tiul9iilQh4uKw9I0zS7egEBQH3DchkgBngU+BwYY1g/BphoWK4ANAImAKNyHUcBpQ3LzsAeoGk+5/MC4gxfyxuWyxu2NTXEc/MfYt4LNDOccyPQ3rC+JlAd2Ak0tHRu7TjPzwBOhuWJxpit4WVneS6ba5+hwExL59ce82zYFgxsRj9Hto+l82uvuQY+zB2TNb3sLM+tga2AqzFWS+fXHvN8xz5fAeMsnV97zDOwJddyB2CnpfNrp3neB7Q0LPcDPrZ0fu0g1/nuBwzCcE0HvAAsK87c2V3Pq6ZpFzVNO2BYvgEcBwKBLsACw24LgK6GfRI1TdsHZN1xHE3TNOOdBWfDK78HhNsCkZqmXdM0LRmIBNoZjrFb07SL94tXKRWA/qL+D03/r/5DrtiOa5p2skAJMBM7y/MWTdOyDbvuBoIeMA3Fzs7ynJJr11L3OL9F2FOeDSYDo+9xbouyw1xbJTvL8xvAZ5qmZRhjfcA0FDs7y7NxHwX0BJY8QArMws7yrAHGXsByQMIDpMAs7CzP1YFdhuVI4LkHSIHZ2Fqu/2G/3DH/BDxl7AEvDnbXeM3N0G1dD/1dCD9jwg1fKzzA+x0Nw2YS0f+D78lnt0DgfK7v4w3rHlSg4T0P+36Ls7M890N/587q2EOejUNOgN7AuAIc12xsPc9Kqc7ABU3TDhfgeBZh67k2GKL0w+HnGod3WRs7yHMY0MIwHO1/SqlGBTiu2dhBno1aAJc1TTtVgOOajR3keTjwheFv4ZfA2AIc12zsIM/RQGfDcg/0I5Ksko3k+n5Mx9b0nUHXAe8iOvZd7LbxqpQqDawEhmt5e3wemKZpOZqm1UXfE9dYKfVYfqfK760FOE1h329R9pRnpdS7QDawqADHNQt7ybOmae9qmhaMPsdDCnBcs7D1PCulPIB3sdIbA7nZeq4NX2cAVYC6wEX0Qy2tip3k2Qn9MLemwFvA8uK8q/8w7CTPRr2wol7X3Owkz28AIwx/C0cAcwpwXLOwkzz3AwYrpaLQD8vNLMBxzcaGcn0/Zm3L2GXjVSnljP4XYZGmaasMqy8bhhcYhxk88LAjTdP+Rv/caTulVBN1u6BBZ/R3LnLfzQniPkNAjHdHDK+PDO/PPUz1vu+3JvaUZ8OD652A3oahJ1bDnvKcy2KsbAiPneS5ChAKHFZKnTWsP6CU8n/QuM3BTnKNpmmXDRcNOmA2YDWFV8B+8mzYtsowPG4voAOsphCZHeUZpZQT0B1Y9qDxmosd5bkvYIx/BfK5UVyfzyc0TXtG07QG6G/GxD5ozOZiY7m+H9OxDZ8h5YBrDxp3gWlW8NByUb7Qt/5/AKbcsf4L8j4A/fkd2z8k7wPQvoCnYdkd+BXolM/5vIAz6O8Klzcse92xzz89AL0P/R1l48PmHe7YvhPrK9hkN3lGP+b/GOBr6bzaeZ6r5drnTeAnS+fXHvN8xz5nsb6CTXaTayAg1z4jgKWWzq+d5vl14CPDchj64WnK0jm2tzwbtrUD/mfpvNpzntE/29jKsPwUEGXp/NppnisYvjoYfqZ+ls6vref6XvsBg8lbsGl5sebO0v94xfDL8AT6ruo/gUOGVwf0Y6+3AacMX70M+/ujv2OQAvxtWC4L1AYOGo4TzX2q7qEfmnDa8Hol1/rPDcfTGb5+eI/3NzScIxaYjuGPMtDN8L4M4DKw2dL5tdM8n0Z/MWT8OaypCq495XmlYf2fwDog0NL5tcc837HPWayv8Wo3uQYWAkcMMawlV2PW0i87y7ML8KNh2wGgjaXza495NmybD7xu6bzac54NP0sUcBj9M44NLJ1fO83zMPQVfGOAz7CSG142nut89wPc0I8iOI2++nPl4syd8R9YCCGEEEIIIYSwWnb5zKsQQgghhBBCCPsijVchhBBCCCGEEFZPGq9CCCGEEEIIIayeNF6FEEIIIYQQQlg9abwKIYQQQgghhLB60ngVQgghhBBCCGH1pPEqhBBCCCGEEMLqSeNVCCGEEEIIIYTV+39a3zmxXjT/BAAAAABJRU5ErkJggg==\n", 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" ] @@ -1344,28 +1368,28 @@ } ], "source": [ - "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, period=2, \n", + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,10)], max_slope=180, period=1, \n", " plot=True)" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 135, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 41, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1377,13 +1401,13 @@ "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", - "ax.plot(data['2013/1/5':'2013/1/9'], label='original data')\n", + "ax.plot(data['2013/1/5':'2013/1/13'], label='original data')\n", "ax.legend(loc='upper right', shadow=True)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 136, "metadata": { "scrolled": true }, @@ -1391,16 +1415,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 42, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1421,24 +1445,28 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 137, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Drift detected in period 5 to 6, slope: 51.6\n", - "Drift detected in period 6 to 7, slope: 52.53333333333333\n", - "Drift detected in period 7 to 8, slope: 44.86666666666667\n", - "Drift detected in period 9 to 10, slope: -48.266666666666666\n", - "Drift detected in period 10 to 11, slope: -41.06666666666667\n", - "Drift detected in period 11 to 12, slope: -34.6\n" + "Drift detected in day 5 with slope: 449.0\n", + "Drift detected in day 6 with slope: 499.0\n", + "Drift detected in day 7 with slope: 354.0\n", + "Drift detected in day 8 with slope: 474.0\n", + "Drift detected in day 9 with slope: -317.0\n", + "Drift detected in day 10 with slope: -70.0\n", + "Drift detected in day 11 with slope: -303.0\n", + "Drift detected in day 12 with slope: -176.0\n", + "Drift detected in day 13 with slope: 157.0\n", + "Drift detected in day 14 with slope: 158.0\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1448,30 +1476,33 @@ } ], "source": [ - "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,15)], max_slope=30, \n", - " plot=True, period=2)" + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,15)], max_slope=68, \n", + " plot=True, period=1)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 138, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Drift detected in period 5 to 6, slope: 55.285714285714285\n", - "Drift detected in period 6 to 7, slope: 56.285714285714285\n", - "Drift detected in period 7 to 8, slope: 48.07142857142857\n", - "Drift detected in period 9 to 10, slope: -51.714285714285715\n", - "Drift detected in period 10 to 11, slope: -44.0\n", - "Drift detected in period 11 to 12, slope: -37.07142857142857\n" + "Drift detected in day 5 with slope: 449.0\n", + "Drift detected in day 6 with slope: 499.0\n", + "Drift detected in day 7 with slope: 354.0\n", + "Drift detected in day 8 with slope: 474.0\n", + "Drift detected in day 9 with slope: -317.0\n", + "Drift detected in day 10 with slope: -70.0\n", + "Drift detected in day 11 with slope: -303.0\n", + "Drift detected in day 12 with slope: -176.0\n", + "Drift detected in day 13 with slope: 157.0\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1481,28 +1512,28 @@ } ], "source": [ - "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=35, period=2, \n", + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,14)], max_slope=68, period=1, \n", " plot=True, drift_type='B')" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 45, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1520,16 +1551,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 46, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" }, @@ -1558,7 +1589,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -1567,13 +1598,13 @@ "text": [ "Drift detected in period 4 to 7, slope: 90.5\n", "Drift detected in period 5 to 8, slope: 103.42857142857143\n", - "Drift detected in period 7 to 10, slope: -98.71428571428571\n", - "Drift detected in period 8 to 11, slope: -99.28571428571429\n" + "Drift detected in period 7 to 10, slope: 98.71428571428571\n", + "Drift detected in period 8 to 11, slope: 99.28571428571429\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1589,7 +1620,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 142, "metadata": { "scrolled": true }, @@ -1600,13 +1631,13 @@ "text": [ "Drift detected in period 4 to 7, slope: 90.5\n", "Drift detected in period 5 to 8, slope: 103.42857142857143\n", - "Drift detected in period 7 to 10, slope: -98.71428571428571\n", - "Drift detected in period 8 to 11, slope: -99.28571428571429\n" + "Drift detected in period 7 to 10, slope: 98.71428571428571\n", + "Drift detected in period 8 to 11, slope: 99.28571428571429\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1622,22 +1653,22 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 49, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -1655,7 +1686,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -1688,16 +1719,16 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 145, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 51, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" }, @@ -1758,16 +1789,16 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 52, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" }, @@ -1800,7 +1831,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1814,22 +1845,12 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 53, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1875,7 +1896,6 @@ "\n", "ax.plot(ds, 'C1', label='Slope2')\n", "ax.plot((s+ds)/2, 'r*')\n", - "#print(df1)#(s+ds)/2, ((s+ds)/2)+df1)\n", "#ax.plot(df1, 'k--', label='detrended drift org')\n", "\n", "ax.plot(df1+((s+ds)/2), 'b--', label='fixed drift')\n", diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 843e99c3a..17e8ff680 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1657,11 +1657,13 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals detrended_values = signal.detrend(series[start_index:end_index]) line_segment = series[start_index:end_index] - detrended_values[:] slope = (int(line_segment[-1]) - int(line_segment[0])) / 1 - if slope > max_slope: n += 1 print('Drift detected in day {} with slope: {}'.format (series.index.day[start_index], slope)) + #combines the indexes where the slope was larger than the max_slope over a longer period + if m > 0: + list_value.append([start_value, end_value, 'm']) if n == 1: start_value = series.index[start_index] end_value = series.index[end_index] @@ -1682,7 +1684,6 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals list_value.append([start_value, end_value, 'm']) m = 0 - # combines the indexes where the slope was larger than the max_slope in a longer period if series.index.day[end_index] == series.index.day[-1] and n > 0: list_value.append([start_value, end_value, 'n']) if series.index.day[end_index] == series.index.day[-1] and m > 0: @@ -1729,7 +1730,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals m += 1 print('Drift detected in period {} to {}, slope: {}'.format (series.index.day[start_index], series.index.day - [end_index - 1], slope)) + [end_index - 1], -slope)) if m == 1: start_value = series.index[start_index] end_value = series.index[end_index] @@ -1738,7 +1739,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals list_value.append([start_value, end_value, 'm']) m = 0 - #combines the indexes where the slope was larger than the max_slope in a longer period + #combines the indexes where the slope was larger than the max_slope over a longer period if series.index.day[end_index] == series.index.day[-1] and n > 0: list_value.append([start_value, end_value, 'n']) if series.index.day[end_index] == series.index.day[-1] and m > 0: @@ -1867,9 +1868,6 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr print('Not yet possible with period = 0.5') pass - elif period == 1: - pass - else: """ for n in range(len(self.list_value)-1): From 24de81a5711043a4be4dd926fe438f9f270ebfe7 Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Fri, 31 Aug 2018 12:27:31 +0200 Subject: [PATCH 31/42] use dropna instead of own implementation --- Showcase_OnlineSensorBased.ipynb | 447 +++++++++++------- wwdata/Class_HydroData.py | 28 +- .../Class_HydroData.cpython-36.pyc | Bin 52980 -> 63066 bytes .../Class_LabExperimBased.cpython-36.pyc | Bin 7071 -> 7290 bytes .../Class_LabSensorBased.cpython-36.pyc | Bin 6845 -> 7138 bytes .../Class_OnlineSensorBased.cpython-36.pyc | Bin 44069 -> 45442 bytes wwdata/__pycache__/__init__.cpython-36.pyc | Bin 726 -> 610 bytes .../data_reading_functions.cpython-36.pyc | Bin 11143 -> 11811 bytes .../time_conversion_functions.cpython-36.pyc | Bin 5179 -> 5139 bytes 9 files changed, 298 insertions(+), 177 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 167c02242..883412b5b 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,12 +20,13 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", "start_time": "2017-05-09T11:54:53.499498+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -51,8 +52,10 @@ }, { "cell_type": "code", - "execution_count": 95, - "metadata": {}, + "execution_count": 2, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import wwdata as ww" @@ -67,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -76,7 +79,7 @@ "'0.2.0'" ] }, - "execution_count": 96, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 106, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -120,13 +123,13 @@ " dtype='object')" ] }, - "execution_count": 97, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "measurements = pd.read_csv('./data/201301.txt',sep='\\t',skiprows=0)\n", + "measurements = pd.read_csv('./data/data_example.txt',sep='\\t',skiprows=0)\n", "measurements.columns" ] }, @@ -139,12 +142,13 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 107, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", "start_time": "2017-05-09T11:54:55.589786+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -164,16 +168,17 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 108, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", "start_time": "2017-05-09T11:54:55.671616+02:00" - } + }, + "scrolled": true }, "outputs": [], "source": [ - "dataset.to_datetime(time_column=dataset.timename,time_format='%d-%m-%y %H:%M')" + "dataset.to_datetime(time_column=dataset.timename,time_format= '%d-%m-%y %H:%M')" ] }, { @@ -185,12 +190,13 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 109, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", "start_time": "2017-05-09T11:54:55.783330+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -206,12 +212,13 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 110, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", "start_time": "2017-05-09T11:54:55.790638+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -227,12 +234,13 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 111, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", "start_time": "2017-05-09T11:54:55.796021+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -248,12 +256,13 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 112, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", "start_time": "2017-05-09T11:54:55.815534+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -262,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 113, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -272,9 +281,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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r167hxIkTOHnyZONbSERETaJwVSDxyUvYe/VbfPr7x8goSecTPiIiG2eoiwoREUk1KmOj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"text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -316,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -326,9 +335,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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Eq20TEREREZHLUakMiIrSAwCiovRQqRg4WyMrS2oKnAEgL09m6oV2Vux5JiIiIiIil6NU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Pjw4fJ82xUemUyGIU21q/LMKriQMD9zLHxhCQBu+JExrL2hvtrkNd79clWpTQ2Y8Nd2W9xWrHpz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\n5Ejx008/iQ0bNojo6GixevVqo2GrhZVnG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" + "" ] }, "metadata": {}, @@ -349,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", @@ -379,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", @@ -409,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", @@ -439,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", @@ -462,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", @@ -479,9 +488,9 @@ }, { "data": { - "image/png": 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\n", 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Lly/H6NGjtcddtmxZsx6/Zs0aAMB3332HTp06AQCio6MxYcIEw785NdhDhIiIiIiMSxQh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\n", "text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -497,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -505,19 +514,19 @@ "text/plain": [ "array(['TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2',\n", - " 'TSS_line1', 'NO3_line1', 'CODtot_line1', 'CODsol_line1',\n", - " 'Cond_ns', 'Turb_ns', 'Temp_ns', 'Ammonium_ns', 'Cond_es',\n", - " 'Turb_es', 'Temp_es', 'NH4_infl', 'NH3_line3', 'Turb_rz',\n", - " 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter', 'TSS_efflPST',\n", - " 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST', 'TSS_efflRBT',\n", - " 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT', 'Cond_line1',\n", - " 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", - " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", - " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1',\n", - " 'Flow_line2', 'Flow_line3', 'Flow_total'], dtype=object)" + " 'TSS_line1', 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns',\n", + " 'Turb_ns', 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es',\n", + " 'Temp_es', 'NH4_infl', 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz',\n", + " 'PO4_mixinggutter', 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST',\n", + " 'CODsol_efflPST', 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT',\n", + " 'CODsol_efflRBT', 'Cond_line1', 'Turb_line1', 'Cond_line2',\n", + " 'Turb_line2', 'Cond_line3', 'Turb_line3', 'NH4_efflPST',\n", + " 'PO4_efflPST', 'PO4_sandtrap', 'NH4_splittingworks',\n", + " 'PO4_splittingworks', 'Flow_line1', 'Flow_line2', 'Flow_line3',\n", + " 'Flow_total'], dtype=object)" ] }, - "execution_count": 111, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", @@ -546,18 +555,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 112, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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6d+9Ohw4dWLBgAevWrTuvaw0fPpyBAwcyZcoUcnJyzNW8f/vtN6txXbt2pW3btjz11FMEBgYSHBzMK6+84vCa3bp1Y8mSJYwaNYrY2FhatGhBixYtuOKKK5g9ezbJyckkJCQwb948jh8/fl5x+5pfM/V2EaWmUoIDg+sY7Xv2nt5LdlF2rWO6JHTxy0qXQgghRGMQHqS3t2weab91saCsgBWHVphfnyrUV2n6cp9poxWYI/Hh+grWBy57gENn9QkzY2m7P5GZaVGrUaNGsWTJEs6ePcv48eO599576dq1K2vXrqVFixbndc3hw4fz8ccfs3v3bq6//npeeuklZs+eTVJSEjExMU6Je+7cuYwZM4annnqKm2++mfz8fD755JPzvt6CBQsYPXo0TzzxBDfffDMVFRW8/vrrVmNCQkL4+uuvSUpKYuLEidx///0MHjyYxx+3b43w+uuvExkZyXXXXUffvn155513AL3/9qWXXsr999/PpEmTSEpK4rXXXjvvuH3JT4d/AvxzCZDhTPGZWs+rqv8TQgghhO8x9gmn56Y7PL89a7v564936CsXa0tIneX2Hrfz3JXPOf26uSW59Rp3ouAEAF/d/JXTY/A0mZkWdRo1ahSjRo2qdcyqVatqPOdo+cqtt97Krbfean597Ngxdu/ezbhx48zHhg4davfemu6TkZFh9TohIcFhX2bb69V3CXViYqLDZNz2ej179mTt2rV24+6++26r1wMGDGDTpk1241JSUvjuu+/qvI8/a0zfq63QoFCfLkQihBBCNGbZhfoKtLVH7X8X9KR/XP0PSircW8jWpJkIUAEEBQSx8bheXDWlaYpbY3AHmZkWbldcXMx9993Hl19+yerVq3nvvfcYPnw4ERERdkmnaDwkidTbSJSb/K/SpRBCCNEYtIvV27z+ruPv6hzrziXPjyx7hJEfjXT6dWtbTWeqNFGpVRKgAriu03UAfL7zc6fH4GmSTAu3CwwM5OTJk/zpT39i+PDhTJs2jY4dO7JmzZp6tdcS/skoTuGP+2kM/Vv3r/X8jlM7XNIHUgghhBCuZ7SKqqnK9agO1Ss9p/aZClTvLXalT377hP1n7Nt1Xaj4iJpjN/aCnyg4QURwBADPrHrG6TF4mizzFm4XEhLCV1/5354JcWHaxrQFan/K6cvax7anXdN2dY7bmb3TDdEIIYQQwtlOF50GYHf2brtzCREJpDZNdXdILhUUUHMqWVhWCMD/dvyProl6f+2Kygq3xOVOkkwLIbxC/9b9mT1iNk1Cmng6FJeYfOlkWkSdX9E+IYQQQng/o2vHtqxtdueeHvQ03ZtX95k2VuL5coIZE1p34WClFN/u/xbw7e+1JrLMWwjhFZYdXMYjy86v3ZoveH/r+yzcu9DTYQghhBDCRVpG6X2mr+14rd25p1Y+ZU4qAQKUnoZp+G7h1VJTaY3njFo4wQHB3NnzTqB+ybevkWRaCOEV1h9bD0BReZGHI3GN3Tm7WbRvUa1juiR0YfxF490UkRBCCCGcKTQoFHC8l7iwvNDq94BjeccA983WBirn16TJK82r8VxiRCIA9/S+x7xn2h/r4kgyLYTwChsz9bYJ7m7d4E7+/L0JIYQQjZ2RXO7N2evwvGWR0c92fga4ZxJhcu/J/GP4P5x+3dxi6z7Tqa+lcrLgpN24o3lHAVh6+1Knx+BpkkwLIbyKLy93ulDhQeGNus+2EEII4cuMAmQbjm/wcCTWHh/4OCPaj3D5fQ6dPcSXu74E9CXggSqQsKAwtpzYAkCzyGYuj8HdJJkWQngFf63i3RCVWiVlpjJPhyGEEEKI89AhrgMAv+/8+zrHhgeHA+75/Wf6j9P5/fy6Y2ooY1+0JeP7qqiswKSZCAwI5MZuNwIwb8s8p8fgaZJMCyG8wj297wH8N6kOCgjiqnZX1TpmW9Y2hxVAhRBCCOH9jFZRYUFhDs//rtPvzF/f1esuoPZezc7y8Y6POZh70OnXjQ2LtTt2cbOLATBV6n2mM85mmPtvv7j2RafH4GmSTIs6zZs3j44dOxISEkLTpk0BSElJYdKkSeYx77//PkopMjIyzMdmzJjBypUr3Rxt3RzF2hBKKWbMmHFe9503z/+eyDlLclQy4Pgppz9oG9OW5CbJdY7LOJvh+mCEEEII4XRGa6wdp3bYnUtqkkSLJv7VItO2oNhzVz5Hq+hWQHWdmM93fc6mE5sAaY0lGqHMzEwmT55M//79WblyJStWrADgq6++Yvr06bW+d+bMmV6ZTHuKJNO169OiD3NGzCEuPM7TobjEvX3uZWT7kZ4OQwghhBAuklOUA8Cu7F125x4b8Bi3XHyL+XVwQDAKRbmp3G3xOVtCRILV65mrZ3Ku5JzduBXpev5g0kxuicudJJkWtdq/fz8mk4k77riDgQMH0qdPHwB69epF+/bt3R5PaWnN/eyEb1t6YCnTlk3z22Xe7219j4X7pM+0EEII4a+MWdnru1xvd85Rn2kNjUqt0m3xOZttnZfyynLOFJ8BqvtoRwRHVC9pD3f9knZ3k2Ra1GjSpEkMHToUgGHDhqGUMi/ttl3mbctYqvv888+jlLJbGr169WqGDRtGVFQUkZGRjBw5kt9++83qGkOHDmXgwIEsWrSIXr16ERoayptvvglARUUFL7zwAl26dCE0NJQWLVrwyCOPUFJi3XooPT2da6+9loiICBITE3nooYfqnZCbTCaefvppkpOTiYiIYOjQoezcudNu3IEDB5gwYQLt2rUjPDyc1NRU7rvvPnJzq9sFDB06lNWrV/Pzzz+bfx7GzzY7O5spU6bQqVMnIiIiaN26NbfeeivHjx+vV5z+Yu3RtQDkl+V7OBLX2JW9iy92fVHrmM7xnbn5optrPF9RWcH83+ZLxW8hhBDCCwUHBAMQHRptd66ovIhPd35qfp2emw64b+lzTGiM0695tuSs3bGtJ7cC1ZW7J/aYaN4zbfzpT4I8HYDwXtOnT+fSSy/lwQcf5I033qB3794kJibW673r16+nX79+TJo0iSlTpgDQqpX+tG7JkiWMHTuWa6+9lo8++giAl156iUGDBrF9+3Zat25tvs6+fft48MEHmT59OqmpqcTF6UuAb7/9dhYtWsRjjz1G//792b17N9OnTycjI4Mvv9RL8peVlTF8+HCKi4t54403aNasGf/+979ZsGBBvb6HGTNmMGvWLKZNm8aIESNIS0tjzJgxduMyMzNp1aoVr776KrGxsaSnpzNr1ixGjx7N+vXrAXjzzTe5/fbbMZlM/Pvf/wYgOlr/h/bMmTOEhYXxwgsvkJiYSGZmJrNnz2bAgAHs2bOHsDDHRSz8zfas7QAUlxc7/BASMHvdbB7/4XFMlSZu63Gbp8MRQgghhIVzpfoS5x2ndnAz9g/HjX7LAF/t+QqA4opil8f158v/TKf4Tk6/rm2f6ZoYDw6WTVjm9Bg8TZJpN/nzn2HrVs/G0LMnvPpq/ce3b9+erl27AtCtWzeuuOKKer/XGNuyZUu79z300EMMGTKEb775xnzsyiuvJDU1ldmzZ/OqRZA5OTksW7aMnj17mo/99NNPfPrpp3zwwQdMnDgRgKuvvpq4uDhuv/12tm7dSs+ePfnggw9IT09n/fr15hiuueYaunfvXmf8ubm5vPLKK0yePJl//vOfAIwYMYLAwEAef/xxq7GDBw9m8ODB5tf9+/enQ4cODBo0iC1bttCrVy+6detGdHQ0FRUVdj+Pzp0789prr5lfm0wmBgwYQJs2bfjuu++4/nr7pUL+rDH3mY4Miax1P5FtoQ8hhBBCeA9jpjYtM83DkVibfOlkc6LvTLUVjS0qLyJABRAZEsnObH1lpytmxz1NlnkLt9q/fz8HDx7ktttuo6KiwvxfREQE/fr1Y82aNVbjU1JSrBJpgO+//56QkBBuuOEGq2uMGKE3ozeusX79elq3bm2VvAYEBDB+/Pg649yxYweFhYV2Y2+55Ra7sWVlZcyaNYsuXboQHh5OcHAwgwYNAmDv3r31+KnAW2+9xSWXXEKTJk0ICgqiTZs2DXq/P/DXvdINUVefaaPdRPs499crEEIIIUTtOsd3BuCGrjfUOdZYhWfsLXalF9a+wK1f3ury+4A+MQD6/ulKrZKggCBu666vpnt1QwNm9XyEzEy7SUNmhP3ZqVOnALjrrru466677M4bSaQhOdm+ldCpU6coKyujSZMmDu9x+vRpAE6cOEHz5s3tzjs6ZuvEiRMOxzp67xNPPMHcuXN55pln6N+/P1FRURw7doxx48bZ7eF2ZO7cuTz44INMmzaNl19+mdjYWCorK7niiivq9X5/8cBlD/D0j097OgyXiQ6NZlCbQbWO2XpyKycLTropIiGEEEI4k5EYG/2mbY3tPNb89YQeE9i2fJtbinJ9uP1Dl1zX0Uxz1wR9VauxF3zf6X3mvdJzf53L7JGzXRKLp0gyLdwqPl7/B+OFF17g6quvtjsfEmJdmMDR8pH4+HjCwsL46aefHN6jRQu9h19ycrLDgmFZWVl1xmkk8VlZWVx00UW1vnf+/PlMnDiRp5+uTgQLCgrqvIfl+4cNG8bs2dX/uBw6dKje7/cXCREJemVLPy2ulRCRQGx4bJ3jakum95/eD8DxvMZVnE4IIYTwBcZn+JaTW7iTO63OtYpuZddKytfZzqq3jWlL26ZtASit0Av+fr3na3OVc2mNJUQDhISEUFxsXVShc+fOpKSksHPnTvr06WP3X48ePeq87qhRoygpKeHcuXMOr2Ek0/369ePo0aNs2LDB/N7Kyko+++yzOu/Ro0cPIiMj7cbOnz/fbmxRURHBwcFWx9577z27caGhoXY/j4a8399d3OxiXhz2IslR9qsR/MHUPlMZkTrigq7RIa4DUN16QwghhBDe43SxvjrywJkDducevuJhiiuK+XKXXig3LCgMhaKkwndXITZvYr1i8/C5w5wuOm11TCnF6sOrAXy6DVhNZGZauEy3bt1YsmQJo0aNIjY2lhYtWtCiRQveeOMNxo4dS1lZGePHjychIYGsrCzWrVtHmzZtmDZtWq3XHTp0KH/4wx+48cYbmTZtGpdddhkBAQFkZGTw7bff8tJLL9GpUyfuuOMOXnzxRcaNG8esWbNo1qwZb7/9Nnl5eXXG3rRpUx5++GGef/55oqKiGDFiBBs3buTdd9+1Gztq1Cg++OADunfvTocOHViwYAHr1q1z+PN48803+fTTT2nfvj1RUVF07tyZUaNG8dJLLzFr1iwuu+wyVq5cyRdf1N5CyR8tO7iMZ9c8y6MDHvV0KC7x/rb36RTfiQmXTDjvaxgfuLXtqxZCCCGEZxgPux21uZz+43SKyosoLi/mhm43+EWf6XJTud0xo8+0sdQ9JjSGyb0n887md0hqkuTW+NxBZqaFy7z++utERkZy3XXX0bdvX9555x0ARo8ezZo1aygsLOTuu+9m5MiR/PWvf+XkyZP069evXtf+6KOPmDFjBl988QVjx47lxhtv5PXXX6djx47mfc0hISEsX76cnj17MnXqVO644w7atWtntRy7NjNmzODJJ5/kww8/ZMyYMSxbtoxFixbZjZs7dy5jxozhqaee4uabbyY/P59PPvnEbtxjjz3GsGHDuPvuu+nbt6+5ZdgzzzzDlClTeOWVV7j++uvZvn07S5curVeM/sR4aumoZ6E/+O3UbyzYXXtbtk7xnbjlYvsidwbjZ7TpxCanxiaEEEKICxeo9K4bEcERdueKyouA6pZYe3L2AHqhLndwxao2Yybe0rasbUD1rPX4i6qL+Tr6ufg6mZkWtbr66qsd7mHNyMiwej1p0iQmTZpkdWzAgAFs2uT4l/5+/fqxePHiWu+9atWqGs8FBATw0EMP8dBDD9V6jdTUVL799lu740YiW5vAwECee+45nnvuOavjtj+PhIQEh8u/bcclJSU5jCU8PJy33nqLt956q9b3+7u9p/XK5UXlRTQNa+rhaIQQQgghGsaYENh8YjM3XXRTrWMX79d/Dzb2FrvSkwOfdEmf6Qe/e7Be4/ad2QfA97d97/QYPE2SaSGEV2lsDxEsRYdGO1wyZUhpmgJA88i6K9ILIYQQwr3ySvWthFuztno4Ems3drvR4SzyhVoP6zpvAAAgAElEQVSyf0mN5/JK8whQARSWFbIqYxUA4cHhTo/B02SZtxDCK0ifab0wR6mp5ifU3RK7AZgrZQohhBDCe1zUTO8Ac1O32melAeLC44Ca22g505wNc7hroX1LWlcw+meXm/Q+08+uedZ87tnVz9b0Np8lybQQwis82l8vPKbhnzPTzSOb1/nhuvnEZtYcXlPjeaNno6nS/1pLCCGEEP4sPEiflb2q3VVAdZGy+AjX95n+aPtHHDl3xOX3AegY1xFw3AZr3pZ5bonBnSSZFkJ4hfiIeGJCY/x2hjoqNKrWwhtpmWlA9RIxRzLOZgCQU5Tj1NiEEEIIceGO5x0Hqj/TLbWPaw/A7zv/3q0xuZvxfTrqPOKPEyayZ1oI4RVaRrXkocsfomV0S0+H4hJTLp1iXtLlSH1aYxh7pv31ZySEEEL4stySXKD64belm7rdRJuYNubfBSKCI1AoisqLaBLSxJ1hulRWQRbNIpt5Ogy3kZlpF2jMBZSE5/j637tVGat47qfn6h7ooz7c/iGL9tm3VjNsO7mtzmsUlhUCUFxe7LS4hBBCCOEcRvupCT0m2J17ce2LfLv/Wxbs0dtk+kOf6ZHtR9odMwqdhQSG2J1LbpLs8pjcTZJpJwsODqa4WH7RFe5XXFxMaGiop8M4bz9m/EilVkl2YbanQ3GJ7Vnb+XrP1zWer0+fyXVH1wH63mohhBBCeJcApadWwYHBdueKK/T8YMFuPZnekbUDoNYuHs7UNaGr06/5wGUP2B3bnrUdgKQmSXbnYsNjnR6Dp0ky7WTNmjXj+PHjFBUV+fxMofB+mqZRXl7OmTNnOHbsGPHxri9i4SrGkqii8iLPBiKEEEIIcR6MPtMbjm2oc+zSg0sBau3i4SzPXfkcM4bOcPp152yY06Dxi/+w2OkxeJrsmXay6Gi9HHxmZibl5e550iQat6CgIMLCwmjTpg1hYWGeDueC+WNxivqoT+/oDnEdAGgR1cLV4QghhBCigYwJgb2n93o4EmvDUodxsuCk06+78tDKGs+dKT5DgAqwWsbuaMbe10ky7QLR0dHmpFoIUT9K+WcV7/oyiovVpnNCZ0AKkAkhhBDeqFtiNwDGdh5b59ikJkkczD1IcIDrE8yX173MmsNr+H0X11cSbxrWFKjuM23pL8v+wv9u+J/LY3AnWeYthPAK0wdPB3y/kFpNUpqmMPGSiTWer88+otIKfSmYu/ZXCSGEEMI5jOJb13S4BsCc2Lqjz/SC3Qvc1lazfazeGstRn+lPd37qlhjcSZJpIYRXiAuPo1V0KwIDAj0diktEhUQREVRzn+n6FF47nq/3r8wvy3daXEIIIYRwjiPnjgDwy7Ff7M6lxqYCcH2X690ak7sZq+gqKivszlVqlbyz6R13h+RSkkwLIbxCRHAE47uNp01MG0+H4hI3druRi5tdfEHXaBmlL+/2x9YSQgghhK87V3IOgGP5x+zO9WvVjytaXWHe1hYZHIlCmdte+ovjecdrPT9l8RTOFJ9xUzSuJ8m0EMIrrDu6jtd+ec3TYbjMgt0LWJ6+vMbz27Lq7jOdV5oHyMy0EEII4Y2MPtN397rb7tzrG19nw7ENLNy7EKjuM71w70Kf3eJ2bcdr7Y4Zy8nDgmouiuvLvbVtSTIthPAKPxz6AZNmIjM/09OhuMS2rG18s/ebGs/X54NlY+ZGALae3Oq0uIQQQgjheiUVJQAs2rcIgLTMNAAmL57M4n2ubxnVt0Vfp19zWr9pdsd+O/Ub4LjPtCE8KNzpsXiKJNNCCK9gLAsqLi/2cCRCCCGEEA2XW5ILwJrDa+oc+2PGj+avA5RrU7JXRr7C04Ofdvp1n/nxmQaNf3HYiwDctuA2p8fiKT6VTCulblFKbVZKFSiljiul/quUamEzRimlnlRKHVVKFSul1iilejq4Vjel1A9KqSKlVKZS6lmllH9WPhLChzTWPtP16R3dNaErUL2MTAghhBDew+i2cSTvSIPeV9ssrjP0SupFfqnzt4j9fPTnGs+dLDhp95CgbdO2ALWu1PM1PpNMK6XGAJ8A64CxwGPAYGCxUlb/Sz0OTAdeAq4DCoAVSqkki2vFAisArepazwKPADNd/50IIRwx/sH11X1DF6o+hdfax+ntJlz9oSuEEEKIhuuS0AWA0R1G1zm2dUxr89flla5tefnSzy8xZfEUl97DEBceB+jVvG23sP3hyz+4JQZ38plkGrgV2Kxp2p80TftB07SPgAeBXkBnAKVUGHoy/YKmaa9rmrYCuAk9af6TxbXuBcKBcZqmLdc07W30RHqaUirafd+SEMLw/FXPezoEl2ob05Y7LrmjxvMxoTF1XsOo+GnsuxJCCCGE93G0yq5TfCfz14v3LbZKuI+eO+rSeL478B2F5e6pGp7SNAUAU6V9n2l/5EvJdDBwzubY2ao/VdWf/YFo4DNjgKZphcAi4BqL910DLNU0Lc/i2Hz0BHuIE2MWQtRTTFgMXRO6EhwY7OlQXCI5Ktn8tNaRkwUn67zGqcJTgOPejUIIIYTwrENnDwF6hxJb7Zq2IzQwFIDrPrnO6lyHuA6uD85NLmp2EQAmTZJpbzMPGKSUmqiUilZKdQKeA37UNG1X1ZgugAnYb/Pe3VXnsBi3x3KApmlHgCKbcUIINykzlTGozSDzE01/M6TtkAvuD928SXMAmkU2c0ZIQgghhHCigrICAId9lFOappj3DANEh7p2MeyenD0s2L3A7W2ojpxr2H5xX+czybSmaUuAScA76DPUe4FAYJzFsFigQNPsHoXkAhFKqRCLcWexl1t1TgjhZmmZaby39T1Ph+Eyyw4u46cjP9V4fnvW9jqvkVusVwk9W+Lony8hhBBCeJLx0PzOnnfanXt/6/vsO70PgJHtR1oV53JFy8sFuxdww2c3uH01m7GKLiI4wq339RSfSaaVUlcCbwOvAVcCtwBxwFc2VbgdVS9SDs7VNM7uuFJqslIqTSmVlp2dfT7hCyHqsCJ9BeWV5X77RHPLyS3m3pLny/iw3XZymzNCEkIIIYSblJpKrV6vO1a9FNzVBcgCVABXtbvK6de9q9dddsd2ZesLhi90NZ6vCPJ0AA0wG1ioadpjxgGl1Fb05dpjgQXoM8tRSqlAm9nppkCRpmnG39TcqmO2YnAwY61p2jvoM+L06dOncZYaFsLFsov0B1VF5UUejkQIIYQQouGMPtMr0ldwfdfrax279sha89f1aY/ZUK9seAWA0opS3rr2Ldo1bef0e7y75V2nX9PX+MzMNPpeZqs1EJqm7QWKgfZVh/agL/223cVvu0d6DzZ7o5VSrYFIm3FCCOEyFZUVzPppFsXlxfVqjdW9eXegfm20hBBCCOFeRnvPU0Wnah2nlLJ67YpaKJadP9rGtCU9N93p96jN0byjBFotHvZPvpRMHwZ6Wx5QSnVFr8CdUXVoHZCH3g7LGBOB3m/6O4u3fgeMVEpFWRy7GT0xX+3swIUQdQsLCgMaV5/p97a8x1Mrn+Lva/5Oq+hWdY5vG6MXLpECZEIIIYT3MfpMX93u6lrHfX/ge6uZYle3vJy1dhb3f3u/S+9hSIhIAPTWWI2horcvJdNvAzcrpWYrpa5WSt0GfI2eSH8LoGlaCfAi8KRS6n6l1DDgc/Tvc67NtUqBBVXXmgzMAObYtMsSQrjJjCEzAMe9Gf1Bm5g2TOo5yepYcUUxoFf/jAqNcvAua+dK9e6AshReCCGE8C2d4ztbvb46tTrhzinKcfr9JvSYAEBQQBBrDq9xW2LbOro1gNuriHuKLyXT/wLuB4YD3wD/QF/2Payql7ThReB54AlgMXrf6eGapmUZAzRNywWGoS8JXwTMBF4B/ub6b0MI4Uh8RDyXtbzMPEPtbzrEdaBFE+s9UU3D9NINiRGJZOZn1nkNo5p3YID/L5sSQgghfM3+M3p33jVH1piPVVRW8Njyx0iOSq6xKFf72PYOj18II6m1rBruDpckXQL47+SILZ8pQKbpaz/fqvqvrnHPV/1X27hdgPPL2gkhzkt2YTYd4zqSGpvq6VBconuz7gQHBlsdG3/ReAa0HkByVDKbMjfVeQ1j6VR8eLxLYhRCCCHE+TOWa1su295yYgv/WPcPoPpzHCA2zPXdeBWq7kFOlnE2g55JPd1+X0/xpZlpIYQf25a1jS92feHpMFxm7ZG1pGWmWR0LCwqjfVx7IoIj2J2zu85rnC4+DbhmOZgQQgghLoxR08RYYg0QGRJp/tr4/L469WqrImS/Hv/V6bGYNBMamttXs53IPwFAVEjd29f8gSTTQgivsDx9OaWmUg6cOeDpUFxi04lNLNm/xOrYt/u/Rc1U/N+m/7Mbv/PUTh5Z+ohVQbbfTv0GwI5TO1wbrBBCCK9mqjQ1qoKdvqywrNDuWKVWyY8ZP1q9dqWwoDDGdB7j9Os+dPlDdsf25OiNkZKaJDn9ft5IkmkhhFc4W6K3eHd1RUtvYjw4cJQcj5k/hjkb5nCy4KS7wxIu8MC3D6Bmun+5nRDC/5wsOEnQ34N4O+1tT4cibBi1TZbsq354nnE2w25caGCo1Wx0y+iWTo/l5XUvA1BcXsx/rvsPf+n3F6ff47VfXnP6NX2NJNNCCK/SGJ+0a5pm1SIDYGqfqQA0CWliPtYrqRcAKU1T3BabcI7XN77u6RCEEH7C6Bf84fYPPRyJsBUUoJejyitrWHMgV9RCsZztjg6NdslScv1GCjbdDSXRVofTc9PdXvzME/z/OxRC+ASjsnVjqf4I1oVBWkS1cDhmzvo5PPz9wwDmXtRSgEwIIRovo+tF65jWHo5E2OoU3wmAoW2Hmo85+nz/7sB35rHg+paXf1/zd/6y3Pkz0wCc6g6L/g8W6ysljH3jlVplo2iPJcm0EMIrPDXoKU+H4FKto1tzZ887azxvOQMNsHDfQgBWZqxkWfoyoLoAWX5ZvouiFEII4e0SIxIBGJE6wsORiPqICYtxeHxwm8Hmr13xuX5P73sAvZ3mxsyNTr++2cmqyt3HLwOqHx40hkQaJJkWQniJxIhErmp3FZHBkXUP9kE9k3raLeWOC48D9A+eY3nHrM4FKr365prDa9iVvQuAgrICAL/9GQkhhKhbclQy6Q+mM/6i8Z4ORdgwOnOsOrzKfMyyANnFzS52+D5XtAVtHtkccEN7rKVz9D9z9V7ZvZJ7ufZ+XkaSaSGEV9h/Zj/RodG0j2vv6VBcIrlJst2T5/EXjSfrL1lM6zeN8spyq3OjO462u4axFD423PW9KYVzWfYWFUKIC5FVkEX/ef1ZvG+xp0MRNspN+md5TQms8XAcrD8XXFEvplKrRKFcv29Zs77+4bOHXXs/LyPJtBDCK+zJ2cPSA0s9HYbLbDm5xdzayhAcGEyzyGaEBoXWqyXYqcJTAFLh2wdtmbKFjfe4cJmdEKLRyC3J5WTBSdYfW+/pUISN+Ai9psmt3W81H4sOrS7MZSx9vjLlSqv3rTu6zumxVFRWoKGZi6K5TMwRq5dH844C1RMA/k6SaSGEV1ievpziimKrp7b+ZGPmRr478J3VsYV7F6JmKl7bYN9a4l+//Mvu2N6cvYDeg1r4llbRrejToo+nwxBC+IG8Ur1SdFpmmocjEfVh/O9lSUNj6UHXTiBUVFaY7xUTGsNt3W9z+j0e7f8o9Jqnv+iyAIB9p/cBdfeZ9pfuLZJMCyG8grGnqLSi1MORuM+Rc/rT3P1n9tudiwiOAGBgm4F0Tejq1riE8935zZ3SZ1oIIfyc0Wf6qz1fmY8dPme/7DkyOJItJ7eYX7uiMvsrG14B9Erh9/e9n493fGy1f9sZXl73MvzyYNUr+884aY0lhBBuolTjTTQ0TaNjXEerY5MvnQzo7TWuancVAH1b9gVcU6hEuNb7W9/3dAhCCD/TmFpJ+gqjbZmxdxrAVGmq832uXhK9LWsbANlF2c6/eNND+p9BJVaHd+fsbhQVvSWZFkJ4BaMvYWP65cCyQEnzJs0djik1lZo/lJObJAONZx+SEEIIe0ZHB3mw6n06xusPxvu37m8+9tcVf7Ubt2T/EroldjO/Nrp1uIKmaSzZv8Rl16dphv6nKRioe3m3v5FkWgjhFR7t/6inQ3CpVtGt+GPPP9Z4Pjwo3Or157s+B2DDsQ38fPRnALIKswA4V3rORVEKIYTwdkYV6KFth3o2EFEvtq0vDf1a9TN/vfzgctJz05163wcuewCwXmrtkgKmFfpsPKYQoLoll7/sia6LJNNCCK+Q1CSJsZ3HEhMa4+lQXGJgm4F0TbTe+2xU/WzbtK15/7TBqP7505Gf2JmtFxwrqdCXUMWHx7s6XCGEEF4qqUkS2Y9mM+GSCZ4ORdgwunb8cOgH87GBbQaavx7cdrD5a8vVaXM2zKH9v5zbGtRoo2m5jc4ldWmMZLpSn5lubMU2JZkWQniFtMw0isqL/LbPdHBAMBlnM6yO3dTtJoqeLOKRfo9QZiqzOjes3TC7a0SFRAHWbTaEb2gZ1dLTIQgh/MTx/OOkvJrC5zs/93QowoaxP9pYig8QEhhi/nrN4TXmr43tbQbLRNsZKiorUCjXt8aymZl2VHDNn0kyLYTwCum56S7ps+gt9p7ey8Hcg1bHAgMCCQ8OJzAg0C7RdlS0w1ieVdOSMeG9tkzZwu77d3s6DCGEHzhXco7C8kK//sz0VXHhcYD+sNywKmOV3ThHiXPn+M5OjaXMVIaGZpXMJ0YmOvUegF0yfShXL0hmrL6rib8UnpVkWgjhFZanL6ewvJAtJ7bUPdgH/Xr8V74/8L3Vsa/3fI2aqXhx7Yt241/7xb73tJGM786RpMzXJEYm0iWhi6fDEEL4AaNY1dasrR6ORNSH5d5oS4v2LbJ6veHYhlqvk56bzumi0/W+b3F5MWD9cN5yxtwZHr7iYYtkWl/mfeDMAUD6TAshhFsZy5zLK8vrGOk/MvMzAez2S0N1gZkhbYc4/Wm1cL+bPr8JNVP5zS8PQggh7OWW6H2m5++cbz7m6DM+OjTavL/asOPUjlqv3f5f7bnly1vqHcubaW8CUFhWyA1dbwDApNXdpqshXtnwChyt2hNuCrE6Z6o01dpn2l/aZkkyLYTwCpaFOBobTdPsipMZlb+Hpw5nTOcxQHWrjQ5xHdwboLhgX+z6wtMhCCGEcLEmIU0ArPYpH807Wq/33tXrrlrPt4lpQ6voVucVV15pHuDi37WqCpAZpM+0EEK4UeuY1oD/LPupD8sPtcQIx/uYMvMzzXuljTFGITLhexpTH3UhhGsYCZusWvI+xsPuvi361jpu8b7F9Gjewx0hoaGxPH25629UNTPdIqqF6+/lRSSZFkJ4hT9f/mdPh+BSLaNa1vrU2bJACMBHOz4C9D1xW0/q++KMwmPGMjIhhBCNj1HYyVitJHzTpcmXWr1+d8u7FJYV1jj+yLkjvL/1/Xpf/9H+jwLWD+5d0mc6oKobSVUybWxTayyTI5JMCyG8QouoFtze4/Y6qz/6qpHtR9IzqafVMaOqZsf4jnatJJKbJAOw7ug68z4qY7mUtFkSQojGK7lJMiVPldS5LFi4n/Hwe9nBZeZjA1oPAPSH5pY9mB3tJzZqqThDRHAEYF012+l1aTSgsmoyoKoA2eWtLnfuPbycJNNCCK+wIn0Fh3IP+e1+4LyyPDad2GR17MZuN6L9TWNav2mUVpRanRvUZpDdNcKDwwGIDHFuNU7heqmxqZ4OQQjhJw6fO0zY82H8d9t/PR2KsGHMAltu3YoK1bdmlZnKSMtMMx9v3qS5S2MpM5WhUHYr35zl6LmjUGnRw9roM322fn2mpTWWEEI40fH842zP2u7pMFzmWN4xuyfOpkoTBWUFVFRW2PWOLjVZJ9dQ/cTa6OEofMfmyZs59vCxWiubCiFEfRjFpH4++rOHIxG2YsJiALi+6/XmYyvSV9iNM2arG6J1dGvu7HlnvceXVpSioREaGGo+1jzSeQl8m1fbQGVg9YGqZHr/mf0ANItsVuv7/eXz0D++CyGEz9t0YhP5Zfl8u/9bT4fiEhuObbBa9gXwzd5viHohilk/zbIb/3ba23bHjKe9+07vc02QwmViwmJoGS3L84UQF66ovAjArrWS8E62e6NBn5V1dZeHc6XnAOsWVMbSb6exnJmuqua95cQWQJ95r616uL9U+pZkWgjhFYwnlEa/6cYgqyALgBP5J+zOGdUwh7QdQqf4Tm6NSzjf6I9Ho2YqTJXO7fEphBDCe+QW6wVCLZfg2648A4gNi3X4YLy2jg9H847y5e4v6x3LvC3zAMgvy2dCjwkAVFRW1Pv99aJVzUwHFZtnpp/76TkAyk3lBAYE1vROv/k8lGRaCOEVjKeXjaX6oyUNje7Nu1sdMz74ftfpd9xy0S0ADEkZAiDJtQ/67sB3ng5BCCGEizUNawpUty8DfRtbfdXWRzqlaQq/7/L784rL6HXt9PaMlRbJdGUwlpfflb3L+cm7F5JkWgjhFYwEsTH14bUsvhEXHudwzI5TO9iZvRPQn2SDC5ZpCbdpTH+/hRCuER0aDcBFiRd5OBJhyyg2adu9w9aifYu4tIX18u+R7UcyZ/0cp08qaJrGqoxVTr2mmbHMO7hY/7Oqovc3e75xzf28kCTTQgivMLXvVMB/Z6ZbRLXg7l5313jethDHu1veBWBPzh725OwBIONsBgCni0+7JkghhBBeLz48nuCAYC5reZmnQxE2GlKhunsz6xVpSw8uZfqP082f9bYyzmbw323/5ci5Iwx5fwg/pP9Q6/WfHvy03TFH28ouiOUybzAv9Y4OjW40D48lmRZCeIX48Hju63Of37YQuqHrDVzR6gqrY0Yv6R7Ne9i1kmgf1x6AX4//ap6ZNhJuf20fJoQQom6JkYmc/utp/tjrj54ORdgwWl99f+B787GBbQY6HBsUEOTweGhQqMPjhjPFZ1hzeI35QbuleVvmmQvTGde3TPBNmpP3KRvLvI2Z6aoiZDV9b/5IkmkhhFd4b+t7rD+2nl7JvTwdikscOnuIpQeXWh0b22Us2t80pvadSklFidW5y1tebncNo1ekZZsLcX42Ht9IxPMR5iJwrtY1oStArZVNhRCiPg6fPUz0i9H8b8f/PB2KsBGo9OSybUxb87H48HiHY2tqU1VaYd8a09KRc0cA7H6nALhr4V10f0uf8S6pKEGhCAsKqzvw82Us87aZmU7PTa/zrbUVJ/MlkkwLIbxCdmE2+0/v95tWCbbOFJ8htyTX6lhFZQUn8k9QXF7MyYKTVufyS/PtrmFUBDV6OIrzN2fDHIorill5aKVb7rd5ymbOPnbWb355EEJ4jtFnemWGe/79EvUXFRoFwHWdrzMfc5T02q5Us5RdlO3weKvoVvyxZ92rEYxVbyUVJWhoVsm00SnEaTSbmemqPdPH8o7VeS/pMy2EEE60O2c3heWFLu+76Cnrjq5jRfoKq2ML9y6kxZwWPP/T83bj39v6nt0xoyLogTMHXBNkI9KzuV4cJjkq2S33CwsKIyYsxi33EkL4N2Mlk6NlvsL72O6NBn0G++MdH1/QdR3tSY4MjuTW7rcCkFOUA1i3w3L6LHUNM9PtYtvRLLKZeabeEWmNJYQQLuCvBcgcyS7MtvrTUkrTFACuTLmSjnEd3RlWo2DsSU+ISHDL/Qa/Nxg1U1FuKnfL/YQQQrjf2ZKzAPzf5v8zH3PUGis+Ir7GQmMxoY4fvB7LO8YXu78gPCgcqO7wURMjWc8vzee+PvcBOP8zyHbPdFUy3TKqJSUVJbWuxvKXtlmSTAshvIJRIKOxVH+0pKHZtdG45WK9t/S4ruO4u7deBfzq1KsB6Bzf2b0B+qFAFejafWQ2fjryk9vuJYQQwjMSIxIBSAivflCbmZ/ZoGvU1P6yXdN2jO081vywfWT7kXZjCssLzQm9pe1Z2wGcv5XOtpp3VQGyovIidmXvosxU5tz7eSFJpoUQXsFYBtWYZqYtK2w2DWvqcMzqw6vN/SGjQvS9WO5MAv1ZSUWJ25eZNcaHRUII5zI+Ly5pfomHIxG2jET34mYX1zpu4d6FXN7KvtAoUGMCWmoqpbyynKQmSfxv3P8Y0GaA3ZjwoHDiwuOA6mJoGho/H/25vt9Cw9j1mdZnpree3Oqa+3mhC0qmlVKhSqneSqkeqiGN1YQQwsY9ve8B/DfZqKvPtO3T4jc3vglY95k29krXVJxECCGE/4uPiCcuPM5h1wfhWcbvMPX5XaZLfJcGXTszP5P5v80nuyibl9e9zL7T++zGFFcUs/rwagCeGfKM3fkTBU7uM13puM/04LaDnXsfL1ZnMq2UukQptVgptVcp9ZlSqn3V8RHAYWAjsAVIV0o5bqQmhBB1iAuP46/9/2puIeRvJvaYyNCUoVbHWke3BvS9VR9t/8jqXI/mPQD47dRvHDp7CKhujVXXE29Rt+XpywGpjC6E8D1Nw5qy474d/KH7HzwdirDx6/Ffgfr1mTY+023VtMzbkFucy5aTW2osQGfE4IjLlnnbVPNuTHOstSbTSqmWwGpgNNARuBFYppRKBT4DNOAbIA1oCyyueo8QQjTIa7+8xtd7v/bbPtMbMzfyyW+fWB27puM1rJm0BoBfM60//Hol2f8cjEIeQQFBLoqy8ThXeg6ou5+nsxjLMf2lFYgQwnOOnjtKyzkt+Xzn554ORdgwEmTL2iY1tYhKapLk8HhxRXGt9zD2YBsPhWtSWFaIQhEZHFnruAtSQzXv/afrflAdHBjsqqjcqq5P9WlANHAP0BSYiJ40/w/YC3TRNG2cpmmXA/dVjX3QdeEKIfxVbnEu+07vc1ty424FZQUUlhcCemXNclM5ZaYyc39pywT5TPEZnlz5pN01jpw7AsDu7N1uiFg40+YpmzE9Y5IHIUKIC2Y8DPz+4Pd1jBTuZiSuozqMMh9buHeh3bi+LfrWeI1ThafsjhWVFwHQIa5DnTG0jWkLVPeZDg0KNWs9c28AACAASURBVJ9rFd2qzvc3iHmZd0nVaz1BzszPpE1Mm1rf6i8Pl+v6LoYDqzRNe1fTtDxN0z4CfgD6Ak9qmnbOGKhp2r+BHcAIl0UrhPBbGecyAPhs52eeDcRFfjn+i7mQWPSL0YyZP4aFexcy/ovxgHW1z6yCLIfXMBJvY9m3OH+9k3oD1Plh7yyaplFRWdGoCuwJIVzDKFB18MxBD0ci6sPR9rXgwGDmbZ1X72sUlukP443aKeC4YGtUSBTjuo4DqvdHW7bDCg0MtXvPBTEv89aTfWNmOjU2lYSIhFofIDeW1lht0fdDW9pe9edmB+M3AKkXGpQQovHy1wJktmLDYjlTfMb82nKPU3RotPnrq9pdVa8n0aJhjCTaqHrqav3e7Ufoc6GUmvxz5YUQQojqVQNzf51rPuao6FdiRGKNLbMc9ZnOK80zfx0Zos9+J0Ym2o0zaSbzvugvd39pjunR/o8COP8zqIZq3s2bNKewrLDWhNnpPa89pK5kOgCw/U4LADRNy3Uw/jQgPVuEEA2mqOoz3Qhm7mJCY2gW2czq2LB2w8xfm7Tqdk1/uPgPPHT5QwBc0+EaALokNKwCqLAXFBDk1hZjGzM3uu1eQviCub/MZd6W+s/MCXuN5eGzL0lukgxUFxiF6lVl9RUeHF7reaP91pUpV9qdKyovorjcfs+1UeHb6e0gbZd5VxUgKygrYHdO49iSVlcynQM0tzl2DKipTFxz4EwN5y6YUipIKfW4Umq/UqpUKXVMKfWKzRillHpSKXVUKVWslFqjlOrp4FrdlFI/KKWKlFKZSqlnlapqyCaEcDtj/1Bj+OXgXOk5fsz40fwAAWBM5zHmry2fVi/YvcD8dNmo8Bkc4B9FOzwpQAVQUlFSYz9PIYRrPfj9g9y18C5Ph+GT4sPjAeiT3MfDkQhbxqqnuh56f7P3G/q37u/wnKPaMZbVsZtHNmfRHxY5TKZDA0OJDY8F9J7ThtoqfF8QzXFrrMZU26WuZHoXYNURvmr/dL8axndCb5flKu+hFzj7J/re7McB28cvjwPTgZeA69Bn0lcopcwl85RSscAK9GrkY4FngUeAmS6MXQhRizt63gH478x0cpNkcy9tgO1Z263OW354RoVEmb/ed3qf+UPJeMqbVeh4T7UQQviKy1peZlWkSdRfbHgsbWLacHkr6TPtbYxlzeWVdS9hbh/b3uFxy4JhjmTmZ/Lw0of57dRvdudKTaUsO7gMgMcHPu7wvU5lu8y7qgDZ4LaD/fb3OVt1JdOrgCillONGaBaUUu2A/sBPTojL0fVHAbcAV2ua9m9N01ZrmvaRpmlPWowJQ0+mX9A07XVN01YAN6EnzX+yuNy9QDgwTtO05ZqmvY2eSE9TSkUjhHC72LBY/n7l3/22NdbUvlMZ0b66PmOHuA60i21nfv3Btg/MX1vumT6Ye9CcPBtVQmurAirq59v93wL6wwohhPs1ll+0XaFJSBOW3b6M67tc7+lQhI20zDQAfjj0g/lYTX2ma9pqZDmjbGga1tT89dmSsxw4c6DGZdRbTtqWu3KhSscFyBqTWpNpTdNe1jSto6Zp9VkHZwLGAf9ySmT2/gis1DRtVy1j+qO35zKXA9Y0rRBYBFxjMe4aYKmmaXkWx+ajJ9hDnBaxEKLeZq6eyb83/ZueSXa7MvzCsoPLeHPjm4A+83xdp+u4OvVqc5/pjvEdzWMt90wL1zDalNVn9sAZBrQeAECg7CYSAtDrCHx/QFo7nY9jecfo8kYXvtn7jadDETaMPtPdm3U3HzP2ONuqqc90QVmB3THLXtFG6yzLhN2RvNI8AlSA1Wo3p6thmfeu7NrSNV1dM/C+wmkNvjRNO6Jp2jeaph111jVtXA7sU0q9rpTKq9rrvEApZdkJvQt6Um/bKXx31TnLcXssB2iadgQoshknhHCTovIijuUdc/gh4g/OlZ4z94nML8unsKyQkooS9p7eC1j3ma6pNVbG2QzAfom48H5r/7gW7W8awYGy310IcWGMys6STHsfo3jY1alXm499sesLu3G9k3vXeI0eb/ewO2Y8ZDd6SNemU3wnoKrPtGbdZ7qmxL6hzCtLjGXeNgXIsgqzSI2tvcFTY+kz7U2SgElAT/Tl3ncClwJfqepd+bFAgabZTevkAhEWy9VjgbMO7pFbdU4I4WZGtcv5v833cCSusT1rO78c/8X8+p3N77Bw70LuWaTvoz5y7oj5XEyYfVsMgJyiHLux4vwYS+Xr+rB3luLyYk4XnZalrUJUuaLVFQxPHe7pMHyS0VJIPgu8j/FvvOW/9R3jOtqNCw0M5a20txxew3jw7ujY4XPVpakcfZ5Eh0YzusNoQP/7oaFZ1WRx1gNdc7HYGpZ5t49tT3xEfK3X8JcCoA1KppVSAUqpB5RSG5RS55RSFRbneiml3lRKdXJ+mPotqv4bq2nat5qmfQpMAC4DrrIY5+g3FeXgXE3j7I4rpSYrpdKUUmnZ2dnnFbwQon4aS7Ix8ZKJnCs5Z35dU5/pYe2G1VikRJy/5Ci9fYnlPjRX6vduPxJeTqC4wr5liRCNkUI1iu4NonHJL8sH4JUN1c2GHLXGat6kufkBeX1Y/r5g/I5gtOGylFeaR0mFPku8eN9i/b2l55gxZAaA+ZzTmJd5VyXsVcn0meIzVr2xHWksfabNqmZ1lwOvAu2BfLDo6wKH0Pc13+bMAC3kAjs0TTttcWwtUAZ0sxgT5aDFVVOgSNO0cotxjn6DisHBjLWmae9omtZH07Q+iYn2DdKFEBfOWGDSGH65ig2LJSbUevb5uk7Xmb+2/ICZ1HMSTw16Cqhun9U1sasbotSXlTfkw96XBAcEExYURqVW6Zb7bcva5pb7COEr1h9bz4r0FZ4OQwinahXdCtCLjBqyi5w7EWe037q81eU8uuxRzhRbdyV2VNjM2BJgVBt3GmOZtzJBYKm5mve9S+5tNO2xGjIz/ShwJXrV6+bAfyxPapp2FljD/7N33uFRVOsf/056QhJSSULohN6LVJEmRURRFEURseHVa+/l572IyhUVBLuAIBaQJiBVOiK9IwQCISSkQ3rflM35/TF7Zmd2Z3dnNzvbcj7Pkye7M2dnTsruzHve9/1+gXF2m50UU38RDgC9G0oC4A0gwWCMYY90Egx6ozmOawmgicE4BoPhIIa1GubsKTiMIk0RdqTskPhGissdxdYVS04vwaJTiwDohU2UiFgduH4AMzbOQEFlgcWxpmj7RVs0n9/c8kA3xNfbF5o6jf1X6RkMBkNlooKiAACD4gc5eSYMQ+SCaTk2Jm3Eba1uk9234cENZl8bExyDfTP2oaKmAvOOzMPrO18X9vl6+Qp92+KWMarwbffqP1rm7VUHeNUyNW8LTANwiBDyASGkHvJl0qkAWtllZsZsAdCT47go0bbbAPgCoEv+hwGUgrfDAgBwHBcE3m96u+h12wGM4zhOLG/3IHjP6r/sP3UGg2GJqd2nAmg8Zd6GlkxUXRqQlnknFyQL9heJeYkAlK1yJxck4+dzP8v2XlmDo9SuGQxG46JfXD9M6DDB2dNwS8IDw9EtuhsGtWDBtKtB+5OVLNTSDLMh1LpRjHjxPa04DQ/9/hCOZh0FIO09rq2vxabLmwAArw1+zeg4ciXnDYKWeXtpAe8aQYAsLjiuUVQaAtYF020BHLUwphBAhO3TMctiAAUANnMcdxfHcQ8D+AXAbkLIQQAghGgAzAXwLsdxz3EcNxrAWvA/51eiY30PoBrAeo7jbuc47mkA7wP43MAui8FgOIhQ/1B8PvZzDGk5xNlTcQjdm3WXrFwvO7NMeBzsFyw8zinPEfqOgnyDAACLTy22ePztV/n1Q7tfOD2EjUkbATCfaQbDmXCSbkGGUgJ8ArD8nuUYnzDe2VNhGHAq5xQAvjqMYqryjl7TDVlyeonRNvEie4mmBLnludDW83rLhkJ+dOFdTYT3rqTMu0bITG96aJPqc3AVrAmmqyDfZyymFeRVshuMLsgdBb7feRWAbwDsAfCAwdC5AOYAeAd8NjsUwBhCyA3RsYoAjAZfEr4ZfOn6AgCz1Jg7g8GwzFu73sL/Dv4PPWJ6WB7s5jTxbYJx7cdhRJsROPj4QQBAr9hewn5LPU1KLpQ0iK7WVlsYaZrPx36O1fevtvn1rgz9vdCbEbW5I+EOAFILNAajMXMq5xS2Jm919jTckqzSLNyy5BbZDCbDudB2rH5x/YRtnSI7GY3z8fIx6TMth9grmvZIXy28itcGv4Y+cX1kX1NUVQRvzttIo8UeCJlycZm3t77MW4mFJy1Hd3esuaqfBTCW4zg/QoiRljnHcU3B90sfttfkDCGEXAVgtiaI8DWic3Rf5sZdhFQFnMFgOJG6+jrkV+ajsKoQEYFqFbi4BhW1FSisKkRVbZWg4i32WzQl+jVrP7/e5ygv7lcGv+KQ8zQGtk1jN70MBsM+0GqlNRfXYHqv6U6eDUMMFf8a3ma4sG3lhZVG47pFd7PquFQss3mIXsfE28sbccFxCPSRBqXdm3UHoPOZhtRnun2EfdxBjHymaZm3ToDsyU1PovDNQhOv1r2kEfpMLwHQEsAKjuNCxTs4jgsDsBy8R/P3dpsdg8FoNBRq+A/d387/5uSZOIYfz/6ITZc34dWdrwIAUgpThH2mFhMu5l1UfHx6QW9IGeXk1ZPxzu53bH69KzMwfiAAoGOkWm6OUko0JUgrTnOYejiD4erc2upWjGwz0tnTcEu0hK+oySnLcfJMGIbQIFPs7dwmrI3RuCDfICw8tlDxcan+iVigNKMkA6/veh1nc88K28ICwjCqDZ8rTClKQT2pl/RvKxEwVYJwLSOmBcjCA8PNHkP8O3JnFAfThJDfAPwI4D4AeQCeBQCO404CyAEwCcC3hBC2/M5gMBgKEGeYT+ecFh6H+OvLuW5vdzvahbez+tiP9HwEABAfGi9sS8pPQsevOiq2u9qQtAFzD821+tzuAFXDFaudqsngpYPR9ou2DRaEYzA8BcOFvvWX1mNt4lonzYbBsA9UTPSr43qpJrlFj9jgWIs+zGJKqvU+02EBfNct9bSmtlcAUKwpFsbuurZL2DZ/7HwAsP81iJZ5c1IBMgBGll2GiIXT3Bmr8uuEkCfBe0lfBBAN3paqL4CrAJ4khLxg9xkyGIxGRWNRfzRkSjfBhECyWvtknyfx4cgPrT5eZGAk+sX1E/q3AOCTQ58guTAZmy9vbthkPYAAnwAE+ATY33PTBFSRncFg8Pyd/jf2pe0Tnt+35j48sM5QBofBcC9oFlrcJ12kKbLrOVo2bQlAX2FleN8kLgWnLD+7HICKPtNeUgEyALicf9m+53JRrC5WJ4QsJ4T0ARAMoAWAEEJID0LIj3afHYPBaDSMbTcWQOOxxgKkVhditc+ccv0q9udHPsfnRz63+tgBPgFo1bQV/L31vVJ3drgTABqFyJsl/L39oanToKKmwvJgO9KY/r8ZDIY6NGvSDABfKs9wLWgg2zqstdlxG5I2COXYhmyftl12O6VZk2Y4OfMk7ul8j9E+b85b6EWm/ycAcP7meQCwf6sRLfPm6iUCZI0Jmzu/CSFVhJBsQohj70QYDIZHMrnLZACNNzNdrNEbIYgtMK4WXpX0QyklrTgNG5I2SHqlfL348it79UwxGAyGrfSJ7YOJHScKz4P9gtHEt4kTZ+Q+hAeEY1CLQRjcYrCzp8IwgJZRKxEKjQuJk92+6sIqo23itogvj32JAT8MwOEMXvNZvEirJVqsTuRdOF4YYFwwnFeZZ3FeVlHvDXB1fK2yV60gQNYpslOjuZ/zDBk1BoPh9nh7eWPBuAUY3Xa0s6fiMMRlYEvPLBUei70nizRFgtgM7Z2OaRJj8dhbkrcAADJLM4VtF25eACAVMGmsrLm4BgBwuaBxlKExGK6IOEBYOXklfruvcQhQNhQfLx98PPpjiWI0wzU4k3MGAHAk84iwbUSbEbJjxXZXYn4695PRtmC/YOHxG7vekGSYx7UfJxl7tfCq4vnait4ay4cv8QZ4ETJd2Xdjei9bFUxzHDec47gtHMfd5DiuluM4rcyXYxrQGAyGR/HO7nfw8cGP0a2ZdXYR7szQVkMFn+lBLQYJ22u1tbLjqcq3JVEP8RixwEd0k2gAykW3vrvzO2yauknRWHeD3og4qux6Sle+J15sUcJgNGbO5J7B5it6/YZQ/1A08WOZaSVklWVh5E8jsfvabmdPhWEA1SkZ0mKIsK1DRAejcUG+QVb5TMtdt1OKUvD+8PfRv3l/2dfkVeTBm/NWxW5UsLUi3nwQDfBl3rpgWiyqagrxAoE7o9hnmuO4OwFsBOANIB3AZQAscGYwGHahrr4ONytuIqcsx2Tpk6dRVVslqG2KMzSmguWT2ScBALX1fLD91q63MLDFQKFEXg5xmVV0EB9Mm1oNN+SZ/s8oGsewzJopa5w9BQbDpZm2fhqqtdXIe8POZageSFk1r+L887mfBecGhmsgBNMt9cH0ktNLjMZZcumoqKmQLC5p67VGY6iYmI+XNJzrE9sHAFCtrZbMCbCfHaTeZ9qbV/IGJJnppzY/heK3ik28mkesG+POWJOZfh9ALYDxhJA2hJBhhJCRcl/qTJXBYHgy1E7itwuNpzToj8t/YPZfswHwtlUUattkjoPpB/Hp4U9x35r7ZPfTvmtxf3RWWRYAZZltABj24zA8s8UzA+pB8XwlgKMqIfIr83Hh5gXZGyIGozEyos0I3Nb6NuF5VlmWYtu+xg6trCmoKnDyTBiG0L8Nta0yRYhfCD459InJ/cEfS7O29B5JTHpJOt7/632JrkpEYASGthwKgG9j0hKtxA7L0JLOVmj7makyb8ByFVxVbZVd5uJsrAmmuwNYTQjZqdZkGAwGozEhvpCcu3FOeCwufRrTboxgtSEOjIf9qFf/lmNqt6kAICkjO5VzCgAvTqaEg+kHsejUIkVj3Q3q06k0S99QBv0wCD2+6yG5IcqvzMf/7fk/FmAzGi1M3Z7haVTV8df1709+b3ZcXEicRCDUEiWaEqNtNDMt9pkurCoUrLj+SvsLAC9w+v2d/HyUCKNZhbjM2yCYvllx0+xLPcVnWnGZN4ByAMrSGQwGg2EjjfXmanrP6YLoiPgCO6PXDKEUSlgJFjGj1wzZ48UEx2B46+EI8AlQYbbuT5BvEAJ8Ahx2MU8pSjHa9u+t/8bai2sxtNVQTOgwwSHzYDBchf1p+509BQbD7iREJAAAukZ3NTvOHvc6feP64sD1A5JtHDjZEvKFxxYC0LeJ2Q3DMm+tr7DrSsEV+57LRbEmM70HANPgZzAYqnB3p7sBNC5rLHG/0C3NbxEeS3ymj36OBUcXyL5+cpfJmN5zuuy+Wm0tfL19JYJX93TiPSl7xvRs0Lw9gUDfQGjqNBZL8eyN+AaKZjBodoHBYDCUQHVFRrQe4dyJMIygbhvxIfFmx21I2oAx7cbYfJ6tD2/Fg90eBGD6vqll05bCY9pKZvdKKDNl3pYq6DwFa4LptwC05zjuPc5TOsYZDIbLQP1GG2tmWlwOJfaZTryZKAiPGZJalGqyxzCrLAu7r+2WZF7pRzf7CHe8mrccfWP7AgCaNWnmtDkwGM6iV0wvTOo0SXgeGRipyPaPATT1b4ox7cZIRK4YrgFdoC2pNi7LNkSJPgpFUM8GcG/ne3HnyjtlqzsICH4+9zMA4F/9/mW03+599pIy71pJMN1YsCaYngUgEcBsACkcx63nOG6ZzNdSC8dhMBgMI6pqq7Bw3ELc1ekuZ0/FYYjLwH4484PwWFyaTdU4AaBzVGfJ68/knsGv53+VPfamy7yllbg/mvpfZpVm2T5pD4EK3SUXJjttDk/3expbH96K7s26O20ODIarsOr+VVh530pnT8NteGHACxjYYqCzp8Ew4Fwur39yIvuExbFKbSoBvjWJsiFpAwB9RvqOhDskY6+XXFd8XFsRgnsTat5fjP/C4jE8pRLRmmD6MQAjAHAA2gC4R7dN7ovBYDCs4r2972HuoblGAaMnMyB+AA4/cRgAMKrtKGG7qT5eOU/GLVe2yI6lq+Niz+r4UL7sjPpNW+Lne37Grum7FI11N+ylaKqUJ3o/AYAvL6cE+wWjddPWRrYmDEZj4NyNcxLhJG29FhU1xorFDGOyy7Jx96q7BYEphutAbahGtjFvbhQWEGbWZ/r001KfZjmv6NSiVMwfOx8D4gfIHiOnLAc+Xj6CLaY90QfTPrICZJfyLlk8BhUCdXesCabbKvwyb5zGYDAYMmiJFrnluYqVpj2BytpKrL241mi7nGonAJPl3uYQr/yGB4QDAJr4NjE1XML0XtNxe7vbrT4nw5ilk5aCzCISv89lZ5ah+3fdJbYmDEZjZfqG6Ziydoqzp+EW0MVScUUTwzXw9eYFuPo37292XFxwnNn9dPGbIidAWllbieyybKM+6EEteOtHujBP5wQYV7jZCm2V4su8jTPT358yr2buSSgOpgkh15V+qTlhBoPhmVCFyd/ONx6f6Y1JGwVxsfM3zgvbY4Ib3jcYGRgJAJKsZ3pJOgAgrzJP0TE6f90ZD6x9oMFzcUUGt+D1NPvE9nHI+XLKcnAk44hEbOxA+gFhH4PR2BjddrSk5zevMk/S1sIwDdV6KK0udfJMGIYIHuCV5nuTwwPDMfuv2Sb3x8yT3gfIWVqll6Rj/pH5kgXZyMBI9IvrBwBIzEtEXX0dyqrtL7QpXMtMlHkrwVMqUazJTDMYDIZq0LJbT+mhUYK4nPti3kXhsThzPLb9WLRu2hoAJFlNS9zf9X4AUnGrf27+AwC4XqxszfNywWXZzLknQEvmxX1oajJ46WAMWTZE9qamMf3PMxiUam21x/jMOovGKtjpylTX8QtClqoG4oLjrPr7yS2c0Izz5iubhW0FVQWC+BltoyipLsGiiYtMHqdBGJV5+5ofL8JT3v8mlw84jmule5hFCNGKnluEEJLe4JkxXBZCCDJLMyWS+wxGQ2nsCtNP9nlSKIuqqNWv1j7S4xEhuyx34Xm89+Oyx4sPicfEjhMlgbk35w2A/a4BIMQ/BAE+AaisrXTI+WhVgBhH920z1KNEU4JlZ5bh5UEvs/eXQg6mH3T2FNwW9j/munSK6gQA6NGsh9lxcplma+nRrAf+Tv/baHv78PZG2z47/BkAqY6KXRCXeXszNW9D0gCkAmhv8NzS1zV1pspwFRafWoxWC1vZ1L/JYJhiSle+V64xrbSLgymx97O47Hf+kfl48c8XZV8/rcc0k8F0YVUh8irypD7TnXmfaaYezWf/NXUalyiTbEz/857Kc9uew6s7X8We1D3OngqjEUA9jJmmhetBxb4s2bztSNmBse3H2nyeTVM3YVqPaQDkq5vEC8WEEFwtvAoAklYju1DvY3OZt6dg7if+GQABUGLwnNHIuZTPK/RllmZaFFhgMJQypt0YAI235DWzNFN4LPaZPndDZ7ORZWyzcSjjEMYnjMcwDDPad6PiBo5lHbP/hdNDoL2ZcqIujqJfXD9sSNqAFqEtnDYHhn24vd3tWHF+hRDkMCzTo1kPJEQkCM+bhzSX2AIyTBPqH4rJXSZjaMuhzp4Kw4AiTREAfkHbEudvnrc4hiL2mZ7UaRLuXnU3Hur+kGQMXZhddGoRPvjrA7Pzsxv13tIyb+IN1HOAV+O5lzOZmSaEPEYIeZwQcsPgucUvx02f4QyGteJv3OXKSBgMW8mrzMOX47/E1O5TnT0Vh9EjRl8GJu6vEmeTxfSK6SV5nlachl//kfeZ3pi0EQCQXKD3UT6WeQyANHBvrNBe8GtFziummtlvJg48dkBSlcBwT6iegbeXt5Nn4j7U1ddJFvvW3L8GKyavcOKM3Ifa+lrc1+U+9IrtZXkww6FQMdGzN/SiYL/c+4vs2EHxgywe76+0v1BeUy5ZaKK90PT9M7HDRMlrcstz0a95P+smbgWhH4fC/yPdfYqhmrdu29d3fK1/QXk0oAlRbT7OhgmQMRgMl2DWvlmYe2iuJFPh6fSN6yv4TIsvhqZEOeQEyHak7JAdq6nTAJBmXtuF886FSjOh66asw8HHPbOv0dH9yi8MeAEA0MRP38Pu6+ULX29fvcUIw205k3MGAJBfme/kmbgPl/IvGQknpRalOnFG7kNueS6mrZ+GwxmHnT0VhgFUFGx8+/HCNrn+6WZNmmF8wnij7ZSk55KQXZaNET+NwKMbHkVUUJTRmLTiNCyauEiwwjJFXIjehkuc4bYVas0GwLjMW7ftVM4p/Zh5N4FvE42OEx4Y3uC5uAIsmGZYzdHMowCcm9FheB4EBNll2biUd8nZU3EYlbWV+OE0n5Guhz6gklN8PnfjHE5kG5d6W0Lcj0sVrAN9AhW99r6u92FoK1ZGaA++uOMLI5/pn8/9jMFLB9v0d2W4FlllWQDgMEE7T2T6hul4eP3Dzp6GW0DFq7458Y2TZ8IwhAqGiqsGei/qbTQuLCBMsASVw9/HX7COOn/zvGzLVpGmCKeyT6GqrkqyfVirYRJdI7FFZqfITgp/EoUYlnkDgNYXP579UTqutCVQFWbfc7sIJoNpjuOW2fi11JE/AMPxUF/INmFtnDsRhkeyOnG1s6fgMNZfWo9lZ5cBAE5l61dx40ON+y6raquMtpkjNjgWgH6VHABSi/msT255rqJjRH4aidt/9kyBG/o5NiB+gEPOl16Sjp0pOyVVB4cyDgEAblbcdMgcGOoxocMEAOy6aA3j2o/DwPiBwnNXEAN0N6y9LjDUh1YaZZdlmx3XrEkzzPl7jsn9bb9oi8FLBwMAbm11q+wie0ZJBhafXixUxnAch+igaCOR0RJNidFr7YakzFu3OCAWIasTWWWdly6WqeF/7QzMCZA9ZuMxCYAnbXwtg8FopAg+041I2Vhc3ptSlCI8/v3i70Zjg/2CEeQbpDjzdW/ne7E6cTUiAyOFbVQ8UM6mSY7CxVbbvQAAIABJREFUqkKPVSem/WeOEjwa+MNA5JbnIv+NfEQGRUr2Nab/eQaDUqQpYgE0w+Og1lM/nfsJj/R8xOS42OBYBPkGmT1WQVUBAOCOhDtk3yvBfsGorqrGtqvbMKP3DBBCkFeZJy3DBu8zveSuJZi5eSaKNcWIbhJt7Y8FAPD90NdY9M5EmbdApag83U9qB+YpPtPmyrzb2vjVTsX5MlyA/Wn7AbAybwbDnjzV5ynh8cs7XhYeP9yDX8ntFNVJNpCe3nO67PFaNW2Fh7o/JFEG9/fmBUOYSBJfYhfgE+CwlXG57DPzivUcdl3bBYDvYWQo43jWcSTlJzl7GgyGXaFZ4Z7NzAtL5pbnok3TNoqOacoCrXNUZwD6BVnqhtIhooOk6gOAkAU3V1puiYSIBMQEG1h+yZV5i4Ppimb6x74VNp/blTGn5n3d1i/xcTiOC+U4rpX6PwrDUeRX8QIrrDeMYU8e7fUogMZljSUWweoS3UV2zMrzK02+/qk+T+H5Ac/L7ksvScc/N/6RKIPf3eluAPoLcGMm2C8YmjoNSqpVLH9TSGP6n/dUCir5DBIV/mMw1KRVU/622pyAFcM5RARGAICsYJiYg+kHFWdmfz73s9G29Q+sx2O9H1P0+o1JG4WFPpo5txtyat71otLuav2CPjjptc5Trn2OECB7BQCTZ/Qg4oJ5VUBxxqsxwc3mwM1mGSV7M6z1MHDgGm3J6/Gs42b3y9lZrU5cjQs3L8iOL6wqRGJeIrT1zvNRdmWosIvdbyysoG9sXwDMZtATmNZjGgC9Yj7DMt2iu+G+LvcJz1s3bY3escZCTQxjgv2C8WivR3Frq1udPRWGAVTRP68yT9g2qu0o2bHHs81f9ylrEtdIVLjv6ngXJq+ZjAPXDwDQV5vR+6eFRxfiWNYxYfxLf74kPG7IAnJSfhLWJK6Rbqz30QfR3jI907WiUvY6z/SRZ2reDKuhpSNtw9s6eSYMTyK1KBVf3fEVnur7lOXBHoL4xlGsfBkdZNzP1D68vZFYVllNmeyKNQBsSNoAQN8nDfAr4YDynmlPZv2l9QCA6yXXLYxUj5n9ZuL006eZz7QHQG90HW255s6U1ZRJ+kDXP7geP93zkxNn5D5o6jQY0mIIOkR0cPZUGAYk5vEWUOKF7jeGvCE79rZWtyk6Zn5lvqTKjFrK0QrRce3HScYXaYrQJ7aP8kk3hHpv455prS++v/N7/rEkmPaXvNRTPi9ZMM2wGU95EzBcg48OfIS5h+aidVhrZ0/FYfSI6SErQEKzXEr46/pfstupjYZY5IyWdyv18t4+bTtOzjxpeaAb4uh+5XdufQeh/qEI8Q8RthFCUFBVYGRrwnA/aBZInI1imCe9JF3oNQeAlMIUHMs8ZuYVDMqN8ht4ZuszEvsjhmtAbagmdZokbAsLMLaEig+Jx+h2o00eJ+OVDMwZpVf7jmkSYzQmszQTKyevxG2tlQXl4vnZDVk1b1/sv76ffywOprXSYNpQjNNdYcE0w2poOerVwqtOngnD08gszcTpnNPOnobDqKipkNUeWHhsodG2k9knLZaCyyEum6fK1WKvY3OMTxiPfs37WX1OhjEfjfoIJW+XSH73K86vwJhfxuBo5lEnzoxhDwqrCgE4t23A3Zm+YTqe3vK0s6fhFtDrxvwj8508E4Yh3hxfct01uquwjVpciQnwCRDajeQo0ZRgcpfJAPjFXzmf6ZzyHGy+slmo8KAVMqPajsKZ3DOyx+0Y2VHhTyL/2qndp0o36sq8dz6yU1/mrfXFqgur+Me1TfRjWZk3g8FDe3SoAAaDYU/WXVzn7CmoijjzQkuNlWCtsBF9f4qDN7oAJtd/LQc3m0P/xf2tOq+7QH2mHdVzmFKYgt8v/o7qumphG81m0h47hvsyui2fYWI+08qZ0GEC+sXpF+uqtdVmRjPk0BKmieFq0GowS8r+zUOa45NDn5jc3/277ujyDS9Menvb243srgD+Wv7bhd+EJATHcYhpEuPY8n9dmffZ3LPyat7izHRtoOSlqvpfOxAWTDMYDJfC0wXIcspzbHpdqH+oVaJ/EztOBACEB4YL25ILkwEAWaVZio9zKueU4rHuhK+Xr+S72gz4YQDuX3u/pEeUtcowGjOZpZm4UXHD2dNgMOwKzSCvvGDaiQPgfab3pe1TdMw7Osj7TFN9le1XtwPgA/kbFTdQUWuc8b4j4Q4AeucBW7hScEWfcaboyrzf3P2mpMxbYMcC/eOLUyQvbQw+0wyGLDtTdgIAUopSnDwT50BmEZBZnh3wORNPsUowhakAKjwgXHb7Iz0fAQC0j2gvezGlPtSGtA1ri6f7Pi05brBvMADA19sxAaQrExUUhQCfABRrih1yPnMr8I5YQDqccRgDfxiIxJuJqp+rMbIjZQcA1v5kDf/c+EdxlQyD4S70iu0FABYFwK4VXVN8zP7N++NS3iWj7YbuATQr3imyE4a2HCpsT30pFZcLLgOAbLm4Uro36y6UnutPKqPmrTVxj1HawuZzuzIsmGZYTXE1f/MpLldkeDY12hrVb/j/1f9fADw/M70teZvwWCyC1Smqk+z4X//51eSxnr/lebw55E3ZfYl5ifgz5U+hTxrgyyoBMAVY6H2mHRVMU8SLRfTv74gFpNUXVuN41nHsvrZb9XM1RmgmyFMyLQzXhrqp3NnhTifPhGEIFRuTEx0TY03V14p/VuCNXVJF8LVT1mJm35kA5O+bxPcXv5z7RQjea+vtrOsgUfOWscYSUxkNZPcVnnpK8oQF0wyraRHCryyJy0cbE3I+039e/RNbrmxx0ozUJas0C/4f+ePbE9+qep4B8QMkgZ+nIu4LFK8QWxKhyikzLg//+sTXOJxxWHZ8WXUZ0kvSJWreDD00iHZU8COnHt47hrdG6xbdTfXzU2Ea9v+gDg935ytEmGWkcjpHdcaUrvqyz46RHa1SJW7MBPkG4blbnsPw1sOdPRWGAbnluZLvQMMXPVYlrpLYXN7Z4U5MWTtFuP5T2ywaVM89OFewwgSA/+7/r/C4rNq491opF25eMNZ6Eal5+/nx4muSMm9DyprbfH5XxRHBNKf7YngIfeP4VaW2YeymgXLHijtw1293OXsaqkBXM1clrrIwsmFcyruEBeMW4KVBL6l6HlfiP/v+o3hsx8iORj7TAPDzP/I+0+uT+AvePzf+EbbR/izmMw38cfkPAMrF2NRgZr+ZSH4h2SE+0yyYVhdHW615AnkVeZKAY+ODG7H07qVOnJH7UF5TjjZhbdCyaUtnT4VhQFJ+kuQ7ADx3y3OyY18Z9IqiY5ZVl0laxLYmbwUAFFTx/c+j2oySjK+orUCvmF7KJ90QRGXeNaSc36b1xZK7lvCPY84BcSILNy99EoEqn7s7jgimfwQw0gHnYbgpmjoNXvnzFdl+UHehV0wvh2SXnEGLUL4S4ZEej6h6nk8Pf4qPD36M5iGet2opZkSbEcLjgfEDEeIXYnqwAqyxVerRrAcAoEt0F0XjDzx2AIn/9sweW0eLf3048kN0juosKf2rrqvG+RvnHVJqzoJpdaFZoJsVN508E/ehoKoAf6f/LTw/lXMKmy5vcuKM3Icb5Tfwxq43cCrbMwUi3Rnq4/xAtweEbaaq7g5cP2DyOAVvFmDBOL14V1xInNGYnPIcbH5oM8a0H6N4fjSLbTfEZd70O/HG5iubdQMIECoSPeX016BG7zPNcZwvx3Evchy3keO4PziOe5XjOKO/ECHkOiHkr4ZNk+FKnMzmV5iuFFyx2/EWHluI/Wn77XI8ZxARGIGIwAhnT0MV6E24Um/ihpBeko5D6YdUP48zaeKr91z8/dLvsnYXchzKOGSbz7SoJ4kKj9GLvSWGtR4m8cpk2M7bt76NS89dkryPVl1YhclrJpss1bcnjnj/Nmbk/OIZ1jF9w3S8tvM1Z0/DLaDtQp8d/szJM2EYQu+ZEiIShG2jfh4lO9Zc3/TVwqsY136c8FxOOOxo5lG8u+dd5FXkAQC8vfhM7/iE8Th345zsccXzspaEiARj0VPiDXjVYccjOyTWWMLCmNYf8NEAw9/X/SCe185nNpjmOO5RjuPSOY4bbbDdC8AWAAsA3A3gLgCfAdjLcZyyuzSG2zKs9TAAsFt5ES3zcOebvX1p+yQr7J4E9TfOKM1wyPms8V52R2z1Ba3VWicakhDOXzDF76vL+bya5/Xi64qOwc3m0O6LdpYHuiHUZ3pUW/mbHHuTlJ+E5WeXo6q2Sth2ModfmCzSFKl+/pcHvYwfJ/2Ih3o8pPq5GiPUr5z5TCvn7k53o3dsb2dPg8GwK7T6h15vbWXgDwPR9Vt+MXtix4mY1GmS7LjzN88LSS4vzguxwbFoFdqqQee2inofwEuL8zfOC73TqBeVb9cFAN7VQHddq6DIa7qwqtBx81QRS5npMQBCAOw32P6Qbt8NAE8BeBDAMQCDADxp3ykyPB3aM2WNTYCzCfINsjzIQ6A/a3xIvEPO5ynqjqawpPBp7nWm7LPkGJ8wHgDQ1L+psC2tJA0AkF2Wrfg4qcWpise6EzR7QL+rzS1LbsHjfzwu6zPtCAX76CbReKz3Y0LbBoPhbJLyk5BWnObsaTAYdoUG02svrrXbMccnjMdbQ98yuX/nNd6yVluvRW55LjRajdEY6jN9o9x2b/erhVex8rzIP5uAz0xzWry+63V9Zprogulaf6C4LXB9OOCrW0j++13h5dYmCVwVS3cRfQEcJsQolfII+F/ho4SQZYSQtQDGAigB8AAYHg1VrU4ptI/PNBUAkvPQc0XILIKKdysk2+7scCf6xfVz0ow8C0+3xnprt/wFsVVT8yvJrcNay2YwxX1ZYhIiEvDa4NcQ3SRa2Bbmzwfydu+ZckNig2MR4BOA/Mp8h5yPVniIcaQ11p5re9ByQUucy5Uv/WM0DGp5JxYdYpjnSsEVh1vTMRhq0795f8l3e9AxsqNETNQUtBS8c2RnidJ7yospSClKkYyxhV4xvaQZcpqBpkE07Zmm1lj5nfnvJa0BX10rzE31BTcdjaVgOgaAXLpwCIAbhBDBsJIQUg5gK4Du9psewxUpr+HV+uztVefOGUkvzstjhX1oeffvl35X9TwvD3xZ1eO7CqYuiO3CrS+nfn3w65g9YrbsvmNZx7DszDJJmTfNVjMlfhfxmXagCNrua7uRWZopqMAy7Av9u2rrbWvjYDCsgfa9mir9ZTiPYL9gyXd7sOrCKry5+03JNkEtG5Z9pr878Z2gc9RQO0jJvTrNQNPybsMyb42oEs+vXP9Y62N8LDfGUjAdCkCSguM4LgF86becSlAmANtqGK2A47h4juPKOY4jHMcFi7ZzHMe9y3FcBsdxVRzHHeA4zqghh+O4rhzH7eE4rpLjuGyO4z7gOA/RZ3cArZu2BgBEBUXZ5Xi07NBdes3kfKY3X9mMM7lnnDQjdaGrmHQRRS16xfZCqH+ox3y4WoslAT65DOq8I/OwM2Wn7Piy6jIUaYrYzb0J8ip5wRa5jLEayAXO1BLLEVUt9H3laBXzxsIDXfkKkdZhrZ08E/ehY2RHPNjtQeF592bdG+zH21gI8AnA20Pfxsi2zCzH1aDVluJ2qvu73t+gY/524TdcLbwqPB/VdhRmbp4pPDdsPfzv/v9K7inmHZknPK6olVZWWsO5G+ekivs0aNZlpJuF6NS5iTcvtqrVVcGNfxHwFV1rb3pW3tVSMF0EwDCFcYvuu1zk4ANA3Ttuns9MnOdtAP8B8Al4UbRyALs5joulAziOCwewG3yZ+iQAHwB4DYB8eodhBL0BpEF1Q6FBdEMUBhnuz+mc05g3Zh7eu+09Z0/FJekU2QkD4wcabf/x7I+y47dd5ctOxZnwHSk7AADXS5QJkHkyW6/wGdqc8hynzeFf/f6F3Ndy0b2Z424smB+yOrDfq/VklGQIpacAsOWhLVg0cZETZ+Q+FGuK4e/jb7ekBsN+0AywOPh9vPfjsmNjmsQoOmZ1XbXk+d7UvZLnt7W+TfK8rr5OsMJUFVrOrSvzvlmZLWxfOH4hoOUdRNDyCP/9Wd21LosPJZU6i7g6loLpMwDu5DhObG42FXwgKmd31QGAqncmHMcNAzAewDyD7QHgg+mPCSFf60rQp+jm+rxo6DMAAgFMJoTsIoR8Dz6QfpXjuFA15+4p0AyHvTKINPPpzkIEtzS/BYNbDHb2NFSBVg480ecJVc/zxbEv8L+D/2tUNwf3d72/wZZqZ3PPKh5LM6B9YvsoGn9y5kmkvGgfbQRXw9HBz4JxCzCyzUhEBup9NctqyrAjZQduVNguCKMUWgbIMtPKyS7LxqKTyoI7enPbEHGfxkZVXZWgQgzwrQhLzyx14ozch7yKPMz+azZOZJ1w9lQYBlCHmuk9p1sca+mzX2ylaU48cmLHiUbbTN2j21VA10yZ96///ArU64Jpb11pebNEILAAyOaDaU+537MUTC8FEATgCMdxn3MctwV8xjeFECIp89ZZYg0DoJq6ia4U+yvw2WTDmsch4MvS19ANhJAKAJsB3CEadweAHYSQUtG2VeAD7OFgWITevDdU9p9CfXbdWSHb38cfAT6e550H6G++HXETnlachl0pu1Q/j6uw7uI6xdYQe1L34FjWMavPIenR1QWQSgPJnjE9LQqjuStUxdtRweVzA57D3hl7Ba9vAFiTuAYzNs7AwfSDqp+ffj55SibAEUxcORHPbH0GOWWWcwS0ncKdLR4djeF776nNT2HW/llOmo17QfteFx5b6OSZMAyh11dxy8edK21rXxCXZJsTDssqzQIA4foyseNEXLh5QXZs+/D2Ns0F4LVdHun5iH6DqMx728PbJGref13/C9DqPg+9dMkyDkDzk0Jm2lMwG0zrVLp/ANAKwMsAJoBX7J4pM/wuAOEA1LwTfgZAAIBvZPZ1BqAFkGyw/ZJun3icRG6TEJIOoNJgHMMEtJwkPtQ+Vkn05s6dg9GD6QexL22fs6ehCvSi7ajS4D8u/+GQ8zgLW2+2rRW46xLVBQDg761X7qaK+eLyM3P4feSHmHnKytDcjUEtBgEAxrQf45DzXbh5AV8f/xoVNfqbI7owWaIpUf38rwx+BeumrMPU7lNVP5enkFXG36AqsU8b2IJvwWA908q5p/M9Dm1xYDAcAb1Wn79x3m7HvL/r/YIugxzHs44D4D+rmoc0l1RAqYqozPtywWVjNW+tQWYaAJqf4HumawKRV5HnmHmqjMUrBCHkaQC3AngLvKd0N0KIXIl3JYBXAGyS2ddgOI6LBPAhgFcJIXL1wOEAymVsvIoABHEc5ycaJyffWqTbx3AwtCzuYt5FJ89EOc1Dmjt7Cg6DKlLGBcdZGGkfPN0ay9aypvCAcIlntCVub3c7ACDEP0TYllnGC6NYU4qqNHPOME+/xf3wwvYX5H2mHSC6FxYQhvu63me3RdDGQLfobgDsq8rL0HM657TdKtwaK55+vXRH6N9k4+WNyl+kCQH2zQIy5TO2Y9uPxUuDXjL58j2pewDw2evssmz8dO4nozETOkwAIBVGs5ZrRdf48m2KqMz7lR2vGKt508y0tyhsiz8BEB8gt3eDbLpcCcvLrQAIIYcJIZ/pPKVl/wqEkB2EkC8IIWrdec0BcIwQss3MGLlPFU5mn6lxsp9KHMc9zXHcSY7jTublecYqSkNYf2k9AOXZLUtQVd1L+e7jM531apbwPLc814mz8TwcEVisurAKz219TvXzyGHqQtY1uqvZ17UIbYGSauMM5uQuk2XHd4rshFnDZyE2WNBfRHQQ7zkd6BuodLpO5edzP2PosqGqHLtFaAsE+AQoKuG1B5asS9Tmj6Q/4P2BN87keKbrgBp8M+EbrJ2yFk38mlgcSy3HlGSjCioLkFac1tDpuT3XS67b3WKzscAE71wXWvV0S3MrSpm1fsBf7wM/HJfdHdMkBqdzTls8jCntoeQXkpFalMqfyijnqJx+cf2kivsGat7iMm9+v4nMNABk3+Ix7i2Kgmlnw3FcNwBPAJjNcVwYx3Fh4Hu5AaApx3GB4DPLITIWV2EAKkXZ7CLI23c1hXzGGoSQxYSQ/oSQ/tHR0Q39cdweaiVjb19ld1lhrdXWSlbTUgo9U6CJQm/61lxcY35gA3n31ncBOOb/YGvyVpcTurGlN/m9Ye9h/tj5svt2X9uNOX/PkZSVj2nHlzS3DG1p2yQdzIyNM3A447Aqx6Y+03ILFGoid/PgiP/5g+kHUU/qseta49EkaCgJEQm4I+EORX8f2qakJMh5ZuszGPTDoAbPzxNw5/YuZ9IxsiMA4L4u9zl5JgxDqP5PoI8Vi9ZiD2aZj5t1F9fhzV16n2l/b3/Jtd9SUPrpoU+FhBVt3SuqKsLGJCuy53LnMlDzNi7zNuiZBoDQHCAky6P6ps0G0xzHBXIc105O5ZrjuNYcx63nOK6Y47gSjuM2cRzXUaV5dgDgC+AI+GC4CPq+6UzwomRJALwBGPorGfZIJ8GgN5rjuJYAmhiMY5iAihcolfS3RLvwdgCAtuGGLmyuid9HfvD9UC8iROfvqdBFE7X9eDtFdUKzJs1UPQcl2DcYTQOUl0w7gj+v/ml2v1zQ99HfH2HdxXWy4ytrK1FXX+fWKvlqes5SwZaq2irVziFGLsii1iW3trpV9fMzn2nreX7b8wj+OBhFmiKLY2lQEx9iuYzez9tP0n7RWEmISMC9ne8VnneL7saCQ4X4evlizqg5QjsPw3Wg+jIZpRnCthm9Zph/kY/I+qqgg9HulRdWIrU4VXjeI6YHXtv5mvA8xM/858mS00uEx5W1lfycNs7AvavvFa6FSjiVcwrbkkUFwgZq3l1iOvHPhTJvmcw0wGensxtJMA3eUioZgKT+kOO4EPDWWJPAK2iHAJgIYL+ut9neHAQw0uDrE92+CeB9pw8DKAVvh0XnGQReGG276FjbAYzT/QyUBwFUQd7ui2FAl2he2KhlU/tkt6hftbuqBnu6Oi4NAtT+OQ+lH8KcUXPw6ZhPVT0PAKxPWo+bFTdVP48YSxc7S3SN7iqUj4kxlWHfnbobgNRnevtV/qMwvSRd8XmdmTnqFt1NtfNTz21H/x+IefaWZ1HxboXwmaomgjUWKw9VzOLTiwEoqxywZpGisrYSZdVlNs/LU7haeFXy+bRvxj4suWuJmVcwKPmV+SioLFDUgsBwLLQFUtzK8WC3B82/SPzxUWN8r2BYCSq2lAOgzJq1JB6o0icRRrcdDaCBTjo0aNZlpi/lX+Sz0zQzLZR5Gyzqx58ACjqhqswfnoClYHoYgAxCyFGD7c+CV/g+Aj4THAM+OxwLwHSHvI0QQvIJIfvFX9Bnkf8mhFwmhGgAzAXwLsdxz3EcNxrAWvA/41eiw30PoBrAeo7jbuc47mkA7wP43MAui2ECmumyl3BAtZZfkVM786kWBVUFzp6CqtCe2yf7PKnqeb4/9T0+PvixQzI2zgigqAWcmPu73q84G2+qrSIpX3lBDfWZ7t+8v6LxSc8lOdVn+lrxNdU+FxydoV08cTGm9Zgm+XsXVhXixzM/4nqx+kr5LDOtLnRxRomGxsakjQ7xFncHEvMShcfrLq7D//7+nxNn4z4UVBXg86OfCyrODNeBqv8/1fcpYZvc9d8kdfIBpimfaS/OC/d3vd/ycRdkAp/lIdgvGHtT9+LVna8qn5MpaNDMifqwOa0+Yy1X5g3wmemmaagscJDquMpYCqa7Ajgps30y+Kr+Jwgh1wgheYSQlwBcg9TT2dHMBS9U9g6ALeCz5mMIIcJVixBSBGA0+JLwzQBmA1gAgJkbKuRKwRUA9hMgy6/kLcPdNTPtrosArsi1oms29fC4K+surlMc2O+4ugNHMw3XNS3TEIGP2ODYBmfUG0LHiI6qVUTQ44p9n9VkRu8Z+HXyr5KfZ23iWjy//XmH+ExTJXhH/byehJL3EP27skyhcgzdCf697d+Yd2Sek2bjXtBkxtfHv3byTBiG0AVLsQPKg+ssZKYB4AGdmKhWPpimXvaG1JN6XCu6BgDw97GQ6a33Rbvwdvj94u/CAr3YYcISbcLa4NFej+o3iMq8N03dJDyWlnnX61W+Ke13Aa+0RY8eik/t0lgKpqMBpIo3cBznC6APgMuEkCsG4/fCuGdZFQghywkhHCGkXLSNEELmEEJaEEICCSHDCCFG0qWEkIuEkFG6MXGEkP/IWGoxTDCk5RAA9rNKoh887lou7SnS/qagPx/9sFabrVe2qn4O2qvqcvy4D9j7gewua4PiXjG9AEjLtC/cvAAAvB+kAsI+CUPMvBinLhipJc7VrzmfpXdUz+HxrON4b+97khsX+vewKmthI68NeQ07HtmBB7qZ9ipl2E6f2D4A9G1LDMuMbT8WnaM6Wx7IYLgR9FptWIptkRCds0SdcWvTIz0ewfSe002+9EjmEQD6rLglViWuEh7T6lBL1GprkVachr2pe/UbRWXewj2iV51UgMy7FkYFUbrnnuKGY+m37g8+gyumG3gxMLnakpvQq2wzPBx7SdrTzPSp7FN2OZ7ahAeES2yMqJiDp0KzB/YSnLOEI6wS+jfvb7JkSluvxUcHPrJqtdYu1HPA9RHAgf/I7o4MjESov5EWpEmGtx4OAGjiq8+U5VbwF668CuUWf1V1VQic4xwrrdWJqxtk4+FKDPtxGOb8PUfqM63rX3aEmnewXzDGth8rsUpjmIcuGFulyquA6T2no22YewhuqsmB6wesalNhMNwJapenGB/dorVMmfft7W7H0/2eNvnSv67zkk/UutYcqQUZkudKq8/otTizNFO/UVTm/fKOl4XHEmssQ/Ex8TFNZNvdDUvBdC6A7gbbhoAv8ZZbcgkBoJbPNMNFoCta9irzLtbwjmTuclEtfKsQif9OtDyQYROOCCxSilKkFwQRG5I24D/7/oPXd76u+jwkVIl6h0qNqz5ig2NlA/y7O90te7gDsBskAAAgAElEQVSOkR3x0ciPJIsGzYObA2ig4IgDSSlSr1+7XXg7BPgEWCXGZm8c2b+84p8V4GZzOJt71mHndHe2T9uOQ08cUlS6vSV5CwBe7ZahDCV949xsDs9ve94Bs2Ew7MOwVsMAAAPjBwqfu4rw1mWIZTLTgb6BZvvj6X2TKS/q/ffq79eLi6Shn1JRSllnEAM1b/6xQWbasF9a/PJG4jN9CMAojuNGALxVFoCZun1yZpXdASjXWGe4JbasJJXXlFu06HGXN1WxphjlNXpPQE8X9KEBzW8XflP1PB+O/BC4fiuu7Bmq6nkAoG9sX/h7y/cW0ZLmitoK1echZlSzh/RP0ocZ7ZcTIPtw5IdYPHGx7PG2JW/De/vek5R5j2w7EgDQPKR5A2fr/jTxbcL7TGsc6zMthyM+++iN2P60/aqfy1PoENkB7cLbKbrmhQeEA+AtiyyRmJcosblpzEQFRVkc882JbyyOaWx0ieIdACyqRDMcjp+3n/D9gwPybVuyBPCJJVRFGO1af2k9Xt+lX+APDwjH+8PfNxpH/ccNmfOnXiU/v1AruW9V2sYlK4IqJ0DmXaO3xNKaz0x7CpaC6QW67zs5jjsNvn+6J4D9hBBJ053Oi3ooAOsVchhuRYcI3gNPiZ8mJeTjEIxfMV52H+2Zcpeyt/BPwhHysb4sZnBLBZYEbgxd8VS73LZNWBvgx79x8KsnoHZy2sfLx2RvUUQgfyGjfuqOYu+F8/onZcbBbm298WLUf/b9B8vOLJM9Hl0MqKrT+yjbuvDjLC9TsQetvaG2Jc5s06DtImPbj3XYOT198c+ehH4cirj5cYocGyZ1mgQAiAuxrCWSEJHAeoXBV4eMT9DfF3SO6sx6+hXi7eWNL8d/6dDPDoYyaALiesl1IQAd1XaU5RcG3wC4OqDM+N560alFyC7LFp5HBUXh/b/eF56HBYQBAKKDomUPveviMeFxYXEd3h32rvC8uk5Zz7QstCRdV6I+IH4A/5hm1+t9jW2xPBCzwTQh5CSAx8B7MPcG0Ax8ebec+/gMAH4Adtp3igxXo0OkLpgOVR5MA3zPnhxUxVvJTYgr4uk3pzTotHffoCF7ru0RHpeq3K684vwKSZApZnjr4Tjy5BG8POhlu57TogVWhWh/rfHvunNUZ1kvSeqFa8iB6wcASH2mt1zhS1GzypQXEEUERmDXdLlCJPVpF95O0vNtT/ak8v9vVLPBGTw34DmQWcRkNsGeCNZYzGdaMXQBUZHPtBW/18KqQuSU5dg8L0/hWtE1HEo/JDw/OfMklk9aLjz/+dzPAID/3CavI9GYyS3Pxfmb59n72QVJLeKrTjJKM/Dv/v8GALPiYQJe9YBfOVBt3MMsDqQBILkwWfKc2l6aXKQT3V888/sbaBHaAiPajACAhulo0KBZF0wfzzouDaa1fmYz0+LKOXfGouwbIeRX8P7RAwEkEEIGEkLkmg03g1f5/tO+U2S4GrQkxJrVrKigKJOZbJoZqqhxbFmtvVCzr9MViG7Cr3Q+1vsxVc+z5NivwuMbKluw5pSbvpEN8g1C/+b9hZVee0EtsFqGthS23dv5Xr0qfrlI4K3WuKe5uq5atsyKZliV0DeuLwDglua3KBqf8UoGvp3wLd7Z/Y7ic9iT6yXXUVFboUofvaMXwVZMXoHXBr8mcUHIq8jD3INzHaIXQX+Hnr745yw2X94MQJk67e5ru1FS7fz2AldAXO6+9MxSvPynfhFzxkY+b/PBSCtKZRsJRVVFWHJ6iU12iQzH8MKAF/DK4FdAZhHlbjW+VbI90+YI8g3C9F58sG7yfrS4jf5xdSiOZx23uuUn0FcmoSIE06J4wEcD1OnGan3N9kwrafNwB8wG0xzHPcpxXE9CSBUh5AQhxKQ3DiEkjRByjhDi+cXxbsKcA3PAzebk+xwaQFYpn9WyxiopvzJfyJQZHU+XJesT16fhk3MCN8r5yI/2yTBso7JAL8B1U5n1sir8nf43fD/0xYIjCywPtoHbWt8mPN6QtEEf2NM+KZ9K/YVIxM6UnTiWdcxouyUaEoh6c96Y+vtUzD001+ZjNITu0Yb6l/aD+nHK3iCowP1d78e8sfPg7aU3yFh7cS3e2fMO/r7+t+rnp5URrmpBeCzzGNZdXOfsaciipKedVl7ZexHOkxEvLALAS3++hMWnF6OythLJBfrM26W8S46emstD7+sWnVrk5JkwDKHVAhGBETibexbLzy7Htye+VfZi7xqgWrlrB8AnpC7mXQRgptKqXJR9rg7F/CPzhad5lcrcPWRb47S6+17vGmx8cCP/2IrMtKdgKTO9HMA9DpgHQwX+u/+/AOyvjjwgfgAABWWrBiTmyStgO0K9WU3ojVaN1jM/MOjfx17q7aYoz3NcMG0uM3u9+DoA4EyukUW9XVh1YRVw6DXg+q3SHXUBvJqnX7lsZvrtPW9bdR76PhWXUdGSb1PvRUOaf+4aQmVqCHRRr/HRbUfb/dhy7E3di6c2PSW4FwB6BwNH9G2/PuR1HH7iMKZ0m6L6uWxh0NJBmLLWteY2rv04xWO7NesGQN+2xLDMra1uFTRYxDyw9gF0/Frf+tD1265GYxgMV+dQ+iH0WdQHj//xuFCZZpGS1kDiVKvPdTD9IAAzlWrlsYC/7tpjUEauVICMCjFKgup6ndCYVy0ySnWWW1b0TNPknLujzN2b4ZZQQRR799TQFVFrb3BbN20tu72windTox8Grk6nyE4Y2lKvOO3uiwGWsCRsYS8qC/QKlmqXeXeO6swLnjkAQojkf0RLtMCuecCPBtnIugD+IhRYBFSFN/i8Q1oOASDNvOZX8avWBZWWBZVcgeXnlgPwjPfYhBUTsPTMUqnPtK7k2hFq3oG+gRjccrDDyuq42Rw6fqV+L7haLD+7HIl5ifj09k9N6n2IoTeaSirBHuv9mFFWtjGyLXkbkguTjd7f1DOXwXBndl3TaY3U+UKb0Q+oiDT/ggZA759NJXW8KuIRGqNzLjYoI1fy+QYAdfV1AAw+46hqt3ctXtj+Av9YXKpuITNt78pZZ8GCaQ+GZl5MqRbbyq/n+d5WKrKghPCAcNzV8S7ZfVR1+ErBlYZPzgEkPZ+Eg0/oA393sfSyFfrzqf2hV1uhz8aqLUCWVpxmVa9xQ/D6wAteH4jeg6b+XeoCAJ8qIDgXKFcuxndnhztlt7cLb4c5o+ZIFg3ogpYS31xDnBHQ0r+RGu+xTlGdEOAToHrFhTnoQqcjfreLTi4CN5vDmRx1Ki7kMBTJMcekTpNcyrLt8T8eR2ZpJiZ3mazoZvPPFF4u5lS2ZZ9p1rfOQ/vGvzj2hWT7W0PfcsZ03Ap6X+eqbRuNmZFteAtKQTC0Mhpp81YDSVa4U9h4STDlRd3aeyAQnsY/sbInmyLr6CLKTAtIyrzN90x7yv0zC6Y9mKigKHSI6OASWZ0iTRFKa8xHSK4wTyVklGQIfdIA31dKeXfPu/jp7E8es9oGAJfzeRe81YmrVT3PyDj9haZCZS06WtrrlP+5X0SGB9X6oLZv9FD+IhScC5QpC6Y/vf1TrJi8QnbftuRt+L+9/ycp86b92raodx7NPKpIXMldCPINgqZOg7KaMqfNwZFBFS3xP5RxyMJI59A7tjeGtx7u7GkI3N3pbgDArP2zhIyMOZoF8W1PtBffHGdzz+pLIhn44/Ifkuf0fSHnXsDgoarNkztPdvJMGIZQXQwhkUXto5T0Do98T/oaE8QFx+HVQa8KzwkIssuysTV5q+z4jOxalPon8tZbBposSsu8Ze+XaGbaS/QZaVTm7ZktkGKUBNNhHMe1suZL9VkzFFFSXYLkwmS7+wN3juQ/xK3tDaM2F4b0jOkJAGgb7h4+060WtkLsfH0wMqz1MOHxxwc/xmN/PIbDGYedMTVVcJT1hrYqGF4B5fAJ0KCkrFYI4tWA/kxyix6RQXwpVpeoLoqP98PpH/DhXx9aHlgTCFwbo3++5AT/vaopTv/ZA6gJAQILAY0yEaM3d79plNWhlGj4rE95TbmiY5ljRq8ZGLJsCHp+17PBx7KGKV35HlrxgpW9oAJHznQRoD7Tk7uof0MsWGO5aFa0a3RXq95zakPdKlacX6Go33Fix4kAlGmJtAtvh27R3Ro2QQ+CZvISIhLwcI+HhUW7N4a84cxpuTRenBeW3b0Md3WSr/hjOA+qhSEo1YtEuiwSoFP5rzFfDVNP6vH50c+F55GBkcI13wgC1FUGAQHFQqD7eO/H9bsbklSo11VG6Pqih7YcKg2m04cBqbfbfnw3QUkw/RKAVCu+lEs8MxyCvbNvNOi11hf6ns7yWna0d8xTJPIB3rbCU6CBTKi/dQqT1nIhPR1NmwIRoQE4eu08On/TWfGKqbUsPsV7M8uVGI1sMxIXnr2AFwe+qPh4MzfPFAT/zCK2pwCACl0f+hX+RhxVkYBvBVCrvAzblEooVf4W+0xvTOLVNq3JMLcMbYnl9ywHABRUObbXOj4kHqH+oaos6NC+TKrZ4Axm9psJMougdZi8noQ9EayxXNSXdmfKTpdSJt6RskN4bG+f6eyybKtK4D0dWt6f/EIyVkxegYhAXj/j3b3vArB/q5onkFWWhZ3Xdqp2jWTYTnpJOgCRBacQTCuwk/XTLX5bCKZvVEiFZXrH9jY9uN4bgBcfzPtogNpAjG0/FhM6TAAAtGyqTL9B9jPOoMz7UMYh4RxKCPI1Flt1R5Q0W5QCKLY4iuFyzNo/S5Xj0kxXVW2V4tc0D2mOqED5YJkK8ljjW+1KnL9x3mhbkcZzgmmaqX2k5yOqnicpKxcVXhrEB3REjYa/+KhVhk3t2OQy037efogPjVdUrmk1FTHS57Hn+O9a0bn8yvlgup4DvCz//IYXVXP0i+sHQLnPdNFbRdibuhfPbX0OIX4hktVsR5BZlonS6lJo67USSyl74OgM7capG3Ex76JEeCq/Mh8LjizAlG5TzN8M2RFXzUwvPbPU2VOQ0PrcYlxv+gvQRplt2e8XfwcA5JSZ9rCnHMk80qC5eRq0ym3+4fk4mXMSzYP54Jpm+F4Z9IrT5uaqFGuKserCKtzS/BYMajHI2dNhyPD64Nex+9pu/fVdSWZaYTAtJiwgDDP7zoQX54UpXadg7cW10gHC+asFcbC9qXuxLXmb4nMAJoTKRAJkAjQzvd2yvainJNGULPctIIS0teZL9VkzFGGr6rYlqLWLUMKigOyybOxO3S27jwoAUfVhd0NYfRThTFEjd6W2Mgg1PnnQcIWoqeLX+dTOoskF6wfTDyL8k3DMOzxP8XHu7XwvujdT4IlcYKBunDoaqPcCynVB9vQxgJ+u7FjGHssWGrIg8cPpH3Dfmvvw7clvUU/qHS4W0ieW9563d6sKoFc5V7vigjI+YTxeHfyq5H96TeIa/O/g/xziZECzD/ZelDAFmUVAZrmHDoYhhADXN8wElh/gnyv4v6c3hdFN1HU98CT8vPlF07wK3uf29V2vY9WFVfjmxDeScSbLVxlYdmaZs6fAMEGIv86CypoybxuC6WJNMc7mnkWgb6B8y5C4Z9tHA9QFYsnpJcLuzNJMxecyol7fM73+gfX8YxpMH3vZ9uO6Gax2hmE1NLsVGWidzL8p9WR3V/MTspuZA4CqMOk2D4CWeV/Ku6TqeWorAwH/ElR7FSD5Br9AMWnVJGSWZlpVBaEEam0mp4RKS7Qu5l1UfLxRbUfh7o53y+6b0nUK2oW3458ceM94wAdanQdkCdB+t00XUzlubcX7WIsFyM7mnpV8t8Qbu/Q9ixW1FbiUr+7/gCnUqFCgAj4j2460+7Hl2Hx5M+5ceafEliylMAWAcgGYhvDa4NfwzzP/CH3oDNNU21Ak1TGSXyhjllfKoX3jhu0jhouoP5z5wWFzYjDsxZ5re/gHNJj2saLM24pWLwDYn7YfJZoSPPT7Q8Y7xef3qTJS81Z6fyVbPUrVujngWpGuy9enCiA+AHT3wc1PmDwmvd9yd1gw7cHQFSp7i/dQHztrs0WdIjvJbqdl3jtTdsrudzWGtRomiM0Auhv9qqbAD8eA1fzKnBIRGnchPJD3PFY741JbFQj4l8HHvxqkhs8a7kzZiZYLWkoE3+xBq6atkBCRYLcsXXpJulA6boiWaFGr1ZVAtdJlICc9Btz+pn7Q8ReB6qb8Y19dZrqBwTQt5RYH08XVfFUJrS6xBh8vH/Rs5lgBsu9Pfg/A/RfcAOCBdQ9gW/I2iXq4I62x/H380SOmh/B+VhtuNodWC9xTj7RElAj97s7vEBZgWRCwWsvfaNbWm7aBoTzV5ymXsgFzFmdyeZs2w7LUpv5NnTEdBsOuCH7p1qh527iYfjTrqGyVJAChzLtn886y/cy0SssSsp9t9Xrrq9d3vc5v89EtDrfZz3+/5zGTx3QXFx9LsGDag+kY0RG+Xr52L+tbeWElAN4iSilN/ZtifMJ42X10tetqkXuURh94/AA2P7RZeE5AgBKdgFAan+Ua0WaEE2amDtp6ftGELqKodp4aX8C3Et7+1fCuC5Hsowsu9iK7LBtXC6/qg9wGkpSfJBH6ErP+0nq9DU69DxB1EejzEzDwK+nAIL7UEf66YKtG+jswxbj242S3t2raCnNGzUFCRIKwrUNEBwAmep8sUFdfh5uVllWN7Qn9valxwe3erDsCfAJUr7gwB+1fdsRiwWeHPnOYzzTtG7bG/unezve6jMK1OJie2ecZ2fdLXX2dRLxuX9o+AMp8phlSDN/fTMXbMrSqSrxYynANxrYfCw4chrQcgr5xfYEcvppTzWAaMKOHoctMF9XeEHqmbcGkNZa3wX0UDaZ9NEDYNaCZ6So/T1goB1gw7dHEBseifUR7IRhyJiXVJRaFktxlhepMzhnJDbi/tz9Q2kI/gLiuyI8tXC7gLaqoErRaBHGRCAjg4B2gQVMvdbM2d3Xk7USq6pSXj9eTery0/SVBFEfM5iubhSyLWTRhvD0FAPhqgMnThF23T9e9PkAnXldlOYO4cNxCbHpok+y+rclb8X97/0+y6kx1CWytMvj1n19tep0rEugTCE2dxi7WYdYg/pxzpLI2VY8+kW265M5e/HTuJ6tf0y+uH4a1GmZ5oAMQB9MHLp+TXUh8evPTiPw0Uri+tgjhrwFKgpsT2SeQXZZtn8l6IPR9cXs7z7fUsRVa6UevZQzX5HTOaWDvHP6JHdW824e3lzw3e/+sy4xnVCQDfmVG1ptKEyWyga8oMy1Ag+nqUONA20MxG0wTQrwIIR84ajIM+1JVV4Wk/CRFZWfWQLMHQh+oQlZdWCW7/ZZ4vhy1TVibBs3LUfRd3Bddv+0qPB/RZoQ0mNY0NZmlZJimWuOFsOAABATWoaaKF7V4ffDrqpyLWq3IXYCoevmK8ysQNEcvApZdlo0vj3+JDw8o8JM2hTiYBoCeK4EgPtu7O2sdvy1QF0xrLAfTL+942aS/NbVns6Wk25BXB73a4GPYwsM9HgYAVZTVaU+8o4JpuQW2btHdEBEYgUd7Par6+QVrLAcs9G2+stnyIAMSIhIa/HdOL0nHf/f9VxC0spXSUv3nwqhFk2Wt5OiCAW13GpfAV4goUadtG94WPWMc2zLhitDPYeoz3aNZD8zsOxOpRby46YsDlNsTNkbWTlmLKd2YBoKrceHmBRAQzD8yX7qDU5AwUhhMG7aVmW0tFAugBecClVF4aeBLwm457RjFaH0Brzrh6Yg2I/TBdMatQIF8e6enwTLTHgwtYbV3xrdFKB84xgTHWBipJ9DHhMqg6HhK+tLUYve13UgusM33c/KaydJgujIKdfV1pl/gZtAPWrUtDCqrtOjePAFjOw1Hle6zuKRaHRXXz49+DkBeKG5029G4/vJ1ANLMNc04DYq3zobkqT5P6Z9omiIi3KDtgl5gqYq3FZlpAPji2Bey20/nngYgtW6j9j3WBhvzx/E3BYE++iw3IaTBQYslogKjEBYQporP7KGMQwDU+x8zRO5nmNF7BgreLEBssH01AeRwZDmdLQH7kcwjWH52uU3n23x5MyI+iUC3b7vhwwMfNlgdvaJCNP+qcOEaWqutBTebwwd/fSBUedC/qzU/c2pRKltwhf7zly5g/vPsP1h812J8feJrAMA7e94BADTxtU6MyRX5I+kPxcKPSkgvScfys8slrQYM1yCrlA90K2srpTuUlFf76l5jIZg2FK3sFt3NdKWT2BrLpxqoC8CA+AG4r8t9AIC2YcpMmGSvw/XSMu/9afv1wbQCbGk5c0VYMO3B/Hf/f1U5LrWpKKsuszBST7MmzUy+aai6rTPL0cf8MgYdv+5oeaAMmy5vAsri9BuqIu2SDXQV6CLH1O5TVTuHVgto67xxJu8I/P2B+lp+JVVs32BPqBVEkabI6GbE28sbHDhM6DBBUHwG9Dd+RhdIC/x+6Xf9E00YpvQdKx1Avad9dIG7FZlpABJBKznEQVS/5jqf6XhlPtOUaeunoYlvEzzW+zFh2w+nf0Czec1kfdbtRW5FLoo1xar36zuCbdO2Yfmk5Wgbrr9xKaoqwkvbX8LRzKOKj5Ndlo3VF1bbPA9HlpZbwxfHvrB5YWP2X7NRpCkSqgwaak1YJn5LiUoiaZXX/rT9wt/sz6t/AtBriZgUARJx7sa5Bs3P0+gS1QUAMPfgXExYMUHYnpiXCAAO97dXg3tW34M+i/rY7XhlNWXYmrwVh9IP2e2YDJWgataxClrBvOp5EVIre6ZfGPgCWoS2kFyjBagAmk81H+hq/bHp8ibpvYkCZG0k6/z19y4UK4LpxuQzzXBz7J2RoDe21ojLXC+5btIg/vxN/mZ8dLvRDZ+cs6gQZekrIxvm22eB/Mp8PPz7w/wKoIdArWgKajNxNu8otLX67K2ftx/eHvq2Kudt/2V7RH4qtXg7mH4QrRa24pWXRQtGVARNLsB/oNsDksBbjCAASABowhAWBlz890Xj0iqiG+dXDnB1ijPTprBnOe/K8yuN2kV2XuPV99W0y6KK5LKWHA2EZrsiAiPsfmw5bmt9G2b0niHZ9tuF3/Dl8S9xOOOw4uOM/nk0pv4+1Wq7OGrd5Igybxqwb314K17c/qLVC1Bivjr2lcWA4el+T0uey5VlW8ONwgr9E024cA2l79nRbUcLrQ90kTg+JB6AvtKKYRn6HqSLIO/seQfbr243GleoYdlXU/zyzy/OngLDEk1uAnEnAS+F9+J+5dareWceRZBvENqFybRfisu8dR7QqxP1C7JUT8Mm6gKE4HndFF2rmhXBtLtoJVmCBdMMq+kV2wuA9WXZ+ZX5sttd4c0UHRSNZ/s/i2JNMb44+oVVc4ppEgOUx/CqhQBQFalqSWVeRR5+u/Cbw6zE/L35Vc0LNy+odg4N/ez10SCjIgX1Wm+gnr8hr9HW2D2TM7z1cJP7xL6HpuyuDBnXfhym9Zgmu496WmPbV0C9H366shBdv+2qbwXw0QUaVOmTA99XXR4LFLUB8uSDdEuMajsKgP7vB+jEUACcyLJehKpGW4OUohThOT2uGiXYFDUDv/YRvICLo5T3fzv/G/ot7oebFXpFdPq/Zk3mnXp5WttK8urgV5H6Uioe6PaAVa+zBboIsuXKFnx1/CssPLrQ5mO9+OeLuPXHW82O8fXiNRbeG8b7uNMSbFvJLxEtVIgWtej/Y422Bgcz+FJyGmDTUkkaVDMsU1HLL1psvGxe3HLl+ZWOmA6DoQ6igFMRFTHAyWeBiigovZXcmLQRpdWl8hWp4jJv72q+NLtef902LBk3Oa2aCuONop9NuMf3FX1+DvvI7DHTitMUndvVYcG0B0P7IcQ30/aAriJbezNnSnCF9qWuv7S+YRNrAPWkHtp6Lf5vz//h5R0vCwrWckzuMhnTe04Xnj/a61GgMgqI0r2mMhKtm7ZWba5FGr4M2FGZaepLq2Y5jjiYvlqqC9q1+v9buWxFQ2jWpJnJTLK51wDAE32eMNp3IuuEIGglS15n4MTzAIBcrUFZdBudF6VYEbMqCjj7BPBFKvCN8szvrpRd2JfKW/T0ieVLCv28/YT99L1rqTTcFL1jeguPn+n/DMa0G8Nbf6gE7W33BPuMJzc9idM5p2UFz6xZvJvRi89uWyss6ePlgzZhbRDir8xyrSE80/8ZAMB3J78DIO15rdHWYM+1PVZZK1r6H/vj8h8AgC3JWwAAvt6+Vs3XkNJyfcvRg+2fFT77aPC38NhCoZqAqnLT7LsSh4B/9fsXvwjLAMD3E3s6JW+XoPwd6Xs/oyQDLRe0FATXbMETPhs9DaOqs9pA64Jpymd5wFxlLYOHMg4JloRGGGamAX3pN5THCLLXHFEwHRcSZ3RsdPb89zbw/+ydd5QU1dbFf9WTc4KBIQ45S0aJBpIJRAVUjJhFxPj8nhFQFPWZMKIiKAoIiGQJkoeccxhgYIgDk3Oe+v64dauruqt7AkHB2Wux6K6uNN1Vt+45Z5+9K4Ppqxp1QuoQ6B3o5DPdb1o/Rq8aXeH9ztg3A6Bc1h6B3oH0rGdN45a90sfSKv5AuVAMaj6I+bHz+WbrN4D7atuswbOYfOdk/f3yY8tFf2tYHFACuREuPbUvJi7XQ1QK2V0IVbM02IPpXLt9RNHFV3CWSMlN4WDSQb64+Qu+vOVLl+sZJ/HympBVMCMOpxx2SXc+knIE5vxkX5DlIDZVIh68Pep3dX3Cxa694vs0sPdg9/m1DzdNFhXpqKAo3rvpPZpVbaZ/3ryqUKFfe2JthRghJzLsVftudbqx9MGl5Vb1Lw/kGHMp2Cvto9rj6+l7UUWByouK+EwPbD4QEPTn8uDtlW9fNp/pEJ8Q0/tf99gt1TLyM+j1Sy89AJa4rdFtlkFztYBqdIjq4PZ40gmiVWQrwMAGqSAys4rBVoinVzHRvm11KrccA+5seqe+rmR5SEE7yf6oRNnxT+3jv5gI9gkmwNsspDZ512ROZZxiwvYJ5d6fTJJeLVB9VzkAACAASURBVAJOVxN61u+Jh2J4ZqfXAX9rZmapyA+BjNKtQlVVdX0fmXqm5fzKLoZ2QXPJIl99n7pNm9R9gVKTCFdLMqgymL6KUSekDnVD6urBUEFxAddOuJYFsQsYtXrUZT2XrIKsUukcfyfde9mxZSbhGHf9iH8d/cvU4xibeATyQsAvWQwiueGXdHJwuT2sZZXeVc/7xYAMpmtHRNoH++JLF0xLquvdze9meKfhLtczToxl//SGUxuc1lt+bLnLoOzt69+2Z4YBamwzr3DbMGg+k2u7uOkLPmsIMvIDderXZ30/488h1r/LwljNZ9qgwN2pZicAJu2cpCfFygOjvV1MfAwDZwy8Yv1yfTx9yCvKu6RJIitY+UyX556WFc0fd/xYruNKHYfLEez9vv930/utZ7bqr2XyVCreSnSv053e9Xs77etc9jmOpLoXFKsRJCab8ruR6tASuxJ2lUuULCtLiAD5BxWw/8QZp+eBDNpBPGfBTvM23m+usPbEWs5lnyvz+VztkHoUVzOU0QrXfGtm50kxwookJKUGws0NLn3ivhIVQ9faXUW7WmYNrdhSRrT61fx+nPO2xjEISglKrSrThvmVjBFKg+UcvUhU3asFVLPPe6MMcyFHcbKrFJXB9FWM4pJi9iXu0/shzmefZ/PpzQAu+zvLAknXbhTeqFzbzT44G2W04hRUd6sj+uEuJTW6NDhOtJJzk12u2+fXPnSdaK98ZGXaABv4pYiAOieCJUeWXKpT1ZkGZZm0XSmQwXRUaJipMv3ANQ9c0H4TshIse71l8DJmzRjeXPGm6TNXglSyr7W8glX1w+pDRk1o9z083RoaOvS6RxyBwYP532aDX3SX/5nXSdfujXMtYWwmLBgPwItLXuTtlfYeqR51e+j94LJ/yahVYHxoSn/csmDU9aOclk3aOYlZB2ax4aRzcuFiQfovXwpqskx+lMeV4EJglWBrGdmSplWa8lSHp8q8n/di3tO3LQ8uZwVAKltLGK2/5Njq2CdbP6w+cefOY4XSnB4kvfF4+nHxv8Mzps13bWj0ZdmfV3m5Hth8clF8M5m/Z7WeaJWTSaOugmx36lW/F1C28aFeWL1L2h5xpSEqUNBDO9TowAvXvqAv71yr8991SpcEUmxVQiZi6oZWbO6z+P7FPNj6wdJXrMRlxc6EnRSrxWK+XRiAPj8sK0oc2G8WhQXHa8lt24jJGkvSvO2VaUfGRLmQHwTemXpyUI6DOrwqg+lKXOGQvXlyEuXv5a9/diGVUzkxqhpQtczbyMoB4OQBKvss/kl0pXKJKuVplEbfdPBPhtyIcvczlgfyATyo+aBLdgwjZP9PzeBLJ6wjg+lWNRuZKtOpuamuNyoDoj+PptW3rZyWj4kRohjfbv1WD04k+jboS8qrKU50bpnEuLbmteU6hwUHF4me+sAEqC68Zd0JoAHQ51UY1gIe6SHep4vfnD33if+32YOvD9d9qL+uFVxLv9f2JookgjGZMO/QPP11g7AGZf4bRt4wEjBbY8je0EtpWxXkHUSEX8QlETmTicWK9o+XF1aCbUNaDeHAswfKlaCZuX8mUH4dgcvJ/HH0bzeOVZL+6LjOrDl5zHxgIps2mfcV4hNC62qt3R5v8xnxW0odic2nNxOXGqdXyPs06KOzMsqCYFsUDapFER5qg7wQ/buTCajJuyfrk1fjc7WsOJh0sJIOboBUQN/yxBY+7vOxWLj8XTb8cOnF8v5O2BQbfp5+FRrf4lLjGLt2rBPDoxJ/P6SbQGFJoWl++OJ1L5ZtB0YmWxkhmQoAbH4Gfv3TLl5mpHl7ONO8G4Y3LNMxLK/TgiDwsTNLlsUtM39eSmXasSXoSkVlMH0Vw5HKbczu/7rbgUZSDkhf3vJ4KRsnHNJTUkJWFZxEG/5GWIkEuYS0MPDO1CrTVfhmyzeX5sSACL8Itj+5nUEtLk8wHeQtqoIDmw28ZMeQwfTKk4tMlemFhxdarj9973QOJTmLxCVmJ5oog/nF1tRpY2XJESoqZ7POcmezO03ZXjn5lwJwZcX4mJmATdhjaPi/rv9X+oaR+yE6BnzS7cH02tedVjNWmJtXaa4rKUsYK5JSid9xeWnoObknfp5+3NPiHqfPLmXFMyknieTc5HLbQP0Tsej+Rfw+6HeTz3R6XjqPzn1UF40rDRcjIL4cFWpH2vp9Le/TX8sJWYlagqqqvB/zPrvP7Wb6IhEULHUgbqTnp5eq5i/vU6kJUFxSTOvxrRk0U4yRnjbPUqvbRuTkgL8/+AUWip5FDb6eYgJ6bc1r9XtJLvtp109A2XymL9QH+2qD1BgZs2YMnu9q84CYN2HTC6CKFoCrCa8sfYVRq0ZRWFxIblFuhVq3sguyWR2/mtXxqy/BGVbioqDYAxI00U6fDD7b+FnZtrPSiyll2P6o90d2J4E/v4EjtwjhMyhVgMwK/1v3P4b/aW6BC/F1CHyLvIXyeKGbhGIplWnHlpwrFZXB9L8AUiClPMGvO8iKXXm8lI2TBzn5kJD9dLc2uvUinN3FwR2/3VH2lfVgOgtC4yG1PoeTL8C3rxQk5STx9qq32X1u9yU7xuVGrjbexmXuc+qZjgyI5Kn2ZhrsvbPupenXQo372YXPsiBWqPhGfhxJ9OfR+noPt35Yr+SXFetOrKPFNy2YsW+GycZIUqS/2/ad0zb3t7rfstK7+vhqzp3TKnCGYNrVA8QldXfT887Lcp2t6ebFzmPJUdFiUNoErTxWZyuOrXBKTFyO3n1J8yyLQnJ5IZkwl0tV+dpa13J387tNy6bsmcKknZPYeGpjmfaht5+c7gAF5WvzkNXdy0Fr9/EU9+6T7YT/s7xnYpNjWXxkMSDumT3n9/DGijdoPb41eIne9U3xzgJppQUMz3Z8FrArnRerxTzZ7kldRfzPw3+y7ew2CooLOJh0sNRnYXxiIidyD+Dhlw35wXoCQrbWdK7VmUl3TGLGwBn6c0v6u8re6UqUHTIB+tbKt8SCIgMrKDdcv56uFnyy4RNGrx7NDT/fcMH7MvoFV+IfhmUfwDQxN8Envezb9fmP/XVtjcmZH2y9roZ1J9Y507ULtfcmmrdzZXpXgnOycsqeKXy95Wv353lOY/25ExkrRYCsPEnOfzIqg+l/AXr9InoYjBQNqwpTWdEisgVgpnyWB7KyLfFPUPNzpPQ6UhDdwbtYC4y8s6DqPk19sRbxafEX8xR1pOSmsCB2gT4pvdSQA/SlVD02WmM5qnmfzz7PptObLLe7bsJ1fLP1G/pN66cvM1p4qaiWQd9N9W6yVOUGOJlht+wxXpvurtP+TfrrdkBGTN0zFXK0doiARH25SenTAEv6t6yM5TncbyvedVp18+nNejAtqz1G2wujEJRUQC4rStQSk++2DEZd/S0XA5dSyE9qNPSo2+OSHcOIH7f/SM1Pa5rsS+TropIicgpzWBa3zK2gm4IC51rAD1vg9/JNop/p+AyAk4r2pUDzKqJCLKnVMlh6c8WbjFg8AoAmEU3M96Y24Vt4wIEmWAZIKyw5bheXFKOiOtESf9v7G82+blaqmOKuk4dJLTqFb0CenaZp2H92YTY1gmowqMUgvd2pdkhtwGAPU4kyY+VxB2aGsdKVVc2ZOnqVwdXzrRJXODa8ZH/tm67ba5aKyP0wShH/OmjJ++TGbjeZsW+G8IE2JqLitP5lvTJdaClAZsXgs2IDOQkFqtr46s7+yjDEy2SnEZU+05X4x0NaqEgYqdYXEsDKPtayKgA6wuh7C3YBl192/1Lhc7pQOAbPtzS8xeW6T7V/ihGdRujve9fSbFK8syFyn3h9vkW5AvLyQHqdXi6fadnPeSnpOKZg2kLN2zGQl3Qmx0lIo/BGdKhht9FRVdWyChXiE1Jun2kZfA1tM9Tps3mH5lmqfNcMrmnombKfhyuap9tMcKbjJN19oCmr3EbPXWOFtyIZYeldDTCs4zDuanaXiTp+sTFmjeht/zuV/i8Wnl/8PGcyz1j7TKOy9/xeev/S222LSLBPsH2CFNuPknIMMTJwdRx/LwUeb/e4+H+++D/CL4Lsgmy93zvAK4AQ3xC9txCwV17yQp1+b+M9bYWfdv4E2CubxWoxM/fPNPXDV/WvysNzxGTu/j/u567pd7neYWEAeGXTtFYN/EuidJ0QmQieuGMiM/fNpM34Nnq7iaz4l6VFaFiHYUT4XR30xouBoylHzQsS7OOMkWZ/JaPorSIy/ptBam6qk7Baep6oWvqO8TUlhitxZcLOvjSEWH4pFRt7ZQX5F/eitjEnYkSLyQlDS0SONsYU+YgihYKlAJmrwgJA14ldWX1cMIOc5vxSKM1WtligPKKnVxoqg+mrGJK+eHvj2wFzAF0RWxyJWQdmAZTL2sPfy1+X8ncVZLrrY72UGPDbAPtNnnAN/DXWVIFzxPjbxzPulnH6+/1ntPOWlWmAxBY8v9iCmnsRcbkq+vlFIri9lPYl7irTjiguKXb5+xxOOaxXX/OK8jiRfsKSIphXlMee83uYdMckfr3TtX5Al9pd9NeySmoV2B1JOcKBRGef6T3n9wiBDhA99fJvsHio1A2pa13lvUZLMqVFi/97vwI1NkOyUCeW97cRqqoS4RfBmBvH6Or7AG2qtdFfV0QASdqkAbSp3oZZg2eVOylRHiTmiGr+pbjWu9Tugq+nry5E9nfAeE3FJscC7hkgXh5eRKbYPY4PHS87dVAmJkoLTC8GHOmG9cPqm9pSsguzWXJkCX1+tXuk68F0bhgnM06y4tgKlNHi+2lRtYXb40ldB8nsGNB0gKkNqW5IXW5pZE6Qzj442/UOC/3BK4c6kaHkZnvh7ylYGPI6HNJqCJN2TmLXuV2siV8D2EXQHH28VVWlRC1h2p5pbDolkn//Bl/l8kD2ugOwbyD8bKhU5zm3s1yJ8LB50GlCJ8I/CueJdk+YPtPbCLz89PlRfFq8W/9pPy/RchDqe3V8P1cTboi+wVkDyD+pYq1RgRqTKS8cCn1gwTdwStNF2f4obHtcX1VVVZi83L5tkeyZ9rHPqywEyNw9X9efXM/Quc4FBLFfLZj2KHQWZm0+02l1K62mfwIz9WKgMpi+iiEV+p7pIOh9Fa0kXwzkFOboUv6uaB1/102lC0rlhsL4XbDuv+xeG+WSCjhtzzTmH5qvvz+TrE1ovbMgIBkCz0BCa+bHzrfc/kJxuX2m5ST/r7i/3K53Mv0kX2z6okLHKK0ybbQBe3qBnU5tlVE9nCL61R+a/RCr41eb+p4lHrxG2Il0rtWZ+69xbRNn9BNPzhH9qutPrXdab9PpTRxIOkBGfgb5RfmcyjjFkFlDhJpwvhZM+9iD6X6NnasPYX5h1pnb6zTREmmP5Z0tlMGzI3m7x9vMGOicGAscG0jLb1vy5so3Td9d+xrt9dcVYU4Y/YOXHl1K31/7ci7ryvTL9bR5kleUp1sH/h2QNORtZ7fx4GxxTbr7XeYcnMP52GjwFRXS0yfK3ksqqXyXo0dtzkGz7ZWK6qQH8MVmh7FC3ie5EVTxr2JqYzmQ5JyoMiLYJxibYtP7R8P9wnm92+t6cio+PZ5VG1NgxnQhmiPPS1Wt1egLAsA7G5tvJqoKCSlZ+vqAKYEkn1tNIpoAzureIxaNwOMdD57981mm7JkCCF96d/aL/zaYRNvOtjV/mHd1VKaV0QoHkw4C8Oi8R02fNQpvxPS90wn2CdbVjaPHRfPE/CdcMh0ahjdEQaFnvZ6X9sQrcXFgKyy3rSYAjTUR1g7fQOztsPUZWPy5SDDO+xHm/wAF/kLQERVCjtu3le0Sxd5CfAwsBcgkO9TlKWhK4U5zdENl2tgeB8DgwYKm/i9BZTB9FUMGXfJ/YwbzkTaPVHi/baqL6pYpm1wOSJqyhPSlqxNcPqGoiwVZWZDevQCc6uyyQjTkjyH0/62//j4/RxtQvLWHXpVDkNrgktH4JG1XVmMuF0qzQLp92u08v/j5Cll1lFaZlvTkmPgYJuywZ+utLMjkpNZdf5IMYh6e8zDKaMXUxx/mG2Zad+OpjXSd2FWf8DUOd927FPJBCNf/dD0n0k8wbe80sVBW3AyVaSvfZJcVSdlrHatVoH0yxLKcqryz5h3ej3lfX/W2RrfRLqodOYU5+jIjldaoit0gvOzWWJ/2+dRp2W97f2Pp0aUsP7bcYouLg6FthuLt4V2xSUgpuNzWWFZoVqUZHWt05NG29sl1XGqcy/Xn710B6XXxbiYkr0/H+7pc1xEyEKyo1kV58POun03vM/MzS3drkPdJdlW8PbxpH2VP/JTWa3g++7wpCbH73G4URTFN/k789grsHwy77xd954jEm88YHybumKivV1RShGdJMLc0u56zhSL4OXxGJOTk/mSC0QhZFXdUvP1qy1eASNrK6nx0aLST6v6/GQ+0egCAbnW6gerAzskLZUDTAX/DWV0aWDF59pzfw72z7uVE+glmHZglel81uNOkWPvoWr2lohL/HGw5vYWiAodA0lbE+secE/GlQkEUaM50hJlaMvtUF/YdNjAFkxsTFRQlxvjo1RCQIOZRUs27yMdepLAQIHOc8wDc2fRO3f/9tka3WZ+bQSVcan1IrZZ/GyqD6asYkiL5v/X/A8zUO0ndrQikwFN5JrimTL4DTVZOlBxVvi8loj6Jot137ezUw7TasO8e6PY+VN0LZzqUXaBJqnlrarSEHoe0us7KihcJUi3Wit5bXpxIP8Eby99wqz4uJ8Gl0WFlL31pWU4rWFamf58B580JG6tJrBHto9rrjAzdJsICry57FbD3XI/baKft39b4NgreLNB7pCfumMj6k+v13sjSaLKbTm8ytywUBIFS5NIiwujBbgkZhMdq1exaG8A/EbKrgmr3zAbhHd04orGJcmVU7TayLcqTDHuxs/DHNIq7yd/Z8fdOzkk2VfQvBD4ePoT6hlr6W0r6bEUhae7GieulhBwPjEHlPS3vYfMTm/VJCwgqvau/q623sHoacJvY197YsrdeqKj4e/nzRo83yn3u5UFhIRz/8ns4ZhfTu7PZncSnlyLIKIPptGg2Ht9h+g4c7RQdYWw/ID+AuVs388vuX/R9DG0zFB9f7bkzbyJ8uxcO38ykHZMAM+MiKz+HolxfCjxShDUWkJFubvGQFWbjMlfUbaMAoFQl33NuD1vObHH7N/2bIL+7mKExBBfXN312Q/UBfHPrpbOavNyQ1WkQ/ud+nn4mNkNBcQH/t8xunejqujqcfJjnFj13SZ1DKlExJOYkQp5DgOpRWHFWYcRhEUwb8blhjpEWrRcRyAsVzDXPXPs5WFam7fPtJlWaOB3ylS6v8MUtXzCg6QDqh4l70imxY6B5S7gTxn2o9UNOyy5FovzvQGUwfRXjvZj3ALtSprGyqFfNKoDEbBGkJ+UklXkb4yBi7EMFOJZ6DLCrA18OJGQlsCNhh7BkAVj7X/F/q6lQcwuc7ghqGQe+gkDwyub5zs+J96HHIbMGJ5IT3G5WUQT5BHH4ucOWQljlxZIjS3h/7ftu+wcdaYuuIFXeK5IUycsDlBIxKHsYEj0xZl/l7MJs0Tc0YT0sex9izX2Q285uI+ZEDAD7k/a7PJ5jf76RXl1UUsT2s9u5qd5NeHt468G5DHpNokkuYHpo5gcJirfD5bTswWV8ecuXpfe8eeWY34cdA78UKPF28nf09fSlTbU2lsEn2H8jKF/bR/Ovm+Pt4W1JT3dMjt00+Sa6Tuxa5n27Q0ZBBuezz1vaOf2w/Qc83vHQx6NLhe1ntzsLJFUASx9YyuL7F5t8pnMKcxg0cxBPLzQrwTtSsf848AdJOUlE5glxmadv6woBCazb60Ctc0B8WjzdJnYjNTcVVbVWtr/YOHUKcg52hd9/05cpKCa2hCXyg7VqSiAfzlzG0ji74bSVuJ8RtYJr2d/8vJK3b3/cdI97KB6ojuP56rf4YN0HgNnpYsuROFA98QxOwl8LprMyxeeSUXJdreuczuH7bd8DmNTaAT7u87FJtwBwq8nxb4RkZoxaNYqMVLNI06pD269ahfT/6/p/5BblOiV+9dYzXLdl5Bblsv3s9ssmRFqJciLXIZi2FRE4toJz3ADnVjUTUuvzSZ9PqB1Sm9Yh1wux07BjsO0pyA0RAbacV1kE01ZYeWwl0/ZOY8WxFTpbKszP4W8qhwBZ7/q9eaO7cyK3MpiuxBWHizXplJN/x0mDOxh73hwFoeRE6Y6m5fB2vpgo8Ietw8Travug5mbIiSQoz73ojQ5N+VX3Qg49DtggvfalOFtOZ5zm8XmPV0hAyhGS3iz7gS8EE/tPZONjG03Vy7IiLw9sXgW0iGxBrXADpfN8S6JDoxncYjCgWSvE3g6nOsPa12Dqn3BUtAnIKpQUSlt0eBHgZrBe9TZ8cQjygk2VwZj4GK778Tom7ZxEQXGB3lP70ByRVTVWgiWkn/VT7Z/ih34/mKsJ+cEmirdEz/o9Gd5pOPsT7UH/2z3e5sCzDv2hHoWisg3Q/jsRlPtoFUmHfsIfd/zIB+s+MAUfrrQI1p1cZ7ncCgeSDlBQXMCq46vo80sfTqSfcFkxkVXEi6HA3a12N8C5NQTsyYCKai3IwMgUiFmg/fftafhlwwodw4jW1VvTt2Ff07Jfd//K7/t/d6L4G5M7mfmZ3D3jblp804K9+4pRFJWOrULBO5cN81roHu1WeD/mfdadXMf0fdPpXLsz2YXZTm0NFxvpUhPNo5D7Wt4HwF0z7qL99+1dbwTiPoleBcCCmBO6QjfA2hNr3W4qfaYBvYLzaPMRRAZEMmbNGCbsmECBZGPZCuH6UXDqOsgJp0ONDjx/rV0s8o9NwvP7aNE6PZjOzLBxPvs8ry8Xyb3W1Voz9e6pxAyNYVALwRaQ171jlWd4p+HsetrZZqYScPql0xwdcVRPWI5ePRpyHJ4fhf4mjZKrCT0ni35nozXRQ60fomlE2UUdpSBsJf5hcKxMKxXTKQGcE+qOWPoJW85swd/LnwOnzopgWirif5gGsf3tjD9ZoS62J602ntrotMupe6fyx4E/yMjPcK0rYrTcKgWuNHf+Ti2ni4nKYPpfirub3V2h7YLHBvPdNuF7V1Eas1FlFS6d7c3sA7PLplq+RsuWDdAoKFEiSB03Z3XZDqSJ1eiV9dDjALT07u96mwtAWl4aq+NXsyB2wSXZvyPKqhYaFRRFp5qdXFZF3SEvDzy8CgjyCWLPc3YvZBKb40uo/hAK8Q3hxmzNdzFSo+j/8hdsfRKPdwQFSdKwJaXIqlfzhugbYNVoSGkMcb14rN1j+mcHkw4Kr8YC0W80ctVIwC6cZ6VqPrD5QF7t8irjbx/P4+0eN9OhCoJM4mPuekdf6vySc0+dAqjaNor2MPbVIpZ8c/9rcm4yaXlp1A62J3KMNNMtp+3UUmn3Ux4cSzvGX3F/sfXMVsJ9RZLCw2amfkn6+MUQFHSneiwnoMa/rzyQ1aBudbq5XKcsLISy4otNX+Dxjgcn0+3VZD2oLbGZ/EGN1HlZ0T2ffZ6Zq/ajhh0hl2RIFRXuqVNdH1Pei6qqmqwSjdfBxUaW1EuyFXFro1uBMiZy84O5vmMV8E2FBNd2a1vPbMXrXS9Ty4LVdTJx3VzOZ5/X7bIo9AePPEJHNoBGiwAb7LmPR1o/Qu8GvckvyichK4G+1R8B4MMBz+MfJH6HzEwbfxz4gy83fwkIK6NQ31C61emmJ+vk/SRdNCQOJh3kl11CkX9Q80Glfw//ItQIqsGOszvMyaScKqBoySTPXCj059c9rh0XrgYYmWEKCg9c84D+3sqNohJXABwr0xcCx2D6gb5Oq0zYPoH0vHQKsv3BLxXudZh/yiBaVpFL7M8bq4DWmOSfuFNoSjglYXWat3s9HQkrhlmlz3Ql/vFwJ9pRkb5WEGI90sy9NEEqR0j6r6t+7aFzh9rFwNydQ34mttE25h50YxQPfLv1Wz7d4Cyc5IRtT4r/W2q0xAjRl7t823HL1d/s/iZB3kF8tkGoLNcPbAleOXYxrFBBW88+X81y+wuFPI7sv7vUkNQeRzsPR4xYNALbO7YKDY55eWDzLkBBwcc4dyjx4uB+m+7LvPDQIlauBNpOgGGtYajmqbjgWygWAec1kYJS+dlG8ftYqXn7lhiq1Zp67NyDc8nIz2D4ouGweByMzYBkTaTrfHP4aTnseNjy/H/Y/gNLji5hzJoxzNg3Q/hLo/ky5wfZK8nApDsmufwerITJTMjQqqg+Mpi2VroN8wvTGQLG4L2gxH7PVnQMkBjWcRgPt35YFySUkP2rF8Nn/bXlrwHWCTdJ57+UqshDZg25aPt6Y8UblKglJrqz/ttMnQ9fHdSvYeNvY6wKxB0MgMg9eHt4U/8hITy3cY9rCmDHmqJK26RKE3MPclX3PciAfq7lvU4ytfmSr7cXH677ELCgBzog+ZU0KAwkqoofVNsN58zBtFGsKz4tnqKSIlOvqKWLgOO9kVMFms5lUNubocZWiDgE617lzeUjOZB4gMG/DybqkygOHBN+8K0bRdKrmTiuV0GE6bk1c/9MJu+aTNQnUbruRnq+uCd/2/sbbca30VWY233XTme1yATOiE4jKi2NEHZtA2cONFvm5FSBDt8KJeCgM06tLFcq1JGqSyEno67Fz7t+Ll1HoxL/aAR6BzpXpi8EuYb5SodvhGaKA9bErxHtI3mhojLdxIHNoVtjafPUYnswXVoBRAqXOj0LyukzLcfIqxGVwfRVDPmwHtJqCG+ueNMk0jL3kPtA1BWMlNny0MZ9PHx0USTHSbasYK0/uZ7rf7reaVtHxCbHoqLyzpp33K63P3E/285uIzY51tIDGIBDt0FuFej+HnhqA4JfGvglQXIjy8D/3ZveJbMgk5eWvgRAdo4KXjn2SXLwafDI59hR5arIusm/64ftP7hdT1aKKipA5uFVRJBPkDmYBjjbTq9cfLtkBeRGQB2Nolx3Ldw1BFFlEoHPRQZDJwAAIABJREFUiuMrTFlVK0TmGXp6E9ry5aYvGTB9AA/PeRgKfYX9hOoJ61+B5IYw7wc4fhP8+RVkRzhdF0dSjrA/cT9vrXyLe36/h5aRLdn8+GbxUC0IMtG8pQWSI25vfLv+UOtRt4f1iQdrVU0DzduKZbL73G6ScpIYc+MYk2Bapxqd9NelfUeloVFEI34a8JNueVRYXEhidqIu5nQxgum0PBHYWFW5ZTDlyjqmNNxY70Z8PX31oNzd8cuCh+c8zHN/Pleuc/BQPCA/EI7cCmn1IVFU9Y2Wb/q4UuhDQWJdiNyLj6cPQZ1nQHgs63a6rp7f2/Je4kbE0bV2V15c/KK+3NvD2+U2IALWrWe2EvB+ALMPuPFjtoAMpj08xHFsiq3UCn/Eu9EAVA33FcyghNZCG0GDUXle9kIfSzumL9OfS1uftO/U0VIppwr4J4n2GFsJ9HgXMuqQFtuc5t801xM2r88RgXmR/0ka1qgKQHJaES8seUHf1RPtnmD6vukkZCXoFPQdCcJf+qWlL7Hr3C6nv7l/k/5O9mD/dny95WvAkCwr9hAVPX9Nj8UrBwr9Lxl77XJj4eGFlsuN40zD8IZM3WOnm7gSSAzwEszAirRVVeLSokfdHngUXMTfRQbTAwfD7c9CqEHMsZVIRBUXeFFUpIokom+ak0aLvTJdIpgfBpp3WVlkTvehQYCsc63Obrf1snlZFjYqfaYr8Y+HrNDd2vBW3ot5j76/2qkhZRWVckRFH2r5xfl6JfVCxVeksuD9rVz7A8vjFJUU0eSrJjT/xkK5uMAPpmlU6fbf64sjAyKFemJKI8vJ9JebvjS9D/esQ1iwj32iZCuBsDhIbUDLby7d5OliDELd63andnBt7mx2p8t1yqoWKqmWFTmvvDyIrlKdJQ8swWYclfyS4PgN+tuFyzWaUe319iCypfCWlcH08bTjpmvdCreGi0QIkbvhXCveXfMuoKmFn20nPvM/D9uehi8Pw6ku0HwGFAbC7gedruH9ifs5mmoXqSosLuTdNe+KQC0/GC//0tXzZfUdcGZo3KkF4L00oTyN5t3Fazg/9v/R5T7fXPmmiSZoFEK60Anq7AOz6fJjF73f/qkFTxH5cSTDOgzDz9Ov1IDtQiGThRX1TVZQyCvKc9uzJW37ykIln7xrsm6DVFbYFJvZV3f8LsgLNrXQNIpoJF6cbS9sg6rtxtvDW9DcA85z8IRrIcisgiy2nd1GZkGmuC+LBSXfZXJRQ/S4aK6dIJKfuuNBGSGD6ezCDOF96uo6+24LbNcswbR2hTqRoVBvORT5w+qR+qpGWrocX4xiavrzbOVo+/6NLRAlNjEh9U/Cpti4peEt0HSOoBHvE3oM82O1Sk5mDfBNISTQh+SiEyi2Es4kmhM2Vu4UrSJbmdZxTCrOPzRfV/BeeHhhuRI1VzVUKJa3cG44YDME09kimDY8UwqLC4mJd50A+6dCGV028b8jKUdMdoOhH4ZaJvUbhDfAQ/FwnXitxN+L3IvIPJHsjCBhP4WfgW5dVYzlRTmBZGZq15ivxdhipGLbCk0079IS344Cijo2vqjv27F90xGFJYVX9ZhXGUxfxZCTWakM+Z8u/9E/G9KyYvRFo8pk6+qu+9rcwVHMwKWHnQtIn+ULqXyF+4XD9D/Em64fQKiodvSo24NTL56CsKOQWl/3NzZixOIRpvfnUjPJU8TgJgV3CDsKKQ25q9ldZTqfHpN6mLxO3cHPU/TyVpQm+Pry19l0SlhCdandhRMvnrBUpy0v5OS2IkFabi74WolLNl4AR26GEoWwD8PgZBcRYEfE8vtgzc7GVgLXj4ajvSFbZIMdA6TikmLej3mf9DwRhMZJK996KyCzBpl5IvtvU2yQolXB7nKoIPf6r7ClWvcqvX7uy8iVI10mS+LT4+2T8/wgWtWuW+p3YLRMcULrXwXt0VeLVDSa9/pJ/Rk1137dqCNV1JHm79/Yo2ukWbWNaos7GH/H8beNd/p84eGFbDi1QWe5/LZXtEnkF+eTW5TLimMrOJd1zu0xSsNjbR8jMiDSJBAn8d9uIrFQUbq6rCha9cBL9G8i+s7ubl66xkTfBn3pVLNTqesZUTukNg3zHMbiIzebxjZPmyek1oWdD4OtAOovw6bYhDevfxJe+a6Vjmftn8WgmYNYdXwVZw/UgXeLIL6r5bjmCibbKRfIyM9gwvYJqKrK8XNaIGQrZmfCTuvkWqEPnO0A87REkBb4RlcLt7NONtgr6cZElZUVlV7xMFRbyDOMj7lhyCAt2CeYT/t+Cj7Z0Ggh7B+k0+sByIqCoLP4ePiw6fRGVO90Tpw30xONgpoS0qVCPgMcr0sVlVXHV5GQlUD9sPqlVnL+DVBVFRaMZ/orw8UCKT7mUJk24p3V79Djpx5sOOle4f1KhqMGwMhVI3VrRiN2PLWD4Z2GX67TqkQZseHkBopzgvHwMY+zyx9aTp8Gfcq/Q3kPeGssBePlESiEgCM9GpOZrumXWAXTNkPS2aPARPO20k+5vfHt+nNXal/oKFHgbBtI0RK9tkJOZoh5Rv8m/akXWq/s1rJXCa6YYFpRlEGKosxTFOW0oihZiqJsUxTlPov1nlAU5bCiKHnaOj0t1qmpKMpsbT9JiqJ8pSjK1dGYY4D09XxukaAe1gqupfctX4zehfIEc8asvWOwVVo/3ZGUI6bgSNL8ZO92abil4S1O3sApWRlwVDOXv+lNfXmQd5AI1kPjIaMW20+XfozifG9s3iJBsOToErEw/CikNMBT8XKzpR0xJ2L4z1//KX1F7JX53vV7l2l9R4xdO1b3WT6VcYrnFz3PnnN7XK7vTjDLCDkBqEiSIy8PEvNPMHqVqCz9940CeKgn1F4vaN0r3xFZzZNdoPZ6qgU69KPXWw7Y4JSopp3LNgdxsw7M4o0Vb/DiEjFB/2nNcrwDs0R/vOoJ2UJBfMVDK7g5YjhQAtEr4elr4MFeIpANPwadvoKsKM4fjeK9mPfYl7jP8u9p+50hUC0IIrXEbtOz+H6zD6P0Da/qX1Vf9vWtX7v/wgw92J8/f6PTx7KiCmY694pjK/TXLrPNGoxBkDHrPOmOSfSq30u/j+X/UoBPiv71nNyTMWuclc/LA5tiw0PxsBSY0sW1KsjQ2HNeXPPuAss6IXUA14JdaXlp/HX0L5TRCkuOLnHrgx7iI2jHxor9Xc3uor36NIQYaHvxPUzf99yYw4Idsf1JqL8c/MTYnVWQBf5JFGSE0Oxr6x5omaA5nXGak7u0ic/+gRedNvvemvd4Yv4T7EzYyZZjWqCp2Cdvmx/fbL7uc6qad6AF0yEhsOzJ30XPbLEvpNstgyRtWgreNQpvpH+me1gbA6/Zv8IZwTK5uaaWGPNP4s6md9I4orG471r+BtnVhLK3REoDCIm3P5d80snNMo/jRsEoef1JhsQ9Le4h87VMnalkvHYPJR8i6pMotp7ZWqrd178G254i4UA9CgrgunDNes8QTCtFQXx/u505djhFMKWMNP8rFUNaDdGT40Y4sm0+XPchN/x8g2nZwaSD3D3j7nIzRypx6ZGSmwK5Ydj8zfPsEJ8QXu/2uout3GDAI9B8JkTudf4sUIyLtX1akZGuhXQymK5mmL8qxmC60JR4lE4ERoy6fhQT+k/gwWsepHFEY8BeyOKdEvhuh31lb3srgq+nL8fSjjm1OA5tM5Qgb2dNGOO850rGFRNMAy8BWcCLQH9gJTBVURS9SU1RlHuB8cBk4BZgH7BAUZSWhnU8gSVAXeAe4HlgEGAfra8S/G/9/0zv/4r7S68Kz9w/s0L7NApjlKfqZJxQ3N74dtNn7ipy6XnpNPqykV71Aru/dWmK5LWDa9OhRgfiUuNMFFoAtmo2VvffDB72QUafmIecgBIv7v7xWUpDUYE3irfoa9TVDsOOQmEg0zevLHV7EAPQY20fK31FxGQ84eUERlw7ovSVLeBp82RN/BpeW/Yaiw4v4ovNXzDn4By351YWyAG3tOSIFfLyILMkUfdEHzvGG+qvsItoxLwJMf8HSc2o1jSeEy+afaKpsVXYR52yrrBLGrdMACWd9aUg4KjobwfIEJP2qgFVOXS4CEJOih766nuggZ1yR7Q4v+zYDvSs39PJH9QJm56FvDCOZYvAzdPm6WSPJEXHOte2V6qe6fAMx58/zobHXEy4DergnLMLgEV8FIEyWnEpDtgkwm7b0+SrJiw+sthyPdCC1RIFps1mzOsRADQIa6B7FkvIe2ZC/wn0a9zPJAhmZWlVHuQX53M26yypualOn321+Sv7eV4iLI8Tv72cwDui+6Tu9Pm1j+h7LvImLS/NZY/jsoeWETM0xuQzDfDnuhNQ3TAxSWxGcUkxGfkZBI0N4qWRCYKS1/kT6PV/+mqtqrUSqq15oS7HUGOCqzBfS4qVeJqseIzIK8pjQewCPu79scvx6KedP/HOarNeRatqIlka6B1Ide/G+nHkROmdNe9w85Sb7RtkiWSYzaZdR1owHRyMELRrqgWrn52Cn5dBsScTtk8AYFncMgBT5aNhmGZd5lEANTfZj7NfKJgv3iWSITWr+fFYu8ewKTbino+jVy8FPPLsz4NiD0hqpk9aFRTwTSfHIZi2SkRJTYkB0wcQNNY+afz61q+5ueHNpnWNDK9/M1SDgn1mJnjkabaIAYkiieuVg2dxsOmZItkiEX4Rl/VcLwWGthlqmcyzSkgnZCWYApT8onwOpxzW74dK/MOQF4bqa1a/7vBDh4rR8qvvhsGDwcPAdvHQGJ5a4PxYs5cJVuuYlvF0G+gpRDx1JxBwonlbYX7sfL7d+i2/7v5VZ4G4tBk15LqlNpIVpCCmERWZL/4TcSUF0/1UVR2iquoMVVVXqKr6CjANEWRLjAZ+VlX1XVVVVwKPAEeA/xrWGQQ0A+5WVXWhqqpTgOeAIYqiNOIqgZHaKVFan1xZcCbzjP7asfrnDsbsqZ7d0rDp1CbYPQRGqRB7i+mzzAIRNBip4bJ/qLSMVhX/KkQFRnEo+ZCpV6PftH7Cq7jqPmi4xLTNo220Hj5ZKUp3Tc+9o4nwxS7K89Er0zrCBS0x51x1k3qvK6iqauoBdIe41DhunXprhWhuqqrq9MPx28br34s7qmtZ8WP/H9n7zF46/9gZZbSCMlopsxd2Xh4oXgWmpEvs8FhWDp8GfTXhn+UfANC5o7de3VNHqsSNiAPvXKi+S/hPS5QocKIzFPjraqlxqXH8susX8lOqCjGvYK0CuP4VQKjLHotTRDLEgH6N+xHoHUjLhhFCBfj4jSw9upTTmaeZd2ieazr/Iq2HNl9Mrq0oyfLekNR7EEFQ3dC6run3Nuvqv0zmGHuu3VVupc2dSxy6Aw4NgE0vwJHeHE09yktLX+KWKbc4VYv7N+nPvPvmMazDsDIduyzoUUdMPKxExsL9wqkXWs+JdVJWBPuIAE4yPazw+abP3e5j7/m9kNQYxsXBF4chvptToCnRtEpTJxuuidsnkXkuglrRhdBRu1YSm1NUUsTAGQPJyssmbnNjaDUF+r4ikjsahrQaQmiwh+gvLjbbk0norReo1PLXHm+5ES6p8S8ufpF+0/rRo24Pna4src4khs4dqtvFSUgruBK1hJR0LZFT6M93t4vry8nGTxPUka0dPsUiiAoO1pJ3Df+CgfdAs9/hWE/Y9ByeNk+WHl2qizjd+sQmftc6PZ7q8BSo4pjUNwQXUvU+UySBOzauZUoOVq/qCx3Gi77p1LqQ0hCKffCvaah6+mSQm2Xuma8bUpfZ98zmyHNHeLi1UPhvUbWFaZ2jKWIMeaTNIyy6fxGVcMa2h+zfc3Y2rDuoUZn9k9g/bL9wycjzNCXTJfW0rEnefzJ6/2LNLnOyR9QwatUop2V6O1El/lnIdQ6mwdrGz6oyXCre8oORih4474o/bh+nZDCtIHQHwBxMO9C8jYw1iV93/8qC2AWoqLy58k2U0Qr7zlsz8Yww2dwZMGnnJMs5rit3nysNV0wwraqqlcrKDiASQFGU+kBjYIZhmxJgJqJKLXELsEVVVSNHaA5QAJjTx1cw5h2a57SsXVQ7/bXM7l4IHOlJMfExZQrwHIN6FRU2Pi/ebHiJ9SfX6+vIG01W2s5mnuWPA6LXed3JdW6P06t+L/1BIymWAOviN8Dpa6H2Oj2jtuj+RagjVb03snZdbeBJdT3RnjlIVPeLC+zB9MjrR7L6kdX2YCy1AWNjxro9TxAVuI/Wf1TqeiDUfbef3W6iGpYVun0X7q3TjKgaUDYaTrhfOLVDapuy52VVM8/LA5tXvmmwjfCPEP6sncfBbc/oy4ffdpNpW11gq9ZGON1JiA2pwNyJMHE9vJ8NOwXNc+6huTw05yHyUyOpEpVnD6b33QvAWyvegsRmUMXcn+Zh82Bsz7GCbhi9CuJ76AHMHb/dwT0t7hH92onWkyC2lN7XVp7klAmRzjQ/I53KKJ618cRW+H0KxIj8ojuRsMLiQphuuMZ2271P957fq09qPRQPUnNTuXXKrTw0+yGdoQAXz0PeGJRn5GfQa3IvpuyZckFUT9kn5k4vwHj+khHjhD+/EsrRueEwcwYfxXxiudrYmLEoo80K/6cT8qEwAEKP0+2Z6dD7P5BdneSUEv6K+wsS2ggackM7g8DoQXt9Y21MLwi0PKYuCqiq1PbXfpfsqpZ0O4AjqYLBc+PPN+rjbGntAABT9wr14cMph5m/Z5VY6MrSqNAHDosevGIKWP/oet7pPA4QwbQuJtZyBtwzCKpvh/Wv8NriUUJYMLEJZEfA6lEM0mybFRQxOVQ9xeQxSLuvi7VrX7OUm/DgKNOpBHsHQ+dPRT/h4s91EcNGLUQCVFEU8EknPb0Etg+FtaIVJ7sgG28PbxqEN9CZJY6qyrKPe/vZ7Xy4VliEBfsE895N710VgeDFQEaavYUoKwt7z7RfshDh03qmZesIiHa18beNr7Bmy5UA4xhqxM+7fqbGJzU4nXFhIq6VuAzIC6PY2/mZYcU6MLZllQsKeuD8x47l7DusJZ0DDU4C0grLFEybad7FqrOIp1GnQuJQ8iEocpgzGFuUgF92/2J6b6zEW7WXXg2ON3AFBdMu0AWQDYFyFuvIdzsAhCuKUtWwnmkdVVULgKOGfVzxCPcLF0GFoWJhrD66oiKWB45UwVeXvcqo1aNcrl83RFR5p+yZ4vyhnAzmhdF1YlddfVtWpIcvEsHIo/MeZfw2IYZklU0zQopNgfkm7uw7VHgA1tqoL3Ok4X015GWghJtCnsQR424eR4OwBry18i1UFYrzfXi4gwjCR90wSgweYceAEkhpyJiYMWw7s40O33dg3QnXCQD5/ZQV7ix9JM5mnmXDyQ16z7mxF6us1maSGq1X7V3gjeVvEPKB2Y6mLMFUTHwMqZk5KJ4FJsru+pPrmbhTE9dqb6+g9prTxLR9jaAa7Hlmj/g9C4Jg9Vs8G/0N7HrEvtKcyXaRoQI/irJC8a+SDIHnoaZ2HWRFikl3fqjwujUeI7AGwzsNp3PtzkwY8YCwn0iw90WPXDkKZsyErw/ASefgrPuzvzDv3nl8c+s3Tp9JOu1L173k9Jlb/F8YBJyD89eQkGpmFnjYPPR+X9ljCnB8cyvYOwSWj4XcUPrUt4uh5BTmcNf0u3RNAtMDNuQ4Ri7XDdE38HSHpxnecThto9ry1IKnWHRkEb/s/sVkIXShlennFz/vtKxELTGp3Ro9WssDm2IrVQ/AeP5Nvmri9Hmdopsgrif0GAN33y/Eq472sRQTHLtWJNWkZydA8hlxv5xSYoQgmuZxH3dE+82OaONSg6UMaSUCvX6N++nbzz2uTVyMytUGSA/w9jXak5mtTaryQ7il0S2W68vES3ZhNt9s/YYzL50RytelQD5P5h6cq7MwKPTXNQpM2DdYMB0AxVZC26i2pCaLays8XASwa4euta/f5xXIqgG//CWYJl8fFPeZhuJi7bvVgndvv2J4QqMa5moUwsya2LzzCQ8zT3k+6v0RhJ6EbmMFA2PN2+CbQs/rxHThpno30bdlZ04nZsG8ibDsI8gPZF/iPsZvHY/Xu15sPr0ZsLNCnr/WfM1e/9P1/He5SF493vZxXu/+Os90eMZlQuPfhPeXfqu/zs5GBNPemewcvkkk6ywEyDae2sjTC5/WXQSuFKgjVad5hivc/4drp5KzWWfNvtyV+MchxDdEjD1+9naOZlWaMbD5QMv1JfvHcUxY8sASq9XN0ILpw2cSOZUo5rnDrzeIWtosgmkHmndZk96FxYWQbSiudPoCnnCmboPQA4l/IZ7Vj6wmKjCKwS0G6wyzttXdi59eibhig2lNWOwOQCr1SOK9o4xdqsPnYRbryPWuDvI+cF+r+6jyx1ah3npQTL50cSwwTUbLA+PNrvcHa9h4aiNLjy613M7L5kWLSEGDk37EEiUlKqRFizfJjZDz1++3fe8UsLepZu8P3XhqI454ffnrPDlfBMCuKtc7N2t/Q11BhZ08YLLTOv5+HhBygtgjzlnEEdeO4GjqUT5c9yF5eaCqCtXDzUHkB33fEX23mjJ0hx86sO3sNrad3ca9v9/rZJMR4Rfh1Et+ITibeZbI/0USPS6aLhO76IkUY1Z04eGFeuXKinokkZkvqPZ6YGuB3MJcy8q6k8WTBXr81IMzqSn4+9lMVXCTT6tNxSMyFvwTsWqRbRnZEppp6uyrR/H1UK2SPbyxvc9014OiypYhgswWDbQApI+geHOmPZwTVbi7b2hM5muZDGw+kO51uvNGjzf0Y+3w/Uy8WDZWBM6Fvhz87HOIv0Es/3EDHO6LMY5Uwo7Tr0k/nulor7BL/LhDKBq76ke6p8U9lsuFH7qYUG7dae67S8tL40T6Cd676T1dZRggfYtdOX+A53c81s7eFzv34FxmH5zNf5eJiX9Ojir60LuPEb3lux+EtNqAuI5qBtfky1u/pE31NvZWhsSmcMb+oJQU2IpC7tf4sHf0qiyP371EdkE23et0R1VV/jr6l/uV8wNh9xBSspyz6k8Ezgds0HYSNFwklOZ3Pcxj85z7jdUSRbQeGJB0Whs3tLYQwkVv9v0/aBZPR26BqG0QeJ4pd01BHakyuMVg+w6kf3lBkGVLxT0t7iHxP4l0q9ONNUe3ausGOK0n4UgvrfFpDb1C7QrFJcU6ff3nXT/bE6NF/oT4hjgLLGXadTcUWwlPzH+CD/6agK9fMX7aql3rdLWL8NVbCeGxcKK7YJqAScDs+zVzhJ1hofi77m93JwSfEQkg6c+aUYt6dTxxHOYCvAN476b34IZ34RrtOdD/CT7dKNgF4X7h7E1fL5JnEnE9ebnzy8yPnU9RSZHOxpJihLIf2tHC6/bGt7PlzBbum3UfuYW55VJUv1qxYPd6/XVWFvgU1CQoLJ/W1VujKAoPdhgIhQGUGO5/WTFzpWPwT4Y7jYryICErQW9TsVJirsTfi251uomCjW8qjKjPvn2w/9n9zBw000nj46HWD+mspz3P7DEJlJU29gLglQu2QkpyQygoAJQibm5sqHRbVqbNNO9yJb2NidsO30GgeP7eVE8wBiVD5/dBv+sJfR9PH3w8fPREvdHJoNJn+m+EoijRwFRgrqqqPzl87PjLKBbLrX49xcVyFEV5UlGUrYqibE1MLP/E7XLi8XmPCyrhyQKS9mqT2t9/E57KBkQGRFZo/xW98AtLCvUgOjHH/B1mpPiKvr/wWCgI1pWVn1rwlFO/cVFJkU4DtKKMjF07VheCcaW0fGZfPQg8o09graiuXjYvCD/CqWM+Tlm7kSvt/YI52untTDYH7s92elbQDfc8YLqq/L38mb5P+CLLigYIGrs7v1sruMsmJuUkkZiTqNPjZQXOkWLUuVZn2ke158FrHnTJVijLpGXRkUWCCTFjOkxaCemCVvnF5i+Yf8i5p+u3vb8JCrdEkS/9WvRi2t3T9EWO1grFj7WH5+vRvW5365PwzkHnfYLwha5yGK79QryfN5Fe9XrxcC1RIRx5972k/V8aVNP6UM9dA+eEkFJQreMEegcyc9BM1gxdYxLe+/rAW+LFsV4icH4vF+L6wHWf2SfkUxbDYXtF78U+hgDIAbKS5YruZNUvrKP/EwCsPWSth/DGijf063v38ZNsW1EXOn4NfknMWZBj0lZoUkVUXmUP8p49CNps1A49ycDn4mEY5BPE5F2TafJVE9Ly0sR4craNqBh+v114CG8cwfV17JR8ZbTCq3+96vpvsYBV5djIOAFnilp8WnypCreBYwNp9+prFKfUcjumXVvzWlj9FvwxRVgoOeCz37YRXjOZ/p3aCcG6VlPh4B0iADfg3DnIGbcJPkrmSKx9EpN8WnNECIsTvdsRh4X38dm2EN9VBJDN/hDMCytIIbr8YCbvck4KZuRnMP/QfBKyEkjN1HQdCv1ZcsS64vHJBmeK+o6EHRZrwrHUY9w5/U483/XUBf4KSwrxVrXJVokn+QUl5Bbl6hN/AIrszyJFUUWVLSeCkHDz+PdIm0e0lYAnDdWPAQ/b+8uBYTPeFs8UrYKp61cEndXp3WTUpHYt675y3WliwFB43R+a2yewx1KPcTp/n6DxywpPbripz15eP7KNamGs6Ol2HJ8Xxi4k5kQMv+39jRXHV1iqOP/bUJJjdwTJyoKS7Cr4BdvHu2ZR0QAUF9jHAal0X5qv7T8NZfWZLgvua3Uf9cLq4WXzqrRY+weisBDBkvNL5aPBz9DcLDtBtQC7G0mryFb6WODr6WuyzipTO6ZG9S7JCyI/zwaeeeaA3abpYyiG8ciB5l2udiwZTN99H0TanULk33B93etpUbWFqUBzPO04U/dM1ZMG7trLrlRcccG0oijhwCLgBPCA4SNZgXb0a5Lv0wzrWXk6hWJdsUZV1e9VVe2gqmqHqlX/2TLussr10qda1bbnf0Wges7cX1SaErYrGCf27aPaV+wkHdDKWxswoleL/+UECOj++hirANwlAAAgAElEQVT4+S/IrE5hcSEfb/iYnMIc6oTUMfUOSpTJ6zWpCVTbQ8/6PWletTnX1nJWH/TyEME0KQ1N6sQglGklZDC96oy52p5VkCUqKgDH7AHF+pP2TLyRppZZkMn328smKB/gLSowjj16RjgGCDIgvnWq2S+wc+3OtIxsybhN4wgcG0j1j6tbKieXhsTsRGFbtX+wqNAusNP3+v/W38nq475Z9/HQnIcAuDH6Rmwl/k4+0059RD5ZPNjxLubd66wHoKPpbKi9VtCPBmrOeV550FgE9L9um42aKuj0DRoo4nf2zRBVrHPXiH8hx7mxWSnX9hMdIdTQr1t1n7BYu/1pIZoEMFW7Jnq8Q6Mmrr2Qn2wvmBRrT661/Nxtf2WAqNJ+uFTc9+pIlZ1PmQVA4lKFqfZ9X3wKqg2azYIa2yCxBXU+r6Nfk1Id18fDh/i0eLZu136z6juEur0BCgpr4tcQmxzLxB0TyUkLsFtl1P9LeAgvHke1Jx43JWkcHQZKw4PXPEit4FrUDbW3QDgmhBzfR4+LpvV4637K1NxUXlz8IuwdRMHP82DKIlKyMi3XBbi35b2QLPoXA06ZJzYFRUWkHGxBSuRs5t47l4I3Cwi45i9h6RRndmT89FMoSWwKeWF8/5n9vs1PrAVBp6gdUZWDzx5k/RNrxPd9/EbY+AL4n4frPtNtlhzhE6CJfeUH6ZUBI+bHzufReY/y0pKXSM/UWD6F/k4JTXDdE+7Yly4rrYdTDlu6ABTk26cVh86IZM0rnV8RE6j8QJMORYms1OdUITjUfI/4e/nbPdN9M0QQfdPr0Hoy3PYcPHyD+Cw3gt/3/64H054+WjBdbbd47qlQraQDtWphCT2JZSsRQoZA1mviObcjYQf4pkOJt50WWehvmWCQKrbyeWFsrwDzmFwruNZV3fNbVpRk26di2dkQUFiHiCr278lfY3gXF9h1Hy62rduVhHC/cA48e4AXFr/Aw3Me5tDwQ7zS5ZW/+7Qq4YBFezTtIN9Uy+e3bL8B8Yx7qfNL5L6RS7XAanqhq031Ns7+zq7gm4Z/UZSoTHvmY1Nszn7WjjTvnKqQK+4/WUE24uaGN+u6KPphPH3twbTDnEAWt5Jzk50Ebf/T5T9MvnOyPg90HBuvBlxRwbTmBb0A8AZuU1XVWEqTfdCOfc9NgRRVVRMN65nWURTFG6iPc7/1FYcXr3uRIO8gtiytL4RbWooqKOcFxVoqYJ/NOnvBx9p6ZmuZ1zUG3o5BcOIZjXZYQ9tfjmZ5kVZbVPiO9YIpC/ly85dieVZV6qu9mdBvgtNxVFS3VjmxybGQVg9Cj9Muqh37hu2zNJfPL8oXwXRuFT76a4LoZ7RAtnYFejioeW84uQH6vCzUZLfY1Y2N31lBcYHJsqtWsHm2l1WQZal0WD+sPgoK3eu4qNCCk2qiqqqcyzrn9HfsO7+Pn3f9rCdhzmWf43SmWdykLD7Tm09vhoMDwCNfBLKHbxfq7Fr/sFRll+hVv5eeUVcUBbXQh5jTS3l75duW+3/xOtF72b9Jf9GPZIGdT+0UNmePdYdbnzcrXmvV6fP7mjJv6zZsngW8tXGY/VqptlsE0qc7QfWdlg8XibVD10LNrfBCfXjDT3hQP9tSVMa9c4VoUhcD5b3ldLfWYwFe4vp3lQSaPnC6y23x14a17KpkF2QTE7+WNg9Mg/N2ddCDSQfJKshi/z7F/reGH9FbKnYl7CKnMEenlH295Wuix0WzYUse+KSJ/v9ws7XctL3T9Gvm5aUvM/1XbdY78B54qA+85QU+6SRv705iTuJFnQAXq8WQVRWmzYEDd5iCaaMl2KmMU4zfOp4XFr+g+0QXlhQKhe5Yre84qRm71rtOkLao2kJX9M8+Ha0vV1UVn+faQV643i7i5eHFq/d1Be8MXWALYOKOicxaehbv6G3QdgKrlgSTL+PapDpUqZWhb98isgW0/kX04x8YCM1nERVulfsVyLdpv39BEIeTnRkkctmyuGX2inBhgKUIjrs+TSMebfsoNYJqcDbTxTOkyDB51CjfA5oOYPuT22H8TtEuIM8/yx/yBBspNLwUZk6bydBjrJ1rJr1NZU+tHkxr+6m+U3jUp9Ul6ZwXNV242Fmpz8qEJWDydBfHCTApKMtr28ii6lmvpy5sZ9VCs/7kepfPlH8T1Dz7WJ6VBaEljejQ0G4dJ2n/X/Z2ftZfDXimwzPlEqNLyU1h4o6JbDi1gT8P/0mXiV0s290q8ffibKI2H/RLtUyaGefA1QKroSiKfh3I1saXO79c9gP6puFXXIP8PAU881AUhf90EWKJqNocx5Hmffpa+FAUTmQLphEf9/6YKXdNYUirISy4bwHqSFWwAmUw7TAuyvHsrqZ3ObUsftT7I4a0GqKLNRpt7a6WNoUrJpjW/KFnAo2AW1RVNTXOqaoaB8QirK/kNjbtvdGXYhHQUVEUo9pTf8AHuDgNLX8jvGxeFKRU59T+Wni2mi2U9ryy4LygssmKhLvJvTs0CGugv35i/hOmz2yKjdrBtS23M04oHIPd9bu1SVnUNvF/rnajzTM8QBPa8fK0bwRd/ev9rHr1C9p/fIfTcZYeXao/XBqGN6RTzU6m4/2xc6nIyIUe0+1crHBD9A1QReRW/jd/Lt0nWQeusjKtOATTHjYP8MqHBkvgwN2wRvTBGIPKAdMH0OjLRvpk7PG2j5uC56CxQVz/0/WWx818LZO3rhd04+Vxy1FGK6ZeUsfvuNOETlT/xHnQ+uPAH4KGvv9O0VuvCq9YI4xq0K5QrBaLACJ6Jdw4UtDoQdCgj/R26v2JS43TK0K31OuHWuLB6dw4tpzZYlpPWka80f0Npt411S2jwuqh9dUtGh20Tgx45sDRvqSdC8QjNIHUvBT7NVBtNyS2hNSGUG+lWy/IrnW62o9ZyyxKpftOt//BvjAknvWn1uMKcuLesYa1kIfbLK5vOtgKIDuSR+c9So9x9woLsd/NAfje83vhaF8xFgQki+A4PxRywxn25zAC3g/gpaVCAK1L7S5QYmPWL1VEK4SCSU2dHK0PNauqvQf4TAdqRGcLBWYQfphN58ChfnhjD97qhZo9lsuCUxmnTNd2flE+bH8CDt2Bsupd0/XpM8b+uvZntXlm4TOM2zSOThM68fnGz+1iZedbQPQK8E1h+zJr5VyAdSfW2yupyY1RVZUW37Tg263fQrx2b0pGDfDmja9Cg6Vw+FZ+3TWFbhO78djsJzm6L5iIBkd596nryMqysTRGVIGPHIEk3w2czBAVXB8PH2gzyX4CbSdy5mW7HaEjxt3xnvalBDupqIJ93LUpNkPQGSB0KhzQtXZXp2VWWBO/hjOZZ1zrLBT54q2x+KrYxHf7wpIXaPltS0htYFpVVRX4IB3OdCI4zJq9sfvp3fRtILzZP+79MXuf2cvh5w6Dp6StaxGX1gvu6aMlVKprQfK+wRQXeThRLSWk7/vmx0XbjZGCqKDY7bUkCv0J9w0XrUAG/LTrJ8v9T+g3wUm80Wj1+G+GrdBO/8/OhqQkqGIgXMnKdEmBPeCU1FfHqtmViAFNB5T7WpDsHl9PXxKyEly2bFTi70NWhhgbPh8wynIuIUUkpfioEbL9T+rNlLxtbYNpgk860b5tCLRVoVZ4VbrW7srq49pzSbUYpz0Micusqk7MQYCpe6YyJmYMU/dM5fZpIjgO9wt3GUxLPHftc3xzm7PQKthbatrXsBfXpMDtlY4rJpgGvgFuBd5FqHNfZ/gnZ1CjgKGKorypKMqNwERE8P2BYT+/IyrQfyiKcquiKPcBXwFTVVW98hQtHPDR+o/IPyDofgGtlxLoGwCRe+HUtbDqLcgUvRruqrfuYJTLzy4099heV+s6S1N2MFdkP+79sf46ryiP7QdSBZ1R0kZyIiClnuhDbT4TXtDyHr//Bnvuh9wqUOTPvoWuAx4QNMEaQTVMVZjXZmmq0GHHGNZxmIsttUmoFkyTaO0B+FDrh/Rg2rEyrX+/vTSL803PgapRCktssOdeyBTBrTy/FcdX4Puer6lisem08B4uLinW6bIHkw7S9ru2rDq+ChB9yWCmkDtVb5MbwKLPINe8fNTqUbB/IMz4A36bB5tGMH7reJffi6sKY06mDyQ1g7oxQhjrlZowrDn4psCsqTw27VXiUuOoP64+ymiFuNQ4nR2x87T4nhUHaywQoh3qSJUI/wjua3WfW6E0Kzzb6VkODT+Eh3eRsLQ6cjOk16Ew8AgeNg/77xRp6K9vsKTU+0P2ORvtxVY+vNJe1Q82+Lz7ZLvdn/T8dedH/mP/H52ODYhA1ycD0uoJ+xjZznG+Fex4BBC/2dnMsyKYLtQSJbLSnNzI+Vg7foQDd4o3Z7WHXvBZeECjjS0bC6nR8PF5eKcEUurDkb6cYZN5R01nQ144NZ+/h3PZ54gKjOJY2jGXHsdWuD5aBKxSBA+0e+v4DQB4pLSga80b7euoCBu0abMhuaFpXy8ueZGek3uK+y+pmegFb7yQuM1N+Hz9l7y7+l2n47++4GMhPuV/HnIjOJ+Wxf7E/cKfO64nhBxn0bN2pXmbYoPGCyGzFg9+NU6IICY1g8IAzgbPY7enEPHr/8k7bDm2n4QEIOwotzUSwnDeHt4iCTf4Lrj9KcGAcIP7O2oV9vwgy7YXeT/1a9LPHnSC6K1zgDE4ear9U4zoNAKAdtXbmdY7nHKYfo37WXqGAlDkS3Utb/fcNSLhZ+W84Nv9a9P7WtWse4hbVWvF4gcWo45UebnLy7SIbEHD8Iait1w73rAOw/RkQdVQLQKL3AOUwGqhcdGuncXOgcEtBpPyagoda3Yk478ZJL/qoBLt6xxM+3n5Mfue2aS8mqKLCrasaqfiLz+2nP2Jop9wUItB/HjHj1TCGUOaPEmoNpc+d05Up3dn2cVRZWV6ynZ7Mla2N3l5mJMZVyL6/trXcnlZ+ullz7hRWLYS/wxkpYtr0zcw13LOJHU+HNmIwP+zd97hUZRdFP/tJptseq+QUEOA0It0EASUIqCINAsCCgIqYkVUQEXFLiJiBSuoSBFBQPST3nuHQAhJCAHS62aTne+Pd2Z2J7sJJIQmOc/Dw+70yU55773nnkOP2sJ7XAm0dTqdVT+iNJzpxJYtkJ3lTJC/Cx4uHtbknpssFGy0ad/T2wTTqdF2osAgLK5sn9u66TrB4iklmLYd15cGJ70Tge6BmgS4rbvFzYybKZhWGgA+BraU+BcGIEnSAmAsMAJRZW4C9JUkSfVOkSTJjPCTTkB4Us8GfgPsPZBuQjQPbQ4J7TH6puMemigyTiEHIKkt/Psa/CAevJdr0VAeTOsyjcdb2asV26J3VG9cnFwoshSRZcrCbYYbm/Ynge9p603/52xY+7a44e+aSIsGgdD8K9GHueZdMbAN3w4J7dV+UAUD6g9QfVEHRA/QVOCLLcXoMuTKiG8cET6Oq+ggBzi+pwVt+WIDh1SUbwd8q9K89a7aYFq1ufKPgz5jITcUTsk9wFueht8WwA9/QqGbqqyuZCJtFbAVoYqnVz+N51ueWCQLBUUFnEg7wc8Hf+avk3+pnuK2FWW1QhrXRdCtP4kVdjRbbaxqcgNFwPXHZ8IXu/pmWPcKM//5VPNwreZt5UeWFszWyJEtH6rZBFTBR+CRziIY+fst6syq49AX+Mfdv4lty70+V4KkSUmcevIU7/d8n9bhrZEkiXoB9cifkk/d205BajQkdACfM+SZ86z7C7epiAcdc7xxG7ze9XXGthzLs+2fZcMjG+hasysdIjpYWw8MMsNArtCXZQWhvGzL8g4d2Xwk5589z6JBi3ip00uqIFrGCxkiuXRIVvw+Z+3FYu1bIIkA7WKGfH02li1VfE+L/zNt6Oy273yFHXKfjZK4Yhe2+zHYZfO4nHVSCAfW+4Pxrcdbp9f5C5wK4Fg/dOjU5ImjLHhpcBSwdanZBc43Auc8isx6jso5r+GLh8PGF4UN2rEB8MkJ2DVaBP62iL1LUJ6DDtGkYxJZ6a48Pe8HXv33VbvnSeFF+RkhV5+PnxY3/P6EE3CyJ0Qvt0+C1F8i6PH/ToO/3xDUZoDwnfya8KG4Js62os1MWek88BjLhiwT56vcXw2XQKsvmH779DL/Pl6KuYLJu0wqfS3fWhrhL0+dvQClrdDg6pOr8XPzw93grjI+dNN1TPt3Gv5u/kT6RDoUkXmgyQMY8CRMjstzsx0HPE4GMwV1tOyJhnUde2WXhpY1ZGqiSl8XQfRdDeQkq2uuaB0ye0DgYWLsmYwq/NyEkYeXq5fmOar4TGtQ6CHeJTodfm5+Kj1T2YYCRWV+Q/wGJq4SVmD1Auoxr/88Dc3xVkZuLrh556N3KuaY/NjVe1uFxZTK9Npj1kSxv5s/n/X5TONS8F/DR3d9dL0PoQpXAKUyPfbv+9XxmS0UJxxHbKJafrWQpkqqlo9uuo75e+eXvcNiEZyejCskWx/Pmcwz1r7lqD/hrqeshR0QNG8F6bUdinAqbClbHE89DrkhYmxeIph+up0DG8QSaFu9LReeu6AmyaHKZ/qaQ5KkmpIk6Ur5d9pmuS8lSaorSZKrJEktJEmy84CSJClRkqQBkiR5SpIUIEnSeEmSSi8L3UQI9QyFhPYUhP6Px1uNFRmwUBuxlJSmkNSyVLGZK8EbG95gxoYZpc4f1HAQ0QHRLDy4kNG/j7Z6EmfUFH2ZTjaD7MP3Q6vPwPusePD0GScCW5OvqDBW2wHJLUjOStHsI9uUTUaB0JErWTk3FZswZosqc9vGZfdpOOudWfHg7xBwnMC8TiwaJESlFJ/oKP8oHv/jcbUy/e0gLa1FkzVvtFD8v30CFBmsPdQpzeDwILtKnaJ82zq8Nc3DRBCmKIDbUsI2J25m2bFl1n3a0A51Oh3da94Jy0uImm19CrY+CfP/gXcvwLJ5IhjrMl0IaOUHwsEhmoG5j6v4nYY3Hu6w1xKgOKEVYIFqO/ig5wfq9PoxxUI9evejcMHep1c3XWftsZR7fUrOL48KarhXOLX8ajGp3SS2P7pd3Z7BycCcUTZtCT5nKLIUodPpRE94wEnoPU5LZy4DXq5efNb3MzxdPOkY2ZF/Hv4Hg5OBUS1G8ct9gur8xsovYbwYwZflKRvmFcbJJ0/y/p32Ssq2CPIIYmDDgQR7BLPj0R3sH7tfy0AoctEG07mhkNiGpOwkXlsmv7Sj5V5PZcC6SKZln20Bb2WJxMuq94WatK5IiJUp8EyBAHnEu3GyCDIU9fI2H0H7D1VKm6/RV/SQR/0Jhwbz4eaP1c0YZxjZn7KfH/b/wDub7O3UbDF6+WhAK96UlYXwc44Wg5Rlm0UFcPmRlSJRFbEJHmsB1bfA8i/h4zj4YjtsfB6+XwU/CbVlgg+x3+M9dDoJYkWFSGF7KDDLwXSd5iIpEhsnJ0ni7hDCjtHL1EqCCrdMuO1TiO0NG6aAJNP0/WMFkyDoCKRFIZ2TK5nBB0pNIo1uMbrMv4+LC7i6SkI51gEU/9KmIU3B7IZOL56xHcPsK2LxmfHq59MZp9mXso99Y/dpaIrT100nLT+NT3d8SlSAPashJigGV8lXrUzP2bBAu4CLGIDp9JIqnqegdpS9PkRZWPGQuDZHNX6C9WfWq9ZYQb4eVrXcex6CmIVw5zN2tliXgztq3eGgZ9odi2Tho60foZuuU5Ofyjv15U4vaxbvu6AvH28T13//6P6MaDaCR5o9UqXmDWw+uZeUwpNILtkkyGN3o7d1KKZUposLrYmbTQmbeHzF4xUSyryekKZK4nq6DIz5Y8zlb/c/Yi30X4IkC3vh5liATLn3L7e1xhaKa4AjHDnkQmzeDk6ln1LHrOgt0HYWbWvbtMHZ0ryXfnvZmibFlmLBHHW/qNWkwXHi+1bCTRNMV+HysPXYSdH3GbEFX6Mvvw76FRouEpWSpt+KhRLbaWyZyoOyxDLWx6+3G4wq0Ov01Auox9KjS9mYsFH4kQJhHtUho4ZWGVlBB9EbdDrjtLCdsQghrN69JVFxKPTmZKz2Ev477m/Vy+79Le9DZjWYfRg2Ps+9P99L/oUQMOTg4XtpaknvqN7Ui7aQeiZYrUyvPbEe1r/Eib2BzN01Vw2mw0r4TCtCb4A8uJ4lKmJLvoeM2sLCyf8E7B2BudisEbx68s8neWTZIxy6cIjxrccjSZLaM5pv1tKGbPu+bQPRoxePsnZ9NqTVg3uHC5GsCfWEJ+Gqj4VasHp8qUKoTvFzPXKPmpAAq2/qjwd+dEjJ2Xl2Jx8u2gSBR/n7sSVMbDtRHTTU9K2JT485oq932TxY+rWgnNtCDqaDfbxL7bmvDHj72wzWQ/eq1WI1mXHbZ9B6rspsqCgUW6Z+LdqSNEXQxy/VF1fbr/ZlCb0pCPcKp3FIY+3EnBARTEeuF4kBgK+3Mmn1MySeka8TpSJttDEuKPCG9S9bA7Ktk2DXWNF2YZvg0gFjbYL1QYPg3odhihF6iax0/+j+jG4+msPjDou+1kYLICeMd7/SVnybzm3Kg0se5IW1L5R5noqgmCRJpOWn4TfTj+cXyNX/KCGFcey4fIxnOkJeMLT7AML3wEN3CDeD5l8LD/G1MyHBxkYm8IjoHw8+JMRYssIZ98cEzmZbe5SldNHjHdrwOADxCfK1EtdN0IxrbFBFVRSYXzGL5FSvJ+CuJ60zlL+lbxyk18Irsz3OLmZ0AXGlMj78jH4Op9vC0wsweTkcVA9pNISCKQWiel7khuQmAr58B4+/22vcrvm+9OhSoj6J0ugd2ApJNg5uTMqz2mTmgoMLKChADaY9iqupyTgADOKB+dzbx8FHW/mwBDq2MiwNXh7ifqnn00QMSuXKtLs7qsKxMSweBg2FqIrJoXi4eDikeU/pPEWIumHtcTyWKhJNCgOjpM9076jerD65mq7fduVi/sUqn2kg8WIGFucsJEOuNZj2sgbTSmW6yGQNphX2SGnWlzcyFBZaZcIRVbgK1xd+yDobxnSH735lHK3YUF4unmn3jJV1CMzoNoOiV4pguNWGE2MGOnR245ixLcdav9jSvIGc7HKoa5t87Nk6lM5avBT+K8mgqmD6P4b0ePlGC93D5L8ni+DH8wI8GwoDRoDHOTjbssJN/7ZV1PJ4xVkkCzM2zCAuI06lvwFknHcXtiN+JYLpJ+uATyIBbgGMaTlGZPI6iCqWa5MVPDPwdgAO7nWc3VeVZg8MFz2L/7wheosyaoFfHC90uDy/W9/IJKS0mtw1T9CYT6y+A/6ZISpeWAXIFhzV9sQFuFtpfBHeEUKQqthV0HFbzYGYRaJiHd+ZAfMeURMAIB4u8/fOJ8+cx4NLHtSIctlSSiVJ0ohTKWJdIGcQj/cVQaxsC0XgCXiqNoyLgVHtYGhf8XeeUJ/jEw/SJLQJRK2E0135+/hm1QYtNi1WBMBZYZrfPz0/Hd10Ha2/aC1UsKtvo1utbuh0OtY+JAaaq2JX8dJdo6HzDEhsB3tHCsr52hnKyUK2oCy/ePvEUoUrKgOrkn60fqm+VbWyKYlLeRRfCv/G/wuIADrcK5yVw1Yyqd2kK9pmWRj7tjxAy6gFaVFQd7VQhJZ7SvMPdwE5KOzVWr5GbN97h++DY3dDh5nwsgu0kavISuCNTVBnKLC+iBWBJ5nSPrHNRFydXfmy35eEeYWJvtb6y8Tyi36BrzfCV5vgYumCXwqSspI0Ogv7UvaxPWk7GQUZfP6XfL5hu8AjheR4D1JyUoQAnr6Q10Z3EgKCLvnQaSb0Hw0v+sL4BjDZJrAzij5sKWQ3nOgDHyRh+ukHqn1QDd10HTmFObhkRYNnMr3biirsibgCcc3G3gkRm9g3QSuYB4LVMrBxP2gzG2J+tT85vzjIDSX7VH1cw07h7KQdhBx8/KBqiWKrTl4avL2gkU8Hh36z6QXpfLHrCxGAmN0ICxLPyz8O/3XJ7SrYdVYIQ7o6uWoqa8k5yQz7bZhm2f1nD1FUBCFyYdiUYyTTlGmlThe7QpuPuaNfimAuKPCLJTyyfEJMipXe/sTjon2kRDB9eNxhLjx3gRCPEI1wZnmw9tRazcBRr5doG9Jdta8Ba5tG22pCvfvXw79qpiv488Sf7E/Zz7+n/2VzwuaqIAiQCt2FKrshW+gHAK6eZVemlcTyrewzbZt4LU24sgrXD+npgCEXnM0OnXOUANK2aFEafrnvF5Xt4mf00zgNdKnRRYwBa/8lmGQAxgz0Oj1mizZgtnUg0NC8gdNHvblsmLzt2DqOnHVuNVQF0/81XJAlS4MPkWvOZchvQ8R3g0kMoMN2Q3JLhjUaVuomyoJtMPX3QxXLspotZhYNWkS/6H7kXxSDkoWjZ4oM29MRMCkc/EX2ecMjG5jbd66Q5O82BV5yZ8mp7ykOOAj6Qk4d0wbTnSLFQFrtF1ForxYnKHYWQYVvnH1VrxTUapAK6Ik9JAaDPy6RxWkuxEB2KOtPiAH/wuP2AjOxTwiRp5/v+xmP6qeh1lrCGpyCHnI1rt4fggK6YYqooM/ZDx/HCu+/5Z/BnP1kbBpIm6+sQV+eOU+lgYd4hmgq07aBtUWyiEpd+C41aABEMBR8GCK2QvQK8Xf2uEiQR5BYp+4qKHJj3rJYbp9/OwBFRcDsY/DBWeLPWAeIqq1XRk1REbTtl5bRPLQ56+LXwe2vweB7IPAwuGTDxpdg4W8wMw3mi37UQYv72q2/+7HdfDvgW7vpFcG+7H/EvgOOgney2k+/ZdQW5vS2BvGHxx2+ov38eO+PPN/+eVWxsldUL63VTiXDyUdOTsl+5r4148E1B170ET3LP62EVbPAkIuHr00A011OKP3+NUjO0OIrwQDxknu3vayDgO/v+Z7Cl+UX8Kj28GgrkaSzwXs97QVITk46AsNlm6iEDpDYHpZ9c8lzqjOrDq1nDobfP4dtExizfCy9fpSz7xlCj6B7yyjwj+XsaU+hVB/bC2ps4MVu41kwsAS92JgNQUc1SQSVvs8gHgEAACAASURBVBxm0wZz9F44KtSCvd7ywje/BXXrONG+djPwSuKnf/aIfu2LDaHhb6WyGBbdv4jhjYfbDVoAKwvnTCdyfbcyvInWkiomOIa5febyXo/31Hu9LHh56ajt0VQ8I0tgxfEVPLnqSVYeXwXFRnx8xP2bmp2jLrP4yGLWnV7Hjwd+tFsfIDFbBC2jmo+iTfU2IkmCsN2yq7TJvXuenuDslkdGhviDT+0yVQicmd3BkMezf4nK8ajZ38C9w+CpKPTlHI3o9eDiIvHj7sWsOblGDaaVAKxBUAM8XTw59+w5Yp+MLWNLpWPZ0WWaynRYmI7UzDz+F/c/u2UV+qXy7imZbLatwAS4BVQ4wP8vQTK7i6DDxdqS1by21WjFkc/0rYzafrVJeDpBMw57pfMr1/GIquAIh86cVQW/7o+5326+0i7nyK++JAbFDOL1bkIc8+X/vczkjpMJdA/k3DPnrM4iTsXgKWejjJkimC7WBtODY2z0TxSat6yhUnzR/lnUvXZ3O60gTxdPKPCxY+tUZHzjyJL2ZkZVMP0fQ2h+d3ETe57TKAB+0usT8T18F1xoyIGkk6Vu43JhW029FBTPTRAB+cCGA1k2ZBlhReJh0LyBrwgGfRKFcjBwX8P7aBAkKmmL718MeklUm4CPdryHU9ApEmK1g03FZ1odyKg9pHoRbKTXBr9Tl90zHtVYDsrPynSci9GCng1w+nYWHxCCbqodiw3q+NdBmirRLqIdOS9lw8M9SB5cB1xzWDJ4CUN61gW/WNjyjOjrPN9YWMfMTBc02/ONYflXQoV743Pw50fU/6gpPkYfPAwetKnWBldn6yDD1m/SZJIgqTVECOEWaapEzuQcXuxgFaFQfQgRghgHzx8UQkuumbDxRXYl7Sbyw0j2bg5U+z4/nWUdIPq7yRZJiSLYr9lIG1xlT85my6gtVjGzBkthQgw8Fwx1/xSBS4ENjdW5gP4LtXZnzcOa81DTh+x/mAog0MNPVObHCnr3V3tENrVt9bY83vpx0l9I5+JzF9VrrqKI9IlkZo+ZVyymdrmQ3OS/uyxw9+sTU8R3ZzM81F1dzkky8lTbJzk07hAJTydAx3dFcgOg3u8QIAccsjew4mFd+HIhfer1seoAVNsJ1US1MsQjhP1j91P8arFDC6/afrWhzlpBBX/JAzq/JoJqGx9sZWDRaV4nqn9QnaMXjwr16Q0vCbGzPz/Bss2GopYVAcY0pvV4FkL3cvqYH2SFi/ul7ioMTgZCPUORpkosHbyUN7q+QdxTcXStKVobWoydzZq/iq1Cf7aaEsEH4Jdf4Yyo8qafDaBdk2BR6Q7fAWdbw8EhoCvW9pM7wPf3fE/WK3JfcGuhXL3z0Z10bGzjyhh8kHn959mtG+ETwTPtn7ks6pynp0RapklV+7eF4r5wIUsk1Dy9BdXcbLL+VgN/Gcjt395+ySr43F1z2ZO8hx61e4jBXM45+4Xklg2jEQzueVAgGFB96/Xl98ErRbBtsB7nnbd7QROR+KhIz52bm3WfFHrg7GrS9EZLkkSxpbhUrYdLwWwxa3oDw8Lg1PlzGq0KJUhWE7hAzzo91YGuo99wS+IWkWS8ARGfEc/YP8aWS3W/opBMHuCSI/7JeLKL9XmvJEbGNbP33K1M7/rrhRc6vFAun+lT6aeYsV6rS+NIKb8K1xcZGTpwE8G0Ok6yQbBHMBsf2cgP9/xQ7m03CWkiGDeeIdoZ8v5wzcTFyYVafrWYddcskiYlkfp8KoNiBvF026fV/QMQcAJ0xRSlV+Nk2kmNDsHcPnNZOtgq4CtNlYTonwOat237z+Vi3Yh1LBy4UENbv5lRFUz/x1B4LgqCDoFO2OksHLgQTxdPhjceLhQBQ/eA5MSGnRUTIIsJskqijlym9c50dXLVzLeF7UDJ08WT7/d9j266juQEEQx+ffJVcgpz+PguQTHdOmqr6PeW4eHiwYNNHtRs855O9UlLEA+UP47/wfr49Ww8s5F/4v4RNOsiA6TWsw6Wf1gtrIGCDpOQaa9U6AhBIcXC5iipNfGp50QVNuZnMSBMbIu7FAi6IpwN5Xuxh3qG8lq3aSLYcc4Di0FUbuvINhf1fheVxepbhAr32ndg21Ow+TkOpBwg56Uc3unxDi92fBGKxcDY1qP60AEXKDZC9S2qZYGHi4cIbmQoCYcxLcdQy7cWfwz9g39GreSF6Wmip/pUDxKyEvj06wyRoKn3O1/My+VoyklWxa7i6EVZRjmpDTjnYwg7qjlHTxdPXJ1d+aKvVQTtx3t/FNXx4b3F+Q7pZ13BucCh8mVl4c073gSPi2L/DuBr9NXQ828WPNNdHnwmdACvJOYef9U6s8YmGCuqp8VFTnSM7EjDoIZU964ugsuxzeFlVxjWn7Etx3JswjGhUg1qosNWTM+2h7dDRAfOPXuOxiGNL504MJgErVfx355zGE4KyrDSU7bxzEaSspNo8GkDFh1aJKjU9ReDawYZ221YC5mR4HOGII8gCN9JQa4B9svV3Qitn3f/+v2Z0nkKNX1rqpZi7z7fkB7dnRjUcJBYKHSfdYVHOoms+9aJUGQgIUEiIFweOFTbLtTgd46FWv/YVeZLQqfT4eXpREoKbPilBTO6zaBleEu+GvqWuszDPa6counuIbExdg+zts2yPwb5uavQZL3kYLqw0D7AG9JoSJn7sUgWskxZrDixgot5Fx0H+jbBtJ8vGIuErPdDSx6i9acyE8CQj5/RDy8XL+5tcO/lnWQpcHPToSuWqyJmd9zctc/h/KJ8nF93xum1cvQE2qCkL3poKEhmNyRJsuuV/+GA44HxT/f+dFNVDx9Z9gif7/pc4ypxtaAze1I7JAR/H3HdOLnl4GwjHaFUpm17/JXrNMjDRpfkJkXHyI7l9pmeu0tYVyoWYStOrKj046rClSEn06DVJXGADpEdKo2x9liLx6xJSmOm6sDwRJsnCPcKVwP6ZqGiuHQ+W3bOcckGr7PExhVS95O6qlAiwBe7vmD8Sht3DuT2RZO3XWW6hm8NyotIn0gGNxpsb+N6k6IqmP4PQZKgMLkuBB3mwONCIXZwo8FkT862VmIDhJAOaXVL31AZsBX9MBVr1VfbVG/DbdVuc7jelsQt6ucHfOby0FwhLkZGTfBK4p3tr5NvzldpJe9tsaeMzuo1S1NNdQo+zqlTEt/u/Jm7F9xNl/lWuX1vV28CTK1FRbXVXKi1Vp33YM/G3FnXsb9jSdT0ralWpGre9xmgF6q8YbsJzOjFxfRCcM3C2UFVrjQ4651pHtqcWn61wC8eJtaC0W1E5fa+wTB4AAztD8YsGN1efO/1hLADi72TIxePEP5+OH+d/IvzF4rggyQh7GWD2P3iRUvEVp5pb83q2/bRvNDhBSa0nsAHd36ATqejT70+dK3VlZk59cHtolBGLnTn1OYmwu+7wWLIC2Lt7lh6/diLvgvkACfpNgjbRaFkXxkDeLTlo0hTJaSpEsMaDyPvpTxx7g2Wav2dDflXlfoT6B5IwZQCnm//PN1rd2f50OWXXukmQM0wm5dR1AoSsxI09j6EHHC43vKhy8G5EJwL6RTZic/6fka9gHpkHrmNwYMtwue4BBQxOsAuuVUaJraZyMudXkaaKgnmySDZRu3nxXTyH4RFsthXRS80hOzqooe/1VxIbgFmuYKTVgffsHTqBdSzWprtGgO6Yib0s6c6K+hfvz/nnjlHt1qCDv/LoF/4fcjvwhddgVsmxPwCx/vS0TgeSdKxJvMTAIIayM/O/AD8W69l56Nle0ArCA6GjjXb8VKnl8R2bGKAb88+R+SHkaWseXnw8JCg0LNMIZeWQR3FsQSKoNpcaP/qVwbnIH6z59uLNoD21a0WRLO2zyIxK1Hjaa1oNSy+fzGN/EVywGiEOuGB1DIKFsiOsztUGjaGXJqFNkNCUgPy6t7VNYm+y4XRCPoid+HTbXbH4pyjmV/ZCrO+vqiq4T/d+xOFLxfyTDvxfG0cbG0dWnNyDXuSRRK3V1QvXuv6WqUex9VESeG0qwk3KYi2tRuTViSU5Is9EjR+70pl+q+jm9RpAW4BeLl4abxqb1bcveBuh9MvR3hQGdP977R9y0EVri9yswxaX+dKwLlnzqmtgyVxPO249YtrVql6Ro2DGzOg/gBVzBdDPvicYcFGcX99uuNTddn5++azK3mX+l03XceOxF2CGVaiZ/pKdWb+C6gKpv9DMJthyBCJ98f1VPvaFKi9d36iF9knr3wqgqWh2FKs0v2ea/8cd9dz/HJQ0K3a3QzrGwFz5ZtP7mEGofit2Ds5opj5Gn3VASnAzylTsVh0jPjmDTFBHkvW9a9Ltimb1ET5hRSyHx7qIZSqq29m/vgJl03Bvbve3RC5SQg7bZaD0lr/g7DdpMVFCBqjLPhwufhz+J+4OrvirHdmRrcZ4Hkeqsvq6m6Z0GCZViCqwTIhZhR0GBI6Mv6H90nOSea7/d9RZ9wzwvdv70iyM60B/YFdHoSEm8h5S+uZbEup8XL14pPen+BucNceoHMhtH8PTt4Ff70jqvkxv4gkAvDkT9bsJcXOItCpvu2SHuMK3AxuqmWPIpIlPhdcdc9BV2dXZvaYyV8P/kXfevY92jcjfj1s49cbuQmdTseHd35onVbKmNg2K/5kG6vqtLeXnoUL9bzXewZ7x+x1uO6R8Ud4rOVjDueVxId3faj2fAEQ85tQli92ZcPzc/F6YARRn8g2S0mt4PMdMEdOstRdDTXWC5HC010gszpcrI9XTXEt6oOPgyFHtEcEHuWT/m+XeSwlqXHqdThwKJFj5Sx83VVQ5E7908LiLaS2oGoP6xMhLMCC9/Pb1GFqT3x54Wur/ehzxs7Cr7zw9ATMHmXSXkNcawIQGigCEB9ne5/pg+cPqp/n7Z2Hs96ZEI8QtcqhwMvFiwC3ADWYUYS0YtNi6Rgu2AZGI/j5QW62zaBODqYHN+uPqchETmEOs7bNIu6pOA48fsCetngZcHMDXbG7eF+Y3cmVtHZbFVWYVaAOSh9rySvvnMXdHaRCNzURYHAyqO0NJe3vlGBnzck1DFkkqqkdIzvy5/A/Ne4NNxoUxoatyNrVgCQJn2lvTycrzds3TnM/uLgAOgtHz1lt23Q6Ha90fkVj2fZfw5ROU673IVThCpCTZbDSrisJIZ4h1PF3rLMwsMFAUTEGcL/gsOUHRNvcksFLqOsrt1k5FwihUVmg1Lb9URH6s8Wbr8kJrCJta0J8RrzdsrcaqoLp/xBcXODLuUYmjapp14ejUilc8sErkazk8g9cHOHV/71K2PthJGcn8/G2j3l387ulLjui2QjWbbOpAsX2EL7XftZgWlGpLK1fy9foS+HLhVx47oK2yn68F0yXYPF3xKbGigdBmjxA9z8hAoonomF0B/T6yx9g6XQ69r8n+/8WekPUH+B7BgKOYSl0gwsNwJjJuhGX7n/rWaen3bkp1FOA1Q+sxvSytdq/YOACDj5+kOfaP8eux3ZBQ7lH89efodiJbYnb1IcgwJG91lH6srXnSfFbakcjquUnlr+kEnv798EjBXaMF4rgkZvURIyUVtO6XEoTKHKDatt4oWPZNke2UGmPtgJNzuWju1VBYMOZDSKgBDXhoViYjW5etk+xcr/1qtvLbt4z7Z+xC6Q8DOJ68nfzr1CgIk2VA77AEzD0bjGI/nURZw4HQ4EX/PYTJNsk+nwSofbfwtrv4BDYMBl0EsOH6ojPiMeiM4s+f8A1ovxWOcrgJKzdOk7M/pCkSUmqkN68b4tBX0iOl8jOH0k9LCzAxjWlekjF6Xl6PfzwAwx6ZQnohF7BlcDDg1Ir0/UD6wNQ10tQ/f39xCu/SaA9vdzWEizTlMmu5F2semCVna9ppimTj7d9TISPsLFTqhLPr32eAvkWNhrhYNZGzqTYVDCKRJnx5+PfqBRpU5EJP6MfsWmxqntAeeDmBk28bxe2VGYP1XpLwZVWV8e1li3mwnczZEQGHh6yaBbw5oY30U3XsTpWtOYog8+379AmdIYsGsLPh0TCq0NEB+6qexfDGg0rlxvGtUTPOj35vO/nVPO+ur2M+fkioD6WtcsqQOZ7WvOb6XSgdynQCJBtTtjM82ufr9D1cj0hTZUuOwGgCPRV4eZEbpYrGNOp4VN++nNFML71eOv4MGyPpjXLEbIL5Oekc74YM2RFQNGln0dLv2wkPsRqmZ0O9TNuMVQF07cIjM5G1o1Yx9w+cyHgBFJq+Sl1oPU1BqtVU35RPmtPrdXQuUsi0juS4gM2PXI/rIECP6rXEMGlXqdXHwIllQhtYXAyCBqUnyyidq6pUCwG2P8g/PaToKCnRgmqjbuswF3BcVWNKBs1bB9ZdC1AFiFLbgWumZclcBPpLaoRbs5aBXJ3gzszus2gZ52emgHWkEZDOJ563JqgiP5D0MCTW8GB4eSZ84WCsc9pAJb9T6junj0LZNaA6lvthNaUAfN3A74r9TiXDVkGTkWi9x5ET6mhQKhFOplEJRoEXUgWvSrZq3opqKJkTja/c1UwXSGYi82CfQHgkkNOYQ5LjwnhEEVkDSDGgZxB/2gh+FayZaM0KFWjyqCArpn2nPCtdrsIX+6Az/aL5NAguZfZ+4yokjkXQoMlsG8E7BwHzeZzX8fm1kRRfXGutZsmVeg49o7Zy96xe3FxciHcKxzp3fN4BWRTbHaGwKOkFIj7SqPhYLiyXrfhw2HAoMrxGfb01EGh48r00MZDkaZKXMwUv5veTfS6mWx+7mCPYDrX6KwRiQRYfXI1zT9vrvGZtvVFbRXeiqwXs0hJzYdDAwFYe2g3IIJpJ7ds9CY/gtxlXrtC83bOVxOLOp2Ovgv60vrL1hw6X/5kSLVqEHvAjztqdgezOwajNgl7pZVpN0OJZ7U76M2evHb762xOEM+83cninE9liGRjXIZIDpdMbvSq24sFBxfQ8NOGnEw/eVm2Z9cDOYU55BbmVnqwn23K1lyjiq1kLhfFewXAK9mO5aU3mDTWWHHp4u9rS0G9GWAqMl2VPvT/miryzY6iIsjLcWJar4mcnnj6muxTp9Ox7tuuMNkTJ1fTJe/dIDehZYGzSQTTkpOmMFMafALld1atqtaCkqgKpm8hdK7RWVAT/WMr3DNdLBWrnz1dPAn3Eh7B2xLtbZFK4rVffxNiWiWQ4izW1ev0ql1Ip8jSex9B2EDd07ybGIivmyYm9pFpxgeHEvN+e1GZVqrSMhSBs/Jg+9lt0Ooz8cVfDuC9bAbuxkwG/Dzgktv5st+XSFMlutS09nZ7uHiQ+1Kuhr6+ddRWZveaDcCiI4sAEbykPZ/G3feYhALzlkkkH60OyS2h01sQcIwj+8RgdcsWecBSfYsd9VCxdFEt0xygX3Q/8l7Kg9OitxRvWaxNLwk13j2j4XwD4eUdexcEHWJoh/JR7hYcFAq+vwyx2vHoDIUasbIqXB6ebvc0DB4olLKDD+Lj6sMnvT7RLPPq6pnscjD2VDxKtydtL9c+y+rPvRQOjTvEgPoD6PlDT3BPhy4yBdyYKdoxGi6CHs/CyI7WleovsX7u+iphXmEEugeKFoXmX8OILri0/r5Cx9M0tKkdpbVuc/n+9j2tPpOUVpkAt4BKocAqYjBXCk8PHRS5c3uNO+zmpeal8uaGNzmRIhICzu4iqP5frDX5lfJsCutGrCuVJr4jSfSle7t60yGigzr9XM45oVPx95vw6yJIaEviXPGcdHUFF89cLPneXMhJFb+TWQ5MDXkqO0eHjo1nNlb43O+8E7KyYOPB01DogZOLNiHnpHMiOiC6wl68q2JXqZ/9jH64u4PFosNNb9UpUO6FzpHiGTh/73wxXf57KgH9qthVnMk8w5GLR9hzbg+NghtV6JiuNn4/9juT1kwiNS+10rYZlx6H99vezN05V52msBg83Z2gWB78u2TbJUD0LgUUm6yVaUW3wZZJcTPAOOPyVbsvuS0b5mGzkMp5jlShcpAhky/9Lt32XqnoXLMjsc/uw/xK6YUoBREeMl1cb7YWpdKtBbYlR5Y4WAvC68gsqj7jNNOvZDzwX0FVMH2Lobp3dRFM54aQlWU/P8+cd9mWV9tGb1N98jYlbLrE0sBxuUe1j7a31ux7iNuq3YaniyehnqG8dcdb9I7qfcnNPdLsEWuF2P84706uC2GyKFBOmEgYyLTXCa0nUM2rGqNblE17dYQiS5HVQ7nW3+wZs8da7QYwZgif00pCm+ptGH+b6N88lS6qHflF+fi5+REdGAXN5kNKUyxbx4OuiH/fHSeUhpPa0Pardtz3wXvgZMK/9hmNdRagESMrC24GN2Lul3txO7xjv8Ccw2zbly5Uvxv/pLFhuxycSBO/272NrH3L2a9c4NGWj5ZrO1WQqbxe56DbVNBbeLDJg9ZeYBn9GnXH1YFeT3KOsKFTldkvE8WW4ksvVAoaBjXUPmPazOLXf46JKnWtf8mcnMnPH94GvglE+cutGpHy88X3FHifpdoHgoKa8HQCNf1rQM31rB/5b4WPqST6PSqLtrX8gnd6aK9/fzd/hzZg5YWSiFRVxSsILy8RfDQLaG83b8WJFUz5ZwpbTwvFci8fEcRm51mrot/t+47VsavVBFdJpOSmAPBQk4foGNlRFZk8nnpcPP+L5Atr+efCQQDRC+viIZceTd7M7D6TGh7yNWnI46W/X6Iy0EBu/Tt2QvRMF+i1AaCT3omjE46y/dHyJYsU7DorMlCTO04mzCtMVZdeesD+ed84RAiQKSyPki02tgNOTxdPQjwqp9WqsnDgAHzxBaw5+RdgrbBXBlLzxe9i66urBNNOLmaMOrnf09lE0xBta4mzqxldUQldj1sUt1W7jYwXMjQK4DPumFHGGlW41kiXW6Vf3jz+mgtz1fGvc1lsHIWZ1DwiRm2z5Ndf1PmxaY6FzhISLNBgkWCL2aBk0eZWRFUwfYshwC1A7X09fdp+ft+f+lLjo9L7PGwpWDFzYtQB2KW8EjvX6AzxXSDoILSeq4qOARB8kO1J24WVjKsXL3Z8keZhzS95LndH3w29J0CfsTC2GWey4hnxrCy4lR0GmRHgF0fz0OY81+E5+kT14Xjq8bI36gBp+WnQ7Ft4NoTk91bRJKQJuNn0ObpmlqpifqWY2X0m1byqqUqxOp3O2htz4AGos4bIEG8R7OeEse1wIiS0g7BdDG9+n932ylNRa3z/UpimgwiRSIj0iYSW1soCs4+JfuoWX3H4wuEKnZ+TTUziKNirwuXBln5rq9iuoLT+R+V60Kh/XwbKu3xJKDTxP4b+QdGrRWwomKMySLxdvYkJiiHSJ5IuNbuIZ5Z7Ggztq61WIwLbaV2m0Si4kVVksRJQr4EZXnZhx3vTNH/buKfi2Diy4pVUW/gafZGmSvwy6JdLL1wGPOSYLeGCveCNIuiXnSuCaB8fEdDZ+kw/vPRh7vrxrlK3rwSBs3fM5uD5g7Sr3g5vV29rn5xik3K+ifDpBnr0sAmmC3y5o9YdjG4si9wZ8tV2HiUArSgCFCe7fD8we1A/LEJ77JJESk7KFVdZz+eeR5Ik9W/952GrRoZSgbZtqelVtxcdI8W16qglYu+5vfwd9/cVHVNlIbMgk2VHlzF4MIwZA7kJdeHvN0hOqLxKaoS3+F1s2RhKMO3sUkSosab44lTIfQ21763o0EhaBGrve/hv+Ey/0fUNu7avsrA9aTsT/pygmbbieJU11o2ETPlxmK0/c0VJ56sJJZj+qM9M0b4HQmgWwV7sWqur/UoSmNNDwTvRbtaVPsf/C6gKpm8xGJwMat/vGQcF6EvZLLQIawEX68HukWCxDhKyTFl4GDxoH9GePHOe4xddSmMIl7mmE6Kt02VrmtJEx8rCPd1qQOvPwSUfX6MvScWy+nByS5CcwSee1Q+sJj4jni92f8Gvh34te4MOUNe/Lujgq6FvEuoZKgZHTkVW43pjZoX6/S4HnWt0JnFSoirY07VmV/A/JYTQPM7B3Y/x25HfRGUa4ExHce7VtzKrl73vLECQe5CdqJAj2Kr7Apx+6jRbljbjnh+HQrWtYmK3V+jVrOVlKzuXBVt/0SqUD7YD+Tk75mjmTek0pdQq2LPtn2V2r9mC5VEOXGkv6qR2kwBoF9EOJ72TWiH/8m7hQx0THEP8xHh61unJzO4zAXCL+YdJPYcCMLzxcHVbDzd7mAOPO7b/qiiyTdngbCajQOsVWtO35lVXOS4vPOW8xmeb7XUQlEDOYhaZKm9v8VwuMttX1stilyjP8wu5F1h4cCFZpixrkFhgY812vjEBDffj7AytasutRPl+DPp1EK+skStohjyCPYJxdXLlrrqlB/GXAyW4xewBhR6E+/vaLRP6fiiB7wbaTS8Pvt7zNcdSj6mVaXOBi3pPKSyFhQcXqsvb3h+LBy/msz6fXdH+rybGrRzHgAX3cuSI+L73x4GwYQq/fFW90vahMKRs+8SVYNrX08hpZ5lOH3hU00oGQmTO1md6VPNRAPi5XWMe7VVATd+a5BeVTzvhh/3Cz1xJaCr6GFW4MaAyPl0dUD9vECjBtMHFIpLYioBpsRNLji4Rrjr5PvBOCszZD4m3gckbU77BYTB9o7asXEtUBdO3IuRgetm23Xazbq95e5mr7kzaCT+uhN+/hn0Pq9N3nN1Bu4h2xATFEPRuEL8etgatkiSx/sRO4RurKHA7m+GJuvBoxXrZFCwevBjTyybeuuMtXur0En9lzBHV0r9k0a6w3QR5BKnCSTuTL88b1ha3VbuN+InxjGw+ErAZKCmiKa6ZTGxr3wt+NaA+tIb2g6drgE8S6fnpQiTMyQTrXhGquTU2lLqNI+OP8NeDf11yX7aCSyDOu231tlj0+TC6HTwfAB3fYeXwlfb2WpdASRpyFa4Mtsq28wfM18x7o9sbpQa/Lk4ujL9tfLlpy4rwYEUxrPEwpKkS/m7+ALSr3g6ANtXa2C2rKOA+3fZp3u35Lh/0gAZbogAAIABJREFU/EBr/XUVcHe0sPibvm76Vd1PZUAJKAvz7UVnlN+9gY9InkUG+4OuiCIHPtM+rtag+MUOLzK1y1QAutfqrk6fs3MOGQUZjGtl7ZnzoYag38tw8xeJnUEt5fUKfDl04ZBGgEzxplZQ179uqbYvZUFJJFRzjQazO2fyte0KV5r0KdkLaBtMf93/a6SpEi92fBHQ+kyvPLGSLQlCjLNzjc6MbTX2io7jasLX1Rcybay6TojWmxzTlVm2KZg/H7p00cE0idffNKl900owPbL1MLo9sBtGt4HIzby54U3N+udMJzl+LsF6vEZfwr3Cy/3OuRHxwJIHHE4P8wy75LrKM3/TmctosavCNcPNEEzPmAFBQVC/gZy4UnqgFy3k39P/0uzzZrDqI8gLhvONYcWnkCWz2+Rg+s46VkXvKp/pqmD61oRHCugL2XDAvjTdKqxV2bSjtDrC0xUgwdqjl2fOY2zLsbQKb0WeOU/t9bWuJ6oU3VrZZLsDTlKnkZWaWB6vZlu4OLnwYscXBdXcJc/aXwlqJTzATfABFbpZeRHpE6kZmOl1elWJ+rEOQ3i96+ulrVqpiPCJEPZCekntW0nJTRGfq22Hiw3BK5GYjqdL3UaAe4AaxJSF+oH1kaZKmF42kT/Fmj0P8wwT2Uz3ilv6RAdEawafVbgyKP7hcU/FqS0Hvw/5nW/6fXNV9hfofmWVvpJ4qu1TxD0V55AuFhUQxdHxR3mt62vodXqebvc0QR5Blbr/klAGs1cijnWtoASUhQWl26F46cXfK8jHEyeXIjyd7H8/W3Xktze9Tb45n7r+dWkW2kwTVBqcDBidjWq10WgJAI8LePoL14NmMeKAPDxlhwPF/1QOpj/s+xZZpixMxSY+2voRF567wL6x+yp0TSnnXlTgDmZ3YrP3lXsbZcH2XahDh1FmPjuq7JcM7hTLs2VHl3H7/NsB6FuvLxse2XBDJRPf6PaGdaDsYbW4WbMlCd10HS2/aFluSnWRpQhJkpj+73QeeQT2bxeJmrTlL/D4isd5YPEDajDt5gbjbhsD1QW7SrH1U5BRnEx6trVHONOUyejmowVL6wbF8mPLafxZ4wqLpD3aoko75Fpje9J25u+dT2xa7BXZFWYr5i83cDDdsyecPw9+PvI7I0Rmdh25jwN/thE2WftGiGm11goHmTXvie/eiTzT7hlWPWAVZzyZdvLaHfwNiqpg+hbE78OWgU8Cx07aV5dGNh/JwvsWOlhLRqJsn+J+AeKsL7NuNbvxzd5v+GLXF7g6uZKcnaxm5iUkSK0HwAfDHifvpTyOjj/K2UlniX0yloUDF9IqvFWl2O0cm3CMFk+8A/WWC4sdeZOtq7Vm0aBFpVKfywsnnZNamW5bp+EVV0CuBN1qyarbfcdCxEbo+zg7xpTPqqosuDi5aHriP+t75ZTF5zs8r1Z0qnDlWDdiHUfHH9XYpNwdfTePNC8ffftSmNB6An5GvwonvkqDXqcv0+IlOjC6UkS/LhfX834uL5TKtCnPPpiODhDtNGFGkQA1GiW83Y1EeNi7OVzIu6D5vjVpK9/0+4aogCjN9JzCHD7Y+oFKc76YlQvO+eTki+fhsC4imTN9o3x/F8tiCLKa99P/PKbaZUmShA4d2xK3kWUq/+BTqRTnZhoBPRgqp5qq4Mk2T2q+K7oOFrOBV/55Bd10HUuPCpqt0kP+ae9PAWtVe/Ty0ayLFz3WTYKb0DGyI/fUv6fS76GKYl38OsiWg+mHusMUIzSdL1qGTB7sTt7N+dzzfL7zc3TTdZfl72x43YDrG65MW6FtOUFvhiIDPx74UaVuLz/5KwMbDlQX+WavNgGodzFhsfGZ3pq4ldfWv6YR4bpRsO/cPj7Y8gH9Fvbj4PmDqmYBCJ9phYFzKby2/rWrdIRVKA1f7vqSp1Y9RdQnUTT5rAmZBZkV2o5Smb6v+Z03RSvCrsd2sWeKjfjk8s/he1lgsfsLcPs08TlWFgX2TuK9nu9ptpGYZU/9vtVwYzzNq3BNUS+gnqB6Z9oLjS08uJD+C/s7XE+SJIjvDK4ZYMyA9LpwWlAwXZ1dWXliJbuSd2EqNjFr+yzaf9PeWqFOFQOyunWFUnR0YDRhXqL6M7jRYHY8uqNSBrD1Auqxa9KfMKwfxCzSzBvYcGClUcPMFjNkCCsBveX6KWcNjhnMsMbD2D92PwQfhlGd6NQj084ftbKR9WIWxyYcI/elig1efz30K2P+GFPJR3XrwsfoQ3Rg9KUXvEJISDdVoHkleLjpw4xuXn71/2uNsirTg2IGIU2VSM0SidM8KQ1XV63PdPPQ5txV9y47FeX18evpPL8zi48sVhOdthZT7SLakTM5R9gWGfJE3zJQp66oSOsNgrnj6yxTVovkZ5KTSaXu63Q6un/fnW7fdePYxWPlPne9XgTUITQBwM+7cp/FilAaiGNVgulRTcapytQKxfFMlmB6KeehWmPJf7u76t7F7B2zCXwnkD3n9mCRLJV6rBVFfEa8Wpn+YvhUMJiEyKXkDOdFW5GXqxf7UkTV/47v7C3YFFgkC7rp4nzNFjNssFFtD98BFgNkCxV7pTKdlC+qWooOwpAYrW2jk8FEsU0wHZ8RD1ymg8g1wvak7XSZ34VmnzfjmTVWxwzbgD8tP40tiVsqfd8VaY+ogj2+2vOVmtBLyk7Cd6a9/sLlQAmmvx8y56bwAG8R1oJmEfWhzcdQew2gF2LBAG1mQY1NVn0gAC8r20KxSrwZzvNqoyqYvgXh5eoFxQZI6MC/J7ZqXuprTomMlCNal4QESbdB9W3Q7n0x8WxLQGt5YQu12pBaD7wS8XpPf8MqHJYHp586zcBBQjAtJPjaVcwUKD3PJ9PFQMT2hbr+kfVXff9erl7UC6hX4eTEd/u/Uysc8fFwsooldFPg0x2fXhEF7mbC/AHz+bLfl9f7MC4JpTId42Pfb56Wn8bktZNJSL0ITgWYJRPnTfHsTbSq7+8es5s/h/9ZqgCk4rQQ7BGsES5MyUmh4ZyGIkg25EOQ2Ob0A6IP1NlVbC8jJx8Pg4eoTDsVgF5S730dOvae23tF529wKyAxWWQH9K721cr2Ee3pWadnhba9+Mhi9bOf0c/qOFBsDe6Ud2WP2j0AoXoO9v3Wq2JXkWXKIjU/laMXj6pq39cbFskC2eEYjRKj24vkS81a8rFn1ALE76QMmLcnbS/VFSMpK6nExuV346tO0G2K+JwVQZ+oPlZrLDnp8t0935E9Oduu6qV3MakCemDtFU7OTq7I6V4VdJrXifXx9u/dMM8wOs3rhG66joB3AhysWTHYuik0CW5SadutAnCuCaTVggIvIURZTmRlgcFwE7qT9JoIvZ+wfu/0Bhjkm7SRTeXaxhZrwyMb2PjIRu5tcO81OsgbF1XB9C0Ifzd/qLYDgK4zJ2psjbYmCpVmRybs5zLT4EIMhO2Gll8IBUBZuKRlWEvtwkUuIFmVJ0mLgoAT16yyZWvBcTVQw7cGv/7szL59cNeVCdJWCG2rC7r9pLZCFdnd4M7eMXv5dsC31/5gKgBb9enISKhd+zoeTBWqcBNDqUyHutpXqFYcX8Hbm97m5PlkMOSjQ4dFn0+BTWX60+2fsvzYcpYdW+Zw+6n5qUiSxJCYIXSt2VVV4D568ajwCze7i8r0kAEwpB8Gd1EFV4Ikilz5pNcnhLrVEkE3MGND5Xnj5usuYsoUPbmp5ni7+ZtGbmL1A6srtG3F0eDlTi8T5BGkDpD/jbWvMNYPrA+gJqfLcnhw1jvfMAJaEhLkBRIYKKG8mrs0kwXJ0sWDedHhRbyw9gV1HUVErCQaf2ajeSCB4dQ99O0L5qkm5j0k64pkRlDDp4bGGgtEq4eni6eGDQDg6aHHUmi02eyNZ4nlSIsl0ieSBkENKlV3oV90P4pfLdZQ7at8pisRf70Fc/fBrFMwK5YBX48pt8tMVhYYPQrxeNOdfecqV8PhauLOOndC4HGYFC4sUe94xTpTsT/01GoA6HQ6OkR2uGXYamWhKpi+BeGsd4b2cvY36TaHlWLbarUkSXT7thvVXm0raFqBR4UAVvAhofSHjWl7Wm14PxHeMMEfc3l/y/uiNyy1nqrkXRm90ZfCxDZXX11bp4Mm1ykp7OniiTRVYmjjoeq0pqFNeajpQ9fngKpwS6CyhceqcOVQKtNnU+17/BKyhApyYYETOOeLZ7GzSSOgNeHPCfRb2K/U7UuSRLFUzKztsziRdoImwU1wdXLlfO55sYDZDZzzwScR6i9X11OCJIqMtApvRVP/NmI5UDUYbJOeFR2QObnmQ67o376nyZ128w9fOFxqJfVSUKrOB84foNhSrAbTe2wq+0pwV7JSqiQMHPVGH089zpqTayp0TJUNSZKgwA9fP2uQOqbtg+CWqlKyp62bplknLiOOfef2Mf3f6RoWW6ZJvgYPDIHF32NOrUb//mLMMaC1zJzICWXOzjnWyrSLuczjG9K8P5ZCIzeyrbSi/6HY+AHcUUvQ4fvW61uqnd6Hd35YLp/ptafWcu/P2irgyhMry3u4VXCEi/Vg87MQvRQ6zIS8YP75uis1PrJvhywL2dng7mkmvyj/hkz8lIZVD6xi88jNPHb73WwdJYpqTULkAa7ixNDug+t0dDc+qoLpWxDOemfwPgteSZB0G22/bmu3jO0L8s/YP4X/tJylxk/ugw4+ILyjgezCbOE7vXSeVcxk1xjICRI9JHlB4B8LXBtxHx+jz6UXqkIVqlAu3FnnTur4VfXo3UhQRLj+d3yH3TwlcSmZXcEgB9NOJopM9qbuj7d63G49Bcr74EzmGb7Z+w2mYpP1Oa5UppV15el9GwjaM0VG7v3lXlYfXa9WpkM9Q9Hr9HSp2aUCZ6yF3jUfckQwXSfY3lIoZk4M0bOvTE9g2bFlxGXEqcF0sdlZpT0rFeZFR7QaHYod4x/D/uC3+3+7ov1fTQS6B+JeXB1fG62kdhHtiIrwgVwRBNpaNdXwqYHR2cjnuz5n2rppGuG6YY2HQbEz/LYAZLr/3cJlDh8fcHYWVXCw9kxH+JetzO/mBpJk7fNXROFsqc7XG7V8BR1+aKOhmF428eGdH/JJr08AUWjwdvV2uJ6rk2u5fKbzzHkqg8SgFxV8W3/zKpSOYb8NI/yJ4XiHnaf+q/cxatkonln9jNXmcdNzwqHl7segx4vQ7j3YPYqogiEkZyfz1oa3LkuULCsL3DzKV82+UdAuoh2f3/25yrIZ0XSEmKFY5/naM3+qIFAVTN+i2PDIBmGllHSbRiRDsZuwVc5dd3odSMDfsv+jGkwfFD50OUFsS9oGhwbDmc7QfwSMklUrDw/inXflKrdv3FU+KyuUCpqtd2oVbhzYtQVU4abAhbwLxGdWvVBvJDg5AYa8Mn2mw4y1qR0cSoB7gF1lWoEt7Xhql6m8fcfbANxd7251+pe7vyS3MJfn2j+nBty6Yncw5DOv/zxrJQMY1nQwTs7FUGQkNi1W9FbLlelavrWwSBbMxaIqGRMUQ11/e4Xxyzp/Yx5YxLn/7+wfFdrG5cI2mJ7dezbSVInnOzwPlN672iq81Q3dU/hws4ep5dqSoADtNREYZFGDaUVoaMuoLRidjUiSpP7Wtsw2Z72zqqOiIETkOdDpwC+gWCTWsQbTL97+dJnH93eC+E0V9W9PF0/qBdQrNUC9Hrij9h1IUyUifCJwcXJhYtuJeLgIysi2pG3i+neAcSvHOZyuBOdlwWwR986Os/ZJtCpokZSVxIKDC0j+axDZ54I59r8WfLP3Gz7Y+gGzt8/mxOlcXA6PhGbzwfMC6S+kQ5fXweMC62YPJfz1Jrz0z0v8dOAniixF6nPLEbKywM2zbLbFjQ4PFw+WDVlGv2iZsdT1VeEUU2c1q4avKnvlWxRVwfQtio6RHbk9uiWk1YMT1qbfhkEN8XfzV6lpqXmpvLP5Hdg5RnjNAXjKdLZAWX110wvs+b0dLP4efOKh6XcQsRUCj8A/bzDjDfky8z3Ng00evFanCHBTWBPciqjjX0fNflbh5sGak2vK3UNWhWsAlxzMZfhMO1m8CPD2wOhsJNTXH1e87JbZnGC105u2bhrnc8/TPLS5nc+0uksnF7DokYpcwDmf2LRYRjQdwYDoAYCoojm7iGAaEHRwQz4/3/ezKkz5+a7PKZhSwN6xe/E1Vkw91+CZoX7edaFyxRdLBmzWYNo+GaH4bpfEggMLqD9bPOvuj7mfPWP20KaavVjc9UR6OviVeFUGBktqMB3iGcLIZiMJdA9Er9NjkSzqGMH22njiticgTSRFdOG72FEizvMPKIZcEUzn54Ozs/hXFhLzxTgjTy4gxmfE0yeqD91rd6/IqV5zNA1pSqPgRiLRUBZyA+HfV6HQnf7Rjh1VqlAxKCwRLspjjoQOUCzu4RfWvkC9oV9QWGiB9u8C4Gv05aE2A6D3eEhpAl/sgCP9GbdiHIbXDQS8E8BXu79yaAmVlQXuN2llWoGz3pl+0f2o41+HjY9sJDzqIozqBMZs7qxr30pThapg+pbGyEEydevgYHXak22eZF7/eWq2+Y31b0BKI1hhIziil1+eAXIwveUZ8pe9J6w0Os2wzq/9FxTYvKH9Y3ms5WNX63Q0SMlJAdD4PFbhxsFTbZ5iapep1/swqlBOjGw2kmpe1a73YVShBHQuuQ4r09EB0YR6huKhCyTLco7C4kJaVI/BQ2+vLJyen675vuHMBt7o9gahnqGa6flF+by7+V0RWBXJVFtDHjM2zGDH2R083OxhAMatGIeJTKvydZEbOBcweNFgInysgk3pBemsPLGSjIIMKoK+zdpbvxgrto3SMLGtVXtDh84mmDYwafUkdNN1/HTgJ8DaM/1538+121g9kWOp4l35y6FfaBbaTBVxuxHw3b7vOHexAB9fbQBQLdSAU7747YssRXyz9xuiPonCbDFjkSx8vO1jQKuv0iq8FZGWboAFRnUkrJ5W3ds/0KKpTEvOeXy09aMyj09vEOrBSmV6x9kdfLj1w5sqqadDh/kVM63CW5W+0LYn4N/psHs0H20r+29ShfJBkiQRPCutivFd4L0UWD4XLkbBzrHQ9Hu2P/8zc/uIse63A76FmN9gZEdwyYWfl8LCJZAdQnZhNo8uf5SID8VzLKMgQx0zZ2VBkL8rj7d6nAC3ylNwv17oENmBpElJ5EzO4dSTp6734dywqAqmb2E8ONwg+p5zRFA9ee1komdH039hf0zFJkxFJj7a/Al8dsBu3S2jtmj7J3xOw/j60EpYyVx47gK4WBUn6TId3DLpNK/T1TwlFVdbzbsKV4Zv937L06vLpvdV4cbDreQzfVPBJRdzgX0wfU+De0h+JpnsHDPHMvaSU5hj5zPdt15fBjYYaMcU2XF2B31+6sPSo0tx0okqTkmf6cOPnRZf5F7oP47/QW6h1XveycWMpz4ICryg0EOleSvb0aGj4zcd6b+wP6fSKzZQq13NWj2uGVa5TKSS17oSTPepfS9HLx4F4MiFIwCczRFKtyX9skv2n+um6/j39L+AYwvKa43TF85hKTRqeqYBQoL1FOf6QbGThna8eeRm5g+Y77B/dH/Kfs7EOYN3IpJTAb1/6q2ZXy3ERdMzbdHnCUX4MuDkKi5WJZhWqoFrT60t13leLyw5uoQD5w8QnxHPzrM7S19QHoeRGlWu7UcHXJkewK0Ai2SBzBqiHaTPWLh/INT9E3aPgtnHRaKv05u0rtaaMa3GqOvFPhHLmhdnMHH+t9DjOTh5J8yKhe/WwL7hYNHT9duu+M30o9t33QARTEcG+zGnzxxN0vBmh4eLB7X8Lt1+cKuiKpi+xfHYPQ3xTOlJURG8veltdXpcehzGGUbYZVNJ7jUBHhAUj7bV24LeYrOh1hAkBhGTO04m0D2Qzq1l5d9R7aDrtKt9KhrcTCqKtyK+2vMV53LOXe/DqEI5MW/vPIfUtipcX9QJCcXfyV51NrMgkwkrJ5CRY1KtsZbF/kJyRqq6zPKhy1l0/yKNdoYttiZtxUnvRIR3BDHBMer0tPw0ms+Wq8KyAFl2YTYPLbU6ChTrc8nJM8PbWXD2NjXBmppv3f/J9CszmT9t2qV+1rvk2c3vH92fwTGD7aZfDlRrR4SlpJOToCUXFurUd4zyf5+oPgB8sFUo3o5pOYbScCr9FL3q9rohElN52SJDUJLm7R8g933mBfJP3D/qdF+jL54unjzY5EEMegPVvaur897Z9A6k1wF/8ZuaikyabYYEO+GUH8qA+gNEz7TB8TVnCycXUZlWaN75ZhFVq2ryNwlqflyz7AV2Pyr+Lyg7IeRucNeIrzUPbX6FR/bfh0WyqO0HUwcNgoaLYeCD8EAvcM2Ezq9zf+cWduvV8a9Djzo9+LD3O9DhPRjbDGr/LYR3l/wAX+zk39/DId+X9XEb0E3XcSE9n5XxP1NYXHhDJMuqcG1wiSaOKvzXEdUimZzPq/Pb/2JpENiAIxdFlr3RZ42g0A3WvwI11sGI29k1Zhctv7CKi0zuOJm3RrYH5wLwEL7BiU8nUs1b0EDfmtiYDk4h4HntX3q23tlVuPFgSw2sws2Dx1s9zooTK673YVShBGoEBaqVO1ssP76cT3d8ij73afDOR6fTYdHnYS609vy+vfFtovyjWBvnuNKXWZCJRbIwsMFA2kW0w1RkYsHBBRw6f8i6T2f7net0OvFuMNtY/7QRCseXovaWB1uzfwHEe+lUpr3Q0//bu+/4qKr08eOfZzKTZNIrCUWK9KIgAlYURWwoir33uvpdV13X/lNcy35XXctaWL8W7LA27CJ2VCwgIKIgHUNPIb3P/f1x7tRMyoT0PO/Xa17JzL333HNnDmGee855ztwz5ja77NV5qwGTkM2bf0OclfyUs4aKnqnw6d1UjzfDjUOz3GfFZ9Vbbke62esNptPSgl9PSa8CXFDag8822MF03kBu+++LjBhu2k/ozQALC/IHwpD3/M8DJKZUUVueSGZsNiUVgLOi0aUys1OTWIa/Z7ojvXctpjRgycGy+ocGe79fyQz/e3brIbe2Zs26hD1T9+SGvR7mfuD8QyZz5/mmDT3z0zNcMiAVHBYT+/678YIyfoczTzQr16w4zSTlffNls81VCtXxeICcyhXE3H0GSy9fyujs0a12Xarj0J7pbu7VIpNN8j9vrPCtSerzzd+gpCcHnvcRl+57KWN7jqXm9hqqbzd3rO+dfC+r/jmLqYf24voDrufcvc+lZ6J/CY3tpdsgYQeXjb2MR49+FDBDCttCcWVxm5xHqe7kialPsPEvms27o6mKyiO/sLLO6955vB57aSxBwFkVlEDr5k9v5pTXTmm4/NoqHv7+YdYVrGNg6kAEIbcs1x8ou+r2CAMmmLZ72s68dikM+ALAl+l4317+m7ONBVX1SR7kv3F668S6gcWCjQtY+MfCZpXtNX/dfN8c3VpHGRtzt7H120mw4FZ+etfMgw3Ncn/v12b1C1dU3cRwOUU5fLjmww5xU7Gs0CSIS0sNfv8zM+3nZRlU1VaZAOLxX3ngolN549e5fLXpK6pqq3z5SQCqyqKhNNvXMx3aMxebWAaWg//7dg6VlebGRGNmTDFrOJfV08Q6uvNHn+9bXSTUzKkzzTrT+QE3YooaHho8+YXJQc/nr52/23Xs6twuN6m1wwHIDkgBcfHYizlnzNlA8GoG4VTeVknN7TU8cewT4LDYNusR+PNgOHcKHPQPGP6mf+eYoha/BtWxaTDdzT17zt8hZR2ff1VDydZseGg93F0Gd1rw5Z3Q+zu+jfoHTx3/FGCWzArMSjkkfQjvnfUeDxz5AC9Mf8GX4RNgVI9RABwz+Bgy403Skb5JfdvkutLcaY3vpJRSXcDC7Z+yNb/uDUT/WtD+ZanEWUltdd1Badfu789h4F3DNtTK3JU8segJ/9z5Gm8wXe77f8EbFJ849ETcbocve/Orv/sTc/VK7AXAhN4TIrnMsKKT7C+ujir6Jtf9/+WQWYdw4LMH1nm9Ka6acBVgMp3/UWjfbHZWUVvtJCPGjMAq2GiG13vX//XyBt8fnPUBH5/zcbPO3xbia80w7bS04GA6w7v8c1mG+bzyhoInGk9FIkWbexLrNEF4cZW/3ZVsN9m/SbWD6ZBe5NRU+3l5GhUVEO92+dbrro93HXVvz/TNB98M1J89vaOJjoquN5N3fnm+WWe6xI7wev0I5fV/dznypSODhtwDzFo2q6Wq2mXlleUxf9kvJCZ5cLuDt704/UXW/XkdF465sMEyoqOiiXJEceX4K7HusMhKyGLJlYt5/vpzYcrNcJJ/eos3mO4I0zhU29Bgupvrn9If+i2AjRPhk/vMH/Uxz8HI2dD/c5h6VbPLHpw+GOsOixOHncjJw0/m1om38vfD/95ylW+A906wN3GO6lgO6XdIe1dBqS5DokvCJiDzBrZRtYmcN+40s9STs5Kaqrpf7gMD6BmTZvDI0SZb86kjTvX1MD6/7HlKqkq46SDTW0ilnfwrupjHjnmMkZn+OdUnDDuBfulZvuWViPYHXT0TzAimsmrT3Tih94RmrzMtCP3uOAz+MoCnFj/VrDLq0yO+R93zOSuprXFyaC8zRzq50tw0rm+d6b2y9mLKwCktWq+WtG+aWWIqIz3462B6uh34lmVw8B4H+wM+oChnDy4acxEQ3PvsC6btnmlfO7GFBtN79x7C/+z3Pw3W7+FF95lq2D3Tsc5Y9u25b6e5Yf7Vxq/C5wdZ+BdueeRn87v3vc34DcpTGZ4xPGxZ4aav/bz955aqape1qXATny//laT00rDbB6QOaFbgOyZ7DOeNPo+pg6dy40E3+jfEmL91gckYVdemwXQ3lxybbLIalvWA306BAx+A466CU8+ECw7nuEm9mH/u7g8jckW5uPvwu9vsP0Dv8LnO8h9udzMwdWDQF2+lVPNJdFn4YFoELKitiqZfRg+iHFGM22MvaqudhOacSpQnAAAgAElEQVTGCezxuuWzW1hXsI6JfSfWO+fP5XBBRbJ5ElvIuoJ1XD3hal+yr4LyAhzuQvN/C/iSj807Z55vnekXl72IdYfF95d8T2JM3bWvm3TtIhS5l0HSFhZvXdz4AbtJ7J7pQjuZdY6dj8/b+xgTFdxj+tyS50j5h1lD+8IxF/L71b8zeYAZqtsREhTl55ufoQnI0tP9w7x7Jvbk2N7+nreKglRf8BE4VP2AxDPNLykbSIxO5OKxFweV6TtHeRqVlf7s6A1ZU2KCRW/P9NJtS9kne58OtbxYQwalDWJsz7HBSwquPhrmPQRvvGqel2QDHsj8FWpjGZt6GFTHwP8thN9OhJ/PgurYdql/V+CxPFCSTVpm49MKmuO9s97jH0f4E/iO7meW4PKtb626PA2mFQx9F2LzzfJWBz4IgNvp5rPzPuPdM9/liD2PaN/6NYP3TvDOsp3tXBMVzrMnPMsvf/qlvauhVJcgMSaYDo3NBqUNYniKyVK7NHchVbVVTBt5FJYl1IQs01tSVRL0/NP1n3LV+KuId8UHvV5VW8U/vvkHae40XNV2oBy7i39++0/eXvU2p448FYAb5t/Ar8Xf+A+0e2uOeukoXy+0Qxz8UfgHL//8Mvnl+c269v+e8l9f0svW5A0eo1w1WNUxvLf4JwA2bKzBsmBL8RaojCf+8Z0w7wHfcbd9fhuFlSbyfm7pcwxOH8yh/Q5t9fo21bxffgDxkJwc/HqiO5aouGIoy6CwopAPlv3g21ZZnMg9C+4Bgodyx1b3AvFAbAHFVcWsyQ9OCJeaZu9bkUpFBSze8TX3f3N/g/VzRpscLd6e6SXblvD0kqc7xI2IphKEnOtyGJ012gTFL3/o31iRaPIKxBRBolle7eXvP4LN+8Hm/WHOWybJ1ezwifQ60/vQXkww3ZPUVgqmQ+2ZZUZG1jdXXnU9GkwriC6Dv2bDNXvy/476M48e/Shlt5Zx2IDD2rtmzTY0Q9deVEp1DxJtEjuFZvSeNnQaX59nemvfXTeHyppKnK5awL/W9BX7XsF5o8+rM3f1lx2/cMYbZ/DOqneIjjK93oFznPfrsx9/29cEVMSYYPGjNR+RV+Zf9iohudpfYMAwb++oFIc4mPD0BM5565xG1xuuT2Z8JiMyRwA0e6h4JPbuNYzhqWMo2G5uMlRVml7qHWU7YO2R5G9LhIXXU1/SaZkhvoz4HSEzdW5+LcQUEhUyI8ohDpJTq6AsgyHpQ3zrQycnW0zJOtM3JzwwT8rKnK2mLTjMdR354pFBZQ7oZUfsds90iSef7aXbaUhUdPA6096keu+seifyi20H769+n8VbF7NixwqWbV8WnGwMIG8IVMeZJH72DSeqEmDWl8H7rT0qbPnetq/q5+2ZTs2sapPzjexjkshlJ2Q3sqfqKjSYVmZ9TGc1OCxmHDaj0TlMnYHerVVKdRcXTDDZuEuCO5cpqSrh8rf+Yp64zNJY/2/B3wB/MP3kcU/y/InP++Yvh1qYs5AoRxSD0gYFLf9UXFnMPfMfA0c1uPxR/JXvX+k/v3ODv6AYfzC9pdj0wFlYu73e/Es/v8R/V/wXCJ8R/OJ9LuaKfa9odvkzp87k8AGH+6YMxcRAYSGUbO4LMbsAM1R6+rDpsNM/deXc/rfUW+aa/DUcP+T4ehNTtaXyIje4C+q87rE8FDhWQ1kGP2750QTTMbvo1UtYs8bBcYOPI94VbwJt2/drVkNs3bK8MtPN9Y5MPMSsM+1sfJ1ph6sGxOPrma6qNQFRc0cytJdRT5q59b48A155Q02CQFe5byoElfVMeZj9BlnxWUGjRfbtuW/4fZVPWZlAVSJpGW0TTDvd5u+h94aT6vo0mFbMPmU2f57wZ3645IfGd+4kVuWtau8qKKVUm9hvgPmiXhoyRW/uyrm8vsys+YurzLc0FviD6Vs+vYXZv8zm25xvw5ZdWl1KjaeG44ccz/Rh07l6/NUAppetIhlidxFuVStBTD4O27Ej/UObn17yNNAyXzZfWPaC7/fV+avrbH962tM8edyTzS7/8nGX8+l5n5rkbcC6XWtYuBA8VW4Ya64jLw+TSTx3mO84z64+zT5nW6oojq03mLbcO6Esg7dWvmWC6bhciq3tLF25y5/RPUBVabxpD7bQnvdaqYDoYjxlKab9OSsbXRJtaPoQnNHVYddR73T+2B+eDfl3ljvUZMV3BgTTVYmQmFP3+JUnMf+obUFzca8/8PpWrHDXkI1JDjhhaL82Od8XW82oiRU7VrTJ+VT702BakRCdwCPHPML43uPbuyotpry6K/zPq5RSjVtXapI0hfZM7yzd6V++yml6piUkmL7v6/s4840zGwxqKmoqeOi7h9hYuNGX4Tq/PB8qUnxDvL2CAqysFb7e26n7jPO9nBJrEnKN7+X/P6e560wHDjOeMWlGne1zV87l3VXvNqtsgO9yvmPW0lm+HtEtv5ih5FkHfAIj3gBMML06bzUU9QZ3LgAv/8t8gXc73XXKzCvP493f3+0QPVcVJW7EvavO6w5xQFwulGVQ66m1g+k8ant+S+EuBwtzFlJSVcK6gnW+Y6pKEohP9s9LDR0hVlFTAe58ftu0jYoKkCb0TD9yzCOkJMb4gmnvlIPO4pr9rvHdiOG/r/s3HPE3UjLLcBQMt3umy8z7DWY5OVc5jHgN/hw8LHzvkKTxX2z4ovUq30UUFZjkbXv0bJskbhftZ/JG6DDv7kODadUlZSVktXcVlFKqTTy82MxdDu2ZFhHzRR18Q7EdTjOPuTIkF0/g0i7eNYRDLd66mH999y/ADpQqkyHWBNOhKyecNvI09s7aG/6WQdqMgVw171LfNm9m472y9mrqJdYrMHhPd6fX2T59znSmzZ7W7PIPeOYALnz7Qt9w9LQT7iFr1K9ccNcn4DZDjfPzzdxYSntAr0XmwD8OAuCDsz/g6wu/Dlt2R5iOVFuWTHZG3XXFBfEF032T+/p6pqOTC6gpTSLbbdb0Lqzw30ypKkkgKr7Q1xZCe6YFMe9ZeSqVldA3LbtJ+U3cbn8CspsONsttdZY1fB3i8H/OVQHJ/Pb7N/GZO/CUpNlzpsv9N6bKMs28aXc+pK2DiXfXW/6Ti5o/6qK7+HWDuUnhidu9KSVNFWenqQ+80ae6Nv2kVZekWRSVUt2FI9ZEGqE904L4eqZfPPX/iHXGIi4TRVeEdAoGBiczJs3gP8f9B4ALRl/gCwZm/zKb4spibp14q5nvW5FCUrLZdsehdzA0faivh3nKwClcOvZSiKol31oXdK7UWLNGUlFlEenudKbsOYXB6YObde2C+JKnPfz9w80qIxLJhz3DlLvu4x9H/IOdd5jpRHl5mBsHpT0g1X+tHg8MSR/C/n32D1tWR0hAVluawgn7HFLndRE7mK6JY78eh0FZBpNG7EV0kunFntbHXmc64BqqShJwxhf6EsHdP+X+umW6833rTJ86+nguGXtJg/W75sNrKKzd4uuZjnXGMqn/JN9a5R3dvLXzKK6y8wVUx/k3uCrYXPWrmSrhHebt7an/+EEzb9o77Hvy7XDWVP+xAc1mVa5OaWvMyo0mKWKJa10je7aMRVvMDbVdFXVHfKiuSYNp1SVV15relzhXXCN7KqVU5yYuE0yH75k2fwMHZffCIQ5OGnUsUDeY/mD1B+aXop7c+M7/snz7co4dfGy9vccmmE4mM830am4q3MTNB9/MeaPNesRbi7eaoc8hPjn3E9+cz9d/fZ3cv+Xy8bkfN/tvtYjwR+EfAHWWYmpJ3psEIuK7uZBiRquTlweeGgeUpxGf4p9itHUrPPHjEzj/bhJvXT3+ajZft5mjss4HT/t//bIs06uellbPDvaw4/jqfrgqe9KvZzwxSab3tCg/1i7D8pVVW5rE9H0m8cPmH0hzp3HayNOCinOIww6m06mqato602sL1lIdVeTrmf5iwxdkxWcxdcjUhg/sIHon9mb/PvsztudY8IQMUY/dZaZKeBOQBSTyozoBHAHTAAZ9CL3svDZ3V8Di4DW8Vf3yc82/v9T0tplW8fMOM+3GuySe6vra/6+5Uq3AO8ws3LA/pZTqSiTGBKehPdMDUwcyOHEfAOaseoYaTw0XTzgbqBtMV9RUQGUC/GsLvP4qH6z6mOnDppv5sgFqPbXcs+Ae4qPjkaoU1paZ9ZYfXPggTy56kmMHm2D9zi/u5NEfHq1T1yNePILhGcMBcDlcrCtYx8xFM4OW1IrEf0/5rwlU2kiP+B4kxyRz+uun47pHcMVUU1YGa7fsAhycuO+B0M8sa5STY+akez228D+MG9qLeVfOgqcWtfsw7+JiqK2F//vtf8NuHzdoAACr11dSXRHN86sfIC7FRLX/+tQkfvNYHgCKiqC2VhjeN4ukmCTyy/P5cfOPQeUJYrJ9F5te5SeWPMg9X93TYB1FBIerwtczvXzHcuasmNO8C24ngvDYXovrbkjYBiU9/UtjRYUEe0vP9//usHwJ76iN8U2vUI0ryHOBq4S4Vu5bmTsXZs70L/3nzS+huj4NplWXNCDVfAnIK2/eFzSllOosHDEm0gjtmZ46ZCq37n8vAA//dC8ey0O1owjwr9t728TbuHzfy+mV2AvWTjEvrjuSddet4tJ3L+Xd39/F7TJDxYcUXQH/2AXzHmB8rwkkeHpBTJHvfN9v/p7NRZt9z7Pis9irR92ebe/fZ1eUi9EzR3Pl+1f6lsuKVHx0PL2TzBzsoemNz79tLu8w+IUXL+TxqY+zvcSsj+xwVVNZCVu3mZsOFTGb4KjrANMzLQiUJ8Mrb8O7T7F1q13gtn1ara5NlW+vLlXk2BB2+7A9zHSp2MLR5oW4XJ470wzdtsrspcKcMUFlbaz6iaJK0yamz5keVF6cK870TFeaLv3Cmu2N9t4JgkRX+nqmd5TuAODV5a824Qrb3/x181mYs5ADDwyzMX4nVMdDRaoZ5h3q5LOCn+8RkAncbb7btETega5uV54L4ne0+hzmE06Ayy/3r/2dGZfZqudTHYcG06pLq2/tVKWU6ipeOO0JoG7PdHl1OQ8veMo8cZUiCKe+dTzg75n+++F/Z+ZxMymuLIYtISs6VMewMGchToeTUT1GUfTlhSbp2MLruf0207MZms37hvk3ACb43F66neU7lpMYHbxu7oZdGwDTq1lSFVLpCM1aOou5K+f6zhnqpoNu4paD61/zuTGvnPQKp4883TfPO1SUq4aKCpiQdAIAb2x6wpeYrLDQDH/mnafh92mw7IKgY6OjmjDOuRV5A2AJszQWwEtrHwJgxa/26IS4XNLtwV4DYsaT5k4zc8UDynp3k3+pMm+vtZeIkJrkv2bLGZIFrx6OaH/PtHekRKcdQtv/c7jCvjnhXUasPN0/xHufp/377vl58LHpv/t/t4/dr/d+rVTRrsME0zubvWJApCpqzB9X7woAquvTYFoppZTqxI4cbjJHh/ZMv/HbGyzdZH8Bjy41Q2adwQnIrv7gal5c9iJLti2BvMGQvsosyQPw3AIqayqprq1mcv8jKFzp70199OFowBHUM12fuWfM5cIxF/qev7L8FVOHmsaXRmq0bDuQBliZu7LO9vuOuI97Jjc8lLghZ+51JrNPmU18tMnEfPm7l3P/N/f7Em85o6upqIDqXfYKEsmbfImjSkqgZMGF8Nsp/gLjduI67loALm7naa/eANgRV0+iJLv38+df7aA3LpfnV5kAu7woPig4ybMHgTkT6g9yaz21FFQHZFR2VjQa4OydtTfpiXG+nulf3pkM/9zO8q/3aPC4Duea/iaQPvN4yDZzanEF/IP19kxXJtVbROFtuf4n9jDvK8Zd0cIV7XpqitM4Yq+9GddrXOM7twBv/olVeZocrrvQYFp1aQNTBza+k1JKdWIfrH2H6BhPnZ7pgvICM4wUwFXmGzIL/mHej//4OOfNPc8M5c4fbHq/Jt1pNm4Zj+URyqrLeGT+65SXutj/RDNHuqbGDoLqCaYDgyS3082oHqN8z72rLQT2qjV3qaPAoZv3Tb6vzvZnfnqG55Y816yyAT5Z9wn3LrjX18v0+YbPzY0HW5SrhspKWLN1p3khtiAomK765biQCtdQnbICgOeaX60W4Q2mo+LrCYDdBYCHmh32/6NxuXzxx3yi3MVs2lpCXnkeP2//OagsZ5y/PYRmK7ewIMrfGy1Rjffc3X343Rw6eDzl5eZm0aKXToKyHrzzROfokb114q2mjaZuhAsOhxh/AH3+0Gv9O3p7pgfNMz+vHtJwwXbP9DebvmnJ6nZJO3c66NvL7ZuS0NrO3svkpRiQMqBNzqfaX7cNpkVkhIh8KiJlIrJFRO4Skaj2rpdqOX2S+nBov0PbuxpKKdWqrnz/SiS6LHw276p40+vl8CAiRLnMSgehCcguGnIjbB8NaatxZ/nnL5fv6G2CoJ0madjSjFthv4exPMHBdL/kfv5zAmfvfbavJ2jKi1O4/uPrfWV615kekt5IwNAEgUF4uPWxL3n3Ei5656Jmlz/lxSnc+tmt5Jb5ewUtLCb1mwSA2+2gvBx+2WQyihNTbJY4klpKSiClPGRutKMaErb7nlZXN7tqu80bAB89akL4HRwecOdTm2uCaWdCIQ5x4EzYRXy1+by960x7e6ZdiUX+daatMOtMBwzt3q//6CbN+fWuMz1vHtRURsOQd8lZnUFOTpMvtd0ErTMdomrgm/4nsfZQ+32ehZuSIaNuJvysB7L8T+zpFQ9991CL1bUr8nhg506LVeVfB+VzaE1RDhNKdJa10NXu65bBtIikAp9gVus7AbgLuB6Y0Z71Ui1rV8Uu3xIsSinVVQmCM7YyqGfasuCzV0fDzhHEuGvY/tftpmfaZaLo8pB8R9PG2mshJ+Xw9yNu49pHzHzNSfH2MNLcYQBUpPzEocNG+44bsYcZbnvV+Kt86wsDHNz3YM4aZRIohf4d9g6Zzi/PZ0j6EE4beRqD05q/zrQ3OA/MnN1avF+QZxw2A+sOiwE9Mvn1V1g65wQzbNfhAQGiS9i8GbZsdhITY8Fxl5kCoqogcwVEm7WHf/+9nhO1AW8w/cJZj9S/U5z3JoKHp0+739yQid9FGubz8vY+59q7RcUV+T6PWSfOCirKIQ7/WsrATZP+wjl7n9NgHf/0/p+Yt+ktysvhu+8gOtpi4nmmbX7xRePX2N7eWvlWveuJv7rlbhj/mHnSy6xNjMABg0aG3T9oWkSUmTu+ftf6FqtrV5SbC9XVwje75rC1ZGvjB7QAbxZ7b5JC1fV1y2AauAJwAydZljXfsqyZmED6OhGpf8KK6lRmTp3JdQdc197VUEqpViUiOGMqg3qmP/8c3np4Iqw+jhh3DT3ieyAi3HjoNUBIz3SePwgmtpC/zv8rRVnvA1CzY6DpWds1AFwlEL+D2ER/JL5PP9NrubVkK/cefi+X7HMJYJKM/bTtpzp1feeMd0yyM8zcwlVXr2LOKXOaPQRTRNhcbHqcvJmeW1tgT2NqKqzxLm9dHe/vbY8u8Q3jrpx0LaSbnsYhPQZwzj5nwvmHAwHHtoPcXIiLg9i6Hfp+djDtTqpgRNYQX890YYFZM9mbZGzLFkjPqOW1M15m+Y7lZMVncfSgo4OKEpGgYd4JCY3XcWvJVoo9Oygvh8WLodegPFL6byAmBn7+ObLrbQ8ZcRlM7DuRYwcf65veEGTyLXDqKdDPP1y7uWuuq7p8oxeSclo9m7fX6nzzb31XRT25CFSX012D6WOAeZZlBU72mo0JsHVccBdx9t5nM6F3PcPXlFKqixAEl7siqGf6vff8v8fHRXHbZ7cBcN3B/4OICaYtC1h3GDz7dUBpJlD8IOcVElLLWLIkih3bBf44EBK3gMC8zf5liV5eORMww01v+/w2DhtwGAD3f3M/L/38Up0EU9NmT2N8b5M13O1ysyp3FQ98+0DQMOpIzDllTptM5/Fex4CUAWQnZHPsy8ciM4Sc6uVB+9188M3ml/iAwL40y9cjGxPtMMuFpa4DYN26Vq96vbZuBVdyLkP+HX64/Tl7n+MLpsudOUx4egLJMcnEpRSzZmUMvDqXbz9N9ZXVp3cUg9IGkZ2QzfbS7Xy89uO6hQYM8z7pzaOY8UXDAwIFweEqx+OBH3+E5P5rmbf+fYYPh+XLGzy0Q7AsCxHh/bPeJykmTF9NbDGMfCPopU/Xf1p/gYfeCYPfb9lKdmGBwXRbZfMekz0GgOyE7DY5n2p/3TWYHgYEpf20LGsTUGZvU0oppTqN0J7pjz7y/751QxL3LDAZrTcX5+B2W5SWwqmnAi98ZoK9EFtLtlISs4rP3stg2IAU2LwfCemmRzkoUIwp9v36e97vrM1f63ue7k5nbM+xdcr2rr8aHRXNiCdGcMP8G5o9JNIhDhKiTRfnyMzww2Nb0kfnfMTDRz/sW0vZiskL2r6pcJP5JXmT/8VD7wLLfJFfXv0WT//0NLjzSU622j2YdqcW1LvMVFZ8Fs4Eu3ct0QyRfWH6C1x/wrFYNdGw6gRuvWQ0y5ebnumopO3M+WWOb0nKc94MM4Q7oGe6mK2U14RZXzmAiD9pXnExRCXtoKq2itheq/nll0ivuO19ufFLvtr4FW+vfJt1BS3wYR82A872J7XzBm4qPH8w/Ueb9Ux7R6d4cweors/Z3hVoJ6lAuPEXBfY2pZRSqlN458x3uH5eX9asK+S6h39m185YfvvNv2a0M3UztXavzAHPHECV/MLzrzjJ3x4fXNDeL8KoOf7nIdmW4xKqKQFI3uh/MS04QLjzyzt5cfqLiAh55XnkleeRFJPkCz7BBN0ANZ6aOmsRR+qZn57h/dWmpy7c3NQHpjywW4mA3jvzPeatnUdKbIo5h2Uxd+VcdpaZ7N3FUX8E7X/HF3cAsPeQVH5eBQx+D1wV0Od7OOJvMO4/rM4v4uQRJ7OkdyEvzYmGPkt9x6e50xiWae7p/7D5B2pqa4LKz4jP8H1Z/y5nIR5P8DVnJWQxMG0gHsvDd398V+d6eiX1on9KfxzOalasrsTRp/4euwcXPgix9jz0BP9805qMJYBJrBbl9HDhNVtYtTaT6FELeHDhg77POtxnu0daFt53zOMqrrM9nGrxt518j5kjXJ70Mzk5g7nmf39g/75mBNqq3JXkleUHHRvtjPYlwluxY4UvYZqXO9rNPtnmWpZvX+6bguCVEJPgW0t7ybYllFeZ4H/c4TmkJMYwvvd4UmNTTTb8EIFrqJ8458QmXWtjxmSPYek2f3s5aI+DeG3Fa9R4TDsZkDqA9QXrydsWx6qfMn379U/tR6/E3pRXl7Fk69I65e6ZtifZCdkUVxazfHtwl/8Bx2zkoL4HsmjLIgqLPCz5srdvW2Z8JoPTzfz5b//4ltB/gtmJ2eyZuicej4fvcuq2x95JvemX0o/q2mrfXONAfZP3oE/yHlTUVPDTlrrTRgakDqBnYk9Kq0pZtm1Zne1bl+1FlDOe2vgdbZYQzNuGWmLpP9U5dNdgGur8kwdM2pA6r4vIZcBlAH379m3laimllFJNNyZ7DFlp8Nk8eOjaif4N0y4CRy01e71Mpj1fMzMukxxHCfnbzRfiAefex/oXb4ZxT8Jxf2r4PL1Hcc6JL3Dea5fVu0+6Ox0gaH7oVxd8xbXzriXOFcf7q9/39UamudPITshmW8k2Ut3Nu4+9bLv/C3SP+B51tl9/4PV1XovE1CFTmTpkqu/5DfNv4IVlL/iC6XXli4BzAZBTzvZ9gZg2biw/vwu47AWSHR44+H5fORlxGWxz/EjZzik8dtOB9Zy9sWlKBzSwzQHUVy6Ayzz2WMbIcHN5AZfDRbXbzjJtX8dzS57jpfxngK9xJu9g4OQvWPzmaWafuG/ZL6CscGsg/+XgK7n+cfN7bVRh+HnEATLjMpky7CBm2883VJhAcFnF28DJPHrTBB717d3YwMLGRi40llk8IDP79dns0Tuap45/ijHZY8IG043N4fe2/aaqvr0ap8OJzDBB4RmjzuC6A64j4d4EX5K/80efz/PLnoffToQ5b4UpJY6G20Vine2PlR/EKye/wlUfXEXB5nT4d91M40ZD5TalPTa0PbaR7fH1bh86zOKHW3YR74oPu72lefM/hFtdQHVNUl/K/q5MRHYAj1uWNSPk9RJghmVZ94c/EsaNG2ctWrSotauolFJKNdlf/gKPhCRl/ubXNaRnmqy/PeJ7kOpOpbiymFHDYti0IRqXy2L5ltX0SepNTlEO1Z5qLMsi1hmLhcW0Q/dg1Qo3512WzwtPpXHaaTB7tsX6XesZN7gvGT1qmP/tdpJikkiKSWL9rvX0T+mP0+HEY3lYk7+GNHcaGXEZlFWXESVR1HhqiHPFsbVkK70Se1FUWURZdVmz5xfWeGpYm7+W+Oh4UmNTfZnCW0tpVSk5RWbsaJo7jWdn1XDT1T3JyrJYumY7hRUmQJz7ajqXXALHnFDEQ09v9fXSeoea7pm6J+dfUs6rs5I4/fxdnH+F6VGNd8WTlWCG3W/ctZFaqzbo/AnRCb6bBusL1tfpjU+KTiIjPgPLssJmek6JSSElNo0RI8zzG+/awS1/iw07n7ewopAvvivknGm9eeiZPzjl+BTiXfGsK1jH2t+jGdonk1Xryph6mKnPky/ncP6p6TjEQWFlIZlxmXV6Axd8U8UhB5vkZb9sWc3w7IENDr+trq3m1TlVnH+2+VwfeXYzx55QzndfR3PuCaZzY6U9aW97yfY6meOd4qRvitlva3HdYeXRjmj6JPcBYHPRZiprK4O2x0TF0DvJ3HjKKcyhymNGa/TtX01stJPshGxinDE4HXX7pqpqq9hctNl3Toc46Jfcj02Fm3A6nPRN7ktRZRFVtVXUWrVU1lRiYVFZU4krykWURJHmTiO3LJc0dxrpceZG1c7SneSW5TIgdQCxzlh+z/vdlxTPOwqkpETYsc1fpzR3GimxKVTVVvnab6CMuAySYpKorKn0JfTzGn7qZhgAABJOSURBVDCwml5JPdlesp2KSg85m1y+bYHtMdwwdm979FgeNuzaUGd7SkwKaXFp1Hhq/FMkAqTGppLqTq233unudJJjk8PW+75be7DgswROOw3mzKlzaKsqrSpt9b9FqvWJyGLLssY1tl937ZleScgtTBHZA3Nra2XYI5RSSqkOqmdP8zMuzqzJC3Dg8EF19kuMSaTAnuablSUMzTBDhodmDK2z7+ABsGoFHHFwGi88BRMmmDmse6buydbNIOIkOrqfb//ApbEc4ghaR9qboTgG02vTK7EXgC8Qby6nwxm27q0lPjo+6HzDTByGZQnZCdm+mwIOOz7MSEpiaEb46+ubZYKSkQNTOGb/lDrbh9KvzmvB2wc0sFUYxp4NHg8waXwPkupJpJ4cm8wJk5LZVQBRUf39580YylC7QznO5U/JfeLhfXDbcVYPZ91RAgBJCdG+30f2bHw5NFeUi4wUf/A2Yo/eDEoDl391NobaH8dQ6s79DzSUno1s793I9j4NVzZEdFQ0A1LrfkaB7ccbIDckdNRGZnwmmfH+Idyh67X3TOwJGUD/sLVirwbbRQx717Pd++90r17hjxzaYLkOhje43cnIBrdHXu8lk2HBZ3D44Q0c1ko0kO5eumsw/SFwg4gkWpblnSBzOlAOfNl+1VJKKaUilx3QsXvXXbCrgVVZ+vaFFSsgObnhMmfNgnnz4KyzYORIGBOQ6yimeStZdTmJieanI6RzdZB9X+Gww+o/Nt7+vh265ndbuOIKmD0bDmhopLgtKqr+bT0CYubsJgwuaE67iQ+IS1Lsew69G457leKvf4VRo+DYY9u7Jqqr667ZvGcClcCbInKEPSf6TuBfIctlKaWUUh1eP7sTMy0Nbr8dHnyw/n1vvNH8rGgkP056ugmkAcaOrRswKn8wHReyNPDEibBqFVx4Yf3HeoPE0tL692ktjz4K69c3fkOlMVFR8NVXsKOJS3ynNSPBcWAdvb87nTB5csPtXHVv0dEwbZppK0q1pm7ZxCzLKhCRycBjwLuYzN4PYQJqpZRSqlM54AA48UQzd7oxAwean6NHN7yfapw3mN4rTO6qIeGXb/bxDj89+uiWrVNTuFz+Xt7dNXFi4/t49egBqalw0klNPyawnoG/f/JJ08tQSqnW0i0TkO0OTUCmlFKqM/N44P77Yfr0xgM+1TDLgueeg5NPbl4vb1WV6UHrTmpqTI92U1cqKijw92hXVna/90sp1T6amoBMg+kIaTCtlFJKKdU2amv9Q3X1K6tSqq00NZjWGVBKKaWUUqpDaigBmlJKtTcNppVSSimllFJKqQhpMK2UUkoppZRSSkWoW2bzVkoppZRSncMzz0BCQnvXQiml6tJgWimllFJKdVgXXdTeNVBKqfB0mLdSSimllFJKKRUhDaaVUkoppZRSSqkIaTCtlFJKKaWUUkpFSINppZRSSimllFIqQhpMK6WUUkoppZRSEdJgWimllFJKKaWUipAG00oppZRSSimlVIQ0mFZKKaWUUkoppSKkwbRSSimllFJKKRUhDaaVUkoppZRSSqkIaTCtlFJKKaWUUkpFSINppZRSSimllFIqQhpMK6WUUkoppZRSERLLstq7Dp2KiOwENrZ3PTqQDCC3vSuhVBvSNq+6I233qrvRNq+6G23zwfpZlpXZ2E4aTKvdIiKLLMsa1971UKqtaJtX3ZG2e9XdaJtX3Y22+ebRYd5KKaWUUkoppVSENJhWSimllFJKKaUipMG02l1PtXcFlGpj2uZVd6TtXnU32uZVd6Ntvhl0zrRSSimllFJKKRUh7ZlWSimllFJKKaUipMF0ByQip4rIOyKyWURKRGSxiJwZZr9LRWS1iFTY+0wO2Z4pIo+KyA8iUiUiG+o530wRWWmfq0BEvhKRIyKob4vUo4Hye4vIW3b9ckXkMRGJC9ieJCIz7PILRWSbvf+QSM6j2pe2+zrlN9ju7X1iRORfdpsvE5EFIqKZODsJbfPNO1ZE+onIqyKSb7f7ZSJydFPPpdqPtvmgY48QkTkistFux7+IyNUiEhWy3wwRWS4iRSJSLCKLROT0pp5HtT9t90HHHioin4vIDhGpFJF1IvKgiCRFWo+OQoPpjuk6oAS4FpgGfA68IiL/491BRM4AZgIvAMcAK4D3RGRUQDm9gdOBbcDSBs7nBh4DpgPnYNaY+1BE9m+soi1cj3DlO4F5QD+7jGuAUwme19EXuNTe7xTgcqAn8L2I7BHJ+VS70nbvL78p7R7gUeBi4E7gZMz794mI9IvkfKrdaJuP8Fj7b/pCIAW4EPO+vWhfm+r4tM37XQbEA7cBxwKzgQeBf4bslwTMss9zMvATMFtETonwfKr9aLv3SwOWAFcBR2Ha/PnAK82oR8dgWZY+OtgDyAjz2ivA+oDnq4BnA547gOXAS4GvBfz+ALChieePAjYBjzZh31arh73/mUAtMCDgtdMADzDYfh4PuEOOS8P84bqjvT9PfTT5s9Z279+/Ke2+D1ADXBSwTwywGXisvT9PfTTpc9Y2H+GxmIBjQeD++ug8D23zjb4X9wLlQEwjx34DvNPen6c+duuz7pbtvp5zXgpYQFok9egoD+2Z7oAsy8oN8/ISoAeAiOwJDAH+G3CMB3gNc/cm8LXmnL8W2AVEN7Rfa9fDdgzwo2VZ6wNemwtUAUfb5ZdallUecg35wEbs90x1fNrugzTa7oG9MP9BfhJwzkpMoDF1N86t2oi2+aC6NHqsiCQDJwFP7Oa/L9VOtM0H1aW+9yIW0xvdkDwauQbVcWi7b1Se/TM6knp0FBpMdx4HAr/avw+zf64M2ec3IE1EMiMtXAyniKSLyLXAYODZRg5r8XrUc46g8i3LqgLWBpy/Dvvcg/C/Z6pz0nZvC9PuY+2fVSHHVgL9JGR+teo0umubb4qxgAuwROQbEakWkRwRuVlEpI3qoFqetnm/A4Fcy7J2hm6wryFFRM4GjsQMgVWdV7du9yISJSbvyxjMVIc3Lcva1tb1aAkaTHcC9oT7E4DH7ZdS7Z+7QnYtCNkeidOBasy8ir8Dp1uW9UMjx7RGPcKdI7R87zkaKv9BzDDv2S1QB9UOtN032u7X2D/HezfaAcV4QDBzSlUn0s3bfFNk2z//gxmBcSTmy+HdwJVtVAfVgrTN+4nICOAK/O9F4Lb9MddQgJk/fY1lWXNbug6qbWi7B8wc6ApMD/0O4Nx2qsduc7Z3BVTDRKQ/Zl7F25ZlzQrZHLpIuNTzelPMw3wJzwDOxiS3ONayrC/segS2FcseMtJi9RARB8E3d2ote5JEPeVIfeWLyJWYhAsnW5aVF24f1bFpu6+3HF+7tyxruYh8AzwgIlsw86GuwwyNAjPnWnUS2uabxHvch5Zl3WT//rmI9AFuBp5oaj1U+9M2H7RPKvAG8DNm3nSo5fY1pGCm8TwmIkWWZb3a1HqojkHbvc/JQDJmytr/A14TkeNC9mnJ96PVaM90ByYiacCHmC/J5wRs8t6ZCe158j4P16PVIMuyCizLWmRZ1keWZZ2LyZZ6l12P/pi7W97H2laox7Mh5zg/4BzhethSwpUvItOAfwM3Wpb1VgTnVx2EtnvfOZrS7i8AyoBFmDu7xwOP2GXlR1AP1Y60zTeZt01/HvL6Z0AfCbO0iuqYtM37iUgs8DYmgeQ0e0pP6DWU2tfwiWVZ12Iy2P9vBHVQHYC2+6D6rbAs61vLsv6DSbp6LHBYK9Sj1WnPdAdlz3d8DzMZf6plWaUBm71zCIZhkmwR8Dw/3FybZlgCnGH/voWAoaSYOZktXY87MWn8vbyJl1YSMjdaRKKBPQmZLyQiB2KGdc+0LOv+CM6tOght95G1e8uy1gD72Mk6XMDvmJtJP1mWVR1BPVQ70Tbva/NN8Vs9r3t7KzQpWSegbd7f5sWsKf0KMBI40LKs7U0s8yfgQhFx6d/6zkHbfYN/63+yf+6JuTnaFu9Hi9FgugOyh168hkkWcJBlWTsCt1uWtU5EfsesOzvPPsZhP/+wBc4vwAHYDd++S7oodL+WrIdlWRuADWE2fQicJSL9LMvy/oOahrmD+1FAnUdi/kh9BPw5knOrjkHbfZAmtfvAOtn1yMAsoXVrJPVQ7UPbfGQsy9ogIiuAyZh5016TgbWWZZU0p1zVdrTN1/EEZoWGIyzLWhVBsQcBORpIdw7a7ht1kP3TW79WfT9amgbTHdMTmOEO12Cy1gUusr7EMsvf3Am8JCIbMOsNno/5R3pWYEEicor96xAgLuD5l5Zl7RSRicD1wJuYYSfpdln7Y4aMNqZF6tFA+a9jAoM3ReR2zPyKh4BXLMtabZfdAxNglACPAhPEn9i1yLIszejdOWi792u03dvl/xmzpMRm+/w3Y+bWPdOEa1DtT9t85MfeDrwhIvcDHwOTMIlrzmvCNaj2p23ef9wtwGXAfYAn5L341bKsIhHpBzyH6b1eByQA0zE9jJp0r/PQdu8/7kXMKLqlmGlqY4G/YYahB07haVI9OgSrAyx2rY/gB+ZOjlXPo3/AfpdiMvpWYoZITA5TVn3lTLK398d8cc+xy8nB9PAeEEF9d7sejZTfB7PGbgkmcHgciAvYPqmB8r9o789TH01uR9rug49tsN3b+9yIuZNbCfyBmUMX19Rr0Ef7PrTNN+9YzFzD3zDLwq0Brmjvz1IfTW5D2ub9x33RhGtIBl6y37cKYBtmGOyx7f1Z6kPbfTPb/f8Ai4FCzPeb5ZibpAnNqUdHeIhdWaWUUkoppZRSSjWRZvNWSimllFJKKaUipMG0UkoppZRSSikVIQ2mlVJKKaWUUkqpCGkwrZRSSimllFJKRUiDaaWUUkoppZRSKkIaTCullFJKKaWUUhHSYFoppZTqpESkv4hYIjKrFc8xyz5H/9Y6h1JKKdUZaTCtlFJKKaWUUkpFyNneFVBKKaVUs20GhgOF7V0RpZRSqrvRYFoppZTqpCzLqgZWtnc9lFJKqe5Ih3krpZRSnVS4OdOBc5xF5HIRWS4iFSKyXUSeEpHkeso6QkQWiEipiOSLyFwRGdbI+fcTkddFZJuIVInIHyLyHxHpFbLfSXadvhMRV8i2USJSJiJbRKTHbrwdSimlVJvSYFoppZTqmv5pP5YBj2OGhF8KvBW6o4icAswDxgGvAf8B0oGFwIBwhYvIhcA3wDHA58DDwCLgEmCRiPT17mtZ1pt2HfYD7gkoIw6YA8QA51iWtWN3LlgppZRqSzrMWymllOqa9gf2sixrE4CIOIHPgMNEZIJlWT/YrydggmcPMNGyrEXeAkTkIeAvoQWLyBD7mA3AoZZlbQ7YdjgwH3gEmB5w2PXAgcBfReQzy7I+wgTYI4C7LMv6rKUuXCmllGoL2jOtlFJKdU13eQNpAMuyaoDn7KcTAvY7AUgDXgkMpG13Ej652ZWAC7gmMJC2z/MZ8A5wvIgkBrxeCZwOlAIviMhfgQuAr4C7Ir04pZRSqr1pz7RSSinVNYUGxgB/2D9TA14ba//8MnRny7IKRWQpcGjIpgPsn4eKyPgw5+kBRAFDgMUB5a0WkcuBl4H7gVzgLMuyahu5FqWUUqrD0WBaKaWU6pp2hXmtxv4ZFfCaNyHZ9nrK2RbmtXT75w2N1CEhzGvzgSIgCXgttGdbKaWU6ix0mLdSSinVvXmHcWfVsz27gWOSLcuSBh5Bvd0iIsALmEA6F7hMRA5piYtQSiml2poG00oppVT39pP9M3QoN/YyWmPCHPOd/XNihOe6ATgaM8z7cKAaeEVEMiIsRymllGp3GkwrpZRS3dvbQAFwloiMC9l2J/5h4IEewwTCD9mZvYOISLSITAx5bT/gbmANcKVlWcuBa4HewCy711oppZTqNHTOtFJKKdWNWZZVIiKXYdZ7XiAic4CtwMHAKEy27UNCjlkpIhcBzwIrROQj4HdMhu++mB7rncAwABFJAWYDFnCGZVnFdjkzRWQycApwHfBgK1+uUkop1WK0Z1oppZTq5izLeh0z/HoxcBpwBZCPydq9vp5jXgL2xQzZ3hu4GjgHGAS8DvwpYPdngP7ATZZlLQ4uiUvsc9wnIhNQSimlOgmxLKu966CUUkoppZRSSnUq2jOtlFJKKaWUUkpFSINppZRSSimllFIqQhpMK6WUUkoppZRSEdJgWimllFJKKaWUipAG00oppZRSSimlVIQ0mFZKKaWUUkoppSKkwbRSSimllFJKKRUhDaaVUkoppZRSSqkIaTCtlFJKKaWUUkpFSINppZRSSimllFIqQv8fhf8TmOg7zLsAAAAASUVORK5CYII=\n", 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Sa56jprfDviQjoqe9l4kjMYwTCcdYfGIBV9OjTB2KEEIIIQxk99WdfH9hY4XyEE/VrEJf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w1/22F2ccUwJ5/LuyMUCBHmE8ap9FpcYBcAQHIkN1E9vnKj5jrzWKPVYMPlb7HqzHtYOuAFk76e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xSoc3uKO2q53e9cK877P56CMvpk4Jgd0jOfTV/c4aZZcFW7HNJZmckVuAw5SH0VvDIDPgZA8l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\nFkIIIYQQQgghbEigLIQQQgghhBBC2JBAWQghhBBCCCG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" + "" ] }, "metadata": {}, @@ -568,6 +577,43 @@ "dataset.savgol('TSS_line3',plot=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drift" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tag data points that are part of a drift. Because there was no drift present in the original data, an artificial drift was added to *CODtot_line3*." + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "drop() got an unexpected keyword argument 'index'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n\u001b[0;32m----> 2\u001b[0;31m plot=True, period=None)\n\u001b[0m", + "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 1586\u001b[0m \u001b[0mnan_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1587\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1588\u001b[0;31m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnan_values\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmax_slope\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: drop() got an unexpected keyword argument 'index'" + ] + } + ], + "source": [ + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n", + " plot=True, period=None)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -595,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -604,7 +650,7 @@ "4895" ] }, - "execution_count": 113, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -615,8 +661,10 @@ }, { "cell_type": "code", - "execution_count": 114, - "metadata": {}, + "execution_count": 21, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#dataset.check_filling_error(100,'CODtot_line2','fill_missing_standard',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", @@ -628,8 +676,10 @@ }, { "cell_type": "code", - "execution_count": 115, - "metadata": {}, + "execution_count": 22, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#dataset.check_filling_error(100,'CODtot_line2','fill_missing_daybefore',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", @@ -669,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -682,17 +732,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:326: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:326: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:369: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:369: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", " 'filling of gaps in the data during rain events.')\n" ] }, { "data": { - "image/png": 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\n", 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Gpk2bxm233cYXX3xBmzZtzO09evTg+eefZ+jQoZetITU1q3Jvqhbz9Gyo90Ns\njvq92CL1e7E16vNii9Tvz/P0bGjtEqSW2rlzJxMmTOCnn37CUAPmfRqNRmbMmMFjjz1m7VKqzBNP\nPEGLFi2YNWvWNZ1v9emNVyMnJ4dJkybh5eXF6NGjgZLHfV68eJyzszN5eXnmIXeXay+Pu3sDHB0r\nljzWZfoPgtgi9XuxRer3YmvU58UWqd+LXJ9u3brRuXNnPv74Y8aPH2/tcuq8pKQk9uzZw9y5c6/5\nGjU+9MrKymLChAkkJyfz8ccfm4cjuri4XBJg5eXl4ebmZl6Mraz2evXqlft6GRk5lVh97abfBokt\nUr8XW6R+L7ZGfV5skfr9eQr/5Hq89NJLjBkzhvvuu++anygoV+eNN97g2WefxcvL65qvUaNDr/T0\ndB577DHS0tJYsWKFxWJxTZs2NT9ms1RaWhp+fn7m4CstLc08vbGgoIDMzMzrerNERERERERExHY1\nb96c7777ztplABAfH2/tEqrU22+/fd3XsPpC9peTl5fHE088QUZGBh999BGtW7e2aA8KCmL37t3m\n7dzcXPbv309wcDD29va0b9+eqKgoc3t0dDQODg4EBARU2z2IiIiIiIiIiIh11NjQ68MPP2Tfvn2E\nh4dTv359UlNTSU1NJTMzE4CRI0eaH7OZmJjIrFmzaN68Od27dwdg9OjRvP/++2zZsoW9e/fy4osv\nMnLkSFxdXa15WyIiIiIiIiIiUg1q7PTGr7/+moKCAh555BGL/Z06dWLVqlV4e3sTERFBeHg477zz\nDkFBQSxatAh7+5Icb8iQIRw7dow5c+aQl5fHwIEDmTlzphXuREREREREREREqptdcXFxsbWLqEm0\nuON5WuxSbJH6vdgi9XuxNerzYovU78/TQvYitqPGTm8UERERERERERG5Vgq9RERERERERESkzlHo\nJSIiIiIiIiIidY5CLxERERERERERqXMUeomIiIiIiIiISJ2j0EtEREREREREROochV4iIiIiIiIi\nIlLnKPQSEREREREREZE6R6GXiIiIiIiIiIjUOQq9RERERERERESkzlHoJSIiIiIiIiIidY5CLxER\nERERERERqXMUeomIiIiIiIiISJ2j0EtEREREREREROochV4iIiIiIiIiIlLnKPQSEREREREREZE6\nR6GXiIiIiIiIiIjUOQq9RERERERERESkzlHoJSIiIiIiIiIidY5CLxERERERERERqXMUeomIVDKT\nCaKi7DEtbY+2AAAgAElEQVSZrF2JiIiIiIiI7XK0dgEiInWJyQShoQ1ISHDAz6+QzZtzMBisXZWI\niIiIiIjt0UgvEZFKFB9vT0KCAwAJCQ7Ex+tjVkRERERExBr005iISCUyGovw8ysEwM+vEKOxyMoV\niYiIiIiI2Karnt546tQpcnJyuOmmm3BycrrscX/99Repqan4+/tXSoEiIrWJwQCbN+cQH2+P0Vik\nqY0iIiIiIiJWcsWRXnv27GH48OH07duXQYMG0a1bN1566SWysrLKPH7VqlXcc889lV6oiEhNZso3\nEZUSiSlfq9eLiIiIiIjUBOWO9IqLi+ORRx6hoKCA2267DWdnZ3bt2sVHH33E9u3bWbJkCT4+PtVV\nq4hIjWTKNxH6ST8SMg/gUz8Y3o0kKdFRC9mLiIiIiIhYUbkjvSIiIigsLGT58uV88MEHLFmyhG++\n+YZ77rmH5ORkxo4dy4EDByqlkLy8PIYOHcovv/xi3nfs2DEeffRRgoODGTRoENu3b7c4Z8eOHQwb\nNoygoCDGjh3L4cOHLdpXrlxJnz596NixI8899xw5OTmVUquIyIXi02NJyCz5LExKcCYpseT3CVrI\nXkRERERExHrK/Wls165dhIaGcuutt5r3ubu7Ex4ezpQpU0hPT+fRRx/l6NGj11XEuXPnePrpp0lI\nSDDvKy4uJiwsDDc3Nz799FPuuecepkyZYn6tEydOMHHiRO666y7Wrl2Lh4cHYWFhFBWVLBq9ZcsW\nFixYwOzZs1mxYgV79+7llVdeua46RUTKYmwcgJ9bGwB8/PLw8S0AtJC9iIiIiIiINZUbemVnZ9O0\nadMy28LCwpg4cSJpaWk8+uijpKWlXVMBiYmJ3HfffRw5csRi/44dOzh06BBz587F19eX8ePH07Fj\nRz799FMA1qxZg7+/P+PGjcPX15d58+Zx4sQJduzYAcDy5csZM2YMISEhtG/fnjlz5rB+/Xqys7Ov\nqU4RkcsxOBnYPGobm0Z+y9YxX7F1Sy6bNmVraqOIiIiIiIgVlRt6NW/enD179ly2/amnnmLkyJEc\nPXqURx99lMzMzAoX8Ntvv9GtWzdWr15tsT8mJoa2bdtiuOAnxs6dOxMdHW1u79Kli7mtfv36BAYG\nsmfPHgoLC9m7d69Fe3BwMIWFhcTGxla4RhGRKzE4GejctAucM+jJjSIiIiIiIjVAuaHX7bffTnR0\nNOHh4ZcdIfXSSy/Rr18/Dhw4wP3331/hNb5Gjx7N888/T/369S32p6am4uXlZbGvSZMmnDx5stz2\nlJQUzpw5w7lz5yzaHR0dcXNzM58vIlKZTPkmfjq0m4F31GfQIFdCQxtg0oMcRURERERErKbcpzc+\n+eST/PzzzyxfvpyVK1cydepUxo8fb3GMvb09b731FtOnT2fr1q2XTFO8Vrm5uTg5OVnsc3Z2Jj8/\n39zu7Ox8SXteXh5nz541b5fVXh539wY4Ojpcb/l1hqdnQ2uXIFLtKtrvTXkm+rw7gLjoGyBxJ1Cy\niP2pUw1p1aoqKhSpfPq8lxrBZIJ9+yAwkKoeLqs+L7ZI/V5EbE25oZerqyurV69mxYoVbN26FQ8P\njzKPc3Z2JiIighUrVrBo0SJOnz593YW5uLhgumiYRF5eHvXq1TO3Xxxg5eXl4ebmhouLi3n7cudf\nTkaGnvBYytOzIampWdYuQ6RaXUu/j0qJJC4tDjxdwSMW0gLw8yvEyyuH1NQqKlSkEunzXmoEkwn3\n0H44JhygwK8NGZu3VVnwpT4vtkj9/jyFfyK2o9zpjQD16tVj/PjxfPLJJ4wYMaLcYx966CF++OEH\n1q9ff92FNW3alNSLflpMS0vD09Pziu2lwdeFi+sXFBSQmZl5yZRIEZHr5d3wZpzsncElG8cJPflo\n/VEtYi8iUkGO8bE4JpQsk+GYcADHeK3DKiIiItfniqHX5WRnZ7Nnzx62bdsGYB7d5ezsjL+//3UX\nFhQURFxcHDk550deRUVFERwcbG7fvXu3uS03N5f9+/cTHByMvb097du3JyoqytweHR2Ng4MDAQEB\n112biMiFkrOOkF9UMrK0wCmDxr4JCrxERCqowBhAgV+bkq/92lBg1P+ziYiIyPWpcOiVlpbGtGnT\n6NatG6NHjyYsLAyAjz/+mIEDB7Jr165KKaxr1640b96cmTNnkpCQwNKlS4mJiWHUqFEAjBw5kpiY\nGBYvXkxiYiKzZs2iefPmdO/eHShZIP/9999ny5Yt7N27lxdffJGRI0fi6upaKfWJiJQyj/QCHPPd\nSU/00yL2IiIVZTCQsXkbGZu+rdKpjSIiImI7KhR6paenc//997Np0yY6dOhA27ZtKS4uBqB+/foc\nP36ccePGER8ff92FOTg4sGjRItLT0xkxYgSfffYZCxcuxNvbGwBvb28iIiL47LPPGDlyJGlpaSxa\ntAh7+5JbGjJkCBMnTmTOnDn8/e9/p127dsycOfO66xIRuZh5pNc5VwqW/MyD97TQ0xtFRK6FwUBB\n5y4KvERERKRS2BWXplZXYc6cOaxZs4a3336b/v37s3DhQt5++21iY0vWXNi5cyePP/44ISEhLFiw\noMqKrkpa3PE8LXYptuha+r0p30ToJ/1I+MMN3ttp3r9pUzadOxdVdokilU6f92Jr1OfFFqnfn6eF\n7EVsR4VGen333XcMHDiQ/v37l9nerVs37rjjDqKjoyulOBGR2sDgZGDzqG2sG/d/+PgWANCiRSHe\n3gq8RERERERErKVCoVdGRgYtWrQo95imTZuSnp5+XUWJiNQ2BicDvVp1YsP6XFq0KOLoUQdGjNAU\nRxEREREREWupUOjVrFkz9u/fX+4xv//+O82aNbuuokREaqvkZHuOHi35aE1IcCA+/pofkisiIiIi\nIiLXoUI/jYWGhvLrr7/y3//+t8z2Dz74gKioKG6//fZKKU5EpDYx5ZvIbbzLPMXRz68Qo1FTHEVE\nRERERKyhQgvZm0wm/va3v5GYmIivry9FRUUcPHiQ4cOHs2/fPhITE7n55pv55JNPuOGGG6qy7iqj\nxR3P02KXYouutd+bF7PPPIBP/WBebbuV4EAXPYBMagV93outUZ8XW6R+f54WshexHRUa6WUwGFi1\nahUPPPAAx44dIykpieLiYjZs2MDhw4cZPnw4q1atqrWBl4jItYo+tZuElGOQ3JWkzATq3/K7Ai8R\nERERERErqtBIrwsVFhZy6NAhzpw5Q4MGDWjdujXOzs6VXV+1028/ztNvg8QWXUu/N+Wb6L/iDg6/\nvgbSAnDwSuCX7+1p5elVRVWKVC593outUZ8XW6R+f55GeonYDsdrPdHBwQFfX9/KrEVEpFaKPrWb\nw0kNIC0AgMJTfox4915+fDYCg5OGe4mIiIiIiFhDhUOvpKQkPvvsM44dO0ZeXh5lDRSzs7MjIiKi\nUgoUEakVPPeBR2xJ8OURy7H6XxOfHkvnpl2sXZmIiIiIiIhNqlDo9dtvv/H444+Tn59fZthVys7O\n7roLExGpLfzcjTjWO0fBuC5w/FYohlZuPhgbB1i7NBEREREREZtVodDrrbfeoqCggKlTp9K3b18M\nBoMCLhGxeclZRygoLgBcYONiSAvA3rcARuWCk7WrExERERERsU0VCr3++OMPBg8ezIQJE6qqHhGR\nWse74c042TuTnxpoXtcrKdGR+Hh7OncusnJ1IiIiIiIitsm+Ige7uLjg6elZVbWIiNRKyVlHyC/K\nO7+uF+DnV4jRqMBLRKSUKd9EVEokpnyTtUsRERERG1Gh0KtXr1789NNPFBYWVlU9IiK1TulIL1yy\ncZzQk4/WH2Xz5hwMenCjiO0xmXCMigSTgp0LmfJNhH7Sj0FrQwj9pJ+CLxEREakWFQq9ZsyYQU5O\nDlOnTiUqKor09HRMJlOZf0REbIV5pBdQ4JRBY98EBV4itshkwj20H+6DQnAP7afg6wLx6bEkZB4A\nICHzAPHpsVauSERERGxBhdb0Gj16NDk5OWzdupVvvvnmssfZ2dmxf//+6y5ORKQ2MDYOwM+tDQmZ\nB/Bza6OnNorYKMf4WBwTSoIdx4QDOMbHUtC5i5Wrqhn0OSkiIiLWUKHQq3nz5lVVh4hIrWVwMrB5\n1Dbi02MxNg7A4KRhXiK2qMAYQIFfGxwTDlDg14YCo4KdUvqcFBEREWuoUOi1cuXKqqpDRKRWMzgZ\nMDYOIPrUbgCCvTrphzoRW2MwkLFuIy7fbObc7aFonrMlg5OBzk018k1ERESqT4VCLxERKZsp30T/\n//bgcNafAPi4+bJ11A8KvkRsicmE+4gh5pFeGZu3KfgSERERsaJyQ6/w8HB69+5Nr169zNtXw87O\njpkzZ15/dSIitcSvx382B14ASZmJxKfHalSDiA3Rml4iIiIiNUu5odfy5ctp2LChOfRavnz5VV1U\noZeI2JqjZ46c3zjnituZ3ni7tLVeQSJS7bSml4iIiEjNUm7otWLFCm666SaLbRERudQQn7t44eeZ\n5Oc6w7uRZKYFMGJLIZs352h2k4itMBjI2LytZISXMUBTG0VERESsrNzQq2vXruVui4hIiaYNmrL7\nof0s2xTNgrSS0R0JCQ7Ex9vTuXORlasTkWpjMGhKo4iIiEgNYW/tAkRE6oqmDZoyJTQUP79CAPz8\nCjEaFXiJiACYTBAVZY/JZO1KRERExFZUaKTX1bKzs2Pnzp3XdK6ISG1mMMDmzTnEx9tjNBZpdlMN\nZco3EX1qNwDBXp30lE2RKmYyQWhoAxISHPDz09RvERERqR7lhl4G/d+IiMhVMeWbiE+Pxdg4AIPB\nYJ7SaLFfwUqNYMo3MXBNH5JOJwLg4+bL1lE/6N9HpArFx9uTkOAAaOq3iIiIVJ9yQ6/vvvvuul/A\nZDJx5swZmjdvft3XEhGpiUz5JkI/6UdC5gH83NqwedQ2DE6Gy+4X64pPjzUHXgBJmYnEp8fSuanW\nYRKpKkZjET4+hSQlOeDjo6nfIiIiUj2qfE2vDz/8kJCQkKp+GRERq4lPjyUh8wCccyXhDzeikw9Y\n7gcSMg8Qnx5rzTLlf4yNA/Bp5Gve9nHzxdg4wIoViYiIiIhIVajxC9mfPn2aZ555hq5du9K7d29e\ne+01CgtLFok+duwYjz76KMHBwQwaNIjt27dbnLtjxw6GDRtGUFAQY8eO5fDhw9a4BRGp44yNA/Cp\nHwzvRsJ7O3n2wZ6YTCX7/dzaAODn1kbBSg1hcDKw9b4fWDf8S9YN/1JTG0WqQXS0PUlJJdMbk5JK\npjeKiIiIVLUa/38cL774IikpKfznP//h1VdfZcOGDXzwwQcUFxcTFhaGm5sbn376Kffccw9Tpkzh\n6NGjAJw4cYKJEydy1113sXbtWjw8PAgLC6OoSMPpRaRyGZwMvNp2K6SVhFpJiY5E7zuHwcnAurs3\nMr//QtbdvVHBSg1icDLQ66Y+9Lqpj/5dRKqCyYRjVCSYTJhMMP0ZF3OTk+dBvH2yrFiciIiI2Ioa\nH3pt376dhx9+mDZt2nDbbbcxdOhQduzYwY4dOzh06BBz587F19eX8ePH07FjRz799FMA1qxZg7+/\nP+PGjcPX15d58+Zx4sQJduzYYeU7EpG6KDjQBR/fgpINj1gm/96LQ6cPMmLDEKZ9P4kRG4ZgyjdZ\nt0ixYMo3EZUSqX8XkcpmMuEe2g/3QSG4h/YjPvochw6eX0Y2f/CjJJ/bb8UCRURExFbU+NDLzc2N\nzz//nNzcXFJSUvjxxx8JDAwkJiaGtm3bWjxhsnPnzkRHRwMQExNDly7nFyWuX78+gYGB7Nmzp9rv\nQURsgIuJcRHvw+PdYFwXjuXHM2x9qNb0qqFKHzIwaG0IoZ/0U/AlUokc42NxTCj57HNMOEAg+yx+\nKeDT9rSme4uIiEi1qPGh1+zZs/ntt9/o1KkTffr0wcPDg8mTJ5OamoqXl5fFsU2aNOHkyZMAl21P\nSUmpttpFxDaUBigzd07AocVucMkG4FROCi0a3gxoTa+aRg8ZEKk6BcYACvxK1jM0tbqZwnY3sXVL\nLuu+SGPdxlNsHfOVphWLiIhItXC88iHWdeTIEdq2bcuTTz6JyWTipZde4t///je5ubk4OTlZHOvs\n7Ex+fj4Aubm5ODs7X9Kel5dX7uu5uzfA0dGhcm+iFvP0bGjtEkSqXUX7/cHk/eYApbC4gKauTUnJ\nTsHfw5/vH/6ew5mHCfQKxOCsH/JqiuD6bWnZqCWHTx/G38OfXm262vy/jz7vL2Iywb59EBgIBtvu\nGxXm2RDTju08Fn4bG50O02LLMCLHRXJPKw+gr7WrM1OfF1ukfi8itqZGh15Hjhxh3rx5fPfddzRr\n1gwAFxcXHn30UUaNGoXJZDkdJS8vj3r16pmPuzjgysvLw83NrdzXzMjIqcQ7qN08PRuSmqqFZqXi\nTPkm4tNjMTYOqHW/zb+Wfu9lfzM+jXxJOp0IQANHV9YN/5Jgr0445LrS2qUtuaeLyUXfTzVBSk4K\ng9eGcDTrCC0MLfhk6Bc2/++jz/uL/G9NKseEAxT4tSFj8zYFXxUUlbKfNYaSp2bHpcWxdf926jvW\nrzH/XVCfF1ukfn+ewj8R21Gjpzf+8ccfNGzY0Bx4AbRr147CwkI8PT1JTU21OD4tLQ1PT08AmjZt\nWm67iFSNlJwU+v73NptaK8ngZODVfgvM24dOHzTvl5rFlG9i8KcDOJp1BICjpqMk/+9rkVIXr0nl\nGK/prxVlbByAn1vJFEefRr48u30qg9aGMHBNH3469oNN/LdBRERErK9Gh15eXl6cOXOGU6dOmfcl\nJSUB0Lp1a+Li4sjJOT8yKyoqiuDgYACCgoLYvXu3uS03N5f9+/eb20Wk8l0cKNjSWknBXp3waeRr\n3n52+1T9UFcDxafHctR01Lx9k8Fba63JJS5ck6rArw0FRvWRijKcg20+b7Bl0Je82m8BSZklI2GT\nTicy4rOhNvNLEREREbGuGh16BQcH06ZNG2bMmEFcXBzR0dG88MILDB8+nNDQUJo3b87MmTNJSEhg\n6dKlxMTEMGrUKABGjhxJTEwMixcvJjExkVmzZtG8eXO6d+9u5bsSqbtsOVC4eLRXUmYi8emxmEwQ\nFWWPST/b1QjGxgEW4aSTvVM5R4vNMhjI2LyNjE3famrjtfjf9NDmw4bSf8zTdHQ1mkd9lbKlX4qI\niIiI9VQo9NqwYQNxcXHlHhMVFcXbb79t3u7atStPPvnkNRXn6OjI0qVLadSoEQ8//DCTJk2ia9eu\nzJ07FwcHBxYtWkR6ejojRozgs88+Y+HChXh7ewPg7e1NREQEn332GSNHjiQtLY1FixZhb1+jcz6R\nWs3YOIBWN7Q2bzs7OJdzdN1zk7M/Xul3wTlX/Nza4O3SltDQBgwa5EpoaAMFXzWAwcnA3F7h5u0/\nzxzi1+M/W7EiqbEMBgo6d1HgVQ5TvomolMhLRmxdPD20UdIRNo/axkcDN3FT5kjzZ6St/FJERERE\nrMeuuLi4+GoP9vf3Z/LkyeWGWK+88gqrVq0iJiamUgqsblrc8TwtdikVZco30XtVV46Zks37No38\nls5Nu1ixqoq51n6fkplNp17Z5J/ywdErgZ+/tyf9SDMGDXI1H7NpUzadOxdVZrmXqM0PEaguK/ct\nZ/r2yebtG11v5OfRUTb9funzXirKlG8i9JN+JGQewM+tDZtHbTv/PVTGgwBMGAgNbUBCggPu3ims\n+/IUgc1vsVr96vNii9Tvz9NC9iK2o9ynN65bt47vvvvOYt/GjRuJjS17OHp+fj47d+684hMSRaRu\nik+PtQi8WjS82WZ+k/9NZDL5p24FoOCUH79E72J4dy/8/ApJSHDAz68Qo7HqA6/L/hAqQMmDFp7Z\nPsVi34nsE8Snx9aqcFbE2uLTY0nILBnNVTpV0fw99L/poY7xsSXroRkMxEfZk5DgAEBGclNC3h7B\nrzMW0apR68u9hIiIiMh1Kzf06t27Ny+//LJ5sXg7OzsOHjzIwYMHL3uOs7MzU6ZMuWy7iNRdjes1\nwdHekYKiAhzsHPn0rs9tInQx5ZvwuiUNJ68k8k/54OSVxO1dvDEYYPPmHOLj7TEai6p8llS5P4QK\nABuTPqcYywHONzdsaTPhbG1W40cxmkwWIU9dV/p0xtKQ/ZLvodLpoaXHG4vwujmdU0cag0csRR4x\nDFsfyo4H99TMf08RERGpE8oNvTw9Pfnmm2/Izc2luLiY22+/nYcffpiHHnrokmPt7OxwdHTE3d0d\nJyctDCxia0z5JkZ8NpSCogIACosLSD/7V53/Lf6Fo6taPd2BCc0XMeQ2H5q6lUxrNBio8imNpa74\nQ6jQ4oabL9k3pu0j+qG7hrvw+6yFoQVf3fsdTRs0tXZZ55Uxna/OBV8XhXoGJwObR20rN4g0mbAI\n/b/YlEH3BcMo8ogBl2xO5WQrnBcREZEqVW7oBdC4cWPz1+Hh4QQEBHDTTTdVaVEiUvtEn9ptMbXR\n0c4R74aXBgx1zYWjqw6d/Z2gjudwdS0mKiWy2kekXM0Pobaue/OeuDu7k5GXYd7n4uBixYrkalz4\nfXbUdJTBa0PY/sCOGtPHL1643TE+1mKUU613DaGeyQQD76hPUqIjPr4FbN2SSytPL36dsYhh60M5\nlZOtcF5ERESq3BVDrwvdc889ABQXF7Nr1y7i4uLIzc3F3d0dX19fOnbsWCVFikjtU1BcQHLWkZo1\nGqMKeDe8GSd7Z/KL8nCyd6ZxvSZaV+saVNfUNYOTgXV3b6T/mh7mfV2adrVKSFmrVfNUPmPjAFoY\nWnDUdBSAo1lHatQIoQJjAAV+bcyhUIGxbgU5ZYV6mR0CGPhJH5IyE/Fx82XrqB8svn+i950jKbFk\noeikREei952jVzcXPBt48c7AZQAEe3XS95yIiIhUqQqFXgC///47M2bM4PDhw0BJAAYl0xtbtmzJ\nq6++Svv27Su3ShGp8YK9OtHyhls4fOZPAHzcfG3iN/jJWUfIL8oDIL8oj1+O/2S1dbVq60L21V33\n2cJci+27PruTgqKCWvWeWZXJRKM7+uCcmEiery+nt/xQ5cGXwcnAp8O/oOeqWykoKsDJ3rlmjSQ1\nGMhYtxGXbzZz7vbQOje1saxQL/rUbpIyEwFIykwk+tRuet3Ux3yOg2EX9Rq15OzpAPCIBa9TmPLb\nlHyvpxyjRe4gvgrrjEHPPhIREZEqZF+Rg//8808effRRDh8+zB133MFzzz3HggULmDt3LkOGDCE5\nOZnHH3+co0ePVlW9IlKDOdo5wjlXPP8ayscDv7aJ8KBkpFfJOoZO9k70aN4LP7c2ANU+daeshexr\ng4vrjj61u0pfr3TUUKnSdehq03tmTfn7duOcWBJ2OCcmkr+vav+9SqWf/cv8b5VflEdy1pFqed2r\nYjLhPmIIN0ybhPuIISVz++qS/z2NMWPTt+apjbkFuZc/3mTiznGTST3dhVWNutF28n0Ee7cp+V5P\nOQbvRnJ0wScMDm1Y594qERERqVkqFHotXLiQ3NxclixZwptvvslDDz3EnXfeyX333cdrr73GokWL\nyMrKYsmSJVVVr4jUUPHpsSSdOgHvRpIa8QV3D/aoET/MmPJNRKVEYsqvmmJ+T40mvygfgPyifBIz\nE9g8ahubRn7Lurs3Ep8eW2WvfTFj4wB8GvkC4NOo9oy0MzYOoNUN5x94MH3blCp/z17p+wY3Gbwt\n9tW40UM1VEzeGX6lKyZcKQISHNKr5XVLH9QA1R8oX0lZ0//qnNKnMRoMmPJNzNw+3aK5nn0989eO\n8bE4JyZiIJsHTv+Gf0YmUPJLAq/sEEgr+bc7esiV6H3nqu8eRERExOZUKPT69ddf6d+/P3369Cmz\nvU+fPgwYMICffvqpUooTkdrD2DiAZtkDzT/MnDjciO+jTlq1ptJpc4PWhhD6Sb8qCVKOnrEcbbLv\nzzQ+W+NG44JA7t4wiEFrQxj4SZ9qC76wu+jvWiKnIMf89aHTB6tstFdpn3hw4ygc7Ry5wekGc1t1\njh5KyUnho9gVpOSkVMvrVRaTCaY8MZAe7KQLkeTgStv9qdXy2qXrsc3vv5B1d2+sUSNJS6f/AXVy\nTa+LxafHctRk+b0y+qt7zZ9zBcYATK1KAuRYD9hcP5noU7sZsWEIp1y/xcEzqeSkJvE8u39g9X0+\nioiIiM2pUOh1+vRpWrRoUe4xLVq0ID29en7rKyLV42pGSxmcDNzZ9ZaStVsAPGKJKvqwWuq7nOqY\n7tf/5pDzG1le/N/o8UybVp8eXTxIOnoGOL/eTVWLT4+1WGOntkzViz61m5Sc6glIL+wTh7P+5Ez+\nGXPbja43VsvooZScFDqtCGTa95PotCKwVgVf0fvOkZjqAUAcAfxBIPQOucJZlcOUb2LEhiFM+34S\nIzYMqRlBicmEY1QkwCXT/+qc0ns1mcocEZl5LvP855zBQNrmb7l3agu6jIPmTUsCwdLvvcL/jY6F\nYpIyE2rNZ5WIiIjUPhUKvW688Ub27NlT7jF79uzBy8vruooSkZrjakdLmfJNbD35KYzrAo93g3Fd\nGNV+aDVXa6k6pkOln/3r/EbCEArySz5WCwscIGFIpb9eeWry9K+KcndpXCXXvfA9uthtzXpWy+ih\nbw5vtnj4wTeHN1f5a1aWEw22Uq9RSUDhTyzt2Idj+l9XOKtylBliXxDEVDuTCfeBfXAfFIL7wJIR\n8KXT/+ockwn30H4l9xraj91J31/xFFe3prz67E4+Hf0tm0dtI9irU8n3Xmog/OVfctBf/rT4f/bO\nOzyKcm3j92Z3U3YnvaykkkLCCkoIhF4NLYQahKOiwFFAQUQRVPScowf9hKOioggIWA5I8dBBAkZa\naNJjIsT0hFTY9JBJ3d3s98dkZ3d2ZkuSTQgwv+viCu87ve7MPc9zP/XRD/S9ioeHh4eHh6dr0yrR\na+zYsUhOTsb69etZw5RKJb744gskJydj3LhxVltBHh6e+4ul0VJJJYkoIgsBu1rA9ypgV8uqktfZ\nECsPCf4AACAASURBVGKiw/21KCN7W6rR41dA2OJPI2yE2xNXAFD+WuFeEVZdrjE+GfkFDkw9+kBV\nITT01gKAw1kHOmRZ2nPi+/Hb2cvMOdgpUVdDvIeZbHdVSCWJf11dApv5lDn5NUSizs+h01L5DEXd\nnnb+DCGms4UvUVIiRNlUZKUoOwuipM4x9L8fGHqWlZw/igGFgNTAjsukWN1I4N/eF7Gq72YEBlEF\nCfwCa3Fs8foH5l7Fw8PDw8PD8+Ahas3IixcvxunTp7Fx40YcOnQI/fr1g6OjIxQKBW7evAmFQoHA\nwEAsWrSoo9aXh4enk9GKOsrmplYZfXtLfe7713tSSSK9IhW+jv6YdjAa2dVZCHYOwYlZ5xgvWdrx\nwtzk8IRjq5ZBGdlTUTtwvAObN4PQnD4ewrATOD7vKCoayhHmJu/wlzptRF5mVQb8CD8ce/r0A/Mi\neSb/FKtvakhshy2PEBMorWP7UDVr1DiZF4/Z8jkdtmzAIDqwpR3oHGRk7K5DekUqKhorAEdg/uKr\n6FUKTJy4FIs7KbJJK1hqr1XnP9nm8ap+kdZZGElS8wuTP5yRW61E61kmysyAyscHL269iBVFlF9X\n5AKg1o4ab1fqT/h4+CcAmPekYIdwNG++htwcRwAe6OZfi517qzG4ny0IQnr/NoyHh4eHh4fnoadV\nkV4EQeDnn3/G9OnTUV5ejiNHjmDnzp04efIkqqqqEBsbi127dsHRsXUvjTw8PF2Xwpp8RiqWMaPv\ncK8IRgU+O5Fdp6yfMUglibF7RyB6fxTG7R2J7OoWr6vqLFwqvsgYj5G+2WR5tIiiToE5cc/SbbGN\nGKf+vg9fLu+HpFfPINA5CL6O/jicdaDDI4j0I/IKyAJM3B/VNTyPLMDPiS2kVjZ2rDeko60TZ78/\nEdChywUAN3t3iATUNyexjfiBqRgZ5iaHzOExAJTIcdUX8OvWucI2ISbQTxYJQky02zzeqFehQSqf\nsQgyVXgEVMFUtVRVYBA97UMJQaByxx6ovLwgKiqCWxEl3MrLgF56+rGznQv9/6TCDGTecgEapcjO\ntEVuju476518KVZefAWwe0j3Fw+PHh1dRZqHh4eHxzStEr0AwMXFBatXr8a1a9dw5MgR7Nq1C4cP\nH8a1a9ewevVquLq6dsR68vDw3Cf0U4r8CD+jL+iEmMA/B6+i27nVOWbNiTvyQTCpJJE2db9TW8wY\n9vbZZfQyDdM3U0pSLF7Gybx4qKGi28pmJSobKzBbPgcyiaxTDcvD3OSMNMGCmvwHxhz6Sc9wWgTS\n8tbZNzrsBYFUkkgpu8k5bNbRaVY9TobnOGXGPgkqDXXeKJuVuHrnktWW15EQYgKrR3zK6LMXOXT8\ngvV8uxhVLwmizebxprwKDVP5ROlGriOCQOWJc6g8cBSwsYFr7KT7kmbZKZAkXGZMgqikhNHdDKBE\n7xTQRiySJPDW7KHAd1eArdcQ6C+kUxoBAO7pKHA4/sDco3h42kpnVJHm4eHh4TFNq0SvOXPm4NCh\nQwAAsViM0NBQREREICwsDLa2lKfNTz/9hAkTJlh/TXl4eDocLhGKEBM4MC0Ofo7+KCALjFZNU9Qp\nsDD+73TbXARLRz8I1quYfmICCOj/F5GF9MuWoU9QL69eFi/DnBdTZxuW22q9xQB0dwq87+mlllJY\nk0+LQFo6qvqk9rzbmPw153B1S4qjtZYVtWcYovdHIWrPMDqNtqi2kDHewvi/PzAVHDtF5NJHL+rK\ncewwDN8qbxGRH6eFr7aYx5vyKlSFyXURXD6+UPmaiMQjCMDBQeftZUoke4ARpadCXFjI6rcBMDpP\n165poqqhpqfbIDurRcguk+Of8m14ecMP2LA9Cz6vvgAs7IcesvufAm8R96NYwv0s0MBjVTqjijQP\nDw8Pj2lMil4NDQ0gSRIkSaKmpgZXr15Fbm4u3Wf4r6KiAhcvXkRxcbGp2fLw8HRBcqtzMGhnX0Tv\nj8LwXZE4kRdPC1GFNfkoaElrNPbQxhX1lFmZbnR5Hf0gWNVQyWhroKH/rxXktCLEgWlxOD6DqjBG\n2Fr+8mzozSQUiNDDNYxuD/EexkhjGxMwvi2bYhHpFanIvZdDtwtq8lGrrO2w5VkTX0d/VqSXEEK4\n2btbfVn6550xwlx6WmVZl4ovIreaOia51Tm4VHwRYW5yln+XGmrEZR+xyjI7G4cOFsH0o67ss3MQ\nqtBFyHHtM0ujR8Pc5Ah2oYStYJcQtvjS3Ewtv6gQrtOiTYoP7U2zfBBQhcnRENidbmv0/t70oP5v\nAxsM7DYYABAW1ozgEOpYdQ9qwMtJA7HyystYmivH1oXz8OWET7AjZg+7uEhXE3tIEq5Rw+AaHQXR\n8D4oLc0xP401lqlfFbSr7AueNmH4O9YRv2s8PDw8PKYxKXrt378fkZGRiIyMxIABAwAAW7ZsofsM\n/w0dOhRnz57F448/3ikrz8PDYx0UdQoM2dUfJS3RJkW1RZgdN5OOTjGMhuL6Oj8mYDxEAjGjz1SK\nmiXzbCukksS/LrxrdLhWkNNGmsUeimmT2byvoz+EENJttUZFC32kksRzR5+mI5i8CR9IxR1n2Bzm\nJoeXg5feuugilrq6n0hmZTor0ksNNSYfHG/1ddYXOwKdg+Bmx34BeeH4M1ZZbkrZLUa74F6LH56G\nPa5YL0qvq0IqSbyvd10FOHXv8Kqk+oJSdXcfpHjqhhl6wen7+I3dO8L8MdQY/G1BlJ4KUa5O3BBl\nZ5mO3mpHmuUDA0Eg+Z+v0U2B3t+/Z0lhI7BBM5oxbt8oKgLPjgQWRALzB4KcJ4dKTH2EUGtUmHRw\nHJadWYKhu/ozI30t9FLrTESXLtLngmtRKd5f1bfDozIfpaqgjwK/F18w2ebh4eHh6XhMil7PPvss\nxo8fj/79+6N///4QCATo1q0b3db/FxkZiSFDhmDatGn49NNPTc2Wh4eni3EyLx5qA9EBoKJTkkoS\n6appdDQUhzgkk8jwx9y/sLjPUrovuyoLh7MOcL58aud5YOpRfDLyCwDWE2eSShJR0VhudLhW9Ghv\npFlhTT7UUHMOS69IRXbJHaBwANAoRd692x2a1kCICfxv8iEIBZQIJxSIMMR72APtJ1JSp2AUHbAW\nzZpm+v/7p/7CGl7eUIakkva9aCrqFPjkysd02wY2GO0fxYrI05Jdldmu5XUG6RWpdEEIALCtb4Jd\nYmLHihP6gtJvCfDyoqLkAp2DMNh7KGNUfR+/7Kosk8cwqSSRUdyCkd7o6w+NSCfgqwKDzEdvtTHN\n8kHCcVAUcm2luIIBIKET8K/42dDXlDaNO70iFdn1SYDvVZQ134aN3uNmM5qBRilUBRFAo5S+/1rs\npdaJCAsooZoEtd3/OSrBb7f2tm1mXS2KjadTGBMwHmIb6n7S0RHfPDw8PDzciEwNtLGxwbp16+h2\nz549ERsbiyVLlnT4ivHw8FBoU/DaEolkKea8qSxdB6lYijHdx+H47aPIrc6B2EaMZWeWYOMfXxsV\ny945+yZV0t45BBBQL6s9XEKNjt9eFvdZikV9X4NULEUPl1BkVmWgh0sofB39cUNxDcOcB1g8Lyot\nTwyVRgmAGfnia/c4xN8nQ1kSDHikImDFrA71ryGVJBb+Ng9qjRpCgRBqjQrPxT2Nz0auY4l7/WSR\nHbYebUHfgN+QtxLewIXnrlntXEgqSWSkHFY2VmBF/3ex9voaq8xfi2G6bzOa8Vzc0zg07TjcbN1Q\n0cSsTjkz7BmrLr8jCHOTw4/wQwFZAGkjcPibYniXTYKqR2irIpxafU9rEZSkAI5Mj8fJvHiMCRjP\nmtbQx8+wrb/85Qk6cd4wvVFUmA+BSkm3az6n/N9EN65R4tdDLGyZIuHuX1hpewM1TWHwQx6uYgDs\nnO9hv38NY7wh3sPgKfFCoFMQLfA2Qyc0o1EKbL0GlMkBj1T4vBmLMDc5VFIqPVSUmdFl0kQbY6ag\n6t0PMFhzFWmQo2ddKhbcXA9Y8jNBkhClp1LbUVsLt4lREBbkm71etFVBRdlZlKdcjzDO8XgeHDQa\nDeMvDw8PD0/n0ioj+7S0NF7w4uHpRDorSqeIZBsUA5Svkg/ha9E6aNc19vAkFNYUAKDSCAHjkVT6\n/krZ1Vl0lEZ7Pb7CvSIQ6BTE6hdCiI3JX2PawWgAoKPXDkyLQ+yhGETvj0Lk1kiL9zNlwK57Of5y\n9Df0i3hmuogSvACgTA7V3Y59cdHfl2oNFX2WXZWFelV9h6WRWgNtNUNjFNcWdbjx78ywvzHaPlLf\ndqftcQnJ2VVZKKzJx0tPvswaVlxb1K7lAR2fxkqICRx7+jT8HP3RqxSQl1H9oswMKgXLWCSLQgG7\nndsBhaJd9zRSSWLawWgsO7ME0w5Gm522wYjopS98AsB7A99nCGgsj64eYV0u7e5+cOz8XdSQ1H2s\nAAHoJ7iKdV8sg5u7H2O8ioZyEGIC83rP555RaS9K8AKAMjk+lf9G7f+umCYqk2Hekr8jDdT6pkGO\nf6beMJ/iqJ+qOXYEXCeMpqPGzEaxEQQqDx2H2s+f8pSLjXlkz7mHgbjsI3T6vkqjemD9G3l4eHge\nZFolepWVleG3337Dzp07sXnzZvz0009ISEhARUWF+Yl5eHhazf2u+qOGGkeyDlq0DvrrqhW7tBir\n5Kjv6xXsHEKnHbZXnCHEBI7ExsPNzo21PQAlsGnTNvvJIlFYk0+ve1pZmsX72dfRn5G2oDWxV9Qp\n8NrN4YBHy3w8UlHk8GuHHj/9famPg8jBbGrq/SSpJJFVzVAfFztXqwp1rgbnhA/hyxJ979bdbXcR\nAMMiBwAgFAhhL3TA9r9+ZA2j/b7aSErZLfTd9jijUmRHIJPIcPaZy1gzey80Il2wuOPSRTrzbX1h\nSKGAR185nJYtgUdfOXIzLrb5nmaYkmiYvljVUMVo//PCSs79UNnQ8szSKAUKB2DlyX8zxyMIVB6I\nw70vv0HlgTiICvMZaXeSr78AFA9GtU1r8rgHs7JtsSYAYW6LsHks83y2FzqAVJL4763vuGfkmULf\nGwODmzC4j4tuWBdMEx0xqS8E7tT6CtxTUe+TYrbCKyNVMzsLoiLdPUbt5282ik1UmG+5SMbTpTH0\nHjRs8/Dw8PB0PCbTG7UkJibiyy+/xPXr1zmH29jYYMiQIXj99dfRu3dvq64gD8+jjNZ0O7sqi7vC\nmJXQrziIRin1Jd4zBbCrxebkDfQ6mBKjtKILV2U8rXG8TCJj9Gt9vbSpTgCslspZWJOPikbjgnxu\nlS7Sw4fwhZ+jPwpq8tHTo6fF+/nP0iRa4FM2K/FnaRIGew/FhL2jUdRUSBk5t+zLYK9uHRplpd2X\nl4ovYkXC67hTW4xg5xCEe0XQ4t6DgEQoQZ26jm5XNVaitK4EhHP7X4JJJYlZR6Yy+n4vvoAAp+6M\nPrVGhZN58Zgtn9PmZdkL2VUN1Ro1ph6Kxr2maka/AAI42jrhRF48HEQO9DGzlNzqHIzeM4TRvlR8\nEWM7yDuGEBPo2+AGgUqXvikqLND9v+UlXdUvEoJdP9DjCVQq9DyZiB6eurTi1lwTd8g7RoeRShL/\nOv8Oc/zaYiSVJMJB5MC4p5TWlTJS7Eo9UnHpqWSM7dHiEUaScI2NodPsKg/E0Wl3GgDSdWshWb8O\nZb9fBwLZEaUPK3PGPYnP3XPQXE5ts39gAwb3ccHXt35ljHc46wDGB0Zzetd5SWQogQJur0Xjo9Bf\n0C2oArALBdB1RC5DxoUNh2ZhBFAqh8YzBTZ29WZ9mVRhcjpFEQA0YjEESiVUfn6oPHbKrKinjTbs\nSqmePG3jSc9wiGzEUDUrIbIR40nP8Pu9Sjw8PDyPHGZFr71792LVqlVQqVTw9vZGREQEZDIZbG1t\nUVtbi6KiIiQlJeH8+fO4dOkSVq1ahRkzZnTGuvPwPBq0WEA0KBtQq6ztkEgdbcVBQ68VLIhEGcqw\nZfx/WS+OhhBiAgemxeHr659j661vjS5L388HYItc1hJntJUVjRnNLz+r8/SxAVV5zN3eA0efPQpC\nbdk+NqzOl1WZCQeRgy5yya4WQr8bVLqhgGMGHcC/L/4Dd2qL4eXghV2T9nW5yC5DDP289AUvLd8l\nf4uPR7S/QEpSSSJKG0rptkggwpiA8ZCKpQhw6o68e7cZ/e1hb/rPnP2GghcAaKDBq6cW0O1glxCc\nmHnO4mO37dYPrL4rxZc7RPRS1ClwMi8e4/yHwdHDA6KyMnqYxsYGguZmaERiqHz9AZIE8e1GkJAi\nBb3QCym4U1uE+MUJrRa3c6tzGPtIKBAyzp30ilSWTxoAvHH6VeTX5DH2aUzwFKzcvYeRYleQXQH0\noJosQ/XCfFTGJ0Dy9ReQrlsLABCoVXCbPB4Vl//oUlFJHYnMRYrkS0Dc+UT4OQVgcD9bEAQwNSQW\n6xLX0uNNDYlFgHN3BDuHMAofAJTAS0VX5uGNzP6w/bMJE+r9sHbxaUhdZIaL7BIU1uQDdjWA71UA\nQDOAsrpS1kccBgSBms/WwTWWSt0WKJW49+U3aJwaa9n50pLqSXuCPSLn2MNIZmU6VC0fx1RGPgDy\n8PDw8HQsJtMb//zzT/z73/+GVCrFl19+idOnT2Pt2rV466238Prrr+O9997Dhg0bcO7cOXz++edw\ndHTEBx98gLS0tM5afx6ehxr9amlFtYWYuD+qY6vvGXitoJRKZ9E0a9BPFmnyBZXyZooxKnj5EL4M\nP5+xe0Ygas8w6v97R1h9u0xVVjREa7Jc3lCGUdss8xkilSS2/LmR0efryDZk1/fX6uj0VP0U05L6\nEjx9ZEqXr9Z4Jv8Uo+1m68Ya52D2/g7Zjs3jfoBMIgMhJnA09gS8Wl5EHG2dUNfO9MZ+j/W3fOSW\nNDs0UhXxWnuu9PKgIqyljcDIXGBkDlB0569Wra8lKOoUiNjeC8vOLEH4/gHI2vczNEKqWqhW8AIA\ngUoJUWY69cJeqUIkrmEQrqA/rmGCej9qlbVm7yeG7E7dwWirNWrG+R3mJkeAY3fWdPk1eQCY1Rzr\nlLWA5y1G+vHofo/R06h8/aER21LbJbalBDyCQN2zz0PfhlpYonjk0s5kLlK8OLkHxo60pXWYSoOI\n2srGChBiAp+NWseaXlF3l04ntq1vwrWtwL51BfAYH9VlfavC3ORwFDoy+qYeMuIpp/W1a0l/VQVT\nKfuqHqGWC15aumCqJ0/7MVZgg4eHh4en4zApev30008QCAT4/vvvER0dbXQ8oVCImJgY/Pjjj9Bo\nNNixY4fRcXl4eCxHWy1NS0FNfocIJ+FeEdQLo57XCjxSqTaAGb9MZhg/c6EvuHDxe/EFlnG9dp7Z\nVVk4nnO0/Ruih5u9O4QCYaunK7xXaNE+vlR8EWX1pYw+V3s39HANo32+9PFz9O9wE3k3e3dGu6PO\nF2viKfFktCO7DWSNU1ZfikvFF9u9LP1jI7YRY0C3wfSwq3cuo6TFnLqysQKDdvY1e86bYrT/GMgk\nj5kfURtd+d0V6m+jFM62zq06V7oR3pA2AombgYRtQMJ2YOvHiVYXEU7mxUPZ3AQAUDY34VdBGsqS\n0nDvy29QtXUbY9yqxkqowuQ44zGANgFPhxxl9b2wNdl4JCgnJImX7wbglSuAl16hQMPz+8UnFlo0\nu92pOwC7WmDuKGDKi8DcUahozqOHiwrzIVBS2ylQNkFU2OKtVFTICNhU+/jyaWdge6Sl3aFSXcO9\nIuAjZX4IkEkegxDUfVm/GAKRm99lBURCTCDSexCj715TNfveqmde7xHRi4ryaqhH5c69XceYnwcA\nJeDvTN1uviCBFTCMZv7H+be7/McoHh4enocNk6JXYmIihg4darFPV8+ePTFo0CBcu3bNKivHw/Oo\nQ4gJ7Jv6C0QCKhPZmCG8NZZzdMYJrBi6lPKhmj+Q+muni3b58Pf3caHonNGHNX0jdZkD+2V/iPcw\nxjg+Uh/dwEYpXt32Ha7nWSc6hVSSePrwZDrKqrUYikdcGKY2utm5I9wrAoU1+Swj/25SbxybcarD\nUw1/L77AaHtJZF2uWqMhrvbMyK5x3bk/sGRVZrZ7WfrHRtmspNKWWjifn8AYVwMNRv9vaLuEL4uO\nN0d05ZBuw1u1nB6uYRhQJECoXsCNa4H1o5AMK1IO8R4GyGRUBAs0dBRUM4AVB17AH4obuP7+E/Bw\naFkP9zSgyQFbrm3Hibx4k/cTGpKE6+gh6LnwNWw6DuSv0wlf3aTeCHOT0xGkH/z+ntHZaP3tAGBc\nwARKoNmWABz5AdiWADebALr6ZXWwP7N6o1bYqmdGaNz7cDUvZMDAI+27K3hvzkjklpaAEBN4b9AH\njHFfeHweHYGb4gmkelD9TSEhXVpAnBE6i9GWSR5j3Vv102K1oqmoqAhOK5cDtbXclU25MFYFlccq\n5FbnoO92OZadWYKI7b06XPgy/F2+fS+3y3+M4uHh4XnYMCl6lZeXIyiodSatoaGhUFipqpFSqcSa\nNWswcOBADBw4EB988AGamqgHiaKiIrz44osIDw9HdHQ0zp49y5j28uXLmDx5Mvr06YMXXngBeXl5\nXIvg4enyZFVl0uWutYbw1kabmrj2+hq4OzlQ3iV2zPSuuNwjiD08yWgqotZI/fiMU5jT6++s4do0\nNu04W8e1RIbovSxNjHbGT3+0P5UtvSIVBWSB+RGNMPZ/I1r9IPziEwtBiAlmFcWWyIeSSrZPlbUh\nlSS8JDI6kkkoEOKX6fFd3tPLsJqivYhtAA8AIa492r0s/WNjaKLuIfVijV+nqsXQ3f3b9FKkn5ps\nEo7oyuN5RzHq58EWXwc5RUl48q6G0dcRUUiGFSkrGsp10S0vzaGjoGwA7N8L9I6ajH+9/jVy6yPx\nq2AUhM0CYHsCGr49i9kH5iH28CSzlSZFSYkQ5d2m23ZqIKZF/yytL0WtstZslOl/hn+OE7N0HmkX\ni8+zxMZfr+bRqdfjjsWgMC4OlcdPMSN0HAzOTXvuc/WhoBXCS0zwFAhKn2Dsz93n/gAA1DTdo/pa\n7oW2and0k3QDANTaAZELgIHzgf4LmkHadciWWIXooBj4OwYAoIpt/Dh+B+veqjWfB0Cn/QKAsCAf\nbhOj2JVNudCLFjM7Lk+rIZUkJu4fA1Wz9pmqyWwlzvYyJmA8RAJd9Hegc1CX/xjFw8PD87BhUvRq\nbGyEVCpt1QwlEgkaGxvbtVJaPv30U5w4cQIbN27Epk2bcP78eWzYsAEajQaLFy+Gi4sL9u3bh+nT\np2Pp0qUoKKBecu/cuYNFixZhypQp2L9/Pzw8PLB48WI0t/iN8PA8KJBKEm+eeY3R1xF+EPovjeWN\nZSbHNeU3pBV9uErVrzy/HOP3jgJAiQ+z42ZSA4r6670s9cTyvZswYvdAy6JAjGBJpBYnLS9m92rV\nGP2/ISaXr/VR0tJXRkWRaA39neFDi3nqLb8jLvV029bJAkgliaj/DcPsuJlo1lDih79TADwlbCFH\nO/4NxbUukWJxOOsAo51SdpMzUtDV1rXdy9IXZuNnJjBeWrXHzxBVs6pNL0W+jv4Q29hyD9T38LKr\n5YyuzK/JsyzllyQx4pklWPebrqvG0xUVv55hRiFZIXqESzTUj265Cy9swCs4hgkgIYV3HSDSAARq\n4aKph7qypUqsnl9gbnUO7bXFiUF0lVIAxLXon6pmJeKyj8DX0Z/xUqmPvcAeMcFT6GOtqFNg9ZWP\nOMVG7T0wsyoDaY35LD8lVXgEVHrVGh3ff/fhFCVaKbzIJDJsnf0WY38OD6dCuGKCp0DY5AJsvgF8\ndwU/LV2KXePiITCo7JFTldOlo18IMYFt0bsBUMU2Jh4cwxkFWvPJF6jcuRdqX50tgaqbN4QFLSmy\nLZVNjcEqotBFUz4fJBR1CvxwcytO5MXjTP4plDcwn3E87bl/J62FTCLDxeeuYXGfpfh+/E84NetC\nl/8YxcPDw/OwYVL00mg0pgZzIhBYp0TZvXv3sHv3bnz00Ufo168fIiIisGTJEqSkpODy5cvIzc3F\nhx9+iJCQECxcuBB9+/bFvn37AAB79uxBz549sWDBAoSEhGD16tW4c+cOLl++bJV14+HpLJJKEqGo\nrGGYXHcEbvbuENloUyhtsWbYWqPjCgUikymWl4ovMirk6ZNZlYGkkkTsSduNyqZKapvi9Px93NMB\nzxQUkgUWRYEY49fcY62extBbqayqDmfyTxod/UnPcDrtVCQQMcqQZ1amo7rQhxH54Fg1mGs2VuFS\n8UXk3qNewNQtUYG51Tmc669fTGD8XstM+zuSZ+XPM9pze7+Ik387D0cR0zh68qHxHZqGMth7KCvq\nTIujyKnV86NSKZvYAwzOs+9G7qeELo7oyqWnF5ndZlF6KlwKmOP8svJvgEyvOpiVoke4REOVvQM0\noAQvP+RjCTYhBsfxJJJBQnfPCkUKhG5sv0BAzxOKC8PoKoPHEk+JJwpr8qHSMFOKtTRoGhC97yn6\nPD+ZFw8Nmhli42NvTEVs72j0cAmFtBGYUeWHnnYc9ziCQM3nX9NNUXbWQylKtEV4Saw6yxBvT909\nCIB64f865BZQQQmeBXm2OHGuDhpoIKmRYsM3I/Gf70bi9y3O3Pu8C/Hdn0wvuq9vfKFraK+x2Elw\nfHsZIzpR/6lYFWw6jVM/WoyRWsvTJhR1CvTd9jhWnl+O2XEz8cbpV1njPH98VrvS2M1BKkk8HzcL\nG5O/xn+ufNRhy+Hh4eHhMY5J0et+cuPGDTg4OGDIkCF0X2xsLL777jskJyfj8ccfB6H3BbZfv35I\nSkoCACQnJyMyMpIe5uDggF69euGPP/7ovA3geajpLBPUyntNLJPrBitHemn9r/TD/cPcexqNrFFr\nVPizNMno/Aru5bP6tOKQj9QHr59ejJXnl1MDSnsB5T11I056mfHin1ud02qDe1JJYn3iF+ZHBECI\n9YQVDm+lc4VnuScEWl60qX2m0qgY/lD1qnpWJEmNy6VWbUdruFrMLei/FD+H9TCvH9WXWZVxG5F7\n5QAAIABJREFU36MrAp2DcGV2Et6IWIErs5MQ6BwEmUSGdVHMyphqjbrdaSikksTYvSM4K4YSYgK/\nPs0djff5jU8AtO6614+KCnQK0hU3MDjPfBrG4+a8TM7Kg5Zss8rXH80iXSpVhhsQGjWHMY5REcMK\n0V+SwwcgALAfsVBBl5+Wi2CcA+VNpgIQ9UoD1C9z+wWmlhs/B1XhEVB56oodiKFLbwSodFhzPoeF\nZAEdTcbwJbOrhSw0HyeePwaZRIZDY/cgf6cM+9YVwDcmhnO/qMIjHnpRoi3Ci6+jP0O81T8muRnM\nqJasNHugUQrN1uuYV5OAp5CAmeVXoLp807obYmXK6sqMthnXWFEh3a8RCiG8U0y3a95737QPHEGg\nMj6BnVrL0yZO5sUzBPEa5T3O8bbd+qHD1sHwN3dP2u42f2zqSlHaPDw8PA8SInMjXL16Fd98843F\nM7xy5Uq7VkhLfn4+vL29cfToUXz77beoq6vDhAkTsGzZMpSWlsLLixmO7O7ujrt37wKA0eHW8hrj\nebRR1CkQsb0XlM1NENvYInFOCmQSmfkJ20BpnhdLiEktT4U34YMwN7lVQuS5/K98CF/83/BP8eqp\nBZzTLD31ChKeucy53THBU/CP82/TZsUAJQp5SWQoqi1ijqwVhsrk1F/v66z5vXpqIexFDhjtH2XR\n9p7JP4WyBtMpmgAQ7BKCQ9OOIy77CCXCGa6LZwoq6roZnV6bvqY9D7QveaSSxMqzy3WRJKW9AM8U\njA5pf/VBLlLKbuGrPz43OnxT0np8OvJLuh3mJkewSwiyq7IQ7BLSJbxFAp2D8N6g9xl9A7oNYo0X\n5tKT1dcakkoSkV1F+WxlV2UhqSQRw3xG0MMlYu5oyrSKv5BSdgtj946ESqOESCDGH3P/Mnnda9Nc\nT+bFY0zAeADAitNLEd94jj7P3HxLEBbmAEIiw6Yx32HiwTGs+ZiLMhNlpsNGpbvWXh8PvC6sR4C2\ngySB+nqogkMgys6iRAw3d9j9sBWSzRsgys2hBI7EGyaXA+iiBDOrMtDDJRTxMxOAZ5+HZN1aBOI2\na/xcdAcAvDAdiIiajz9SNlOiiAHf39yMxX1f476+CQKVR0/AY2h/CFQqqMUiHOuhogf/88I7WNF/\npdl1Ty9PwzCfESgiCxn9X4xeD5lEBlJJYvXGSdiRTz0naMVBVb9I5ox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2fkAqSfySxUyP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O5zjbolLFrVqLJI02pB3CyT4uWuQjc8uGUCbtYUAwBu3LluFaNwtLdMpx+lpbKhEn8//jKvrrTW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\nA9auXYtJkyYBAKRSKZRKJW/f0tJS+Pv7t2t7T0TVq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"text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -714,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", @@ -726,7 +776,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_HydroData.py:2033: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py:2033: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", " 'representative average day and/or high standard deviations.')\n" ] } @@ -738,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", @@ -750,15 +800,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:683: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:683: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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l/Xy9Cm6hoaFVJ06cqACAsLCwypiYmGbd1xrn6+tbXfechQsX+g8cOLAkJSXlF+vYHXfcURwcHNxv2bJlPm+99dalmXIXLlxQJSUlHRsyZEiFdezLL790+vzzz13XrFmTMXfu3EIAmDRpUombm5tx9uzZQfv27dMNGTKk4o033nD/9ddftcnJycetOUyePPlir169QvPy8tTX8r1Q+9UuppESERERERERUTuWkuKMmhoFzGbAaFQgJaXd7fDZXEeOHNFmZ2dr77vvvsKamhpYX87OzuaIiIiy/fv3O9nG+/r6VtsW2gDg888/76JWq+W0adPO215j4sSJxQCwa9cuZwDYv3+/k4+Pz2XFPqVSiYkTJxZdj2eltsHONiIiIiIiIiJqXExMCVatMsNoVEClMiMm5obd3fPs2bMqAHjqqaf0Tz31lL7u8a5du1bbfvby8qqpG1NQUKCqqakRXbp0iajvHoWFhSoAyMvLU7u7uxvrHvf29r5ijDoOFtuIiIiIiIiIqHGxsWVISvq5LdZsszdPT08TACxevPjM2LFji+se12q1l+00KoS4YudRNzc3o1arlV999dXx+u4REBBQAwDe3t41J06ccKh7PC8vj/WYDoy/XCIiIiIiIiJqWmxs2Y1WZFOr1bKiouKyJbTCw8MrfX19q48dO6ZbtmxZ7tVc94477ihev369z/nz55UTJ05ssMtv8ODBpR9//LF7SkpKJ+tUUpPJhM8++8ztau5LNwYW24iIiIiIiIioQwoODq5MTk7usmXLlmJ3d3djQEBAjV6vr3nttdeyHnjggeA777xTTJ48ucjT09N49uxZ9b59+5wCAgKqX3jhhbzGrjtu3LiScePGFT344IPBjz32WN7gwYPLFAoFTp06pfnyyy+7rFy58tf+/ftXzZkzp3D16tU+U6ZMCX7mmWfOeHt7Gzds2OBZWlqqvF7fAV1/3CCBiIiIiIiIiDqk119/PdPR0dF8//339xg+fHifhIQETwC47777Ln755Zfp5eXliieeeEJ/9913hzz//PPd8vPz1UOHDi1tzrW3bt16esGCBTnbtm1znTp1ao9p06Z1T0xM9AoODq7y8/MzAoCDg4PcsWPHz3369ClftGhRwMyZM/WBgYFVTz/9dE5rPje1LSHlFVOPqY4BAwbIAwcOtHUaRERERERE1AqEEAellAPaOg97Onz4cEZ4ePi5ts6DqKM6fPiwR3h4uL6+Y+xsIyIiIiIiIiIishMW24iIiIiIiIiIiOyExTYiIiIiIiIiIiI7YbGNiIiIiIiIiIjITlhsIyIiIiIiIiIishMW24iIiIiIiIiIiOyExTYiIiIiIiIiIiI7YbGNiIiIiIiIiIjITlhsIyIiIiIiIiIishMW24iIiIiIiIiIiOyExTYiIiIiIiIiavdWr17tHhgYGKZWqyOdnZ1vAQA/P79+cXFxemtMQkKCuxAiKj09XWMdqxtzrQYNGtRr0KBBvZoTu2fPHkedThdx+vRp9bXmk56erpk3b57vsWPHNE1Ht62kpCRnIURUUlKSc0vPnTdvnu+2bduuOC8uLk7v5+fXzz4ZXikzM1Ot0+kivv76a8drvZbKHgkREREREREREbWWjIwM9fz58/UTJkwoTExMPKfT6cwA8OGHH550cXExt3V+DVmwYEG3yZMnFwYFBdVYx6425xMnTmhXrVrV9bbbbivp27dvtX0zbT9WrVrV1Wg0YsKECSW240uWLDl74cKFvNa6b2BgYM39999/buHChf7ff/99+rVci8U2IiIiIiIiImrXjh49qjWZTJg+fXrhmDFjSq3jt956a0Vb5tWYvXv3On733XfOa9euzbIdb085G41GSCmhVqubDm5joaGhVa19j7lz5xYMGDAg9Ouvv3YcMWJE+dVeh9NIiYiIiIiIiKjdiouL048bN64XAEycODFECBFlnYZ5tVMyjx8/rpkwYUKQq6truEajiezdu3ffjRs3utSNS0xMdA0KCgrVaDSRPXr0CK0vpiHr16/3CAkJqRgwYECl7XhDU19TUlI6TZgwIcjJySnCy8ur//Tp0/3Ly8sFUDstc/z48SEAcNddd4UIIaLqTtNcuXKlR69evfpqtdpIV1fX8HvvvTcwLy9PaXtvIUTU448/7vfnP//Zx8/Pr59Wq436z3/+o7NO+/znP//pEhcXp+/cufMtTk5OERMmTAjKzc297BpFRUWKadOmBXh5efXXaDSRer0+7MUXX/Qymxtv1vv00087Dx8+vIenp2d/nU4X0bNnz9Dnn3/e22g0XpYfAKxZs6ar9RnnzZvnC9Q/jTQzM1N911136a2/x5CQkL7r1q1zs41pzvdrFRUVVdmzZ8+KxMREz0YfpgnsbCMiIiIiIiKiJiUno1NKCpxjYlASG4uy63XfJUuWnI2MjCx/5pln/OPj47MGDhxY7uPjY2z6zPqdPHlSPWTIkD7u7u7Gl19+Odvb29v4wQcfuE2fPj1YqVSefOCBBy4CwNatW51nzpzZ3WAwXIyPj/81Pz9ftWjRIn+j0SiCgoKa7LJKTU3tEhMTc7G5eT388MNBd911V9HDDz988ttvv3V67bXXfF1dXU2rVq3KGTJkSFl8fHzW4sWLA1566aXswYMHlwFAREREBQDMnj3bLzEx0XvGjBn58fHxv2ZnZ6tffvllv1GjRukOHTp0XKX6rfyzefNmd39//6qXX34528nJyRwQEFBz/vx5FQD86U9/Chg6dGjxW2+9dSo9Pd1h2bJlfhMnTlR/9913PwOAyWTCqFGjeh47dsxx4cKFOeHh4RXbt2/v8sILL/gXFBSo165de6aR711rMBhK5syZk6/T6eR//vMfx1dffdW3oKBAtW7dujMAkJycfDw2NrZ3XFxc4axZswoAQK/X1ztltri4WDF8+PBeFy9eVP7lL385ExAQUP3uu++6z5kzJ6i8vFwxf/78c839fm3jBg8eXLpz584uzf291YfFNiIiIiIiIiJqVHIyOo0bh5CaGihWrYI5KQk/X6+CW2hoaNWJEycqACAsLKwyJibmmu775z//2VdKiT179hz38fExAUBcXFzxkCFD1EuXLvWzFtuWLFniFxQUVLlz586TSmVtc5fl/r2bKrZlZ2ercnJyNOHh4c2einj33XcXWQs/kyZNKjlw4ECnLVu2uK1atSrHzc3NHBYWVgkAoaGhFbbfQXp6umbDhg0+Tz31VM6rr7561jrep0+fyjFjxvT+17/+5fLggw9esL3X7t27f3ZycpLWz4cPHwYA9OzZs+Ljjz/OsAwXu7m5GWfPnh302WefOU+cOLHkww8/7HLo0CGn119/PeOJJ54otORdXF5erkhMTPT+y1/+kte1a9d6C6ELFy4ssP5sNpsxduzYkurqarF+/XqfNWvWnFEqlbA+l6+vb3VTv+e1a9e6Z2Zmardv3/7zuHHjSgDg3nvvLR4yZIh62bJlfk8++eQ52yJjY9+v7XUjIiLKN23a5JmRkaHW6/U1uAqcRkpEREREREREjUpJgXNNDRRmM2A0QpGSghbvMtlepKamdhkxYsRFd3d3U01NDayv2NjY4vT0dF1RUZHCaDTiyJEjjuPHjz9vLbQBwMiRI8t8fX2b3JwgKytLDQBeXl7N7sCbMGHCZQWxvn37Vpw9e7bJnUeTkpI6m81mzJgxo8j2eUaMGFHm5ORk2r17t5Nt/PDhw4ttC2224uLiimw/z5gx47xCocC3337rBAC7d+92VigUeOSRRy6Le/DBB4tqamrE119/3amhPDMzM9VTp04N9PX17afRaCI1Gk3UihUr/EpKSpRnzpxpcTPY3r17nb28vGqshTar+++/v/D8+fOqQ4cO6WzHm/v9enl51QC//Q6vBjvbiIiIiIiIiKhRMTEoWbUKZqMRCpUK5pgYlDR9VvtUVFSk2rJli7tGo3Gv73h+fr6qrKzMbDQahbe39xWdTR4eHk12O1VUVCgAQKvVNnvXUU9PT5PtZ61WK6urq0VD8bb5AkBYWFhYfceLioouq/34+Pg0mH/d6bkODg6yc+fOxjNnzqgB4Pz588rOnTsbdTrdZcU6Pz+/GgAoLCyst85kMplw55139sjPz1cvWrQoJzQ0tNLR0dH88ccfu6xZs6ar9ftqiQsXLqg8PT2veBZfX98aACgoKLhsrbnmfr+Ojo4SAMrLy6+6QY3FNiIiIiIiIiJqVGwsypKS8HNbrNlmby4uLqaBAweWLF68OLe+44GBgTVqtVqqVCqZl5d3RXfTuXPn1H5+fo12t1k72uoWulqDu7u7CQA+/fTTE+7u7ld00tXtrhNC1NvVBgC5ubmX5VtZWSmKi4tV1mKaq6urqbi4WFVZWSkcHBwuXcdajPPw8Ki3k+/YsWPao0ePOr7xxhunZ8+efakrbsuWLc3ecKIuFxcX46lTpxzqjufk5LS4q9DWuXPnlNdyPtBOppEKIW4TQmwTQpwRQkghxHSbY2ohxF+FEP8TQpQJIc4KId4XQgTUuYZWCLFGCHHOErdNCNGtTkyAEGK75fg5IUSCEKLJlkwiIiIiIiKim11sLMri45F7IxfaAGD48OEXf/rpJ8fIyMiK2267rbzuS6fTSZVKhX79+pVv377d1WT6rSFq165dnXJycpqsI4SEhFRrtVp56tQprb3ydnBwMANXdlzdeeedxQqFAhkZGZr6nqd3795NTnu1+uSTTy7byfPtt992NZvNuPXWW0sBwGAwlJjNZrz99tuutnHvvvuum1qtlgaDod7/bZSWlioAQK1WXyrQVVVVibr3s8Y0p9Nt2LBhJXl5eeqvvvrqsqmrmzdvdnNzczNGRERUNnRuY06fPq1Vq9Wyd+/eTW6C0ZD20tnmBOBHABstL1uOACIBvAzgvwC6AFgJ4EshRH8ppbXSuBrARABTABQCeA1AkhAiSkppEkIoAXxuOTYMgDuAdwAIAI+34rMRERERERERUTuxfPnynOjo6D6DBw/u/dhjj+V37969qqioSHXkyBHd6dOntR999FEGADz33HNn7r777pBRo0b1ePTRRwvy8/NVy5cv923ONFIHBwfZv3//sgMHDjS4hllLhYWFVSqVSvn22297eHh4GB0cHGS/fv0qQ0NDq2bNmpW7ePHigPT0dAeDwVCi0+nMmZmZmuTk5M6PPPLIufHjxzdr2u+JEyd099xzj37KlClFx48fd4iPj/cbOHBg6cSJE0sAYPLkyRdfeeWV0vnz5wcWFBSo+vXrV5mUlNRl8+bNHnPmzMltaHOEiIiISl9f3+qlS5f6qVQqqNVqmZCQ4F1fbHBwcGVycnKXLVu2FLu7uxsDAgJq6tuoYM6cOYUbNmzwnjJlSg/b3Uj37dvX+ZVXXsm03RyhJb7//vtO/fr1K7NOJ70a7aKzTUr5bynln6WUHwMw1zl2UUo5Skq5WUqZLqX8D4DHAPSxvCCE6ALg/wAskFLulFIeAvAggP4AYi2XGg0gFMCDUspDUsqdABYCeEQI0fl6PCcRERERERERta2ePXtWf/fdd8dCQ0PLly5d6jdp0qSQefPmBezdu9dpxIgRxda4SZMmlaxfv/70qVOnHKZNmxackJDgs3z58uymdiK1iouLK9q/f79zcXGxXWovPj4+pvj4+KyffvrJ8Y477ug9fPjwPt9++20nAFi7du2ZlStXZqSlpTnNmDGj+5QpU3qsXr3ax8XFxdS3b99md3j99a9/zZJSYvr06d2XLVvmN2LEiAufffbZL9bjSqUSO3fuPBEXF1e4Zs0an3vvvbdHSkpKlxdeeCE7ISHhTEPXdXBwkB999NFJT0/PmtmzZ+uffvrpgCFDhpQ8+eSTV0zlff311zMdHR3N999/f4/hw4f3SUhI8Kzvmp07dzbv3r07fdiwYcVLly71mzp1ao9jx47p3njjjdPz588/19xntlVaWirS0tI6190ooqWElFddqGsVQohSAHOllP9sJGYwgDQA/lLKX4UQIwGkAPCSUhbYxB0F8LGU8nkhxBIAcVLKUJvjngDyAYyUUn7d0P0GDBggDxw4cK2PRkRERERERO2QEOKglHJAW+dhT4cPH84IDw+/qoID2UdRUZEiICAgfMWKFZm265S1R0lJSc4Ikor+AAAgAElEQVTjx48P2bJly8+TJk26YTe/uFZ///vfXZ988kl9Zmbm/zw8PEyNxR4+fNgjPDxcX9+xdtHZ1hKWNdZWAtgupfzVMuwDwASg7h+SPMsxa0xenePnLOf5gIiIiIiIiIjITtzc3Mxz5sw5u3r1ah+zudmbklIbWrVqlc+sWbNymyq0NaW9rNnWLEIIFYB3AbgAmNCcUwDYtu411MZ3xbgQ4lEAjwJAQEDAFScQERERERERETXm2WefzTOZTCIrK0td37pj1H5kZWWpbr/99gvPP/983UatFrthim2WQtu/APQDYJBSFtoczgWgBOABoMBm3AvANzYxt9a5rIflvCu+SCllIoBEoHYaqR0egYiIiIiIiIhuIk5OTvLVV18929Z5NGXcuHElUsqDbZ1HWwoICDCuXLnSLr+rG2IaqRBCDWAzajc8GCGlrLuA3kEANQBG2ZzTDbUbKOyzDKUB6GMZtxoFoMpyPhERERERERER0TVpF51tQggnAD0sHxUAAoQQtwAoApAD4CMAAwGMByCFENY11i5KKSuklBeFEG8CeEUIkQ+gEMBrAP4HINkS+xWAowA2CiGeBuAO4BUAf5dSXtpthIiIiIiIiIiI6Gq1l862AQB+sLx0AF60/LwEQDcAEwH4orYD7azN6z6bazwF4FPUdsB9C6AUwHgppQkALO93Aii3HN9siZ/fuo9GREREREREREQ3i3bR2SalTEXtZgYNaeyY9RqVAB63vBqKyQIwrqX5ERERERERERERNUd76WwjIiIiIiIiIiK64bHYRkREREREREREZCcsthEREREREREREdkJi21ERERERERE1O6tXr3aPTAwMEytVkc6OzvfAgB+fn794uLi9NaYhIQEdyFEVHp6usY6VjfmWg0aNKjXoEGDejUnds+ePY46nS7i9OnTauvYvHnzfLdt2+Zsr3waYu/nttWS76Cu+n5H9vTiiy96hYSE9DWZTK1x+WZpFxskEBERERERERE1JCMjQz1//nz9hAkTChMTE8/pdDozAHz44YcnXVxczG2dX0MWLFjQbfLkyYVBQUE11rFVq1Z1NRqNmDBhQklr3ru9fzet5emnny5Ys2ZN17Vr17r/8Y9/LGyLHFhsIyIiIiIiIqJ27ejRo1qTyYTp06cXjhkzptQ6fuutt1a0ZV6N2bt3r+N3333nvHbt2qy2uH97/m5ak5OTk7znnnsK16xZ49NWxTZOIyUiIiIiIiKidisuLk4/bty4XgAwceLEECFElHV65NVOlTx+/LhmwoQJQa6uruEajSayd+/efTdu3OhSNy4xMdE1KCgoVKPRRPbo0SO0vpiGrF+/3iMkJKRiwIABldYxIUQUAKxZs6arECJKCBE1b9483+eee85bo9FE5uTkXNYUZTab0a1bt37jx48PAoD09HSNECJq+fLlnn/4wx+6ubm5het0uogRI0b0qDsts77v5vjx45pJkyYFeXh4hGs0mshu3br1mzFjhr/1+O7dux3Hjh3b3dvbu7+Dg0OkXq8Pmzt3rl9paalo7nPbOnbsmMZgMPTQ6XQRrq6u4TNmzPCvqqq64lqJiYmugwcPDnF1dQ13dHSM6NOnT981a9a428aEhIT0HTVqVHDdc5OSkpyFEFGffPJJZ+vY73//+6JffvnFYefOnZ2uJu9rxc42IiIiIiIiImrS+fPJnc6fT3F2dY0pcXWNLbte912yZMnZyMjI8meeecY/Pj4+a+DAgeU+Pj7Gq73eyZMn1UOGDOnj7u5ufPnll7O9vb2NH3zwgdv06dODlUrlyQceeOAiAGzdutV55syZ3Q0Gw8X4+Phf8/PzVYsWLfI3Go0iKCioqqn7pKamdomJibloO5acnHw8Nja2d1xcXOGsWbMKAECv11c7OTmZV6xY4bdu3Tr3l156Kc8av2XLls5nzpzR/O1vfztne53Vq1d37du3b/m6desy8vLyVC+99JLfmDFjQtLT049qtVpZXz7Hjx/XREdH99HpdOZFixad6dWrV1VmZqZm586dl4pUp0+f1vTv37/ioYceKuzcubPpyJEjuldffdU3IyNDm5SUdKol33NlZaUYM2ZMSFVVlWL58uVZ3t7exsTERM8vvvjCtW7sqVOntJMmTTofEhKSq1AoZGpqqvNTTz0VWFFRoVi4cGEBADz88MMFzzzzjH9GRoZar9dfmpa7YcMGDz8/v+q77rqr2DoWHR1d7uTkZPr888+7jBo16rr9b9WKxTYiIiIiIiIiatT588md/ve/cSFS1iiys1eZ+/dP+vl6FdxCQ0OrTpw4UQEAYWFhlTExMdd03z//+c++Ukrs2bPnuI+PjwkA4uLiiocMGaJeunSpn7XYtmTJEr+goKDKnTt3nlQqlbC5f++mim3Z2dmqnJwcTXh4eLntuDV3X1/f6rrPceeddxZt3LjRc8mSJXkKRe1ExA0bNnjq9frKcePGXba+W6dOnUy2efXp06dyzJgxvdetW+f+1FNPXVaYs1q8eLFvVVWV4ocffjhmW6x6/PHHL021nD59+gUAF4DarrrRo0eXdu7c2TR37tyg3NxcpfX7ao433njD/ddff9UmJycftz7r5MmTL/bq1Ss0Ly9PbRu7fPnyXOvPJpMJd955Z0lubq76zTff9LQW2x577LHCl156qdsbb7zh8corr5wFgLNnz6p27NjhOn/+/BzrdwYASqUSISEhFd9//32bdLZxGikRERERERERNer8+RRnKWsUgBlSGhXnz6e0+m6arSU1NbXLiBEjLrq7u5tqampgfcXGxhanp6frioqKFEajEUeOHHEcP378eWtBCwBGjhxZ5uvrW93UPbKystQA4OXl1ewOvLlz5+ZnZ2drrTuVZmZmqnft2tVl+vTpBXVj6+Y1evToMm9v75r9+/c3WFzas2dPl5EjR160LbTVVVRUpJg1a5afv79/mFarjdRoNFFz5swJklLi6NGjDs19FgDYv3+/k4+Pz2VFRaVSiYkTJxbVjT1y5Ih2/PjxQV5eXv01Gk2URqOJ2rx5s0dGRsale7q6uponTZpU+N5773lYdxpdt26du5QSs2bNuqLA6O7ubszPz2+VHU+bws42IiIiIiIiImqUq2tMSXb2KrOURoUQKrOra0yr7qTZmoqKilRbtmxx12g07vUdz8/PV5WVlZmNRqPw9va+ojDl4eHRYLHKqqKiQgEAWq222buBjhgxojw0NLT8b3/7m+ekSZNK1q5d66FSqTBz5swrFvlvKK+zZ882WFy6cOGCsqlC4ZQpU4L27dvnvHDhwpzIyMhyZ2dn8759+zotXrw4wPpMzZWXl6d2d3e/otjo7e192djFixcVY8eODXFwcDA///zzv4aEhFRptVq5du1az48++sjDNvaPf/xj/nvvvef54Ycfdrnvvvsubty40XP06NEX/P39r7iPg4ODubKy8qrWmrtWLLYRERERERERUaNcXWPL+vdP+rkt1myzNxcXF9PAgQNLFi9enFvf8cDAwBq1Wi1VKpWsO90RAM6dO6f28/NrtGhl7WgrKipqUd3lD3/4Q/78+fMDT58+rX7vvfc8br/99iJvb+8rpm42lFdoaGh53XErV1dX49mzZ684z6q8vFykpKS4zJs3L+fZZ5/Nt47/8MMPupY8g5W3t3fNiRMnruiGy8vLu+w72bVrl1NOTo7myy+/TLfdaXb16tVXFMoGDhxYGRUVVfr3v//dU6fTmbOysrRr1qzJrO/+Fy5cULm6ul712n7XgtNIiYiIiIiIiKhJrq6xZd27x+feyIU2ABg+fPjFn376yTEyMrLitttuK6/70ul0UqVSoV+/fuXbt293tU5ZBIBdu3Z1ysnJaXJqYkhISLVWq5WnTp3S1j2mVqtlQ11if/jDH4o6depkvvfee7ufPXtWM3v27CumkAJA3by++uqrTnl5eerBgwc3+LsZNmxY8a5du1wyMzPrLbhVVFQoTCYT1Gr1ZRssvPvuux71xTdl8ODBpbm5uZqUlJRLU1tNJhM+++wzN9u4srIyBYDL7ltQUKDcuXNnvTu/Pvroo/nffPNNl6VLl/oGBgZWTZgwod4uy+zsbE1wcHBlfcdaG4ttRERERERERHTTWL58eU5paaly8ODBvdesWeP++eefO23atMll4cKFXSdPnqy3xj333HNnTp8+7TBq1KgeH3zwQZeEhAT33//+992bM43UwcFB9u/fv+zAgQNXrKEWHBxcmZyc3GXLli2dv/nmG8eMjIxLxS8nJyc5efLkcwcOHHDq2bNnRUM7aZaVlSlt83rggQeCAwMDq2bPnn3FlFOr+Pj4HI1GYx4yZEjvlStXemzfvt153bp1bhMnTgwCAHd3d1N4eHjZ+vXrvdeuXeu+efPmLmPHju1eXxddc8yZM6ewW7duVVOmTAlOSEhw37x5c5dRo0b1KC0tVdrGjRw5stTJycn0xBNPBHzwwQdd/vGPf7jeeuutvRrqSnvooYcuuLi4GA8dOuRU33p2AHDu3DllZmamw7Bhw0rrO97aWGwjIiIiIiIioptGz549q7/77rtjoaGh5UuXLvWbNGlSyLx58wL27t3rNGLEiGJr3KRJk0rWr19/+tSpUw7Tpk0LTkhI8Fm+fHl2UzuRWsXFxRXt37/fubi4+LLay+uvv57p6Ohovv/++3sMHz68T0JCgqft8fvvv/88AMyYMaPeQhIAPPnkk2e7d+9eOWvWLP2iRYsCQkNDy7/88suftVqtbOicXr16Ve/du/d4ZGRk6UsvveQXFxfXc9myZX4eHh6XilqbN28+FRYWVr5o0aKAWbNm6b28vIwrVqzIbs7z1uXg4CB37Njxc58+fcoXLVoUMHPmTH1gYGDV008/nWMb5+vra3zvvfd+MZlMYvr06cEvvvii37Rp087dc889V2ykAABarVaOHj36gkajkTNnzqx359WPPvqoi1qtllOnTj1/NblfKyFlg78HshgwYIA8cOBAW6dBRERERERErUAIcVBKOaCt87Cnw4cPZ4SHh9dbiKDro6ioSBEQEBC+YsWKzNmzZ9dbOKrP448/7vfmm296/frrr4fd3Nwu22AhPT1d07t3734rV67MnDdv3k35+62pqYFer+83cODA0q1bt56uL+a2227r6ebmZmzouD0cPnzYIzw8XF/fMW6QQERERERERERkZ25ubuY5c+acXb16tc/MmTOLFIrGJxd+++23uqNHjzq8+eabXlOnTi2oW2i72RUVFSkOHjyo27Rpk3tubq5m4cKF9W5wsW/fPt13333nfPDgwaPXO0crFtuIiIiIiIiIiFrBs88+m2cymURWVpZar9c3utbbvffe26OwsFA9dOjQi6+88kpOY7E3o3379nUaP358iJubm3Hp0qVZQ4YMqagvLicnR52QkJARFhbWrOm+rYHTSJuB00iJiIiIiIg6Lk4jJaKWamwaKTdIICIiIiIiIiIishMW24iIiIiIiIiIiOzE7sU2IURnIUSAva9LRHQjS0sD4uNr34mIiIiIrgOz2WwWbZ0EUUdk+W+rwQ0smlVsE0IECyE+E0JcFEIUCiHeFUIENRD+FIBW21qViOhGk5YGxMQAzz5b+86CGxERERG1NiFEbkVFhUNb50HUEVVUVDgIIerdDRVoRrFNCOEFYC+A8QCcAbgCmArgByHEnfZKlIioo0pNBaqrAZOp9j01ta0zIiIiIqKOzmg0vpiRkaEpKyvTscONyD7MZrMoKyvTZWRkaIxG44sNxamaca3FALwBbACwBEA1gD8AeA7Ap0KI+6SUW+2RNBFRR2QwABpNbaFNo6n9TERERETUmiIjI3ccOnRo7i+//PK8lNIHXLOdyB7MQohco9H4YmRk5I6GgoSUstGrCCGOA6iQUkbUGR8IYDsAFwD3SCmTLOPPA3hOSqm81idoLwYMGCAPHDjQ1mkQ0Q0sLa22o81gAKKj2zobIiIiIrIlhDgopRzQ1nkQUcfQnMp2IIBddQellN8DuA1AIYCPhBC32zk3IqIbWlp2GuL3xCMtm4u0ERERERER3SyaM420AoCpvgNSyp+FEAYAuwF8IoSYYMfciIhuWGnZaYjZGINqUzWUZ4ZCbEyBsUYJjQZISWF3GxERERERUUfVnM62TADhDR2UUp4AEAOgBMBWALfaJzUiohtXakYqqk3VMEkTan65FdXVghskEBERERER3QSaU2zbC+A2IUSXhgKklD8BiAVQidrCW4sIIW4TQmwTQpwRQkghxPQ6x4UQ4gUhRI4QokIIkSqECK0T4yqE2CSEuGh5bRJCuNSJ6SeE2G25xhkhxHNCCO7KQkR2Z9AboFFqoBRKqIO/hUYjoVRygwQiIiIiIqKOrjnFts8BaAHMbixISnkEtQW3C1eRhxOAHwH8EbXTVutaCOBpAI8DGAggH8BOIYSzTcz7ACIB3A5grOXnTdaDQojOAHYCyLNc4wkACwDMu4p8iYgaFe0fjZRpKVg6YilSn4nH17uUWLqUU0iJiIiIiIg6uibXbJNSfimE0KGBddvqxP5XCBEMoMEuuAbO+zeAfwOAEOKftscsnWdPAlgupfzEMvYQagtuUwFsEEL0QW2BbaiUcp8l5jEAe4QQvaSU6QAeAOAI4CEpZQWAHy3nzRNCvCab2paViKiFov2jEe1fW1lL+7WNkyEiIiIiIqLrojmdbZBSVkkpjc2MvSClzLy2tC4TBMAHwFc296gA8A2AIZahaAClAPbZnPctgLI6MXss51rtAOALQG/HfImILknLTsOs9RsxYqQJzz4LxMQAadyclIiIiIiIqMNqVrGtPkKITkKICCHEMHsmVA8fy3tenfE8m2M+AApsu9MsP+fXianvGrb3uEQI8agQ4oAQ4kBBQcE1pE9ENyvrjqQbPklHVZXkBglERFcjLQ2Ij+f/U0FEREQ3jBYX24QQ3YQQnwA4D+AAgK9tjg0VQhwTQhjsl+Ildad5ijpj9U0DbSpGNDAOKWWilHKAlHKAp6dnS3MlIsLGwxtRaayE1O8ClNUQChM3SCAiaom0tNqWYLYGExER0Q2kRcU2IURXAN8BmAggCUAafitYwXLMC8B99koQQK7lvW73mRd+60zLBeBlu7Oo5WfPOjH1XQO4suONiOiapGWn4a3/vgUJCfjvh3rG7Xhs/q/cIIGIqCVSU2tbgtkaTERERDeQlna2PY/aAlWslPJu1O7ueYmUsgbAHgC32ic9AMBp1BbKRlkHhBAOAIbhtzXa0lC7o6ntP2GjAXSqEzPMcq7VKAA5ADLsmC8REVIzUmE01y51KSDwfxP6Yv1fA1loIyJqCYMB0GgApRJsDSYiIqIbRUuLbXcA2CalTG0kJgu1mw40mxDCSQhxixDiFktOAZbPAZa111YD+JMQ4m4hRBiAf6J2Q4T3AUBK+ROAL1G7M+lgIUQ0gA0Akiw7kcISWw7gn0KIMCHE3QD+BIA7kRKR3bk7usMszQAAmf075H45g7OfiIhaKjoaSEkBli4FW4OJiIjoRqFqYbw3gBNNxNSgtqOsJQbAZu03AC9aXu8AmA5gBQAdgDcAuKJ2uupoKWWJzTkPAEjAb7uWbgMw13pQSnlRCDHKco0DqF1zbiWA11qYKxFRkwrLC6GAAubsQcA7KfjM7IAdb/HfikRELRYdzT+cREREdENpabGtCIB/EzEh+G2dtWaxdMqJRo5LAC9YXg3FFAH4fRP3OQLgtpbkRkR0NQx6A7QqLSozRkKaNJBScWm5If6bkYiIiIiIqONq6TTSbwFMEELU3WgAACCE6AlgLC7vUiMiuulE+0cjZVoKHovrBa1WQKEAhADc3ds6MyIiIiIiImpNLS22vQLAAcBuIcTtABwBQAjRyfJ5OwAzaqdnEhHd1KL9o7F+1jQkvK6EUgmYzcCTT4JrtxEREREREXVgLZpGKqX8TgjxKIC/AUiyOVRseTcCeFhKedRO+RER3fAKC2sLbWYzOJWUiIiIiIiog2vpmm2QUr4thNgLYDaAwQDcAVwEsB/AWpvdP4mIbnpp2WnIcjkBlfoBAEpoNIDB0NZZERERERERUWtpcbENAKSUJwA8ZedciIg6lLTsNMRsjEG1qRrKaW/hEZd3MG1SILvaiIiIiIiIOrCWrtlGRETNtPHwRlScvgWmbxbAaDYiYNz7LLQRERERERF1cFfV2SaEUALoBcAVgLK+GCnlN9eQFxHRDS0tOw1//+xH4J1kwKSBWVmNC0O+Aoa1dWZERERERETUmlpcbBNCPIvaKaRdmgittwhHRHQz2Hh4I0ynhwImDSBVgEli5fsHMSnWG9H+bG8jIiIiIiLqqFpUbBNCLATwImo3RNgEIBu1O5ASEVFd+lRAWQ2YJKCsgSkwBakZjiy2ERERERERdWAt7Wx7BMAZAJFSyoJWyIeIqEOI6BoB+P8NeCgGODwNAKBWqGHQG9o2MSIiIiIiImpVLS22+QP4OwttRESNKywvhEIoYAaA/z4EmDRQ/PgoMENZ+5eUiIiIiIiIOqSW7kaah6vcVIGI6GZi0BugUqiADMOlddtqahRITW3rzIiIiIiIiKg1tbTY9iGAUUIIbWskQ0TUUUT7R+PhWx7+bd02UQOlygiDoa0zIyJqP9Ky0xC/Jx5p2WltnQoRERGR3bS0S+05AIMBfCyEeEJKeboVciIi6hAuW7ctw4CnHvgdoqMntXVaRHS9paUBqamAwQBEc4MUq7TsNMRsjEG1qRoapQYp01K4gQwRERF1CC0tth0FoAbgC+AOIcRFABfqiZNSyuBrTY6I6EZ2ad02//1QBPwHLj1eAsBiG9FNJS0NiIkBqqsBjQZISWHBzSI1IxXVpmqYpAnVpmqkZqSy2EZEREQdQkunkSoAGAFkWV4XAYh6Xi29LhFRh2PQG6BVaqEUSmiVWu5ESnQzSk2tLbSZTLXvXLjxEoPeAI1SA6VQQqPU8G8kERERdRgt6myTUupbKQ8iog4n2j8aKdNSkJqRCoPewI4NopuRwVDb0WbtbOPCjZfwbyQRERF1VNxZlIjoOjiSf4T/oCS6GUVHA6tXA598AsTFcQppHdH+0fybSERERB0Oi21ERK0kLTsNhncMqDZVAwAEBBxUDlwEnOhmkpYGPPlkbWfbnj1Av34suBERERF1cI0W24QQ0yw/bpFSlth8bpKUcuM1ZUZEdIPbeHjjpUIbAEhIVJmquAg40c2kvjXbWGwjIiIi6tCa6mz7JwAJYD+AEpvPjRGWGBbbiIissgcDGQaIoL1cBJzoZsI124iIiIhuOk0V2x5GbeHsrOXzjNZNh4io45gWPg1v/vAmajKjgHdSAJMGCg2Ah1WAf1tnR0TXRXQ0kJJS29FmMLCrjYiIiOgm0GixTUr5zzqf32nVbIiIOpBo/2jsnr4bTz6Th+/NWkiphNnIWWREN53oaP5HT0RERHQTUbR1AkREHVm0fzRWz5wEB60SSiVnkRER1ZWWBsTH174TERERdQTcjZSIqJVxFtmNI/FgIj459gni+sbh0ahH2zodog4vLQ2IifltSbuUFP6NJCIiohtfU7uRnrrK60opZfBVnktE1CGkZachNSMVBr0B0dHRl/4Bedk4dyVtNxIPJuKxpMcAAF+d+goAWHAjamXcrJWIiIg6oqY62xRoevfR+oirOIeIqMNIy05DzMYYVJuqoVFqkDItBdH+0Q2OU9v75NgnV3xmsY2odRkMgEoFmM2175xmT0RERB1BUxsk6K9THkREHUpqRiqqTdUwZQ1EZcZIbHQ7gehZ0b+NSxOqTdVIzUhlsa2diOsbd6mjzfqZiFqflJe/ExEREd3orssGCUKI/kKIaddwvlIIsVQIcVoIUWl5f0kIobKJEUKIF4QQOUKICiFEqhAitM51XIUQm4QQFy2vTUIIl2t5NiKi+hj0BijPDAXeSYHc9SLenvcA0tJqxzVKDZRCCY1SA4Pe0NapksWjUY9iw7gNGN19NDaM28CuNqLrYMW6XFTXSEhZO5U0NbWtMyIiIiK6dtdrN9K7ALx9DecvAjAHwBMAegP4o+XzYpuYhQCeBvA4gIEA8gHsFEI428S8DyASwO0Axlp+3nQNeRER1SvaPxoPu7wDYXYApArV1cDGrZm1u5OOXY2YoBisHruaXW3tzKNRj2LHgztYaCNqDXW2HU3cegRb/+ViWbBEQqE0cxopERERdQg3ym6kQwBsl1Jut3zOEEJsA/A7oLarDcCTAJZLKT+xjD2E2oLbVAAbhBB9UFtgGyql3GeJeQzAHiFELyll+nV9IiLq8KZNCsTbCSZUVdVAKmrwj/MPIvcDd3xx8gsYzUbsydqDfl79WHBrR7h5BVErqWfb0U++qALMfVC71K8JEbcfRnR0ZFtnSkRERHTNrldn27XaC2CEEKI3AAgh+gIYCeDfluNBAHwAXFpsR0pZAeAb1BbqACAaQCmAfTbX/RZAmU0MEZH9dEtD+IKnIUY+DzwUA6PfHmxN34oqU9Vla7ZR+2DdvOLZr59FzMYYpGWntXVKRB1HPduOxt3uDqiqAVEDqKvwf9PVbZ0lERERkV3cKJ1tfwXgDOCYEMKE2rxfllKusxz3sbzn1TkvD4CfTUyBlL8tvyullEKIfJvziYjswlq4qVJWQQ4zQ0BctrWzgOCabe0MN68gakUGA6DRQFZXwahS4Hgfdzw6qR/w4RF88kUh4m63fCYiIiLqAG6UYtt9AKahdkroUQC3AHhdCHFaSvmmTVzdfaxEnbH69rmqG1M7KMSjAB4FgICAgKvPnIhuStbCjRlmAEBvj944df7/2Xv3+Cjqe///+dnNBVARjSigkaD1hqWAKDIqsgiVotiDek57WtugokEsWnpDpLXl1AqS2lPUihIvQLy0nt8XpRYvRQMriAPUCEjlJpfAIqAQwRsk2d35/P6Yndmd3dlbshsS+Dx58Nj9zHx25zOzM5OZ17zfr6Sm4cIAACAASURBVPc2QkYIr8fLrf1upbxvuRJz2hAlnUrwCA8SqYRQhTu6bkZo+XygqWM3KzSNdS/M5P+b9RMW9wzz/ocTqRnQh4rRGhWjj/TgFAqFQqFQKHJLexHb/gg8JKX8W6S9TgjRE7NAwtPA3sj0bkAg5nOnEo122wucKoQQVnRbxOutK4kRcUgpq4AqgIsvvlgVo1coWsix5oXlK/Ph9XgJh8MAbDuwjUdGPkL9ofpjZhu0J/SAzt2v303QCOIVXlW8QpGIi+eYEtyyY2FJPdOukISlgTcmjf5Y+tugUCgUCoXi2KC9iG2dgHDctDBRz7ntmGLat4F/AQghOgCDgV9F+ujA8ZjebZZvmwYch9PHTaFQ5Jiq2iomvDaBsAxT7C2mprzmqL+p0ko1bu13K7NrZyORNIWbWL1nNY+PevxID03hQvXaahrDjQCEZZjVe1Yf4REp2hwunmNKbMsOX5mPIm8RTeEmirxFlHQqMdPtQ414PB4eu+YxVQlYoVAoFArFUUF7KZDwD2CyEOJaIUSZEOJ64OfAy2B6rwEzI31uEEJ8E5iLWRDhhUifDcAbmJVJBwkhNGA2sFBVIlUo8oce0PnJaz8haAQxpEFjuPGYKQpQ3recQq9p+C2RzFkzR5nutxP2frU3fSfFsUXEcwyv13z1+Y70iNod2i7Y8MUYnul6OzXlNdQfqqcx1IiBQcgIMeG1CeocqVAoFAqF4qigvYhtdwH/D5gFbAD+BDwJ/DqmTyXwv8BjwHtAd+BqKeWXMX1uAtZiVi39Z+T9j/M9eIXiWMZf58cwDLstEMeMF5YV3SYQAISMEP46P7oO06ebWWmKtkF533K8wmu3X9/yurrpVzjRNDN19P77VQppc4ik4fb805OU/2Ie2i4z0s3jiV6KhmX4mHkYo1AoFAqF4uimtdJIReR/s4gIZhMj/5P1kcDUyP9kfT4DftTccSgUiuzxlfko8BbQFG4CwLRKPHboH7oTzzunEO5ZQ0HZ+5TUj2LYD5XtU1tExtTKUdVIFa5omjpg05DUn9MlDVe7914eu+Yx7nziWYztgyk4+91j5mGMQqFQKBSKo5usItuEEM8IIb6bps8oIcQzsdOklFOllO0lik6hUOQQrVTjmnOusdshI0T12uojOKLWQ9fh7h/0JlzzO5j3FuGdA1mtd06wfcr7OAI605dNV5FaKah8txJDRiMwJZKDjQeP4IgUivaHHtAZVj2M+5bcx7DqYc5zTpI03D5NFYhna5CLf48xbxHsUmKmQqFQKBSK9k+2AtjNQL80ffoCY5o1GoVCcXRyjNbzNQM5BMgCCBcS3jYYyt5uVdunlDe/CsDcRn/f+PeE6Wv2rDkCo1Eo2i/+Oj9N4SbCMmxHh9okScOtfH4VoSYPyAJCTYLJT71+ZAavUCgUCoVCkUPyEW1WTGLlUIVCcYyiB3Re/ehVu13oKaS8b/kRHFHrUXLBOjwFIRBB8AYpPHs55aPOaVXbp5Q3vwrArEQqXRThG3vfeARGo8iGdhG1eQyZNFrVRr3CS5G3KDElVNPg3nsdJ77dJ78A3ib7PLnM84e2/XsqFAqFQqFQZEBzPNuSxqgIIYqBKwFVxk2hUACmkBE0gnb72nOuPSZ8sPSAzsQPhyHLL8Jb5+O6EZ2Z9P3p5rqXtp7tk3Xz2xRucr/5VbhycseTqRhQcaSHoUiBFbXZGGrE4/Hw2DWPtb3fLFIU4Kg2adR1M4zX50PTNGrKa9w92xK7o2kw9j96s2r3MKjzQZkfccYq5ZeoUCgUCoWi3ZNWbBNCbIub9DMhxC0uXb1AV8zItidyMDaFQnEUsH7fekf7s8OfHaGRtC7+Oj+N4UaMM5bjKdXpdn4F/joz6Lc1byK10vQ3v8c65X3LeaLW+WfrtONOO0KjUWSKv85PY6gRAwPDMJjw2gT6nNqnbe3jLkUBjiqxzU1MPCN196FXhWlqEhQVSZYs9lKhVbD1v7fy0LuVIKG4oFg9FFAoFAqFQtHuySSyzUM0mk2SvLJoEFgH1AB/yMnoFApFu6ch1JCyfbRS0qnENtw3pMHTq5/GkAZF3iJqymvaliDQRkla1TDHaKUaN/W5iefXPW9PK+1cyvRl05VA2YbxlfnweDwYhnmchYxQ24uIsooCWGJUvk0aW5s4MXHHgmqGnjDHjqRdMmaJ4/eoXrCDxsbTQXppbAxSvWAXmtaT0eeN5ouGLwBT/G5Tv6FCoVAoFApFM0grtkkpy6z3QggD+LOU8vf5HJRCoTh68PXysWr3Krs99qKxR3A0rcfqPavt9wJByAghkbZvWmvdTFqpdtbNb3sR+lp73Bd2vdDRXrRtEW9tf4tib3G72WZHmtYSRy20Uo2faz+ncnklYFaQLelUkvflZs2YSM2o8vKjK6oNEsTEF7vupfHrRgAaw41Ur6127AtdjRcpEBMIIcEbhLK30QPnmOnAdRfh2XEV/e88Hq30CK2PQqFQKBQKRY7I1rNtKFCXh3EoFIqjED2g8+jKRyGgIep8/PC7p7c9T6U8oAd0nl79tN32CA8FngJCRqjVfdPcCiS0B+EodtwNoYaEm/Zc4yvz2b+RhSENGkON7WabHUmOlKjbpbgLHuHBkAYe4aH+UH3el5kx8SmW5UdhYRirwmjEhG17fTXUJumr6/zuL7/l28bLLPb4ePmadygfVWmmA9ddhDF3EUa4iAlLJX2WHH26pEKhUCgUimOLrKqRSinfllLusNpCiM5CiFIhROfcD02hULR3rJso5r2FXPx7XvjlrVQtWHekh5X3CobxRSGklDwy8hHuH3o/NeU1AK1WQdFX5sPr8SIQeD3eduOFZI0bzIilJ99/Mu/ba9Q5o/AKr2OagdE2o6XaGNaxHl76KxrrLmq1qre+Mh/F3mK8wkuxt415fbn5tR2NxFQY3XbAafP7/p73ow2/H08wyGBWMEU+yHWHzOO5pFOJWRwhXASygFDQQ/WCHSgUCoVCoVC0Z7IS2wCEEF4hxGQhxBbgAGak2wEhxJbI9OZUOFUoFEchvjIf1A2xb6JkqIDxj73YKiJTMqwInPuW3Mew6mGtMhbjvbH8cfxwSjbcy7pP1zFk7hB+s+Q3rbZ8EbHZFK52m20TrVSjd9fedjssw3a6YK6x9ol/bP4HQji3kYfWi5aqqq1ixLMjqKqtapXl5ZKDWy7AmLsIFt+PMW8RJfWjWmW5WqnGzO/MZFivYcz8zsy2FYFopVh6vUenX5sLywPLHe1Vu1dFz3E+H6ECL0EBQS8sKZNUr61m4hsTkT2XgCcEhJEiyDMHxxzRvxMKhUKhUCgULSUrsU0IUQS8CTwAlAEBYFXktSwy/a1IP4VCcRSTSXSYVqpx3oC94G0CEQRvEKNnTatFvbjhllaZa/p37x9tvHcbLJzNln/1Ytw4yR3/U0vQCJopiuHGvG8Lf53f9ouzDOTbA3pAZ+3etY5pu7/cnZdlxe4TsWmkYEbVtUa0VFVtFeMWjmPRtkWMWziuXQluekDnTy+8Z4vqIlxM/YY+rbbsiW9MpGZ7DRPfmNh2BBpdNyPZZs6E++83Uy2P1rxIXYfp01m3oIqvg18nzJ5cM9l8o2ls/NtfuH94AVff7OH9smIAmsJNSAzHZ9rTuUqhUCgUCoXCjWyj0H4O+ICFwC+klB9ZM4QQZwN/Aq6L9HswR2NUKBRtjGz8mS4a2MiGMcPMNKEyP5SuoKTTLa063lh8ZT6KvEX22PMhpNQfqkcgkEjYcGNkqgAkcv31MMAUUrwi/2mdrbG++cBf5ze3XwxdOnTJy7Jit1FYhh3zJJJ1n67Le8TU/PXzE9rtxd+wem014Z5rwDsFwhLpCVJywVYg/4KbqyfhLmwPsSMicOm6uexgEAoLzbEczUJbxJfuPC8M+jGsiCtusPWzrfb7PqMr+GpAHzrW+flj5Fw0b+08GuquQhoFgBcMA8+Oq9rNuUqhUCgUCoXCjWzTSH8I/BsYHSu0AUgptwI3AB8CN+VmeAqFoi3ir/PTGG4kLMMpo7P0gM6LH74IpStg8INQugKBOKIm5lqpRk15TV7900o6lUSFogssEcXZLvAU8Jdr/pJ3EadNp9ml4GDjwYRp8X5QucLaJ6479zrX+dOWTcvLcmPp171fynZbZv3+9eYxPmYYXPVbGDOM1QWzWmXZllDqFV6KvEWMqi8xxZ/77jNf9SMQ6VZdbXq0SWm+Vle3/hhaixhfOk8oTPlamLwMBgWiXU4/4fTkn9+lMWLDVs5vLKew0IPHKyksgsfu/K92c65SKBQKhUKhcCPbyLZvAI9KKQ23mVJKQwjxOnBXi0emUCjaLCWdSjAipwFDJjeQ99f5MQzn6eJIR1fpAR1/nd8eg2+ej2A4SKG3EP+YaNXJ2H7Z3vSt3rM62rj4KYTwmBFtF8yncOA8xva/g/K+5a1yM2ml2TWGGllctxigXURM+bf7E6ZdesaleVueVqolTVP9ouGLvC3XoktxNGpPIBztts7+r/ebb0pXmP8BaB2x0BJKrWO1z3P+xKIEuYwqs9JDj1TUXFvD5zM96QwDA8ktq6HAgCYvDBtjRrltrt9sd4+NivZ+fAVybg3Bpu5ANzzeMN/9wX4m3dkNTWudNGSFQqFQKBSKfJGt2NYEHJ+mz3FAME0fhULRjqk/VI8HDwZGSgN5X5mPAm8BTeEmADzCwyMjHzliEQt6QGfovKF2SuXIb4y0x9YUbqJ6bTVaqZZVmqzbMp58/0nHtB/e/DUnFP0dKKC879us+3QdU/1TubH3jXkXvvx1fhpDjRgYGIbBhNcm0OfUPm0+aqTHCT0Spp1QdEJel9mhsIPr9IONB9EDel63WaxgLZHtqgLqKcedAvujbYGgvG95qy1fK9Wiv40PsxhBU1OzihKkFNljUiYpKkruw1ZeDnPmQGOjKUT175/Y5yjCkAZCSrwSCokkzIfBV2eKbbHHj1W11tg+GOPznsigVZBEYIQFCz+ZxaQzRgJt+/ykULSUljzQUygUCkX7INs00g+A/xRCdHWbKYQ4BfhPYK3bfIVCcXTgK/NRXFCMBw8ejyepMKCVagw6Y5BjWiYppJkUX2gO1WuraQw3IpE0hhtZsWuFY/7er/YCLSui4K/zJ/h+fVT/EY+PepzHRz3Ouk/XtaoRvq/M56iwGZbhdmE8PunySXji/kQ9vfrpvBng6wGdhmCD6zyJzGklVLf9+/Utrzv6PP7e4zlbXr45ucPJjvbgMwe3zs1jjDH/+IXjGb9wPPoZmCJYM4oSpK1UHJMyaUfNuaFp8Mgjpl+blDBx4pFJZ20F9s6qRARDCKIXlBKQAvxl0X7Va81U2pL6URjzzKq1cvXNeL32JyIFdBa3i/OTQtESjkRVdIVCoVC0PtmKbX8BugKrhBBjhRBnCSE6CiF6CSFuAVZG5v8l1wNVKBRHBjdhwPIB83q8GNJIWgWwqraKpTuW2u1UKaexy8vXRaglptntr53tbsd3AxJ9oLJJe/WV+RAIx7TYiCk3I/zWpDWKMuQCrVTju+d/1zEtaATtm/ZcYu1z7+1+L2mfTfWbcrasIXOHMGXxFIbMHWLv37u/cKawrtm7pv1UJHXu7pzc6WT3frkkEmUm7/sNZ//XONYseIInap9g6LyhpuB2771Zp3mmFdl9PrCEayFSR83V14NhmP9TCXPtnPjUa2tX2HxyYqEEgPoNffAYHc2qtbKQble+Sr9RK/Be8iSeW4ZTXPZ+uzg/AbbY22pCamsvT5E3/HV+GkINhGWYhlCDEpgVCoXiKCUrsU1K+X+YVUZ7AlXAR8BXwBbgKaAX8MdIP4VC0c6pqq1i8JzBTFk8hSueuYLrX7zeFgfqD9VjSANDGkmjv9yEJIefmQstiSpLx2eHP0s5v3OHznZqx8zvzLSLKGQTpaOVavTt1tcxLTbyJ974/sbeN5JPqtdWOyLtwkY4Re+2gx7QeXXzqwnTn3z/yZxHAVj7nIGRIJRabNy/MSfLrXy3kqBhOi0EjSCV75oRc2MvGpvQ9+n3n27x8lqDbsd1S9nOC5EoMxE2KIykLAKu54xMI2V9ZT4KPAUIBAWegkTRZ906CIXM96GQ2U76ZT4z1dTrbVY6a3vhywvPAezyL/br+fXRIgkePHZasc8HhYUGeEJITyMf95rGmosvo8/Yx7l4YJC7Lr0Lf50/8bdqa0KTrhO+cjByyhTCVw7O/7h0HYYOhV//2nxtK9tB0SwONh60iyhJpGtBIIVCoVC0f7L1bENKOUUI8QowFugPnAh8DqwGnpFSqisAheIoQA/o3PnqnbZQY2CwYOMCXt38Km/f/LYd/WX5mrlFI9zY+0YWbVvkmDZnzZyUxQEy+d7msvOLnSnn+7f7eXTlo83yarPQAzoffPKBc6KIzpu5YqY92Su89Dm1dY3AJRJ/nd/2pmurnjHVa6ttUSqWsAzb3nq5whJZjLBhvkojIRVYInOy3DV71zjaVkSb237w8Zcft2hZrUX/7v1TtvNCRMwKNzYQ9Eg7ZdEjPI5zRrxP45IxS1L+hrE3wAnMn5/YrkjiuahpZhrrUV5MQe7fRxjzYlISjWzzyqhnm4HBuk/Xmdv9DB1Zfi9s1aDMbxfUsI6LVbtXIRB0KOgQPf9m6pXXimybMp5eobCZPhsKs23KeM5asibt55pNdbXpAQjma3X1Ed8GiuazZs+alG2FQqFQHB1km0YKgJRyhZTydinlxVLKcyKvtyuhTaE4enDzHoNoKp9VBTBV9FfFgApmj5rNGSecEf18OMhU/9SkUSZWiuqwXsOY+Z2ZtiiUCw+3ZJ5cFj0692hxVJ2/zm9XarWwIn38dX6C4SAEBsGyyYR3Dsx7+kh533K8wmu3rYid9uwZs3Tn0pyP15CGLbDMunYWxd7ihD7r969v0TKqaquoO1jnmObr5QNwTY892NA+oh3io1XTRa/mhIiYteuX4xh5SyErSk3xeta1sxznonifxlRpyP46PyEjhEQSMkKJx2a/fqnbbmNsRjpre6Jk5I3oDGIak1mB6c8ZcWBzeLZZUZr+Oj/BHkth8IMxlWsjRM6LMnApjeHG6PbP1CuvFZEbNwKgM4jpTOa9Dzo374vaWsSeolWIj2jPd4S7QqFQKI4MWUW2CSF+C/illEtT9BkMDJVS/r6lg1MoFMnJd1SSr8yHV3hdBbds6HNqH0adO4qnVz9N0AhiYPDW9rdYtnOZq0inB3QmvjGRpnATy3YuA7DbzY02szip40kJPm1gpnneNuA2Rp83mn9u+acjqi7b7RzvSecV3mgKVZkP78dXEJr3BoSLwNtEyaitzVqXbBBC2PldlqDklq7blqLbOndIfvO6ft96hlUPa9G+EEtsFF3QCLJ6z2pGnjOSBRsXOPqlE2vT4ZZW/fCKhxl93ugEP0GAToWdWrS89kSzzmeaRk9N46ba/nRYP9+1um/8dl2/L7lgerDxoC2Uu/pLdulierVJab526WKKJEd59FoqdDTuFOWEZSFeDGZxJ7fzFGvP7cyK0i/sfh0KTN/K2PQ5B4FBMK/GPi+Km0dEIxStlNxmVpjNB1u6evlk7yCGU0MTRXgPNHFwwToqRqeJVI7dX8BMCbXWa8mS5PtQeTk884zZ9xiocKtQKBQKxdFAtpFtUwFfmj5XAr9rzmAUCkVmHOmopP7d+2c0BqtPVW2VIyXQkAYNoQbXKJN4EWj++vk583CbOGii6/TPGj6z0ztjo/WArLdz/aF62/dLILj9otsd4sG5X9xu3lDKAggXslpvZkREhvjr/A6ftpARonptdYuKQOQbPaDzv/r/puyTaz+/eEZ+Y2TCNDdftWyI9+sDohE8LlZx+w/tb/Gxna/KvrGU9y2n2FuMQFDsLbbF5UxpyflMD+jc9fpdvLntTe56/a6Ez1pFTyze2flO0nPVQ+8+5JiWUDnZ54MOHUyxo0MHKCkx0xvvu898PQajkx6u+oywLAa8hCngTmaxVAzizduvcvTr3bU3kCJdrs7nOC9eV/yn6HnTSsltRoXZfPHwlUUsFj6aKCJMAU0U8vDf0qQCWumw1v5SWWmmhEoZTQ1NhqbBo48eExVujwXi/Tjbiz+nQqFQKLKjWWmkaSgAjLS9FApFs8lnEYHYZSSLanv8vcczGkOs8Xw8EsmcNXMSbnzjRaAbe9+YM1Goz6l98CQ57TWFm+z02HsH34tWqjVrO5d0KnH4Pln+VVW1VQyZO4T1xz8O3iYQQfAGoeztZq9PJvjKfHhE4jpnkgZ8pIgXCN2I9+ZqCfFRdJ07dM5LKmSX4i6u0z/c9yGvbHwlYbrlr9dcqmqruPyZy5myeAqD5wzOm+CmlWosGbOEB656IK0nWiyWEFi9trrZ5zPrsxJpH8OxxP+2BoaryF+5vDKa/h1JZzy45YLEBY4ZA7ffboo+9fXR9MbDh03x5BjDGaMmCOPl99++nQ/OOs4x58umLwF3wRkw/dsi58XCIsGkmwY657exlNyvB3yLv13rR3ii53JZ5k/9odh02IYG2JRlheNjpMLtsUCPE3o42lbkp0KhUCiOLrIukJABA4D9efhehUIRId7QPR9RSQkpVIFBZvRBmZ+1rGT8xePTFjKwhLPDocOuywiGgwnpi5YIFJtS1ufUPjlJmfXX+d1TmCLM3xBN8+vfvT87P99JgacADDIW+l7f8npCu8+pfaLFJkp1GDMM6nwUnLWc8lEzmr0+maCVavzisl/wx+V/BKDQW2hHHmmlWpsS2Sx8ZT6EEEiZ/LcKGsGo6XoL8W/3J7Qv6n5RQr/56+cnpClmQ7L95/l1zyf9zBtb32Dn5ztTFhVxQw/ojFs4zm6HZZg7X7uT1ePy46eW7b5kRbM1hZvwerxZH2cW6Xz0/rHpHwnT9n69NyFtdfeXZqGK2HTGPy0zGH1+RN+JN+kvLzcj3URMSOKCBXDPPTAjv8d0W2JixcmM+2cTGEUAeApC/GHqrZT/a7qj38pdKwF3wVkgkKUrYMxwyg7eTL9BB+GMwUDbOzdZPDj8QS7beRmcNsz+u3jd8CtTf8jnM6Miw2EzOm3LFjNSLRQyX8vTRIS2wXRaRfOYdPkk/rH5H/YDTX2Xjh7Q2+TfY4VCoVA0n7RimxBicdykm4UQPpeuXqAU6An8teVDUygUqQjLMBLZYk+1ZDgie+L8dOSYYdQfqk8QxeKxih3c//b97PpyV+JCRFSAiL35dfueXFyE+sp8eD1eQkbIdf6+Q/t4ovaJuCEK/uP8/2DSZZMyGsOm/ZsS2tVrq52/U+kKKF2B18WEP9foAZ1HVz4KmN5tEwdNbPMX9Os+XZdQZMKNlopfFvFRBR0KOlDet5wn33/S8bsdCRPrpTuWsnTHUuasmZNV1JhbdNi6vetyPDoTPaDb0WKZioJ3zn6Wwyt+CmV+wqUruGPAHZx54plZCepVtVUs3eG0kLUiqCyES37uh598yJC5QwjLMMXeYmrKaxh70VhW7V7lSGc0QmH8/ojY5vebqX6GYb76/Wak1UUXwapV0S9/6CEYPbrNRGDlm4rRfWD+Op6eG6THCT2YdGc3NA0u3XMpWw5ssftdesalgMtDnAijzxvNq55XqSvV6bYRXrvVy/E/mUWf0S0/vvOBVqrR77R+rGGFXejh5fX1zBieQmjVNLj1Vpg92xTbDMOMkjzzzMw8/46RCrfHAlqpxnXnXWf7gsYWnlIoFArF0UMmkW2+mPcSKIv8j8cA6oEXgZ+1cFwKhSIF1WurbcHI8uDK60Waw09HQp2PR1Y+YqdbJsMqdtAYanSdP7zXcLvaqBXlYkU0SaR9I5yrddNKNW7rf1uCoJYKieSVTa8w6bJJGfUvLihO2Y4laCRG9uUaf53fjiyUUvJn/c+MPm90m76odysk4EbSlLQs6d21N0t3LnW0tVKNWdfO4o6Fd9jRkFsPtKyYRVYpoTGRpJSusL3dMv3d3ESNVFGdzUUP6Pjm+WgKNwFkJAr+6JHHWDPjIVu8Z8ww3u/xPo+PejyrZbvtJ39d91d+cslP7OWPOm9UQvTbRwc+st9b29UW+a10xrDEU2Dg80Uq+ZaUmOIImK8lke07dqxTbDMM03vrGBJCKkb3oWK0c9oJRSc42oHPA0DU0zJ2X5RIdn+5m6ARZFAAauZBUTiMXDYBlvRps9tyy2dbHO2PDnxEVW2V+wMAXTf3i717zai0UCgaIZnN+mlam90eiizJ/elYoVAoFG2MtJ5tUkqP9R/Twnlq7LSY/wVSytOklD+UUu7L/9AVCkU+Ke9bjldEbjRj/HRMnzE/e7/ey4hnR6T8jlSebW79wjJMyAgRlmEMaXA4dDjnfnTxkS+ZYEgj43Hs/Hyno32w4aDt2xaPV3jzXpjgYONBRztkhPJaWCAXZBpBtnn/5pwsL/73sdrPr3veIQpULq/knrfuafZyfGU+Cj2F6TtakaSL7zdfA4Psz2dKgrk/cNZJZ2X8+Uzx1/kJhqPFT9w816pqqxjx7AiqaqsAeP3Nww4zfOp8rNq9yp6fEbrO/Ss7Me1NeP1ZuO09c3KsJ5se0BNShOOxjkF7zKUr4Ds/hbNqOGn076O6Rn09eCKXTB6P2Qbo08eZSqpwEvG/W7q8iaraKvMY8CYeA5ZY66uDojAUSPCGwm3al6zAk/i82tXoXtfNSLQnnjBTjYNBuO66NlPsQREl/lyVL/SAzj82O1PcU1XgVigUCkX7JNsCCbcAf8/HQBQKReYkEwdyiRXZ0/PEnuYN6JhhcNVvzddI2sxb295KeWEaW+zAFu5i6HpcV0c/R8pX5CZtwZuf5Gyd9IDOX//dvCz3D/d9mLZPVW0Vnx3+zDGtY0FH6g/VJxRm8AgPf7nmL3mPMIuv/ieEaFOVR92oGFDBGSeckbbfpvosDcaTEPv7ePDYQtXWzxIj2VoquIlMhJm4yozU+QBYsGlBxsuJj2wTCKqvT1HtsJnEFgQBEjwkq2qrm7EZMwAAIABJREFUGLdwHIu2LWLcwnFc+uSlFJ39blS894Th8zMhMIjfLP4N4xeOT1/IIeKfdvFjC5i8HEZshaqFUcENop5w/9r9r6RfU+ApsI9BW5QODII3HoZtwzmw4LfoeiRNtstOwkWFpudWcXHUL8tNDOqf+/Nxe6O8b3mCaDztBTPicdAZgxx9BcIW2/xl0OSFoICQV7RpX7KKixMj2OKN7wFzHwlGBWkMA1591Xyv6zB9evrKopn2UzQLPaBz/d+ud5yr8im4uRWgSlqpV6FQKBTtlqzENinlPCnl2nwNRqFQZEZ8pcR8VE60UkB3fbHLFMpKV8DgB22hDcwoklQXprEVLy8/8/KE+e/sfIfpy0wj7ZryGgafOdicEXOTtmr6NLpPvCEnF76V71Zm5AXmxvPrnudHL/0oZR+3qIaJgybiK/NRXFAcFRMDgzCWTmLrB6c2ayyZogd0OhV2ckz75WW/bNMppAD3vHWPw+PPqkQbz3mnnJeT5Vm/j1d4KS4otsWim751k2v/yuWVzdof/XX+pH6BDlwiSbNdbvw54T/O+4+8/O6xQqVAMLb/WMdy4o+JVbtXsfekl03RfsBT5sTa22FeDfs2nc0TtU8wZO6Q1IKb3w8NDfYFjCVf3rjBfO3coTP+Oj+N4cakqbO9T+nN0puX2il/dgScw7OtgOoFOxhWPYxb9z3JsHLJjl/c7oxIskzvLYSIRr0djWQo+milGl32Xu8QjQ9vMSuMNgQbzE6RBypnfvF9zi05F4AVpTBsDPz2KhhWDnp6zf2IMWP4DG7qEz1HCAQjzxmZ2NHnMwsgxBIOm2mlw4bBffeZr8m2qVWcI10/RbPQAzpD5g5JeJiRqZ1Bc7CKXMVyJDxBFQqFQpFfshLbhBDfFUK8KITYLoT4SgjxpRBimxDir0KIa/M1yMiyuwsh5gkh9gkhGoQQ64UQQ2LmCyHEVCHEbiHEYSGEXwhxYdx3nCSEeFYI8Xnk/7NCiMTSWApFG2fFxysc7fX7Ulfkaw7+Oj+NoUY7pTMdrukzmDddvjIfy3cuT5i38/Od3LfkPoZVDzPbX0RSMNeWQ6jYvknb++/zGLdwHNe/eH36qJckVNVW2WbEWRG5ISQwiOfXPZ8yqik+qqHfaf2oGFBhi45dO3V1CIkPjftO3u6b9IDOlXOvdNxAFHoKGX3e6KT9py+b3uztm0teWv+So93t+G707to7od+hpkM5WV6sKBzrEThj+IyEiESLZPt7Kko6lSQ/lmL2s2SRpAC/W/K7jJYVf06Ij7jMFbFCpVVYIpamugHwt/nwpA7v3RadUboCTtwJRkFCBJ9lFp6UkhKQpowmiFofzb/AfPVv96fe1sC5p5zraNvbJ0bo9BSEoextO8X9ndPDvDDqTGfqn6bBz38ebUsZ9XM72shS9Dn3ot0O0bis7w4Axl401tzP5/ih5gECDz/LuYfHJHw+LMNtPuX9yp7RCqQSyYTXJrifQ2+9FfrFeEwaBqxfDw0NpvDW1JQ8ZdbvN+en66fImKraKi598lKuf/F6JtdMJmgEE/rkU/zSSjUeu+YxLjjlAnp37c3sUbNzUuxHoVAoFG2LjMQ2IcRxQohXgZeB/8KsONoJOA6zWML3gVeEEK8IITrmepARQWw55nX1tcAFwF3ApzHdJgG/iEy/JDLvTSFErEvvC8BFwEjgO5H3z+Z6vApFPtEDOmv+1SF6Yw7s+HxHzpdT0qnE9lqTSPqe1jdl//iKjrEkVOOMISzDNIYameqfyq4vdpnrtPoWzNOTNNPMIpE9CzYu4Mq5VzZLEGqOOOLmnVW5vDLp8uOjGsZfMt7R/vTQp87ImXBB3u6bKpdXJkRRBY0gt71yW8L4rZQ7S/g80oLbDb1vcLRv+tZNzLpmVkK/RdsW5STiMbYSbnz01xU9r3D9zL92/yvr7eSWTgwk7Gdd9o10jSQF2Pv13ozWuSHUkLKdK5IJlQBVC9ax5sE/w8br4eNLYWGVU3BLEsEHsGKXc70dRCLHYoW2MPDv08z3HQo62Eb8yViwcQG+eT77N7SKiMQKnbNe3ET5qHO44mMvU5YJrvg4icdily7ufm5HG1mKPh93+X8O0XhTp7mAmSbeb+sLYBQBHoywlxeregIwKAA/euY2amte56ZnbmNUfdsWLuPFb6vojY0lUFZVwQcfOD+8fLkpzgIUFCRPmfX5zGIKXq/52oZTa9sDVmr7qt2rWLBxQUJFY4tk03OBlTmwuX4z2w9sp8+pffK2LIVCoVAcOTKNbJuNKVDtB/4ADMcUvHpH3k/DrER6LZB5mb/MmQTskVKWSylXSSm3SylrpJQbwIxqAyYCD0op50sp/w2MAU4AfhjpcwGmwFYhpXxXSqkD44BRQojc5CIpjmlay1i38sVlCQJQ4ItAzgWS+DS0Xl16pbx5bQ5WipeVjhoyQqYYZRRg3kob0H+OQ3AIGSHGLEiMgkjHgYYD2Q8wiXdWsqib2Bt8gXCY1Ns3YLGRM95Q3u6b1nzi7v+yfv/6hDS92AIVbgb3rc2M4TOYdPkkvnHSN5h0+SRmDJ+BVqo5okgsWprqowd0hs4byq8X/5qh84YmHEcPDnvQ9XMSSfXa6qyO+9goMEcKUdx+1mP/D5g9ajYFwr1geCbCsa+XL2W7NZj/ej0YhZjHcuTc8c690Q4pIvjWfLIm+TnN54OCAjuyzfrvqzNnn9zx5AQvOTeawk32sXxh15hA+NIVXD3mfSpG90HbBW/Nk/x+ieSteRJtl8sX+Xymj1u8n9vRRpaiT9fjujpE488bP7ePk4N1vRx9d2w8Ea/wctZbtzFBVvEmI5ggq3hjegZp10eQgw0HE6Y5BFm/HxobzUg2Iy7SMhzzAOqWW5IXS9A0M3X5/vtVUYUckOnfjFc2vZK3McT+zT0cOszENyY2+xquLUWlKxQKhcJJWrFNCNEPU7D6APimlPK3UsrFUspNUsqNkfe/Ab4J/Bv4kRDiWzke52hgZSSF9VMhxBohxAQRdZruBXQDFlkfkFIeBpYCl0UmacBXwLsx37sc+Dqmj0LRLOJNwPMpuO1ed26CAGRIo0UXa264paH96vJfJe2/bOeypOtd3rc8aTpeArHRLgWN0DdR2Pros4/S+zrFUFVbxUeffZSyj0DgER46FnS0hZ5kkTd7v9rr+h2xN/gS6TCpd1Q7jAgMP3jo6bzcN+kBnbqDdUnnx0dfWP4xApFgcH+kmDF8Bh/d/REzhs+wp8X6I+WK6rXVtrdXY7gxQUjVSjWzSIgL8zfMz+q410o1Zn5nJsN6DeOxax5j9qjZdCnukrCfnXvxHioGVPDzy37u+j3rPlmXdr26FHdxCL9divPjmJAqKvLGkdb+HyN6NUU9BIu9xUkj+MCMznRF0+CxxxBeLzLy7UGvaa4PsPCjhby+5fWMxr/3673oAZ3FdYsd061jYMeCakRTEK8BNAXZscBFaD9WxJAs13PQ6Wbk9aAATF5mvlpCR4OIS2suOMRZXc5i9QErdc/cd1/aflFOVyHXnN/1fEf7nJPPcUbH+nyZVav9Mk2VbE2De+89evetViTT9NB8VG+28JX58HqiXo+rdq9yfdiTjrYWla5QKBQKJ5nc/f4Q81q2XEq5L1knKeWnQDnmFdIPczM8m7OAO4FtwAjgYeBB4CeR+d0ir/FlCz+JmdcN2CeltK/6I+8/jeljI4SoEEK8J4R4b9++pKutUACJT0rzaaw79vqzXQWgVbtXMXjO4JwJfW5paDOGz0gw9bVI5VejlWqZ+5GkiHaJZemOpRmtrx7QGb9wfMo+YPqZ/WHoH6gpr2HG8BlUX19NQc/3XMfyyuZXXNez/lA9HhGpaik8jsg2R1pcRGDYd5L797SUyTWT0/aJF9QMaSCRzS4g0RrEbk+LxdsX5/0GI0Fcjfir7dt0tmPytGXTUn6PHtC5+/W7eXPbm9z9+t30ObWPaQzv2OeHM+n7ZqGQGcNncHKHkxO+53D4MCOeHZFyWb4yHx0KOtheavkSUFNFRVaM7sPAkZbIHfnT23+OPb9zceeU3737y93JZ1ZUwLJliDvu4J/Dyhh6s2muD2b0a6aV/T479FlC4YpCT6G9vV79bAUeaY7eK+FDw11oV2JIIuV9y7l8l4clc+EPNbBkLowP9aOqtoq9F1rel+Z+UXD5LPYd2kdDn/mO6Vff2Njaw86KWdfMsitte/AwrFeM4KHrZhEEGRdh6fFE044t/vpXVfiglagYUMHAHgPT9rvzkjvzNgatVOPWfrc6pjWGG5M/YEhCW4tKVygUCoWTTMS2S4H3pZQfpOsYqVRaCwxK1zdLPJEx3CulXC2lnAM8QlRss4cQ1xZx09xySuL7mB2lrJJSXiylvLhr164tGLriWCD+SemNvW+0RZ7xC8fnVgwo1ZOKUWEZTm7QnCXnlJzjaFtpaFeVXZX0M2EjuaF1ed9yW4iySBrtliLaBbDFjvDOS9Kur7/Ob3vPxWLdIFkUFRRx7+B77agErVRj6c1LOaP3rsQqrNJwTSX1lfko9kaqWnqLHQJHcUFxQv83t73p8I3KBXpAz8hrZt2n0eioyncrbYPotOb0rYRbaoybYBSSIS575jI6PtCREc+OyDqdprxvOUXeIgSCIm9RgsE/wJknnhltuPj4Wez4fEdK8dctim7sRWPNmZF9ftJ/D3ZExkwfPt31uxZtW5RyPVN5qeWSdFGRK187j5vu+JiTTz/ABaP/Ad+eYs+7pf8tqb87XeqrpsHjj/O7759qC20WmXrULd25lGc/cFq3fu/C76GValTVVtHtHVO0s2KTBn6Qn0IT7YIsCyRopRqzD1xBcRi8QHEYBi3ebD6MuvgpGFUBp6+E81/m5/9xNV06dmH7t5+i1+UV9Dr5n1ww5G7+5/EhKZdxpNFKNZbdsozR543GwOCJ2icYPGcw6xZUmdto9uzE9NHhw+EHP3BOk1IVPmgl9IDOqt2r0vZze7iTq+VPXzad/t37U+hxVqldsGlByiJM8ZR0KsEjPHiEhyJvUZuISlcoFApFlEzEtnOA97P4ztrIZ3LJHiC+3OIGwLoDsh41x0eonUo02m0vcGpM6qnl9daVxIg4hSIrKgZUMHvUbK4+62pmj5oNwBXPXMETtU/wRO0TzUoPSMb89fNTilG5qOCmB3T+9u+/OaZZaWipUisMDCrfrXS9WNRKNX7wTecNxi8v/2VSwS3pk+c4sSO4Y0BKcSjZxecvLvuFo+32FFsr1Qj8POD6+Tlr5iT8pqkEjmJvotgmkQ7fqJaiB3Sm+qdm1Hfmipn2Z17Z6PSmSZYmmy33vHUP5zxyTlY3D9aY3FJjtFKNbscnBCIDpriyaNsipiyeklU6jVaq4R/j54GrHsA/xu8qSs0bPS+6nybx8bOwtmumxJ87YtNmrfndj+/u+tl0URBaqeYQkPNFyAghkQkFOSyee/wM6nedzPqXv5uwriUdkxvgZ5r6aguWMez9OvN9eMP+DY72R/VmNN7T7z9Nj7jsvlMO5qfQRLugGVUxG+NEz91f7qZf90hVztP+DZ98CzZ9lz/dcQ3f6/xnALZ/+ym23z2SLcNmt5u0uNiqz2EZxj9vqrmN4qPavF6YOhUuvJAEjtYqtm2MlNdHMVWhDzYm+vG1lFiP0AmvTSBsJBaO+uPyP2a031tFFqzz7l2X3pX3c71CoVAosiMTse1EIJs8yv1Ars1hlgPxRQzOBawSjNsxxbRvWzOFEB2AwUQ92nTgeEzvNgsNs6JqrI+bQpER8ZE3fU7tQ6fCTvxuye8Yt3CcI5qqMWxW28z3jYMHT0JEVXNwqx4a6z+WioMNB6lcnii46QGdlza8ZHujTbp8EoBr1BlAh8IOvHvru4w+b7RzhovY8UTtE1lt2xMKT3A14U9Gv279EqYlS9lY9+k6/HV+R+QYwA+/levseieWQPXWtrcy6r9x/0Z6PdyLyTWTk/4GLeGet+6hcnklWw5scd0fUpEqNeZ/fP+T9vOHQ4eZ/Fb6VNpM0Uo13rn1Hfqd1i9lBU2ATfWbku6L5X3L7UiGQk+hHUVXMaCCf/74n0lTraf6prpOr9le06z1ySWTaybb54qwDKdNYY5f18FnDk7aN9Ob3YoBFWmrJUMKAT+OA4cPMH3ZdDoUduDp/uY0WzIZmyjsHTM0oyrmiYPMPjKm/UXDF2Yj5lweDgq+2Nif0edHz/eOB0e6DtOnt8lUyztfS3xQ8/JpB8xtFJsu6vXCrFlmRKa1LS2khAkT2uT6tVuS7DNJr4/iHuT98cV3cn7NFhvdHDSCrn97JTKjB6b+Oj+NoUbb/uHP+p/bjTitUCgUxwqZiG0dgaYsvjMIdGjecJLyZ2CQEOLXQohvCCH+C7gbeAxs77WZwGQhxA1CiG8CczELIrwQ6bMBeAOYLYQYJITQMKusLpRSbsrxeBVHObGRN755Pq7/2/Vc8cwVLNi0IGlExaJti1oc4aYHdBZtW5R0fqfCToz4Rmovp0xYv2+94wkvRFMqyvuWJ6RgujFn9RxH2xJQJNI2bH905aNJP79271q0Uo2X//tlW5gDkoodycQVt4vW8QNNDzc3E343Zl0zK2GaRCZctKcqlDFj+Axu6nOTo6KrQFDsLXZNXcwWa/tmKpxJJHXrurH0ucvM3zjm904WPZYNc1fPTdlOha/MR5G3CK/wJqTGVAyoyKgq7tKdSzMS+NJVI7XQSjVmXTsLUboypaegIY2UN0pCCAQCkYlpeoSKARVccMoFCdO/avoq4+/IFxv2bUjZTsekyycl/T1fWv9SxufLdPvE1WddzcrbV1LkLUrZD2DLgS3ct+Q+3g28y1MXQ8Uo+OfZsHTKTaZX3NFAc8SrZhSCOHuHKayJuDbgci5/m5HfGGnPNqRhPuTJMn21tVn/aXziBRzod765jf7wBzOVdNo0WLYsuv9oGtzq9OwiGDQ93hQtR9fhyiuRU6YQHnwFC+bck95iIO5Bntx+Zc4tFeILTyUjkwemvjKf4+9IKhsPhUKhUBwZMiwPeGSRUv4LsyLp9zArnj4A3AfE3gFXAv+LKcC9B3QHrpZSxiaB3ASsxaxa+s/I+x/ne/yKo4/4yJsFmxaYIkecQBWPW7XDbKh8tzLlMr4KfsWCjQuyqtTpxo5/94jzpdLsi79Yj5pUN7j7Du1L8NuKFVAONh7kcOhw0s+f1PEk+/2M4TPs9NxkBRSWB5a7fk98dIwHT2K0XBq0Uo1OBZ0SpsdHrz288mFH++n3n3a0n7vhOZbfupxpV01j0uWT+PZZ3+aRkY/kJPXD2r5uv0mXDl3M6qqxxD7Fn7vE/B/5vfuHWm4MHZLOlMJ4v75UpPMbu6THJRl9T9V76YuFpKtGGou/zm9Wm02Rxi0QSW+U/HV+wkYYicz6xmj9T9Yn/LYSmdVx/vnnOjt2TOfzz3MnVsSLgFY702VppRrLb11uRg3GseXAloxTglMWUyAaHThx0MS03yWRhGXYTs966mIY+WP4m3ZC2s+2C3Sdd674JdOmfME7V/wyvXgVK8w1oxCEiHst71serUQbcy7vfM6HZpGZyKWph0iRmWakr7Ymbg+fZl07K7qtKirct1l5uRntpsg9kycjQyEE4AkbdPmfSqYsnsKQuUOSn+NdHuTlylIBzAc78SnryYi/tkhGTM03DIyMMxAUCoVC0TpkevdzsxBicSb/gTH5GKiU8lUpZV8pZQcp5blSykfiK4tKKadKKbtH+gyRUv477js+k1L+SErZOfL/R1LK3JsytDJuRuKK/OIqbKQwTo+lJRdvm1afnLgMF/GtpSb3YsdQxxPe7vU/cAgeVsTZyR0TKyXGjid2DPECin+7P+UY7r3iXke7YkBFVCRzETvCMuxqTh9fldAgdeRRMiZcOiFh2vhXo8Uv9IDOxn0bHfN7nNAj4TNaqSlcPrryUWq21zDxjYk5OXat7Xv2SWcnzHvth68x/Kzhzonx6bjhQvv9aj11lch06AGdzw47jeSPLzq+Rd8Zy8rbV+Il/U3qwcaDaavVrvjYKZit35888sBX5stKNHT7fKpiAun41eW/SpiW6XH++ec6a9cOY/v2+1izxsemTeNzIro9OPxBhzjy4PAHHctau3ZYRoLb6jtWu0bvHQ4dzmgdTyw+MeV8y9/OijBNhVXkJJMI3vbIorFPcLXxJr/lfq423mTR2CeSd9Z1wlcONiOErhycfVRZeTkUF4MQ5mt5OVqpxpIxSzil0ymOc/k/Nv3DLDJTECkyUxCxRGhG+mprcsnpTvG/32n9MnuAomlmWqnXa26foiJzeylaztatjuZZkT9HQSPorAweS+kKPDdf7XiQl4sobzCj3ofMHcK+Q5m58sQ/qHOjem11QiT761teb9b4FAqFQpEfMr1rKAN8Gf4vy83QFJmQzEhckV8sYWPcgHHRiWmM0+1uB+uavdzzvhrnXMbacmd00sJZtuj21PtPNXt/OPNb2xxPeM/p/7FrvwS/pTjB8a9v1Dlmxxq2dyhMzDYf2GOgbZ7u5l816fJJKdPA3C5Qux7nrCbsFd5medq5iViGNOybeLeqp+eecq7rd1leK2EZpjHUmLPUD61USyhgMbDHQLRSjc4d4gS0+Kf43qAjnasluK1PNiJzJqmdv7j8F4kfdBGepy2blvRhhB7QWbPXKcbuPLgz6bi0Uo1fXvbLlGNP57cjI+5VErfi2KmZMXxGgqhUsy0z37aDB/0YRhMQRsom9uyZnZEQlgkF3oiA6C1IWJZhNHHwoD+j74k/Vi2eXv102nNZbCSsG7GRb8/d8Bzdjkt+E/3TQT/l/qH3JxRR6d+9f8pltBfe3l5KE0WEKaCBYp7bkryA/LYp4/GEwmaEUCjMtinjs1uYpsGSJfDAA+arFq32XOApcHQ90HDAPaq1GemrrclN33KKt+MvyWIbVVSY6aUPPGBG7LWxdWu3RARZ6yzrL4vOWvvJ2qQfE2c4H+Tl4pjXAzp3vnqnWfE7TfaDRZORjXtPlN1fpI7wVSgUCkXrkonYNrQZ/6/Kx2AViaQyElfklwQBI41xusWaT9Zw2kOnZV2hEeDcAbsdy/AIb4z4VgzvVdgRbyEZ4rJnLuNHL/0o+5U7U3ek95x8nrutocNLDRIEx8839kt6k/xF4xcJ07p06JLSKN6qHDntqmmuZuduF6hWZUGLc0vObVbaZrInzQs2LaCqtso16ik+qs6ipFMJRmAgLJuMERiY09SPeE8YK8IsYSyxKVw3DzX/X/VbxJhvUz6qZQWl3dbnq+BXaaPMLDJJ7UyoVJkksnTH5zv49eJfuz6McPvejoUdU45txvAZdCxI3SfZ79mSNFKLw0Fn6vWOz3ck6emkSxcfHk8R0WQ+iWE02kLY7t1VrF07gt27M/uNLNzWKbosLx5PEV26+ByfSbas3qf0dl1GJpG68RVJBwVg8jLz1W1+eb/kEURr9qzh3sH3OvYxgbB9K9s7Db11vIQwJV8PL4Ru4UePPOba98RVTmHCs/kj134pSZJ6enKHk13brlV0m5G+2lrUH6q3z/0e4cl+P7EKJvj9bc6Prt1y4YUYmGc7A9hwanRWqgcdwuNM1X/9o5ZHitkFpzLMfoDUD30s3IRAt8rMCoVCoThypBXbpJRvN+d/awxekdpI/FimtVJrX1r/UrSRxEvMjU+//jTrCo16QOdPgf+E7/wUzqqB70yk88AFpviGVTnUmxBV9/y657n0yUuzWs47O95xpPckiwLRSrW0xQvcBAU9oLN5/+aE6Tf2vjHt+KwbsZW3r+SME85wzFu7d23Cbx6fynleSXxh48zo0TkxJdRi/vr5rlFPydZn9aoOjovu1atyU1NGD+h8/KUzCtGKdHMdS2w6buT9+f0PtNhDbvWe1a7TM0mNyRRfmc8peqWILJVIGkINGYlbF3W/KG2fuy69K+X8Oxbe4SoslnQqwSM8eISn2efr+MIKmRZaOPFEjb59a+jcOTYa1WD37tns3l3F5s3jOHBgEZs3j8tKcHNLjbWW1avX/XzjGzM5eNBvR9ClWlZ53/KkPpDJCs9YVAyosCOlpr0J7zwND9TAkrlQfuicBAHf7fxjYR0rH+770J4mkRlXR23rfPLr7vQ7bQ4iIkeE8fLXVxIjYpYNKePkQ85p+85IHUGYDT8d9NOU7faCr8xnpx43qxp4fAGIqqo2W3m13eDz0VQIQQGNBc7ItliKvcWOdrzdweq97n/LssGO6o79GxUqAv/vkgpunzV8lvbhVKy/IZgPK/qc2qfF41UoFApF7mgXBRIUyUlnJH4sEl8pdPzC8XkT3S49I07EivMSi7+Qiye+YmcqqtdWE955CbzxMGwbDm/MZODpA01h7+Iq8DYmjapbtXtVxkUT3HxAElIQY7CKFwzsMZATzv4wQXB89oNnHcKn9fscCjnv4s45KfGGOFsk0k7rtJh0+SQKPYUAFHoKE6PxMmTSZek/Z22LVKmwAHs/PN8hDO398PxmjSme6rXVSZ/aVwyoYPao2XQ7rhsdvMnFvc31m/N2vHQoyExULO9bbnsiFnmLXKu1Wuc+28cvTWSpRCZEnLl9b3wkpBszhs9g0uWTOP2E0+nXLdHYXyK5Y+Edju2oB3QmvjHRNt2/69K7mnW+jvcQysZT6MQTNRobnRETjY072Lz5Dse0urr7s0ovdUuNPfFEjVDoIJs3j2P79imsXj2YrVvvYds2pxfjrl0z7fdaqcbgnnGp6dGFpOXCrhdy23swebl5ceMBisNw67qChL7JCioM7DGQigEV6AGdF9a94JiXzmeyvXBh1wtZNaoaWRD9m2GULXY+/NF1Ll9qRk0KzM0vgedHnJ6zcVjnpHTny7ZOi6/DYgtANDbChAlttvJqu0HT+OfsSfz2Khg2BlaUunc766SzHNcH8fvgjs93ZByRnQz7HF3mB08I8+Go17yOSxHh9rM3fpbyb7GvzEeTxJi+AAAgAElEQVSht9Bub9i/QdnJKBQKRRsja7FNCDFECDFFCPEXIcSjkfdD8jE4RWa4plwcw8Sn1s6unZ1wAZKryLcLu16YMO2CUy5g2lXTePfWd7ml3y0pP98YasxugXHRO5trezB7/C1cffcCTr/rx86oujhvkKU7lib1v4rFLYIkWTqkRcWAClbevpKHrn4oQXDcsH+DI43P+n3i+eXlqb2w3OjdNTHt7JXNrzjWUSvV+NkZ/8c3Pnyan53xf80+TrRSja6d3D2l3tr+FnpARw/o1B+qZ6pvasobx24XbnQIQ90u3Ji0b6boAZ0nFqxN8IPp1z0qBlUMqGDPL/dw+DeHk5rEh2U4QbB0W5bb8VNVW8WIZ0fQuUNn+wYmlj1f7sno2LPShR+46gH8Y/xJfzOrUEeRtyijyNL4lCCtVOPKnlc6pqWKYIxlxvAZ7Pr5Lr7X+3uu8yWSyW9Nttv+Oj+HQ4eRSAxp8Gf9z806/0wZPCVlO5b4iqC7d1fR2FjnOtpYmpp2sWbN0IwEt2Spsbt3VxEIxO5HYQKBSkIhZ+GMQ4c2OKLb4lMLLTIRFR+/9nHGRgJRYuPjvmkkHrfJ0q1q99SiB3RX4dqt4El7xFfmcz1eKpdXmsKCrsPUqYBzO67pBsVX+nI6looBFSmtA9oLLboOiy0A4fGYolsbrbzanhh9ywxmDeucVGgD2LR/E3+55i9Mu2oab9/8tqs3a0sjshPTPSNHVRp/30OhQ2mv2YLhoP0+mwhuhUKhULQOiY97kxAR1B4HrBysqPGLOX8jcKdKIVU0B0uE8ZX5WiwaWqm1DaEGZOSf5WenlWp2ZFVTuIkib1GLIgLd0op+/K0fc+/gaPTGnDVzaAy7iGqBQXxR52MEU/nnvVPTLqtzh87R6J2wBG+QA91epmLA62Ykhk/nsmcus7+beTWmMOdtsm+mLP+rVOvrljKaSXonmDdOP3ntJ3b0jkXsb+Ar8+H1eAmHw/b8FvshBQaZF6xlfozSFY51vGfOAiorroZwEZUvN3H2SeuoGN28VItb+t/iLkRJM6ps3tp5Ge1X/Qc2mL9JZMz9B6YWZdOhB3RGTJ8K895K+M0317unyz13w3Oc3vl0nv/geQxpsOerPfY8S7B0G3+y46eqtopxC82CIYu2LXKkt1h8dOAjrpx7JWEjTJG3iCVjluTkIUGPE3qYhUesdNgkrPkkUTR+cNiDDJk7hKARNCMfM4hgjCVVytg7O9+xt2P8uSJkhOxzUjZYosT89fO5sfeNSUUKqyKoYTTh8RTRt28NW7fe69rXDSlNP7cTT0w9Put8a+0P1vbYvv13GS9rz56n6dHDXI9kotqXTV+m/R6tVKP+m1fCx0sd00t6JYryFQMqmL9+Pou2LXJMD0tTMHQr6JGs4El7w0r/r1xemXC8/GvqOMa+JvAa0o5oAzMWZ8K1cF28V6Ki5VgFIPx+OHgQ/vxnc3obrLza3jCkkXo+BvWH6u1rtqn+qQl93Io5ZcPzHzxvvqnzgVGAGecggXBKf1+AxnAjlcsrefm/X06Y55aF4BbBrVAoFIojR0aRbUKIG4E3gfOBPcBfgRlAZeT9HuAC4E0hxA35GariaCXXFVVjK4VaPiqxN4G5rAT5VO1TCdNib761Uo0lY5YkClgxRrmLfjuJe+YsSLus//v3/yVEI/S9OJqKqZVqzB4122y4+VdFIt1qlh5y/X6L8r7ldlSSQDDp8klZRR24RfsB9m+glWpcc841jnmW11O2dD2uq6vpsHWjrAd0/vj8vxzb4umXt2a9HIsZw2fQqaBTwnQrlaMh1EBYhtM+Xa4/VI/nzFUw+EE8Z65qkdCoB3R883x8ufmimPUssp+Wp6pOZkVnzf/efIdXliENKt91j26LPX4aQg22cX3803/HTUBMlGXICNmFD2Ijv+LXKZtzwr1XOEUkt6g6wPW300o13r75bTuyIVvxSyvVoqmsccRWJo1PQRSIZntsZhINFF8RdOfOSsLhz5L2d2P//vTnJbcUut27qwgGM68+29gY3UeTpaw/v+55qmqr7OjJZKldJb97EOn12mmPTR5YN8K9ouBU39SEaVa1YjfRL12Eb3tixvAZTkE8MIjShZMpXzgIj2FKbAI40KsbVRcLhtwmWNOro/KFzReaBiUlptAWDIKUcMklR3pU7Z4Tik9IOT/+POxWETlZtG2mbDuwzXzjsDpoNO0/0vj7QrQIUzx7v9rrWt20NQu5tJY/skKhULRX0ka2CSF6APOAEHAX8JSUMhzXxwOMBWYC1UKIFVJKVX+6lchlVNiRwK2iakvXQyvV0Eo1yvuWJ2ybkk4lthBgYDT7KaAe0PmswXnzWuwtThi7Vqrx0vdfikadQZwYJpnz8nZmpAlusj2GYqJ3Hhz2rqOPdfM9LjDHEQFHx/12pNtHbzdRdVnq6C4hBAJBobcwqZCQjDsvudOOcIpl8JmD7cjChZsXOub9TPtZs37zfV/vS9iW1Pn47LC5Xfx1fmTZYvD+2t4WPfokN0bPhLIuZazf76z4OfIbI+ncobPDuyqVmbplqB0fDdQc7LTcjvtBegFpvnbcD2RenSw+ZS6ZSBd7/EgkVe9X0b97/6QeWMmiLAGW7lxKVW1VgmiU7TkhPtpr64GtrhGIbtXbcnH+HHnOSBZsShSmYm/k4qMjvtXtW3k9X1sVQa3Itq++yt7o+8svV7FqVW8GDlyfsp91vrXYsye7tKtwOFqZ2CFKxkSsUrqCacum2dVXrYi0BMFR03ju0ds59MwTpsdYPw/XltTjdrZzE5RnXTvLXpeq2iqHaJxphG97oUuHLubfsMgxuitUxAiaqGEYGiuQwMl9B/GtRyYxqs7PQ+30GqNdoOvwk59AKBIVLiUsXQpDh8KSJW2yCmt74NLTL3U9N1vEVxDf9/W+xE6Z1aBJSu+uvdn15a7ow9KYc5rFHQPuoHOHzkktHMa/Op4+p/ZxHn+7NJg3wfG3tbhsdasJ4rnMElEoFIqjlUwi2yYCnYCbpJSz44U2ACmlIaV8Ergp0rd9lpRqh1hRLb9e/Gt883zt8ulSPiuquvmo1B+qty+wPMLT7KeAbpFL/9n7P5OOw446gwQz9/CZNSmXpQd0mgynz1mngk6uFzYVAyp493f/y5X3/T7qx3P4FEd018N/Sx6hkcyDKVOSbU99l+74/li6NDM16cbeN7oa41sijq/MR0HP9+xoQO/NI5j0/SQG7BniVjHvH5v/wcJNTgExlZm6Vqpx16V30atLr2Yb5VvYYvHhUzCTvYT5evgU2+w9Hf46f0IVyGQiXfzva0iDCa9NcKShOkhRJRRMgSye5pwTYqO9ku1PL6x7IcG7MRdRtcn2+Y6F0WqpvU9xpjIOOt3dFDtXxFYE7du3hlDoQLO+59ChDWzdmnnVZHBGqmVCScl19nvbF80lYnXH5zsckRzJvJTOGVXOz0d35K7rvLxflrw65Kb9mxztnif2dBwvBV7zeWRzInzbA9OHTzffRI5RSQFNFOLHF+3UrZvyhW0N/H4wXFIelW9bi4gtkOSGlTZu4SqoZ1CcJRlVtVXmgwHrvAUOT1uP8DB71GweH/U4M4bP4MTiE12/x5BGwrVYt33fd/xtFXVDeWTkI612nLo9FFMoFAqFk0zEtu8AK6WUiYYBcUgpFwArgZEtHZgiM6rXVtMUbrI9sdIZm7dFWruiqq/M50iTtMSKdOlJ8cRHLl1wygU8d8NzSftXDKjg3Vvfpfvx3RPSQYOnL036OcBO1Yvl4h4XJ+2vlWq8/dsHEYNnmMuKE6TWHz8r6Xr6ynwUeAoQiGaldyaLFPw6+LUpDpf5Ep4mNze6sGJABX0vPuxqjD9//XzWfbrO9GwpXYHnykpm3fHjFu9fFQMqKOtS5pgWluFoqkiEeHE0lqraKiqXV7LlwJaoKXkzmfWvWeabMj8URH7jgia69l7PyttXZvQdvjKfo1Lo1WddnVRYcPutgkaQcOJzGAA8vZalrBLqdnPT0nOCr8xHx4KOCdMl0hHNlKubBV+ZD6/wJkz/Ovi1XQU4kwqruebEEzV69ryX/fsXOKLHLE499Sa6d7+DkpLReL3uxT8APvnk+YyXuXXrPTQ17UqYftJJV9O9+x10734HnTpdEJnq5dRTb6J37+h5064WnCwVPkaA67BnqOsYMt1/zjvlPEc7NvLRX+e3vSeFEM1+INCWqRhQYRZKifx98BCkiCA+/EjAEEB5/vdTBaY3m8flkrygQPm2tYBYm4BkBY4+3Peh/b5iQEVC0ZxsKj7HM3/9fOd5q3oxd5w6j9mjZjPtqmm8c8s7jr+1P/jmD6IfjksRjb/mLB/dE2+hYf9tlWV+5q+f32oP3fP5oFyhUCiOFjIpkNATSDSmSs67wO3NG44iW+IrR/5909+TGpvnC103H7z6fG0v0+Get+7hpfUvcUPvGxh93mj8dX5KOpXYN1FhGebu1+92pJ0lTU+KIz5y6YSi1N4gYF74zf/efDOlNCYd9Msmc6xdirtknM724PAH0/Y5+6Sz2XJgi2v6wswVnyddx9h0yGxJFuVjPZn1lfkSTItb4jHy+LWPc9knl7n6ntz56p32sgxpsHpP9ql0bpx54pmmIX8MDeEGRztVpdn4iJyZK2Y2O2rGjs5x/MZv8/df/Snj77Ai7WKPAbf0TohGhlrbVSAQQiQ1oi7s+R6NSVJnwKyS67ac+NTEbLDElslvTWbpTqeQHbsPJDP3b87yrjvvOhZsTExXChrB/5+9e49vos73x//6JG3K/V4pSLCCiKIIWFQGFQJl8cbur+r57rqLFsFj8HZWd90t4q5HVxQo6+7icRWpuqv1spdzUM/RFVEqEYQBlNvigoJCocpFKPdb0ySf3x+TpJnMJE3aSdqkr6cPH+lMpplpSZPJe94XeKo9mHn1TDxz3TPhUtd0vkZ/880zhnU5Ofm6IBegDVXYsGG0YVuv91scPao2OiwBAL799jnDOputE4YNW5LQsSpOBf0698M3UcNgUOgxlIz3+C52i9hEnj9lo8vwj23/MB2O0bNDT91rR7Y2HX/tptdQc2wslqMYZ29yoXK9B5cHVsMngN2zyzCwtb2pZzNp8n4rmlnDSOHXgiN1R0wvSH9crZ/rNnnoZCzf1fC+YdZ+ICmRr1s+iS2f5WPB3eY5CaXDSrFw3ULImisM7Rfe3fYuyieUN/xcCvDg8+9h3utrwu+tH+4QWLF7RVouXIfeZzO5jQ0RUaolktmWCyB2ioZRPQDjJX5KiejG+9GZG6mmqkBxMfDII9qt2oQLare+eStG/+bnePg3x3DV47+07KrcrW/eqsseGv2n0fjVR7/C3e/ercvC8fq9eHPLm7rvNSttixaduRQvkymS4lS0bIIoT618KmY5W3TT8MlDJyd0YlN5Y2VDeaBzta58YevBraYZVc0tI3UVukwnUQLaB9iHlj5kCOLF62/WGMWpYHjv4Yb1Ow7vMGRbmU0YbIrokkAzg3sOjnlf3y59dctf1n7ZpOd9xboKfZAv+G88aNjBpE98o5u/x/obCPWbsws7cmw5uKzvZXF/1jp/XcNzDzA0c3598+spuRKvOBVce961hvW7j+4O78/KrNqy0WWGUtyQI3VHoNao+Onin+LDHR/ip4t/mtaSfynPGNbZbHmGdV27KhgxYhUAh+G+TZv0Q00qKoBrrtFuIwUCpw3fK0QipxoNfnLJTxqCx0UvAsNf0e6IytAtuOiLpB43WqzhGKoKLKo4P/w8bfa05FZubvFcOArXo2bSXPxq6hq8ccsQfPHmQgx8qLzxbyZreDzmwbb6eqDSmNlOySufUG5aVjqg+wDdcvRFuaZepFNrVHy440PD69bXXf4U83sUp4LnJz1vmtlbfbjasH2387Y2VDAA4eFD6SrpZIk5EVF8iZwB7wVMewvHchEAaz7RUqPMSpHiTR+0msejtRTx+5vWWmTG0hl4/b2vwyn2gZc/wMTZjzZrupFao+LGv96I1zcbS58kpGFUuk3YcNMQfYZEIo2w9x3fF3c5HrMsuAACMcvZogcKbNiX2Mmf4lSwctpK9Ovcz/R+q/plRe/zB4N/YHpf7alafHHQ+AE5Xn+zRBR2LzSsG9B9gKG0rznlIJFKh5UaSmGjXTcodjV92egyw/c35eT4Mc9jpuuPnEk+eBn9PbEeQ3EqmH/tfBT1KYI/4MfaPWux9eDWxndg0oMrxKxM2gpmgV8JqctusOrDguJU8Msrf2l6n2enB5WbKlHnrwt/GLLqZz56VMWuXXNw9Kj56+WePRUwazrUu/dPTLfv2lUxDY4FAkewZ08Fdu2ag8pKFdOnAx98AEyf3hBw047BWE4c2ZMtEeUTyhtKuTZOAdbdqT1nAF3/xdJJg5J6XDPR//6hC0gfvnh1+Hna2MCTTKc4FXimeDB7/Gw89ehK3PaXf2FoSXb1p2v1XC4gL08rJY0sJ5USeOGFpl3JJIPyCeX4+PaPw+cGdmE3VAlEX5TbciD+gJhY5q2cp11YjGobMvmGAXG/z13kRsHFXxraL9T5jdnyrkIX7Db9eU5oojIREbW8RIJtywF8TwhxQWMbCiEuBHBN8HsoDRSn0tDjJijR6YNWcLkAhwOw27XbRFuLhMaFP7PmGcMVvBPbi/DwRw83aeCDWqPi6j9fHXf6VLRbLr4lfMXzvO7nJdwI+2T9Sd3yGZ8xeyQZIvif3WY8UTrpPRl3OR7FqeCey+4xvW94H2NGmBXN+82CWqETwK7tjA2Avf5kkmdNmCQEVO2sQvG5xbp10RmCTaU4FfTt1DfuNvEyYRSnggU3LECuLRc2YUOePXYT95DQ30zk38TRM0dNt72w14Wm6+P56tBXuuVYATS1RsUD7z+AtXvWJlxmfHbns+MOSjDLODT7eZOlOBVD1gLQUG5vtfIJ5drPGqVdTjvLsiojHT2qYtOmYuzc+Qg2bSo2DbgdOGAMqOflDcLAgbGzloQw7zCxbdvd2LnzERQUFGPIkIZ9zZ+v3R454jF8j83W2VCumoi5xXMhqscZnzPBLMk7/7+LU5JNEbqAJAN23fM0OvMz2zBDpYUpClBVBTzxBOCOOv/w+5ndZiHFqWDF1BWYPX42Vkxd0ehzfvnu5ZixNLkhMUDEBPmIqco251pdKWgsPc7/0tCLNiADhvctxang2euf1QUP/3j9H/l3TETUSiQSbPsjtFLSd4UQMWungoG2d6CVkD5rzeFRIgZ2H9hi+w6dH86apd0m0t5FrVEx7pVxePijh3Had9p0miSgBWDu+Yd5kCiWeSvnxWzUHstfP/8rZiydgT+of8COIzvwzJpnGv0grtaoOO49rlt3Se9LEt6nWUaiDP5nVoo2omBE3OXGuApdpo977IyxaboVzftLh5Ua9ieDJTLd23U3bJ9oCW4y/NKPtd+u1a2LnhjaHIfPxJ/u2FgmzNCzhuKOEXfAfam70RLGWFOHr+p/lWFbAZFQP79o0QM3QgMtooWGCiTjpPdkzL9zM1ZNCQWAo3XGgKSEDGeWJTsYpTGX9b3MuFJoE2sjNbsPELTgViDgBeBHIOA1DXbl50dn6dowZMgrcR/37LNjve4GAPhht3sxfHhoXxJHjmsXGrp1cyG6i8R55z0Vd1+xKE4F3S7YZHzO1IwCVszECF9y7w2JCl1AEja/7nmaSLYzUbMoCjBzpjaUwh7VjeXdd40129Rk8YLLZhcLm3Iu1K1dN0NGd58jsftMRrr/ivsNrT8kJB5a+pBh26FnDUWOLZEW3ERElG6NBtuklOsA/BbAAADrhRBvCCHuEEJMFEJ8L/j1XwBsCG7zeynlZ6k9bIoUXQqYSL8xK4XODxPtoxwqpwqLSrGPbKC+cf9GXPPqNQkfS/hKYhL80o95f12O+o8fRGD35TjtO91oSd8P//uHhnXJBDgUp6KdiAGGiVP1/nrD/qNLEuOVKMba37CCYYb1ZuUR0c37o5cT3Z9ZSZ2n2mOaedmrfa+k9xEpVnnoKd8p/XL9KdPtkqXWqDhdb+xNFSleJkwomFSxrgIvbngRm7/bHPex5q2cp5s6HAoUdXB00G3Xp1MfrJy2sklXtaMz60IDLRrbDoDhORytW7tumDi2S8y/8+qj1brtrZoSCgAX5sfO8qtYV4Hp707HBzs+wPR3p1sScDN7Lm6v3W64CLD4q8XN2s/RoyrOnNkdzEKzw2ZzBINdeh07DkVDAEzg/PMXNDroYODAcnTvPjHGvXb4/Q5s3Dg2vGavb2tEQDR0WiHgdJahb9+mlyMOLTqhf84AwQ+uj+OnPx6Skso6RQHmv7EZhUWP4rbhxRiF1XGn8xJZTlGA557TB9y++UZfs00pE6tNxH3v3Ydb37w14YzrLQe2GDK6v+9I7OKDu8htqFoBtCy76PcpT7UH9f56ANr57H3v3ZfWnqBERBRbQl2LpZQzADwW3P4WABUAFgN4P/j1j6Cdzc8CYHx3oJSKvuLe2q/Ar/42Yhph6EM6ELOBemgyYiJc57qSPyCTXlKRo+DNRAf1BETSAY7CboUx+1hFBzQiSxKb2qz74MmDhnXLdy83nJS1y20XdzlRJYNLdD3Tcu25cBW64C5yN/RjChqS3/jAgXjMMgUBoEOOPhg1vMBYNtsUnmoPRCNT4uL9HXqqPajz1SGAAHwBH+7+x93h53h0+aRaoxqyokKiGzc77I4ml4+YTVuMNYFRFzgyeQ5H9wicefVMLLltCSZfPxCdi581TCT99ti3uuXm9g2MNLfYPAi+5eAWQyDZigsVZhlre0/sNaxrTm9NbWro1di793lIWY8+fe7EsGFVpkG0ffsq0dBHTeL48cT6PQ4btgQ2WyfD+q5dr8QLL1Rhy5aIfdm1htzavurD+/L5jJmzyZg7Ya4+uyPig2udVybdIzRR7fY+hy3rn8SfP1uNZS8Dxz5O/D2IyBJuN7BiBXB2VFn6S8lf/KLkKE4FP774x/qVNaNQ//GDeP29r/HwRw9j7MtjGw1oDeg+QJfRLXL8KC05J+Hj0PWujPD06qd1y65CF2wRff78MvnBVkRElBoJjwiTUj4OYBC0gNoyAF8A+BKAJ7jufCnlo1KajVOiVAs1AW+saXsqJNNbSa1RsXFfMOMn+kP6Z/8es4H6z97/WULH0i2vW/I/gEkvqb9+/lfc+LcbY/5MfTr30S337Ry/f5eZUWePitq3A6h2GQY4ANAF/5rarPsnl5g3RG+sUXuPdj2S3hegb/gvIDB1+NRwIGhu8Vzk2fMgIJBnz4sZLEuU4lRMA3Z9OvcJ/23k2nJNrxQ3RSgYFEuPdj3iZsJEnxwHZAD3vXcfKtZVGMonPdUeRL6s2oU9/PvqmNtR97jRy8moPVVrKP01C+oaTuJN/n5G9h2JhZMWYuKAiVg4aWH4d/HaTa/h2MxjGNRD39j+7C76D5Sbv9uMoWcNxfcHf7/ZU0IB89fF5buWY93edbp1VlyoSDQQ3pzeml9//RAaAmgBnDy5JWa22rFjq+Mux9O7962GdUeProDTGVoKPi97bEPPDj2btS8zilPBWR3PalgRVYqcaI/QZBVVbUGeX7uCmOcHSjelP2OcCIoCXBZVlt43+XMNSt722u0NCyYXlOoD9Zjy1hQM+q9BMfu5Tb5ksq5y45cL30+4AiSeyH7BoXOEnys/h13YISCQY8vhgAQiolYiqciMlHJXMKA2QUp5kZRyiJSyOLhuZ6oOkmJTa1Tc+9694QBN6EN7ulLIk+2tVPpWRFAl+kP61ptjNlA/5TuFK164otHjiZWJEz2RUMekl5Rf+vH2F2/HvHo5+ZLJcZcTUTqsFGhfC0g7AKndtteyzyIDYGqNir98/hfd9zalWXf5hHKc0zX+VVW1RsUnuz7RrWvqBM/I7KR2Oe10ATXFqWDZlGV4cvyTWDZlmSXNfO+/4n7Duq0HtyKAgOVNg0MTOWMxuxod/f0/V36uW+cL+PDblb9Fnb9OVz7pKnQhLycPNtiQY8vBczc8F/45Jg2epHuM+0cZfweJMuvrZ5bhaQj0mvz9FHQqgLvIjSW3LTENOl6Uf5Fuef+J/eG/s1Bp59o9a/H2F283WmLbGE+1BwFpDGADWgbA8N7DkxqM0phY/RF7tOuBC3tdiCH5Q3QByKY4efKfcZcjnTmzK+5yPAUFZkFwidwOoRLY4M95qjdqT9U2a1+x3D789oaFiA+uw8setOSDq5k6k2E3rT1jnLJUWRmQm6t9nZurLVPK6fqSxhjus/3w9nBv2xlLZxguPIczz4PZucfylyR9HGfqja9F+07sQ8W6Ct359+9W/Q4BGYjZ9zdVrBhkRESUzRoNtgkh8oQQa4UQVUKI3DjbOYLbrI63HVnLU+2BP6DvBeQL+NKWQh4qh/NLP+p8dXH3q9ao+OpwxMTD6A/pFy4yb6AeLDVdu8bW6Bt6dFldyFXnGBvJh8XpGVcfqDfN/Pr7v/6uW25K8EtxKig559+hZagI7fa0sXeZWbCgqR/8rjvP2OstsndU5aZKXWadDbYmZ50pTgVVpVWYNW6WaXaS1dPv3EXumIFBv/THfG40VbwMpkQy6KKzMCUkvj78NQIyAJuwhcsnQ7/HJ8Y/geW3Lw8HadQaNVxOIiCaHSxSnIohQ/PNrW8a+8Ps9Oi/Mervx97/06SfM3tP7MXVf74aao1qeQ/KWMGvkI37N+Krw19h/ur5lnxgUJyK6TTYw2cOY1vtNuw8vBNDzxrarH3YbPrSbr//JHbtmmM6jdRmy4u7HE/Xrgpyc41/U76zFgWnkWqZbTkX/28wk0L/e05mX7GUTyjX/10HP7jefaM1JeFmcm+/A16bNg7CawNeH25r9r8ZUdJUVRuP+8c/ArNnAx9/nHhzXGqW/Sf3NyzEG+4TPD/9/d9UXHXvK3h46qUY8x+vQq1RDROomzKROtzbN0Kdvw7T352OeavmhXub+qU/PB0+EK0AACAASURBVB3crO9vKlg5yIiIKFslktk2GUARgN9JKetjbSSl9EIbpHB58HsoDVyFLkOJlISMmeFltZ4dejZk1SEQd78PVT2kb6QeHeQa+aIx6BWVvj/lmefjHo9Zw/+yK8swt3hu/Kt9UVOfIlWsr9CdRMxYOgPVR6p12zQ1+FVw0RdATvAkLscbPonr0q5LeJvo3+nkoZMtbda96+iu8BCK6N/fJb0vaVYwzOqAWmNG9TNv0J8K8Z7rb3/5dqPfb1YKHDpZ7tupL+ZfOz/8ezP7PUYOGpGQppNlkxVdanzad9owOMC0ZDr492Pv/6ku8y4Ws6CoX/pRuanS8h6UsYZ1RIscPNFcZhmGElK7KOGPf1EiEXZ756g19di582Fs2lRsCLh16TIq7nKy+5IAJg3dgt/9bgKGDFkFCD9+PupBDOkC+P2HdNu2b39+UvuKpfjcYsO6pvStTNShT5fDFqyQlUKbpMweSJRWqgqMGwf86lfAffcBu3e39BG1KbqMslgXZCPOT31/+hCBdxYAX0+E73+fxbz/Omx4n2tKlYDZOW3InmN7zNtZiBiDjCyWzMV2IqK2KpFg200Adkgp32tsQynl+wC2A/h/zT0wSoziVDC0t/GKeyo/iETvJxTsswlb3P2uWiWNPdmCH9KFcw1ybbnGoFdU+v729X3jTic9eEo/BKCgYwHKJ5RDcSpYOW0lxvQfg3b25Br+R09lfHPLm7r7e3Xo1eTgV+mkQXBMu95wEheZKVd7qrahJx9shhK8pPYXI+Pogx0fYMbSGTgTVT4Vry9Za1Q2ukw3lCHS+r3rLd1XvOd69HPEjCFDLMI3x7/BXe/epesFE1muodaohmmWTblqHq1kcAlybDmG9ZHZZWVX6n/HAgIlg0twV9FdWDF1RUJ/C6XDSk1Lu6t2VmHD3g3h++zCbklGUcngkoS223Iw9gebZLiL3Jg4wHyaZ0DGvyiRiEDAfBJuIFCHI0c8unX9+5cBCP2b5gSXm74vAUAIwOE4g+HDlwPSjj/850BsMglUduzYvMEnIbr+SUFN6VuZEFXF6PI3kCO1E6RcPzC2Oj0fXonCKiuBujpASqC+Hli4ECguRkpG8JKB2Xm1QeT5aSBU0KNd1N24uAilw0rhsDsgIOCwO5pUJTCgx4CY97nOdaGqtApj+uvbVkw4d0JaLnAmc7GdiKitSiTYNgLaEIRELQeQuvoOMtD1lghK15ueq9CFPHse7MKOPHtezA8kFesq4NtxpWnfi36d+2HltJX4+PaPMXv8bCyctLDh5CGUvg8fICTQ/mA4MGTGkaMPDp3fsyGzQnEq+Hjqx/hoykdon9MeNthgF3YMLxiOy/teHveqY+QHuyv66XvHXTMwdvCvMYpTgefXczBm8ipdVt3wPg1/Qq5CF3LtuRAQ4Ymezdlffod80/ueWvmUoWRhUM9Bptu2Vpu/26yflBlh7Z61lk4UNMsqDblpyE2Nfn9jQzUkJOatnIeKdRWoWFeBsS+Pxa+X/RquV1wY+/JY7Dqq74e180jz22Z6qj0IBIz9zSKzyxSngjsvvTOcKWoTNlx+9uVYMGlBwif4ilPBVf2Npd3bD23H8+ueD5/Ah7LdmivRxzDrj9NUj7kei5lN29yS5t69zYedAAH4fPogVNeuCkaMWI5zz52NESOWxxykkMy+pASEkOjf/18ABPx+iQMHooO9IkbPt+T17WL8W2lK6X5CPB7Y/RICWhafDcCEnpelLTuXyJSUgNeLlI3gJZ25EyKqIWJMjUehB7D5oL1S6F/rz0B7HZ42fBqmF02HZ4qnSa8hsaZpA8CxM8egOBVce961uvVVO6vSUtL5+ubX4y4TEVFiwbZeAPY3ulWD/QB4eSONzu9hLNVJV2ZbY325QhZtWWTa92JM/zGo+XkNFKcSLpVzF7lx7XnXaic6ztXAtfcDtgAgbcD7TwM1o/DnDX827EOtUbF5v76ZutmEysgeWCumrsCG6Ruw5s41xlKliJLXyCykyElQZsvJMpukGV0S6Av4ICHhC/iatS8AuDDf2E8K0K5MLt+1XLfOLKOkNWusv5eVEwUVp4JfjP6F6X2JZFJdN8jYP8/MnE/m4N737kV9oB4BGUC9vx71AWNF/57jexJ6vHhchS4IEb+5cugkPjT8ItRbLlm7j7W+sijXuS7LHqtyU2W4LNhqAweWw27vanrfd9/9zbCua1cF55wzM+lAW2hfOTkNvSQltMw2ALjqqrcB4UPAfhqOzvrXrC5drm7S/szoek0GX5fzD//Aksc2cLlgE0L38Xn8kSZMuSZqjtJSwBFx8dBm05ZTNYKXdELVEIVdC2MOSIBzNTDiz9C6O+rfN9sV7ERxZTFeWP8CXtn0SrOOo+QC8/OJfSe1CxzRg4ysukjVmH/u+2fcZSIiSizYdhpApyQesxMA69IDqFHRZV25tuZlPyUr4b5cJn0v5k4wv2rnKnShXU6w3PN0Ly3QFnGic9JrDHB5qj24fLcfD60ARtVoJWix0vbNjvnAyQMNG0RdyVy7xh4OMkRnVFiRYRGvke5DVQ+Fs7X80q/1vmuGeFdKz/j1f7rhf4MMEZkRaMbqiYLlE8oNwxBswpZQ75JEs5uOnD6iyzYTEFrJdRSzRsrJUpwKrux/pWH9S+tfAqAF2sa9Mg7Pr3sedf46XNn/yrhB9nikTCwQNaLPiKQfO1rpsNKYWYiRoodWWCKyT2WQFT/TWWf92HR9Xd03zX7saPn5/xb+OvIjZYcOJzHktmkQU76HgGOH7nsaidkmpfZUrXbxJeJ1+e9l7tRV1ImGnEQBADdzEimlmaJoWWyzZ2slpE88AVRVcUBCGilOBW/c/AZs566IPSBhWCWQUwcgdCFUe1/rf8HB8GTx5vbpLBttXvp/6JTWI9PssZfvWp7y7LZu7fXvl+1yM+t8kYgoHRIJttUAuCyJxxwJoPWlLGQxxanguRueM+2B1BqoNSo+3PGhthDRk63syrKYH9JD2Wd3Fd1lWkp6yndKVxKo1qg4uux9PPnyKHSqegizXx6Fp7vdklQQQBeIMVzJHIt5K+cBAGpP67MG2+e2T3gfiTp0uqHR+NYDW3X3RS8nS3Eqhh4fsRyra37T/XSKFywp6Fhg6WCJkJLBJdYHuyMCNEfrjsJua3h8u82OP17/R8PP2r1d9+btE9rf0Se7PjGsD5XxRQ5lALQT+s3fbTZsn4hzup6T0HZWTJFVnAoW3LAgZj+/ECsvUpQOKwVqFNPyo+h+e00Ru0TTjz17rCuXjrWvUDDtR5Nehey3Cu2EPlBfX3/A8D1NFSqlj3xd9vtyUlNRV1kJRJZSjxkDuK1/3SBqlKIAM2dqz7+ZMxloawGKU8GCu0pjTqwPX0QeuBThDDfhRw+cH54i39w+nYpTwZhzjOdsB04dgFqjml5823JwC8a9Mi6lAbcfXvRD3fLBUwc5kZSIKEoi0RkPgFFCiJGNbSiEKAIwGsCyZh4XJWnD3g3hPkf1gfq0pJAnylPtMZRTdcrthPIJ5XG/T3EqWDBpARbePVUrJRUSCNiBxc8ANaPCUxLVGhVjXx6LPe94cYO/Co9iFm7wV+H4+3lJHae7yI2FkxZi4oCJOHvoV4YrmXuO78Gtb96K497juu9zdnEmtR8z0f3iVuxeET5pubCXvuwzerkpzMprzUQPnGjtXIWumH2yUpWlV7mpUtcn7rrzrksoyFs6rNQ8+BOVVSlrrsCIgoZMKF/Ahw17Nxieh1aUQD5U9VD4dSRSqPfhO9veMdzX1NLcHu17JLRdvGlsyXAXubFi6gpc3vdy0/snDphofV+uGOVHe441v+S3a1cFNpt50vk338xv9uPH21dkUqKzg3Z7wKt/vW3ffrBl+1ecCjxTPCi5tjvsuQHY7BJ5DpGeiroh1gx5IKLM5C5ya+ehURPrw/1vnasB12+0c0X4Yc+BPvsNzb9oNLd4ruHcJr9jPoori7H7qHl+g9fvTemE0OgLfv6AnxNJiYiiJBJs+yO0vOj/FkLE/JQvhLgAwH8D8AN4zprDo2xgli0SmR3TGHeRGyVdH9c+sMIO+POATVqmxaItizBv5TzUB+rhgQteOOBHDrzIxetHz0v6WN1Fbiy5bQnyB39luJI5qOcgLN5uzEjZcXiHySMlp3RYqe5ESkLi3//v36HWqBjlHKXLdBrlHBXnkRKTaBnbTy6J1Yi9dVKcCn4y1PyYhxekZ25LvEEbkRSnguLcXxtKDM0CNJGBNQmJLQe3hK+ah1hRAvn1oa9N1z+16inMWDoD3x7/1nBfU0tzY/6eosoureztpjgVzL92vmlAdkD32FPfmsJT7QEKl5mWH1lR8nv0qIpA4ITpfadOfYGjR63LMIjeV2SJ6JHgfJ4v6kegOVNPG6M4Fbz1izKs8DjwxCyRuoq60lIgL0/7IfPytGUiatPcRW5Dy4gnxj+BhZMW6jcUAjm2HEMftdXfrkZzKE4Fz096vmE3EOjRvge8fq/pBTLtUERKW8q4Cl26ifVN7d9KRJTNGg22SSm/BPA4gHMAbBBCvCaEmCaEmCiE+J4QYqoQ4jUAGwAUAvhN8HtSRgjxsBBCCiH+GLFOCCEeE0LsEUKcFkJ4hBAXRX1fdyHEq0KIo8H/XxVCZEXn49JhpeFU8lxbbpNGjKfK21++bVhX2LUwqcco6Nwn5n1rvl0DABie54EDXthRDwfq0e6sD5LaR6SDpw7qSl4B4JPdn6B3p96GbROZPNkYxamgd0f9Y285uAVjXx6Lyne36zKd3lna/GyzRK6ytre3bzT7sDXS9d6LEH2ibJXSYaXIs+dBQCDPnpfw356qAksffch8wllUgGbrQX3p8IGTB3TZolb1aZx8yWTT9QEZwMsbXzas7+zo3OTSXNPMPpOpb+1zrC3TjheQtZKr0AV7/09Ny4+sCNAfOeKJc69ETc28Zu8jkX2d8AF59jzcMLSsWVNPExWqrEtZRZ2iAMuWAU8+qd2ydI+IoPVoDVU/LJy0EO4iN9xFbqyatgoT7XNgQx4gbfD5gD3/1E9y//aY8UJVc0hIvPPlO8ix5cAu7KY9Sbs4uli6z2ihjOMx/cegX+d+uH/U/ZzaTEQUJaEmX1LKxwH8Glqv4J8AeAHAYgDvA3gxuM4G4FdSyidSc6gaIcQoAHcCiB57UwbgQQD/Aa3H3HcAPhRCdI7Y5g0AlwK4DsC1wa9fTeXxppNN2CAgEmoEnk5v/PMNw7pXbkxuOlNpKYAcLwA/YK/TmtICWL9vPXwBH0bVAP+jrsZSFGMW/hMfoBi/6Hi4ycdsltG16+gu7D2xV7cukXLYRJn1R6sP1GPf54N1mU5il8uS/TXmsn7JtGpsPcwyrcacMyZlJ4GKU8GyKcvw5PgnsWzKsoT34/EAAX+u+YSzKcVA0YvAcPO/k8iryQBww6AbLPn5yieUx8yQO1FnzKIa2GNgk/cV6jWp6zdjktX3wKgHmryPWC7K112HiTtMpakUp4Lvn/99Q9AesCZA362bCzZb7EBkXV3zS1Ub25cQwND8c8LP++ZMPW1NVCiYg5lQkdk/BxFZK1T9EHmRSXEqeOx2F/IcAna7NjS271D9JPezO5/d7H2HBhWF+KUf1513HYrPLcaPLzYOzDlSdwTFlcUp7aO2+bvNWL57Ob45/g3mrZyn66VMREQJBtsAQEo5G8D5AGZB68n2BYAvg18/DuB8KeWcVBxkiBCiK4DXAdwB4HDEegHgAQBzpZSLpJSfA5gCoDO0QCCCJbDXAnBLKVdJKVUA0wFMEkJY11ymhXiqPaj310NCot5f36r6JkT3a+vXuV/SgQFFAcoqFgPFvwZuHxf+4Fp7qhYjCkbAVQ3YAsBorMZDmIsrsRo/GnNPk4+5fEI5OuR0MKw/cuaIbvm8nsmXqsZSH6g3vyMq0+n+HzW/HDJmv7AIo/o1v1y1JYTKPUKlgrm23LgTWK2Q8ETeCC4XkJsDAH7A5of93E9QdmVZw6CTjVOAdXfqs96C9p/Yb9mxR+varqvp+lO+U4Z1db7Ey8HNuIvc+Pj2jxv6qEU91/OHbEnJUAtXoQvtc9rDBhtybDl47obnUhKMNSuV7ZbXzZIAfdeuCoYNq8K5586GEMZAmM3WzrJS0sh9deig72HWK+cUepyqtLRstSWpKlBcDDzyiHabsomnRJQ1FAWYP197zZg/Hygeoz9/tOJ8qm/nvrplAYHFXy1G1c4q/O1ffzP9nlT3bYsOAEYvExG1dUmlQEkpd0kpH5VSTpBSXiSlHBL8+jEp5a5UHWSECgD/I6X8KGr9uQAKAITrBqWUpwEshzawAQAUACcArIr4vpUATkZsk7F6dugZ7tsQQPMmH1lJrVGx57g+wyL6hCFR5VNLMOzfFusyRCQkttVug6cQCNi05oICWq8K1NbGeqiE3HfFfY1uM+ps6wJS4wrHmd8RynQa/58Y88jjcJcMbfa+FKeCFVNXoGRwScwpthv3bmz2flpK+YRyrJy2ErPHz8bHt3/caksbbMIOIWywBwM+5RPK8cm0T+Couca0sX7IvpP7dMuR02ubq2Nux4S3Hdyz+dcpQn3UAOie65hSjJsnNu21IpF9VpVW4YnxT2D57ctTEtADYJotFxo2YYVQJlmXLsYs1KNHl2PDhtH49NPhlgTCQvvq1+9+3Xqf7wD27n0eGzaMzoqAm8cDeL2A36/dpmTiKRFlFVUFHngAqKrSbnds1rcF6dKu+SWdZVeWaZngwb6mvWq/D6/fC7/0IxAIGC6g2oQt5X3U2uW2i7tMRNTWta56wziEEHcCOA/AIyZ3h9IHotM99kfcVwDggJQNc9SCX38XsU3Giu7B1dzJR/FUrKvANa9eE04XV2tUzFkxxzRV3WwSaXOagyv9jEGT6qPVWO0E7r0eqLcBfgGgXTs0d1RdrOy2SIkOGkjEY67HYt8ZLEVzXvyNZftTnArKriyL2Vx3eJ/0DBRIlaZkm6WTxwP4fICUAgjkoHarFkRVnAoeuGW4vm9b+4PGQQoRth/abrq+Ke4fdX/jGwVZ1QdPcSooGVyiLQSf67b+a1PaezJdz4/oYLYVU2OjDRgQO3Pz5MlN2LhxjGWBsL593Tj//IVwOPoZ7vvXv/6fJftoSS6XVgYWKgdLy8RTIspokUH6Oq/EB1Ve3f1WXLxUnArG5Twc7mt6YMFfIGuuAADk2nPx4OgHYRd2CAjYhR0j+4zE/Gvnp/Q9rke7HnGXiYjaupzGN2l5wTLP2QCullJ642wqo5ZF1Lro+822Ce3TDcANAP3790/qeFvCvhP74i5bpWJdBaa/Ox0A8MGOD7B813K8ufVNeP1eOOwOVJVW6d7YzTLsmtMcPN7VwRdHAp/3BmbUX46Su+Zb0ti6U14nrXyuZpSWXVTo0WXW1Z5qXvZcJE+1BwLCEJyM9OGODy3bX2ifsVgx3ZJiC32o93qNH+rLp5YAeBvzXl+jBdref1rLdLN7dY32Q07Xn7bsuNxFbizasggf7Ig/YKSwW6GlJ/FlV5bhva/eg9fvhV3YU1bamU6mFxtS8HfVtasCm609AgHz54GUPhw54rGsl1rfvm7U1i5Gba0++O/1WtcnLhZV1T7YulypmV2gKFp2Sir3QUTZJfL9XBtstEx3v1UXLz9ZnhOR9S6BaheEcw2mDp+KbnndEJABSEj4pR9r96zF+n3rASBl2dvVR6t1y9FTWImI2rpMyWxTAPQC8LkQwieE8AEYC+Ce4NehiEd0htpZaMh22wfgrGB/NwDhXm/5MGbEQUpZIaUcKaUcmZ+fb+1Pk8HmvOHRZdi8vvl1nPGdgV/6UeerMwRvzIJRzWkO3tjVwbX9bej9hDWBNiA4NdVkQmKIlen5rkJXo8MtzJrUN3ef0c32AeumW1JsoQ/1s2Zpt9FP2fKpJTjvB/8DnO4Vt6QUAC4puMTSY1ty2xL06RR7AjAAzLxqpqX7DE02mz1+NlZMXZGyDwfp1LNDz5RMjTXTqVP8gSbdulm734Z30kipvX6Xrn5qKZ94SkRZJfR+fuedgPPq6E431l1ksQ9YYZhW7rA7UDqsFEfqjjS83wRLTX27RuK+9+5L2ZCEPcf0F1i2H9puui+1RsXd796Nu9+9O6UDG4iIWpuMyGwD8DaAz6LW/RnAdmgZb9ugBdO+B+BTABBCtANwNYBfBrdXAXSCFrgL9W1TAHSEvo9bRopuxG3WmLu5Kt7ejOr/etGQYRN6czfrFXekTj9Q4PK+lzerOfjNQ26Om3FzSe9LLM2GOXT6EFD9b4YriXCutnzCpeJUUNSnCGv3rI25jTD/hNusfXqmeFC5qRJbDmzBgVMHMLjXYJSNLsv4rKJMoCjxP9D3aN+jYWiAX4ZPrqOlYgBE7069DdN3QwZ1H5SSYJjiVLLqeVd7qhY22BBAAAICd4y4I2U/38CBc7FhQ/raj+bmGt9junRJ7b+dWT+1Vh0QS3UaHhG1Kq+8Apyp+x5gGxM+R7YLu2UXWToO+CeOTynWVVpc2Gu41n7i/eDk7tAF4uC5ev2UYniqPSl577mg1wX4bvd3unWVmyp1+1JrVIz+U8N7U8W6Cnwy7ZOseq8nIoolI4JtUsojAHRRGyHESQCHgpNHIYSYD+BXQogvoAXffg1tIMIbwcfYKoR4H8DCYP83AWAhgHellF+m7YdJkejeYVY0Y42k1qiY8eIKwP9zQ9ApxCZshky2F9e9qFv+6tBXzToOd5EbP3v/Z6aTEQHgyOkjpuub6op+V+Ark2CHgEhJgOOOS+/QB9uiyldLLiixfJ/ZFuDIJtrzYbp20m5SxgwA7eztUvLv57BFZTxGPBf7nGPMhiQjV6ELeTl54TL7VPag69pVgdNZhpqaeab3W1lGCgAFBaXYu/cFAP7gGhsGDkzt1N94pdetTigNL3SwZumrRJQ1QhcDZMAOyNzwOfItF99i2Xt0j3Y9sM+5WncesHH/Rqg1asNwgmqX4QJxqso7506YqwukAcCWA1t0y1PemqJbDiCAe967Bxump663NBFRa5EpZaSJmAfg9wCehZYF1wfARCnl8YhtJgPYBG1q6ZLg17el+ThTovZULQQasp7+oP7BslTtGUtnYPSfRuNIwVuG9PVIAsKY2XbmSNzlpjArewypPW1dDzUAuCj/ooYJiUUvAsNfAQD88spfpiTA4S5yY+GkhWhnb2coX7V/cxVeu+k1y/dJrVfo+XD5FQH0nPiCIdAGoNHS46a649I7GhainovbNraOacetXWjq6axxswz9LFPB5zsW877cXOv/zUR4+p0d55+/wNJgnpnGSq9bFY41JWpTXC5tsAqEH7D5w+fIK2tWWraPWAOMPNUeDOk1RFso9AA2H4CG4/jL539JSfmm4lQa9ht0xndGtxzd1w0ANu3bZPmxEBG1RhkbbJNSuqSU90UsSynlY1LKPlLKdlLKsaGst4htDkkpb5VSdgn+f2sway7juQpdsNsaxn7XB+rjNr9PVMW6CsxbGcyUCAWdxv+naZN2v/Tjgfcf0L2h59pzdds0ZxJpSGG3wpj3+QP+mPc1ha6P2sYpwLo7ISqXoaRj00thG+MucuPp656OujqZi8HHM7+HFSXPXeTGmjvX4J0fv2N6/40X3piy/S6ctBCdcjsZnouBHVenZJ/ZKJ1TcevrYw3GsaG+3toLEUeOeCBlw+ut1Y8fS8b0U+NYU6I2RwgAUt/u44TXul677iI38ux5hvVH6o7EzZyWUlrymcBMdGXNoJ6DdMs5wlhEJSFRsa4iJcdDRNSaZGywjfQUp4IfXfQj3Tor0safXvO0foVzNXD1XNMMG0C7ohV6Q5+xdAbq/HW6++dMmNPsYxrVb1TM+6wOPChOBQtuWABRPS4cbBCBvJQnKbiL3Jj8g366TML7b7FmmhVlJsWpYNW0VRjeezjswg6H3YHJQyenNNvRXeSG89gPgaP9tSvlwefi7Teem7J9UtPV1x+KWmMDYIfNlmf5gIRu3Vyw2RzBx3dY/vgZL6PS8IiouTwewOcDABsQsIeHGV1acKml+zHLbvv753+H4lSQawuWrwZyANh1x5Gq4TwHTh7QLa/5Zk34ortao+K033xK9qIti1JyPERErUlG9GyjxGyv3a5bfmPzG7j3snubnFGh1qjYsr4LUP2Qaa8oMxIyXEr65pY3dff16tDLkqbqpcNKsXDdQt2UP0AbvpCKwIO7yA3ctxn3fSLh90nkOURakhRe++m9GNN/MxYtrsXN1/WEu2Ro6ndKrZriVLDhrvT1OVFVYNsfngPq7VqwrehFXFj8GcqnvpS2Y6DEBQL68p0OHQajd+/b0K2by/ISz65dFQwbVoUjRzwpefys0NgEFCLKGqFk1tN1PsBmPszICuUTyvG7Vb+DPyKzeP/J/QCAvp37YpdJn2EJic3fbbY8w1qtUbWy0Yierl9jDYori1FVWoXKTZUxv/fmITdbeixERK0Rg21ZxOv36pYlpGEqUDIe+vP/6iYamZWOAtDeZDcF09eHVWLDXi0YcNOQmxpKUAFMGzGtSccRTXEqWDltJe75xz3YenArurfvjt+4fpOS6Ygh7pKhGLos/YPl3CVD4bZ+JgJRQjwewF9v18pHAxLouhudB37e6PdRy+jT5w4cP94wYKVfvwfQt2/qXhe7dlUYZCMignZeOH8+MKfia1T3eSp8vpyKoFKH3A447m1oSZ1j0z7O5dpyG1q+RA1V+tn7P7P0PFmtUVFcWYzTO4cDLy8D/LmAvR7y9nGoc66Fp9oD9Rt9nzi7sKOoTxHuuPSOlJ6zExG1Fgy2ZZHDZw4b1u07EauHT3wV6yqwfLkwTDSKDLZNHjoZr7/3dfBNNthDYsNUVIgJeH2zcRrqwO4Dm3QsZtKd4QMwSYHaHpcLsOX6EKhvuELet3Pflj4siiEUWDtwYBHy829OaaCNiIgaqCrw0/v9qKsbCNifBnr/JaK3HwAAIABJREFUC2W3XJ2SoNKIPiOwfNdy3TIQcZE7amIpAJzyncKMpTNQPsGansOeao92kX/TbcHPAALw24BNpQg4V6Nnh57YfXS37nu65HXBmjvXWLJ/IqJMwJ5tWaSjo6Mlj6PWqLj3vXu1K2Im00dtwoaFkxbitZtew1kHfqhdzYLQ/vfnIrDzahz3HtdddQPYn4Eo4/RTYZsyMTwUxd7/U5RdWdbSR0Vx9O3rxrBhSxhoIyJKI48HqKuDdoHa5wCqx2LbwW0p2dfc4rnhbLYcWw7mFs8FoJWYThwwUas4WfGQdhthwdoFlh2Dq9AFh90R8/7FXy02DHMwG+5ARJTNGGzLIpPOn2RYV9CpIOnH8VR74Av4TKePllxQgk+mfhK+Und7yblaIA5S+9/m05qp1xiHGAzvk9kN/lUVmDNHuyVqCzzVHgTOXhl3KAoREVFb17MnAGmDdj5sB850wbba1ATbFKeC5bcvx+zxs7H89uW6djGPnbcEOa99DHw0S2sFE3E+frz+OGYsnWHZMVSVVmHM93cB9joAfu12mNan7YOvPsD5vc7Xfc8oZ+wBZ0RE2YhlpFnk2JljhnXRI7kToZtiGpGK3jm3M9760Vu6bcunluCdL+/A1qqRwInewPbrgXV3AhunGHq8mR1fplBVoLgY8Hq1BrgcLkdtgavQBZvNhkAgAEDrA+mp9ljeZJkoUaqa/t6ZRESN2fD1LgBnQ/toJQH1QThcNSnbn+JUTN+LPR5A+hxazM+kBcy8lfNQMrik2e/jFesq8NL6l9B3cF+I24shq8foesSd8p3Cil0rdN9zfs/zTR6JiCh7MdiW5ULDChKl1qh4ffPrpvf16NDDdP2WuS9B/EZoKetf/iBmj7ctB7ckdSyticejBdr8fu3W4+EHPcp+ilPBs9c/i/veuw9+6UeePQ+uQldLHxa1UYaLHvM3Q6l91/LIm1qjwlPtgavQxcAyESVkX/7fANsD2jAhCEDasP/zC9N+HKGpqHVeiYAwn4ra3ItmhfMLsevoLm1hDwAnAOcqw3YSUre8ce/GJu+TiCgTsYw0i5QOK4WA0K1LdkDCPe/dE/O+h69+OOZ9XRxd9D3ebH5DOemBkweSOpbWpGdPwGbT/nc4tJMZorZgqNeNG77chZHrPsX8i9Yw+EAtRnfR44wfnnv+DjzyiBaBs6i+PzRh75Flj6C4shhqDfsGEFHjDvX8B3D9vYCtHhA+wO6Fr//StB+HomjVF0/MErh85sOmLSCeWvVUo4+j1qiYs2JO+DVQrVFx97t346zfntUQaIvQs13PRh8zFZNZiYhaM2a2ZRHFqeCS3pdg0/5N4XWHzhxK+PtnLJ2BjfvMrzpd2OvCuBOVfjvxt5j+7nStdHRTKbBhqqGcNL9DfuI/TCuiqsADD2gf8Gw2bbQ7s9qoLVBVLbDs9fYB0AeblgBDl/H5Ty3D5QIcOX54/QE4ZD1c/qUArE03Dk3Y80s/vH4vy6aJKCEHTx0ERi4Hen+uVXYUejDisi4tciyKAqCfCo9nK7Aj6s6aUThU7ULhdz/G9JJLTDN4QxcdTvtOAzWjIKrHQxZ+FLd36+wJs7XPATE09jmCiCgbMdiWZaIn/ZypP5PQ96k1Kn678rem9+V3yMeWe+OXgIbeQF9a/xK+3n8KtYEcQznpkPwhCR1LaxPKpggEACGA2tqWPiKi9PB4gPr6hmWWUFNLUhSgaurr8Cz8Ei75ERSs1l6ULUw3Dk3Y8/q9cNgdLJsmooQ4coKTOSN6HQMTW+RYQsGyOl+d/o6aUdrQBL8Duz724uGjE+AofAzXn3c9CjoVYESfEVjw6QJs2r9JKwENbi/9DsD+K+3iORAOJoZ+TofdAXeRGz9f8nOcrD9pekyX9rk0dT8wEVErxWBblrnj0juwds9a3XIiPNUeQ2+FkP+95X8Tegx3kRvuIjfUi4HRS04BPqlNKg32iygdVprQ47Q2of4XoT5BLCGltsLlAnJztec+AOTk8PlPLUspHQTllbu0J6XdAUybBpSWWhYBDk3YY882IkqGw+YwrGupsslQhm4AAdhgQ4fcDjhRf0ILkvkdERfDx8ILibc9FwSDZ8/rHyh6+02lWsWK36G1jQlWrow6W2sZU3JBScy+z4u3L07pz0xE1Box2JZl3EVuLN+1HG9tfQtd2iWevh7r6n1TJhYpCmCfcg38O68KX/myC3vGfmgJ9b/gBDxqaxQFeOYZ4J57GjI7iVpUGl6QY035IyKKJfpi9+Shk1usbDI6Q/d31/xOK/EM9Vb2By+Gtz8YznSDzQeM+DMwrLIhMy9ye5sf2DsiKljngs25FnMnzAUAvHbTa1i0ZRHO+I1VNcfqjqXvF0BE1Eow2JZlKtZVhK8qnTpxKtw/oalv+GVXljXp+4ou92Jtv7kNy32KmvQ4rUXo85zHo18mynahsmkptb6FLCOlFqcofBISUasSOs9etGURbh5yc4v2JzPL0F20ZRE+wAdaNlqoDFSXuWYHPrtL67l8+7iGctjIXszfjgRgDw6AqEeHQZ9i6bRPdBcnnr7uadPebed2OzddPz4RUavBYFuWWbRlkem6xt70562aZ1g3qPugJl/dL7mgRHeFr+SCkiY9TmuhqtrAu1ApaVUVP+tR28Ayamp1VJWpxkTU6oTaqbQG0Rm6S25bgj5P9cE+XU85aBltfntwQQD+PC24FtrGuVoLygVyoH1s9AEDlgKux7H04d8ZPieEfv5Hlz2KfSf3BR9V4JUbX0nND0pE1IrZWvoAyFpm/SES6RlRtaPKsK45b4yuQhfa57SHXdjRPqd9eppMqyowZ452a7HQkAR/xOA7orYgVLU3axaDzNQKhK58PPKIdpuC13siomy09xd70d7evmGFc7VWOgoJIHafiK4XbNTKSUU9kOPFmCkerHrUGGgLcRe5sfcXe7Fq2irMHj8bK6etZGk+EbVJzGzLMu4iN74+/DXmrdQy1ezCjqFnDY37PRXrKnDce1y3rr29fbPeGNPeZDrFqWfM7qG2jFV71GqYXfngk5OIKCFVU6ow+k+jG1YMq9RKRP152rLNq60DMCR/CO6/4n64i9yomLAZixbX4ubresJdMtfkkY3Y/5KI2joG27LQttpt4a/90o95q+bhrR+9ZdhOrVHhqfagYn2F4b6u7bs2+zjS+iab4g9gHJJARNTy1J6T4BGn4bJ9BMWx3vzKB8tMiYhMKU4Fq6atwpS3pmD74e1adtvt44BNpci1OWAb/houGHEaC25YpTuHd5cMhTuzO8IQEaUdg21ZaM+xPXGXAS3Q5nrFhXp/PSSk4f7QGO+MkYbUM2b3UFsUCsqnJUOVKA5VBYofGApv4GI47I+gav4XUJShJhuxwSYRUSyKU8G2n25DxboKvLT+JfS9oABljw4Pvsff0dKHR0SUNRhsa6MqN1XC6/fGvP+6Qdel8WgswNQzIsupNSqKK4vh9XvhsDtQVVrFgBu1mHACc0DAK3LhqR0Kw7ORZaaUqUIZmT17amOgeS5DKdaaBjoQEWUjBtuy0NaDW3XLm/ZvMmyzdMfSuI9Re6rW0mNKC6aeEVnKU+2B1++FX/pxxncGlZsqGWyjFpNQAjMbbFImCmVk1tUBgQBgswF5eczMJCIiymCcRpqFenfsrVuu89ehYl1DX7aKdRX46vBXMb/fLuzpmR5KRK2aq9AFu80OAJCQeGnDS1BrOP2RWkZCk3E5PpcyUSgjMxDQlgMBjj4nIiLKcAy2ZaEJAyYY1i3asgiAVhZ217t3xfzeXFsuVkxdweyVKGqNirsXVOLuGbugMtZAbYTiVHD9oOvDy/WBelRuqmzBI6K2TlGAmTMbiaEltBFRKxLKyLQFT8ttNsBuB3bvBk86iIiIMhODbVmodFgp7MKuW3fzkJsBaGVhZgMRQvzSz0BbFLVGheuJmXj+p/+G5397NsaN9/Pcl9qMgo4FLX0IRETZLZSR+cQTwMKFgNsNCAG88IJWXsqTDiIioozDYFsWUpwKHhz9oOl9rkIXbHH+2TvmdkzVYWUsT7UH9V9fCfgdgMxhZQe1KaXDSuGwOyAg4LA7UDqstKUPiYgo+4QyMt1uoH9/wOfTD/ogIiKijMJgW5Z658t3dMvzV88HoAXifjD4BzG/76mJT6X0uDKRq9CF3IErAbsXEPXsuU1tiuJU4JniwZPjn4RnioeZr0REqRYqK7XbOeiDiIgoQ3EaaZY67TutWz585nD460NnDpl+z8QBEzN3BLiqald+XS7L+/QoTgWeX89B5fD/AarHorTkHLYCojZFcSoMshERpYuiYPMb81G7eBF6XnczhvKkg4iIKOMw2Jal+nftj+oj1eHl/Sf2Q61RoTgVnKk/o9s215aLnyk/Q/mE8jQfpUVUVetp4vVqV4BTMIFOcSpQ7ubJLhEREaWWWqOi+F8PwHu2F45/rUBV0VBe8CAiIsowLCPNUj3a9dAtS8jwFMFu7brp7htXOC5zA22AltHm9aa8t4mqAnPmsE8xEVFL4msxZTtPtQdevxd+6YfX74Wn2tPSh0RERERJyohgmxBiphDiUyHEMSHEASHEO0KIi6O2EUKIx4QQe4QQp4UQHiHERVHbdBdCvCqEOBr8/1UhhD7ylCUKOsWeILhx/0bd8o7DO1J9OKmVht4moeS5Rx7hYDAiopbC12JqC1yFLjjsDtiFHQ67A65CV0sfEhERESUpI4JtAFwAngMwGsB4AD4AS4UQkelbZQAeBPAfAC4D8B2AD4UQnSO2eQPApQCuA3Bt8OtXU33wLWFEnxG6ZQGB0mGlqFhXge9Ofqe776YhN6Xz0KynKFrp6KxZKSkhBdKWPEdERHHwtZjaAsWpoKq0CrPGzUJVaRVLSImIiDJQRvRsk1JeE7kshLgNwFEAVwJ4RwghADwAYK6UclFwmynQAm4/AbBQCHEhtADbVVLKVcFtpgNYIYQYLKX8Mm0/UBrUnqrVLUtIbP5uMx71PKpb39nRObNLSEMUJSVBthCXC8jJAQIB7ZaDwYiI0i+UyBxq0cnXYspWHExDRESU2TIlsy1aZ2jHHhqxeS6AAgAfhDaQUp4GsBxaNhwAKABOAFgV8TgrAZyM2CZruApdsAn9P+/jHz+OfSf26dbl5eSl87Aymt8PSKndEhFR+qUhkZmIiIiIqNkyNdj2NICNAELdWkINyvZHbbc/4r4CAAeklDJ0Z/Dr7yK2CRNCuIUQnwkhPjtw4ICVx54WilPByD4jdeu+Pf6tYbtLCy5N1yFltMpKwOfTvvb5tGUiIko/RQFmzmSgjYiIiIhar4wLtgkhfg/gKgA3Symjc4xk9OZR66LvN9tG21DKCinlSCnlyPz8/OYccovZVrut0W2+Of5NGo6EiIiIiIiIiKhtyKhgmxDiDwB+DGC8lDJyhGaoNjI6Q+0sNGS77QNwVrC/W+jxBIB8GDPisoIv4NOvqBkFrHhIuw066T2Z5qPKTKWlQF4eIIR2W1ra0kdERERERERERK1RxgTbhBBPQxt2MF5K+UXU3TuhBdO+F7F9OwBXo6FHmwqgE7TebSEKgI7Q93HLGpf2jSgRrRkFvFIFfDRLuw0G3KKnlpI5RQGWLQOefFK7ZfkSEREREREREZnJiGCbEOJZAFOhZbUdFkIUBP/vBIR7r80H8JAQ4iYhxMUAXoY2EOGN4DZbAbwPbTLpKCGEAmAhgHezbRJpyNziuQ0L1S7A7wBkjnZbrQ1QKBtd1mLHl2nYJ4iIqOWpNSrmrJgDtUZtfGMiIiIiohaQ09IHkKB7grdVUet/A+Cx4NfzALQH8CyA7gDWAJgopTwesf1kAP+Fhqml/wfgvhQcb6ugOBW0z2mP077TQPuDgLQDkNpt+4NYcMMCjpUnIqKModaoKK4shtfvhcPuQFVpFd/HiIiIiKjVyYhgm5RSJLCNhBZ4eyzONocA3GrZgWWAHFvwn/h0LwB+aP/kPji8Z8Nd5G7BIyMiIkqOp9oDr98Lv/TD6/fCU+1hsI2IiIiIWp2MKCOlpvvB4B9oXxR6gBwvIOqBHC+GXPZdix4XERFRslyFLjjsDtiFHQ67A65CV0sfEhERERGRQUZktlHTvXbTa9heux1rsRqYUgxUuyDOXY7npj/V0odGRESUFMWpoKq0Cp5qD1yFLma1EREREVGrJLTqS4pn5MiR8rPPPmvpw2iWinUVeGn9S+jbpS/KRpfxAwoREREREVGQEGKdlHJkSx8HEWUHBtsSkA3BNiIiIiIiIjLHYBsRWYk924iIiChjqCowZ452S0RERETUGrFnGxEREWUEVQWKiwGvF3A4gKoqQGFXBCIiIiJqZZjZRkRERBnB49ECbX6/duvxtPQREVmIaZtERERZg5ltRERElBFcLi2jLZTZ5nK19BERWYRpm0RERFmFwTYiIiLKCIqixSA8Hi3QxlgEZQ2ztE0+wYmIiDIWg21ERESUMRSFMQjKQkzbJCIiyioMthEREVHGUFVmtlEWYtomERFRVmGwjYiIiDIC21pRVmPaJhERUdbgNFIiIiLKCJxGSkRERESZgME2yh6qCsyZo90SEVHWcbkAux0QQrtlWysiIiIiao1YRkrZgbVFRERtghD6WyIiIiKi1oaZbZQdWFtERJT1PB6gvh6QUrvlSz0RERERtUYMtlF2cLm0jDa7XbtlbRERUdbp2RMIBCQAiUBAomfPlj4iIiIiIiIjBtsoOygKMH++Vko6fz5LSImIstCGr3cBIgBAAMKvLRMRERERtTIMtlF2UFXggQe0Xm0PPMAhCURE2ajwY8BeB4h6wO7VlomIiIiIWhkG2yg7sGcbEVHWK500CI5p10OMfwyOadejdNKglj4kIiIiIiIDTiOl7OByaf3aAgHtlj3biIiyjuJU4Pn1HHiqPXAVzoHiZMsAIiIiImp9GGyj7BEIaCPqAoGWPhIiIkoRxakwyEZERERErRrLSCk7zJsH+Hza1z6ftkxERERERERElGYMtlF22LMn/jIRERERERERURow2EbZ4Y474i8TEREREREREaUBg22UHYYO1QYjANrt0KEtezxERERERERE1CYx2EbZobIS8Pu1r/1+bZmIiIiIiIiIKM0YbKPssG9f/GUiIiIiIiIiojRgsI2yQ0FB/GUiIiIiIiIiojRok8E2IcQ9QoidQogzQoh1QoirW/qYqJlKSwGHAxBCuy0tbekjIiIiIiIiIqI2KKelDyDdhBA/AvA0gHsAfBK8XSyEGCKl3N2iB0dNpyiAx6P973Jpy0REREREREREadYWM9t+DuBlKeULUsqtUsr/ALAXwN0tfFzUXIoCzJzJQBsRUTarqGjIZBYCuOIK/f233gr07KndNvXxhwwBLrpI+/qaaxr2lU3/d+gAqKr+Zzf7WXNzAZst/mN16QKMHQs4ncCMGcZ/j+jtCwvj//5zclr+99PY/336aMeaicfelv6Pfn0gIiJKEyGlbOljSBshhAPAKQA/llL+d8T6ZwFcLKUca/Z9I0eOlJ999lmajpKIiIhMVVQA06cb119+ObBmjRbYef31hvWTJwOvvdb8x89mq1ZpF6muuQb44ANrHrOsDCgvN/57RDrnHKC6Wr8uE3//CxcCbndmHntbEXp9IGqEEGKdlHJkSx8HEWWHtpbZ1guAHcD+qPX7Aeg66gsh3EKIz4QQnx04cCBdx0dERESxLFpkvn79eu128WL9+ujlpj5+NvN4tNsVK6x7zDff1G7j/f53m3TuyMTff+iYM/HY24rQ6wMREVEatbVgW0h0Op+IXielrJBSjpRSjszPz0/fkREREZG5m282X3/ppdrtddfp10cvN/Xxs5nLpd1ebeGsqJtu0m7j/f779zeuy8Tff+iYM/HY24rQ6wMREVEatbVg20EAfkRlsQE4C8ZsNyIiImpN3G6tbC83t2FdZInYa69ppaM9eiRfQhr5+BdeqPVtW7gQmDjRuuNvTdq3byghBYAlS8x/1lAPsng6dwbGjAH69WsoIQUa/j2imZWQAg2/f7s9qR+lRRQUNJSQApl17G0JS0iJiKiFtKmebQAghFgDYJOU0h2xbhuARVLKmWbfw55tRERERERE2Ys924jISjktfQAt4PcAXhVCrAWwEsBdAPoCeL5Fj4qIiIiIiIiIiDJemwu2SSn/JoToCeDXAPoA+BzA9VLKXS17ZERERERERERElOnaXLANAKSUzwF4rqWPg4iIiIiIiIiIsktbG5BARERERERERESUMgy2ERERERERERERWYTBNiIiIiIiIiIiIosw2EZERERERERERGQRBtuIiIiIiIiIiIgswmAbERERERERERGRRRhsIyIiIiIiIiIisgiDbURERERERERERBYRUsqWPoZWTwhxAMCulj6OVqIXgIMtfRBEacbnPbVFfN5TW8PnPLVFfN43OEdKmd/SB0FE2YHBNkqKEOIzKeXIlj4OonTi857aIj7vqa3hc57aIj7viYhSg2WkREREREREREREFmGwjYiIiIiIiIiIyCIMtlGyKlr6AIhaAJ/31BbxeU9tDZ/z1BbxeU9ElALs2UZERERERERERGQRZrYRERERERERERFZhME2IiIiIiIiIiIiizDY1goJIWYKIT4VQhwTQhwQQrwjhLg4ahshhHhMCLFHCHFaCOERQlwUtc2vhBArhRAnhRCGemEhRL4QYknwMeqEEDVCiGeFEF0TOMaxQoh1QogzQogdQoi7ou4fI4T4PyHEt0IIKYS4PcGfPU8I8YwQ4mDwuP9PCNEv4v5hQoi/BI/1tBDiSyHEL4UQfC5nOD7vYz/vg9sUCyFWif+/vXsPtqss7zj+/XEplDvB0sglE5ThLoOA0CiXCFqgBUuBoaI4Ax0BdWgtFRymdmyKtwIjhFELiFpGLgWhWlvacmsooCZtCbW1LSnQwgQx4RZICJdw8e0f7zpksdnn5Jxk49n77O9n5p2Vvda73mets5+cvc+73vWu5NkkS5Kcn2SD8bSv/jXkeX96kjuSPNPsN3OUekckmZ/k+abuP46nffWnYc35JNOa3/OLmnN6JMmlSbZp1VmvaXdxE3tJkquTbL+m9tXfhjXvm/2uSPK/zTk9keT7SXbvqLN1kquSLG/KVUm2Gk/7ktSv7KDoT7OBPwfeDRwGvALcnmRaq86ngU8Bvwe8C3gcuC3J5q06GwHfBeaOEufnwPeAY4BdgFOAw4Erxjq4JDsBfw/8CHgn8CXgK0mOb1XbDPhP4JPAC2O112EucDxwEnAwsAVwU5L1m+37AU8AHwH2BP4E+Cxw7gRiqD/NxrzvmvdJ9m5i39rE/iDwAeDPJhBD/Wk2w5v3m1Bzes4Y8Y8FrgOuauLPAr41gRjqP7MZzpzfDti+Obd3ACcDhwB/2VFvHnAisCv1c+FtzXlosM1mOPMe4J7mOHYHjgBCPfcNW3WuBfYFjgKObP591QRiSFL/KaVY+rxQP9xeBY5pXgdYAnymVeeXgWeBM7rsf0J9q8cV6/eBJWuocz7wQMe6bwDzR6m/EjhlHLG3BF4CPtxatyP1i8MRY+x3AbBwst8nS2+Leb8674EvAv/Wsd8x1C+7m0/2e2XpXRmWvO/YZ3+gADM71q8PLAZOm+z3xfLmlWHM+da+v9H8rt9ijDofaP5/bDzZ75Wld2XI837vJqd3bV7v3rx+T6vOQe06FovFMojFkW2DYXPqKMSnm9c7AdOpIwIAKKW8ANxFvWK2VpJsBxwH3LmGqrPasRu3APt3XKWaqP2ADXn9eT0C3MfY57UFq382mjrM+9XntRHwYsd+LwAbN/tr6hiWvB+P/agdz6uS3JtkaZJbk7zzTY6rX6xhzvktgFXA8902NqOePgz8cyml8zNAg20o8z7JpsCp1AspD7dir6SOqhvxQ+A51uHcJWmy2dk2GC4BfgzMb15Pb5aPddR7rLVt3FLnQHseeJR6Be3UNewyfZTYGwBvmWj8jnZfBZ7s0nbX80qyL3Vo+qXrEFf9ybxffV63AAcm+UiSDZr5ez7bbHvrOsRW/xmWvB+PtzXLz1FHd/4m8FPgzuYPSE0NQ5nzzXxUnwOuKKW80rHt/CTPAU8BM4CjexVXfWOo8j7JJ5KspHaqHQUcXkpZ1Yr9RCnltTnomn8/zlqcuyT1Czvb+lySi6hDqY8vpbzasblzYtR0WTceZ1HnRjiW+sfNa/NAJFnZKpetIXa39V0l+aOOtmeMVb1bu0l2Bf4OmFtK+avxxNVgMO9fa7sAlFJuBc4Gvkod4XY/dW4VqB11mgLM+zcY+Y7yhVLKjaWUhcDpwDPUeTs14IY155vRPX9L7Qj5dJcmLqTOm/Xr1N/xVydJl3oaQEOa99dQc/pQ6neYG5JsMkbskfhrc+6S1Bd8kl0fS3IxdSL095ZS/q+1aWmznA480lq/LW+8KrVGpZSlTZuLkjwF3J3k882tbPu0qq5oxe+80rQtdbLXp8YZ9jLgO63XP2vaXZ96Be2Jjrbvau+cZDfgDuC6UooPR5hCzPvXtf1a3pdSLmp+Nm+l3nYykzqB8UPjjK0+NoR5Px5LmuV/j6wopbyS5AHqaB8NsGHN+SSbsfpiydHdbg8tpTxJHe18f5L7qD+Hg4C7xxlffWpY876UshxYDjyQZAH1e8zx1IcgLAW2TZKR0W1N5/KvsBbnLkn9ws62PpXkEuqH8exSyqKOzQ9RP5jeD/xrU39j6lMMz1nH0CMjCTYCKKU82KXOfOqVsrb3A/eUUl4eT5BSyjJgWXtdkoXAy01b1zbrdqBOnPqjVr09qE/r+k4p5azxxNNgMO9Hz/tm/0Lz5TXJSdQv5PeOJ7b61zDm/TgtpM5ntSvwA4Ak6wFvp95arQE1rDmf+lTJf6CO2DmylLJyoseswTWsed9FmjKS0/OpD4yYxervPbOATen4HiRJg8TOtj6U5GvUW2SOBZ5OMnKlaWUpZWUppSQs7ah4AAAGFklEQVSZC3wmySLqcOw/ps6DcG2rnRnANOoIGJKMXMl6sJSyMsnRwDbUP2hWAntSb11YMMoH8YjLgDObY7gceA913rSTWrE3A3ZuXq4HzGjiLyulLO7WaClleZJvAhcmeZx6Je0i4D+A25t296R2tN0BfLH1sxm5iqcBZd6PnvdN2+cAN1OfXHcccC5wYpdbUDRAhjXvm/2mU0dS7NKs2iN1HqvFpZRlpZQVzS1Of5rkp9TJtM8EtgauHuOY1ceGNeebjrZbqQ9FOBbYtLmdlGa/l5LMot769wPq7dJvp87r9nCzTgNqiPN+Z+oIttupo/d3oH5/WQXcBFBKuS/JzcDlSU6jdsRdDtxUSvmfMY5ZkvpbLx9taulNoc5P0K3MadUJMId6m82L1KcM7dXRzpWjtDO72f4+6tWkZ6hPNryf+ujvrcdxjIdSR9Ssol6N+1jH9tmjxL5yDe1uDHyF2uHwPHVOkx1b2+eM9vOZ7PfNsm7FvB8975s681rHvAA4arLfM8u6lyHP+9F+n5/SqrMhcAF1xMcK4J+AfSf7fbOsfRnWnB9jn/Yx70O9mPhUK/alwA6T/b5ZzPu1zPsdqaM5Hwdeoo7IvwbYraPeNOpFlBVNuRrYarLfN4vFYlmXklIKkiRJkiRJktadTyOVJEmSJEmSesTONkmSJEmSJKlH7GyTJEmSJEmSesTONkmSJEmSJKlH7GyTJEmSJEmSesTONkmSJEmSJKlH7GyTJGmAJJmZpCS58k2McWUTY+abFUOSJEmaquxskyRJkiRJknpkg8k+AEmSNCGPArsDyyf7QCRJkiS9kZ1tkiQNkFLKy8CiyT4OSZIkSd15G6kkSQOk25xt7TnWkpyR5CdJXkzyWJKvJ9lylLbel+TuJM8lWZbkr5Pstob4Bya5McnSJC8leSTJ5Um266h3XHNMC5Js2LFtryTPJ/lZkm3X4cchSZIk9R072yRJmjouaMq/A1+j3nJ6GvC9zopJTgBuAfYHbgAuB7YB5gM7dWs8yanAD4GjgDuAucA9wEeBe5LMGKlbSvlucwwHAl9otbEJcD2wEXByKeXxdTlhSZIkqd94G6kkSVPHrwHvKKUsBkiyATAPeG+SA0op/9Ks34zaufZz4OBSyj0jDSS5GPiDzoaT7NLs8zBwaCnl0da2w4DbgEuA327t9ing3cDZSeaVUm6mdsDtAZxXSpnXqxOXJEmS+oUj2yRJmjrOG+loAyilvAL8RfPygFa93wKmAde2O9oac+j+8IWPAxsCn2x3tDVx5gF/AxyTZPPW+lXA7wDPAd9OcjZwCnAXcN5ET06SJEkaBI5skyRp6ujsOAN4pFlu3Vq3b7O8s7NyKWV5kh8Dh3ZsmtUsD03yri5xtgXWB3YBFrbaeyDJGcA1wIXAk8CHSimvruFcJEmSpIFkZ5skSVPHM13WvdIs12+tG3lgwmOjtLO0y7ptmuU5aziGzbqsuw1YAWwB3NA5Mk6SJEmaSryNVJKk4TNym+ivjrJ9+hj7bFlKyRjldaPlkgT4NrWj7Ung9CSH9OIkJEmSpH5kZ5skScPn3mbZeasoSbYE9umyz4JmefAEY50DHEm9jfQw4GXg2iRvmWA7kiRJ0kCws02SpOHzfeBp4ENJ9u/YNofVt5m2fZXaUXZx82TS10nyS0kO7lh3IPB54EHg46WUnwBnAdsDVzaj3iRJkqQpxTnbJEkaMqWUlUlOB64H7k5yPbAEOAjYi/q00EM69lmU5HeBbwH/leRm4H7qE0pnUEe8PQHsBpBkK+A6oAAfLKU827RzWZLDgROAPwS+/CafriRJkvQL5cg2SZKGUCnlRurtnQuBE4GPAcuoTx19aJR9rgb2o94SujdwJnAysDNwI/CJVvVvAjOBc0spC1/fEh9tYnwpyQG9OSNJkiSpP6SUMtnHIEmSJEmSJE0JjmyTJEmSJEmSesTONkmSJEmSJKlH7GyTJEmSJEmSesTONkmSJEmSJKlH7GyTJEmSJEmSesTONkmSJEmSJKlH7GyTJEmSJEmSesTONkmSJEmSJKlH7GyTJEmSJEmSesTONkmSJEmSJKlH/h8kZQ9WZ5BfNgAAAABJRU5ErkJggg==\n", 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JTJ48uUx9Wlqa+fvGjRuXOTcqKoqoqKhyz8vOzgagYcOGFnXu7u7XHbdUHyW9RERERERE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\n45kzZ8jLy6N58+Y4OTldtt1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jVm6tMTbysWJEIiIiIiJSHWr9RvbZ2dlMmTKFrl270qtX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\nLFBAgM8+7hUb0tqV8FP4QSmwyPU6sEgKP6gpnaYzygsA4DfsT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"text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -780,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", @@ -792,16 +842,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n", - "C:\\Users\\joras\\Anaconda3\\envs\\wwdata\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: \n", + "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:3: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" + " app.launch_new_instance()\n" ] }, { @@ -812,7 +862,7 @@ " dtype='object')" ] }, - "execution_count": 119, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -829,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", @@ -841,19 +891,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:817: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " wn.warn('When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.')\n", - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:1410: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n", - "will be corrected to return the positional minimum in the future.\n", - "Use 'series.values.argmin' to get the position of the minimum now.\n", - " return (np.abs(df[column]-value)).argmin()\n" + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:817: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + " wn.warn('When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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\n", 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88cQT140hMTH99j5UBVanjoveh1Q66vdSGanfS2WjPi+Vkfr9n+rUcbF2CFJB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\nodpxT3Ybh6Ag31uq+1aPd3YnTgDZ2ebX58/LkJfni9hY+7aJGl9T6/dEg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"text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -878,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.917107", @@ -890,10 +936,10 @@ { "data": { "text/plain": [ - "(2.4506423271968965, 0.6721532140851265)" + "(2.450642327196896, 0.672153214085126)" ] }, - "execution_count": 121, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -912,7 +958,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", @@ -924,7 +970,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best ratio (2.5328218826106403 ± 0.16586491872475548) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" + "Best ratio (2.53282188261064 ± 0.16586491872475553) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py:1400: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", + "You can access Timestamp as pandas.Timestamp\n", + " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" ] } ], @@ -941,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", @@ -953,15 +1008,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:462: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:462: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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\n", 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yduzYAuXJycnm31epUqXAuzExMcTExBT63uXLlwGoVKmSRZm7u/s9xy2lixJlIiIiIiIi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\ntW5ZVa+ipKaYB6Mewc/Fr0OLtkd4RRkMYcypyO4yBbL7+8ToAkdazx1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2U+QqVhLu3F9g2zFKvyanJRrL7JaFsx+F2nUh35TMqf2P8oVBoVqyJjZZOK83pMCkxB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" + "" ] }, "metadata": {}, @@ -984,32 +1039,40 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 31, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", + " from pandas.core import datetools\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "slope: 0.4055129249855649 intercept: 0 R2: 0.9737746563763395\n" + "slope: 0.405512924986 intercept: 0 R2: 0.973774656376\n" ] }, { "data": { "text/plain": [ - "(
,\n", - " )" + "(,\n", + " )" ] }, - "execution_count": 124, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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pyGQyLl36By+vWTRvbkrnzl24dOkfrWCkUinR0Xmy3427eTOPhIR4LCzuP+1N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obE5mFXN4J6A6r9DAbTQrTaN+jU7Rz2SinbEf0z0eoVKspNRA1Od63Mmjh276VApFcI7Gxm7dq9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\n7rv06NGD1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" + "" ] }, "metadata": {}, @@ -1030,28 +1093,28 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.016129", "start_time": "2017-05-09T11:55:05.261370+02:00" }, - "scrolled": true + "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:569: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:569: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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pzJaj3ixVq1YF4J///CdeXl5Fyhs0aHDFd00mE0FBQTz11FNFymrUqIHFYgEgLS3Nqqww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\n2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R8ItLpS0+1Mi54BEUwPhFRaFeth0vih8Wj5YVS3VHF9HAAlBhISAk/1nMMUyTiEkFvK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"text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -1076,28 +1139,28 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.731819", "start_time": "2017-05-09T11:55:06.018568+02:00" }, - "scrolled": true + "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joras\\OneDrive\\Skrivebord\\wwdata\\wwdata\\Class_OnlineSensorBased.py:961: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", + "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:961: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", " 'ensures the proper working of the package algorithms.')\n" ] }, { "data": { - "image/png": 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\n", 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pzJaj3ixVq1YF4J///CdeXl5Fyhs0aHDFd00mE0FBQTz11FNFymrUqIHFYgEgLS3Nqqww\n+SdSWlqaKiIiIiIiIiK3HTs7O6vrRo0a4eLiwunTp/H09DR+paenM2/ePLJKOHTCz8+Po0eP0rJl\nS+O9evXqMWfOHJKTk2nYsCFubm5s3LjR6r0tW7b8LW2TO5dmxImIiIiIiIjIbadwBtx3333Hfffd\nh7u7O6NGjeK1114DoG3bthw/fpw5c+Zw3333lTgjLiwsjAEDBjB69Gj69u2LxWIhJiaGkydP0qJF\nC2xsbAgPD2fKlCnUqlWL9u3bs2HDBvbv318kIShSEiXiREREREREROS2YzKZCA4O5p133mH37t18\n/PHHDBo0CGdnZ95++22WL1+Oi4sLDz30EGPHjsXGxuaKdbVs2ZLY2FiioqIIDw/HycmJVq1a8a9/\n/Ys6deoA0K9fPwAWL17M6tWradeuHSNGjGDJkiVl0l65M9jk5+fnl3cQt5KUlMzyDuGWUbt2VfWH\nVDga91IRadxLRaMxLxWRxv2fateuWt4hiEgFpj3iREREREREREREyoAScSIiIiIiIiIiImVAiTgR\nEREREREREZEyoESciIiIiIiIiIhIGVAiTkREREREREREpAwoESciIiIiIiIiIlIGlIgTERERERER\nEREpA0rEiYiIiIiIiIiIlAEl4kRERERERERERMqAEnEiIiIiIiIiImUkPz+/vEO4Ke6UdpQ1JeJE\nRERERERE5JZx4sQJBgwYgKenJ7179yY6OhpfX1+j3Gw2s2zZMgDi4+Mxm82kpaXd0DcnTpzII488\nctXnTp8+TVBQEOnp6Rw/fhyz2cxnn31W6u8kJyfz7LPP3kioN1VCQgJms5l9+/aV+p1Tp04xdOhQ\nzpw5A3Bd/VAa4eHhrFu37qbWeSuwL+8AREREREREREQKrVy5koMHDxIZGUndunVxdXUlICCgvMMC\nYOrUqQwcOBAXFxcqV65MXFwc9913X6nf/+yzz64p6XUr+u677/j222+Nazc3t2vuh9IYN24cTz31\nFB07dsTV1fWm1l2eNCNORERERERERG4ZZ8+epUGDBjz44IO0bNmSunXr4uXlVd5hkZiYSGJiIk8/\n/TQAjo6O+Pj44OLiUs6Rla+/qx/uvfde2rRpw6JFi25qveVNiTgRERERERERuSUEBgYSHx/P4cOH\nMZvNxMfHF1maejVbt26lX79+eHl50alTJ+bNm0dubq5RnpOTw+zZs2nfvj2tWrUiIiLCqvxKli9f\nTmBgIM7OzkDRJZkTJ04kPDyc2NhYunTpgpeXF4MHD+bIkSMAREdHM3/+fM6dO2e0DeDcuXPMnDmT\ndu3aGe8cOHDA+G58fDz+/v4sXboUf39/AgICjDrWrFnD8OHD8fb2JjAwkNWrV1vFnJ2dzeuvv05g\nYCBeXl488cQTVrPZivOf//yHvn374u3tjbe3NwMGDCAxMdGIZdKkSQC0bduW6OjoYpemJiYmMnDg\nQFq1akW7du2YMWMG2dnZRvngwYOJiIggMjKS9u3b4+3tTVhYGKdPn7aKpWfPnqxdu5azZ89e9edz\nu1AiTkRERERERESsZGVBQkLB72Vp/vz5BAQEcPfddxMXF0fnzp2v6f1t27YRHBxMgwYNmD9/PkOH\nDmXFihW88sorxjOzZs1i1apVBAcHM3fuXJKSktiwYUOJ9WZlZbFlyxa6detW4nPfffcdH374IZMn\nT+aNN97gl19+YeLEiQD069ePJ554AmdnZ6Nt+fn5hIaG8umnnzJmzBjmzZuHo6MjgwcP5tdffzXq\nzczM5OOPP2b27NlMmjSJypUrAzB79mxMJhPR0dF07dqVGTNm8P777wOQl5fHsGHDiI+PJyQkhOjo\naOrXr09ISAjffPNNsfF/9tlnvPTSS3Tu3JnFixcTERFBRkYGY8eOxWKx0LlzZ0JDQwFYunQp/fr1\nK1LHli1beOaZZ6hduzaRkZGMGjWKTz75hOHDh5OXl2c8t3btWvbu3cusWbOYNm0aCQkJREREWNXV\nqVMn8vLy+Prrr0vs99uJ9ogTEREREREREUNWFrRuDUlJ4O4OiYlgMpXNt1u0aEHNmjU5ceIEPj4+\n1/x+VFQU3t7eREZGAgWJnOrVqzNp0iSGDh2KyWTivffeY8yYMQwZMgQomNnVpUuXEuvdsWMHubm5\ntGjRosTnsrOzeeutt3BzcwMKDnd49dVXOXPmDHXr1qVu3brY2toabfvmm2/Yvn07K1asoF27dgB0\n7NiRnj17snDhQiMxlZuby8iRI+nYsaPV9xo3bsycOXOMtp48eZK33nqL/v37s3nzZnbt2sXSpUuN\n9wICAnjyySeJjIwsUhfAr7/+ysCBAxk1apRxz8HBgZEjR/Lzzz/TrFkz7rnnHgA8PDyoWbMmx48f\nt6pj3rx5eHl5ERUVZdxr0KABw4YNY/PmzQQGBgJgZ2fHW2+9hZOTEwBJSUlGErGQk5MTjRs3JiEh\ngccee6zEvr9daEaciIiIiIiIiBj27y9IwkHB7/v3l288pXX+/Hn+97//0aVLF3JycoxfhbOqEhIS\n2Lt3L7m5uXTq1Ml4z8nJ6aqHQfz2228A1K1bt8Tn6tevbyThLn/+/PnzxT6fkJBApUqVaN26tREv\nQIcOHdi+fbvVsw0bNizyfo8ePayug4KCOH78OKdOnSIxMZEqVaoUSbj16NGDAwcOkFXMdMeQkBCm\nTJlCRkYGe/bsYd26dfznP/8BwGKxlNh2KEhEHjhwgIceesjqfseOHalevbqxxBUKTr8tTMJBQV8V\n10/169c3+v9OoBlxIiIiIiIiImLw8CiYCVc4I87Do7wjKp2MjAzy8vKYM2eOMUvscikpKTg6OgJQ\no0YNq7KrncqZmZmJo6MjdnZ2JT5XqVIlq2tb24L5T5cvybxceno658+fp2XLlkXKHBwcrK5r1qxZ\n5JnLk36XP5Oenk5GRkax7XJ1dSU/P99qz7ZCKSkpTJ48mf/+9784ODjQtGlT7rrrLgDy8/OLbcPl\nMjMzyc/Pp1atWkXKatasaZX8+2tf2djYFPsNZ2dnTpw4cdVv3y5umUScxWLh8ccf5//+7/+M6Zjb\ntm1j9uzZHD16FDc3N4YNG2a1/nj79u28+uqr/Prrr3h5efHKK69w7733GuWrVq1iyZIlZGZm8tBD\nDzFlyhRjHbWIiIiIiIiIFGUyFSxH3b+/IAlXVstSb1SVKlUACA0NJSgoqEi5m5sbP/74IwBpaWnU\nqVPHKEtPTy+xbhcXFywWCxaLxUjm3QxVq1alVq1avPXWW9f1/pkzZ6yu//jjD6Ag6VW9enVSU1OL\nvJOSkgJQ7Cmn48aN4/Tp08TFxeHh4YG9vT1btmxh48aNpYqnatWq2NjYGHFcLjU19bpOVs3IyLij\nTqa9JZamXrx4kRdffJHk5GTj3s8//8zw4cPp2rUrH374IS+88AIzZszgq6++AuDkyZOEhoby6KOP\nsnbtWlxdXQkLCzOyzBs3biQqKoqpU6eycuVK9u3bx2uvvVYu7RMRERERERG5nZhM4O9/+yThAEwm\nE+7u7hw7dgxPT0/jl4ODA3PnzuXUqVP4+vri6OholVjKyclh69atJdZdr149AE6dOnVDMRbOkCvk\n5+dHWloalStXtor5448/NpaElmTz5s1W119++SWNGjXCzc0NPz8/srOzixzMsGHDBjw8PKyWhRba\ns2cPPXr0wNvbG3v7grlbhe8Xzlb7axsuV6VKFZo3b251gmphHZmZmbRq1eqqbfqr06dPG/1/Jyj3\nGXGHDx9m3LhxRaYfrl+/nubNmzNixAgA7r33XhITE/n4448JDAzk/fffx93dneDgYKDg1JP27duz\nfft22rVrR2xsLIMGDTKy4NOmTeO5557j5ZdfNrLkIiIiIiIiInLnCA8P54UXXsBkMtG1a1fOnDlD\nVFQUtra2NGvWjEqVKjF06FCWLFmCs7MzzZs3Z82aNaSmphqHEBTHz88PBwcHdu/eXeJzV1OtWjXO\nnz/PF198gZeXF126dMHT05OQkBBGjhxJvXr1+Pzzz3n33XeZPn36Vev75ptvmDFjBoGBgWzevJlN\nmzYZhyR07twZb29vJkyYwNixY6lXrx7x8fHs3buXhQsXFlufp6cn69atw2w2U716dTZt2sSaNWsA\nuHDhgtEGgE2bNtG+ffsidYwaNYqwsDDGjBnD448/zsmTJ5k7dy6+vr5We/OVRnZ2NsnJyQwfPvya\n3ruVlfuMuO+//x5/f3/i4uKs7j/88MNMmTLF6p6NjQ0ZGRkA7N27l9atWxtllSpVwsPDg927d5Ob\nm8u+ffusyn18fMjNzeXgwYN/Y2tEREREREREpLwEBQURExPDDz/8QGhoKLNmzcLHx4eVK1cae5KN\nHj2akSNHsnr1asLDw6latSr9+/cvsV6TyUS7du2uOnPuanr27ImHhwdjxozho48+ws7OjmXLltG+\nfXveeOMNQkJC2LFjBxEREQwYMOCq9Q0bNoxffvmFsLAwtm/fTmRkpHFQgp2dHUuXLqVbt25ERkYy\natQoTp06xeLFi694SmxERASNGzdm0qRJjB07liNHjrBy5UoqV67Mnj17gIJTZjt06MDMmTNZvnx5\nkToCAwNZsGABv/76K2FhYURHR/PII4+wdOnSq+6x91fbtm3DwcGh2BNeb1c2+aXZba+MmM1mqyN7\nL5eamkr37t0JCwtj6NCh9OrViyeffJJBgwYZz4wZM4Zq1aoxduxYHnjgAT7++GOaNWtmlLdr147/\n+7//45FHHrliDCkpmTe3Ubex2rWrqj+kwtG4l4pI414qGo15qYg07v9Uu3bV8g5BblMJCQkMHz6c\nb7/9FtMtsGbXbDbz0ksvMXTo0PIO5W8zYsQI7r77biZPnlzeodw05b40tTTOnTvHyJEjcXNz4+mn\nnwYKjv796waJjo6OWCwWY7rklcpLUqNGZeztry1DeyfT/0hJRaRxLxWRxr1UNBrzUhFp3IvcGH9/\nf/z8/Hj33XcJCQkp73DueEeOHGH37t3MmDGjvEO5qW75RFxmZibDhw/n+PHjvPvuu8ZUUicnpyJJ\nNYvFgouLi7HhYHHlzs7OJX7vzJlzNzH625v+XzOpiDTupSLSuJeKRmNeKiKN+z8pISk3YubMmQwa\nNIj+/fvfUSd53ormzp3LhAkTcHNzK+9QbqpbOhGXlpbG0KFDSU1NZeXKlVYbItapU8c4crdQamoq\nTZs2NZJxqampxtLUnJwc0tPT77gfoIiIiIiIiIiUjfr16/PVV1+VdxgAHDp0qLxD+FstWLCgvEP4\nW5T7YQ1XYrFYGDFiBGfOnGH16tU0atTIqtzb25tdu3YZ1+fPn+fAgQP4+Phga2uLp6cnO3fuNMr3\n7NmDnZ0dzZs3L7M2iIiIiIiIiIiIFLplE3Fvv/02+/fvJyIigkqVKpGSkkJKSgrp6ekA9O3b1zhy\n9/Dhw0yePJn69evTtm1bAJ5++mmWL1/Oxo0b2bdvH9OnT6dv375UqVKlPJslIiIiIiIiIiIV1C27\nNPWzzz4jJyeHIUOGWN1v1aoVa9asoUGDBkRHRxMREcGiRYvw9vYmJiYGW9uC3GLPnj357bffmDZt\nGhaLha5duzJx4sRyaImIiIiIiIiIiAjY5Ofn55d3ELcSbWD6J23oKhWRxr1URBr3UtFozEtFpHH/\nJx3WICLl6ZZdmioiIiIiIiIiInInUSJORERERERERESkDCgRJyIiIiIiIiJSxrRTWMWkRJyIiIiI\niIiI3DJOnDjBgAED8PT0pHfv3kRHR+Pr62uUm81mli1bBkB8fDxms5m0tLQb+ubEiRN55JFHrvrc\n6dOnCQoKIj09/Ya+l5yczLPPPmtcJyQkYDab2bdv3w3V+9e+utX8Nb7w8HDWrVtXjhGVvVv21FQR\nERERERERqXhWrlzJwYMHiYyMpG7duri6uhIQEFDeYQEwdepUBg4ciIuLyw3V89lnn1kl3Tw8PIiL\ni6Nx48Y3GuJtZdy4cTz11FN07NgRV1fX8g6nTGhGnIiIiIiIiIjcMs6ePUuDBg148MEHadmyJXXr\n1sXLy6u8wyIxMZHExESefvrpm163yWTCx8eHypUr3/S6b2X33nsvbdq0YdGiReUdSplRIk5ERERE\nREREbgmyWT+fAAAgAElEQVSBgYHEx8dz+PBhzGYz8fHx17zccuvWrfTr1w8vLy86derEvHnzyM3N\nNcpzcnKYPXs27du3p1WrVkRERFiVX8ny5csJDAzE2dkZgOPHj2M2m4mNjSUwMBA/Pz927NhBfn4+\nsbGx9OrVC09PT3x9fXnuuec4dOgQULA8c/78+Zw7d85oY3FLUzdt2kTfvn3x8fEhICCAqKgocnJy\nStUHH374IV26dMHb25vhw4fzyy+/WJX/5z//oW/fvnh7e+Pt7c2AAQNITEw0ys+dO8fkyZPp0KED\nXl5e9OnTh40bN1rV8cMPP/Dss8/i7e3NAw88wMyZMzl//rzVM8uWLaNLly74+PgwYcIELly4UCTW\nnj17snbtWs6ePVuqtt3ulIgTERERERERESs5WTlkJGSQk1W6xM/NMn/+fAICArj77ruJi4ujc+fO\n1/T+tm3bCA4OpkGDBsyfP5+hQ4eyYsUKXnnlFeOZWbNmsWrVKoKDg5k7dy5JSUls2LChxHqzsrLY\nsmUL3bp1K1IWExPD+PHjmTJlCl5eXixfvpzZs2fzxBNPsGzZMqZMmcLhw4eZNGkSAP369eOJJ57A\n2dn5im2Mi4tj5MiReHl5MX/+fAYNGsTy5cuZOHHiVfvg/PnzzJ49m/DwcP71r3/x888/M2TIEM6d\nOwcULIt96aWX6Ny5M4sXLyYiIoKMjAzGjh2LxWIB4NVXX2X79u1MnjyZxYsX07hxY0aPHs2RI0cA\nOHz4MIMGDcLGxoaoqCjGjx/P+vXrGTNmjBHHsmXLmDNnDn369OHNN9/k0qVLxMbGFom3U6dO5OXl\n8fXXX1+1bXcC7REnIiIiIiIiIoacrBx2td7FuaRzVHavTKvEVtibyiZ90KJFC2rWrMmJEyfw8fG5\n5vejoqLw9vYmMjISKEjyVK9enUmTJjF06FBMJhPvvfceY8aMYciQIQC0bduWLl26lFjvjh07yM3N\npUWLFkXKevXqRY8ePYzrkydPEhYWZhzG0KZNGzIyMoiIiCA7O5u6detSt25dbG1ti21jbm4uUVFR\n9OzZk6lTpwLQoUMHqlatytSpUxk2bBju7u5XjDU/P5833niDtm3bAtCoUSN69erFp59+Sr9+/fj1\n118ZOHAgo0aNMt5xcHBg5MiR/PzzzzRr1oydO3fSvn17Hn74YQBatWqFq6urMSMvJiYGV1dXFi9e\njKOjIwD33XcfAwcOJDExET8/P5YsWUK/fv0IDw8HoGPHjvTu3Ztjx45Zxevk5ETjxo1JSEjgscce\nK/HncCdQIk5EREREREREDOf2n+NcUsHsqXNJ5zi3/xzV/KuVc1RXd/78ef73v/8xduxYqyWchTOu\nEhIScHV1JTc3l06dOhnlTk5OBAQElHhi6W+//QZA3bp1i5Q1bNjQ6vof//gHAGlpaRw9epSjR4/y\n1VdfAWCxWKhSpUqJ7Th69ChpaWk89NBDVvcLE3M7duzAbDYXWU5rb1+Q4qlataqRhANo2rQpd999\nNzt37qRfv36EhIQAkJGRwdGjR/npp5+s4gO4//77ef/99/n999/p0qULnTt3tpqNl5CQQFBQELa2\ntkZf+/j4YDKZ2LZtGzVr1uTMmTNW/WxjY0O3bt2ME28vV79+faOP73RKxImIiIiIiIiIobJHZSq7\nVzZmxFX2uD0OEMjIyCAvL485c+YwZ86cIuUpKSnG7K0aNWpYlV3txM7MzEwcHR2xs7MrUlarVi2r\n6yNHjjBlyhR27txJpUqVcHd3N5Jv+fn5V21H4V5pf623atWqODo6kpWVxbp164ylroUK96D763sA\nNWvWJDMzEyjoh8mTJ/Pf//4XBwcHmjZtyl133WUV3z/+8Q/c3Nz46KOP+Prrr7G1tSUgIIBZs2ZR\ns2ZN0tPTiYuLIy4ursi3UlJSjDaUtp+dnZ05ceJEyR1zh1AiTkREREREREQM9iZ7WiW24tz+c1T2\nqFxmy1JvVGGyKzQ0lKCgoCLlbm5u/Pjjj0DBbLU6deoYZenp6SXW7eLigsViwWKxGMm84uTl5REa\nGoqLiwsff/wxTZo0wdbWltWrV/Ptt9+Wqh0uLi4A/PHHH1b3MzIysFgsuLi40KVLF/79738X+35G\nRkaRe6mpqTRr1gyAcePGcfr0aeLi4vDw8MDe3p4tW7ZYHcbg7OxMeHg44eHhHD16lM8//5yYmBjm\nzZvH9OnTMZlMBAUF8dRTTxX5Vo0aNYyZdWlpaVZlV+rnjIwMo913Oh3WICIiIiIiIiJW7E32VPOv\ndtsk4QBMJhPu7u4cO3YMT09P45eDgwNz587l1KlT+Pr64ujoaJV0ysnJYevWrSXWXa9ePQBOnTpV\n4nNpaWn88ssv9O/fn2bNmmFrW5B2+eabb6yeK7xfnIYNG1KjRg0+++wzq/vr168HCvZrq1GjhlUb\nPT09rWLYv3+/cb1//36OHz9OmzZtANizZw89evTA29vbWM5aGF9+fj65ubk88sgjvP3220DBHnOh\noaH4+Phw8uRJAPz8/Dh69CgtW7Y0vl+vXj3mzJlDcnIyDRs2xM3NrchJq1u2bCm2zadPnzb6+E53\n+/wXJSIiIiIiIiJSgvDwcF544QVMJhNdu3blzJkzREVFYWtrS7NmzahUqRJDhw5lyZIlODs707x5\nc9asWUNqair33HPPFev18/PDwcGB3bt3l/hcrVq1qF+/PrGxsdSqVQs7Ozs+/PBDNm/eDBTsYwdQ\nrVo1zp8/zxdffIGXl5dVHXZ2dowcOZKZM2dSvXp1goKCOHToENHR0Tz00EPGzLYrcXR05MUXX2T8\n+PFcunSJ2bNn4+7uTvfu3QHw9PRk3bp1mM1mqlevzqZNm1izZg0AFy5cwM7ODi8vLxYsWICTkxON\nGjVi79697Ny5k+nTpwMQFhbGgAEDGD16NH379sVisRATE8PJkydp0aIFNjY2hIeHM2XKFGrVqkX7\n9u3ZsGED+/fvL7K8Nzs7m+TkZIYPH15iu+4USsSJiIiIiIiIyB0hKCiImJgYFixYQHx8PCaTiXbt\n2jF+/HgqVaoEwOjRo3F2dmb16tVkZGTQrVs3+vfvz/bt269Yb2E9W7dupXfv3ld8zsbGhujoaF55\n5RXGjh2LyWTC09OTFStWMGTIEPbs2cNdd91Fz549+fDDDxkzZgyjR48ukowbNGgQzs7OLF++nA8+\n+AA3Nzeee+45wsLCrtoHd911F0OGDGH69OlkZ2cTEBDAlClTjCW1ERERTJ8+nUmTJuHk5ITZbGbl\nypWEhISwZ88e2rRpwz/+8Q8qV67MokWL+OOPP7jrrrt4+eWX6devHwAtW7YkNjaWqKgowsPDcXJy\nolWrVvzrX/8ylvwWPrt48WJWr15Nu3btGDFiBEuWLLGKd9u2bTg4ONCxY8ertu1OYJNfmp0CK5CU\nlMzyDuGWUbt2VfWHVDga91IRadxLRaMxLxWRxv2fateuWt4hyG0qISGB4cOH8+2332Iymco7nDvG\niBEjuPvuu5k8eXJ5h1ImtEeciIiIiIiIiMhV+Pv74+fnx7vvvlveodwxjhw5wu7duwkODi7vUMqM\nEnEiIiIiIiIiIqUwc+ZM3nvvvauesiqlM3fuXCZMmICbm1t5h1JmtEeciIiIiIiIiEgp1K9fn6++\n+qq8w7hjLFiwoLxDKHOaESciIiIiIiIiIlIGlIgTEbnJsrJg505bsrLKOxIRERERERG5lWhpqojI\nTZSVBd27VyY52Y6mTXP5/PNz6EAlERERERERAc2IExG5qQ4dsiU52Q6A5GQ7Dh3SX7MiIiIiIiJS\nQP9CFBG5iczmPJo2zQWgadNczOa8co5IREREREREbhWlXpr6+++/c+7cOe666y4cHByu+Nwff/xB\nSkoK7u7uNyVAEZHbickEn39+jkOHbDGb87QsVURERERERAxXnRG3e/duevfuTUBAAA8//DD+/v7M\nnDmTzMzMYp9fs2YNffr0uemBiojcyrIuZbHzdCJZl3RCg4iIiIiI3F7y8/PLO4QKo8REXFJSEkOG\nDOHw4cM88MADdOrUCRsbG1avXk2fPn04cuRIWcUpInLLyrqURfcPOvPw2iC6vtODrt0q8fDDVeje\nvbJOThURERERuUYnTpxgwIABeHp60rt3b6Kjo/H19TXKzWYzy5YtAyA+Ph6z2UxaWtoNfXPixIk8\n8sgjV33u9OnTBAUFkZ6efkPf+7uUth2X++KLL5g6dapx/df+/jsFBgYyY8aMMvnW9bg8vpSUFIKC\ngm54rJWYiIuOjiY3N5fY2FhWrFjBW2+9xRdffEGfPn04fvw4gwcP5scff7yhAApZLBYeeeQRvvvu\nO+Peb7/9xvPPP4+Pjw8PP/wwW7ZssXpn+/bt9OrVC29vbwYPHswvv/xiVb5q1So6deqEr68vkyZN\n4ty5czclVhGRyx1KO0hyesHfhUeSHTlyuGDVvw5rEBERERG5ditXruTgwYNERkby6quv0q9fP2Jj\nY8s7LACmTp3KwIEDcXFxKe9QbprY2FhOnz5tXN9K/X0rqV27No899hivvvrqDdVT4r8Qd+zYQffu\n3bn//vuNezVq1CAiIoLw8HDS0tJ4/vnnOXbs2A0FcfHiRV588UWSk5ONe/n5+YSFheHi4sK///1v\n+vTpQ3h4uPGtkydPEhoayqOPPsratWtxdXUlLCyMvLyCjdE3btxIVFQUU6dOZeXKlezbt4/XXnvt\nhuIUESmOuWZzmro0A6BxUwuNm+QAOqxBREREROR6nD17lgYNGvDggw/SsmVL6tati5eXV3mHRWJi\nIomJiTz99NPlHcrf6lbp71vRs88+y8aNGzlw4MB111FiIi47O5s6deoUWxYWFkZoaCipqak8//zz\npKamXlcAhw8fpn///vz6669W97dv385PP/3EjBkzaNKkCSEhIfj6+vLvf/8bgPfffx93d3eCg4Np\n0qQJs2bN4uTJk2zfvh0oyOgOGjSIoKAgPD09mTZtGuvWrSM7O/u64hQRuRKTg4nP+21mQ98v2TRo\nPZs2nmfDhmw+//ycDmsQEREREbkGgYGBxMfHc/jwYcxmM/Hx8de8VHLr1q3069cPLy8vOnXqxLx5\n88jNzTXKc3JymD17Nu3bt6dVq1ZERERYlV/J8uXLCQwMxNnZ2bh34cIFXn/9dWM13oABA9ixY4dR\nnp2dzeuvv05gYCBeXl488cQTfPvtt0Z5QkICZrOZ9957j/bt2+Pv78+xY8cIDAxk9uzZ9O/fHy8v\nL5YuXQrAL7/8QlhYGL6+vtx///1MmDChxKWSWVlZvPLKK3Tp0oWWLVvywAMP8PLLL5ORkQHA4MGD\n+f7779m8eTNms5njx48X6e9Lly6xePFiunfvjqenJ7169eLjjz82yo8fP47ZbOarr75i6NCheHt7\n07FjRxYuXHjVPi3sw0mTJuHr60uHDh2IjIwkJyen1G0A2Lt3LwMHDsTX15c2bdoQHh7Ob7/9ZvWd\nlStX0q1bN1q2bEnPnj1Zv369VXlKSgrh4eH4+fnRsWNHPvzwwyKxVqtWjQ4dOhhLo69HiYm4+vXr\ns3v37iuWjx49mr59+3Ls2DGef/7561oj/f333+Pv709cXJzV/b1799KiRQtMl/0r1s/Pjz179hjl\nrVu3NsoqVaqEh4cHu3fvJjc3l3379lmV+/j4kJuby8GDB685RhGRqzE5mPCr0xoumnRiqoiIiIjc\n9rKyskhISCCrjDc9nj9/PgEBAdx9993ExcXRuXPna3p/27ZtBAcH06BBA+bPn8/QoUNZsWIFr7zy\nivHMrFmzWLVqFcHBwcydO5ekpCQ2bNhQYr1ZWVls2bKFbt26Wd0fM2YM77//PsOGDWPBggXUqlWL\n4OBgfvnlF/Ly8hg2bBjx8fGEhIQQHR1N/fr1CQkJ4ZtvvrGqZ8mSJcycOZNJkyZx9913A7BixQqC\ngoKYN28egYGBpKam8vTTT3PixAn+9a9/MX36dPbs2cPQoUOxWCzFxj1u3Di++uorxo0bx7Jly3j+\n+ef55JNPiImJAQqW2rZo0YJWrVoRFxeHm5tbkTpefvllYmJi6N+/PwsXLsTX15fx48fzwQcfWD03\nadIkvL29WbRoEV26dCEqKqrIFmPF+fDDD0lNTSUqKopBgwaxdOlS5syZU+o2ZGZmEhISQp06dYiJ\niWHmzJkcOHCAF1980ahj/vz5vP766/To0YNFixbRrl07XnzxRePnnpuby9ChQ/nhhx+YOXMmEydO\n5M0337RasluoW7dufPHFF1fs86uxL6nwwQcfZMWKFcZS1CpVqhR5ZubMmfzxxx9s3ryZJ598ErPZ\nfE0BXGlKZ0pKSpEBUKtWLU6dOlVi+enTp8nIyODixYtW5fb29ri4uBjvi4jcTFmXsthz/EcmDGzP\nkcP2NG2aqxlxIiIiInJbysrKonXr1iQlJeHu7k5iYqLVJJm/U4sWLahZsyYnTpzAx8fnmt+PiorC\n29ubyMhIADp16kT16tWZNGkSQ4cOxWQy8d577zFmzBiGDBkCQNu2benSpUuJ9e7YsYPc3FxatGhh\n3EtKSuLrr7/m9ddf57HHHgPg/vvv5/HHH2fXrl0cOXKEXbt2sXTpUjp27AhAQEAATz75JJGRkcY9\nKJiZFhgYaPXNxo0bM3z4cON6zpw5XLx4keXLl1OzZk0AvLy86N69O+vXrzdiKHTx4kUuXbrEtGnT\n6NSpEwD+/v7s3r2b77//HoAmTZpgMpmoXLlysf196NAhPv30U6ZPn86AAQMA6NChA1lZWcydO5fH\nH3/cePbhhx8mPDzc+M7nn3/Of//7XwICAkrs23r16rFw4ULs7e0JCAggMzOTd955hxdeeAEHB4er\ntuHIkSOkp6czePBgYyZfjRo12L59O3l5eWRlZbF48WKGDRvGmDFjjDZkZ2czZ84cHn74YTZv3syh\nQ4eIi4sz+uG+++6zal+hFi1acOHChSITxEqrxETcCy+8wNatW4mNjWXVqlWMGTOGkJAQq2dsbW15\n8803GTduHJs2bSqyxPR6nT9/HgcHB6t7jo6OXLp0ySh3dHQsUm6xWLhw4YJxXVx5SWrUqIy9vd2N\nhn/HqF27anmHIFLmrnXcZ1my6LQkkKQ91eBwAlBwUMPvv1elYcO/I0KRm09/30tFozEvFZHGvZTW\n/v37SUpKAgqSTfv378ff37+co7q68+fP87///Y+xY8daLW3s1KkTeXl5JCQk4OrqSm5urpHUAXBy\nciIgIIB9+/Zdse7CZY5169Y17u3atQvAKoHm6OjIJ598AsDrr79OlSpVrBJuAD169CAiIsJqtmHD\nYv7h8Nd7CQkJ+Pj4UK1aNaN99erVo3Hjxmzbtq1IIs7JyYnly5cDBctHf/75Z5KTkzly5AhOTk5X\nbOvlCpfZPvTQQ0Xa8Omnn3LkyBEqV64MYJXIs7W1xc3NzTg0Mzc3l/z8fKtyW9uCRZqBgYHY2/+Z\nnurSpQtLly41xt3V2tCkSRNcXFwYMWIEPXv2JCAggLZt29KmTRsA9uzZw8WLF+ncuXORcbF27VqO\nHTvGrl27qF69ulUbPDw8uOuuu4r0SeG933777eYn4qpUqUJcXBwrV65k06ZNuLq6Fvuco6Mj0dHR\nrFy5kpiYGM6ePXvNgfyVk5NTkSmwFovFWIvt5ORUJKlmsVhwcXExfhjFlV++lrs4Z87oZNVCtWtX\nJSUls7zDEClT1zPud55OJCk1CWpXAdeDkNqcpk1zcXM7R0rK3xSoyE2kv++lotGYl4pI4/5PSkhe\nnYeHB+7u7saMOA8Pj/IOqVQyMjLIy8tjzpw5VksbC6WkpBgTdmrUqGFVdqV8R6HMzEwcHR2xs/tz\n4s7Zs2dxcHCgWrVqV4ynuHpdXV3Jz8+32sO+cIbb5WrVqmV1nZ6ezt69e4v9edSuXbvYGL788ksi\nIiI4duwYNWrUoGXLljg7OxsHXV7N2bNnjRWGf20DFMyeLEzE/TXfYmtrayTfhgwZYsxgA+jTp49x\noOZf+6iwLzIzM0vVBpPJxDvvvMOCBQtYt24dq1evplq1aoSEhBAcHGxso1Y4o++vUlJSyMjIKDIm\noPh+LWxnYXzXqsREXOEHQkJCisyEK84zzzzDgAEDOHr06HUFc7k6deoYGfhCqampRifUqVOHlL/8\nCzc1NZWmTZsaybjU1FSaNSs4yTAnJ4f09PRi1zuLiNyIBlXvwcHWkUtO2dgPb0/s/Xtp6+2iZaki\nIiIiclsymUwkJiayf/9+PDw8ymxZ6o0q3E4rNDSUoKCgIuVubm78+OOPAKSlpVkdTnm1Pe9dXFyw\nWCxYLBYjmVe1alUuXbpEZmYmVav+meDdvXs31apVo3r16sUebFmYy/hrcutqTCYTnTp1MpZ/Xq64\nrcR+/vlnRo8eTZ8+fXjnnXeM2XyjR4/myJEjpfpm9erVjXzK5fEWtqu0bZg+fbpV4vHypNdfJ3P9\n8ccfQEFCrrRtaNq0KVFRUVgsFnbu3ElsbCyzZ8+mTZs2xs9mwYIFxR5I2rBhQ1xcXIzvXq64cVF4\nSMS1/vwKlXhYQ0mys7PZvXs3mzdvBv7sOEdHR9zd3a+3WoO3tzdJSUnGNEaAnTt3GtMEvb29jWmg\nUDAF9cCBA/j4+GBra4unpyc7d+40yvfs2YOdnR3Nmze/4dhERC53PPNXLuUVzMDNcThDzSbJSsKJ\niIiIyG3NZDLh7+9/2yThoCBmd3d3jh07hqenp/HLwcGBuXPncurUKXx9fXF0dGTjxo3Gezk5OWzd\nurXEuuvVqwdgte984X5kX3/9tXHPYrEwZswYPvroI/z8/MjOzi5yMMOGDRvw8PAo9fLQQn5+fhw9\nehSz2Wy0rVmzZsyfP98q/1HowIEDXLp0iZCQECOBde7cOXbu3FlkmWhJ3wT47LPPrO6vX7+eWrVq\ncd9995Uq9kaNGln9TBo0aGCUbd261Sqezz//HJPJRIsWLUrVhv/+97+0bduWtLQ0HB0dadu2LVOm\nTAHgxIkTeHt74+DgwB9//GEVQ3JyMgsWLAAK9p3LzMxk27ZtRhxHjx4tdvu1wgMcCsfEtbrqjLi/\nSk1N5dVXX2XTpk3k5uZiY2PDgQMHePfdd4mPjyciIoL777//uoK5XJs2bahfvz4TJ05k1KhRfP31\n1+zdu5dXX30VgL59+7Js2TIWLlxI165diYmJoX79+rRt2xYoOATiH//4B2azmXr16jF9+nT69u1b\nbJZYRORGGDPi8izYX6pB2uGmZFVByTgRERERkTIWHh7OCy+8gMlkomvXrpw5c4aoqChsbW1p1qwZ\nlSpVYujQoSxZsgRnZ2eaN2/OmjVrSE1N5Z577rlivX5+fjg4OLB7927jOQ8PD7p06cLMmTPJysri\n3nvv5b333uP8+fM8+eST1K1bF29vbyZMmMDYsWOpV68e8fHx7N27l4ULF15z25577jk++ugjhg0b\nxjPPPIODgwPLly9nz549xiEEl2vevDl2dna88cYbPPXUU5w5c4bly5eTmppqtad+tWrVOHjwIAkJ\nCXh7e1vV4e7uTvfu3XnttdfIzs7GbDbz5Zdf8umnn/LPf/6zxCReaf3000+8/PLL9OnTh8TERFav\nXs2LL75o/Hyu1gYvLy/y8/MZOXIkwcHBODg4EBsbS7Vq1fD396dmzZoMHjyY1157jbNnz+Ll5UVS\nUhKRkZEEBQVhMplo3749rVu3ZsKECYwfP57KlSsTFRVV5OwCKJjxaDKZivRVaV1Tj6WlpfHkk0+y\nYcMGvLy8aNGihZGBrFSpEidOnCA4OJhDhw5dVzCXs7OzIyYmhrS0NB5//HE++ugj5s+fb2RNGzRo\nQHR0NB999BF9+/YlNTWVmJgYYxD07NmT0NBQpk2bxnPPPUfLli2ZOHHiDcclIvJXxoy4i1XIeWsr\nA/vcTffulSnjk95FRERERCq8oKAgYmJi+OGHHwgNDWXWrFn4+PiwcuVKKlWqBBQsaxw5ciSrV68m\nPDycqlWr0r9//xLrNZlMtGvXrsjMucjISHr37s2CBQsYOXIk6enpvP3229x1113Y2dmxdOlSunXr\nRmRkJKNGjeLUqVMsXrz4qqe0Fqd+/fq8++67VKpUyUju5eXlsWLFimJX/zVs2JDXX3+dQ4cOERIS\nwuzZs/H09GTq1KmcPHnSmNk1ZMgQLBYLw4YN48CBA0XqmT17NgMHDuTtt98mNDSUXbt28cYbbzBw\n4MBrbkNxnnvuOS5dusSIESNYu3YtL7/8MsHBwaVug4uLC0uXLsXJyYmXXnqJkSNHcvHiRVasWGHs\nNzdhwgTCwsL44IMPGDZsGCtXruTZZ5819qmzsbFh4cKFdOzYkVdffZWpU6fSp0+fYld8bt26lc6d\nOxebpCsNm/zL5/9dxbRp03j//fdZsGABXbp0Yf78+SxYsICDBw8CBSd4DBs2jKCgIKKioq4roPKm\nDUz/pA1dpSK6nnGfdSmL7h90JvkHF1iaYNzfsCEbP7/SbYIqUp70971UNBrzUhFp3P9JhzXI9UpI\nSGD48OF8++23t9WSXbl5UlNT6dy5Mx988MF1b312TTPivvrqK7p27XrFzK2/vz/dunVjz5491xWM\niMjtyORg4v/Zu/OwKMv1gePfAYZ1EEQ2EXBDR3BDcDkq4oKKa5qlv7IsT0pmmUdNO1idPGVpncwl\nl1JLS8tcyVJzzaU0F1zQVEBAVBYdQNYBhBng98c4A8MmKMMSz+e6vOpd5n2eed93hpl77ud+Do4/\nTkjQ/2jroZkO282tAFdXEYQTBEEQBEEQhL+LXr164evry5YtW+q6K0Id2bx5MwEBAU80/0C1AnFp\naWm4ublVuo+TkxOpqamP3SFBEISGSCaV4dfah90/5eLmVkhcnDHjxonhqYIgCIIgCILwd7Jw4UK2\nbt36yFlWhb+fpKQk9uzZw/vvv/9Ex6nWZA3Ozs7ljhcu6cqVK7qZLARBEBqb+Hgj4uI0v3FERRkT\nGey7LnIAACAASURBVGkkhqcKgiAIgiAIwt+Ei4sLR48eretuCHXA0dGxRq59tTLiAgMDOX36NFu3\nbi13+8aNG7lw4QKDBw9+4o4JgiA0NEqVkly787rhqe3aFSCXiyCcIAiCIAiCIAiCoFGtyRqUSiXP\nP/880dHReHh4UFhYyM2bNxkzZgzXrl0jOjoad3d3duzYQZMmTQzZb4MRBUyLiYKuQmP0uPe9bsKG\n9Bu0tfDmM6/DeHc0Q9RwFRoC8X4vNDbinhcaI3HfFxOTNQiCUJeqlREnk8n48ccfee6550hISCAm\nJoaioiJ2797N7du3GTNmDD/++GODDcIJgiA8rrCki0QpEiC+JzHpUVi0uiKCcIIgCIIgCIIgCIKe\namXElVRQUEBsbCyZmZlYWlrSpk0bTE1Na7p/tU78SlRM/GomNEaPc98rVUoGbhrK7c+3Q4onxo5R\n/HnMiNYOjgbqpSDULPF+LzQ24p4XGiNx3xcTGXGCINSlak3WUJKxsTEeHh412RdBEIQGKSzpIrdj\nLCFFM4V1QVI7xq1/lj/mrUQmFWlxgiAIgiAIgiAIgka1A3ExMTH8/PPPJCQkkJ+fT3kJdRKJhJUr\nV9ZIBwVBEBoEh2tgH64JxtmHk2BxgMjUcHydetR1zwRBEARBEARBEIR6olqBuHPnzjF16lRUKlW5\nATgtiUTyxB0TBEFoKNo1lWNinoc6qAckdociaG3bFrmdZ113TRAEQRAEQRAEAysqKhJxEKHKqjVZ\nwxdffIFarWbWrFns3r2bI0eO8Ntvv5X5d+TIEUP1VxAEod6Jz7qDukitWdj3JWw6jtH6C5AnhqUK\ngiAIgiAIQnUlJiby3HPP0blzZ8aMGcPKlSvp1q2bbrtcLuebb74BICQkBLlcTmpq6hO1GRwczKhR\nox65n0KhICAggPT0dAC2b9/O8uXLn6jt0iZNmsS0adNq7Hhnz55FLpfz119/VetxgwYN4sMPP6yx\nfiQnJxMQEPDE16qhq1ZG3NWrVxkxYkSN3hCCIAgNnau1O1IjU1TJHXV14mKiTYiMNMLXt7COeycI\ngiAIgiAIDcumTZsIDw9n2bJlODs7Y29vT//+/eu6WwAsWLCAF154AVtbWwC++uorBgwYUONtGBlV\nK2+qQXBwcGDs2LF8/PHHfP7553XdnTpTrUCcmZkZDg4OhuqLIAhCgxSfdQdVYb5enbh27QqQy0UQ\nThAEQUupUhKZGo7czlNMZCMIgiBUKiMjA1dXVwYPHqxb5+zsXIc90ggNDSU0NLTGM+BK+ztPjPny\nyy/Tt29frl+/jpeXV113p05UK8Tq5+fHyZMnKSgoMFR/BEEQGhxtRhxm2ZhM68sPP8Vx8GAOMvE9\nUxAEAdAE4QJ3DGD4rgACdwxAqVLWdZcEQRCEemrQoEGEhIQQHR2NXC4nJCSkzNDURzl16hTjx4+n\nS5cu+Pv7s2LFCr04hlqtZsmSJfTt2xcfHx8WL15cpTjHhg0bGDRoEObm5rq+JiQk8MMPPyCXy4mM\njEQul3PgwAG9x+3Zs4dOnTqRlpZGcHAw06ZNY/369fTu3Zvu3bvz1ltv6Ya6Qtmhqenp6bz77rv0\n6dMHHx8fXnnlFSIjI3Xbb968ycyZM/nHP/5Bp06dGDRoEKtXr660tn9pycnJzJw5E19fX/r168fu\n3bvL7POodsaNG1dmBGVeXh6+vr5s3rwZgCZNmuDn56cbWtwYVSsQ9/bbb5OTk8OsWbO4cOECqamp\nKJXKcv8JgiA0FrqMOEAtTcPOI0oE4QRBEEqITA0nKv0GAFHpN4hMDa/jHgmCIAiPolYrycw8i1pd\nu9/vV61aRf/+/XFzc2Pbtm3VHvZ5+vRpgoKCcHV1ZdWqVUyZMoWNGzfy0Ucf6fZZtGgRmzdvJigo\niKVLlxIREcH+/fsrPa5SqeTEiRMMHTpUr68ODg4EBgaybds25HI5np6e7Nu3T++xe/bsoX///jRt\n2hSA8+fPs23bNt5//33ee+89/vzzT6ZPn15uu2q1mn/+85+cOHGCOXPmsGLFCh48eMCUKVPIyMgg\nOzubl156ifT0dD799FPWrl1Lr169+OKLLzh27FiVzllBQQFTpkzh6tWrLFy4kODgYL744gsUCoVu\nn6q0M2bMGE6dOqUXVDx69Ch5eXmMHDlSt27o0KEcOXKE/Pz8KvXv76ZaQ1MnTpxITk4Ohw8frnRC\nBolEwvXr15+4c4IgCA2B3M6TdrbtiUq/QTvb9mK2VEEQhFLE+6QgCELDolYruXixBzk5EVhadsDH\nJxQTk9r5pdnLyws7OzsSExPx9vau9uOXL19O165dWbZsGQD+/v7Y2Ngwf/58pkyZgkwmY+vWrcya\nNYvJkycD0Lt3bwYOHFjpcc+fP09BQYHecEovLy9MTU2xt7fX9XXs2LEsXboUpVKJTCYjNTWVU6dO\n6foDmqDWtm3bdENQbW1tmTZtGufOnaNnz5567R4/fpzr16/zww8/0L17dwA6duzIs88+y9WrV7Gx\nscHd3Z3ly5djZ2enez5HjhwhNDSUQYMGPfKcHT9+nMjISLZt26Z7Hq1atWLcuHG6fWJjYx/ZzujR\no/nss884cOAAzz33HKAJQvr5+ekeoz1vDx484PLly/To0eOR/fu7qVYgzsXFxVD9EARBaLBkUhkH\nxx8XtY8EQRAqIN4nBUEQGpacnGvk5EQ8/P8IcnKu0aRJrzru1aPl5uZy5coVZs+ejVqt1q339/en\nsLCQs2fPYm9vT0FBAf7+/rrtZmZm9O/fv9JZRRMSEoBH16rTBqMOHTrEuHHj+PXXX7GystLL7JPL\n5Xp14Pr3749UKuX8+fNlAnGXLl3C2tpaF4QDsLOz4+jRo7rlLVu2oFKpiI6O5tatW1y/fh21Wl3l\njLOLFy9iY2OjF/js2LEjLVq00C136tTpke3Y2dnh5+fHvn37eO6550hPT+f333/ns88+02tPe9yE\nhAQRiHsU7ZheQRAEQZ9MKkNu50lY0kUAvB19xBdNQRCEEmRSGb5Oje/DtiAIQkNkadkRS8sOuow4\nS8uOdd2lKsnMzKSwsJDPP/+83Fk5k5OTMTU1BdANE9Wyt7ev9NhZWVmYmppibGxc6X7NmjWjX79+\n7Nu3j3HjxrFnzx6GDRumaxcoMwmmRCLB1taWjIyMMsfLyMigWbNmlbb55Zdf8s0335CVlUWLFi3o\n1q0bJiYmVa4Rl5mZWeZ8lNfPqrTz9NNPM2vWLBQKBceOHcPc3LxMVp62xl5WVlaV+vd3U61AnCAI\nglA+pUrJwK19uJ11C4C2th4cHv+7CMYJgiAIgiAIDY6JiQwfn1Bycq5hadmx1oalPikrKysApk+f\nTkBAQJntjo6O3LihqVmampqKk5OTblvJumblsbW1JT8/n/z8fL2gWnnGjBnD3LlzuXHjBmFhYbz9\n9tt620u3VVhYSFpaWrkBN2tra1JTU8usP3PmDK6urpw/f54VK1awYMECRo0ahbW1NaAZNlpVtra2\n3L9/v8z6kv3cvXt3ldoZOHAg1tbWHDp0iGPHjjFs2DDMzMz09snMzNS12xhVGohbvHgx/fr1w8/P\nT7dcFRKJhODg4CfvnSAIQgNxOvGULggHEJMeTWRquMj+EARBEARBEBokExNZgxiOWpJMJqNDhw7E\nxcXRuXNn3fqIiAg+/fRTZs2aRbdu3TA1NeXQoUN4empqlqrVak6dOoWlpWWFx27evDkA9+7dw93d\nXbfeyKjsHJgBAQFYWlrywQcf4Obmhq+vr972iIgI7t27pxvmevz4cdRqNb16lT3f3bp1Y8OGDVy8\neBEfHx9AkyUXFBTEe++9x/Xr13F2dub555/XPebatWukpqZWOSOuV69erFu3jtOnT+sCazdv3uTO\nnTv07dsX0AyRrUo7pqamDB8+nD179nD9+nU2btxYpj3tJBDac9rYVBqI++6777C2ttYF4r777rsq\nHVQE4gRBaGziMu8UL+RZYZvZD1czr4ofIAiCIAiCIAhCjZs5cyZvvPEGMpmMIUOGkJaWxvLlyzEy\nMqJ9+/ZYWFgwZcoU1q9fj7m5OZ6envz444+kpKToBdhK8/X1RSqVcunSJb39mjRpwrVr1zh37hw9\nevRAIpHoglHbtm3jjTfeKHMstVrNa6+9xowZM8jIyGDJkiUMGDCArl27ltl34MCBeHl5MXv2bGbP\nnk3Tpk1Zv349jo6OjBgxAmNjY7Zu3cqqVavo2bMnMTExrF69GolEwoMHD6p0zvr27UuPHj2YN28e\nc+fOxdLSkuXLlyOVSnX7dO7cucrtPP3002zdupUWLVro1bbTunTpEjKZrNzn2xhUGojbtGmTXnG+\nTZs2GbxDgiAIDdHItk/xn1PBqHJNYX0o6SmejDtUwMGDOcgaRia/IAiCIAiCIDR4AQEBrFmzhtWr\nVxMSEoJMJqNPnz7MnTsXCwsLAP71r39hbm7ODz/8QGZmJkOHDmXChAmcOXOmwuNqj3Pq1CnGjBmj\nWz9t2jQWLFhAUFAQBw8e1GW5+fv7s23bNp566qkyx/Lw8GD48OG88847SCQSRo8ezdy5c8ttVyqV\n8s033/C///2PRYsWUVhYSPfu3fn222+xtrZm3Lhx3Lp1i61bt/L111/TokULpkyZQkxMDBcuXKjS\nOZNIJHz55ZcsWrSIjz/+GBMTE1555RUOHz6s26c67Xh7e9OkSRNGjx6NRCIp096pU6cYMGCAXqCv\nMZEUVTVXsZFITm6cxQLL4+BgLc6H0Og8yX2vyFHwzf4wlk9/Vrdu//5sfH0La6p7gmAQ4v1eaGzE\nPS80RuK+L+bgYF3XXRAaqLNnzzJt2jROnjyJ7BG/tv/3v/8lMjKSH3/8UW99cHAwV69eZe/evYbs\nap26cuUK48eP5+DBg7Rq1UpvW0pKCgMGDGDHjh26ocGNTdnBzIIgCMJjcbJ0YmZgIO3aFQDQrl0B\ncrkIwgmCIAAolXDhghFKZV33RBAEQRAeT69evfD19WXLli0V7rNz504WLlzI9u3befnll2uxd3Xv\nr7/+YuXKlcyZM4cBAwaUCcIBbN68mYCAgEYbhINHDE3t2bPnYx1UIpFw9uzZx3qsIAhCQyaTwcGD\nOURGGiGXF4phqfWUUqUkLOkiAN6OPmJ2W0EwMKUSAgMtiYoypl07MWxfEARBaLgWLlzIiy++yIQJ\nE8qd9fPq1av8/PPPvPjiiwwbNqwOelh3cnNz2bhxI61bt+a///1vme1JSUns2bOHHTt21H7n6pFK\nh6YOGjTosQ989OjRx35sXRLp2sVE+rrQGD3ufa9UKYlMDUdu56kX1KlovVB3lColQ7b7E5MRDUBb\nWw8Oj/+9UV8f8X4vGNqFC0YMH26lW67rYfvinhcaI3HfFxNDUwVBqEuVZsTVRDBNqVSSmZmJi4vL\nEx9LEAShPlKqlATuGEBU+g3a2bbn4PjjyKSyCtcLdSsyNVwXhAOISY8mMjUcX6ceddgrQfh7k8sL\nadu2gJgYY9q2FcP2BUEQBEFovAxeI+7bb78lICDA0M0IgiDUmcjUcKLSb0CeFVFXbQmLv6G/HohK\nv0FkanhddlN4SG7nSVsbD91yW1sP5HaNt0aFIAiCIAiCIAi1p95P1pCRkcHcuXPp2bMn/fr1Y8mS\nJRQUaAqhJyQk8Morr+Dt7c3w4cM5ceKE3mPPnDnD6NGj6dq1K5MmTeL27dt18RQEQfibk9t50tbC\nG9aHwtdnmfdCX5RKzfp2tu0BaGfbXgR76gmZVMbhCb8TMmYvIWP2NvphqYJQG8LCjIiJMQYgJsaY\nyMh6/xFUEARBEATBIOr9p6APPvgAhULB999/z2effcbu3bvZuHEjRUVFvP7669ja2rJz506efvpp\nZs6cSVxcHAB3795l+vTpPPXUU+zatQt7e3tef/11CgvFUAhBEGqWTCrjM6/DkKIJtMVEmxB2LQ+Z\nVEbI2H0sG7iKkLH7RLCnHpFJZfi18Mevhb+4LoJgYEolvDXXTLcsdbiJa1tRp0oQBEEQhMap3gfi\nTpw4wcsvv0z79u35xz/+wahRozhz5gxnzpwhNjaWDz/8EA8PD1599VW6devGzp07Adi+fTsdOnQg\nKCgIDw8PFi1axN27dzlz5kwdPyNBEP6OvDua0dZDrVmwD+fNK37EZtxk3O6RzD42g3G7R6JUKeu2\nk4IepUrJBUWouC6CYGCRkUbE3iwuS6wa8QrxedfrsEeCIAiCIAh1p94H4mxtbfnll1/Izc1FoVDw\nxx9/0LFjRy5fvoyXlxcyWXEmg6+vL2FhYQBcvnyZHj2KC29bWFjQsWNHLl26VOvPQRCERsBMSdDK\nDTC1FwT1IEEVyeifAkWNuHpKO5HG8F0BBO4YIIJxgmBAcnmh3g8Vbb0yxFB9QRAEQRAarXofiFuw\nYAHnzp3Dx8cHf39/7O3tefPNN0lOTsbR0VFv32bNmnHv3j2ACrcrFIpa67sgCI2DNqgTfHYaxm4X\nwSwbgKQcBW7W7oCoEVffiIk0BMHwtFmnmCk5fCiXkD0phOxL4vCLv4oh4YIgCIJQTxQVFdV1Fxod\nk0fvUrfu3LmDl5cXb7zxBkqlkoULF/Lpp5+Sm5uLVCrV29fU1BSVSgVAbm4upqamZbbn5+dX2l7T\nppaYmBjX7JNowBwcrOu6C4JQ66p739+Mv64L6hQUqXGyckKRraCDfQeOvXyM2+m36ejYEZmp+OJZ\nX3hbeNHSpiW3M27Twb4Dfu17NvrrI97vhZqkzFfiv34QESkRdLDvQGhQKE+3tgf613XXdMQ9LzRG\n4r4XGorExETmzJnDtWvXaNOmDYMHD2bDhg26EW5yuZy3336bKVOmEBISwvz58zl9+jR2dnaP3WZw\ncDBXr15l7969le6nUCiYOHEiu3btQqlUEhAQwIoVKxg2bFiV2lGpVMyfP58jR44glUp55513CA4O\nZufOnXTu3Pmx+/84jhw5wu+//86HH35Yq+1WpKrXQCs+Pl7v/B87doxvv/2W7777zsA9fTL1OhB3\n584dFi1axNGjR3F2dgbAzMyMV155hfHjx6NU6g8lys/Px9zcXLdf6aBbfn4+tra2lbaZlpZTg8+g\nYXNwsCY5WRRTFqpPqVISmRqO3M6zwWU9PM5972jkTlsbD2IyogGwNLEiZMxevB19MM61oo2ZF7kZ\nReQiXk/1gSJHwYhdAcRl3cFN5saOUXsa/fUR7/dCTbugCCUiJQKAiJQIDl8/gYWJRb35uyDueaEx\nEvd9MRGQrP82bdpEeHg4y5Ytw9nZGXt7e/r3rx8/5ixYsIAXXngBW1tbLC0t2bZtG61atary4//4\n4w/27NnDW2+9Rbdu3VCr1Ybr7CN89913WFpa1ln7NW3gwIFs2LCB7du3M2HChLruToXq9dDUq1ev\nYm1trQvCAXTq1ImCggIcHBxITk7W2z8lJQUHBwcAnJycKt0uCIJhKHIU9N/6j0ZVe0smlfHZgOW6\n5diMm7r1Qv2iVCkZsXMQcVl3AIhTxhH/8P8FQag5cjtP2tm2B6CtjQfzTsxi+K4Ahmz352TC743i\nb4MgCILw+DIyMnB1dWXw4MF06tQJZ2dnunTpUtfdIjQ0lNDQUCZOnAhoRt15e3s/MuGnpIyMDACe\nffZZevTogZFRvQ7LNDhTp05lxYoVjxwNWZfq9RV3dHQkMzOTpKQk3bqYmBgA2rRpQ0REBDk5xRls\nFy5cwNvbG4CuXbty8eJF3bbc3FyuX7+u2y4IQs0rHeRoTLW3vB19aGvjoVued2KW+KJZD0WmhhOn\njNMtt5C5itp9gmAAMqmMg+OPs/+Z3/hswHJi0jUZwzEZ0Yz7eVSj+aFGEARBqL5BgwYREhJCdHQ0\ncrmckJAQVq5cSbdu3ap8jFOnTjF+/Hi6dOmCv78/K1asoKCgQLddrVazZMkS+vbti4+PD4sXL9bb\nXpENGzYwaNAg3Ui8+Ph45HI5Bw4cADRDK2fOnMl3333HwIED6dKlC5MmTdLFMYKDgwkODgagd+/e\nuv8vKTg4mFGjRumtO3LkCHK5nPj4+Co/x0GDBrF+/XoWLFhAz5498fHx4d///rduZOGkSZM4d+4c\nx48fL3PskuRyOTt37uTNN9/E29sbPz8/tmzZgkKh4NVXX8Xb25vAwEBOnDih97jDhw/zzDPP4O3t\nTf/+/Vm+fLle9l9Vr8GmTZsYOnQonTp1YuTIkfz6668VXB2Nvn37olar2b17d6X71aV6HYjz9vam\nffv2vP3220RERBAWFsZ//vMfxowZQ2BgIC4uLgQHBxMVFcW6deu4fPky48ePB+CZZ57h8uXLfPnl\nl0RHR/Puu+/i4uJC79696/hZCcLfV2MOcpTOiotJjyYyNRylEi5cMEIpvm/WC3I7T72AqdRIWsne\ngiA8CZlUhq9TD7wdfXTZcVqN6YcaQRCEhkqpVnM2MxNlLQ+dXLVqFf3798fNzY1t27YxYMCAaj3+\n9OnTBAUF4erqyqpVq5gyZQobN27ko48+0u2zaNEiNm/eTFBQEEuXLiUiIoL9+/dXelylUsmJEycY\nOnRopfv9+eef7N69m3fffZfPPvuM27dv6wJur7/+OtOnTwfg66+/5vXXX6/Wc6vOcwRYu3YtmZmZ\nLF26lFmzZrFv3z6+/PJLQDPE1svLCx8fH7Zt21ZmssuSFi9eTMuWLfnyyy/p1q0bCxcuZPLkyfj4\n+LBmzRqsra2ZN28eubm5AGzbto0ZM2bQpUsXVq1axYsvvsiGDRv0Ao9VuQarVq3i008/ZcSIEXz1\n1Vf06dOHOXPmVHqtTExMGDRoEPv27av2ea0t1aoRt3v3bjp06ECHDh0q3OfChQucOXOGN954A4Ce\nPXs+fudMTFi3bh2LFi3i5ZdfRiqVMmzYMObOnYuxsTFr1qzh3XffZdy4cbi7u7Nq1SpcXV0BcHV1\nZeXKlSxevJivvvqKrl27smbNGpH2KQgGJLfzpHWTNsRmaoZmmhqbPuIRfy8tTDvgmPoUSVa/0c6p\nBa5mXgQGWhIVZUy7dgUcPJiDTIxWrVMyqYwP/Rbzwj7Njza3MmM5nXiKIS0D67hngtDwVLUeqDY7\n7vSty7y942sSLA7QzqlFo/mhRhAEoSFSqtX0uHiRiJwcOlhaEurjg8ykdkrMe3l5YWdnR2Ji4mON\naFu+fDldu3Zl2bJlAPj7+2NjY8P8+fOZMmUKMpmMrVu3MmvWLCZPngxostMGDhxY6XHPnz9PQUEB\nXl5ele6XnZ3N2rVrdYEthULBxx9/TFpaGu7u7ri7uwPQsWNH7OzsuHv3bo0/R21cxNnZmaVLlyKR\nSPDz8+PcuXP8/vvvzJs3Dw8PD2QyGZaWlo88z926dWPu3LmApgzYoUOH8Pb25rXXXgNAIpEwefJk\nbt26Rfv27Vm+fDkjR45kwYIFAPj5+WFtbc2CBQuYOnUqzs7Oj7wGmZmZrFu3jqlTpzJr1izdcbKz\ns/n8888ZPnx4hf318vJi79695Ofnl5nEsz6oVlQqODiY3377rdJ9Dh8+zLp163TLPXv2ZMaMGY/X\nOzQXecWKFZw9e5aTJ0/y3nvv6dJAW7Zsyffff89ff/3Fvn378PPz03ts//79OXDgAJcvX2bTpk26\nG14QBMPJLyweix+bcbPRZDwo0rPxGwhJX/yMyTeX+H7Ir8THWBMVpZmFOSrKmMhIw/8QoFQpuaAI\nFUO+KnFPeU9vee7xmeJ8CWVsv59Mm2sXaH7tAsOirnEtN9tgbf2Sdp921y7gcu0C/SL/4ny24Yup\n/5GVwcjocP7IynisxytVSgJ3DKh6PdA8Gf+dPISE5TtpujmGdQN2ijqagiAI9di1nBwiHpaBisjJ\n4VpOw5jUMDc3lytXrjBw4EDUarXun7+/P4WFhZw9e5bLly9TUFCAv7+/7nFmZmaPnAwiISEBQK+G\nfXlcXFz0ssu0+2uzxZ5UVZ6jVufOnZFIJHp9yXmMa1myPp+9vT2gqd+vpa2Rl5mZyc2bN0lNTS0z\ni+zIkSMBTUCzKtcgLCyMvLw8BgwYUOZ5xsXFERcXR0VcXFzIz88nJSWl2s+1NlQa0g4JCeHo0aN6\n6/bt20d4ePlfrFUqFWfPnq1WoUJBEP4+IlPDSVAW1xZws3ZvNBkPR0LjUSV1B0Cd1I4/w84zprcj\n7doV6DLi5PJCg/ZB+8U4Kv0G7Wzbc3D8cfFFtxRFjoK5J2bqrbubfZfI1HB8nXrUUa+E+mb7/WRm\n3CuexONi/gMG3oxglbM7E5rV7KRPv6TdZ2riLd1ypDqfEbdusKCZM284t6jRtrT+yMrgmTuamm3P\n3Inmrab2/NulZbWOEZkaTlT6DaB4mGllr6HISCPdDxNp8U4ErB7H6bfX0NqmzWM+C0EQBMGQOlpa\n0sHSUpcR17GBzKyZmZlJYWEhn3/+OZ9//nmZ7cnJyboMqaZNm+pt0waYKpKVlYWpqSnGxsaV7mdh\nYaG3rB2VV1hYM98FqvIcK+qLRCKhqKio2m1aWVmVWVf62FraySiaNWumt97a2hpTU1OUSiWZmZlA\n5dcgPT0dgOeee67cdpKTkyscTqvtW1ZW/ZwputJAXL9+/fjoo490EVOJRMLNmze5efNmhY8xNTVl\n5syZFW4XBOHvy868GSZGJqgL1RhLTNj51C+NIhCkVClxbJWC1DEGVVJbpI4xDO7hikwGBw/mEBlp\nhFxeaPBhqdX9YtwY7Yv5hSL0P3y4W7dsNAHjhqyqwyBrwsdJCeWun3HvDm3MzeluZV1jbX2kKL+t\nD+7fo52FJUNtmpa7/Um8l6A/U/DnaSl4Wsh4qmmzCh5RlnZWVG3g/1GvIbm8EEf3VJLu2IF9OIX2\nlxn9UyBnXrjUKP5OCIIgNDQyExNCfXy4lpNDR0vLWhuW+qS0AaPp06cTEBBQZrujoyM3bmg+L6em\npuLk5KTbpg38VMTW1pb8/HyDD3eUSCRlgnbZ2cWZ+VV5jnVJm5h1//59vfWZmZnk5+dja2urQ+WZ\n+QAAIABJREFU26eya2Btrfm8tXr1ar19tFq3bl3hNdMGA+trklilryYHBweOHDlCbm4uRUVFDB48\nmJdffpmXXnqpzL4SiQQTExOaNm2KVCqKXwtCY6NUKRn38yjUhZpirgVFalIf3P/bZzuUzEJrPacL\n01zWMPIfbXGy1fyBlMnA19ewmXBa1f1i3Bi5NSlbouBFr8kiEFDPlXyducnc+PXZozhZlv1AVlPe\ndWyhlxFX0tKke2xpXXOBuPecWuhlxJX0sSLBIIE4JzNTwnPy9dZ9pEioViBOW/etsuCoUoneDxF7\n9qfRe/loCu0vg1k2STnZ4gcDQRCEekxmYkKvJk3quhvVIpPJ6NChA3FxcXTu3Fm3PiIigk8//ZRZ\ns2bRrVs3TE1NOXToEJ6ems/LarWaU6dOYVlJ5l/z5s0BuHfvnkHLXllZWXH//n0KCwt12XQXLlzQ\nba/KcywvcFUeQ9TQb926NU2bNuXAgQN6E1toZzv18fHBxcXlkdega9euSKVS7t+/z+DBg3XHCQkJ\n4dChQyxZsqTCPigUCkxNTR+Z5VhXHhnWtrOz0/3/4sWL8fT0pEULwwyVEASh4QpLuqg3LNVEYoKr\n9d+/LmPJLLTYB1fo2i0PK6siLihCayVzp6SqfDFu7Hq79KWpaVPS8tN068yMzeqwR0JVlHydxSnj\nGLErgBPPnTHYPZ6pVmMClDdH3HT7mv2VOUOtxgIor2rMu06G+by1wNmV4zcj9Na9V8Nt/ZGUxYuH\nk8j9shVtC2UcPpRLawdHTr+9htE/BZKUky1+MBAEQRAMYubMmbzxxhvIZDKGDBlCWloay5cvx8jI\niPbt22NhYcGUKVNYv3495ubmeHp68uOPP5KSklJpgM3X1xepVMqlS5cMGojz9/dn8+bNfPDBB4wY\nMYIzZ85w5MiRaj3HqmrSpAnh4eGcPXuWrl276urxPwljY2NmzJjBwoULsbGxISAggMjISFauXMmw\nYcN0/XvUNbCzs2PSpEl88sknZGRk0KVLFyIiIli2bBkBAQHIZLIKM+LCwsLo1avXI4cR15Vq5Zc+\n/fTTABQVFXH+/HkiIiLIzc2ladOmeHh40K1bN4N0UhCEhkddpCY+645Bs1bqA1drd6RGpqgK85Ea\nmWJn3kzUaXsMtTXsUCaVETJ2HwO399Gt6+HUs04Cp0LVye08cZO5EafUFOWNy7pjsEyqrxV3eScl\nUbdsBZScpsHSuOaG5mxOVvBWUrzeug7GUh4AHzV3M0g2HEBHCyuOtenA/Pg73C7IY6GTW7Wy4UDz\nmh2yw5+Y9Gja2npwePzvutfP+ewsnkm6Ad7AV2HEvOZN2DU1fr3McLB05Ksh3wDg7egjXnOCIAhC\njQsICGDNmjWsXr2akJAQZDIZffr0Ye7cubraYf/6178wNzfnhx9+IDMzk6FDhzJhwgTOnDlT4XG1\nxzl16hRjxowxWP/9/f2ZPXs233//Pbt376Z379588sknBAUFVes5VsXkyZOZPXs2U6dO5bvvvsPH\nx6dGnsOLL76Iubk5GzZsYMeOHTg6OvLPf/6T119/XbdPVa7BvHnzsLOzY/v27XzxxRc4Ojry8ssv\nVzohqHbugtmzZ9fIczEESVE1K/VduXKFt99+m9u3bwPoCv1JJBJatmzJZ599ppce2dAkJ9fPYn51\nwcHBWpwPocqUKiUDt/XhduYtgDJfzBqK6t73FxShDN9VXJth2cBVzD5W/Idh/zO/1dqwq4Y6WUNt\n97v0NdPWNWxI56ymVfe+r816bVqxGTfp+2N31IVqpEamXHzpmkEC/R7XLpJZqo6gm9SUOFU+7UzN\nOdimA7Ia+nXV8/ol7hfpD11f1rwlL9jVz2EUJZ1M+J1xP4/SLYeM2YtfC83MZxNjoziSk1m883kp\nIf5qvF3ba17rigTccofz6+srdcP4a5v4jCM0RuK+L+bgUHMlBoTG5ezZs0ybNo2TJ08iM3QBaOGx\nHDp0iA8//JDffvsNM7P6OfKlWgOCb926xSuvvMLt27cZOnQo8+fPZ/ny5Xz44YeMHDmS+Ph4pk6d\nWuk0soIg/H2ZSEwgzwqH+6PYMuRAowhoaDLiNHUxpUZS+rj40c5Wk25d28OuypusoSEo3e+wpIsG\nbU+bXaWlrWvYkM5ZXdIGTofvCiBwxwCUKmWttJv64L7uWqkK84nPKr+G25MKtm+ut+xgbMJiJ1fa\nSUwwKiriUk7NPd93HFz0lo0Bd6mUp6LC6RoRxi9p98t/YA2JzcvlhdgbdAy/xPb7yY9+QAm56vIG\n02rMcXQuXigqwslsEd6u7TWvdUUCrA8lbvkORgRao6yd20cQBEEQakSvXr3w9fVly5Ytdd0VoQIb\nN25k+vTp9TYIB9UMxK1atYrc3FzWrl3LihUreOmllxg2bBgTJkxgyZIlrFmzhqysLNauXWuo/gqC\nUE9FpoYTk3QX1oeSvHIPY0fY14svWEqVkguKUIMFC64kh6EqVAGgKlQRnR7FwfHH2f/Mb4SM3Udk\nanitBSrkdp60tfEAoK2NR4OpvSS386R1k+JJPd46PtPg5+yT/ktpIXPVWyc1Mm0UdQ2fVFj8DaKu\n2kKeVa0GL7WTkYBhg9xTnZqzyN4Fa2C6jT1ftWjFi/E3iSpSE6nK45k70fyRlVEjbU1ycOJzR1ea\nAhOsbdnu7sEzd6I5k5/D3YICpibeMlgwLjYvl17R1zmck0VyYSEz7t2pcjBOqVISfOItvXXmRsU1\nZbpbWbPL1Q2LjDA4/xqyQk2g3dXaHcfsAEjRXLu4WCvCruXV0DMSBEEQhNqxcOFCtm7d+shZVoXa\nd+TIEUxMTJg4cWJdd6VS1QrEnT59moEDB+Lv71/udn9/fwYNGsTJkydrpHOCIDQccjtPnLOH6L5g\n3b1tw7EL9+q0T7WRuROXqZ+Vc+1WCj9vt8VO3ZGxu4czfFcAQ3b411owDkmp/zYQOeoc3f/HZtw0\nWFac9p54Yd94TCQmNJEWzwRmyCyr0hQ5Cn4I34QiR1Er7dUUpRLemtgbvj4L60Npa+FdawFfbX2/\nZQNXETJ2n0Ezbqc6NSemoy8fuLbky5SkMts/vpdQY209bWfPltYd+KRFK7anp5bZ/pGi5toq6ce0\nsm19nFS1tiJTw4lT6r9WJv76rN77nOWD2+SGzYacG8SkRxOWdJFxu0eSZPUbxg4xmp2aRTLv+pDa\ne38UBEEQhBrg4uLC0aNHsbW1reuuCKUMHjyYzZs3I5HU7y9D1QrEZWRk4ObmVuk+bm5upKaW/XAn\nCELDVZWsMplUxrCercD+YXaMfTgXCr+tlf5VpDaGag50L641RpYj/5v4KrNnW9Cnhz0xcZoaSdov\noYYWmRpOTHq0rs2GMswyLOkiipzaCdqWvCduZ90iU1Vcx6q5VfNaCSopchT4bOrI7GMz8NnUsUEF\n48Ku5REbY6pZSPHkw/a/1NoQdKVKybjdI5l9bAbjdo80SPBGWVDAfxJu4XHtIl8r7gKlhlk+NL6a\nExtUZPW9BDwiwhgeG0HgzQheLqc2XE3PZqr1fFO7MuvedaxaW+Vljqbnpeve57bfT+bFFCPMOy8B\nU2ddJqP2tVfwMIsYiohJj2ow71WCIAiCIAg1oVqBuObNm3Pp0qVK97l06RKOjo5P1ClBEOqPqmaV\nKVVKDt/bCUE9YGovCOrB+M6jyt23ttTGULbUByWGjUWNRK3SvK0WqI0hamSNt1eZ2hq6VxuampUN\nEtSEkueotH84962VoNKR2wdRFeYDmiy8I7cPGrzNmnLX8rBesP2B3flaa7vcwLpSicmFUGpiHLyy\noIDuEWGsTb9PJkW8k5LI14q7mmGW7h6YPUwzbWEi5f+aPvlkCl8r7vLB/Xtop2qIyn+ARGLEsTYd\n+IepJc2NjfnapVW1ZzOtqtZmFpz18GKIpTUORkascnZnQjOHKj323N3TFW7bfj+ZGffucB94YOcL\nvbewbswhvB19NK+95I5wv4Nm5/sdcMsd3qDfqwRBEARBEKqrWoG4IUOGcPnyZVauXFlmm0qlYunS\npVy+fJmhQ4fWWAcFQahbVc0qC0u6SIIyHsyywfUcmGXzoKDiYt61QSaVGbxem2ayhocZQu0OgPHD\nekfGedh1Pgto6rV5O9bMVOCP8mn/pYSM2dugZv8sXasN4OfoEIO0pb0nvgncVLbNmz/VSnZaHxe/\nSpfrK6VKyX/OzdALtt/MuVxr7ZcONHcwc6dp4ACaDg+gaeCAJw7GReY9oHQ+/ycpmqy4ftY2/NSq\nHV5GphQWFnI088lrwmiPrWUEyM3M6WhhxQr3VnQys2R+Neq2PY7WZhYsdW1Ff6smvKeIZ3Ny1e7/\nM4nlB+KamtmVM7xVwo6MDMiT8V+XU3zQbS2t22gm3XBrnc2vr69sMO9VgiAIgiAINcGkOju//vrr\nHD16lDVr1rB79258fX2xtrZGoVDw119/oVAoaN26NdOnTzdUfwVBqGXaQJOqML9axexdrFrUeZaD\nUqUkMjUcV2t3xv40nJiMaNraeHB4wu96X/y0+8ntPHGgetPZayZr0GQ3YX0XozltKIwMxFh+mP2T\n95L64D5yO0+Df9HUZi5Gpd/ATebGr88ebTBfbo/d+a3MujEe4wzWnkwqIzmnbHCjsKiAI7cP8oLn\nSwZrG0plUT5cbm3TpoK964/I1HBS81LBDE2wHSiqxfa1QVTta9XmSjgmUZofCUyibmASGY7at8dj\nH19uZo4d6AXjtDOoXsvNZsStG7r1UxNv8TU8UbZasH1z3klJ1C3/p5kzMmNj3SQKWjPuaWqxVTVb\nrToUqnw63/hLt/xWUjygmUSiMhXdr1vCN/Ou11xdnwEoKmLPsdnsX3mQ2JvWgD3N3bP5YUcGvX1N\nkcmsnvh5CIIgCIIgNCTVyoiTyWRs3bqVp59+mvv37/PLL7/www8/cOTIEdLT0xk3bhxbtmzB2rp6\nX2QFQai/4rPu6A2jq6iYvbejj97Ml2YmdTtdtFKlZMgOf4bvCmDojv7EZDysnZYRzenEU3r76Q29\nza96Vo0iR8FL+57XLUuNpPz2z50se8uXsDeO0dqmDa7W7vwcHWLwTKuSmYtxyjhG7ApoMAXQ3ZqU\nDe6m5Rm21qi1aZNy17vLWhq0XQA782aYSDS/g0mNpA1mpla5nSdOFvr10tratq3VPsikMnydeiCT\nylDLPVG302TIqdu1Ry2vXuC/dO1LmbEx5zt4M822GU2QsMjehalOmkDcV+VM2DA38Rbb7yfT9toF\nWly7gP+Nq5zPzqpy+9rZWbVtveGsqc9W3iQK8+/d4Ze0+7S7dgGXaxfoF/lXtdqqyJGszDLrFibF\ncygjDa9y2tKes2spf5V5HICNmS0TmjmwytkdG4DbV+DkVOKuKYm9Wfzb7907VgSfeg3MlPyRlUG3\nh211j7hcYzPSCkJ9YejZ2wVBEISGp1qBOABbW1sWLVpEaGgov/zyC1u2bOHnn38mNDSURYsW0bRp\nU0P0UxCEOlJyOJibzK3CoIFMKuO93h/olmMzbj6yALchP5yGJV3UTVxwNztRb9vbJ2br2iw99PZa\n0rUqt3Hk9kEKUOuWVYUq0vJSecHzJZwsnWq1KL/czlNviGdc1p0GUwC9i4O3LjClNe/ELIN9aVGq\nlBUGEibsHVuj16n0Pa6ZcGAU6iLNfaMqVFVab6s+kUllLPL/n946cxMLwzdcog6c3myzMhlpB4+T\ntv830g4eB1nVM0Arqn0pMzZmYYtWRHf00QXhAF6zL1v7Nh1NtloWoAIiVHmMuHWj2sG40m2VN4lC\nFposvAxADUSq86vdVnkGW5cNSKcDL8bfJKVUW39kJOnO2Q8Rm8s9njZTboSZA/ZBfjB5Jnyxl9bu\nxrrhqAA0iyTOYj9b74XzzJ1oEh62dadAzTN3okUwTvjbqI3Z2wVBEISGp1qBuJdeeondu3cDIJVK\nad++PT4+PsjlckxNNTWSNm/ezLBhw2q+p4IgGFx5gTGZVEbI2H24WbsTp4yrcLZCRY6CVw/+U7f8\nqEwfQ384zVXr16eTUDyFdYIyXhekKl13qqNjxyq38ajaXrVdlN9UW6sOaNWkdZ0PDa6q+Kw7usCU\nlqFmfdXed2suf1Hu9oKHw1Nrqq2A7X4M3xVAwHY/3RDohOx4vf1ePfjPBjNzaq0E3kpSKnV14KyH\n+NFvvefDwLaXLhin9u1RrSAcVH9G5Y4WVsy1Lzt7anmWJj3ZDMCtzSz4xrVVrbTlJDXlr/adkUsf\nncH88d3bunNW0aDkrHxNhl1kpBEx0Q+D6ymevOf5HdNWb2D1pmhavDEJXvWlnVMLduaVfz99okgs\nd70gNDS1MXu7IAiC0PBUGoh78OABSqUSpVJJVlYW586dIzY2Vreu9L/U1FROnTpFYqL4ACUIDU1s\nxk3+8UM3hu8KoN+WHhy+fVAXHIvPukPcwyGpFX2QLC87LCotssL2DP3hNP1Bmt5yUYkvjtogoTYw\nEjJ2H/uf+U0zwYFp1b/Ql671ZSwxoV1TuW65j4uf3hDEwS0DH+epVElkajixmTd1y3FZd8hWZRus\nvZrkau1eJiPOGGPszGt+tsiS911F5LYdaqSt04mniM3QXJPYjJucTjyF3M6zTH2tAgrYF/NLjbRZ\n2ywMHJgziSyuA2cec5P2iuJMwvLOWVWzbOV2nrS19QCgra1HlYLWL9tVrUbbHMeqBewqM1BmS/mD\np2u+LSepKZtaPnqI8VM2tsUT05TDCCN6Ne8NgFxeSFsPzbVq1eYB08J6EXx2GjNjPVn/6mSWDfuU\n70dupyPlT+gT7OTyGM/EcPQyMQWhGkr/HTPE3zVB+LtKTEzkueeeo3PnzowZM4aVK1fSrVs33Xa5\nXM4333wDQEhICHK5nNTUJyttEhwczKhRox65n0KhICAggPT0J5+86UmVPA/1TU33LSIiglGjRpGf\nn19jx6wrlU7WsGvXLj766CO9devWrWPdunWVHrRr165P3jNBEGqNIkdBny3dKXiYlZSQncAL+8bT\n2qYNv004qcsai0q/QTvb9uV+aR3cMhATiRR1kUq3bt6JWRwe/3u5kwZU5ZiPS6lS8p+T8yvcrg0S\n/vvEHF37jzPLqKu1O8YYU0ABAAVFaqLSInGydEKpUjJx77O6TC8XWQuspIYrSi6388TRwpGk3KSH\nfSmeeKDkZBT1cQKHqLTIMhlxBRQw+qdAzrxwqUb7rA3AxKRH09qmDRkPMkjN0w+oTtr/HKGTrjxx\nu9dSruotx2XeobdL33KTiSoLcNQXSpWS90u8rlo2aWXw2YC1deBMom6Q0aoF1xyKZ+QsXVtQWxcy\nJj2atrYeFb736BSV+u8jOElNOevhRe/o6xSWs93DWMoXbq3pbvXkdXJlxsacat8Z3xt/Ud5HzVbG\nJqxxa1MjbYEmC29BM2c+uF9+hp2b1JQukvTiiWkAewsHUnKTaWHlyt2cRAqLChm6cwAXX7qGlZkV\nBPlDlClK9yTUhZofRgqK1Iz6aSiFFIJNL+i6mBLJyjgaGfGlaxv6WdvUyPOqCYocBd2+80JdpMJE\nIuXSy9dxsqx8MgtB0Poz8WSZ5YYwOY8g1AebNm0iPDycZcuW4ezsjL29Pf3796/rbgGwYMECXnjh\nBWxtbeu6K2zbtg0Xl/r1A5ahdOjQgU6dOrF69Wpmz55d1915IpVmxD3//PMEBgbSvXt3unfvjkQi\noXnz5rrlkv969OhBnz59GDt2LP/73/8qO6wgCPXMkdsHdUG4kmIzbhKWdFE3W6Eua6ycL7dOlk5c\nevk6r3edqVsXkx7Nz9Eh5WanaI8ZMmYvn/ZfCtRczbiwpItlgislaTNhnjQjLz7rji4IV1pkajgx\nSXchvifkWXE785ZBh6TIpDK2jd6NscQY0GTn9XHxa9D1aZJyFHoTa9SUwqLiMMquMXvKbL//IIWw\npItP1IYiR8GnZz/WLRthxED3gDKZi1ox6VFP1F5tiEwN1016AqAuLPueUeNK1oE7dBxHR80X2NY2\nbTRBzRJK1oWMSY+u9BqGJV3Um8Clqq/N1ILCcoNwAG84utRYYAw0gT/zklGqEsba2tdoWwDr0srO\nJAww2LIJJ9p60a6JO5I8a917WtqDVL4J3ER+YZ7uNaUdgh+ZGk5Mbhi4niOl8BZGJT5uFlIIeVbg\nNhMk+s+vi7msXgXhAPbF/KL7gUldVH4mpiBUZHDLQKRGUsDwmfGC8HeTkZGBq6srgwcPplOnTjg7\nO9OlS5e67hahoaGEhoYyceLEuu4KAN7e3jg6lq1l+3cVFBTEhg0bSE4u/3NLQ1FpIM7IyIjly5ez\nefNmNm/eTFFREePGjdMtl/y3adMmvvnmGxYvXoy7e8OYAU4QGoLamG3rUbXOqppRZSW1YnCrobpf\ne6VGUmYfm1FpAOjfJ+Yw7udRDNnur5vl1JABo9e7zmT32P14O/ro1YZztXbXnOdqzJqqGVIp1S2X\nzBByNfNC+s1l+PosrA+lpXkng9ZsU6qUvHpoMgVFBRhLjCkoUjNx37OEJV2s9/VpSk4yUdq84zU7\naUNY0kW94aJpeanM7V5x9uTjKj1Uu5BCJu57Fldrd+xMyxbjHy9/rsb7UNPkdp64ydx0yyVrLVZH\ntd/THtaBs7J14penD7Js4Cp+efpgmfei0nUhSy+XbP+t48U/GFR1aCqA3MycsldP44t78fS7cbVG\nJxoItm9e7vpf05LpGXGFQxlp5W5/HO86tih3vSLvAX2j/mJZzCWK1oVq3tNWX6MgsxlnEk+TnKv/\nQbiPi59mCHaJWbT1wpd5VrA+FBYPLpONWBNDbWtaUZF+JzPyxEQSQvVo76HS95IgCBUbNGgQISEh\nREdHI5fLCQkJKTM09VFOnTrF+PHj6dKlC/7+/qxYsYKCguIf0NVqNUuWLKFv3774+PiwePFive0V\n2bBhA4MGDcLc3ByA+Ph45HI5v/76KxMnTqRLly6MGDGCX3/9VfeYs2fPIpfL2bp1K3379qVXr17E\nxcUBsHfvXkaPHk2nTp0YPHgwmzcXT4g0f/58AgPLBvCfeeYZ5s2bB5Qd/hkREcHUqVPp2bMnPXv2\nZN68eaSkpOi2lzf89siRI8jlcuLjNXWMk5OT+de//kWvXr3o2rUrEydO5Ny5c5Wel9jYWKZMmUK3\nbt0YMmQIf/zxR5l9rly5QlBQEN27d6dTp04EBgaydetWQHM9+vbty4cffqj3mHv37uHp6cnRo0cB\naNu2La1bt+b777+vtD/1XbUma4iIiGDGjBmG6osgCKXUVjZTgjK+3PXGGNNC5lqlPmj7Ou7nUcRn\naf6wqAo1WQQVBYBK1uuKyYjWZbM8acDI29FH70tgyeez5vIXjP1pOIAuyy9k7D7G7R7J8F0B9Fjf\no8rnWTPJQPFQ3GUDV+mCA1GRJqiSHtZdSvFEfU9e3iFqTMlzWVCk+RARkx5NrjpXL+BY3yZw0M4i\nWpHE7ASDBw/Hy/9Pb7mFlesTD7ksL7gdkx5NfNYdpnSZVmZbYnZCmXXVZeigvUwq49dnj+L2cBKW\nx7mfnuQ9TalSMvan4cw+NoOxPw1/5GMfVBCIKxmMBXin1/tVHoYsMzbmfAdvptk2wwQwB/qZWQIQ\nW1RApCqvRmf9nOrUnEX2LpgAZoDvw0kVbhQWcKtAxYvxN2ssGDehmQOrnN2xBkyBDsaaHxn+Ksjn\nbkEBGyT20P5htlpmS/j6HDYSV73gLGhqZ8qkMiZ3mlp+Q8kdIcUTLjjCPC+aqaCrmTm/tmpf41l+\nNeFMqazcT84tFLXihCrTZFRqfpRRF6lFRqXQ4OTmqYm8nUpuXi1kwZewatUq+vfvj5ubG9u2bWPA\ngAHVevzp06cJCgrC1dWVVatWMWXKFDZu3KhXdmvRokVs3ryZoKAgli5dSkREBPv376/0uEqlkhMn\nTjB06NAy295//328vLxYtWoVHTt2ZM6cOZw8qT88ff369SxcuJD58+fj5ubGTz/9xFtvvUWPHj34\n6quvGDt2LIsXL+brr78GYOTIkdy6dYuIiAjdMeLi4rh69Wq5tezCw8P5v//7P1QqFZ988gnvvPMO\n58+f58UXXyQnJ6fK52/evHncuXOHxYsXs2bNGiwsLJg2bVqFNfGUSiWTJk3i/v37fPbZZ7z66qsE\nBwfr7ZOYmMhLL72EpaUlK1asYPXq1bRu3ZoFCxYQGRmJiYkJI0eO5MCBA3oB0b1792Jra4u/v79u\n3dChQ9m3b1+Vn099VGmNuNJSUlK4ePEiycnJKJVKLC0tcXNzo0uXLtjZVfQbsSAIj6u8CQ18nXrU\nWvsFFPBL9E9V6kPJvmoDcFoVzaBask5cWxsPkGiCFU8aMJJJZfwy7iD9f+xFal5x0VbtMNKYDM2w\nNb8W/vg69eCCIlTX94iUiCqfZ1drd6RGUlSFKqRGUt1EDYocBW/+NRrsQzRfOO3DSbA4YNDrV/Jc\nlmRhYsHB8cfrbY24sKSLZWYRLcnWrGmNBg+bmun/rWohcy0TiL6Xc49sVfYTnavSE3kAGEuMMTe2\nYNP1jWW2xWXeeey2QFOPbuzuEWTkp+tqOxriWjtZOnHiuTPVup9KZtQ+yXta6eGk2tewVvoD/Q+H\n750MZqD74DJ9THvw8D0hzwqSOxJ85L/l7lcRmbExC1u0YmGLVgCMjC4bKP5EkVhjQyynOjVnqpMm\nM25ibBSo8vS2f6xIYKhN0xppa0IzByY000xK8WZcLBGZJYpeSyTwWiycfpi1ltGSbpJJDBrSixE/\nDdbtZm5sgVKl5NurX5ffiMM1sA+HFE9ap9vym4dvdSe9rVVDWw/n55s/6ZaLKNLV3xSERyldy7L0\nsiDUZ7l5auYsP0F8khJXRxlLZ/XHwqxa4YPH5uXlhZ2dHYmJiXh7e1f78cuXL6dr164sW7YMAH9/\nf2xsbJg/fz5TpkxBJpOxdetWZs2axeTJkwHo3bs3AwcOrPS458+fp6CgAC8vrzLb+vWPUH8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jgZp/1dflSVZSJxTTDhCWDx4sU4efIkPvnkE1y5cgXHjx/HwoULkZeXh4CAABAEgTfffBPfffcd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\nznLNNGx7CyYxob1sTGi6lvr6XgCfT0Am4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qFWlGxMc7gy0PHIzZ2IS5fG2wzGVVdb/x2j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"text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -1113,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", @@ -1123,9 +1186,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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EdnY2P/zwg9E7rVu3vuX38/Ly2L59Ox06dDAkyQAaNWrEk08+ec/6K1JIM8pERERERERE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\n57C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" + "" ] }, "metadata": {}, @@ -1139,6 +1202,18 @@ "ax.tick_params(labelsize=14)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## De-drifting data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -1155,20 +1230,20 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", "start_time": "2017-05-09T11:55:07.433945+02:00" }, - "scrolled": true + "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", 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KwocOHdL999+v4uJiDRo0SD179lRISIhOnz6trVu36sMPP9TYsWP17rvvKjw8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\nD3dGzQAAuDVGrIDGMySxk82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"text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -1188,12 +1263,13 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", "start_time": "2017-05-09T11:55:07.833046+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -1211,24 +1287,6 @@ "Finding and replacing a dataset with drift." ] }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No drift detected.\n" - ] - } - ], - "source": [ - "dataset.detect_drift(data_name='CODtot_line2', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n", - " plot=True, period=None)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1236,24 +1294,24 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 131, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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MevfunfuCeQgICHA/ztohqaivAyA4OLhI9yktha7xYIyZbIw5YYzZnK1tujFmfebXPmPM+sz2JsaYxGzH3s92zkXGmE3GmF3GmLeN9pESERGRMpaclkzzt5szetZod9vlzS7H39e/TO7/3IDnuLbttWVyLxERKVyfPn2YPXs2iYmJxMXFMW/ePPexuLg46tatS2pqKtOmTXO3V6tWjbi4uEL7ZdewYUOio6PZuXMnzZo147LLLmP8+PF5Fh7+eP2Svo6iKs59i6ooi0tOAQZlb7DW3mit7WKt7QLMAr7Mdnh31jFr7T3Z2icCdwEtM79yXFNERESktD3w7QPsObOHzzd/TlJaUpnfPy45jsTUxDK/r4iI5K1bt27ceOONdOnSheuuuy5HIeC5556jZ8+eDBw4kDZt2rjbR40axauvvkrXrl3ZvXt3vv3+qGfPnrRq1QpwTb04fPgwl112Wa5+Q4YMYfbs2TkWlyzJ6yiqmjVr0qtXLzp06ODxxSVNfsM0cnQypgnwtbW2wx/aDXAAGGCt3VlAv7rAImttm8zno4F+1tq7C7t3VFSUzVpoQ0RERKQkVh1eRY//9qBFjRbsfGBnmd8/4pUIRnUYxYTBE8r83iIi5dGaNWtyLIIo5deaNWtYtmwZAwcOpH379gAYY9ZYa6MKO7ekazz0Bo5ba7P/5G5qjFkHxAJPWmuXAfWBQ9n6HMpsy5Mx5i5coyNo1KhRCSOKiIiIwPgV41l7dC0nHjlBZHCkYzmK8qGPiIiINylp4WE08Fm250eBRtbaU8aYi4A5xpj2kOfeUfn+1LXWTgImgWvEQwkzioiISCWVkJpA8L/PL6hVo2oNR4sOWuJKREQqo2IXHowxfsAIwD0uxlqbDCRnPl5jjNkNtMI1wqFBttMbAEeKe28RERHxTmeTzhIaEOqxX9C3ntya43lqeqpHrlsSNv/PXkREKqWMjAx8fIqy/KA4JSMjo0Tnl+T/7hXANmutewqFMSbSGOOb+bgZrkUk91hrjwJxxpiLM9eFuA34qgT3FhERES+z58weqr9cnYmrJ3rsmlmFh6nXTqVuSF3ev+b9Qs4oXSbPQaAiIpVXUFAQx44dK/EvtlJ6MjIyOHbsGKmpxS/eFzriwRjzGdAPiDDGHALGWWs/BEaRc5oFQB/gWWNMGpAO3GOtPZ157F5cO2RUBb7N/BIREREBYF/MPgCWH1jOfd3v88g1Z2yZQVW/qoxsP5KbO93skWuWxKOXPkq7yHZOxxARKTeaN2/O77//zpEjRzQdrRxLTU3lwIEDAMUanVJo4cFaOzqf9rF5tM3Ctb1mXv1XAx3yOiYiIiJSK7gWACPajvDI9V5f+TpjOo/h6X5PU8W3ikeuWVKP9vLs9mQiIhWdv78/TZs2ZerUqRhjCAwMdDqS5CMlJQUfHx9q1ap1weeWdHFJEREREY9Iy0gDwM/HM29P/v7D3wGw48rPmgpH444S6BdIeNVwp6OIiJQboaGhjBgxguXLlxMfH+90HMlH9erV6dWrFzVr1rzgc1V4EBERkXIhNjkWgPk75zO8zXCPXfdM4ply84t+1/90ZXib4Y6vNSEiUt7UqVOH66+/3ukYUkq0dKiIiIiUCx1qdcjxvaRuaHcDAD5Gb3dEREScpJ/EIiIiUi74ujbGck+5KKn3rn6PHffvICwwzCPX8xRry8/UDxERkbKgwoOIiIiUC1tObgFg3bF1JbrOr4d+5eYvb6b///qTnJ7siWgeoxXbRUSkMtIaDyIiIlIunEk8A8DpxNOF9CzYl79/yaebPqWqX1UahDbwRDQREREpARUeREREpFxpFNqo2Oc+u+RZXlnxCgB1QupQPbC6p2J5xLi+42ge3tzpGCIiImVKhQcREREpFyyutQ/+3O3Pxb7G+6vP7xbRrW63EmfytHui7nE6goiISJnTGg8iIiJSLmQtKunnU/zPRVLSU9yPJw2ZVOJMnrb79G6OxR9zOoaIiEiZUuFBREREiu3Vn1/l2unXXvB57/z6DvVfr8/c7XPZeWon8Snx+Pv6A/Cvn/7FjTNvZFv0tgu+7qnEUwBsvnczNarWuODzS1ufKX146qennI4hIiJSpjTVQkRERIrtsQWPFblvbHIswVWCSc1I5cHvHgRg2OfD3Mf7Nu5LVb+qzN85H4AZW2Yw/6b5tI5oTURQBKEBoQVePyktyf24SfUmF/AqylbWlBIREZHKQoUHERERKTXpGenEJMVwwxc3sGjfohzH7o26l4mrJ7qfL9m/hCo+VXL0GfzpYPfj+MfjCfYPzvde/137X/fj7ae2l8s1HgzaTlNERCofFR5ERESk2DrV7sTG4xtztV82+TJ+PvhzgefeG3Uv/Zr0I6peFFtObGHo50NJzUjNt//w6cMZ03kMt3S6Jdexw7GHeeDbB9zPy9tuFtlZqxEPIiJSuajwICIiIrlsOr6JKz65gnF9x3Ff9/vy7bfhng3ux9uit/Hyzy/jg0+uosMjlzxC5zqd6dekHz/s/oGR7UcS4h9Cx9odAQgPDMdgCpyGsGDPAhbsWUCP+j1oVbMVAImpiaw/tp5LJ1/q7vfu4HdpWr1psV53aTNGIx5ERKTyMeW96h4VFWVXr17tdAwREZFKZe72ue71F2L/GUu1gGq5+sz+fTZv/foWzcKbMXPrTOJS4nIcv63zbXy84WMe6vkQbwx6o9B7nko4xYGzB6gfWp+aVWvy3qr3ePu3t9l1eleOfq1qtuLaNtfy8s8vExEUQXRCtPvYFc2u4MdbfyzOSy4Tn276lPrV6tO3SV+no4iIiJSYMWaNtTaq0H4qPIiIiFQc0QnRrD26lp71exIWGFZq9/ny9y+5bsZ1ABx8+CANQhvkOH4s/hh1X6ub57n/6PUPOtTqwKgOo0q0NWZ2Xf/TlfXH1hfaL6+sIiIiUjqKWngodDtNY8xkY8wJY8zmbG1PG2MOG2PWZ34NznbscWPMLmPMdmPMldnaB2W27TLG/LM4L0pERKSy+/XQr1w59Uo+Wv9Rqd4nNf38WgsxSTFsObGF1hNa89qK1zDPGG7/6vYc/R+++GE+GPIBC25dwEtXvMQtnW7xWNEBYN3d63JM68jL1vu2lvuiw8bjG9l7Zq/TMURERMpUUd4RTAEmAB//of0Na+347A3GmHbAKKA9UA9YYIxplXn4XWAgcAhYZYyZa63dWoLsIiIilc6OUzsAePj7h3no4odK5R4nz51k+pbp7uefbPiEV1a8AsAjPz4CQLuIdoQGhDJ56GQ+3vAxt3W+rcAdJzxh4Z6F+R6LDIqkbWTbUr2/Jwz5bAgDmg7go2GlWzgSEREpTwotPFhrlxpjmhTxesOAz621ycBeY8wuoEfmsV3W2j0AxpjPM/uq8CAiIlIEj/7wKFtObmHrydL/0Tlo2iDWHl3rfp5VdMi+g8ULl79AoF8gAPd2v7fUMwGM7TKWBXsX8MaVb9B6QmsAutfrTp/Gffhbz7+VSQYRERG5cCUZA3m/MeY2YDXwd2vtGaA+8Eu2Pocy2wAO/qG9ZwnuLSJ/MOCZASxiEWcfOUtocKjTcUTEw8avHF94Jw/YHr3dXXQY1noYN3e8mUX7FjFx9UTu734/LWq0oEWNFu6iQ1kKrxrONzd9A8CW+7ZQN6Qu4VXDyzxHSZX39bVEREQ8rbiFh4nAc4DN/P4acAeQ1x5RlrzXksj3p64x5i7gLoBGjRoVM6JI5bLy7EoIg5hzMSo8iHiZ3ad352ob3mZ4ia87ffN01hxdwysDX3G3tXm3jfvxk32eJKpeFDe0v4F3B79brraCbBfZzukIxWLyfKskIiLi3QpdXDIv1trj1tp0a20G8AHnp1McAhpm69oAOFJAe37Xn2StjbLWRkVGRhYnokil06dGHwBCg1R0EPE22ddbyDKgyYASX3fUrFG8uuJV9/PktGT34+9u/o6oeucXqS5PRQcRERGpWIpVeDDGZN8/61oga8eLucAoY0yAMaYp0BL4DVgFtDTGNDXG+ONagHJu8WOLSH58TLH+WotIOZaWkZar7cHvHvTY9bOG/u+Nce220K9JP65scWVBp0gxvX3V29wTdY/TMSQP7339HtFno52OISLilQqdamGM+QzoB0QYYw4B44B+xpguuKZL7APuBrDWbjHGzMC1aGQa8FdrbXrmde4Hvgd8gcnW2i0efzUildhPMT9BNTgTf0ZTLUS8zOHYw6Vy3drBtTl+7jhpGWlk2Awu+fASAP5+yd9L5X4CQ1sPdTqC5OHnLT/z1zV/5ZVFr7DvtX1OxxER8TpF2dVidB7NHxbQ/wXghTza5wPzLyidiBRZ96rdWclKqvpXdTqKiHjYudRzReq3P2Y/J86doHv97gX2i06IJvLVSEa2H8n4geOpNb4WMUkxAAxsNpCrW15d4sySt18O/UJoQGiFXaPCWx07c8z13R5zOImIiHfSmGwRL1E9sLrTEUSklEwdMZX9D+0vsM+WE1to8lYTevy3R452ay0JqQk52mZsmeH+vufMHnfRAWD8n8ZrPYdSdNOsm3hp+UtOx5A/aFqnKQCX173c4SQiIt5JhQcRL7E+ej0ASalJDicRkdJQO7g2AG0i2uRoT05LZuhnQ+kwsUOe5y3Ys4DGbzbmTOIZd1uP+q7ixJBWQ+jbpC/JTyYz/6b5bLp3E51qdyqlVyBZbP4be4lD/Hxcg4DDfMMcTiIi4p1UeBDxEscCXcND09PTHU4iIp4UnRDNsM+H8dPen/j+lu9Zdvsyrm93PQCJqYkEvhDIvB3z3P271OmS4/y0jDSiE6LZdGITACfOnWB79HZ2P7ib2TfOBsDf15+rWl5Fh1p5Fy/EczSapHyqGVKT8F/CaVutrdNRRES8UqFrPIhIxRCYFEhiYCJV/Ko4HUVEPGjZ/mXM3T6Xh3o+RP+m/QF4tt+zNA5rzB1z78jVv2udrjmeN6/RHIDfDv9G3yl93e2LxiyiWXizUkwu+cnaRUTKj4SUBM5cfIYNMRucjiIi4pVUeBDxEpeGX8pCu5CIsAino4iIBx2Ld41myj7Fom1kW15b+Vqe/ZPTk4lJinGv+/LCMtd6zx9v+DhHv7YR+mTXCQaNeCiPTsadBGBTzCaHk4iIeCdNtRDxEpozLOKdjsYfxcf4UCu4lrutoO01P930qbtYAZCe4Zp+lTXVAiAiKILaIbVLIa0UZtKQSdqutBw6fvo4APsTC17EVUREikeFBxEvsThuMQDHzxx3NoiIeNSx+GNEBkXi6+Prblu0b1GuftGPRvNMv2cAiE2OdbefTT7rfjys9TCS/pXEiUdOlGJiKciApgPoWrdr4R1FRES8iAoPIl6ifzXX3G8tXCbiXWoF16JXo1452jrW6pjj+VUtrqJmUE36N3H9OxCXHOc+FpscS6uarTj292O8fdXbBPgF6N8JB/209yfWHFnjdAzJh/5uiIiUDq3xIOIlQvxDINnpFCLiac8PeD5XW+c6nTn56ElS0lOo/3p9gqoEARAaEAr8YcRD0lla12ytqRXlxD1f30NUvSg+ve5Tp6NINrXDXX8/rm5wtcNJRES8k0Y8iHiJlcdXAtpOU6SyiAiKoF61egT4BrDz9E4g78LDs/2fpWn1po5kFKkofI1rKlOIb4jDSUREvJNGPIh4iZOBJ52OICKlYPSs0UQGRfL2VW/neTzmnzHunRIahTXi5KMnCQsIcx8f2nooV7fUp7jliRYDLn+aRDYh/Ldwmgxt4nQUERGvpBEPIl4iKMk11LpqQFWHk4iIJ209uZUDZw/kezzQL5AAvwAAfH18iQiKoIpvFQD2xezjcOzhHAtTirO0hkD5lGpTOdPjDGvPrHU6ioiIV1LhQcRLRIVFAVCnRh2Hk4iIJ6Wkp+Dv61/k/s8sfoY52+YAMG7xOLp/0L20ool4jcOnXVvUborZVEhPEREpDhUeRLyEhu6KOOepn57CPGOYuXWmx6+dkp7iHtFQFJ9s/IRpm6YBsOLgCjrX6ezxTFJ8n1z7CU/1ecrpGPIHR6KPAHA05ajDSUREvJMKDyJeYlniMgB2H9ntcBKRyuf5Za6dJ2744gaS0pI8eu2U9BT8fYo+4qFT7U7M3DoT84xh1+lddIjs4NE8UjI96vegXWQ7p2POr3ZaAAAgAElEQVSIiIiUKRUeRLzEkPAhAGRkZDicRKRyS0lP8ej1LmlwCe1rtS9y/1s63ZLj+dguYz2aR0pm3vZ5LNu/zOkYJZKRkcH83+Y7HcOjstbe8PHRW2MRkdJQ6K4WxpjJwDXACWtth8y2V4EhQAqwG7jdWhtjjGkC/A5szzz9F2vtPZnnXARMAaoC84G/WWs1NlzEQ7IWkxMRZyWlJbm3tfSEGTfMuKD+17a5lhnXz6Bx9cZsObHlgooWUvoeW/AYnWp3onfj3k5HKbZrX7mWuclzmRwzmdv/dLvTcTwiLNi1E8x1ja9zOImIiHcqSll3CjDoD20/Ah2stZ2AHcDj2Y7tttZ2yfy6J1v7ROAuoGXm1x+vKSIlsOTIEgAyrEY8iDjJ01MtLpQxhhva30CP+j24vat3/FLobSr65y7rTqwDYP3+9Q4n8RwffCAZQnxDnI4iIuKVCi08WGuXAqf/0PaDtTYt8+kvQIOCrmGMqQuEWmtXZo5y+BgYXrzIIpKXU4GnnI4gIkByWnKBx1PTU/ls02ekZ6Sz+/RuzDOGr3d87T6++shqfj/5O+AqJDZ5swkTfptQqpml7Bgq/naa3riYcacmnai+pTphNszpKCIiXskTE9nuAL7N9rypMWadMWaJMSZrHGF94FC2Pocy20TEQ0ISXZ/SVAuq5nASkcrnyuZXuh9/tf2rHMfGLRpHkzebcPLcSX4+8DMTfpvATV/eROf3O9Nrci8Aluxbwu7Tuxn4yUC6f9Cddu+148VlL7L6yGr2n91PYmpimb4eKV0V/Rf39298n4E+A7n7T3c7HcVjrLHEdIthQ8wGp6OIiHilQtd4KIgx5l9AGjAts+ko0MhaeypzTYc5xpj2kGd5P9+fusaYu3BNy6BRo0YliShSaXQK7cTPGT9Tr2Y9p6OIVDrf3PQNfs+5fqQ++uOjDG45mO92fceYzmN4dumzANQaXyvHOVtObnE/Hr9yPONXjs9x/ImfnoCfXI+DqgSVYnopS1mLGFZkV/e8mqt7Xu10DI/afdy1I9Sm2E0OJxER8U7FHvFgjBmDa9HJm7MWibTWJltrT2U+XoNr4clWuEY4ZJ+O0QA4kt+1rbWTrLVR1tqoyMjI4kYUEREptuiEaOJT4ovU93RijhmJfLPjG/7+w985k3SGYa2H5TiWtfDkHV3uAOAfvf5R6PWdXjdCPOeLG77g5StedjpGiUyYN4GaD9Vk+ablTkfxmEMnXANzo1OjHU4iIuKdijXiwRgzCPgH0Ndam5CtPRI4ba1NN8Y0w7WI5B5r7WljTJwx5mLgV+A24J2SxxeRLCtSVkBV2LJvC+2baBV7keI4cPYA7d9rz/LblzN+5Xg+3/w590Xdx/Fzx3myz5OsPrIaf19/moc3p2eDngAkpCZwyYeX8HTfp3l6ydPA+aH0Ld9p6b52vWr1+EvXv/Bknyfdu9C8eMWL1AquRRWfKjy/7Pl8c8WlxJXSK5ay1i6yndMRSuzlRS9zOvw0M3+dyWUdL3M6joiIVABF2U7zM6AfEGGMOQSMw7WLRQDwY+aQwaxtM/sAzxpj0oB04B5rbdbHQPdyfjvNb8m5LoSIlNDIyJFMj5/udAyRCudo3FHe+OUNnuj9BP6+/sSnxPPIj4+wYM8CAN7+7W0Apm/J+fdr1shZfLvzW6ZvmU5cShzhVcPZ/eBu6oTUIfjfwTn6fnvzt/Rv0p8Av4Ac7bWCXdMvBrUYxPPLnufqllfzzc5vAOjdqDfLDiwD4IneT3j+hYsjvtjyBWGBYfyp+Z+cjlJiFX13juyypsD4+vo6nERExDsVWniw1o7Oo/nDfPrOAmblc2w10OGC0onIBfOmN4IiZWHm1pm8uuJVXl3xKkNaDQFwFx0Kct2M63I8H9JqCE3DmzJjy4wc7ROumsCgFgXvIN2rUS8y/i8DYwy7T++meY3mgGthyjYRbfD39b+QlyTl2LNLn6VVzVZeUXjwJoFVAgG4udnNDicREfFOJVpcUkTKjx8O/gDhTqcQqXhu6ngTD373IADzdszLceyn235i0tpJRAZF8s5v77DqzlV0qdOFkV+MZPa22e5+L17+Ik3DmwIwsv1IRrYfecE5sj5xzSo6ADzT/5kLvo5IWfGGrUGz+Pn4QSyE1A1xOoqIiFdS4UHES8RUjXE6gkiFVDOoJqM6jOLzzZ8D8P0t37P6yGoe6PEA1QKq0b9pf6y13NH1DrrU6QLAlzd+ydg5Y9lxagdzR88lIijCyZcgFYxGppU/l7S9hND9oaQ3Snc6ioiIV1LhQcRLhCaGcjbwLDVCazgdRaTC+GrbVwyfPpyPh3/M/d3vJ6peFAF+AbmGwRtj3EWHLFOGTynDpOItvGGUwLQx0/h42cc8NOwhp6N4jK+fL7EdY9lydkvhnUVE5IKp8CDiJdpWa8svab9Qr2Y9p6OIVBibTmwC4LY5t2HH6VNokaLo07kPfTr3cTqGR23ctxGAzfGbHU4iIuKdfJwOICKekZqeCn6Qlp7mdBSRCiMuWdtUStmaN3oeEwZPcDpGibz4xYsE/r9AFqwpfBHWiuLwycMAxKRp2qKISGlQ4UHES6xLWwfA5n36tEakKJLSknhlxSsAXNLgEofTSGXRuHpj6lUrfyPTHv/f4zR/pDnTfppWaN93V75Lclgy89fPL4NkIiLiDVR4EPESo+qOAiAjI8PhJCIVw/6Y/e7H80bPK6CniOf8b/3/mLNtjtMxcnlp30vsqbaHW5bdwuhXRmP+YSrVCDqLa6qVn59mIYuIlAYVHkS8RNZWfCJSNJtPuEYHVfWrSs2gmg6nkcrijV/eYMr6KU7HKNChs4cgCFJSUwrs5027c/gY11viv7T6i8NJRES8kwoPIl5i3j7XJ7YZViMepGhS0lOYvnm6V/3ycCE2HN8AwPFHjjucRCqbrE/Xy5M6MXUACIkPITAwEMj/54k37MzxR/4+/hANwb7BTkcREfFKKjyIeIm4qlokTy7Ms0ueZdSsUXy942unozhiw/ENtIloQ7WAak5HkUqkvI5OS4hNACAoJYiV8SsBSE9Pz7NveSyclNSV3a+k2slqnDx10ukoIiJeSYUHES9RPbE6AHXC6zicRCqKw3GuVdyjE6IdTlL27p9/P3O3z6VZeDOno0glVB5HGWUV4Bqca8C5gHMApGfkXXj4+p6veb7N8zx+w+Nllq+0+fr6Etc2ju3x252OIiLilVR4EPESzYObQyo0iGzgdBSpIK5rex0AHWt3dDhJ2Tpw9gDvrnqXgc0GMmXYFKfjSCVTXqcpVAtxFR62h5z/xTu/qRZdWnXhXzf+i1phtcokW1lYud01ymPrua0OJxER8U4qPIh4iaS0JKhS+GJgIlmyFlOrbOuCTPhtAgAP9HiAyOBIh9NIZfP9Ld8zZfgUp2Pksi14GwDnws/RxXYBoEZojTz7PjntSXz+6cO8Fd6zG8zhE64RYHHpmrYoIlIaVHgQ8RLb0l1vGtftWudwEqkogqsEc3nTy6kV7D2fWhYmOiGaqRunElUviiGthzgdRyqhyOBIalTN+xf68iLABkDa+eLkH01ZMwVb1bLo90VlnKz0VLYCrIhIWVPhQcRL3NzoZkBvnqTojsQdYeHeha7RMpXEqsOrALi0waUOJ5HK6j+r/8O0jdOcjlGgX6v8Cn5wJvaM01HKTNa6GwFVAhxOIiLinVR4EPES5XXesJRfe2P2AnAo9pDDScrOVS2v4tubv+XlgS87HUUqqQ/WfsBnmz9zOkaR9HyuJyu3rnQ6Rpm6t+29TkcQEfFKRSo8GGMmG2NOGGM2Z2urYYz50RizM/N7eGa7Mca8bYzZZYzZaIzplu2cMZn9dxpjxnj+5YhUXl/s/QLQiAcpul2ndwGwP2a/w0nKRobN4FzKOTrX6UygX6DTcUTKjakLpxIcE5yrfWfITt7+5m0HEpW9AJ8AOAwhfiFORxER8UpFHfEwBRj0h7Z/AguttS2BhZnPAa4CWmZ+3QVMBFehAhgH9AR6AOOyihUiUnIJgQlOR5AKprKNkjkce5iQF0P4aN1HTkeRSs5SfrbT/NsHf+PW5bdyrvq5PI8HBuQu0hnj+rfDx8d7Bs4Ou2wY1ZKqse3gNqejiIh4Jb+idLLWLjXGNPlD8zCgX+bj/wGLgX9ktn9sXZPlfjHGVDfG1M3s+6O19jSAMeZHXMWMijHeUKScC08M53TgaRpGNnQ6ilQQ/r7+wPlfIrzdvph9ANSrVs/ZIFKplbe/b78d+K3Ad4OpNjVX26IHF7Fy20qG9PaeBVqNMcQ1iWNPwh6no4iIeKWSlKprW2uPAmR+z1oWvT5wMFu/Q5lt+bWLiAc0rtoYkqFRrUZOR5EK4uZOrgVJG4VVjj8zWWtaNA1v6nASkfKjSpUqBR4/FJN7DZgWjVtw65W3Uj2oemnFKnMLNy4EX9iWqBEPIiKloTTGyOVVyrcFtOe+gDF3GWNWG2NWnzx50qPhRLxVfFo8BEBCkqZcSNFkTbWoLOuC7Dq9C4OhcVhjp6NIJbZk7BJm3jDT6RhuVzW7Cp/9+b8drOKXuzDxt8l/wzxjmLm4/LyOkjp68igACRn6GSoiUhpKUng4njmFgszvJzLbDwHZx3o3AI4U0J6LtXaStTbKWhsVGRlZgogilcf+dNcCgat3rHY4iVQUcSlxXN70cjrV7uR0lDLx6+FfqRVciwA/bZcnzgmqEkTVKlWdjuH2+C2PM3n05JyN0ecfvjjqxVznzNo8C4CVu71nx4vKUoAVEXFKSQoPc4GsnSnGAF9la78tc3eLi4GzmVMxvgf+ZIwJz1xU8k+ZbSLiAbc1vw3QmycpugNnD7Bw70LSM9KdjlLqDsUeYvfp3Tzd72mno0gl99Yvb/Gf1f9xOobbwvULGbt0bM7GiPMPI0MqxwdAWQt+BlUJcjiJiIh3Kup2mp8BK4HWxphDxpg/Ay8BA40xO4GBmc8B5gN7gF3AB8B9AJmLSj4HrMr8ejZroUkRKbnKtkOBlNyhWNfc7e2ntjucpHTFJMXQ8I2G7D6zm9u73O50HKnkPtv8GbO3zXY6htuQz4YUuLjkh19/WHZhnJQ5+feBTg84m0NExEsVdVeL0fkcujyPvhb4az7XmQxMzuuYiJTM1F1TobpGPEjRHY1zzWnedXoXA5oOcDhN6flu13fux5pmIZJTanruXSuyW3NsTRklcVZwlWB89vtQvZ33LJgpIlKeeM8GzCKVXFJgktMRRMqlzSc2A7D3b3sdTiLiYvNeW9sR1hScZcnJJbnafPEFwK9KkT6/qhCu73c9gRmBLN261OkoIiJeyXt+YohUcjWTahIdGE2Lei2cjiIVRLWAagD4GO+tQe86vYv5O+czusNomlRv4nQcEYwpX9PirE/BhYdz4edytS1/dDlbD2zlsi6XlVYsRyTUT+BQSu7tQ0VEpOS8992mSCVTN6AuJsnQqFYjp6NIBZG13kF4YLjDSUpPy3dasu7YOsb1Hed0FJFyqbARD3lpWLchV/a8kuCA4FJI5Iy5v80Ff9iRvMPpKCIiXkmFBxEvEZsaiw20xMTHOB1FKgjXkjzeuS7IiXMn6DTx/DahLWu2dDCNyHkr/7yS728pP5t6dYztSMPjDQvvmM2d79+JecYw7YdppZSq7B0/fRyA5Ixkh5OIiHgnFR5EvMSJjBMArNq+yuEkUlG8+MGLsAf89jsz62579Haum3Ed51JcQ7nPJJ7hyZ+e5KN1H/HZps84HHu42NdevG8xm05sAmBY62FePZ1EpCQ2vLeBUd1H5XvcP8k/V9u3O74FYO3BtaWWq6xlFWJFRKR0aI0HES8xtvVYJh6f6HQMqUjCgGZQtWpVR27/8PcP8+2ubzmVcIrejXrz/LLncxzvWKsjG+/dmOe5R+KOsC16G30a98HPx/WjbMXBFbz161usOryKYH/XEPB7o+7VNAspV15e/jJVfKvw/y75f05HIcNmcDj2MAdPHsy3T5BfUBkmcl6Yf5jTEUREvJI+AhLxMt44bF5KR6q/axu9vcmu3R6staSkp3j8Phk2g9OJp93P0zLSeOqnp/h2l+tTUz8fv1xFB4CGYQ25/avb2XNmDwDzts/DPGP46zd/pf7r9bn848t57MfH+HTTpyzYs4Bek3sxY8sM9sbsde9k8eagN6kdUtvjr0mkuObumOv+s++0w9GHafRmIzL8M/h0wKd59onxqxzT97JGPPyt698cTiIi4p004kHES0zeMdn1CbZIER2LOQbA7yd+B2Dc4nE8t/Q5Ep5IoGqV4o+COBR7iCo+VagdUpuE1ASC/+0affDNTd+w49QOHv7+YQCub3c9vRr24v4e91PluSq5rjN/53wApm6ciq/xJTndNff6vdXvufu88csbBWbx9809TFxEXE7FngLAN8OX0b1Hc9MPNxXpnWF52g7UU6oHVsd/rz8R3SKcjiIi4pU04kHES6QEeP6TavFuJ0+eBCAmxvWJ5nNLnwPgms+uofnbzfM971zKOf618F8kpiaSmp7KNzu+wVpLcloyX+/4moZvNKTOa3V4funzhPw7xH1e47DG7qIDwNRrp/LQxQ/h5+PHxKsn8tLlL3FXt7sIC8hZQUvLSHMXHYoiPDCcIa2GsPC2hUU+R6QslZf1BE7HuUYihQSEuDIdLdp5/rgKek5N0yoNNw64EWMMM1bOcDqKiIhX0ogHES8RkRTBycCTtG/c3ukoUkG0bdyWHWd20LRp0xztP+39qcDz3vzlTf69/N/UqFqDA2cP8PZvbzNv9Dw2Hd/EEz894e731KKn+HDoh9z99d1s++s29sbs5ak+T1HNvxrXtbuOAL8Ad997ou5xP46qF8VdX9/lfv7jrT8y8JOBRX5dux7cRY2qNYrcX6QsGYzTEdzOxJ8BILRqKMYYQg+EEtswttDzVj25ioMnDtK+pXf9vEmuk8wpe8rpGCIiXkmFBxEvUcu/FtEJ0TSIbOB0FKkgLg+7nK/OfIW/nz/pGel59tlwbAP1Q+sTERTBQ989RIsaLWgY5tp6LzEtkU82fgLAkM+GuM+pHlgdPx8/moc3Z0znMdzR9Q4AmtdozhXNrig0150X3cmdF93J4djDxKfE0zqiNY3CGnHg7AF3n3mj5zG45WDWHFnjXkgyMiiSoCpB7uci5VFQlaAcRTcnxZxzjXYKDQwFoEq73FOe8hJRI4KIGhGMGDeCpNQk5v97fqllLCufL/8cAmFX3C6no4g4LiMjg7NnzxIeHu50FPEiKjyIeImY1BhsmCX6bDQRYZqjKoXbvXs3WIg+GU1ssutTzgahDTgUewiAfTH76PKfLlzb5lpe+9NrvPXrWwDUq1YPgDnb5nDw4YOEvBhCo7BGnEo4RafanVg0ZpFHfrGqH1rf/Tgr04SrJnBliytpUaMFAN3rdy/xfUTK0g+3/uB0BLcWkS3ofKwzPQf1BIq+dsNN79zEZ6c/gxQISQwp/IQKIPpMNACpGakOJxFx3lOfPsUPC39g1Ufaol08R4UHES9xNuMsAL9t/43BPQY7nEYqgjVn1kACtGvczj21IesXfIBmbzUD4M5ud/KfNf9xtx+JO0L7yPbUCamDxbL3b3sJDQglITWBOiF13NtbetKvf/mV5LRkejXq5fFri1RWfbv1Zf3E9e7nGeTeFck3zTdX25I9S6A6EAjxgfGlGbHMlJd1N0TKg/dXv09yWNHXVhIpChUeRLzE7e1u552j72g7TSmyhh0awjGoX78+G3ZtyHXcYqkVXIum4U0Z/KmrmDX12qnUDqmdY8pEiL/rE8/SXFchql5UqV1bpCw9u+RZDIan+j7ldBTOJJ7hWPwxmtdojr+vf56Fh/rUz+NM7xURoBGDUrmdTTjL6fDThXcUuUAqPIh4CWPKz4JlUkFk7jS5Nm4taRlpDGg6gKnXTmXX6V38Hv07pxJO8UDPB3j0h0cBeHXgq9zc6WYHA4tUfAv3LsTH+JSLwsM/PvoHH5z8gGXXL+Oy9pcRFBhELDkXlzzgd4Bdp3fRMLRhuVmbojRkFe0f7v5wIT1FvNvWfVudjiBeSoUHES8xcdtECEUjHqTIzpxyrWi/+Nhi9sbsZVSHUdStVpe61erSu3Fvd7+HL3mY/k37M7L9SKeiyh+cOHGCWrVqOR1DKrjYRFeRoXpwdQC2Pb6N6m9Wz9Wv5Tst+erKrxh68VBXgxfWuWuH1CZ4dzC1L6ntdBQRx2RkZPDgtAfdH0yIeJKP0wFEBIIeDsI8XrJ3cmn+aR5KI5XFsaPHADiQcAA/Hz+uanFVnv1a1WylokM5sHbtWpYsWcLKlSupXbs2M2bMcDqSFFN5WU/gXMo5AGqG1gQgLCwMkvLuGx9/fi2Hqraq+/HFXFx6AcvQqMtHkVIlhfd+fM/pKCKOGPXaKHyf82W1/2p3W2xC4dvrihRVsQsPxpjWxpj12b5ijTEPGWOeNsYcztY+ONs5jxtjdhljthtjrvTMSxCp+BKrJ0Jgya4RmRQJQFRLzYWXomlR37UzxM7knfSs35NLGl7icCIpyK233sqQIUNYunQpAIsXL3Y2kBSLKUfDBbIKDzWqZVufJZ+fRct2LnM/Xv/c+QUpe9funVf3CiktMo1YH/2iJZXT9PjpOZ4/1uIxqlWt5lAa8UbFLjxYa7dba7tYa7sAFwEJwOzMw29kHbPWzgcwxrQDRgHtgUHAe8aY3Esli0ix1KhSA59zPtSrWc/pKFJBzImb4368/I7lpbIbhXhO3bp16dixI4GBrt8MJ06c6HAiKY6IoAhqBtV0OgYACakJkAYBVQpfuyH7OkIhISGExYQB8MOm8rM9aEl8uOBDbFXL3vS9xTp/+pLpJKXkM1xEpAIa3nW41g8Tj/LUVIvLgd3W2v0F9BkGfG6tTbbW7gV2AT08dH+RSu906mkygjM4dPJQ4Z1FgDRc03NG1RvlcBIpjLWWhQsXsnXrVtLT0wFo166dw6mkOGaOnMmskbOcjgHAsFbDGJAwIEfbFT5X5Nk3+0iN4a8P52x11xbOm0I2lV7AMhQTGwNAekb6BZ87++fZjFo8ioEvDPR0LJEysWz9shzPBxwfwOjXRvPNim8cSiTeyFOFh1HAZ9me32+M2WiMmWyMCc9sqw8czNbnUGZbLsaYu4wxq40xq0+ePOmhiCLeLdWmArBqxyqHk0hF09/2dzqCFCJrPYeYmBhatmwJwLFjx5yMJF7g8dseZ+FrC3O0/X3Q3/PsGx4U7n686tD5nzMZQd6xoHFJ1t3YsM+1HfH22O2eiiNSpnp36c1jjR9zP79l+C3sb7SfX/f86mAq8TYlLjwYY/yBocAXmU0TgeZAF+Ao8FpW1zxOz/NfeWvtJGttlLU2KjIysqQRRSqF2zveDmhXCyncsfhjfLfrO7rX6A4/Q40aNQo/SRy1ceNG9+OhQ107C5w+rX3WK6InFj7BP378h9MxiD0Xy8DXB7Lv9L4c7Ys2Lcqzf3hIeJ7t3sJmviWtXzXPz8QK1Kd1HwBq+Ojf0oro9TmvE/ZYGAnJCU5HcdTLY1+maWxT+tg+NK7VGICjZ4+6jw96fhA3v6EttaX4PDHi4SpgrbX2OIC19ri1Nt1amwF8wPnpFIeAhtnOawAc8cD9RSq8OQPn8EHPD0p0DR+jTWqkcNZavtv1HVdNu4p/dfsX/IjmcFYAjRs3djqC1ztw4ACHDx8u9fv8cugXVh5aWer3KczIN0ayIG4BN7xxQ472jo075tn/sk6XFXi9bfu3UXNMTf499d8ey1iWskY8PHzxwxd87oCoAZh0Q8PaDQvvLOXOrI2ziA2OJfTxUNo+1tbpOI54duazhP4rlNn3z2bJ00toXrc5ACfiTjDtp2nExMfwffr3fBr7KSmpKQ6nLV8yMvSBX1F54jeV0WSbZmGMqZvt2LXA5szHc4FRxpgAY0xToCXwmwfuL1LhDbt0GH8Z9JcSXeON398Ays82bVL+WGtpNaEVt3/lGh1zYvsJAM6dO+dkLCmCu+66y+kIXq9x48Y0aNDA6RhlYs6cOXy/4nsAZtyXc1vWWy6/hRuCM4sR2XZprlOtjvtxrM2980NcUhynm51m26ltng9cBhpVb0TI9hAiAy98pO3mI5v5+uav+f7R70shmZS2xMREANLD0tmWUTH//JbUpr2biPOPw8+4FpluENEAMmBu8lxuWXYLHf95viD57y8qZnGxNPz6+6/4PuPLbW/e5nSUCqFEhQdjTBAwEPgyW/MrxphNxpiNQH/gYQBr7RZgBrAV+A74q7X2wlfwEfFCvo/5YsaV7FPnDD9VXKVgKekpHI8/DsCw1sNYt3odoGq9JyQllf5q9tmnW0jZsNZy9OjRwjte6HXznmlaZj7f9jn0BJKhad2muY53btQZAL/k8zvdHDtxfk2RtPjzFYnRYaMB2Ljf9efzh8MVc5eLUVeMIqVaCq9898oFn/uX9/7C1VOvzmcCsZR3O+N3nn9SDVZtr3xrZcUkuRZXbRjpGrXj6+tLYPz5vXUPRZ5fuDzAt/BdcCqLWb/MAh+YfWR24Z2lZIUHa22CtbamtfZstrZbrbUdrbWdrLVDrbVHsx17wVrb3Frb2lr7bUnuLeJNMoIzSjz+qFZSLQAua1/wcFipvAL8Aoj5ZwzLbl/G7Btn06JFCwBGjBjhcLKKr2fPnjz++OOldv1u3boxa9Ys6te/8Pnnkr9169blKLz9ccTDd999R7169fjiiy+YMWOGR0aUOT216V+f/IvpydNdT6rk3Wf9gfUApAWfLzBM/226+3HCpPNz4RPiXY8TklzfE1MTPRm3zFhrSQtL45zPhY8Ai06KBj+oeXdNzDOGtTvXlkJCKS0JgTnXdvhw4Yds3rs5n97e6WzSWUiD0OBQd9vjV+f9M+2JbU/Qe1zvsopWru0+uRuAH+/80eEkFYMmhYt4iVC/UHzjfalTo07hnaXSybAZTF43mWPxx3j/7M4AACAASURBVLis0WUYY9y/cDn9i5A32LhxIy+99FKpTHWKjY1l3bp1pKenM3z4cG688UaP3yMvR48exRjDvHnzyuR+AEeOHMEYwxdffFF45xJauXIl3bp1w9fXl/T0dGrUqMGwYcNy9MnaQWTkyJHceOONzJkz54Lv89hjj/HUU0+5nzcMbUjDUOfWAnh3zbvnn+TzLvDdP7/LRSkX4Rd3fsRD9u00rbWYRNfzr9K/4p2575RK1rL0zvx3yAjO4IA9cMHnnko7BUBMA9enxtN/nl5QdylHzsSdISMkA5N0/s/3f07+h44fd2TqwqnsP77fwXRlJy41DpOS873A/w34Pwb5DaJqTFUA6p89X/he7rOcbQe38dWKr8o0Z2lJS09j5PiRFzza5eBZ14aNr3z0CovWLGLQ84NYvGFxKST0Dio8iHiJ06mnSQ9JZ+/RvU5HkXJo1tZZ/Hnun5nw24T/z95Zx0WZvX34GrpLREVFwEQQdXGNtXXtXmtX13rtWLuwsVfswFpdsWNt1+7uRFFXFEVSmqGZed4/HplxBBRkKH9c+9mPM6ee8wwT59znvr+3oizVhfzSpUt5Na18g7e3N6tXr1bJFHHw4EEcHBxITk7O9DiLFi0iKUm9wlvnz58HoHbt2qxevZrdu3czcuRITE1N1Xqdz7l37x4Aa9euzdHrfMrLly8B2L9/f45fy8zMTPHYx8eHO3fu0LhxY2QyZRRouXLlMDQ0VDzX0dHJcDxBEHB1deXBgwcq5Tdv3uTatWuK51s6bmFn553quIVvwljDWPH4h8Qf0m1jZW7F3Xl3VUJCUg2UKbIUDKcbIugr6yJiI9DSEo0U+jr6OTHtHCdGGgN8W+hZrKaql4SBjkGm+snlcq56Xc3y9QpRH8b6xhyvd5wbv95ANl2GRKbcfPfa3wvbdbZoj8vANeg7wkpuRZGwImnKT0w9Qfif4XTU68i1SddYbr1cUeew2YGOZzoW+NCUgLAAio8vzr7YfQzeMjhLfR2MHDB8a8hB3YOMOzyOU7JTND5QmKI8IwoND4UU8p2gKdEE4PbLQs3WQtKy/NZyHCwdmNN4jqIsVVTSz0+02AuCoLLp+l8hLi6OypUr88cff/Dq1SsEQSA5OZlp06bx/PnzLIlvTp48We1inQEBYgIoFxcXRZm2tjZRUVGEhoaq9VqfkpIiutmnbihzA3t7ewCaN2+e49e6fv264vGaNWs4duwYXbp0wdbWVlHu5eWlYuCpUaNGhuPJ5XIWLlzIzJkzVcqvXLnChQtiisrExETF65pXvDcVY7U7G3Tm1KQviyFqamkqHqemavYP9SdBR1XTxMTAhBJmorZ4zwoFM93et3oryeVykvVVjZNh0rBM9e27si/199dnw4kN33TtQrJHaFQoR28dpVXTVtSqWgsNDQ38xvspGxQT/0kxSSEkQhRjthhtge0429yfbA5y7NYx7G3sCdkYkm69no4eBycdpEyxMowaOIo7v92hfoIy1KLm7prp9isotF3ZljAz8TMblBhEdGw0ZceX5ditY1/t6+nqie8yXwAeaD6AGEDjf1MnJDMUGh4KKeQ74f+q/R+gXBwWUkgqybJk7gXco3X51mhqKDcSY8aIaeN0dUWhqLlz56KlpZUrQon5hTNnzqicaNeqJS4+dXR0sLa2pkqVKpiYmBAZGZnpMRMTE9U6x4CAADQ1NSlaVKm2n3ryXrRoUX744QcmTZrExIkTkUgkVKmSfjrErGJkZATApEmTvtjuwoULrFu3Ti3XTDVy5MbmfNKkSQwbNowKFSrw6tUrRThEVJRCtort27crDD8AxsbGacZJRVNTE2NjY8qWLZthGz09PWwG2DDs32FquIOs8+z9M0ySTWih24J/JvyDpanlF9u3tWireLzsvZg5ySdQjGlOzXjRxKAJg1oPwlzfHOPXxpQxK9ipX+2N7LPUPik5ib66fTEMMoQUWFJ3CYPbZO7U1DvCG4A3YYWeinlB/+X9+eXsL3Re01lRVtKkJDETY1j1g2r4kNseN+IS4ogwj+CtyfcVftHueDu2RGwhOi5ttpr0qFGhBqPajMrhWeUe76OVwpmBpoGUnFqS18av+WVvWv2re/fu8fDhQ8XzYGkw5sbmiud72u8BGYzcPjJnJ11AKTQ8FFJIPuBK5yvsaZy9mNDCOP1CMuLS20skyhKpWVL1VEJfX9UlesaMGQAq4QbfO76+vhnWnT17FkdHRzQ1NTE3N/+i8cHSUrmBU7fhpk6dOkycOBFNTaXRKNUoULVqVR48eMCiRYtwd3cHxFP6zBITE0NQUFC6dT///DM9e/akXr16X3xPeHp6KoxY2eXFixdA7oT/REdHY2JiQvny5bl+/TrR0eKiOyYmhtevXwNKI8SmTZsAMDQ05M4d1ZOssLAwAgMD2bZtGzExMfj7+xMQEEBoaCjDhikNDKlZSWIMYngY9JDcRhovxXGTI9Ha0UxvN/3rHUg/+8bbEHHTZRYjhqroJOlgoG2AQxkHBASlYaKAkWq0H/vT2Cz109PV4+/pf1PHtg5owbhr42jq1jRTfX8s8SMATRyaZG2yhaiFa5FiCNThN6o6BUb6RoxoN0KlzCPYA5cZLnxvJCQlKHaDpoaZD9/r9FMn7GOURrq4uLgvtM6/pMhSCNUNhU9+tqXmUgCSTZKxHmNNUrIYPnnizglq7K9BPXdRxF0ul1N8UXFaLm7JgsoLONryKN0adENHqsMr6St+mv4TXr7i7/GqA6soOrooYzeN5fjt44z5Sz2/mQWNQsNDIYXkA+o51aNbg27ZGmPh04VAYWrEQtJyL+AeP1r/SNsKbVXKU92/P9cwePjwIXfv3s21+eUlenpiurDKlSszefJkunXrxs8//6yoP378uOKxubk5EomEAwcOpBlnx44disfq9nho06YN8+er5k13dnYG4NGjR9kau1u3bpQoUSJN+dWrVxk+fLjivvbu3ZvhGJ6enmoztqR6cnTs2FEt42VEYmIiycnJGBsbU7Vq1TRGpbJly5KYmMiHDx9wcHCgX79+WFmJmYMaNWrEhAkTuHXrFpGRkVhaWmJtbU3v3mIe93379lGyZEmKFi2qoo9RtaqYorKIZdo46tyg+BRReFiSIKGuY91M9Wlg2SBN2fsw8XRwdpPZOEmd+O/Nf+w6s4sUIQWpvZTXMa/VN+lcpELRCpg+NcVcy/zrjT/hQ/QHPC550Ltmb0VZpFHmPKQSksTPTUHVxSjIBEQHKNzryeDcxquPF496Kb9jnxs+xyxWNLjlhJBwXvD4tWgQLRpZ9CstVdHQ0MBnsQ9acaKXmqG7IU6TnDj/8Lza55iTnL53GkFfoL1eew7WO4hutGqq0ECzQNadWMe5R+dofbw16EJshVhSZCn0W9UPNEFIEJjcdTJta4lrrOXtlhNqFsoNrRtU8ayCxE3CyCcjCTUPZdn7ZbQ50Ybl/sv/J9frhYaHQgrJB0hmSZC4ZdNjofDTXEgGTKo3idsDb2OgrSp4lnpya2hoqCKIOGHCBNq3b5+rc8wrUjeL2traLFiwgD179nD69GlevHjB9evX+e+//9DV1aV27dqKPr///jtXrlxRGadpU+UJ54kT6s0WHRUVlUY3ok2bNl/sk1lBzEqVKim8J1J5+vQpf/zxBx4eHoqyoUOHcv/+fWbMmEFCQgJJSUkcPHhQ5ZRLHYuo1MW8uXnWNn9ZJdWDw9TUVGHEAVi/fr3isaenJydOnMDKygqJREJwcDDly5cnLi6OxYsXU7t27W+a51vftwQHBwNijHmKLOfDSgLCAog1E99DKfMyf73R/zeaaXbTFKeB0ngpZwPOoumriVNpJxZ1W4RPaR+uvb7Gifvi+/5m+E21zz83+PXnX4kvGs/M4zO/3vgTzj84z/CLwzn+QGmkTNbO3Ofv0WtxU3vw+sEsXbOQ7FNlmjIkbW7Nuem2cbR1xNnemen2Sg+h4sbFMZIa4f3OO8fnmBs8ffsUZDD8x+Hf1H9HD6XR/anBU3bf2Y3mBE2O3TrGmXtnKDamGO9Csp4pJrf49/6/APRp2oeOTTuSsCSB5z2fs8x2GTYBNgCMejCKnw99PJD4+NHeeWEnW4O3AjCo0SCVMYc0GpKpa2tN0cJ0vOn/lAGicKtSSCH5ATVESRRLEFWQmlVvlv3BCvlukMllGep+pGoG9OzZk6tXlcrqz549IzAw8Ls50cmIVNd6gPfvlTGeEomEChUqUKdOHaysrAgLC+PkyZMMHDgQLS0t4uPjadCgAadPnwbETf6nYStjxoxR62vXqlUrOnXqpFIWHx+fYfvGjRtnWiPBzMwMqVSq0t7JyUklhjUVFxcX5syZw5QpU7C3t+eXX35h6tSpivo3b96QmJjImTNnmDZtWqau/zmp93X7ds6J5J47dw5ra2sAKlasSMOGDRV1derUYfPmzVSrVo2jR48SFxen8rf9VGiySZMm2NnZoaenx9atW7l//z6HDh1i2TJRC2HGjBnpG4hS4PWH11j3tabo8qLUml4rh+5USeM5osr6hY4X0NDI2tKvdY3WIDoGYbzQmMvSywyuMZjG1RtjqCvqoySmJBIbJxo2klLUm9Ult0iWJZNsmky8ZsafrVS2Xd/GEM8heL/zJlwqGrEcSjgo6uUGciLjv+71MLTlUABMTTJ2cW81rxWrj67OsL6Qb8NSVwyPq5lck8ldJ3+x7exes9nTeA9FIotwbtg56mrVxXGLI3329smNqeYo3et351a7W4xoOeLrjdOhW9VuXOl8Bc0YTUZajWRjwEbkRnIevH9A82PNCTELwWGRA1vPblXzzNVDbZva/BD8A42rKjNRVCxXkdF9RvN2/Vt0YpSZjOyi7djcYDMA045PA12Y7zA/jceyRCLhaperzK00lwWVFwDgGOdI7ZTavB/xHj5qeAr6AtHG0bgfdWeIxxBGbhjJ2E1ZC/UqcAiCkK//d3FxEQop5HuHWQjMIltj2I+zFzTHaappRv87yGQyoerkqsIf6//I66nkCOvurBNKLS0lvAh9kaZuxowZgvgzIAiAAAhLlixRPD5y5Iggl8tze8q5xp07dxT3+uTJk0z3mzJliqKfkZGRsHbtWgEQmjdvrii/fPmyUKFCBeH9+/dCdHS08O7du2+ep4uLi9CmTRuVMrlcLvz666+K62lpaSkeL126VKXt0qVLBSsrKyE+Pl6l/MGDB4o+O3bsEARBEGbPnq0oAwQPDw8hODhYqFixoqChoaFS97X/W7duLSxatChL93r06FFFX3Uil8uF9u3bC1OnTlXMr3z58kJcXJwgCIIQGRkpXLx4UdFeJpMp2p0+fVpRHhUVJfz333/C/PnzhZSUlAyvFxkZqXj8+vVrARBGjRoluLm5CTRF8Z3PLATNqZrCvZf31Hq/nyKTybL1G3P7+W2V+TILYdrWaYIgCMKdF3cEZiH0WtZLWHFohcAsBJPRJuqcfq6xYP8CgVkIemP10q2XyWSCIAhC5YmVVV6L0RtHC8xC2HZ2m/Ds7TOh+ZzmArMQZhybISQkJ3zxmtvObhOYhTBl65R065NTktWyPigkLSXHlBR0xuh8U985u+Yo/i6SiRLhyevM/35876S+LkVGFUnzvXHV6+oX+w5eM1iwHWsrhEWF5dJsv87Z+2eFRf8of8fCosIEpiNoumoKJTqUEKKkUVke86/LfwmNNjRK8/oU5M86cFfIxL6+0OOhkEK+E8JSwpAZy74b97/cIiklCe9Eb/Y/35/XU1E7ckHO7MuzKWNahrLmaZX2U0+5//77b0XZuHHjFI+LFi1KmTJlWLp0qeq4cjmHDx8u8B4R9+/fB8DHxwcnJ6dM95s7dy6lSpUCQCqVMnSoeGrZqVMnhZhgkyZNePnyJdevX6dfv37Y2Nh88zyTkpIU2gepSCQSdu3apfgbfOqx8NNPP6mkRfXz8yMkJEQlO4O/vz/Vq1dXPH/y5AmCICgERmfOnIkgCAwdOhQrKyueP3+OTCZj165dij47duzgwoULdO3alYEDB6aZ9/HjxxWZNubOncu4ceNYuHAhRkZGyOVyHj9+rDIegIWFBfDl7BHfwv379zly5Ajz5s1TlG3fvl3hzWBqaqri+eDtrfwebdZM6UVmYmJCuXLlcHV1VRH7/JxPPSPs7Ox49OgRS5YsYcqUKSxsvhDvXt601GqJbpQuMm0ZLjtdkLhJ6LegH9JYaabSuGWW0KiPKVfV6M3r2tUVAEM90eMhISVBIXBsomuivgvlIqlhQxl9r2nO0UTiJuGZwTOV8uX+yyERutTvgoONA5PaTkI7RJvZd2ejP0ufIzePZHjNlVdXAhAdnzabwMVHFzEer97PQSFKpHIp+vJv09aY9qvSo0swEKiytQo+AT4EhAXQel5rhXZHdGw0i/5ZRKQ081mRcpsOKzrQzSN7GmOfUk9eD71wPcLiwyARrnZRelO23NYSiZsE29m2afrVnVGX9R/W42viS5FlRfjnyj9qm1NGyOVyph+aTnxyxl5OTas3ZULnCYrnFiYW9LXqi4aGBheWXsDEMOvfd/3r9+dM/zMIMwXOtDmTpj5VzPJ7pNDwUEgh3wnGGuIC5c7LwtzBWUFPRw9zwZxwIa1q/66Lu3jo8xDJFAnFxhQjLiEOwzGGOEx0SGek/Mcerz0ExATQy7mXShrNVFI3CqkZBFq0aMHatWtxdnZm6NCh1KlTBz8/P+bNm0doaChlypRh9+7d/PXXX3Ts2JFt27bl6v2ok7179zJt2jTMzc2xs7PLUl+JRIKfnx+TJ6u653bt2pXVq1eza9cuZs2aBUCDBg2oU6cOIBospk2bxrZt21ixYkWmdRgSExPTGB7SIzk5mYMHD1K7dm1u3lTG2fv7+wPwzz//IJFIqF69usJwAjBnzhzmzp3L0aNHAVizZo1i/p/z66+/IggCcrmcHj160KhRI/bu3cuGDRtYvnw5VatWxcTEBC0tLcaPH6/oN336dI4dO4arqyuxsbF4eXlRtWpVevToobLRS9XScHBQ32fs6tWr/P777yrPBUGgZs2Mc89XrlyZw4cPq02p3dnZGU1NTbS0tJg0cRKV7CtxYuoJEpYmUCJSKe65JWkLJnNNaHeyHZ5nPdVy7dQsE73Me31T//Ily0MsEAilo0ojmy7DQE/UiyliLApl7ovdx+mnYujRiBrf5rKd13zJkNpggVJk0+iDEXof9FTqWxi0QE9HLGtSrQk9K/YUx9QW6HCqg0oM90Ofh5y6e4qnvk8JjhK1PtKL8R68fTAJZuIG1jwuZzVPcppTd0+x+uhq6s2oh09A/sh6kqCRgKGG4dcbZsAQqyHgB3zUUnVc60izJc04kXKCrku60nBWQ0wXmzLp6SQs3dKmrb3se5lOezpx5+Udzj04983zyC5nfM9w6T/1ZRG64naFmKUx9KjUA99RvtR1rKsIN0gJFA3k7+LfqXze/rnyD9c1r6uM0/V8V4qNKcbIDTmXltInwIe5j+YycENaw/mX+HvE3yTMTqCifcVvvraWhijK+XONn9lcRwzf0IjWYHOdzehof/33vqCildcTKKSQQtRDvx/6McdnTl5PI18xY/sM5vjM4UnvJzjZKU+0F+xdQJ2KdWhUtRGrjqwi2CwY5LD38l5aurTEQM+Abku6cTD+IJJ4CehDiG4Ihn8aghk85zkzts+gTNEy9GzckyUHl+B22412xdqxf2L+8ZyYf1XMhNDELv1Ubf369WPevHncu3cPgLFjx9K8eXOGDBmiSB8I4oY5JCSEd+/e8dtvv7F8+XJAuQFfsGABixcvxsDAIN3r5Ee6d+8OwMqVK785FW2bNm1wdnamUaNGSCQSihQRN2G//vqrYszixYsr2k+frprC8Pnz56xdu5a9e/fSqlWrDE/50/N4+JQGDRpQvHhxtLS0KFZM1Hr5VL8i9WR+48aNAAr9hgoVKijSV4aEhNChQwdAFJz8Gum9ZqNGjWLUKDG3e2JiIjKZDE9PTz58+ADAy5cvKVWqFO/fv1dkeACoX78+VapUwdXVFRsbGzQ0NDKtUZEZtm/fzvPnz/n3339p06YNFy9epG7dL2d1kEgkuSawGrAsALlczspTKxlzewyCnrggr2jz7YvaVJKSk2i3vR3ogpWJ1TeNYWZkhrAo/U25lbkVPax6sDNkJ6eCTmEZZYld/awZ8vIbGnINJqycgPtId0WZd4g3iMkMSNZJJmFpAnEJcfiH+uMX6kcj50YqY3yuq6M/Xp+iFGVtt7W0P/XJ++qjY0zZkmXpuawnZYuWZY7PHDroduCl0UsAamvW5oLbBbXfZ27S8t+W4gNNGLd1HIcmH8rbCQFtDNpgW9z2m/uvHbqWtYhZa2zG2hCoEYiNhQ3Pkp9xLOmYinaXzEhGaFQolqZKA4TbVjfOC+c59PwQkgQJ70q9o7hFcbQ0c29rJo2XEq8fT1lZWo/I7KClqcWOMUrRycldJzM2eSy688WMEd7DvCnhWoKEhATG1hrL9vvb4aPGsV64HgkWosEtxCyEVYGrWMlKtc4vleveorHDxjjrHolZ1cr5Ev2a96Nf835qGy8/U+jxUEgh+QCvPl6cbns6W2OkyMWFekF3f1cna+6uAeDwbWWObrlczhTvKTQ+1Jh5e+Yx8sFHa7oGdL/QHdPFpmjP1eZgvKgyXlu7tsIa/SlzfOYw4OYALCdZMu35NJJNknkQ/OCrcwqJDeF1xNfTzW28t5HeB3t/tV1GxCbFUsmyErMazqJ8kfLptilXrhwAXl5ePHv2jEaNGinqevfuTXx8PPHx8fTs2RNHR0dF3ejRowHRHf/PP/9k7dq1XLp0CUEQiIiI+OY55xap6TAdHBz4448/vnmcevXq8dtvv1GiRAkVAwNAu3btMuyXmpZx3bp1dO3ale7duzNyZManOuPGjaNLly4Z1l+6dIk9e/YAYigAQExMjKI+1Qjx6d8X4MwZpYunlZUVPj4+7N69m8aNG5NddHV1MTAwICQkhKSkJEaOHMnNmzfx8/Nj+/bt2NnZceLECczNzalTpw7r1q2jX79+nDx5ErlcrhLqkB2mTJnC+vXrMTMzo0WLFnTv3p369eurZWx1oqGhwehWo1XK6uyqg/c7b/qt7IfWBC1SZCm8j36fwQjpM3nrZMJ0wzCIM2BOz5wxTBfREg1uLpYuRAvRHLxZMDM0yD/GoiRrJbPKd5WifP/V/YSahWIbbYtnXU/u/iGmGjbQM6B8qfI0qdYkzSZkVrdZlI4qTXFpcYqnFCfJNAl/U38Vo0OpZKXX0ZpLa9gZvVNxeHA48eNvVjycH3deJetQQeNzt/GHH9IK1+YF++fuZ8mIJWoZ693SdyQvTmZB6wXw2U9g1QTRyGq10IoJmycQlxDH3Zd3OS8o004KegKlPUpjPd5aGRqVg0jjpWhO0sR4kTHoQkennE1fDKCjrUMDoQE20TYEvQ4iWD+YKPMoZr6cyX+a/wFQSlqK+BXx/CT7SaVveHRaj1R1cN9XDLd0sXfJkfELSUuh4SGH8Anw4cazG3k9jUIKCOVLlqdp9aZfb/gFFnovVNNsvh9K6YkLu0ollSe4n55ETXv+ZeX9RU6LuDjjIv2a90OYKSj+n2CjjPeLNYtVxE6/1XxLY7fGdFzYEY9jHpy5d4bNFzajO0MX7wBvpElSii0uhpOHE5EJkTwOfswNP+X3REJKAhHx4qrlwPMDbHu8je2Pt3/TvUcnRrO3y16mNpiapk4QBLZs2aJS5uDgoHKqrq2tjZ6eHnp6ejx7poxptre3Vzx+8+YNFSpUAKB169bMnDkTCwsLKleuzNq1azl27Jji5PrZs2dfzMSQm2zdKqpr52SoyIEDBwgMDMTCwoKBAwdy8eJFoqOjuXz5Mv7+/oqNyj//iHGsr1+/JiEhId2xRowYkenT91SviU89HlINCVu2bEEQBK5cucL8+fPT6E7Y29vTvXv3b/YAyQhtbW1WrFhBrVpi9oaePXvy+vVrWrZsSXh4OO7u4sny+fPnFQaYfv360b9//wxfk8yyYIHo4uvu7o6mpia7d++mQYMGX+mVd2hHa6s8rzy7MlsitiAzktFweUNKLyvN8UfHM+idlohY8fvEtbor+rrfFs/+Nc4/FTdQk3+eTJJ9Eu8Ts2YcyS84Wztj8diCFOMUEk0TAdEFvO+2vgAMrj2Y3j/3VvGeywi7Ena8W/qOQPdAfFx94KRYbhBpQOAfgcimy3g35x3d33eHEHhtLBqj5zjOwSpMNEy20WlD8KRgyo4pS9lJ6j2Rzk22nN0iPvgPCIS3Jm/zcjoAPPN/hsclD8KkYWodt1q1asS6xxI2MYyYiTEkT0tmx2Dx5F/QE1jstxijqUbUWqTMZCOJUn7ffjD7QK05yroUWQpDPIZw6u4ptc7zwLUDyA2Ua6EJv0z4Qmv1cWnWJd4ueUvDhg051OyQIk1v05imfBj+AT93PwCuzb7GFPspin5LDisNRCERIQSEBaAOngc/B6Bu5S97wBWiPgoNDzmEwxIHftr309cbFlIIoDtfF805GQuVqYMFexfgccwjTfmA1QPSLf8eKGshLtaq2VcDoNTYUpQcX5J9TfaBuK7ENMKUxCmJXO96HWGmwOtBrxlgOYBXA18xofOEdGPtFvVbxNGWR7nQ8QImESZsqbeFzgadMUwy5CIXOZx4GLdzbjQ/1pz+l/uTpJmE01onjBeIm8Iptaew4/EOqq6ryk+bfyI8PhxBENCfp0+3f7qx5PoSTr4SV6peIV5ZumdBEBh6bCjWS63Z92yfIo7wU7y8vOjXT+nW9zUvhQoVKrBhwwZkMhk+Pj4IgoCXlxd79+6lW7duClf+OXPE0zpvb2+GDRtGu3bt2Lx5MxKJBEdHR4UIYypSqZQdO3bkupdO8eLF6dKlCy4uOXfKoaWlRfHixQkLC2PDhg00bNgQY2Nj6tevj5aWFjKZDEEQOHDgkkNjfwAAIABJREFUAN26dcPR0ZGGDRsyceJEOnTooPBgAHj79i2RkZkTJ0vP42HcuHFUrlwZXV3RzbVevXq4urqq8W6zz/z5YlhQaljGnj172Lx5M3fv3v3mMT+Nm69WrVr2JphLhM4O5d9W/yoLSisfXpeKbsHPnj1j/vz5mfrcxCSK74OudbuqdZ6f8q/rv/Qy7YVPiBi7/0z67Cs98ifdm3Yn3Fl5svrXyb/oer4rUmsphpGGX025mBEGBgYkX03m1cBXxC6LpbhFcTQ0NJBIJKx2Xw0fI2DmVprLtC7T8Fvix6ofVnFg/AGszK3ElHsG0YSGhqpNcyQ3OfvoLACPZj9iYu2JmISaYDjGMFfEAzNizdE1DL84nNve6k/ba6BtgIW+BUb6RmhpauFo68g0+0/EKE0Efij/A7WSa3Go2SHkS+Ucb3ycGqE1APA18KXGlBrojdVjzF9jWP9hPS3/bYnbPjek8VK1zDE0RvSq6JjUkcutL3+TQGJ26fBTB4QF4mHO2TVnsbRU1cGY10spBLzwykIk4yXYTbGj2MpilFxdkmHrhmVbhPF1xGuQQnGL4l9vXIhakOR3t+waNWoI2Vl45BUSN9GCKczM369vfiIoPIj4xHjsShTs+NBvQR3vlxJjShBkFkTYmDAsTCzS1BuMMcAYY4KXBav92vmVBXsXsOrGKm5MvkHpoqUVxp3y0vK8dH/Jf+//o6x1WbXF6q0/vp4hd4YAUCyymKgd8REzXzMibcXNY6U9lXCZ68KOJ+JJyKW+l9j+eDsb729MM2ZJ45K8H5v2BNHzoSd1bepSzqKcouxt5FtmXZrFlodbAFjXZh2DawxO07dPnz5s3bqVO3fuYGVlla2MCwBBQUH4+PhgYWGBk5OTyoZvwIAB/PXXX4Bo8ChVqhQmJiaEh4cze/ZsVq5cyd9//03fvn2zNYessGfPHqpXr67w1sgPuLq6snChqtdS6u+zjo4OY8eOTVOfHjKZDFdXV9q2bUuDBg2Ijo4mMDCQMmXKiMYt/Zw59VYHN2/eVAhxfkpQUJBCuyKzREZGcuXKFdq3b4+dnR0PHjxQyTSR31l+aDlXXl7hQPyBNHV1IupwI/IGD6Y+oFr5jA0qySnJ6MwTDacxE2Mw0jfKsfla97cm0DgQzEVjbuTy/Kvi/znrj6+nzY9tWHtqLfN95qfbJmFKArraujly/bfBbyldtHSGv0PN5jTjrPwsBEJDeUMubriYI/PIDpUnVsZb35ur3a5S11H19DgiIoLLty7ToaWoIVNxTEVemon6FRPKTmDR74vSjCeNlzJl2xSW9l+aI5oH3RZ3Y1/svjT6TznJ4euHGbhvIJMbTGZsp7Hptnnh94KA0ACaHElfl6l4ZHEClwVmey6unq4s9F3IzoY7+a3Rb9keL6d4E/gGl1UuROimfzhSLaEaDxZ8PcQ1PdzPuuN53ROdOB3uL7yfnWkWAkgkknuCINT4Wrtsr7YlEomvRCJ5IpFIHkokkrsfyywkEskZiUTy38d/zT+WSyQSyUqJRPJKIpE8lkgkP2T3+oV8P1j/aY39BvuvN8wDUmQpvAt5R4pMfYJn6kZXQxetaK10jQ4A8UbxhGuoL05ux/kd9F7eW20W+JxAX0efQLNA9l/fz57Le9CNEheO9qbi+6x8qfJqFQga3HowcyvN5UnvJ3jN8qJWci1KRpXkl+hfmF5tOuwEPGDkHyNZ1WwVbrXd+NXpV/yi/BRGh3uD7vFk6BN2d96NWyM3/GP8WXVLGW+cLEvmn2f/0PdwX1w2uPAo6BGBMYFc9L2I7QpbhdFhcbPF6Rod5s+frwg1qFq1araNDiB6ENStWxcHBwfFSf7r1685efIkS5cuZfDgwbx58wZ7e3tsbW3R0NDA0tKSlStFwajUdJ6+vr5ER0fnWEhGYmIiP/30E9ra2vnK6ACku7Hu0qULZmZmJCcnY2iYOfV1TU1NFi1apAgnOH36NJUqVeLx48f52ugA4OLikq42xueinJmhS5cuivAUNze3AmV0ABjdcTT7J+6nodCQAZYDVOpumN8AOwiP/fL3+cm7JxWPc9LoAIAJUMASL8jlcpznODPkzhAqzK+QxuhgK7UFxJC7nDI6AJQpVuaLv0OVi1cWH5SAZ4Y5400SEhWCxSQLdMeK91l7Wm3Kj09fG+hTTtw5QemxpfE29AYNqLejXpo25ubmCqMDwPQ6ys9zU6emSNwkSNwkzNujPOE2XmTMqsBVaM/VVtSvPro6O7eoQnic+NnJjrhkVunwUwdCloVkaHQAqFi6IvWd61NeqnztGwjK0LAgsyDGrx+fbhaUrCBLkIE/2Fhm//c/J7ErYUf4/HA0pel7BD/Ue8jWE1u/aew5/84hLCCs0OiQy6hrxd1YEIRqn1g6JgPnBEEoD5z7+BygFVD+4/+D4KMc7HfM4euHv96oEEDMhZxf8fL1oszaMnRclPMCPN9KmCyMFJMU7v+XwZeoFqSYpDWcmEeYYxSR9UXpxssb2Ra1jX6r+7HhxAYm/j0xy2PkNMkyMV1hcFQwPU71wFpijVcfL45MyjivenaZ2n0qTnZOWJpacnPuTd4vfc/+Jfvp37c/vIQB7QcwdOhQenbvycyWM7G7Z8eN96LOw4wGM/ihxA84WTnR3ak7I2qKaem8Qry4+f4miSmJrLi1gq77RLfp6MRoqq2vhvVSaxp7KgUBD3Y/yLifxqWZm5+fHy1biuric+fORVtbO00bdWFnZ0eLFi0wNjZm3bp12Nraoq+vr4jzB+jYUfw89enTBycnJ+zs7DA1NeWHH3LGJu3r68uNGzeIjY3NkfGzQ3obj/379xMVFQWQqXSaqSQkJPD+veglc/fuXbS1tVWySORXtLW1OXLkCJ6enlSuXFlRvnHjxiyF43h7e3P+vKg7YGFhQcWK2c8OkVdcnHWRjcM3YhqR1nDy2PcxP03/KcP0hD129wDgSIuc+75LpZie0nBW1Khojl8vuzz1fcrwDcN5In8CQLyZ0th5qdslzvU4h8+fPiROSWRC59yJf88Ip9LKE/kPRh9y5BqXHl0iwiCCJNMkgsKDuKV9i1fGr77YJyEpgdbHW/Pe9D3c+1ioC9uObaPU2FLUmlaLpOQkqsypwvITyxX9fu/2O4lTEnk79C2LFy1WlE97Po2KEyqSIkuhpH/JNNf74/63CwF/TmRiJCTlgkHuG9DS1OKl+0tOtjmJ/wh/Ls26xEhrpfjwkqAlLNi3IFvXWDR4EckeydSpnNbDLD/yesJrHvV6lG5dn9t9MB9rTqmxpbJkkJHqSDEzMVPXFAvJJDml8dABSE1A7Ql0/KR8qyByEzCTSCQl0hvgeyEqLiqvp1Bg0IjVUIj05TcWHhJdnP9N+pfGbo15F/KOuIT8FWtZTEtc+N33yZz1ds+lPSzYuwAdiQ4aaKAzTodui7sRlxBHiiyFp75Pv9g/KlF8b18OuMzg24Nxf+f+xfZ5wYIr4o/zoreLQA8almmIo61jnuRINjU1RRAERUrDVF2EBQsWYJAkpqFsUa6FSh8LfQtSpqfwIOgBdTbVof7f9Xn6If2/y5jaYxj0wyC8hnrRsVJaA9nbt2+xsbFh9OjRCILA1KlpRSdzgxMnTnDo0CG8vLw4ePAggiDQqlUrnj5V3tfz58+RSCQqKT3VgY+PuEErWzb/CbUNHDiQ1atXs27dOoXg5KeYmWV+gdS/f39Kly6NRCLhzJkzODs7K/QdCgK9e/fm9m3V2OvUmPjU1K9fonLlygpDxdmzZ3NUyyO3ONTvENZR1iplYx6N4YbWDeosr4NMLlOp23VnF1JzKSRBqx9b5fj8htQfong8rdGXRXvzmm6Lu+Hk6cS64HVp6nY22EkDhwY0KS9mqsiL34rPaViloXJtpAW+b33Vfo1UEVKAVwGv0InXwSDOgNiEWCQzJNRwrUFQeBAyuYzEFFEgqf+a/mKHZIjZGcP93vfRSdSh973e+Jv6c1v7Nn8e+hMvuRfXvK6pXE9HWwcbKxvO7DxD2Ziy2ETZgB+8NHqJ20433m94z6VOl2iMaoadaq7q0WqJTopGIzl/y9y1qNEC6yLiZ37FwBX4j/DnF/1fAJjmNY3flvxG+fHlv9n7QUtLS60enzmJjZUNzvbO/NvqX5ppNAOgRKRy+xhpGom/qT/tFmacTepT3oW8QzAQKG1c+uuNC1Er6njHCcBpiURyTyKRDPpYVkwQhECAj/+mJo8uCfh90vf9xzIVJBLJIIlEclcikdxNFZoqSKSqzzaiERbGFrjtdMvjGRUMjJKMMIsy46HPQ7Ur+H6N8Ohw3PerbpzvvLhDXEIcWuO12CNVCr1d5CJl1pbBcaojcrmc4euGqyjsftOPgOzrTb5G3xp9M6wbuUE1Vd+ui7sYdHAQU7ynEGwWTLR5NMkmyeyL3UeTuU3Qnq6Nk6cT5qPN2X91P0CakIroZFE1P1Tn66mfhq8bjsEYgzz3APqjlfpOTLKLh4eHIgWjLEl8A9ibKEONEhISuH//PtIYKT2r9ATgTsAdRShF0rQkPFp7YKZnxqW+l1jaYinr263H0cqR9BgzZgzAF9M25gYSiYQOHTqopOcsUaJEuqEPnp6eacqyQ342POjr6zN8+HAGDx5M586dGThwoEq9kVHmT+Y6dFC6Nd+/f58aNb4adpnvMDQ0TDelZo0aNTKldZFKQkKCwshXkGlUtRH+S/3Trftg+IFmk5qRlJyEXC5n+aHl9Dguejs01WmaIzHynzO4tTKs6/DzwySnJOfYtd4Gv6Xm1JrfnHZwX+w+leepm7kZejP4rXH+i3evULICH8Yq18IVJqk/TCwyVqnJERwZjCRJQpxBHKWmlgJNuKd3jxKrSqA1Rwu9eXoYjzFmZ/ROtKK1iJkag5GREdXtqnOw40FIAVKgkm4lZjybAUD5YhmHbbxa/Iq3S9+yv5+41pj7ei7NlzSngXMDTk87DfGgESduVwRD9XjGujV2Y3rZrIdw5SXWRazZP1F8jdCC3dLdvDJ+xexdszEZbZKldXO3Vd0oNb3U1xvmM1rXbM3p6acRZgq8X5JW++p48nH2XNqTTk9V1p0UjY5tnNuofY6FfBl1GB7qCoLwA2IYxXCJRPKlPFXp5ehK8y0iCMIGQRBqCIJQo2jR/O+y9zmdd3QG4L+o/2h3sh2z/puVoStkIUpalGqBta41DVc1pOW/LXP12uVmlGOi10TFF9amU5uoubsm5VzLITNWtQr86fgnAL4mvuy7sg+PYA9Kri7J0oNLef/hPboTdZG4SdCaoJVx2MNn+P3hx70e97IVt5d6YvFpukgQ9SlWvVNqBGhM1qDHpR5EmyvT7fkO8VU8vqV9Cz4ejkaaR9LlXBce+jzEeJExPZb2wGasDVoTtJDKREPEpymZGs5qyLkH55DGS5m9azYAree1xiPYg3izeBafUrpV5jSTtkwiwlx8TYwijdjdaDc/lM8/sjKlS5cmMDCQuLg4IiLFeZZwKMHUqVNJSUmhZcuWuLi4cOnSJUbVHsX/Vfs/THVNGVpjKHMbz0VbU5uhPw4lYlIEDcp8OT2gTCbj3LlzDBo0iC5duuTG7WWZq1ev4unpiUQiUWgRWFpa8vLlS7Vlvbh79y5FihTBysrq643zmFWrVnH16lXFc1tb20z3/eWXX2jcuDHNmoknQ23btlX39HKFSpUqceLEiTTlrq6u9OnTJ8N+JUsqzzM+Ddn4nrlgdEGRHWnMozGK8o4/5F544J624u/nwbCDHLmWc+EdwzYN447OHX5bmXUjQbnxSjFe/xH+JE9LZv/E/QgzBdwm5d9DIktTS/5pKnpCJTsk88LvhVrH/9TjodeaXop0opEm6YuESs2kkAw/6v+oEq7QumZrPoz/QNTkKHZ13aUo7/Bjh/SGUaFjnY6UiS4DwI9WPwJi2MGAmgM42O8gwkyBwRUGozlYk6DwoKzf5Cf81vo3Zg2ela0x8ooDP6sKzm67tY0Y8xjG7s5YO+JzLr2+RFB89l7DvEZDQ4P1NdenKf/90O94HPPI0CtZEARO3jgJcdC/ef+cnmYhn5Ftw4MgCAEf/w0BDgI1geDUEIqP/4Z8bP4eleRQlALUk4w1HxFrJsYP1y9ZX1HmG+ybR7MpOATFBPHM4JnKhjinCYkIwWmSEyaaYiqhXy/+ilwuZ8BNUcyruV1zQMyt/keJPxBmCkzsMpGGQkMA+h7qqxhr3ONxlPYoTYqxqKMgM5LhstOFAasHoDlRU7ERzwjbYrbZcntb9mpZuuU7L+wEbbCKFDdbgr64iasorYh2tDYeNTwoU6wMwkwxrdEU+ykUjSyKS6LSPfnADfGHblfMLvxM/ZAZyZAiRTNGPEmUxIs2xcuSy/x85GdsXG2Y+XImWhO0OJGi3Dhc1bjK2n9zR9pl12Plouevjn/RvWH3XLluVtHX1+fo8aPikyqi+KO2tjaXLl0C4OVLUf17U4dNRE6OxKONB1MbZC1M4sSJE0RHR1O/fv2vN84jihYtSu/evQkODlbM8+DBg1SsWJFFi9KqnmeVW7duceTIERo0aIBEkp4NPH+hq6tL3bp1sbe3p3///tStm/k841paWpw/f57169ezfPly2rQpuKc6LVu2JCAg7TJh69atBAcHM3PmTJ49UwruCYKgSDno7u5e4EQlv8bOhjvZ02gPtr62X21bNaEqI9qNyPlJfaRjNaWR45z3Oe68vEOJSSV4E/Lmi/2auDWhiVv6Kv7pkZoiuIptlSzP0cfYB+sYa4SZAtZFrHPFG0RddPypIx1NxNe40uZKah07Mk5pYIivpNS70JAq1yRzKsxRPLYMt0SYK3B97vU0Y1maWmJiaEK1stUQZgqEjQmjlkOtNO0+R0NDA98lvggzBZVUiht/2Uj7SqJQrMddD+TWctwPfntoZ4oshTYr2nDiSVqjZkGgU91OKl6yr4u8BuANb7AaY8WO8ztYfXQ1DWZmfCARqhWKjZC/hSUzw6BWoqO9RqIGpu/F7/oUsxSG3xtO8cnpp8gMiw+jsmNlNjbemC81Pr53smV4kEgkhhKJxDj1MdAc8AKOAKnHEX2AVP/qI0Dvj9ktagNRqSEZ3wsrDq8AxHR9kzpOUpR7v0/rMlqIKt7ROf8apchSaDirIWP+GkNoVCh15tXhqcFTNCVKV1zN6crN9OqBqxlRfAQvxr9g5aCVijZdXMRT41qmtVhfcz37muzjdNvTivoDPx+gaKTorbMpbBNyQzkzX87k3INz9Fjag0FrBvEppT1KU2RZkWznJE6PPtfEj+LQGkM52eYkt3+9TZHIIhz84yBJS5IY2maoSvt5veYRsiyEu/PvUldWF+Rw2zdtrus4szgu9r2ISYQJaxsojQklo0oqPA1kRjJmlpup0s/zhid6Y/WwH2dPUHgQ07dNx2qMFUsOLGHo2qHZVmtOZf1v66mWIC588qvRIZUWtqK2Q6NajdiwYYNK3fPnz7M9fnBwMJ07d6ZTp07ZHiunKVq0aJrN4po1azh8+DChoaG4u7tn+T3y+PFjateuTWRkpNrDN3IaHx8fRTrSrGJnZ8eoUaMKhKHlS5QoUQJBEHj37h2jRo1SlP/888/Mnj0bR0dHJBIJDx48IDg4mIgI8fundu3aeTXlHOO3Rr/RrWE31o5aSx+bPph8MEm3nUGkAfv/2J+rc2swRbnR2f9iP+7/uhNkEMTrgNeKch8fH85eOavS78LH/zJLRHwECLDw18yH3AB4nhE/+2HysCz1yy9oamjS3rF9mnLvd94s2LuACZu/XQRzYIOBjDYfzfaW21XKD3RSnq5P+20aSxyWgAzOjjz7+RAZklGmrW/h+kzR0LH0/VJuPBNFmeVyOUlJmV87bTy5keORx5m3b97XG+dTnBKd0I5WFYeON47ng9kHfr/yO3/c/4MrGleQuElw3++O+353Ws5tyfrj6+m4sCNyIzmWepZ5NHv18qzfM/zH+1OxmKqIcIx5TLrtWy5oye6g3QxoOSDd+kJyFkl2XFglEok9opcDgBawUxCEeRKJpAiwF7AB3gFdBUEIl4irn9VASyAO6CcIwt0vXaNGjRrC3btfbJKvSJGl4DjZkV2DduFs74z2XOUXw81uNzNl9f1fReKmujgWZqo3y8Xb4Lf8ffZv3F6J7pTGEcaKL6aOeh2Z/9t8Shctzf+t+T+OBBwhZE4IJobpL+pSZCkMXDOQP3v9iZW50m171o5Z1K9cn6bVmyKXyykytghRelEKL4P2uu05kqh0Qd1cZzO9mvZSvE+yk2vdeow1gQaBxExTHaPjwo4cTjzMvR73shVqEB4djk+gDz9W/JESY0pgpm2G9yJvEpISSJGlUGFKBYbXHs7U7lPp6t4VQ11DutfpTqsfW3Hx0UVqVqzJibsnmHxo8hfVsmeVn8XMHjMzrM8M155ew6W8C3o6etkaJ7eY4z6HGXEzaJTQiAsLLijSUY4YMYKTJ0+SkpLyXcSpZ5b4+HhOnz6tyHrh7OzM48ePFfU3b97k7NmzVKpUiY4dO371tVmzZg0jRozA0tKSgqgbVIiSbdu20bt37wzrtbW1SU4W9QW8vLxUtES+Rz7/3UzlF/Nf2D8ydw0PuuN0STJRbgB1o3RJ1kwmdn4smx5s4pXXK5b7idkNEqckIhfk1Jheg6cGorDs2fZnOe91Hrcebl/0RHCc6MgznvFm2JsvhiCFxoWiIdHg4YuH3P7vNkvvLOWD4Qe0orVIXpJzGhQ5za6Lu2hVoxVmRmbc/+8+Nf6qocgK9k/Tf+hcr3O2xt98ZjP9r/ennWE7jow/wtvgt4CY9jM/UG9GPa5pXqObYTeCo4K5pHWJ8lHleblU9A6Uy+Vf9B7t6t6Vf+L+4XrX6wUmq0NGPHn1BOcdzt/Ut768PpfdLqt5RnlHiiyFSGkkZeeVJdpQ9JxuRCMuzFQaNQVBQH+APnIzOUlL1H/Q97+MRCK590l2y4zbqSt2NqcoaIaHz7EZa4Ofqain2VBoyMVZF/N2QvmYnDY8aE7URG6oPCntadqTW+9v4S/zJ/zP8BzbpJ57cA4TAxMMdA2osrZK2rShMuDjviliXARmRt+W3sdmrA3BkmASlyRmb8I5jGSGBDShk34nTgaeVEljBvBj0o/0r9ufcGk4rt1cszz+hhMbGHx7MGVjyvJq8ZfTgeUXxi4ey7LYZYyuMpplvyhDZjZv3kz//v05d+4cTZpk3hX5U/z8/DA2Ns5SVoT8wrVr1/D39+fWrVssXbo03Tb79u1T6FZIpVJu3bpF06ZNFfU+Pj78/PPPREZG8v79ewwNDXNl7oXkDKdOnVKkhP0S3t7eVKqkXnf0/Mj+q/vpfrg7MiOl77WJ1ISnE59SqmjuiseZjzYn0vwzTYAUxGOpdKiWUI2Heg/FJ0nAxwQS635cpyJW+TmvXr2i/KLyFDMoRtDyjOPUK7lXwkLPghvhNxRlOpE6PBz9EIcyDpm4o/yJIAjExMTwLuwdVbaqhpu00GzByWknszReiiyFFu4tcCnnwqIui5DL5QxbN4y5PediaZr/TsXlcjllZpahrV1b1vkpM5OYRpsi1ZAiM5KxxmUNw9oOS7d/qbGlCNIIImlRUoHJ6vAlyowrwzuTd1nqYxhpyK1Rt3C0/f4Ms+FR4ZSZXAZpcVGH7NO9xNI9Sxn3fBykgDAnf+9/CxqZNTwU/E9cPufd0ncIMwU0YjXwiS4UmMwL5HI5tafVVhgdOul3QjZdxvbR23mx6AXSJdIcPRlvWr0pP1b8EUdbR671ucbyasuRTVcuEivEKxWqXwW8osXcFlx8dDHL1wkTwkgySeLGsxtsOLGBH1x/wHacLeM2jVPHbaiN2z1v86fjnxyYeICAOQG42rkSNT4K2XQZ0+yn4R/vz5A7Q5jiPSXT4pwgLp6iY6MZfFtcsA6qPegrPfIPrwXRFTlJU9UC36tXL44fP07jxo3T6/ZVBEGgbdu2ODk5IZVKv94hn1G3bl26devG4sUZi5J27dqVmTNFD5n9+/fTvHlzwsLCiIiIoFatWpQrVw5fX1/WrVtXaHT4DnB2dqZq1apfbZcVMc6CTOd6nXnr+pZWxVrRoXgH5jaey4Y+G3Ld6ACwo8cOAIqFFqOZqShsmpHRARCNDh+9oZuaNUXiKx4+vAx8+cXrlCtXjhKGJYjQiPhiu7cBb3nzWlVfYk2LNQXa6ADgccgD02WmaYwOAN6JWQ9Z7ezemfOJ53F/KuomaGhosG7YunxpdABxfn5z/Fh3RzUdapRJlMIAN/zecJynOGM2yoyADwGcunuKURtHcfTaUfx1/bFMsfwujA4ALxa8wG+YH/Xl9VnivASvPl5c6Pjl0CXpMul3aXQAsDC14K/OyvDEU3dPsffyXgBi9MQvHENp4Vogryj0eMglio8pTohuCPKF6olf/x6RuEkwjTBl0k+T0NTQZGKXidkeMyg8iBKrlLl+lzgvYWynzCv/5iRn7p3Buog1jraO/L78d3ZE7VCpP9TsEB1++roSdCqSaRLQBo8aHgy/MlwR3tHbrDeeowpOXLvC8yUBSiaW5N3id5laIGi4aiDoifdcOqo075Zm7QQgL1l5dSWjzo1iba21DGk5RKUuPj6eSZMmIZVKWbt2Lbq6YsqRQ4cO4eLiQunSGeeh9vDwYPjw4SxevJhx4/KXASqrODg4ZFrvonnz5jg7O6sYLGQy2Xez0CxE9G6pU6cONjY2HD9+XKVu69at9OrVK49mVgjA2TtnaXaoGbYJtoTLwulTuQ/HXhxjQpMJDLub9iR6mtM0OlfrTPXt1dFM0qSyrDLdq3dnave0QrrtV7Xngv8FpHpSznY4S9PqTdO0AZCMkqAn1SPBJgGA3ia98RxTcH4LM+LIzSN0OKW6NjAMNeTe6HtULFuRLvO68C76Hbf/TKvNBDDJcxIbX2wkcHYg/iH+lPUoC9pgEmFC1PKo3LgFtVBrWi1R8CVaAAAfcUlEQVRua4uaVWFmSt0OiwgLws3DFc9/TP6R8PBwfIopD/9+NfqVXeN28T2z9/JeEpISFDpfExwn4P7IHbTU71GcH6kxpQb3tO8pj9g/el9pJGjw8o+XlLXOf2m1CzKFoRb5jHYL2nEs6RjP/+85FUtX/HqH/0EkMyTUFmqzqtcqHr5+qBbhF7txdvia+AIwxGoIa4fmTkaFrJIiS8F4vDEJZgkq5Q9+f0C1stW+2DchSeyjv0BMQbjGZQ2HHx7mtOw0BpEGxC6LzZlJ5xBjN43F84kn3St1Z22w+Pd6O/QtNlYZKzB/briJnRSLgZ5Bjs9VXWy8tZFBJwcxUX8if078U6UuIiICCwtRnGvYsGG0atUKgHbt2gFi1ovy5dPmSI+Pj8fAQHwNvodY9+3bt7No0SKePHkCwJEjR9i0aROHDx/+Sk+YMWMGbm75N1VeId9OYGAgy5Ytw93dnXr16uHp6Ym9vX1eT+t/HkEQOHjyIOXLl6dKufSzT2y6v4kT905w8OVB9rTfQ+sfW2P4p+pJZNGQomjoaHBu1DkcbR2Ry+VoTtekeFxxgsyCqJNSh+tz0mZWgE+M2PFQTVKNe/PufTfGx3Ljy+Fj7IPDBwe8i4peDi01WrJvzD5KjSpFVKkoZlVMXy/p07BWBxxICEqgi0sXZvScUaBU/uVyOX4fxFDmDac3MLTlUHyDfannVA+dMTokmyl1PPST9InXEcM6n/R5QqXSlQpURhN14RPgQ2hU6P+E3lxcYhxDFg9hW8o2lfKz7TM2Vhby7RQaHvIZs3bMwu2VG5NtJ7Ogz4K8nk6+ZOjaodx5f4dncc+IN4tHNj17J5RyuRzdibqkGKdwtctV6jpmPh1dXpGUnMSdl3eo9089APqY92HLyC2Keo9jHgxoMQAdbR1FWZVJVfAy8FI8H1F8BHf87jCj/Qxa12yda3NXN596q5SOKk27Cu1Y0GsBJoYmJCUn8feZv3E97YqTqRPVS1VnZYCYdSRsTJhaVbRzg4m7JuL+0p2eQk+2z9qepn7AgAFs2rQp3b5ly5bl1au0WhZr165l2LBhrFixgpEjR6p9znlJaGgolpaW4ibkK8KST548oXLlyt/NhqOQ9PHy8sLOzq4wnKaAc8v7FrV31wYNKBZejGDzYPi4T24gNGBFrxVU316djnodORd4jniteJIXpy8U+ekG++3/vcWmdMFPH5iK5xlP+l7vy7Ym21h0bBFPDESDrKGfIbGllYcNn4vrvY18i+0KW5WxRpcczbIB6afjLqhceHWBJjuUukhtddpyOvQ0hjJDwpeHf6FnId8LcrkczTni+qCWpBa3hFsQB8Kf+XvfW1DJrOHhf8/cl0d0rduVY0+P0ap6q7yeSq6SIkuhs3tn5nSbg7O9syINXnqbAGcbZ9aFrFMITCWlJGVLe8F1qyspein0Ne9bIIwOADraOtR1rEunfztxMP4gx98cJyQihJj4GB6+fsjwe8M58lDMinHM9Rj3Xt5TMToA+Ib7ckv7Fnde3SnQhofiFsVJnpaM9lxt/Ez98Aj2wONPD3SkOox0Gsliv8VgDle4wpWAK7we9JoIaUSBMzoAePuKJ1beSenH5/711180a9aMJ0+eMG+eagqwN2/ekJycTGhoKH5+fhgZGSm8G44fP06LFi1ydvJ5gKWlGHssk8mwsrKiZ8+eyGQyVq5cmaatk5NTbk+vkDyg8O/8fVDLoRZP+j6hXMly6OnoER0bjeliMb3uy+iXVN9eHYCadjXp16wfpx6dQqevDiPqj2B8x/FYF7FmwOoBFDEqohizZFTJ78roANCnWR9qV6pNxdIVKW9Zntp7a1M2pixJJknEojQ8XOQi/73/j/KlRK+4X9b/AkDRiKJ8MP+AJF7CnJ5z8uQecpLG5RrTz6Ifulq6Ck/XuIS4/0kvh/9Verkrw+1uzriZhzMp5FMKP4G5hKOtI3fnF3zPjayw5MASJl+fTIpxCke2KVNIGkYaEjQ/SMWlTxov5dyzcyr9E5ISvsnwkJCUQN9VfWlXvR1/lfiLfs36fftN5BEHJh6gxtQa3DO7R7GVxQAoGlkUzOB8xHmSTZL5demvPAh+AMZQUVqRF0YvAJALonFHU6Pgp1/U0tRicZXFjH8yXizQhCTTJC74XAAd0I/UJ94sHvsYe+xK2GGHXd5O+Bspp1EOADN5xpknunfvTvfu3Tly5Igi3GDevHlMnToVHR2ddPs0aPD/7d15fFX1mcfxz3OzEiAERJBNtiqrCrJoLR1ZagvWYuloB1sQUVpwawVEUUehdamChVocq4wwZWqnCloVFbWCooyDS4qIIIsoRCIBwhK2iNl+88c5CZc1CUnuyT35vl+v88q5557zu0+Oj+Te5/6Wfwn1N/1JSUls37697PGVV15JYWEh/fv3Z8OGDWWFThGJH93bHy4ipddPp/juYtZnr2fdlnX85KWfkFyQzI0/vJH0+unkbMuhsH0hM7NnMvPRmTza61Hm7JoD/pD/5J3JzB45O6DfpGaVDtu9oMsFZWP2h9w/hC1FW4447+sCb4jBU0ueYsWhFTTOacz7k9+nRcsW7N6/O66GV1TG3JvnHvE4noZfStV9sucTqBd0FHK08L4jrYWytmfRekJrrp11bdChVJv8Q/mM/uNofvu33/LYy4/RfmJ7NuV4s0hPXTaVooZF3olRKzwerH+Q2a/NpuOtHVm9aTXX/+l6srZn8Vz+kWuOl85dUBklJSU0vb0pzxx4hhFLRjD6ktFx+8Er8/5MGuQdfkOQm5ELQGG61630ufznyCnO4bQ9p7H6wcO9Hoac4/WqMTv++u7xZuJPJtIsr9kRx1YWrIQCyJ+Zj5vi+Pzh+F4xpl7E++uYRFK5565atYpdu3aRnZ3NddddR7NmzY45JyEhgY8++qjOdTvv168fAwYMwMzo1KkTXbrE9+z1IuL1kOxyZheGfWcYe/59D/sf2k96/XQAxl46louKLyo796nlRw5V+2bWN3Hd86+yHv65N6HuyEYjGdnI+8b362+8wsPI//Uet2zUkvbt2pOanErL01oGE6hIjPQt7Bt0CBJFPR5i6ONNH/NVo6/4r93/xVzmln9BLVY6xv6JZU/wUepHULqqVTp0mN2BrvldmTNsDjv27mDcpd4s/eOfHM/nOz9n/m3zSZ+ajmvoOOe/zyF9Tzo//+7Pj32NooJjjpXnZzN/xsEMr5vhP37yj7gtOpT602V/4uo3ri5boeJoo84edcyEmaU9HcLQ46FU1kNZFBQWMH/ZfMYtHkdSSVKovs0uTi+GfOjSq2IflEsnmwTYvn07S5cupWvXruTn5zN16lTuuuuu4044KSISzzIaHNsr7N3fvgtAUUkRBQUFbNy6kTlL5tCnQ59Yhxe4bu26lfV+GPOoN0H3gfwDLF61mM45nVnXYh1Pj3s6yBBFYurMRuEaZhXvVHiIofM7nl+2v+qLVZzb4dwAozl167esp/Pczt6DqJEQyfuSySjJYEfGDj5N+5T+5/SnWePD38bOGjsL8IoWzQuasy1tG5EDERJJZMCfB0BDr42CdK/g0KRh5cfqv/DVC9AIFl26iEt6XXLqv2QtMWLQCEYMGsHSj5fy4EsPMubiMbRt1pZ9+fsYcN6AIworqXmpHMo4xIrNKwCIWHwXXaKlJqeSmpzKmMFjGLdkHIcaHcK+DkePDoCtxVsB2FN08nXpT6R///5l+3/+85+rISIRkfiSGEkkMTWRczucyyMdHgk6nMAlJ3pD8L738vcAuLjdxaydfPx5hETCprYvnlBXqfAQQ61Pb80fe/6RmW/P5Ly/nAcQF6tcrN+yntdWvMbq7NU8ue1J7ut+3xHPzzp/FlcPvJr0+umUlJRw4xM3knsg97jfTIA3gWLOzJyyx++ueZd+z3irOMwePJtNOzaRlpJG73t68/iIx1mwfAHPrn+Wi864iF8O/CUv/fMlBnUfxCU9L+GVD1/hqv5XlbX1wU0f8GXulwzpE65JPPuf15/+5/U/6TnNI83JIoturbpx8LqDR6x8ESZWYjTY04A/DPlD0KFUm0G9BvHUa09xfuPzyz9ZRESkHBP/dSIXb7mY4S8NB+CS9vH/ZYxIRTVPb87qotV0bNIx6FAkipbTDEDqhFS+aeRNetA8rznbZm4LOKITy9qeRbvH2wHQZE8Tdjf2liEa33o8nVp04t/+5d9OWGCojH0H97F87XJ+0Nubgd/uNEjxnmuQ14ADGQdOeO3jfR5n7KVjqxxDvCtdOmzmeTO55ce3BBxNzUmZmEJz15wvZ3wZdCjVZt6787hm8TXcdcZd3Df2vvIvEBERKcfVc67mL9l/AWDBwAVc8d0rAo5IRMKoostphqcvdhyZ1HNS2f72jO1cMb32/CHYuXcn9u+G/cbbxsweU/ZcadEBYMZ1Mxh76dhqKTqAN3N1adEB4IXLXijbPyv1LFrktTjmmrMOeGPYn3z3Sfrd0w/7jTHusXHVEk88y9ycyTm3n8Mb/3wj6FBqhDkjOymbSXMnlX9ynFizcQ0An+V+FnAkIiISBm9++GZZ0cHyTUUHqVOccxSVFGnIRS2jwkMA7h15L26Ko8P+DoC3OsG+g/sCjgp27NlBpymdiJ5Yf/2e9aTvSadXQS9ybs7hx6k/Zu+te2s8lssvupziu4u5re1tLL5jMVtnbiXr+iyWXL6EqWdN5e6Od7Nh+gYS9yfyydef8G6CN7lUi4xjCxR1TdaeLFanrWbdV+uCDqVGRFwEl+b4w6fhGWqxbqv33+qzfBUeRESk6l5e9XLZfo9IjwAjEYm9H933I5LuTeKG2TcEHYpE0RwPAZoyeAqj3h0FwIavNtD77HJ7qNSY7Nxs2jzWBhpDi7wWpERSSImksHr6ahITDqfJ87c/H7OYIpEID13zUNnjM5udyZnNzmRgj4Flx1KLU8uGYQxJHMKUn02JWXy1VYnzVnsIy3KaR7vp3Jt4eN3DQYdRrc5M8GZdblGswpmIiFRdYVFh2X5iRG/3pW7ZfGAzpMGu/buCDkWiqMdDgEYMHMGwesMAmPrc1MDi+N3833lFB9+yScvY9PtNrJu+7oiiQ2209s61LLp0EVekXcHCyQuDDqdWuKz7ZUC4ltOMNm30NNoUtcFceAorSeZ1M0pySeWcKSIiUr6ikqKy/ZsvujnASEREPCo8BCgSiTD3hrkAvFLwCk8seiKmr/965uucdetZ3Ln2TgCurH8lxXcX07Fl/MwA2/r01gzpM4QFkxbU+iJJrJT2eAjTcprRXs98nR1uR6gKD/Vb1geg7XltA45ERETCILrwMPKHIwOMRETEc8qfTMysjZm9ZWZrzWyNmf3aPz7VzL4ys5X+dmnUNXeY2UYzW29mPzhx63VHRoMMJrebDMC4D2M3MeKiDxYx+JXBbGy4EYBOBzox/9b5RCLh/LBaF6Ts9ZYBWZG1AorC2+Nh+FPDOdToEEZ4Cg8t0rwhFm0bqPAgIiJV161Ft7L9Dzd9GGAkIrHn8CaVDNN7xTCoyqfMImCic64LcCFwo5l19Z+b6Zzr4W+LAPznhgPdgMHAY2YWzk9GlfS7Ub8jY4+3OkTW9qxqaTNzQyazFs6i4fiGdJ7UmaLiw5Xv7rd354ev/rDscc9DPVk3PZwTEdYlpfME9GnfB3evY8zgMeVcEZ8SSCByMMLzV8VuvpGaNuyCYUz6ehI/+JbqsSIiUnXXDLmmbH/K/2j+K6lbzj7tbAC+1exbAUci0U65b7pzLgfI8ff3m9laoNVJLrkceNo59w2wycw2An2B5acaQ5hce861zMieQecHO/P1zK9PqY2de3cy9OGhXNXnKn710a+8gxmwnvWM/MNIPt7+MU3rNWVNmrd039CUoaQkpjB26Njq+jUkQJ8leysiRBeZwihiEVILUxnSZ0jQoVSbbdu2Mf2h6fS7qB/dunUr/wIREZGTyKiXwQfDP6Dv031JTkgOOhyRmHr+tvB8ORUm1dKv3szaAT2B9/1DN5nZKjOba2aN/WOtgC1Rl2Vz8kJFnTJ99HQa5DWgILHglJfWfPqdp1meuJzx74w/9rkDT7O2/lqWRZYBcFvb23hx8ovMv3U+g3oOqlLsUkv47yve/uxtOt7akXdWvRNsPDUkgQTyM/KZOGdi0KFUm/Xr1wOwbp16HomISPV49t1nAbigwwUBRyIiUg2FBzNrADwH3OKc2wf8CegI9MDrEfH70lOPc7k7QZu/NLNMM8vMzc2taohxIRKJsPf3eymeXkx6/fRTaqND8w4AFDcsLjv2i6a/oMvBLtjXh2//He3vOGKZSgmX7P3ZfNHwC7bs3FL+yXEowR+hNXv17IAjqT7btm0DICcnJ+BIREQkLKZtmQbA+R3ODzgSEZEqDLUAMLMkvKLDX51zfwdwzm2Pev4/gZf9h9lAm6jLWwNbj9euc242MBugd+/exy1OhFEkEqGgsIDmk5qT1ziP1aNW061dxbtdf77t87L94Q2G87eJfwOgpMRb5eD9de/TrW23Uy5sSHwJ60Shdw68kxv/eWOoVrW4/PLLmTBhAqNGjQo6FBERCZnTG50edAgiIlVa1cKAOcBa59yMqOMtok4bBqz29xcCw80sxczaA2cBH5zq64fVzn07yWucB8C8t+ZV6trpS6eX7T/wswfK9iORCJFIhG93/baKDiG2etRq3vvpewztOhQI76oWN1x2Axl7MkI1U3GHDh1wztGjR4+gQxERkZCwPIOt0Ll156BDERGpUo+H7wAjgU/MbKV/7E7gKjPrgTeMYjMwFsA5t8bM5gOf4q2IcaNzrviYVuu4lqe1pMmeJuxuvJszMs4o9/zd+3Zz4NABpj4zlS2NvK71ubfk0rRR05oOVWqZ0t4xzy73xnRGLJw9Hl798FXyGuWRsTcj6FBERERqrRRSyEjLIK1eWtChiIhUaVWL/+X48zYsOsk19wP3n+pr1hW5M3L59X/+mnvfvpfFny5m0V3eLZ04ZyIrv1rJknuWADDvjXlc83/XAJC4LxHSoVdBLxUd6qjMDZlc8cQVtKrfCiswkpPCOYv1hAUToL7WZhYRETmZQxmH2Ma2oMMQEQGqaVULqV6RSIRHfvEIBVbAq0WvMuyhYbye+TozsmfwpnuTNZvXMGvhLK5Zek3ZNZ0TOzOt+zQy788MLnAJ1I68HWSlZ9GrdS9KHixh6IVDgw6pRiSaVy998+Y3A45EREREREQqokqTS0rNiUQiLB23lL5P9eWFQy/wwt9fgBTvue7zuns7/hfav2r5KyYMnUDb5m2DCVZqhdI5HYpKigKOpGYlRBKwQ0aPjpoPQURE5EQ67O9AlmUFHYaICKAeD7Van059mPvduQB8J/E73sGjZsX45OpPeOQXj6joICQlJgHw1hdv0XJ8SzI3hLP3S1FJES7V8eRrTwYdioiISK21/5v9WIGGJYpI7aAeD7Xc6O+PZvT3RwOQnZtNelo6W3K3sG3PNh546QHOaFL+BJRSNyRGvP+dcwty2dV4F7v27Qo4oppxsOggAMvWL2PM4DEBRyMiIlI75TbNDToEEZEyKjzEkdantwagW/1udGvXjUE9BwUckdQm9VLqETkYIcG8IRdhXdXip+f8lGlZ02hUr1HQoYiIiNRaAxhAYUlh0GGIiAAaaiESGn069aF4WjHDzhoGQEJCQsAR1YyurboCkJGm5TRFRERO5M0pb7LsN8uCDkNEBFDhQSR0ip03EUhYezwsyFwAQKsmrQKOREREREREKkJDLURCYlPOJi6cdiGNEhuR6BKpl1wv6JBqROlQkh9d8KOAIxERERERkYpQ4UEkJPK/yWdHxg4GNBjAhokbgg6nxrw4+UUKCgtITkoOOhQREREREamAcPbFFqmDEhO8OmJRSVHAkdQ8FR1EREREROKHCg8iIZGc6H0YX/rVUprc0oS1X64NOCIRERERERENtRAJjdJVLHY13gXAwUMHgwxHREREREQEUI8HkdBITUo94nHp0AsREREREZEgqfAgEhLNGjeDwsOPEyMqPIiIiIiISPBUeBAJESu0w/tmJzlTREREREQkNlR4EAmJQwWHcGmu7HFaSlqA0YiIiIiIiHhiXngws8Fmtt7MNprZ5Fi/vkhYJScm03ZfW+7tdC9uiqN9i/ZBhyQiIiIiIhLbVS3MLAH4D+ASIBv40MwWOuc+jWUcImEUiUTY/PvNQYchIiIiIiJyhFj3eOgLbHTOfeGcKwCeBi6PcQwiIiIiIiIiEiOxLjy0ArZEPc72j4mIiIiIiIhICMW68HC8afbdMSeZ/dLMMs0sMzc3NwZhiYiIiIiIiEhNiHXhIRtoE/W4NbD16JOcc7Odc72dc71PP/30mAUnIiIiIiIiItUr1oWHD4GzzKy9mSUDw4GFMY5BRERERERERGIkpqtaOOeKzOwm4HUgAZjrnFsTyxhEREREREREJHZiWngAcM4tAhbF+nVFREREREREJPZiPdRCREREREREROoQFR5EREREREREpMaYc8esZlmrmFkukBV0HFJpTYGdQQchtYJyQUB5IIcpFwSUB3KYckFAeRDP2jrnyl2KstYXHiQ+mVmmc6530HFI8JQLAsoDOUy5IKA8kMOUCwLKg7pAQy1EREREREREpMao8CAiIiIiIiIiNUaFB6kps4MOQGoN5YKA8kAOUy4IKA/kMOWCgPIg9DTHg4iIiIiIiIjUGPV4EBEREREREZEao8JDHWFmbczsLTNba2ZrzOzX/vEmZvaGmX3m/2zsH+9sZsvN7BszuzWqnVQz+8DMPvbb+c1JXnOU3+5nZjYq6vj9ZrbFzA6UE3MvM/vEzDaa2R/NzPzj081snZmtMrPnzSyjqvenLglTLkQ9f6uZOTNreqr3pa4JWx6Y2c1mtt6PYVpV7k1dE6ZcMLMeZvaema00s0wz61vV+1NXxGkeHPc8M0sxs2f8/HjfzNqd2l2pm0KWCxPM7FPz3jMuMbO2p3pf6pow5UHU81eY935Rq2cEwTmnrQ5sQAvgfH+/IbAB6ApMAyb7xycDD/n7zYA+wP3ArVHtGNDA308C3gcuPM7rNQG+8H829vcb+89d6MdzoJyYPwC+7b/mq8AQ//j3gUR//6HSmLXVvVzwn2sDvA5kAU2Dvr/xsoUpD4ABwGIgpTTWoO9vPG0hy4V/RO1fCiwN+v7GyxaneXDc84AbgMf9/eHAM0Hf33jaQpYLA4A0f/965ULdzIOo3+Ed4D2gd9D3ty5u6vFQRzjncpxzK/z9/cBaoBVwOTDPP20e8GP/nB3OuQ+BwqPacc650ipikr8db6KQHwBvOOd2O+f2AG8Ag/023nPO5ZwsXjNrAaQ755Y771+L/46K7R/OuSL/1PeA1hW8DUK4csE3E7jtBK8tJxCyPLgeeNA5901prBW8DULocsEB6f5+I2BrBW6BEH95UM550TE/Cwwq7RUj5QtTLjjn3nLO5fsP9Z6xEsKUB7578Yomh8prR2qGCg91kN/lsCdexbF56f+g/s9mFbg+wcxWAjvw/oF4/zintQK2RD3O9o9VVCv/mvKuvxbv2y45BfGeC2Y2FPjKOfdxJdqTo8R7HgBnA9/1u1S/bWZ9KtGuRAlBLtwCTDezLcDDwB2VaFd8cZIHJ1PWtv9FxV7gtGpqu04JQS5Euw69Zzwl8Z4HZtYTaOOce7k62pNTo8JDHWNmDYDngFucc/tOpQ3nXLFzrgde1bivmXU/3ksd79JKvEy515vZXUAR8NdKtCu+eM8FM0sD7gLuqURbcpR4zwP/ZyJet8wLgUnAfH27WXkhyYXrgfHOuTbAeGBOJdoV4ioPTqYm264zQpIL3guYjQB6A9Ors926IN7zwMwieL1jJ1a1LakaFR7qEDNLwvuH46/Oub/7h7f73VZLu69WuIuycy4PWAoMNrMLzJvMa6X/LXQ23tj7Uq05SZfX0kqov/3Wvz66O9wR1/sTzlwG/NzvaiuVEJJc6Ai0Bz42s83+8RVmdkZF467rQpIH+M/93e/O+QFQAmii0UoIUS6MAkrjXwBocslKiLM8OJmyts0sEW/Yze6Kxi2hygXM7Ht4X1QMdf6QPKmYkORBQ6A7sNR/v3ghsNA0wWTMJQYdgMSG/+3fHGCtc25G1FML8d6oPej/fLGcdk4HCp1zeWZWD/ge3qQy7wM9os5rAjxg/ky3eBNCnrDLq3OuOPp6v439ZnYhXreuq4FZ/vHBwO3AxVHj9qSCwpILzrlPiOre5/8x6e2c23myuMUTljzwn3oBGIj3puJsIBlQHlRQyHJhK3Ax3hvbgcBnJ4tZDovHPDiJ0piXA1cAb+pLiooLUy6Y18X+CWCw0/w/lRKWPHDO7SXqywgzW4o3+WVmeddKNXO1YIZLbTW/Af3wuiutAlb626V4Yx6X4L05WwI08c8/A6/yuA/I8/fTgXOBj/x2VgP3nOQ1rwU2+tvoqOPT/PZK/J9TT3B9b/81PgceBcw/vhFvDFjp7/F40Pc3nrYw5cJR52xGq1rUyTzAKzQ85T+3AhgY9P2Npy1kudAP+CfwMV5RolfQ9zdetjjNg+OeB6Ti9XjZiLcCSoeg7288bSHLhcXA9qjfY2HQ9zdetjDlwVHnLEWrWgSylf6hFhERERERERGpdprjQURERERERERqjAoPIiIiIiIiIlJjVHgQERERERERkRqjwoOIiIiIiIiI1BgVHkRERERERESkxqjwICIiIiIiIiI1RoUHEREREREREakxKjyIiIiIiIiISI35fyAK68zIiTSqAAAAAElFTkSuQmCC\n", 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RDCIiInITKMEgIiLihDaeW8+rm14EwGhWgkFERESKnxIMIiIiTuhA9H72Ru2hpn8tOlTq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BAqryyf/ItYsIi2oF3xjvwZZ5hYr4NpZ5YMXv5CgZk1B1cw+fA3xKpjGFVtNKM7jqfrz+25E+HH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\nRK5CYII=\n", "text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -1262,21 +1320,74 @@ ], "source": [ "from scipy import signal\n", - "data = dataset.data['CODtot_line2'][:].copy()\n", - "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])\n", - "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "data = dataset.data['CODtot_line3'][:].copy()\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line3']['2013/1/5':'2013/1/8'])\n", + "line_segment = dataset.data['CODtot_line3']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", "line = line_segment - line_segment[0]\n", - "line10=10*line\n", + "line10=5*line\n", "fig, ax = plt.subplots(figsize=(18,4))\n", "\n", "ax.plot(data['2013/1/1':'2013/1/14'],'k--', label='original data' )\n", "\n", - "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']+= line10\n", + "dataset.data['CODtot_line3']['2013/1/5':'2013/1/8']+= line10\n", "\n", - "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/14'],'g--', label='data with drift')\n", + "ax.plot(dataset.data['CODtot_line3']['2013/1/1':'2013/1/14'],'g--', label='data with drift')\n", "ax.legend(loc='upper right', shadow=True)" ] }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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LkoS3vj6AXw4V6p0UIopaGt8jOXsWqSVyjvp0cTU27ynAko/36p0UoohQXF6Ltd8fRm1d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\nKAhgyFPhc9RKiipe/XiP42/3J5an39r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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.data['CODtot_line3'].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "drop() got an unexpected keyword argument 'index'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n\u001b[0;32m----> 2\u001b[0;31m plot=True, period=3)\n\u001b[0m", + "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 1586\u001b[0m \u001b[0mnan_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1587\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1588\u001b[0;31m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnan_values\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmax_slope\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: drop() got an unexpected keyword argument 'index'" + ] + } + ], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", + " plot=True, period=3)" + ] + }, { "cell_type": "code", "execution_count": 132, @@ -1622,7 +1733,7 @@ "cell_type": "code", "execution_count": 142, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -1923,39 +2034,47 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.0" }, "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, - "eqNumInitial": 0 + "eqNumInitial": 0, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "nav_menu": {}, "toc": { - "colors": { - "hover_highlight": "#DAA520", - "navigate_num": "#000000", - "navigate_text": "#333333", - "running_highlight": "#FF0000", - "selected_highlight": "#FFD700", - "sidebar_border": "#EEEEEE", - "wrapper_background": "#FFFFFF" - }, - "moveMenuLeft": true, + "base_numbering": 1, "nav_menu": { "height": "282px", "width": "252px" }, - "navigate_menu": true, "number_sections": true, "sideBar": true, - "threshold": "3", + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "324px" + }, "toc_section_display": "block", - "toc_window_display": true, - "widenNotebook": false + "toc_window_display": true } }, "nbformat": 4, diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 17e8ff680..565540973 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1577,22 +1577,24 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals series = self.data[data_name][arange[0]:arange[1]].copy() #removes NaNs and infs from the dataset and other values that signal.detrend can't analyse - index = 0 - nan_values = [] - for value in series: - try: - signal.detrend([value]) - except ValueError: - nan_values.append(index) - index += 1 - series = series.drop(index=series[nan_values].index) + series.dropna(inplace=True) + + #index = 0 + #nan_values = [] + #for value in series: + # try: + # signal.detrend([value]) + # except ValueError: + # nan_values.append(index) + # index += 1 + #series = series.drop(index=series[nan_values].index) if max_slope is None: return KeyError('Please specify a maximum slope') """ - if the period is not specified or the period is the same as the length, it goes through this if-loop. - it is faster than the else-loop. the loop calculate the slope by using signal.detrend and compare it + if the period is not specified or the period is the same as the length, it goes through this if-loop. + it is faster than the else-loop. the loop calculate the slope by using signal.detrend and compare it to the max_slope. """ if period is None or period is arange[1].day - arange[0].day + 1: @@ -1710,7 +1712,7 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) """ n and m is for separating the positive and negative slope. There are different methods - used for positive and negative slopes. + used for positive and negative slopes. list_value stores the indexes where the slope was bigger than the max_slope. """ if slope > max_slope: @@ -1932,7 +1934,7 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr # ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') #series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) - """ + """ detrend = signal.detrend(series[value[0]:value[1]]) df = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) detrended_values.append(df) diff --git a/wwdata/__pycache__/Class_HydroData.cpython-36.pyc b/wwdata/__pycache__/Class_HydroData.cpython-36.pyc index 54eeaa2054fff8925e72bcbd7dbe7121f4e054f5..4eec8132fed30e48793315376aa56ca8fb47f5fe 100644 GIT binary patch delta 12461 zcmd^Fdw5*Mb-#1(zI1mbtzNq;S(dJB*^;mxwt{&W8-p=61TbJojD?L@Yp$eSt#;S< zURe*WSiq2vM;ruG+J=~fJPLuNX~?EPAZe47qlVA&rD6%cZ?heuQ(Y%6Q!f(lRJV)2QZEz@+Mu{Z+ad1JZf^a3rYNQ|!niV` zayFuIm22F`{VAdPx(g<+=g~XaNQjT~2HuEV4R7MjcPb+~pWrbbM@g7Z@&s>1F2dXR 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zcmZ1+(;m)c%*)HgvcWkj$zUSaOHN~E1_mw#Aei_godrZsj%QSXFq#=%A&e7@o)Cs0 z6I3LONfjbe&Ex=K>}IluFn%%FF@ubG$f!Iyidh&Un9B?`VH@*Z<|;O>$ro66Gb&B) zVVl9IGFg?qh*53w4EDW@ER(}H7Bf|`PR`}Cn!K0m2)8~114FS0kYHfm+{3+@nbB^u z1OGNw#?;NF_icW|g| U{>CrM$hdRzP7dYGCW7qD05H-d3IG5A delta 208 zcmbQNv0H=Fn3tD}WrK6nkBOW;obk*I3|tC8F!=+U Date: Fri, 31 Aug 2018 12:28:16 +0200 Subject: [PATCH 32/42] change max_slope to required argument instead of checking value --- wwdata/Class_HydroData.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 565540973..1c62b438f 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1547,7 +1547,7 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return slope, intercept, r_sq - def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=False): + def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): """ This function calculates the slope of the data in a certain given period by fitting a line through it and compare it with the maximum @@ -1589,8 +1589,8 @@ def detect_drift(self, data_name, arange, max_slope=None, period=None, plot=Fals # index += 1 #series = series.drop(index=series[nan_values].index) - if max_slope is None: - return KeyError('Please specify a maximum slope') + #if max_slope is None: + # return KeyError('Please specify a maximum slope') """ if the period is not specified or the period is the same as the length, it goes through this if-loop. From cb5f317e93b0ed1d6a64e05475bac146ea842ae2 Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Fri, 31 Aug 2018 14:23:50 +0200 Subject: [PATCH 33/42] make arange argument more flexible by checking type --- Showcase_OnlineSensorBased.ipynb | 387 +++++++++++++++++- wwdata/Class_HydroData.py | 67 +-- .../Class_HydroData.cpython-36.pyc | Bin 63066 -> 62938 bytes 3 files changed, 404 insertions(+), 50 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 883412b5b..21b10add2 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -123,7 +123,7 @@ " dtype='object')" ] }, - "execution_count": 106, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -178,7 +178,7 @@ }, "outputs": [], "source": [ - "dataset.to_datetime(time_column=dataset.timename,time_format= '%d-%m-%y %H:%M')" + "dataset.to_datetime(time_column=dataset.timename,time_format= '%Y-%m-%d %H:%M:%S')" ] }, { @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -212,12 +212,13 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", "start_time": "2017-05-09T11:54:55.790638+02:00" }, + "code_folding": [], "collapsed": true }, "outputs": [], @@ -234,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -256,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -271,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -283,7 +284,7 @@ "data": { "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -591,21 +592,336 @@ "Tag data points that are part of a drift. Because there was no drift present in the original data, an artificial drift was added to *CODtot_line3*." ] }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "code_folding": [ + 141 + ] + }, + "outputs": [], + "source": [ + "def detect_drift(self, data_name, arange, max_slope, period=None, plot=False):\n", + " \"\"\"\n", + " This function calculates the slope of the data in a certain given\n", + " period by fitting a line through it and compare it with the maximum\n", + " expected slope.\n", + "\n", + " Parameters\n", + " ----------\n", + " data_name : str\n", + " name of the column containing the data to detect drift\n", + " arange : 2-element array of ints\n", + " the range in which to apply the function\n", + " max_slope : int\n", + " the maximum slope a signal is expected to have over a certain period\n", + " period : int\n", + " the period, in days, which a certain slope is allowed\n", + " plot : bool\n", + " if true, a plot is made of the orginial data, detrended data and\n", + " slope\n", + "\n", + " Returns\n", + " ----------\n", + " information about the drift\n", + "\n", + " !!Doesn't check the last day mentioned in the arange!!\n", + " \"\"\"\n", + " from scipy import signal\n", + " data_series = self.data[data_name][arange[0]:arange[1]].copy()\n", + "\n", + " #removes NaNs, infs and other values that signal.detrend can't analyse from the dataset\n", + " data_series.replace(0,np.nan)\n", + " data_series.dropna(inplace=True)\n", + "\n", + " \"\"\"\n", + " If the period is not specified or the period is the same as the length,\n", + " drift detection is applied to the complete given series. This is faster\n", + " than the other, periodic algorithm. The slope is calculated by using\n", + " signal.detrend and compares it to the max_slope.\n", + " \"\"\"\n", + " if period == None:\n", + " full_period = True\n", + " else:\n", + " try:\n", + " full_period = period >= arange[1] - arange[0]\n", + " except TypeError:\n", + " raise TypeError('The type of the period argument ('+str(type(period))+') and that of '\n", + " 'the difference between arange elements ('+str(type(arange[1] - arange[0]))+\n", + " ') does not match.')\n", + " if full_period:\n", + " detrended_values = signal.detrend(data_series)\n", + " line_segment = data_series - detrended_values[:] #constructs a straight line of the dataset\n", + " slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1)\n", + " if slope > max_slope or slope < -max_slope:\n", + " print('Based on the specified maximum slope, a drift was'\n", + " ' detected with a slope higher than the maximum one. \\n'\n", + " 'Slope detected: {}, maximum slope:+/- {}'.format(slope, max_slope))\n", + " self.line_segment = line_segment\n", + " else:\n", + " plot = False\n", + " print('No drift detected.')\n", + "\n", + " if plot:\n", + " fig = plt.figure(figsize=(16, 6))\n", + " ax = fig.add_subplot(111)\n", + " ax.plot(data_series, 'g', label='Data')\n", + " #if slope > max_slope and slope < -max_slope:\n", + " ax.plot(line_segment,'b',label='Detected drift')\n", + " #ax.plot(data_series.index, detrended_values, 'r', label='detrended values')\n", + " #ax.plot(data_series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data\n", + " ax.legend(fontsize=16)\n", + " ax.set_xlabel(self.timename, fontsize=20)\n", + " ax.set_ylabel(data_name, fontsize=20)\n", + " ax.tick_params(labelsize=15)\n", + " ax.legend()\n", + "\n", + " else:\n", + " if type(period) is not int:\n", + " return ValueError('the period must be a integer')\n", + "\n", + " if period < 0.5:\n", + " return ValueError('period must be larger than 0.5')\n", + "\n", + " start_index = 0\n", + " end_index = 0\n", + " new_index = end_index\n", + " n = 0\n", + " m = 0\n", + " list_value = []\n", + "\n", + " if period == 0.5: #Need a solution\n", + " print('Not yet possible with period = 0.5')\n", + " pass\n", + "\n", + " elif period == 1:\n", + " count = 0\n", + " day_list = []\n", + " for value in data_series.index.day[:-1]:\n", + " count += 1\n", + " if value < data_series.index.day[count]:\n", + " end_index = count - 1\n", + " day_list.append([start_index, end_index])\n", + " start_index = count\n", + "\n", + " for value in range(len(day_list)):\n", + " start_index = day_list[value][0]\n", + " end_index = day_list[value][1]\n", + " detrended_values = signal.detrend(data_series[start_index:end_index])\n", + " line_segment = data_series[start_index:end_index] - detrended_values[:]\n", + " slope = (int(line_segment[-1]) - int(line_segment[0])) / 1\n", + " if slope > max_slope:\n", + " n += 1\n", + " print('Drift detected in day {} with slope: {}'.format\n", + " (data_series.index.day[start_index], slope))\n", + " #combines the indexes where the slope was larger than the max_slope over a longer period\n", + " if m > 0:\n", + " list_value.append([start_value, end_value, 'm'])\n", + " if n == 1:\n", + " start_value = data_series.index[start_index]\n", + " end_value = data_series.index[end_index]\n", + " else:\n", + " if n > 0:\n", + " list_value.append([start_value, end_value, 'n'])\n", + " n = 0\n", + "\n", + " if -max_slope > slope:\n", + " m += 1\n", + " print('Drift detected in day {} with slope: {}'.format\n", + " (data_series.index.day[start_index], slope))\n", + " if m == 1:\n", + " start_value = data_series.index[start_index]\n", + " end_value = data_series.index[end_index]\n", + " else:\n", + " if m > 0:\n", + " list_value.append([start_value, end_value, 'm'])\n", + " m = 0\n", + "\n", + " if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0:\n", + " list_value.append([start_value, end_value, 'n'])\n", + " if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0:\n", + " list_value.append([start_value, end_value, 'm'])\n", + "\n", + " else:\n", + " \"\"\"\n", + " the first while-loop makes sure that the calculations of the last period is right and that\n", + " it don't overextend.\n", + " the second while-loop finds the indexes for the right period length(could be improved).\n", + " \"\"\"\n", + " while data_series.index.day[new_index] + period <= data_series.index.day[len(data_series)-1]:\n", + " checked = False\n", + " while data_series.index.day[end_index] < (data_series.index.day[start_index] + period):\n", + " if data_series.index.day[end_index] == (data_series.index.day[start_index] + 1):\n", + " if checked is False:\n", + " new_index = end_index\n", + " checked = True\n", + " end_index += 1\n", + " if end_index == len(data_series)-1:\n", + " break\n", + " detrended_values = signal.detrend(data_series[start_index:(end_index-1)])\n", + " line_segment = data_series[start_index:(end_index-1)] - detrended_values[:]\n", + " slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1)\n", + " \"\"\"\n", + " n and m is for separating the positive and negative slope. There are different methods\n", + " used for positive and negative slopes.\n", + " list_value stores the indexes where the slope was bigger than the max_slope.\n", + " \"\"\"\n", + " if slope > max_slope:\n", + " n += 1\n", + " print('Drift detected in period {} to {}, slope: {}'.format\n", + " (data_series.index.day[start_index], data_series.index.day\n", + " [end_index-1], slope))\n", + " if n == 1:\n", + " start_value = data_series.index[start_index]\n", + " end_value = data_series.index[end_index]\n", + " else:\n", + " if n > 0:\n", + " list_value.append([start_value, end_value, 'n'])\n", + " n = 0\n", + "\n", + " if -max_slope > slope:\n", + " m += 1\n", + " print('Drift detected in period {} to {}, slope: {}'.format\n", + " (data_series.index.day[start_index], data_series.index.day\n", + " [end_index - 1], -slope))\n", + " if m == 1:\n", + " start_value = data_series.index[start_index]\n", + " end_value = data_series.index[end_index]\n", + " else:\n", + " if m > 0:\n", + " list_value.append([start_value, end_value, 'm'])\n", + " m = 0\n", + "\n", + " #combines the indexes where the slope was larger than the max_slope over a longer period\n", + " if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0:\n", + " list_value.append([start_value, end_value, 'n'])\n", + " if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0:\n", + " list_value.append([start_value, end_value, 'm'])\n", + "\n", + " start_index = new_index\n", + " end_index = new_index\n", + "\n", + " if len(list_value) == 0:\n", + " plot = False\n", + " print('No drift detected')\n", + "\n", + " #Makes sure that list_value don't have two values in the same index\n", + " for l in range(len(list_value) - 1):\n", + " if list_value[l][1] > list_value[l + 1][0]:\n", + " ind = len(data_series[:list_value[l][1]])\n", + " list_value[l + 1][0] = data_series.index[ind-1]\n", + "\n", + " if plot is True:\n", + " detrended_values = pd.DataFrame()\n", + " fig = plt.figure(figsize=(16, 6))\n", + " ax = fig.add_subplot(111)\n", + " ax.plot(data_series, 'g--', label='original data')\n", + "\n", + " for value in list_value:\n", + " detrend = signal.detrend(data_series[value[0]:value[1]])\n", + " df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])])\n", + " detrended_values.append(df1)\n", + " line_segment1 = data_series[value[0]:value[1]] - detrend[:]\n", + " ax.plot(line_segment1, 'm--')\n", + " ax.plot(df1)\n", + " \"\"\"\n", + " detrend = signal.detrend(series[value[0]:value[1]], type='constant')\n", + " df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])])\n", + " detrended_values.append(df2)\n", + "\n", + " b = df2.iloc[-2][0]\n", + " a = line_segment1[0]\n", + " slope = (b - a) / len(df2)\n", + " f = [a]\n", + " s = df2\n", + " s[:] = a\n", + " for val in range(len(df2)-1):\n", + " a += slope\n", + " f.append(a)\n", + "\n", + " ds = pd.DataFrame(f, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])])\n", + " ds = ds[:] + s[:]\n", + " ds = ds / 2\n", + " ds = ds.squeeze() #from dataframe to series\n", + "\n", + " if value[2] == 'n':\n", + " #ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift')\n", + " series[value[0]:value[1]] = series[value[0]:value[1]] - line_segment1 + ds #series[value[0]:value[1]] - (line_segment1 - line_segment1[0])\n", + " elif value[2]=='m':\n", + " #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift')\n", + " series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment1-line_segment1[-1])\n", + " #ax.plot(series, 'k--')\n", + " \"\"\"\n", + " self.list_value = list_value" + ] + }, { "cell_type": "code", "execution_count": 114, + "metadata": { + "code_folding": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ValueError('the period must be a integer')" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "detect_drift(dataset,data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n", + " period=dt.timedelta(5),plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "drop() got an unexpected keyword argument 'index'", + "ename": "ZeroDivisionError", + "evalue": "integer division or modulo by zero", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n\u001b[0;32m----> 2\u001b[0;31m plot=True, period=None)\n\u001b[0m", - "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 1586\u001b[0m \u001b[0mnan_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1587\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1588\u001b[0;31m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnan_values\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmax_slope\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: drop() got an unexpected keyword argument 'index'" + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n\u001b[0;32m----> 2\u001b[0;31m plot=True, period=None)\n\u001b[0m", + "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 1601\u001b[0m \"\"\"\n\u001b[1;32m 1602\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0marange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0marange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1603\u001b[0;31m \u001b[0mdetrended_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1604\u001b[0m \u001b[0mline_segment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_series\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdetrended_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#constructs a straight line of the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1605\u001b[0m \u001b[0mslope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_segment\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m 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\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mrnk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2521\u001b[0m newdata = reshape(transpose(data, tuple(newdims)),\n\u001b[0;32m-> 2522\u001b[0;31m (N, _prod(dshape) // N))\n\u001b[0m\u001b[1;32m 2523\u001b[0m \u001b[0mnewdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# make sure we have a copy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchar\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m'dfDF'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" ] } ], @@ -1294,16 +1610,16 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 86, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, @@ -1311,7 +1627,7 @@ "data": { "image/png": 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RDCIiInITKMEgIiLihDaeW8+rm14EwGhWgkFERESKnxIMIiIiTuhA9H72Ru2hpn8tOlTq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BAqryyf/ItYsIi2oF3xjvwZZ5hYr4NpZ5YMXv5CgZk1B1cw+fA3xKpjGFVtNKM7jqfrz+25E+HH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\n8Xt8G7fNy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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1335,6 +1651,35 @@ "ax.legend(loc='upper right', shadow=True)" ] }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dataset.data.to_csv('./data/data_example.txt',sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 87, diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 1c62b438f..09897a0f4 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1557,8 +1557,8 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): ---------- data_name : str name of the column containing the data to detect drift - arange : 2-element array of ints - the range in which to apply the function + arange : array of two values + the range within which the drift detection needs to be applied max_slope : int the maximum slope a signal is expected to have over a certain period period : int @@ -1574,10 +1574,11 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): !!Doesn't check the last day mentioned in the arange!! """ from scipy import signal - series = self.data[data_name][arange[0]:arange[1]].copy() + data_series = self.data[data_name][arange[0]:arange[1]].copy() - #removes NaNs and infs from the dataset and other values that signal.detrend can't analyse - series.dropna(inplace=True) + #removes NaNs, infs and other values that signal.detrend can't analyse from the dataset + data_series.replace(0,np.nan) + data_series.dropna(inplace=True) #index = 0 #nan_values = [] @@ -1593,38 +1594,46 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): # return KeyError('Please specify a maximum slope') """ - if the period is not specified or the period is the same as the length, it goes through this if-loop. - it is faster than the else-loop. the loop calculate the slope by using signal.detrend and compare it - to the max_slope. + If the period is not specified or the period is the same as the length, + drift detection is applied to the complete given series. This is faster + than the other, periodic algorithm. The slope is calculated by using + signal.detrend and comparing the obtained slope to the max_slope. """ - if period is None or period is arange[1].day - arange[0].day + 1: - detrended_values = signal.detrend(series) - line_segment = series - detrended_values[:] #constructs a straight line of the dataset + if period == None: + full_period = True + else: + try: + full_period = period >= arange[1] - arange[0] + except TypeError: + raise TypeError('The type of the period argument ('+str(type(period))+') and that of ' + 'the difference between arange elements ('+str(type(arange[1] - arange[0]))+ + ') does not match.') + if full_period: + detrended_values = signal.detrend(data_series) + line_segment = data_series - detrended_values[:] #constructs a straight line of the dataset slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) if slope > max_slope or slope < -max_slope: print('Based on the specified maximum slope, a drift was' ' detected with a slope higher than the maximum one. \n' 'Slope detected: {}, maximum slope:+/- {}'.format(slope, max_slope)) self.line_segment = line_segment - else: plot = False print('No drift detected.') - if plot is True: + if plot: fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) - ax.plot(series, 'g--', label='original data') + ax.plot(data_series, 'g', label='Data') #if slope > max_slope and slope < -max_slope: - ax.plot(line_segment, 'b-',label='slope') - ax.plot(series.index, detrended_values, 'r', label='detrended values') - ax.plot(series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data + ax.plot(line_segment,'b',label='Detected drift') + #ax.plot(data_series.index, detrended_values, 'r', label='detrended values') + #ax.plot(data_series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data ax.legend(fontsize=16) ax.set_xlabel(self.timename, fontsize=20) ax.set_ylabel(data_name, fontsize=20) ax.tick_params(labelsize=15) - ax.legend(loc='upper right', shadow=True) - + ax.legend() else: if type(period) is not int: return ValueError('the period must be a integer') @@ -1656,19 +1665,19 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): for value in range(len(day_list)): start_index = day_list[value][0] end_index = day_list[value][1] - detrended_values = signal.detrend(series[start_index:end_index]) - line_segment = series[start_index:end_index] - detrended_values[:] + detrended_values = signal.detrend(data_series[start_index:end_index]) + line_segment = data_series[start_index:end_index] - detrended_values[:] slope = (int(line_segment[-1]) - int(line_segment[0])) / 1 if slope > max_slope: n += 1 print('Drift detected in day {} with slope: {}'.format - (series.index.day[start_index], slope)) + (data_series.index.day[start_index], slope)) #combines the indexes where the slope was larger than the max_slope over a longer period if m > 0: list_value.append([start_value, end_value, 'm']) if n == 1: - start_value = series.index[start_index] - end_value = series.index[end_index] + start_value = data_series.index[start_index] + end_value = data_series.index[end_index] else: if n > 0: list_value.append([start_value, end_value, 'n']) @@ -1677,18 +1686,18 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): if -max_slope > slope: m += 1 print('Drift detected in day {} with slope: {}'.format - (series.index.day[start_index], slope)) + (data_series.index.day[start_index], slope)) if m == 1: - start_value = series.index[start_index] - end_value = series.index[end_index] + start_value = data_series.index[start_index] + end_value = data_series.index[end_index] else: if m > 0: list_value.append([start_value, end_value, 'm']) m = 0 - if series.index.day[end_index] == series.index.day[-1] and n > 0: + if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: list_value.append([start_value, end_value, 'n']) - if series.index.day[end_index] == series.index.day[-1] and m > 0: + if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0: list_value.append([start_value, end_value, 'm']) else: diff --git a/wwdata/__pycache__/Class_HydroData.cpython-36.pyc b/wwdata/__pycache__/Class_HydroData.cpython-36.pyc index 4eec8132fed30e48793315376aa56ca8fb47f5fe..6a6f59f6f8419f6d6e893a9867a72bf684eed8b1 100644 GIT binary patch delta 2408 zcmaJ?TZkM*6s_v+>FIg(%+BmQdUj^=bTuy%5-}J=B*vg1(P&5{?y@Y@64MixY;AvR zq?!f?13?ys^p}v2AU+_3kk}v*Om;uS4>}wR}fkzOAQr<#OqNoip#Ass4bVA-KHvdUMWoQ)7aEz&}78?!&)H$&Ql-|{jn>1J`=(K-kzCCEf>I?F6S z0edMaOE3eVB>6v{?dI7tEIUvAjL5hJmIGK>k#&belG!ZJ3iCSnz;%iZu_CSmB}lqO zKwo0RAT)Y12$#YW*Wh7gR_Qw^u{5j79A({MLEJK{!5Z_Ibt{4ntg6gmb){KiBaqt$ z>UGWOF|Q_!P5{Yiry!A6J%=?r^c_rlt4%=iBS_Yugi${SuPtmp58nd3L&u0;l$si7 zK7d@T#awia>2n~}+gjJ)TS2b`dc$)Eyt-R-u{fKsIQz)>9=|L~Oy5lcza+}>wkSD} z^sdGmxCIro#jrafa>70qC%@$9y4pK5qU-=zhxj=) z2iAZW$q@8|!4*@~N(7HmX7g&wFnxs`U7ha~1w`%_;MD7Y9Z));X3ilZtynU|XxjkI zqA0=vDx%8D$8^aRu!P*`d=#zFCh#kAY6H^hbPHU^rf>93*T<%qc%-FBOV{VbkOVRG z1Q2nkZZFiTNHNGj=il^0DU?`Wbt-@r&)60y4kEwXhj_PdryxFJvsKz?+y$_BES#;% zNB|Xep`zOK`(160DJJA0tWhQL%4>S{4n;gf`5qnNG!i1XAko#!O!;#5Em`L zzk7vPCMN^LdSc)VQK5l|kf#K{|39JQ+MB%I4?V!Mpm&~L71^_rG$wyr5e;WO(kP-m zVzXV@oK{3LA%8ua-mjO3u8{X_DCLeL1bRmiI7Qom286n#`uRruN9SDGT5ubxjO}Io zu%)bDxVM06Pa=FD7Lf`<^HxKoxuhJ&F;8px{ZJco!i_>6AHWgq!TC zdChMK3p!T=^8x%`uX~)2|^u&;n?;4yVvnwVak8OA%%m5!^=42aX5*?85ovzHhF3} z$)-F$HO<0X*LPAq%r30AU&T#xc8VXLn&rE~%?lf8D|~ZdYwl=Z!l7D| zg0u6Wf7AkJ6DN+(dVDV|&n|AJ^>EAL3)MPkkb0K3bem?&qclwmbO$X)^NTHm7NU#a z-?z*bdRY^Fd80z96JEQy-IRXo;a@ios?G5Ar46(Zez4R8zn?7ar=!tNO9%7Ri8lV0 zr}Rqn-0!6}r(p}NEzej2E Kl%-n*tMVVpWJz-X delta 2547 zcmai0Uu+ab7@yhQ+uhr{>;3<;*WUGnN-2U=NfeN1q7{82P>e;SAs6SU-76=ZJDS7H zW{nq3G?HAh4<-f^6Tv4)(2ImbEtv3v3FSc^Odpi^q=FbD@xl1uH?w=^QCdy2?Kkty z_xJakZ@PR-UcM~{w?!hxPW7FapZGMm^ZY;wAryx18Tjgl+0P#QBF0opVa5z@VT`6wt*D?zplVPHBmTpJmdbRldkNK~1BmHFejM5Q{ProtG)rR| z3!2E%iEQE> zvq0xCxh3&hSeT*11GO;G?Eol)VlO}pNJpm!8c{1oHj)TbG{6R|I2nM{qAY49m`0)` zM&c)B@WIz%k|4>xwPxv1K#HVcPw~%a5G~A$&N)yM$&hT%AW1?bcakR*wNeaOX=1`! z66*}ZeXA_VlR^Jvk_-`p6!^KeT07LpF@^W18f;56(Fl;D-}+&wUZNwgQ{p=WJ9Z2h z5NB~a0e_S5rGO1-Kxw!t!O=5Jen%M25;HIW_jWf|nS#}e*{2lv_HuU28rrj@KAveQQqnZC!8BJ(6vy$pxjaB$4`$B)m|+tP#pfQlYV4C_hOKuU{0T#U%%@dkE0ss6OxK$fa&|JV#DO zuM@rz*lZRy61@V+^18Uxwef=ipjQA;srG7HY7*Z=0>Ua;-ygv2z#Zq50C%{p(%W$T z^37u_!wCq$3`i89Bdck}(sGHD+OYF1F8T>1hk$-OQSgPm$Z0F{7hrDj ziKlTQ@nr5LQ+H<-LTy<|sQ(>90eZ8KVCtX zJ0#wa@fB}~bKfJy?=z?`4euqK-jBu}DNoAo-mgt} z;VS8Tckw4_;~1tg{EM;Y_2S;SO1WMs)sIzPnLTo%R4yGYH)oF?KU%8Ky>_hPUj5>X zJATb{-)|Q>AAk82c7OhAH!iy4i>c9NjZ_-6QX}+Pa7DNAU;O_T;hFvj(y~m-C)^{8 zkGdZ&7Ts%$C0ugvEN(6?tMv+4oGF21g&k>Yxrj z4$sZj8?Jk46E@w&OFPCkgBIn(xF{=F!3LIa3=hB;#tBKsy0!uKVt5>9Is=!B3J!O+ zT)C^O1@5!h8NHUlIPX5Wv{e;ROt-$Y&sVzNEUm}G?wzFqL<(Qujn{SdU*8wS`OcZ| zqZps-eEvf+j3+x+Z%t!K-qRWV^(^4z-nc`s*4gpfw-_6p$nS4TV9@?!2;;4tb9Xmj L?YJ)MF+KASE1PW? From 7a56533bafc6afbce8a1db5dfd2e47098b363e05 Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Fri, 31 Aug 2018 15:20:45 +0200 Subject: [PATCH 34/42] add _get_slope function to allow for more flexible calculations of the drift slope --- Showcase_OnlineSensorBased.ipynb | 162 +++++++++++++++++++++++++++---- wwdata/Class_HydroData.py | 78 ++++++++++++--- 2 files changed, 208 insertions(+), 32 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 21b10add2..accbccfa9 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -594,15 +594,71 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def _get_slope(data_series,arange):\n", + " \"\"\"\n", + " Calculates the total slope of a given data series\n", + " \n", + " \"\"\"\n", + " slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1)\n", + " \n", + " if xdata == 'index':\n", + " self.data[xdata] = self.data.index\n", + "\n", + " date_time = isinstance(self.data[xdata][0],np.datetime64) or \\\n", + " isinstance(self.data[xdata][0],dt.datetime) or \\\n", + " isinstance(self.data[xdata][0],pd.tslib.Timestamp)\n", + "\n", + " if time_unit == None or date_time == False:\n", + " try:\n", + " slopes = self.data[ydata].diff() / self.data[xdata].diff()\n", + " self.time_unit = time_unit\n", + " except TypeError:\n", + " raise TypeError('Slope calculation cannot be executed, probably due to a \\\n", + " non-handlable datatype. Either use the time_unit argument or \\\n", + " use timedata of type np.datetime64, dt.datetime or pd.tslib.Timestamp.')\n", + " return None\n", + " elif time_unit == 'sec':\n", + " slopes = self.data[ydata].diff()/ \\\n", + " (self.data[xdata].diff().dt.seconds)\n", + " elif time_unit == 'min':\n", + " slopes = self.data[ydata].diff()/ \\\n", + " (self.data[xdata].diff().dt.seconds / 60)\n", + " elif time_unit == 'hr':\n", + " slopes = self.data[ydata].diff()/ \\\n", + " (self.data[xdata].diff().dt.seconds / 3600)\n", + " elif time_unit == 'd':\n", + " slopes = self.data[ydata].diff()/ \\\n", + " (self.data[xdata].diff().dt.days + \\\n", + " self.data[xdata].diff().dt.seconds / 3600 / 24)\n", + " else :\n", + " raise ValueError('Could not calculate slopes. If you are using \\\n", + " time-units to calculate slopes, please make sure you entered a \\\n", + " valid time unit for slope calculation (sec, min, hr or d)')\n", + "\n", + " if xdata == 'index':\n", + " self.data.drop(xdata,axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, "metadata": { "code_folding": [ - 141 + 76, + 94, + 142, + 201 ] }, "outputs": [], "source": [ - "def detect_drift(self, data_name, arange, max_slope, period=None, plot=False):\n", + "def detect_drift(self, data_name, arange, max_slope, period=None, time_unit=None, plot=False):\n", " \"\"\"\n", " This function calculates the slope of the data in a certain given\n", " period by fitting a line through it and compare it with the maximum\n", @@ -618,6 +674,10 @@ " the maximum slope a signal is expected to have over a certain period\n", " period : int\n", " the period, in days, which a certain slope is allowed\n", + " time_unit : None or str\n", + " if None, it is assumed that the index value can be used\n", + " as is for slope calculation. In the case of time indexes,\n", + " the time unit is needed for this. Allowed: 'd','hr','sec'\n", " plot : bool\n", " if true, a plot is made of the orginial data, detrended data and\n", " slope\n", @@ -653,7 +713,9 @@ " if full_period:\n", " detrended_values = signal.detrend(data_series)\n", " line_segment = data_series - detrended_values[:] #constructs a straight line of the dataset\n", - " slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1)\n", + " slope = _get_drift_slope(line_segment,arange,time_unit=time_unit)\n", + " \n", + " \n", " if slope > max_slope or slope < -max_slope:\n", " print('Based on the specified maximum slope, a drift was'\n", " ' detected with a slope higher than the maximum one. \\n'\n", @@ -678,11 +740,11 @@ " ax.legend()\n", "\n", " else:\n", - " if type(period) is not int:\n", - " return ValueError('the period must be a integer')\n", + " #if type(period) is not int:\n", + " # return ValueError('the period must be a integer')\n", "\n", - " if period < 0.5:\n", - " return ValueError('period must be larger than 0.5')\n", + " #if period < 0.5:\n", + " # return ValueError('period must be larger than 0.5')\n", "\n", " start_index = 0\n", " end_index = 0\n", @@ -691,11 +753,11 @@ " m = 0\n", " list_value = []\n", "\n", - " if period == 0.5: #Need a solution\n", - " print('Not yet possible with period = 0.5')\n", - " pass\n", + " #if period == 0.5: #Need a solution\n", + " # print('Not yet possible with period = 0.5')\n", + " # pass\n", "\n", - " elif period == 1:\n", + " if period == 1:\n", " count = 0\n", " day_list = []\n", " for value in data_series.index.day[:-1]:\n", @@ -858,25 +920,89 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 145, "metadata": { - "code_folding": [] + "code_folding": [], + "scrolled": false }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for +: 'int' and 'datetime.timedelta'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m detect_drift(dataset,data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n\u001b[0;32m----> 2\u001b[0;31m period=dt.timedelta(12),plot=True)\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msecond\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mloop\u001b[0m \u001b[0mfinds\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mright\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcould\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mimproved\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \"\"\"\n\u001b[0;32m--> 148\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0mchecked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mend_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'datetime.timedelta'" + ] + } + ], + "source": [ + "detect_drift(dataset,data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n", + " period=dt.timedelta(12),plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ValueError('the period must be a integer')" + "8.5794354961686032" ] }, - "execution_count": 114, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "detect_drift(dataset,data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n", - " period=dt.timedelta(5),plot=True)" + "(test[-1] - test[0])/ (arange[1].day - arange[0].day + 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Timedelta' object has no attribute 'hours'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhours\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'Timedelta' object has no attribute 'hours'" + ] + } + ], + "source": [ + "(test[-1] - test[0])/((test.index[-1]-test.index[0]).hours)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(test.index[-1]-test.index[0]).days" ] }, { diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 09897a0f4..1a2b58c8f 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1547,7 +1547,8 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return slope, intercept, r_sq - def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): + def detect_drift(self, data_name, arange, max_slope, period=None, + time_unit=None, plot=False): """ This function calculates the slope of the data in a certain given period by fitting a line through it and compare it with the maximum @@ -1557,12 +1558,16 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): ---------- data_name : str name of the column containing the data to detect drift - arange : array of two values - the range within which the drift detection needs to be applied + arange : 2-element array of ints + the range in which to apply the function max_slope : int the maximum slope a signal is expected to have over a certain period period : int the period, in days, which a certain slope is allowed + time_unit : None or str + if None, it is assumed that the index value can be used + as is for slope calculation. In the case of time indexes, + the time unit is needed for this. Allowed: 'd','hr','sec' plot : bool if true, a plot is made of the orginial data, detrended data and slope @@ -1611,7 +1616,8 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): if full_period: detrended_values = signal.detrend(data_series) line_segment = data_series - detrended_values[:] #constructs a straight line of the dataset - slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) + slope = _get_slope(line_segment,arange) + #(int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) if slope > max_slope or slope < -max_slope: print('Based on the specified maximum slope, a drift was' ' detected with a slope higher than the maximum one. \n' @@ -1635,11 +1641,11 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): ax.tick_params(labelsize=15) ax.legend() else: - if type(period) is not int: - return ValueError('the period must be a integer') + #if type(period) is not int: + # return ValueError('the period must be a integer') - if period < 0.5: - return ValueError('period must be larger than 0.5') + #if period < 0.5: + # return ValueError('period must be larger than 0.5') start_index = 0 end_index = 0 @@ -1648,13 +1654,13 @@ def detect_drift(self, data_name, arange, max_slope, period=None, plot=False): m = 0 list_value = [] - if period == 0.5: #Need a solution - print('Not yet possible with period = 0.5') - pass + #if period == 0.5: #Need a solution + # print('Not yet possible with period = 0.5') + # pass - elif period == 1: - count = 0 - day_list = [] + #elif period == 1: + # count = 0 + # day_list = [] for value in series.index.day[:-1]: count += 1 if value < series.index.day[count]: @@ -2319,6 +2325,50 @@ def plot_analysed(self,data_name,time_range='default',only_checked = False): ### NON-CLASS FUNCTIONS ### ############################## +def _get_slope(data_series,arange,time_unit=None): + """ + Calculates the total slope of a given data series + + Parameters + ---------- + data_series : pd.Series + series containing the data to get the slope for + arange : 2-element array + can be either int or or timedelta values + time_unit : None or str + in the case of datetime index, the time unit to calculate a slope with + is needed; options: 'd','hr','min','sec' + + Returns + ---------- + the slope of the series + + + """ + date_time = isinstance(self.data[xdata][0],np.datetime64) or \ + isinstance(self.data[xdata][0],dt.datetime) or \ + isinstance(self.data[xdata][0],pd.tslib.Timestamp) + if date_time: + if time_unit == 'sec': + return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]).seconds + elif time_unit == 'min: + return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]).seconds/60 + elif time_unit == 'hr': + return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]).seconds/3600 + elif time_unit == 'd': + return (data_series[-1]) - data_series[0]) / ((arange[1] - arange[0]).days + (arange[1] - arange[0]).seconds/3600/24) + else: + raise ValueError('Could not calculate slopes with time index. ' + 'Please make sure you entered a valid time unit for ' + 'slope calculation (sec, min, hr or d)') + else: + try: + return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]) + except: + raise ValueError('Could not calculate slopes, most likely due to an ' + 'an unrecognised index. Currently avaible are ' + 'datetime and integer indexes.') + def total_seconds(timedelta_value): return timedelta_value.total_seconds() From aeb2b723c06cac1f28a31160c9dc7b19299fc179 Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Fri, 31 Aug 2018 16:09:10 +0200 Subject: [PATCH 35/42] replace separate negative and positive slope stuff with abs() --- Showcase_OnlineSensorBased.ipynb | 447 ++---------------- wwdata/Class_HydroData.py | 204 ++++---- .../Class_HydroData.cpython-36.pyc | Bin 62938 -> 64457 bytes 3 files changed, 119 insertions(+), 532 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index accbccfa9..a01aeaac9 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -53,9 +53,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import wwdata as ww" @@ -97,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -123,7 +121,7 @@ " dtype='object')" ] }, - "execution_count": 51, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -142,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -168,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -190,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -212,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -235,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -257,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -272,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -284,7 +282,7 @@ "data": { "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -594,333 +592,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "def _get_slope(data_series,arange):\n", - " \"\"\"\n", - " Calculates the total slope of a given data series\n", - " \n", - " \"\"\"\n", - " slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1)\n", - " \n", - " if xdata == 'index':\n", - " self.data[xdata] = self.data.index\n", - "\n", - " date_time = isinstance(self.data[xdata][0],np.datetime64) or \\\n", - " isinstance(self.data[xdata][0],dt.datetime) or \\\n", - " isinstance(self.data[xdata][0],pd.tslib.Timestamp)\n", - "\n", - " if time_unit == None or date_time == False:\n", - " try:\n", - " slopes = self.data[ydata].diff() / self.data[xdata].diff()\n", - " self.time_unit = time_unit\n", - " except TypeError:\n", - " raise TypeError('Slope calculation cannot be executed, probably due to a \\\n", - " non-handlable datatype. Either use the time_unit argument or \\\n", - " use timedata of type np.datetime64, dt.datetime or pd.tslib.Timestamp.')\n", - " return None\n", - " elif time_unit == 'sec':\n", - " slopes = self.data[ydata].diff()/ \\\n", - " (self.data[xdata].diff().dt.seconds)\n", - " elif time_unit == 'min':\n", - " slopes = self.data[ydata].diff()/ \\\n", - " (self.data[xdata].diff().dt.seconds / 60)\n", - " elif time_unit == 'hr':\n", - " slopes = self.data[ydata].diff()/ \\\n", - " (self.data[xdata].diff().dt.seconds / 3600)\n", - " elif time_unit == 'd':\n", - " slopes = self.data[ydata].diff()/ \\\n", - " (self.data[xdata].diff().dt.days + \\\n", - " self.data[xdata].diff().dt.seconds / 3600 / 24)\n", - " else :\n", - " raise ValueError('Could not calculate slopes. If you are using \\\n", - " time-units to calculate slopes, please make sure you entered a \\\n", - " valid time unit for slope calculation (sec, min, hr or d)')\n", - "\n", - " if xdata == 'index':\n", - " self.data.drop(xdata,axis=1,inplace=True)" + "arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)]\n", + "data_series = dataset.data['CODtot_line3'][arange[0]:arange[1]].copy()\n", + "data_series.replace(0,np.nan)\n", + "data_series.dropna(inplace=True)" ] }, { "cell_type": "code", - "execution_count": 139, + "execution_count": null, "metadata": { - "code_folding": [ - 76, - 94, - 142, - 201 - ] + "collapsed": true }, "outputs": [], - "source": [ - "def detect_drift(self, data_name, arange, max_slope, period=None, time_unit=None, plot=False):\n", - " \"\"\"\n", - " This function calculates the slope of the data in a certain given\n", - " period by fitting a line through it and compare it with the maximum\n", - " expected slope.\n", - "\n", - " Parameters\n", - " ----------\n", - " data_name : str\n", - " name of the column containing the data to detect drift\n", - " arange : 2-element array of ints\n", - " the range in which to apply the function\n", - " max_slope : int\n", - " the maximum slope a signal is expected to have over a certain period\n", - " period : int\n", - " the period, in days, which a certain slope is allowed\n", - " time_unit : None or str\n", - " if None, it is assumed that the index value can be used\n", - " as is for slope calculation. In the case of time indexes,\n", - " the time unit is needed for this. Allowed: 'd','hr','sec'\n", - " plot : bool\n", - " if true, a plot is made of the orginial data, detrended data and\n", - " slope\n", - "\n", - " Returns\n", - " ----------\n", - " information about the drift\n", - "\n", - " !!Doesn't check the last day mentioned in the arange!!\n", - " \"\"\"\n", - " from scipy import signal\n", - " data_series = self.data[data_name][arange[0]:arange[1]].copy()\n", - "\n", - " #removes NaNs, infs and other values that signal.detrend can't analyse from the dataset\n", - " data_series.replace(0,np.nan)\n", - " data_series.dropna(inplace=True)\n", - "\n", - " \"\"\"\n", - " If the period is not specified or the period is the same as the length,\n", - " drift detection is applied to the complete given series. This is faster\n", - " than the other, periodic algorithm. The slope is calculated by using\n", - " signal.detrend and compares it to the max_slope.\n", - " \"\"\"\n", - " if period == None:\n", - " full_period = True\n", - " else:\n", - " try:\n", - " full_period = period >= arange[1] - arange[0]\n", - " except TypeError:\n", - " raise TypeError('The type of the period argument ('+str(type(period))+') and that of '\n", - " 'the difference between arange elements ('+str(type(arange[1] - arange[0]))+\n", - " ') does not match.')\n", - " if full_period:\n", - " detrended_values = signal.detrend(data_series)\n", - " line_segment = data_series - detrended_values[:] #constructs a straight line of the dataset\n", - " slope = _get_drift_slope(line_segment,arange,time_unit=time_unit)\n", - " \n", - " \n", - " if slope > max_slope or slope < -max_slope:\n", - " print('Based on the specified maximum slope, a drift was'\n", - " ' detected with a slope higher than the maximum one. \\n'\n", - " 'Slope detected: {}, maximum slope:+/- {}'.format(slope, max_slope))\n", - " self.line_segment = line_segment\n", - " else:\n", - " plot = False\n", - " print('No drift detected.')\n", - "\n", - " if plot:\n", - " fig = plt.figure(figsize=(16, 6))\n", - " ax = fig.add_subplot(111)\n", - " ax.plot(data_series, 'g', label='Data')\n", - " #if slope > max_slope and slope < -max_slope:\n", - " ax.plot(line_segment,'b',label='Detected drift')\n", - " #ax.plot(data_series.index, detrended_values, 'r', label='detrended values')\n", - " #ax.plot(data_series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data\n", - " ax.legend(fontsize=16)\n", - " ax.set_xlabel(self.timename, fontsize=20)\n", - " ax.set_ylabel(data_name, fontsize=20)\n", - " ax.tick_params(labelsize=15)\n", - " ax.legend()\n", - "\n", - " else:\n", - " #if type(period) is not int:\n", - " # return ValueError('the period must be a integer')\n", - "\n", - " #if period < 0.5:\n", - " # return ValueError('period must be larger than 0.5')\n", - "\n", - " start_index = 0\n", - " end_index = 0\n", - " new_index = end_index\n", - " n = 0\n", - " m = 0\n", - " list_value = []\n", - "\n", - " #if period == 0.5: #Need a solution\n", - " # print('Not yet possible with period = 0.5')\n", - " # pass\n", - "\n", - " if period == 1:\n", - " count = 0\n", - " day_list = []\n", - " for value in data_series.index.day[:-1]:\n", - " count += 1\n", - " if value < data_series.index.day[count]:\n", - " end_index = count - 1\n", - " day_list.append([start_index, end_index])\n", - " start_index = count\n", - "\n", - " for value in range(len(day_list)):\n", - " start_index = day_list[value][0]\n", - " end_index = day_list[value][1]\n", - " detrended_values = signal.detrend(data_series[start_index:end_index])\n", - " line_segment = data_series[start_index:end_index] - detrended_values[:]\n", - " slope = (int(line_segment[-1]) - int(line_segment[0])) / 1\n", - " if slope > max_slope:\n", - " n += 1\n", - " print('Drift detected in day {} with slope: {}'.format\n", - " (data_series.index.day[start_index], slope))\n", - " #combines the indexes where the slope was larger than the max_slope over a longer period\n", - " if m > 0:\n", - " list_value.append([start_value, end_value, 'm'])\n", - " if n == 1:\n", - " start_value = data_series.index[start_index]\n", - " end_value = data_series.index[end_index]\n", - " else:\n", - " if n > 0:\n", - " list_value.append([start_value, end_value, 'n'])\n", - " n = 0\n", - "\n", - " if -max_slope > slope:\n", - " m += 1\n", - " print('Drift detected in day {} with slope: {}'.format\n", - " (data_series.index.day[start_index], slope))\n", - " if m == 1:\n", - " start_value = data_series.index[start_index]\n", - " end_value = data_series.index[end_index]\n", - " else:\n", - " if m > 0:\n", - " list_value.append([start_value, end_value, 'm'])\n", - " m = 0\n", - "\n", - " if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0:\n", - " list_value.append([start_value, end_value, 'n'])\n", - " if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0:\n", - " list_value.append([start_value, end_value, 'm'])\n", - "\n", - " else:\n", - " \"\"\"\n", - " the first while-loop makes sure that the calculations of the last period is right and that\n", - " it don't overextend.\n", - " the second while-loop finds the indexes for the right period length(could be improved).\n", - " \"\"\"\n", - " while data_series.index.day[new_index] + period <= data_series.index.day[len(data_series)-1]:\n", - " checked = False\n", - " while data_series.index.day[end_index] < (data_series.index.day[start_index] + period):\n", - " if data_series.index.day[end_index] == (data_series.index.day[start_index] + 1):\n", - " if checked is False:\n", - " new_index = end_index\n", - " checked = True\n", - " end_index += 1\n", - " if end_index == len(data_series)-1:\n", - " break\n", - " detrended_values = signal.detrend(data_series[start_index:(end_index-1)])\n", - " line_segment = data_series[start_index:(end_index-1)] - detrended_values[:]\n", - " slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1)\n", - " \"\"\"\n", - " n and m is for separating the positive and negative slope. There are different methods\n", - " used for positive and negative slopes.\n", - " list_value stores the indexes where the slope was bigger than the max_slope.\n", - " \"\"\"\n", - " if slope > max_slope:\n", - " n += 1\n", - " print('Drift detected in period {} to {}, slope: {}'.format\n", - " (data_series.index.day[start_index], data_series.index.day\n", - " [end_index-1], slope))\n", - " if n == 1:\n", - " start_value = data_series.index[start_index]\n", - " end_value = data_series.index[end_index]\n", - " else:\n", - " if n > 0:\n", - " list_value.append([start_value, end_value, 'n'])\n", - " n = 0\n", - "\n", - " if -max_slope > slope:\n", - " m += 1\n", - " print('Drift detected in period {} to {}, slope: {}'.format\n", - " (data_series.index.day[start_index], data_series.index.day\n", - " [end_index - 1], -slope))\n", - " if m == 1:\n", - " start_value = data_series.index[start_index]\n", - " end_value = data_series.index[end_index]\n", - " else:\n", - " if m > 0:\n", - " list_value.append([start_value, end_value, 'm'])\n", - " m = 0\n", - "\n", - " #combines the indexes where the slope was larger than the max_slope over a longer period\n", - " if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0:\n", - " list_value.append([start_value, end_value, 'n'])\n", - " if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0:\n", - " list_value.append([start_value, end_value, 'm'])\n", - "\n", - " start_index = new_index\n", - " end_index = new_index\n", - "\n", - " if len(list_value) == 0:\n", - " plot = False\n", - " print('No drift detected')\n", - "\n", - " #Makes sure that list_value don't have two values in the same index\n", - " for l in range(len(list_value) - 1):\n", - " if list_value[l][1] > list_value[l + 1][0]:\n", - " ind = len(data_series[:list_value[l][1]])\n", - " list_value[l + 1][0] = data_series.index[ind-1]\n", - "\n", - " if plot is True:\n", - " detrended_values = pd.DataFrame()\n", - " fig = plt.figure(figsize=(16, 6))\n", - " ax = fig.add_subplot(111)\n", - " ax.plot(data_series, 'g--', label='original data')\n", - "\n", - " for value in list_value:\n", - " detrend = signal.detrend(data_series[value[0]:value[1]])\n", - " df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])])\n", - " detrended_values.append(df1)\n", - " line_segment1 = data_series[value[0]:value[1]] - detrend[:]\n", - " ax.plot(line_segment1, 'm--')\n", - " ax.plot(df1)\n", - " \"\"\"\n", - " detrend = signal.detrend(series[value[0]:value[1]], type='constant')\n", - " df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])])\n", - " detrended_values.append(df2)\n", - "\n", - " b = df2.iloc[-2][0]\n", - " a = line_segment1[0]\n", - " slope = (b - a) / len(df2)\n", - " f = [a]\n", - " s = df2\n", - " s[:] = a\n", - " for val in range(len(df2)-1):\n", - " a += slope\n", - " f.append(a)\n", - "\n", - " ds = pd.DataFrame(f, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])])\n", - " ds = ds[:] + s[:]\n", - " ds = ds / 2\n", - " ds = ds.squeeze() #from dataframe to series\n", - "\n", - " if value[2] == 'n':\n", - " #ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift')\n", - " series[value[0]:value[1]] = series[value[0]:value[1]] - line_segment1 + ds #series[value[0]:value[1]] - (line_segment1 - line_segment1[0])\n", - " elif value[2]=='m':\n", - " #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift')\n", - " series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment1-line_segment1[-1])\n", - " #ax.plot(series, 'k--')\n", - " \"\"\"\n", - " self.list_value = list_value" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 145, + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": { "code_folding": [], "scrolled": false @@ -933,76 +637,15 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m detect_drift(dataset,data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n\u001b[0;32m----> 2\u001b[0;31m period=dt.timedelta(12),plot=True)\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msecond\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mloop\u001b[0m \u001b[0mfinds\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mright\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcould\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mimproved\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \"\"\"\n\u001b[0;32m--> 148\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0mchecked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mend_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n\u001b[0;32m----> 2\u001b[0;31m period=dt.timedelta(5),time_unit='d',plot=True)\n\u001b[0m", + "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, time_unit, plot)\u001b[0m\n\u001b[1;32m 1697\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msecond\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mloop\u001b[0m \u001b[0mfinds\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mright\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcould\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mimproved\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1698\u001b[0m \"\"\"\n\u001b[0;32m-> 1699\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1700\u001b[0m \u001b[0mchecked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1701\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mend_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'datetime.timedelta'" ] } ], "source": [ - "detect_drift(dataset,data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n", - " period=dt.timedelta(12),plot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8.5794354961686032" - ] - }, - "execution_count": 151, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(test[-1] - test[0])/ (arange[1].day - arange[0].day + 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'Timedelta' object has no attribute 'hours'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhours\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'Timedelta' object has no attribute 'hours'" - ] - } - ], - "source": [ - "(test[-1] - test[0])/((test.index[-1]-test.index[0]).hours)" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(test.index[-1]-test.index[0]).days" + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n", + " period=dt.timedelta(5),time_unit='d',plot=True)" ] }, { @@ -1032,30 +675,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "ename": "ZeroDivisionError", - "evalue": "integer division or modulo by zero", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n\u001b[0;32m----> 2\u001b[0;31m plot=True, period=None)\n\u001b[0m", - "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 1601\u001b[0m \"\"\"\n\u001b[1;32m 1602\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0marange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0marange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1603\u001b[0;31m \u001b[0mdetrended_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1604\u001b[0m \u001b[0mline_segment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_series\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdetrended_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#constructs a straight line of the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1605\u001b[0m \u001b[0mslope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_segment\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_segment\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0marange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/scipy/signal/signaltools.py\u001b[0m in \u001b[0;36mdetrend\u001b[0;34m(data, axis, type, bp)\u001b[0m\n\u001b[1;32m 2520\u001b[0m \u001b[0mnewdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mrnk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2521\u001b[0m newdata = reshape(transpose(data, tuple(newdims)),\n\u001b[0;32m-> 2522\u001b[0;31m (N, _prod(dshape) // N))\n\u001b[0m\u001b[1;32m 2523\u001b[0m \u001b[0mnewdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# make sure we have a copy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchar\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m'dfDF'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" - ] - } - ], - "source": [ - "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=20, \n", - " plot=True, period=None)" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 1a2b58c8f..780546dcc 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1563,7 +1563,8 @@ def detect_drift(self, data_name, arange, max_slope, period=None, max_slope : int the maximum slope a signal is expected to have over a certain period period : int - the period, in days, which a certain slope is allowed + the minimum period in which trends are expected to be drift and not + part of the signal time_unit : None or str if None, it is assumed that the index value can be used as is for slope calculation. In the case of time indexes, @@ -1575,8 +1576,6 @@ def detect_drift(self, data_name, arange, max_slope, period=None, Returns ---------- information about the drift - - !!Doesn't check the last day mentioned in the arange!! """ from scipy import signal data_series = self.data[data_name][arange[0]:arange[1]].copy() @@ -1585,25 +1584,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, data_series.replace(0,np.nan) data_series.dropna(inplace=True) - #index = 0 - #nan_values = [] - #for value in series: - # try: - # signal.detrend([value]) - # except ValueError: - # nan_values.append(index) - # index += 1 - #series = series.drop(index=series[nan_values].index) - - #if max_slope is None: - # return KeyError('Please specify a maximum slope') - - """ - If the period is not specified or the period is the same as the length, - drift detection is applied to the complete given series. This is faster - than the other, periodic algorithm. The slope is calculated by using - signal.detrend and comparing the obtained slope to the max_slope. - """ + # Determine if the full period of the dataset is to be analysed if period == None: full_period = True else: @@ -1611,13 +1592,17 @@ def detect_drift(self, data_name, arange, max_slope, period=None, full_period = period >= arange[1] - arange[0] except TypeError: raise TypeError('The type of the period argument ('+str(type(period))+') and that of ' - 'the difference between arange elements ('+str(type(arange[1] - arange[0]))+ + 'the difference between arange elements ('+str(type(arange[1] - arange[0]))+ ') does not match.') + + # If the full period is to be analysed, drift detection is applied to + # the complete given series. This is faster than the other, periodic + # algorithm. The slope is calculated by using signal.detrend and + # comparing the obtained slope to the max_slope. if full_period: detrended_values = signal.detrend(data_series) line_segment = data_series - detrended_values[:] #constructs a straight line of the dataset - slope = _get_slope(line_segment,arange) - #(int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) + slope = _get_slope(line_segment,arange,time_unit=time_unit) if slope > max_slope or slope < -max_slope: print('Based on the specified maximum slope, a drift was' ' detected with a slope higher than the maximum one. \n' @@ -1631,15 +1616,12 @@ def detect_drift(self, data_name, arange, max_slope, period=None, fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) ax.plot(data_series, 'g', label='Data') - #if slope > max_slope and slope < -max_slope: ax.plot(line_segment,'b',label='Detected drift') - #ax.plot(data_series.index, detrended_values, 'r', label='detrended values') - #ax.plot(data_series-(line_segment-line_segment[0]), 'm', label='without drift(?)') #some interesting plot/data ax.legend(fontsize=16) ax.set_xlabel(self.timename, fontsize=20) ax.set_ylabel(data_name, fontsize=20) ax.tick_params(labelsize=15) - ax.legend() + ax.legend(fontsize=20) else: #if type(period) is not int: # return ValueError('the period must be a integer') @@ -1654,16 +1636,17 @@ def detect_drift(self, data_name, arange, max_slope, period=None, m = 0 list_value = [] - #if period == 0.5: #Need a solution + if period == 0.5: #Need a solution + do = 0 # print('Not yet possible with period = 0.5') # pass - - #elif period == 1: - # count = 0 + + elif period == 1: + count = 0 # day_list = [] - for value in series.index.day[:-1]: + for value in data_series.index.day[:-1]: count += 1 - if value < series.index.day[count]: + if value < data_series.index.index.day[count]: end_index = count - 1 day_list.append([start_index, end_index]) start_index = count @@ -1712,56 +1695,41 @@ def detect_drift(self, data_name, arange, max_slope, period=None, it don't overextend. the second while-loop finds the indexes for the right period length(could be improved). """ - while series.index.day[new_index] + period <= series.index.day[len(series)-1]: + while data_series.index.day[new_index] + period <= data_series.index.day[len(data_series)-1]: checked = False - while series.index.day[end_index] < (series.index.day[start_index] + period): - if series.index.day[end_index] == (series.index.day[start_index] + 1): + while data_series.index.day[end_index] < (data_series.index.day[start_index] + period): + if data_series.index.day[end_index] == (data_series.index.day[start_index] + 1): if checked is False: new_index = end_index checked = True end_index += 1 - if end_index == len(series)-1: + if end_index == len(data_series)-1: break - detrended_values = signal.detrend(series[start_index:(end_index-1)]) - line_segment = series[start_index:(end_index-1)] - detrended_values[:] + detrended_values = signal.detrend(data_series[start_index:(end_index-1)]) + line_segment = data_series[start_index:(end_index-1)] - detrended_values[:] slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) """ n and m is for separating the positive and negative slope. There are different methods used for positive and negative slopes. list_value stores the indexes where the slope was bigger than the max_slope. """ - if slope > max_slope: + if abs(slope) > max_slope: n += 1 print('Drift detected in period {} to {}, slope: {}'.format - (series.index.day[start_index], series.index.day + (data_series.index.day[start_index], data_series.index.day [end_index-1], slope)) if n == 1: - start_value = series.index[start_index] - end_value = series.index[end_index] + start_value = data_series.index[start_index] + end_value = data_series.index[end_index] else: if n > 0: list_value.append([start_value, end_value, 'n']) n = 0 - if -max_slope > slope: - m += 1 - print('Drift detected in period {} to {}, slope: {}'.format - (series.index.day[start_index], series.index.day - [end_index - 1], -slope)) - if m == 1: - start_value = series.index[start_index] - end_value = series.index[end_index] - else: - if m > 0: - list_value.append([start_value, end_value, 'm']) - m = 0 - #combines the indexes where the slope was larger than the max_slope over a longer period - if series.index.day[end_index] == series.index.day[-1] and n > 0: + if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: list_value.append([start_value, end_value, 'n']) - if series.index.day[end_index] == series.index.day[-1] and m > 0: - list_value.append([start_value, end_value, 'm']) - + start_index = new_index end_index = new_index @@ -1772,25 +1740,25 @@ def detect_drift(self, data_name, arange, max_slope, period=None, #Makes sure that list_value don't have two values in the same index for l in range(len(list_value) - 1): if list_value[l][1] > list_value[l + 1][0]: - ind = len(series[:list_value[l][1]]) - list_value[l + 1][0] = series.index[ind-1] + ind = len(data_series[:list_value[l][1]]) + list_value[l + 1][0] = data_series.index[ind-1] - if plot is True: + if plot: detrended_values = pd.DataFrame() fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) - ax.plot(series, 'g--', label='original data') + ax.plot(data_series, 'g--', label='original data') for value in list_value: - detrend = signal.detrend(series[value[0]:value[1]]) - df1 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrend = signal.detrend(data_series[value[0]:value[1]]) + df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) detrended_values.append(df1) - line_segment1 = series[value[0]:value[1]] - detrend[:] + line_segment1 = data_series[value[0]:value[1]] - detrend[:] ax.plot(line_segment1, 'm--') ax.plot(df1) """ - detrend = signal.detrend(series[value[0]:value[1]], type='constant') - df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrend = signal.detrend(data_series[value[0]:value[1]], type='constant') + df2 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) detrended_values.append(df2) b = df2.iloc[-2][0] @@ -1803,18 +1771,18 @@ def detect_drift(self, data_name, arange, max_slope, period=None, a += slope f.append(a) - ds = pd.DataFrame(f, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + ds = pd.DataFrame(f, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) ds = ds[:] + s[:] ds = ds / 2 - ds = ds.squeeze() #from dataframe to series + ds = ds.squeeze() #from dataframe to data_series if value[2] == 'n': - #ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') - series[value[0]:value[1]] = series[value[0]:value[1]] - line_segment1 + ds #series[value[0]:value[1]] - (line_segment1 - line_segment1[0]) + #ax.plot(data_series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') + data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - line_segment1 + ds #data_series[value[0]:value[1]] - (line_segment1 - line_segment1[0]) elif value[2]=='m': - #ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') - series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment1-line_segment1[-1]) - #ax.plot(series, 'k--') + #ax.plot(data_series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') + data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-(line_segment1-line_segment1[-1]) + #ax.plot(data_series, 'k--') """ self.list_value = list_value @@ -1869,15 +1837,15 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr org_dat = self.data[data_name][arange[0]:arange[1]].copy()#for plotting self.detect_drift(data_name=data_name, arange=arange, max_slope=max_slope, period=period, plot=False) - series = self.data[data_name][arange[0]:arange[1]].copy() + data_series = self.data[data_name][arange[0]:arange[1]].copy() if period is None or period is arange[1].day - arange[0].day + 1: - new_data = series - self.line_segment + self.line_segment[0] + new_data = data_series - self.line_segment + self.line_segment[0] self.data[data_name].update(new_data) if plot is True: fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) - ax.plot(series, 'm--', label='original data') + ax.plot(data_series, 'm--', label='original data') ax.plot(self.data[data_name], 'r--', label='new data') ax.legend(loc='upper right', shadow=True) @@ -1889,19 +1857,19 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr """ for n in range(len(self.list_value)-1): if self.list_value[n][1] > self.list_value[n+1][0]: - ind = len(series[:self.list_value[n][1]]) - self.list_value[n+1][0] = series.index[ind] + ind = len(data_series[:self.list_value[n][1]]) + self.list_value[n+1][0] = data_series.index[ind] """ for value in self.list_value: - detrend = signal.detrend(series[value[0]:value[1]]) - #df1 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) - line_segment1 = series[value[0]:value[1]] - detrend[:] + detrend = signal.detrend(data_series[value[0]:value[1]]) + #df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]]) - 1:len(data_series[:value[1]])]) + line_segment1 = data_series[value[0]:value[1]] - detrend[:] #method shown in Showcase_OnlineSensorBased if value[2] == 'n': - detrend = signal.detrend(series[value[0]:value[1]], type='constant') - df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) + detrend = signal.detrend(data_series[value[0]:value[1]], type='constant') + df2 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]]) - 1:len(data_series[:value[1]])]) b = df2.iloc[-2][0] a = line_segment1[0] @@ -1913,21 +1881,21 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr a += slope f.append(a) - ds = pd.DataFrame(f, index=series.index[len(series[:value[0]]) - 1:len(series[:value[1]])]) + ds = pd.DataFrame(f, index=data_series.index[len(data_series[:value[0]]) - 1:len(data_series[:value[1]])]) ds = ds[:] + s[:] ds = ds / 2 - ds = ds.squeeze() # from dataframe to series + ds = ds.squeeze() # from dataframe to data_series - # ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') - series[value[0]:value[1]] = series[value[0]:value[1]]-line_segment1+ds + # ax.plot(data_series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') + data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-line_segment1+ds elif value[2] == 'm': if drift_type == 'A': - series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) + data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) #if value[1].day - value[0].day == 1: elif drift_type == 'B': - detrend = signal.detrend(series[value[0]:value[1]], type='constant') - df2 = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrend = signal.detrend(data_series[value[0]:value[1]], type='constant') + df2 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) b = df2.iloc[1][0] a = line_segment1[-1] @@ -1939,27 +1907,27 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr a += slope f.append(a) - ds = pd.DataFrame(f, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + ds = pd.DataFrame(f, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) ds = ds[:] + s[:] ds = ds / 2 - ds = ds.squeeze() # from dataframe to series - #ax.plot(series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') - series[value[0]:value[1]] = series[value[0]:value[1]] - line_segment1 + ds + ds = ds.squeeze() # from dataframe to data_series + #ax.plot(data_series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') + data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - line_segment1 + ds - # ax.plot(series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') - #series[value[0]:value[1]] = series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) + # ax.plot(data_series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') + #data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) """ - detrend = signal.detrend(series[value[0]:value[1]]) - df = pd.DataFrame(detrend, index=series.index[len(series[:value[0]])-1:len(series[:value[1]])]) + detrend = signal.detrend(data_series[value[0]:value[1]]) + df = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) detrended_values.append(df) - line_segment = series[value[0]:value[1]] - detrend[:] + line_segment = data_series[value[0]:value[1]] - detrend[:] if line_segment[0] < line_segment[-1]: - series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[0]) + data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-(line_segment-line_segment[0]) else: - series[value[0]:value[1]] = series[value[0]:value[1]]-(line_segment-line_segment[-1]) + data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-(line_segment-line_segment[-1]) """ - self.data[data_name].update(series) + self.data[data_name].update(data_series) if plot is True: plt.figure(1, figsize=(16, 6)) @@ -1969,7 +1937,7 @@ def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, dr plt.plot(self.data[data_name][arange[0]:arange[1]], 'g--', label='new data') plt.show() - #ax.plot(series, ) + #ax.plot(data_series, ) #ab = fig.add_subplot(212) #ab.plot(self.data[data_name], ) #ax.legend(loc='upper right', shadow=True) @@ -2345,25 +2313,25 @@ def _get_slope(data_series,arange,time_unit=None): """ - date_time = isinstance(self.data[xdata][0],np.datetime64) or \ - isinstance(self.data[xdata][0],dt.datetime) or \ - isinstance(self.data[xdata][0],pd.tslib.Timestamp) + date_time = isinstance(data_series.index[0],np.datetime64) or \ + isinstance(data_series.index[0],dt.datetime) or \ + isinstance(data_series.index[0],pd.tslib.Timestamp) if date_time: if time_unit == 'sec': - return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]).seconds - elif time_unit == 'min: - return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]).seconds/60 + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]).seconds + elif time_unit == 'min': + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]).seconds/60 elif time_unit == 'hr': - return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]).seconds/3600 + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]).seconds/3600 elif time_unit == 'd': - return (data_series[-1]) - data_series[0]) / ((arange[1] - arange[0]).days + (arange[1] - arange[0]).seconds/3600/24) + return (data_series[-1] - data_series[0]) / ((arange[1] - arange[0]).days + (arange[1] - arange[0]).seconds/3600/24) else: - raise ValueError('Could not calculate slopes with time index. ' + raise ValueError('Could not calculate slope with time index. ' 'Please make sure you entered a valid time unit for ' 'slope calculation (sec, min, hr or d)') else: try: - return (data_series[-1]) - data_series[0]) / (arange[1] - arange[0]) + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]) except: raise ValueError('Could not calculate slopes, most likely due to an ' 'an unrecognised index. 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Bin 64457 -> 62789 bytes 3 files changed, 221 insertions(+), 169 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index a01aeaac9..09a978b36 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -282,7 +282,7 @@ "data": { "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -606,12 +606,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2013-01-01 00:30:00')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_series.index[5]" + ] }, { "cell_type": "code", @@ -624,56 +635,92 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": { "code_folding": [], "scrolled": false }, "outputs": [ { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for +: 'int' and 'datetime.timedelta'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n\u001b[0;32m----> 2\u001b[0;31m period=dt.timedelta(5),time_unit='d',plot=True)\n\u001b[0m", - "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, time_unit, plot)\u001b[0m\n\u001b[1;32m 1697\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msecond\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mloop\u001b[0m \u001b[0mfinds\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mright\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcould\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mimproved\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1698\u001b[0m \"\"\"\n\u001b[0;32m-> 1699\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1700\u001b[0m \u001b[0mchecked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1701\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mend_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mperiod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'datetime.timedelta'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Drift detected in period 2013-01-04 00:05:00 to 2013-01-09 00:05:00, slope: 92.06687774164706\n" ] + }, + { + "data": { + "image/png": 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xhGW7p+ftZw326dMHg8HA3LlzuXTpEgMHDsTFxYX+/fsDsHDhQsaNG8fw4cMp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BUIAPh/e3ceHtP1/wH8Pdk3S4RExK5CJLJUpEIsIUEsQcVSpGi1qkUXVZRS+9KqvfZfEZSvllBK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\n7X3oVRavxEl7H42do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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=5,\n", - " period=dt.timedelta(5),time_unit='d',plot=True)" + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", + " period=dt.timedelta(),time_unit='d',plot=True)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[Timestamp('2013-01-01 00:05:00'), Timestamp('2013-01-14 00:00:00')]]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.drift_periods" + ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "test= [[1,4]]" + ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "test.append([3,5])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(test)" + ] }, { "cell_type": "markdown", diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 780546dcc..37ee42454 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1579,11 +1579,20 @@ def detect_drift(self, data_name, arange, max_slope, period=None, """ from scipy import signal data_series = self.data[data_name][arange[0]:arange[1]].copy() + drift = False #removes NaNs, infs and other values that signal.detrend can't analyse from the dataset data_series.replace(0,np.nan) data_series.dropna(inplace=True) + if plot: + fig = plt.figure(figsize=(16, 6)) + ax = fig.add_subplot(111) + ax.plot(data_series, 'g', label='Data') + ax.set_xlabel(self.timename, fontsize=20) + ax.set_ylabel(data_name, fontsize=20) + ax.tick_params(labelsize=15) + # Determine if the full period of the dataset is to be analysed if period == None: full_period = True @@ -1603,24 +1612,14 @@ def detect_drift(self, data_name, arange, max_slope, period=None, detrended_values = signal.detrend(data_series) line_segment = data_series - detrended_values[:] #constructs a straight line of the dataset slope = _get_slope(line_segment,arange,time_unit=time_unit) - if slope > max_slope or slope < -max_slope: - print('Based on the specified maximum slope, a drift was' - ' detected with a slope higher than the maximum one. \n' - 'Slope detected: {}, maximum slope:+/- {}'.format(slope, max_slope)) - self.line_segment = line_segment + if abs(slope) > max_slope: + drift = True + print('Drift detected over the whole data range, slope: {}'.format(slope)) else: - plot = False print('No drift detected.') if plot: - fig = plt.figure(figsize=(16, 6)) - ax = fig.add_subplot(111) - ax.plot(data_series, 'g', label='Data') ax.plot(line_segment,'b',label='Detected drift') - ax.legend(fontsize=16) - ax.set_xlabel(self.timename, fontsize=20) - ax.set_ylabel(data_name, fontsize=20) - ax.tick_params(labelsize=15) ax.legend(fontsize=20) else: #if type(period) is not int: @@ -1628,134 +1627,140 @@ def detect_drift(self, data_name, arange, max_slope, period=None, #if period < 0.5: # return ValueError('period must be larger than 0.5') - - start_index = 0 - end_index = 0 - new_index = end_index - n = 0 - m = 0 - list_value = [] - - if period == 0.5: #Need a solution - do = 0 - # print('Not yet possible with period = 0.5') - # pass - - elif period == 1: - count = 0 - # day_list = [] - for value in data_series.index.day[:-1]: - count += 1 - if value < data_series.index.index.day[count]: - end_index = count - 1 - day_list.append([start_index, end_index]) - start_index = count - - for value in range(len(day_list)): - start_index = day_list[value][0] - end_index = day_list[value][1] - detrended_values = signal.detrend(data_series[start_index:end_index]) - line_segment = data_series[start_index:end_index] - detrended_values[:] - slope = (int(line_segment[-1]) - int(line_segment[0])) / 1 - if slope > max_slope: - n += 1 - print('Drift detected in day {} with slope: {}'.format - (data_series.index.day[start_index], slope)) - #combines the indexes where the slope was larger than the max_slope over a longer period - if m > 0: - list_value.append([start_value, end_value, 'm']) - if n == 1: - start_value = data_series.index[start_index] - end_value = data_series.index[end_index] - else: - if n > 0: - list_value.append([start_value, end_value, 'n']) - n = 0 - - if -max_slope > slope: - m += 1 - print('Drift detected in day {} with slope: {}'.format - (data_series.index.day[start_index], slope)) - if m == 1: - start_value = data_series.index[start_index] - end_value = data_series.index[end_index] - else: - if m > 0: - list_value.append([start_value, end_value, 'm']) - m = 0 - - if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: - list_value.append([start_value, end_value, 'n']) - if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0: - list_value.append([start_value, end_value, 'm']) - - else: - """ - the first while-loop makes sure that the calculations of the last period is right and that - it don't overextend. - the second while-loop finds the indexes for the right period length(could be improved). - """ - while data_series.index.day[new_index] + period <= data_series.index.day[len(data_series)-1]: - checked = False - while data_series.index.day[end_index] < (data_series.index.day[start_index] + period): - if data_series.index.day[end_index] == (data_series.index.day[start_index] + 1): - if checked is False: - new_index = end_index - checked = True - end_index += 1 - if end_index == len(data_series)-1: - break - detrended_values = signal.detrend(data_series[start_index:(end_index-1)]) - line_segment = data_series[start_index:(end_index-1)] - detrended_values[:] - slope = (int(line_segment[-1]) - int(line_segment[0])) / (arange[1].day - arange[0].day + 1) - """ - n and m is for separating the positive and negative slope. There are different methods - used for positive and negative slopes. - list_value stores the indexes where the slope was bigger than the max_slope. - """ - if abs(slope) > max_slope: - n += 1 - print('Drift detected in period {} to {}, slope: {}'.format - (data_series.index.day[start_index], data_series.index.day - [end_index-1], slope)) - if n == 1: - start_value = data_series.index[start_index] - end_value = data_series.index[end_index] - else: - if n > 0: - list_value.append([start_value, end_value, 'n']) - n = 0 - + start_index = data_series.index[0] + end_index = data_series.index[-1] + #end_index = 0 + #new_index = start_index + drift_periods = [[start_index,end_index]] + +# if period == 0.5: #Need a solution +# do = 0 +# # print('Not yet possible with period = 0.5') +# # pass +# +# elif period == 1: +# count = 0 +# # day_list = [] +# for value in data_series.index.day[:-1]: +# count += 1 +# if value < data_series.index.index.day[count]: +# end_index = count - 1 +# day_list.append([start_index, end_index]) +# start_index = count +# +# for value in range(len(day_list)): +# start_index = day_list[value][0] +# end_index = day_list[value][1] +# detrended_values = signal.detrend(data_series[start_index:end_index]) +# line_segment = data_series[start_index:end_index] - detrended_values[:] +# slope = (int(line_segment[-1]) - int(line_segment[0])) / 1 +# if slope > max_slope: +# n += 1 +# print('Drift detected in day {} with slope: {}'.format +# (data_series.index.day[start_index], slope)) +# #combines the indexes where the slope was larger than the max_slope over a longer period +# if m > 0: +# list_value.append([start_value, end_value, 'm']) +# if n == 1: +# start_value = data_series.index[start_index] +# end_value = data_series.index[end_index] +# else: +# if n > 0: +# list_value.append([start_value, end_value, 'n']) +# n = 0 +# +# if -max_slope > slope: +# m += 1 +# print('Drift detected in day {} with slope: {}'.format +# (data_series.index.day[start_index], slope)) +# if m == 1: +# start_value = data_series.index[start_index] +# end_value = data_series.index[end_index] +# else: +# if m > 0: +# list_value.append([start_value, end_value, 'm']) +# m = 0 +# +# if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: +# list_value.append([start_value, end_value, 'n']) +# if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0: +# list_value.append([start_value, end_value, 'm']) + +# else: + # The first while-loop makes sure that the calculations of the last + # period is right and that it doesn't overextend. The second while-loop + # finds the indexes for the right period length(could be improved). + while start_index + period <= data_series.index[-1]: + #checked = False + #while data_series.index.day[end_index] < (data_series.index.day[start_index] + period): + # if data_series.index.day[end_index] == (data_series.index.day[start_index] + 1): + # if checked is False: + # new_index = end_index + # checked = True + # end_index += 1 + # if end_index == len(data_series)-1: + # break + end_index = start_index + period + detrended_values = signal.detrend(data_series[start_index:end_index]) + line_segment = data_series[start_index:end_index] - detrended_values[:] + slope = _get_slope(line_segment,arange,time_unit=time_unit) + + # store the indexes where the slope was larger than the max_slope. + if abs(slope) > max_slope: + #n += 1 + drift = True + print('Drift detected in period {} to {}, slope: {}'.format + (start_index, end_index, slope)) + # firstly, if your start index is larger than the end index + # of a previous drift, then a new drift has been detected: + # add a new array with start- and endpoints + if start_index > drift_periods[-1][1]: + drift_periods.append([start_index,end_index]) + if start_index > drift_periods[-1][0]: + drift_periods[-1][0] = start_index + if end_index < drift_periods[-1][1]: + drift_periods[-1][1] = end_index + + #if n == 1: + # start_value = data_series.index[start_index] + #end_value = data_series.index[end_index] + #else: + # if n > 0: + # list_value.append([start_value, end_value, 'n']) + # n = 0 + #combines the indexes where the slope was larger than the max_slope over a longer period - if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: - list_value.append([start_value, end_value, 'n']) + #if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: + # list_value.append([start_value, end_value, 'n']) - start_index = new_index - end_index = new_index + #start_index = new_index + #end_index = new_index + + start_index = start_index + dt.timedelta(1) - if len(list_value) == 0: - plot = False + if not drift: print('No drift detected') #Makes sure that list_value don't have two values in the same index - for l in range(len(list_value) - 1): - if list_value[l][1] > list_value[l + 1][0]: - ind = len(data_series[:list_value[l][1]]) - list_value[l + 1][0] = data_series.index[ind-1] + #for l in range(len(drift_periods) - 1): + # if drift_periods[l][1] > drift_periods[l + 1][0]: + # ind = len(data_series[:drift_periods[l][1]]) + # drift_periods[l + 1][0] = data_series.index[ind-1] if plot: - detrended_values = pd.DataFrame() - fig = plt.figure(figsize=(16, 6)) - ax = fig.add_subplot(111) - ax.plot(data_series, 'g--', label='original data') - - for value in list_value: - detrend = signal.detrend(data_series[value[0]:value[1]]) - df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) - detrended_values.append(df1) - line_segment1 = data_series[value[0]:value[1]] - detrend[:] - ax.plot(line_segment1, 'm--') - ax.plot(df1) + #detrended_values = pd.DataFrame() + #fig = plt.figure(figsize=(16, 6)) + #ax = fig.add_subplot(111) + #ax.plot(data_series, 'g', label='Data') + + for driftperiod in drift_periods: + detrended_values = signal.detrend(data_series[driftperiod[0]:driftperiod[1]]) + line_segment = data_series[driftperiod[0]:driftperiod[1]] - detrended_values[:] + #df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:driftperiod[0]])-1:len(data_series[:driftperiod[1]])]) + #detrended_values.append(df1) + ax.plot(line_segment, 'b',label='Detected drift') + ax.legend(fontsize=20) + #ax.plot(df1) """ detrend = signal.detrend(data_series[value[0]:value[1]], type='constant') df2 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) @@ -1784,7 +1789,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-(line_segment1-line_segment1[-1]) #ax.plot(data_series, 'k--') """ - self.list_value = list_value + self.drift_periods = drift_periods def drift_analysis(self, data_name, arange1, arange2=None, plot=False): """ diff --git a/wwdata/__pycache__/Class_HydroData.cpython-36.pyc b/wwdata/__pycache__/Class_HydroData.cpython-36.pyc index d1257b5b1eb3242cfdcec4ec63d7c82c277ef8a9..272adf0a7066d875c2887f2b6ae1e9dc690765bf 100644 GIT binary patch delta 1696 zcmaJ?eQXnD7{BN4TCUgjdcC!4J6Jh3HFiuyhY2DutM^x5xPK znh`1yKNdS)#edL5B_@7EXNrkm3{3D3|A0v}F_|Br|F{SdLnaDp;`83dGBvT6``z>X zJiq6?=05vP`t&DBZHq?L6Zt0}ZaMs}de=xSAqawo_g#3G(h}P=(mF`wa>!F`nSdLx zgB1}3)aH-?2@V0uL3+ql>=0=rp&=-%o@%Q^tw1^Kh3&AXkT8jm=#YewVLRdpc9du& zHpHuhZOwyOT8!u&1&koqC19CMRitOB0YBV$$vjuCR}Ny<)fYXwO=3hHStdBLhg6FyaqWJtpd)%2jx z3`45m?8l8|2xNq^?n_ye;-2osyTmRi8#^aZmQ#GM88Sa3^gval2c~Qo=MT-J`LNT& zPiy}}VsJJKk?xshF&9~2GBT0;`E}1AW=0?qww1AMyJy%9JfcgS@h?C>4)6Onqt75s 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zT6iH>$W9ltvzT}>dZGPz1!V$B2+5a`TtI>bYZ^G8GJs4k=e(2W76 z3=#)pBf4uY>Kbd{Tw77l+hR>~twrGwrW!#zTAZZQ5S4{7Lxd0$a2;eHa2Bki-PQd+ zNob_0dhE4LLr-HRdc>o@_+h}U$}VAU91jiPb{CRPvA8i1ki}vtadG3bb;y4<`iUia zu5|}fsP`ZcaroMP(qDbp-P2#VybK_9YWcXL|MTOT1GuD*Ed*M#h?7T$TA zkdKKIx6fLSfkMr1pI^>Svkq|Wl1}7a0dh$E Date: Fri, 31 Aug 2018 17:44:47 +0200 Subject: [PATCH 37/42] save notebook without outputs --- ...howcase_OnlineSensorBased-checkpoint.ipynb | 1118 +++++++++-------- Showcase_OnlineSensorBased.ipynb | 1067 ++-------------- 2 files changed, 719 insertions(+), 1466 deletions(-) diff --git a/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb index 3a624c825..214b8a1af 100644 --- a/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb +++ b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb @@ -25,21 +25,10 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", "start_time": "2017-05-09T11:54:53.499498+02:00" - } + }, + "collapsed": true }, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'pandas'", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mos\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlistdir\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mscipy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" - ], - "output_type": "error" - } - ], + "outputs": [], "source": [ "import sys\n", "import os\n", @@ -63,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -79,20 +68,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.2.0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ww.__version__" ] @@ -106,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -114,31 +92,9 @@ }, "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Time', 'TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", - " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2', 'TSS_line1',\n", - " 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns', 'Turb_ns',\n", - " 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es', 'Temp_es', 'NH4_infl',\n", - " 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter',\n", - " 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST',\n", - " 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT',\n", - " 'Cond_line1', 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", - " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", - " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1', 'Flow_line2',\n", - " 'Flow_line3', 'Flow_total'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "measurements = pd.read_csv('./data/201301.txt',sep='\\t',skiprows=0)\n", + "measurements = pd.read_csv('./data/data_example.txt',sep='\\t',skiprows=0)\n", "measurements.columns" ] }, @@ -151,12 +107,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", "start_time": "2017-05-09T11:54:55.589786+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -176,16 +133,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", "start_time": "2017-05-09T11:54:55.671616+02:00" - } + }, + "scrolled": true }, "outputs": [], "source": [ - "dataset.to_datetime(time_column=dataset.timename,time_format='%d-%m-%y %H:%M')" + "dataset.to_datetime(time_column=dataset.timename,time_format= '%Y-%m-%d %H:%M:%S')" ] }, { @@ -197,12 +155,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", "start_time": "2017-05-09T11:54:55.783330+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -218,12 +177,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", "start_time": "2017-05-09T11:54:55.790638+02:00" }, + "code_folding": [], "collapsed": true }, "outputs": [], @@ -240,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -262,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -277,25 +237,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", "start_time": "2017-05-09T11:54:56.050129+02:00" } }, - "outputs": [ - { - "data": { - "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'.g')\n", @@ -331,25 +280,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", "start_time": "2017-05-09T11:54:56.761091+02:00" } }, - "outputs": [ - { - "data": { - "image/png": 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89dZbWLp0KRQKRZMAV6vVQhAEeHl5QaFQAGgaBNfV1cGzPj+5uW0YX3s6Y2I+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Pjw4fJ82xUemUyGIU21q/LMKriQMD9zLHxhCQBu+JExrL2hvtrkNd79clWpTQ2Y8Nd2W9xWrHpz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.get_highs('Flow_total',0.95,arange=['2013/1/1','2013/1/31'],method='percentile',plot=True)" ] @@ -364,22 +302,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", "start_time": "2017-05-09T11:54:57.350077+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "47 values detected and tagged as filtered by function NaN tagging\n" - ] - } - ], + "outputs": [], "source": [ "dataset.tag_nan('CODtot_line2')" ] @@ -394,22 +324,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", "start_time": "2017-05-09T11:54:57.361076+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2464 values detected and tagged as filtered by function double value tagging\n" - ] - } - ], + "outputs": [], "source": [ "dataset.tag_doubles('CODtot_line2',bound=0.05,plot=False)" ] @@ -424,22 +346,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", "start_time": "2017-05-09T11:54:57.394331+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "199 values detected and tagged as filtered by function moving slope filter\n" - ] - } - ], + "outputs": [], "source": [ "dataset.moving_slope_filter('index','CODtot_line2',72000,arange=['2013/1/1','2013/1/31'],\n", " time_unit='d',inplace=False,plot=False)" @@ -454,22 +368,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", "start_time": "2017-05-09T11:54:58.315777+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2810 values detected and tagged as filtered by function moving average filter\n" - ] - } - ], + "outputs": [], "source": [ "dataset.moving_average_filter(data_name='CODtot_line2',window=12,cutoff_frac=0.20,\n", " arange=['2013/1/1','2013/1/31'],plot=False)" @@ -477,32 +383,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", "start_time": "2017-05-09T11:54:58.363535+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "65.77546296296296% datapoints are left over from the original 8640.0\n" - ] - }, - { - "data": { - "image/png": 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Lly/H6NGjtcddtmxZsx6/Zs0aAMB3332HTp06AQCio6MxYcIEw785NdhDhIiIiIiMSxQh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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = dataset.plot_analysed('CODtot_line2')\n", "ax.legend(bbox_to_anchor=(1.15,1.0),fontsize=18)\n", @@ -512,31 +400,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", - " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2',\n", - " 'TSS_line1', 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns',\n", - " 'Turb_ns', 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es',\n", - " 'Temp_es', 'NH4_infl', 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz',\n", - " 'PO4_mixinggutter', 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST',\n", - " 'CODsol_efflPST', 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT',\n", - " 'CODsol_efflRBT', 'Cond_line1', 'Turb_line1', 'Cond_line2',\n", - " 'Turb_line2', 'Cond_line3', 'Turb_line3', 'NH4_efflPST',\n", - " 'PO4_efflPST', 'PO4_sandtrap', 'NH4_splittingworks',\n", - " 'PO4_splittingworks', 'Flow_line1', 'Flow_line2', 'Flow_line3',\n", - " 'Flow_total'], dtype=object)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dataset.columns" ] @@ -550,39 +416,113 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", "start_time": "2017-05-09T11:54:59.892052+02:00" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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7HyQ/P59vvlnK9evXaNasOVZWVowZM5b333+X+vW96NSpC7t2befs2TPY2Oi/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\nMnjwYHbt2sWSJUsICwsDlHd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1/Tl4bb/O/W3LmWZe08lAWQghTCDCp73WD9HU3BRSciRH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\nfiH7OtAjCNG+Hewn+gBg8P9b2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GMNqUhXq8lYLvOg71uDsI88uyab1WOQSvAylmZ6kZhX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\n5rjy4otaYJeUdObfZdnZ4OIC7u7lr2/a1JviYgNdu5b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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.savgol('TSS_line3',plot=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drift" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tag data points that are part of a drift. Because there was no drift present in the original data, an artificial drift was added to *CODtot_line3*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)]\n", + "data_series = dataset.data['CODtot_line3'][arange[0]:arange[1]].copy()\n", + "data_series.replace(0,np.nan)\n", + "data_series.dropna(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_series.index[5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "scrolled": false + }, + "outputs": [], + "source": [ + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", + " period=dt.timedelta(6),time_unit='d',plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.drift_periods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test= [[1,4]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test.append([3,5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(test)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -610,64 +550,41 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4895" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(dataset.data['2013/1/1':'2013/1/17'])" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average deviation of imputed points from the original ones is 39.46857910106997%. This value is also saved in self.filling_error.\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ - "dataset.check_filling_error(100,'CODtot_line2','fill_missing_standard',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " nr_small_gaps=70,max_size_small_gaps=12,\n", - " nr_large_gaps=3,max_size_large_gaps=800,\n", - " to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " only_checked=True)" + "#dataset.check_filling_error(100,'CODtot_line2','fill_missing_standard',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# nr_small_gaps=70,max_size_small_gaps=12,\n", + "# nr_large_gaps=3,max_size_large_gaps=800,\n", + "# to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# only_checked=True)" ] }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average deviation of imputed points from the original ones is 54.261283673154466%. This value is also saved in self.filling_error.\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ - "dataset.check_filling_error(100,'CODtot_line2','fill_missing_daybefore',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " nr_small_gaps=70,max_size_small_gaps=12,\n", - " nr_large_gaps=3,max_size_large_gaps=800,\n", - " to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", - " range_to_replace=[0,10],only_checked=True)" + "#dataset.check_filling_error(100,'CODtot_line2','fill_missing_daybefore',[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# nr_small_gaps=70,max_size_small_gaps=12,\n", + "# nr_large_gaps=3,max_size_large_gaps=800,\n", + "# to_fill='CODtot_line2',arange=[dt.datetime(2013,1,1,0,5),dt.datetime(2013,1,17)],\n", + "# range_to_replace=[0,10],only_checked=True)" ] }, { @@ -700,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -708,31 +625,10 @@ }, "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:324: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n", - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:367: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", - " 'filling of gaps in the data during rain events.')\n" - ] - }, - { - "data": { - "image/png": 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d++67j2+++YaWLVua2rt06cJLL73EoEGDrltDUlJ6+d5UNdawobPeD7E66vdi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HAAAg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\nu9DfRrwGBJ4CfC5wK3wuAIGncG/vUV17YIQQQkgbK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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)],\n", - " plot=True)" + "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n", + " order=3, plot=True)" ] }, { @@ -745,23 +641,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", "start_time": "2017-05-09T11:55:01.063627+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py:1593: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", - " 'representative average day and/or high standard deviations.')\n" - ] - } - ], + "outputs": [], "source": [ "dataset.calc_daily_profile('CODtot_line2',[dt.datetime(2013,1,1),dt.datetime(2013,1,8)],\n", " quantile=0.9,clear=True)" @@ -769,33 +656,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", "start_time": "2017-05-09T11:55:01.105608+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:675: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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Bl156ib59+142hrS0nBt7UzcxLy93PQ+xOxr3Ym805sUeadyLvdGYt+bl5W7r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vTs2RNpaS0LVBMcoyO/eFzhPqlftVMqlIITsgjvKFGybL7+8wte29tDl/MmojRF4//6v2TUdTBz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\ndjO6q+tLFb3zAIkEuB3FKVvEgr5shte2L9jjb3Vhj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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_standard('CODtot_line2',[dt.datetime(2013,1,14),dt.datetime(2013,1,17)],\n", " only_checked=True,clear=False,plot=True)" @@ -811,35 +679,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", "start_time": "2017-05-09T11:55:01.847864+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " interactivity=interactivity, compiler=compiler, result=result)\n" - ] - }, - { - "data": { - "text/plain": [ - "Index(['.sewer_1.COD', '.sewer_1.CODs', '.sewer_1.NH4', '.sewer_1.PO4',\n", - " '.sewer_1.Q_DWF_UB', '.sewer_1.Q_in', '.sewer_1.TSS'],\n", - " dtype='object')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model_output_ontv_1 = pd.read_csv('./data/model_output.txt',\n", " sep='\\t')\n", @@ -852,33 +699,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", "start_time": "2017-05-09T11:55:02.251053+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:811: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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xMf32PlQ5Vru2q96HVDjq91LRqM9LRaR+LxWN+ry12rVdbR2ClFMxMTEMGzaM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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_model('CODtot_line2',model_output_ontv_1['.sewer_1.COD'],\n", " [dt.datetime(2013,1,18),dt.datetime(2013,1,22)],\n", @@ -897,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.917107", @@ -905,18 +733,7 @@ }, "scrolled": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.450642327196896, 0.672153214085126)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dataset.calc_ratio('CODtot_line2','CODsol_line2',\n", " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)])" @@ -931,22 +748,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", "start_time": "2017-05-09T11:55:03.919697+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best ratio (2.53282188261064 ± 0.16586491872475553) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" - ] - } - ], + "outputs": [], "source": [ "avg,std = dataset.compare_ratio('CODtot_line2','CODsol_line2',2)" ] @@ -960,33 +769,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", "start_time": "2017-05-09T11:55:03.980745+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:454: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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UXaxmTVe9D6lwNO+lotGcl4pI814qGs15SzVrulo7BLlLJSYmMmzYML7//nsM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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_ratio('CODtot_line2',\n", " 'CODsol_line2',avg,\n", @@ -1003,43 +793,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "slope: 0.405512924986 intercept: 0 R2: 0.973774656376\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " )" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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pyGQyLl36By+vWTRvbkrnzl24dOkfrWCkUinR0Xmy3427eTOPhIR4LCzuP+1N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obE5mFXN4J6A6r9DAbTQrTaN+jU7Rz2SinbEf0z0eoVKspNRA1Od63Mmjh276VApFcI7Gxm7dq9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\n7rv06NGD1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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "dataset.get_correlation('CODtot_line2',\n", - " 'CODsol_line2',\n", - " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", - " zero_intercept=True,plot=True)" + "dataset.get_correlation('CODtot_line2', 'CODsol_line2', [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", + " zero_intercept=True, plot=True)" ] }, { @@ -1051,33 +810,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.016129", "start_time": "2017-05-09T11:55:05.261370+02:00" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:561: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] }, - { - "data": { - "image/png": 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YPHiwrUP5xwwbNox69eoxceJEW4dyy9h8aWpxnD9/nuHDh+Pu7s7TTz8N5B39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IpEFVu7zUFJDc6r+lODY1Yw4rm0BXB39MAI/ziLvvPPWT+hts180CvcPQK7HjyA1Lw9eP3kEv3A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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "scrolled": false + }, + "outputs": [], "source": [ "dataset.fill_missing_correlation('CODtot_line2',\n", " 'CODsol_line2',\n", @@ -1096,33 +837,15 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.731819", "start_time": "2017-05-09T11:55:06.018568+02:00" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:955: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] }, - { - "data": { - "image/png": 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YPHiwrUP5xwwbNox69eoxceJEW4dyy9h8aWpxnD9/nuHDh+Pu7s7TTz8N5B39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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "scrolled": false + }, + "outputs": [], "source": [ "dataset.fill_missing_daybefore('CODtot_line2',\n", " [dt.datetime(2013,1,25),dt.datetime(2013,1,27)],\n", @@ -1132,25 +855,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", "start_time": "2017-05-09T11:55:06.734413+02:00" } }, - "outputs": [ - { - "data": { - "image/png": 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2Y7id75mXl3dH3/9Bc98kygpPogBYsWLFLZ/39vYudm1voUGDBhVZUywiIiIi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qXIk+VhhP8o8rq4JgLcGgAAn6/Gxy9Pph3LHC6zzwKlNSS9V1hdgOEHWr7+owUk8mR5+Pnvn/BX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\npBR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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = dataset.plot_analysed('CODtot_line2')\n", "ax.legend(bbox_to_anchor=(1.3,1.0),fontsize=18)\n", @@ -1158,6 +870,18 @@ "ax.tick_params(labelsize=14)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## De-drifting data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -1174,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", @@ -1182,18 +906,7 @@ }, "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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KwocOHdL999+v4uJiDRo0SD179lRISIhOnz6trVu36sMPP9TYsWP17rvvKjw8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\nAAAAbOUHj59TP8N2Ue0AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.calc_daily_average('CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,2,1)],plot=True)" ] @@ -1207,12 +920,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", "start_time": "2017-05-09T11:55:07.833046+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -1221,6 +935,380 @@ " ['TSS_line1','TSS_line2','TSS_line3'],\n", " 'TSS_prop')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data with drift\n", + "Finding and replacing a dataset with drift." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import signal\n", + "data = dataset.data['CODtot_line3'][:].copy()\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line3']['2013/1/5':'2013/1/8'])\n", + "line_segment = dataset.data['CODtot_line3']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=5*line\n", + "fig, ax = plt.subplots(figsize=(18,4))\n", + "\n", + "ax.plot(data['2013/1/1':'2013/1/14'],'k--', label='original data' )\n", + "\n", + "dataset.data['CODtot_line3']['2013/1/5':'2013/1/8']+= line10\n", + "\n", + "ax.plot(dataset.data['CODtot_line3']['2013/1/1':'2013/1/14'],'g--', label='data with drift')\n", + "ax.legend(loc='upper right', shadow=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dataset.data.to_csv('./data/data_example.txt',sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.data['CODtot_line3'].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", + " plot=True, period=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", + " plot=True, period=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=180, \n", + " plot=True, period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,10)], max_slope=180, period=1, \n", + " plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/5':'2013/1/13'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "dataset.data['CODtot_line2']['2013/1/9':'2013/1/12']+= line10.values[::-1]\n", + "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']+= line10\n", + "\n", + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/5':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,15)], max_slope=68, \n", + " plot=True, period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,14)], max_slope=68, period=1, \n", + " plot=True, drift_type='B')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/5':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(18,4))\n", + "\n", + "ax.plot(data['2013/1/1':'2013/1/14'],'k--', label='original data' )\n", + "\n", + "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", + "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] += line10\n", + "\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/14'],'g--', label='data with drift')\n", + "ax.legend(loc='upper right', shadow=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, \n", + " plot=True, period=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, period=4, \n", + " plot=True, drift_type='A')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(18,4))\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/15'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/4':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", + "fig, ax = plt.subplots(figsize=(18,4))\n", + "\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])#, type='constant')\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']+= line10\n", + "\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/15'],'g--', label='data with drift')\n", + "ax.plot(data['2013/1/4':'2013/1/12'], label='original data')\n", + "ax.legend(loc='upper right', shadow=True)\n", + "\n", + "asd = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']-line10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(18,10))\n", + "ax.plot(asd, 'm--')\n", + "\n", + "\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'], type='constant')\n", + "df = pd.DataFrame(detrended_values, index = data.index[len(data[:'2013/1/4']):len(data[:'2013/1/8'])])\n", + "\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "#ax.plot(line_segment)\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/4':'2013/1/9'],'g--', label='data with drift')\n", + "#ax.plot(df, label='detrended drift')\n", + "\n", + "detrended_values1 = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])\n", + "df1 = pd.DataFrame(detrended_values1, index = data.index[len(data[:'2013/1/4']):len(data[:'2013/1/8'])])\n", + "line_segment1 = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values1[:]\n", + "ax.plot(line_segment1, 'c--')\n", + "\n", + "b = df.iloc[-1][0]\n", + "a = line_segment1[0]\n", + "slope = (b-a)/len(df)\n", + "f=[a]\n", + "s = df\n", + "s[:] = a\n", + "ax.plot(s)\n", + "for val in range(len(df)):\n", + " a+=slope\n", + " f.append(a)\n", + "\n", + "ds = pd.DataFrame(f, index = data.index[len(data[:'2013/1/4']):len(data[:'2013/1/8'])+1])\n", + "\n", + "ax.plot(ds, 'k--', label='Slope')\n", + "ax.plot((s+ds)/2, 'r*')\n", + "#ax.plot(df1, 'k--', label='detrended drift org')\n", + "\n", + "ax.plot(((s+ds)/2)+df1, 'k--')\n", + "#ax.plot(df1+ds, 'r--')\n", + "\n", + "ax.legend(loc='upper right', shadow=True)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "\n", + "\n", + "dataset.data['CODtot_line2']['2013/1/9':'2013/1/12']+= line10.values[::-1]\n", + "fig, ax = plt.subplots(figsize=(18,6))\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/3':'2013/1/15'], 'g--', label='data with drift')\n", + "asd = dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'] - line10.values[::-1]\n", + "ax.plot(asd, label='original data')\n", + "ax.legend(loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(18,10))\n", + "ax.plot(asd, 'm--')\n", + "\n", + "\n", + "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'], type='constant')\n", + "df = pd.DataFrame(detrended_values, index = data.index[len(data[:'2013/1/8']):len(data[:'2013/1/12'])])\n", + "\n", + "line_segment = dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'] - detrended_values[:]\n", + "line = line_segment - line_segment[0]\n", + "line10=10*line\n", + "#ax.plot(line_segment)\n", + "ax.plot(dataset.data['CODtot_line2']['2013/1/7':'2013/1/15'],'g--', label='data with drift')\n", + "#ax.plot(df, label='detrended drift')\n", + "\n", + "detrended_values1 = signal.detrend(dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'])\n", + "df1 = pd.DataFrame(detrended_values1, index = data.index[len(data[:'2013/1/8']):len(data[:'2013/1/12'])])\n", + "line_segment1 = dataset.data['CODtot_line2']['2013/1/9':'2013/1/12'] - detrended_values1[:]\n", + "ax.plot(line_segment1, 'c--', label='slope')\n", + "#ax.plot(df1)\n", + "\n", + "b = df.iloc[0][0]\n", + "\n", + "a = line_segment1[-1]\n", + "print(b,a)\n", + "slope = (a-b)/len(df)\n", + "print(slope)\n", + "f=[a]\n", + "s = df\n", + "s[:] = b\n", + "ax.plot(s, label='Slope1')\n", + "for val in range(len(df)-1):\n", + " a+=slope\n", + " f.append(a)\n", + "\n", + "\n", + "#print(f)\n", + "ds = pd.DataFrame(f, index = data.index[len(data[:'2013/1/8']):len(data[:'2013/1/12'])])\n", + "\n", + "\n", + "\n", + "ax.plot(ds, 'C1', label='Slope2')\n", + "ax.plot((s+ds)/2, 'r*')\n", + "#ax.plot(df1, 'k--', label='detrended drift org')\n", + "\n", + "ax.plot(df1+((s+ds)/2), 'b--', label='fixed drift')\n", + "\n", + "#ax.plot(df1+ds, 'r--')\n", + "\n", + "ax.legend(loc='upper right', shadow=True)" + ] } ], "metadata": { @@ -1240,39 +1328,47 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.0" }, "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", - "current_citInitial": 1.0, + "current_citInitial": 1, "eqLabelWithNumbers": true, - "eqNumInitial": 0.0 + "eqNumInitial": 0, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "nav_menu": {}, "toc": { - "colors": { - "hover_highlight": "#DAA520", - "navigate_num": "#000000", - "navigate_text": "#333333", - "running_highlight": "#FF0000", - "selected_highlight": "#FFD700", - "sidebar_border": "#EEEEEE", - "wrapper_background": "#FFFFFF" - }, - "moveMenuLeft": true, + "base_numbering": 1, "nav_menu": { "height": "282px", "width": "252px" }, - "navigate_menu": true, "number_sections": true, "sideBar": true, - "threshold": "3", + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "324px" + }, "toc_section_display": "block", - "toc_window_display": true, - "widenNotebook": false + "toc_window_display": true } }, "nbformat": 4, diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 09a978b36..214b8a1af 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -68,20 +68,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.2.0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ww.__version__" ] @@ -95,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -103,29 +92,7 @@ }, "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Time', 'TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", - " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2', 'TSS_line1',\n", - " 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns', 'Turb_ns',\n", - " 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es', 'Temp_es', 'NH4_infl',\n", - " 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter',\n", - " 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST',\n", - " 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT',\n", - " 'Cond_line1', 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", - " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", - " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1', 'Flow_line2',\n", - " 'Flow_line3', 'Flow_total'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "measurements = pd.read_csv('./data/data_example.txt',sep='\\t',skiprows=0)\n", "measurements.columns" @@ -140,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -166,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -188,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -210,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -233,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -255,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -270,25 +237,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", "start_time": "2017-05-09T11:54:56.050129+02:00" } }, - "outputs": [ - { - "data": { - "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'.g')\n", @@ -324,25 +280,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", "start_time": "2017-05-09T11:54:56.761091+02:00" } }, - "outputs": [ - { - "data": { - "image/png": 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89dZbWLp0KRQKRZMAV6vVQhAEeHl5QaFQAGgaBNfV1cGzPj+5uW0YX3s6Y2I+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Pjw4fJ82xUemUyGIU21q/LMKriQMD9zLHxhCQBu+JExrL2hvtrkNd79clWpTQ2Y8Nd2W9xWrHpz\nExHey486foYA7wCz7X7o8TM2vhpu9HiOAE9bQ7+G5hsBWNDrD4N9lj4YN6/ZEvtePwp3V3c8H8zv\n5Tz55gU0KVsc2j5iO2+bw/EHMWD7Cwb7GYG1H7qLFyrRvbnqCp3ZAh8R4dlCcXd1N5qiYAn69Sxd\nXVzRvGYLuLu64/vuP7El38yhG876NE8javTv7dUGdeKvjr2DFxr0Q12/ekidZrokGqBJE2i79lk0\nXlEHP1/6kXNs1on/4c192lJZJ5/+J0kostBr0pmRlzC6+dt4Mln7ffqzDzdypIF/IzTwb4h/X95o\n9fwYhux+hd1+mvcEXTbwl+DTpe6ymhzj8LvzXxsYekx5wqAlVRG42B+TjoxDqzXNELjYH4GL/bH5\n3gYoVUr02txNondSvjBh07/0MgxfZvKQdUse3s8UvkjTNqAdb/lIW1FHJ6WrdUAbVPWsxv6Wwwfx\n3xfPJp5G1w3teI/ZmrEHRmL+xe95yxDpM68s9aRUWcou6kw7xvVCfn7qI4PzLEGuMm886+ea68Pc\nL/fF7mH3fXX2c/a6CAAxemknUqBfKcEUpapS7IzehkMPtRVX1t3WhKSr1Wr8eOH/cDbhtMk+nuQ9\nBgDehW4p0M0r12dF/zVwdTE8ru/BdYTqGoR5HM54jo6ORpMm/KGjUVFR6NyZm1vbpUsXNqQ7KioK\ndevWRf362htr586dUVBQgLt37yIjIwPx8fGcPnx8fNCyZUu2D0I83etqH0ZLFJobSkZxhrHmgqnr\nV8/itvo52EJpXrMF7/5SZanNJfrHNh9vUTs+z6wlBJhQFTdGcFlJEUtoUdN42GVCWYjmcxs7YPKR\nd0z2Y2m48LgWpvsxxYk3z6FzHY2Bu2PwPgxtNsygjW7JsdfDTNcIfmX7i5zXTK51DS/D0EtL0b2Z\nXkvTiP8oVUqE398suk9jnBl50XyjcoZPsEuf1Gm5BtEEplBDDZVaxSp8M7QJaGdw3Tn8BndhZka7\nWRaPo89PF3+QpOSL0VJVRkLrGlXVhGZ7unoiZWoOUqflYtSzYzhtIt8SJjQFaDycluomXEy+gKQC\njY5Ak6ohJv9eRx9Zt7g9M2Iq6iytjrs64lQVGUbpmC+6qkTBGM9azzMjtCWEf1/ehP6NXsb5t4SH\nx4rBT0fMqb4fd3GkV/0+WNTXMcK0GWJ5dAF0ebu59r70++Vf8DAnDvWW1UKrNc1wIfGcgWNh9a0V\nFldw0KVEIu0ElVqFdw5zrwHt12mfg746+7kkitfWMPnoO5ySgTWqaO6rMyKm4I8rv/CmHfBhrL64\ntZiKALL0GYCM54qBQxrPiYmJGDFiBJ5//nmMHz8eN27cAAAkJycjKIibXxgYGIjkZI1CZ0pKCgID\nAw2OA0BSUhLbzlQfhHj6N9KGUyYWJPCW/rEXPu4+Vp0/q8MnvPs33l3HCqrYiv8zok6sjz3rBpfo\nKSCbwpTRO3LfUOyL3YOY7GiDElDWUKuKea+4OWQyGZb2W42UqTkY2mw4AODY8FMcz69MJuMNOWaI\nSrmIo/GHDNRCrcp51gvryijKwJb7GzH9+Hui+zSGkAUqR8fd1Z13MYSPnpu78C7mVOOpM94uqAPu\nTniIq2PvIGVqDuZ0q3jlkjx01N91v18X3rqCCS0n4dF7KRwvyaMPHnHO3xu7Gw9z4rD0+iLOA3Wz\nVQ3QZEUwIpMucNozUUPGOP/WFbi7uuPkmxdMtpMKS0qpVRSixtzkvGY8z7o54Ew1iHuZdzF6/3Ck\nFJh/3mHuLyHVmuHhu1qBMF2jUEp0Uxga+hvPu+fD2LNGqbKUN6f64mhDHQSpqeLmhWtva6s16EZZ\nvLZrAN8p2B69FZMOj+Pk3ptDLpHxXHuJ4bVOl9jsGE6otyPwyckPUKQowtb7mwyOPcyJw8WkSJ6z\nhJXBFEINL+MVWTxdLROSdfQwbjLuNYhLmrQRxcXFePLkCWrUqIFPP/0UHh4eWL9+PcaMGYOdO3ei\nuLgYHh7cki8eHh4oKdF4OYuKiuDpya2n6u7uDplMhpKSEhQVaW4g+m10+zBF9erecHMzHpbhjAQE\n+HG2b029hZZLWlrUXgzn3jmH51Y/Z7adp6e71WN1q9cN55+e5+xTqpUcARkA6B/SH4NDB6NDnQ5W\njwkAAbCsD3/fKuLGK7DcEGZw9VJL8t4AGKxuG0PIeEfGHkb75cYFnoSOsf0t43lsb7YfiomHjZdn\nG31Ao+TZKlDrgQ+oXk305+fqyl0kuV94HedTpVMd1kWqv7EtWT5wucXz3P5WOLbd2Ybh4cPNtuVT\n+vXz9uEdy9LfqD3hm2cVb/6HRGOfX0BAO3RptoLniB82DN2A0Ts0uZETD2vrQa+5sxKxM2M5YoCD\ndvbnnD2jxxSjwlAP33+IwGoa46dWLesihvS5OOkiOq807LN9E/7IIntjze+NOTcgoCVUX6sQmRCJ\nmQdn4lLiJWyMXY3HedoFD7VMhYAAPzy3eTRiMmPw6/Uf8c/gf3A9+TrmnZmHZQMNvboBtfwR4Fs2\nBjT39l/O/4LFryzCnewbiEqUNlrv0+c+xc/nNDmhdYIMjTT/xCoG+3QJXOyP6BnRaFpDm5a0/c52\nXkO0Vk0/RM+IRrO/zJe+NPo3MmNDeFVxR5vGYRjZciQ23+KPEvJw9cCq11Zh7E7N7+n7srKUe2J3\n4n/dJ5s1pNRqNUotCNuu6AxrPgznnpxDYp5hxQt9wU6/6u7wcvNC4GLNYoXya6WBo8Hfh/+6bi2m\n7gutG4YioKrhcT8/rlFd1d+7XO/D5sZ2d9fYQB4ebhXiecFWOJTx7OXlhUuXLsHDw4M1kufPn4/b\nt29j48aN8PT0hFzOrS1XWlqKKlWqsOfrC3/J5XKo1Wp4e3vDy8uLPcdYH6bIyrJMCMFZCAjwQ1oa\ntwxMoMx0PVj99kJp6mXcMNfFXe1l9VjV3C0Ls4nPfIThjTQ3P2vHFEJhYamo8TKKCsw30j8nJ0fQ\nWE2rNeMtdyMEIePVcxOnQi308wsI8EN6umVhqTdTtR6hogKl6O+GSs9pnZSRji23bZOLaM/vr1ge\npycKmmdOjvjr9nOBPSvEZwLw/+0KCvkXhcV873Nz+UuyxWXFIS0tz6TKblpaHlKm5uBRbjyquHvj\nYU4cXtv5Ek6PvAgfeU3JPuPwQbux9f4mhD/QGCvBrk3wRZev8TAnDpvurUc1z2q4O+Ghw/xNrZmH\n/rkhni2Qlq8RUPrgMDf94GH2QzxMTEJMpqaUWmae5nr+6oaBSMh/yns9ycwshEuRdoxAWQP8/NxC\n5GcrsHvQYV4VcKF82ukLVhfg47ZfIcijHoJ9gnk/l9w84yUBGZr91QyXxtxAQ/9GSC1MxZR9/As2\nmZkFaODfEClTc/Ag6z56bDa+aDNjzyx83Y2nXJ6Z7K2iIs39ecHzf/Eazy826I+NAzX6LV91/YY1\nnBm2Xd2D3vUNdTR0kbLcnRCeTk5HvWW17DaeN/xxbew9TD4yATtjtuPdVlOw4uZS3rat/26Ds29p\nF3YeJSYb1ndWuNn1GhDkXRtepdV4x8zP516jc3OLyu36xPdMr49crok0Ki1VOMx11FaYWhxwuLBt\nX19fjnfZxcUFTZs2RVJSEurUqYPUVK56bWpqKhuGXbt2baSlpRkcBzSh2nXqaNSE+droh3ITjosx\nMS9dutaxXhjG0tBvqRRzheIi8ucrJiqoWEDYNgDsGypdXWpHQ0xYlZsJIRGz45X9z5Tumnpskui+\nAOO1ore/tteqfu1FkHdtu401wYLSIoQmB99YfvWlMZq0K5lMhkZVGyPIOwhd63RD6rRchNYIk3Qe\nver34YROyiDDBx0+xp8vLEbqtFw8mPiYV7SnshBvospAkxXadJqSstDuhHzjObYuJq5zUoWWDg8d\nyXk9tvl49G3Y30hry+i0vjWup15FyzVNzQosymQyhNYI4wgBjgrjRkYtuvqHydrj5vB09UTqtFzc\nnfAQF97SlnnboCOsOSJ0lMF5I/aaF1UsD69zzMQnnLQPe9CilsZpsqz/P0idlosfevzMCYnXJTr7\nASeMf8rRiQZtZrb70DYTNUK7oA4Wt3X0sG0G5y5U5WDG861bt9C+fXvcuqXNR1Iqlbh37x6aNWuG\nDh064NIlbgmRyMhIdOzYEQDQoUMHPHnyBElJSZzjPj4+CAsLQ82aNdGoUSNcvKjNtSwoKMCtW7fQ\nqZNlCq1ExeC91tOs7sPRczvEXmTFGN0jwwxLWZjCGoEsR0fM98KaB3ZGwVOKvO4DQ48hYsRZDGj0\nisGxHvV68ZzheOg/cJvDGoG/imJoGVN5TS5I4t0vNRnFGUbz+hv6N7LLHPioKA+iUrH3dcsWLfNK\n83AzzXTer6n7hFT3RjEl1ixBV0mfD77vRfLUbDx+LxV/vrAYawZwFeZbrAnB9geaVJ7UwlRkFWcK\nnlPNKjXRpFpTpE7LReq0XM4cavvwl4mMSubXBVGpVVCr1ZLlOwvBz8P+WjZ81/xg37p4Otl8qaoj\njw6x2w38GqKubz2LKoeIxdJqDwwVrV6ys11TjeFQxnNYWBjq1q2Lr7/+GtevX0d0dDRmz56NrKws\nvP3/7d15eExX4wfw72RfRBARhFiiIXtCFkRICGlriT12JSooftVW7brQRqldWy1tqe7vq9a2aumL\nolV51Voq1NKqrWiVFyE5vz/STDOyzHa3mfl+nsfzyJ0755w7c+bes59BgzBgwADk5uZi0aJFOHXq\nFBYuXIiDBw9i8ODBAIDY2FjExMRg3LhxOHr0KHbs2IE5c+ZgyJAh+t7sxx57DMuWLcPnn3+OEydO\n4Omnn0aNGjXQvn17NS+dJOYIP3BLCzCWfDaNqhqfG+YoLPn8nC3ckxso2uIisVYLvNJ6rtFz29dL\nR1AFW3zF1UyAp4sn3nn4fYPjZx63nQUTLV0oz1aE+UXg7PBLuDzqBj7tXLSyuLFRASUrIYt/WIAt\nZ4oKjFJuFVhRI8TdgjsohLp7kJe1RZ6955UHJdZqjrfav2v0vD2/7cLzeyrep7miz06q52uQTz3E\n1miKF1q+bPxkCZX17HTSOcHj723wHm3YqdTrI7cOQ6EoRMSKRmj8Tn2T9zO2xqOfpeGXv87h858N\nf/+RK0IQ8IYvjl5VfuE7NcpW5TXkuDm74eAg0xZwvZn/F879dbbC0RZSaOBb9m5BprP/sqs90NST\nxcXFBcuXL0eDBg0wYsQI9OrVC7///jvef/99+Pn5oXHjxliyZAm++uordO3aFV9//TWWLl2K4OCi\nPVh1Oh2WLFkCPz8/9O/fH5MnT0avXr3wxBNP6OPo27cvRowYgZycHGRmZuLevXtYvnx5qYXIiOy1\nAq71HnWts+TzC6na2OL4Iv2jsaHbV0gKbG303KERj6N9/XSj5z3YU+nl6mVx+rTOFlr256YsQkZw\ndwDA59236Ifop9Rti0sj/0RynTZIrpMCAPj58d+wt3/p+cX7L+WiUBRixrfT9YvWKWXQF31x9bbx\nXiC51PKujbR6pfO9Uve6ZR1WKBKPKTIadTfpvG/O76jw9QorzxJ9ri5OLviq53aMjBktSXhS2th3\nY6lj5k5fkkKzVREYsqk/Bnxe9JteceRtXLldNB2x+7rSlXx7ZG1Dzs9/nETou9ZWak0zOXG6Wec/\nmH6Wz2yDphYMA4rmJs+dW34PS0pKClJSUsp93d/fH6+99lqFcWRnZyM7O9vSJBIZqFOprvGTNMRe\nGwWUYsnDzdQ9qyvi6eKJ5R1WYtjmweWek1K3Hbad26L/e0fmd2jzSXMAwHf9/9m3V6fT4bkWM/HC\nt1NR29v0Pbxtkdz7sktBBx3e6vAuXit8q9R8wuLf6786r8X/7v8PlVwroZJvJWzothmz9s7A7t++\nAQA8vLotAisZ32rsuRYzJU//0auHMeCLTMnDNVV5W2HJea9zc3LD+m6bEFylEXzdqyCjUXfsOr8T\nG0+twztHylqxXBlSXbNOgZ5nc0hZqTAlrI4hHZEc2MagkeFOgfFFy+Sy+ewmFEhI2CwAACAASURB\nVBQWWLRnt1S6mtgwI7WK8psp32XSR/H60TnF21DKpY5PGeVBMxpwbaV8ZgvPVTlpqueZyBRaa5mz\nlXmRxbT2+dkacx5u6fUfwaWRf0oWd5dG3Sp83dnJ2eD7DfUL08+xa+gbbHDukIhhyIocjn93WS9Z\n+rTIFh7yOuig0+kqXIjHSeeESq6V9H8n1mqOdx5eZXBOySGJc/aVvV98UzMWr7EVahQ4ezfui6YB\ncfAtsRd4q8DWmNV6rkFDla3S2pB3Nb7j1RkbkJM8R//3LzfOKZ6GktSsOANA94eUHdFSrMIyiwn5\nokAUoLKbLwBgUuI0qZIlC62Xz/Tps4ERXXLS1t2RiORnIy2bWmXOw626p7/ihT5T4/Ny9UJO8qt2\nP5/dFoZtW5pHqpZYWfpB5VWelfw82tRJVSwupQ2LGlHuaw19g7G0/dv6v2NrNC1z3vqPV4+WOnbr\n3q1yF4pSktYqz1Iy5/eWFfnPKMXJu56VIzkmW/XjClXjV6shUoopBDfyixqxy1ucjUxTvDCkPd8f\nTOHYV0/kgLTesql15hS8hkRYt61UWXb3za3wdX6/jmVjty3GTypByQJwjxB1eqqU4GdkR4HuD/XC\n5VE38POw8/ii+zYk12mDo4+dwoSEKfpzUj4pvaXiY1/2w6OfpUmeXnNZuhWiVsUF/LOXs6X3yH0X\n90qVHJukVkNkRd+Xud+lm5MK6xuZUWbQ+rBtAVaeAVaeiTTp8+7mFYjNwcqVcuTY1sPovFaNP3yV\nZgvDtq0piCTUSpQwJdbLbNxP8ThVuaeZ+Dur5Oajn9rj7+WPcL9Ig9en756Mb3/bDQA48vth7Pj1\nP9Km00L2Vjj+uNNqi99bssHDkal1L61wzrOZzzutVU5T67Yz+Fvr5bPinmetfY5Ks6+7I5GE1LyJ\nxdc0XiDuGWLZAj32VihSmjn5Qo485OniiZ4hmQitFq5YnLbMFoZtWyvAq6bJ58pdAH64QUf9yuDW\nrDJvDlsqyD24oNDSg0uQsfYR/HDpv2j7aZJKqSrNnp4TLWu3QmV3X/3f5uaXVoEV7xvtKOyh51lr\n6vjUxYUR15H8dx4LqRqicooqVpwH7G1kirkc++qJbFgVj6oWvc+WCppapPbnp9Pp8HraMuzo863B\nnpLrun5Z9LqNFyakZgs9z9Z6O32V8ZP+JncB+F5BPt575CN81eM/aBoQJ2tcZVLo92np7yyieiSe\niis9dzZ9tbbmh6t9n5PSg9+Vud+db4mKt6nk/J3JMaLJFNrseTY9nOktZkiQGgsYyQvOTs74qNNq\n7BtwCA2rNFIoUZYp/DsP2FPjmiUc++rJJtnTQ10N5hYcHP0maQ258+rHnT7D4PAsnB1+CS1qF/Va\n8fsyZAs9z9Z+Z1oaui0g4O3qjVg7XNVbKsMiy19sjLTH3dld7SQYGB41UqWYtXcvVXskWFl+Hnbe\n7Pe4ObuhXuX60idGYlwwrIjm9nkmInmZ+wBx1tnWVlxaIvfDuoFvQ8xpM98wTjtuXFJqGLDSlBwt\nIHfvkRq9U2qMtrAmzioltrdyVEp+Z6XuiWbeI6tVsKq9Gvy9aqgSrxYbIs2qPCv0bKzk5qNIPGoo\nrjw7+toqjt10QOSAzH2AOHoLozXUqMja87DtZR1Wmv0eRxi2bY7a3oFqJ0FytpbnXZzYb2FLjXy+\n7lVQyVU7FSIddFiY+rr+75HRYxSJV4v3UnPyka3dJ7SouAHF0T9LloqJymFLD3dzmHvTq+xm/nwv\nS/2SfQWdGmZgXspixeK0N/b6UGsX1B6hfmFqJ0MWSjZQyb2vtxZ7p+Rg7fMhJ3mORCmxTYr2PFs5\n5xkAvuqpjVXQi/UNHaD/f3Kd1orEqcXftnk9zzIm5AEGWwjaUVmSW1UVceyrJ9KwZhUstvNJpzUW\nh2tuoc/b1dviuMyLpxLcnd3xzsOrMCBssCJxyk2V4aR29KAu6f+aPq12EmRjr9+ZUmzx88uKzFY7\nCY5DZ33l+SENr4Ic7d9UkXjY82w6La1DIaWxTZ8CAAwOH6pyStTFyjORRo2Pn1zqWOs6qfB08URK\n3bYWh6vFBcM+6bQGJ7N+kT2ektZkfC57HLY2F1NJNb1rmXW+v5e/RfFY2lvycqvZFr2PlFdyGLRS\n+V/J35k5q6nbCnuf9+3q7CZ7HCsf+Qivtllo8b3RXJqsPGtwwbBSNNhjb6meIZk4n33VqjKoPeDE\nG1JM94d6qp0Em9I2KK3UsdfTlqGGlYuFmNtL4+7sYVV8ptDpdHB2kn9hsuQ6Kfjh0n/RpFookgKT\nZY9PlTnPtlF3xvbMPWjyTgOTzw+uYtlwY0sLfJbuo24JW2nw0KIq7lXQLCBe7WTIqnNwhqLxHRx0\nXPY4hkYON+t8XyumD5Uatq3ATXJM7DjZ43ikQUfZ4yhJi8O2zXng2eIIFS1ydXZVOwmqY88zKeL4\n0NN4I+1tScIyVtCUqqdUiwVaNdL0Vod3FY9TLh7O7vj58fP4osdWtZMiGy3m27I4KfT4sbTAp2RB\n62GFC8GWSKjZXO0klCkn+VWDe37zWi0BAIPC5B1WKEUv3DNxE9HQN9ikc9d3+0r//8jq0VbHXZ7j\nQ0+jVqXasoUPAOu7bsK4ZuPNek+7eh0sju/BX7IS90g/Tz/Z4yhpSuJzssdRVcFVx9OCOuCd9Pdx\nativFZ6n5Z5nW3kWk/lYeSZFVHGvqlhhdE+//0oSjhZvfFJ9htNavGjyuY2rNZEkTi1whIfn3gvf\nKR6nJZQYaQAAcTUTLHqfEt9d/coNsLnndni6eMoel7WCKtdTOwll0m+d8rdHGnTEjszvMKv1q7LG\nK0Uv3LMJk/Ftv/3w9yx/NFHQ33u/Nq/VAr9kX0FO8qtY21W+KSfVPOSv9DULiDd7xXFrGsXtrcex\nrIaboZGPyxZf6zqpyEmeg9S67WSL40HL0leiU3AX+LhVrvA8LW5VVSy8eqSi8ZFyWHkmRShZibCn\nfYn39P2vwdwSqT7HtCDLW/HlEO6nzEMmpoYyC6uoafdv36idBJP4uFXG1ObP46OO/5Y1Hi03/nw/\n4KDd5Uml50W2DWpv8LdOp0OoX5gs20FNTpyu/79U16nT6fBtGQ2+7z78ATycPfBJp9X6Y+7O7siK\nHG60QmGvWtdJteh9pYdtS5Eax1Gvcj1kRWYrWvk0taxjXqMKv3iSBivPpAgpb7rGbqpa7DG2VKOq\nD+HTzmv1f0v1MYb6hWFTj6+lCcxKHs4eii14UrxSpFK0uMCKloxt+pRVwzFN9UrreWa/x956q2yF\nuT2Mlg6PtaS3v+SzRcrfdmX30vN5OzbsjHPZly2e669llv62Puj4qcQpsU2mlnFS67ZDrCSNc9rd\nNcKs1bYVvqdrco44SYKVZ5Ldhm6bJQ3P3cVd0vDKU03hOUumkLJhoGkFW2Epyc1Zmu9zTpsFJsQl\nzQqoi9sulSQcOUxvMUPtJGjOY+FZ2NhtC2p7B5r8HnN+a0Mj5BsyKYV+TQaqnQSTfdfvByxp96bs\n8azN+MLs90T6R/3zh8QF4+yoUZKGZ4kTQ8+afG6fJv1lTEnZ3J3dzZpyVOzBSpPcDew7MuWdOmNq\nw83HnT6Dk42OxDP1OzKngmpPHSukLlaeSTZt6qTifPZVJNaSdqGZqc2fr/B1qVoXxzV7RpJwpCT1\nzT+ptvwrThsjVQ/O4PChWPnIR5KEZUxmk37Y1kubw6NNXYBIS+Su8Ot0OiTUSjRvKK8Z95FZreda\nkCrlNK4WKkk4SixsVt+3AXo37it7PLEBzczeri617j87IEg9qkRXosf99bRlkoZtqrJ6wMvzTNxE\nAMCitm/IlZwyPSpBHpS7EhXqF2b2e6ydZvDgNUX5x8ja0zozaVapY5HVoyX77crS86xw5Xli4lQA\nwIjo0YrGS/Jj5Zlk868u62RZ0t7Y/rBS9WT6uFXG8y1fkiQsqUj9MBwerX5vh5RDmx6u/6hkYRkT\n6R+t6AIqpnJXYH9RqSnV69amrmVzJk3x24hrGBj2mGzhW8PDxQPR/rFoFdjaqnDesbP9hpMCkzEg\ndLBJ5/Zu3Nfg/iv1kMzi4epeLl5Wb5M2OvZJq9JgiqDK9XB51A2LeqCtqcRYMoxdiUpTfM1Eq95/\nbMjPEqUEyMs6p5+W1azECLOtvXbi9bRlmJQwzazwOjbsVOrY8OhRWN1lg8Gxbb2/waxkaRbqM/U7\nc3d2x1PNxiPAq6bxMBUetp1e/xFcGvknWga2UjRekh8rzyS5BamvYVhktqxxvJ2+Cm5OpSsJ01vM\nQIBXgGTxjIoZg4sj/zC5gCU3qQsB7eulo4FvQ0nDLJZj4kNUyh4cnU6HjODukoVnzJJ2b1X4uhpz\nnqRqPFKSUvtGPt9ypsnnmvtbc3FywattFpqbJEX0bTIAm3tux2cZG60Kx8XJxejQXqny/MjoMZKE\nY8y81MUmjTLKijDcl1jqnufiiuuDq4dbYnqLFzE+fpJi00ui/GMUiafYxZF/oJa36dtpqbHPs7l8\n3avg8qgbuDTyT4veXzI/+rpX0fdkT2n+PN5OX4Vfsq8gyj8GPUMyMS5uPAaGDTEp3KY1mpValK9Y\nq8DWeDxyBKL8Y3Ay65cyz3m4/qMWLeJqzv13YuI07OxjfKi8GsO2tZjXyHqsPJPk+oUOxMvJc2SN\no3NwBn4d8TsujvzD4Pjo2P+TPC4nnZNFw7DkIPWN2MXJBbv75prUamuurMjh+KL7Vqzvugl7+pa/\nfZjUFczX05ahhoQNKBWp7lldkXjM4WqDPc9KMWeVYksKWjqdDv/X9Gmz3yc3DxcPye4dVTyqWt2D\nbYpRsWNlj6PYpMTpODT4pwrPKa6c1KlUFwBQWeIVr4vzmxSVZwAYHz/J6h5sUz0RY953ZW1edNI5\n4cCgYxa/X45KVFxA0ZZ42dFPWBWO1M94TxdPdA7OgPsDjapzUxbi8qgbRt8fUMFIP51Oh5eSZ2Nr\nr53/DPkvI/2WXJO576nqUQ2BlepIGiZReVh5JpvmpHPSP7SSA9vIFs8QjSwIJMdD38XJBTv6fCt5\nuEDRHrvNa7eUZduY8rg6u2Jzz+2KxKXFh7Grgp+1Ldo/8KhJ51n63U5OnI7B4VkWvddWfJax0WDb\nJjkEeAWgspvpc3CtVdWjWoWvF1eev+n7PXIHHEYlNx9J49f3PEOaynPJMO2RTqfDhRHXTT5XbjW8\nAvDbiGuYkZQje1xl8XLxBqDs1CWT6HQWNZBbUtbZP/AoXm41u9zyBhcMI6nY752VHI6cBQUXJxeM\nMrN1XRYyFQKqefihinsVWcIGgADvinq2pR/arORwaa0M6S/WtEYcnombiC97bFM7KZpUx6eurOHr\ndDrMaTPf6Hmh1ZQZzfLeIx/LEu6TCiyomDvgkOxxmMvb1RtBletJHq7T3/d2Ke9dOp0OhwefkCw8\nqUhViXF2Mm048IMVWjkq0wJCskZiSxZhc3ZyxoUR1/Heo+b93o0NqTZ3eoJU362lvdXDokbgtxHX\n8NuIa6VfZ+WZJMLKM5GJnm85s8ze7a6NeiiWBjlv/h4W7Htqqor2VJVqmKJa5qUuLvc1NfZ51ul0\neDZhMpoFxCseN/3jh4E/Vvi6sV0DpNKydpIi8RSTMs9X8aiKQ4N/wrDIbHRqmCFZuGUxdm+Vu0FO\nJ+Gc55ICvGuWWtjJ0TSs0kjtJJilT5P+mJ+ypNzXy8uLpjYmlJQ3rOy5ylLRQWfRPcHaso6LkwtW\nPPyhYZgaHClGtomVZyIzlDWXe1KieStXOqryFiaTo4KpdKX11LBfFY2PtC/Qpw7ODr+kWHwvt5qt\n/3/xXrjru24ya/shLarpXQsvJ8/B0Eh1p87IfU9x+rs4Jkc8yXXa4NzwyxWeE1k9WvJ4yyNlJcZY\nI1RZIy+kaIT2cvEy+Fvq761/2CBJwytPJddKFc99NrPRSM2e5wdFP7CQHXueSSqsPBOZoXG1Jviw\n478MjtnzvDIprc34oszjaqxILTUft8q4POoGfpRwuxGSz2cZGzG79XzkZZ1TLQ1SF7aHRY3A6ccv\n4NLIPzEm9klcHnUDzWu3lDSOB3m7Vip1rLyVea0le8+vkYK1Oas7W0Lu54i99rqNbfoUzjx+sczX\nYms0xcMNypgDbOFn8VCVkHJfkyN/vtJ6XpnH5fgu29SRZhu/B9Omg2VznqUQ6GO4gJi9/gZIeSz1\nE5kprV468rLOIbVuO7Svl65o3HK2nMr9gKtVqTbyss5heYeVsvdyqDFcGtDm6tu2YlmHFfik0xpF\n4moV2BqPRWTBV8Z5/oDyPR3ert6KFhAf3PprxcMf2uVv4IvuW2WfK692I6wtVyy8XL3KPG7tytcP\neqP9cknDM6a8feOVrIyq9SyVyhfdt+r/H1ujmYopIXvCyjPZDSVv8b7uVfBJ5zX44IFeaKqYr3sV\ndGnUDdt6f6M/ZsuFNmNsveChpIxG3ZEa1E7VNMxL+Wf+utyVGXvIGYPDh+LCiOv4ssc2rHzkIzza\nsJNsccn9W6roPhTpL/+Q5jZ1i3r+hkeNlCV8ex+y6u9Zo9Sx8iqZ8nwW0udPFycXo8PtpSLVc1hr\n+SyuZgIujvwDRx47icbVmqidHLIT3NOEiADYV0VPK0PBa3sHqp0EMkOAVwA+774FeddPlNoX1RL2\n3DBUzNnJ2e4Xp1OiQtAsIB7HhpxGNSNbZlnK2DVordJjrn91WYeRW4bh2LV/tqKT+pmmxnPFw8VD\n8ThLsvaatXAPdNI5oYZX6cYVIkux55mI7I4WGgIygrtbtPopqcfdxQPxNRPRL3SgJOFVVCHRSgOP\nrVBzzrNSFUs/Tz9NVDZsUZhfOHb0+dbgmNQ9zxU9V+TMn082lX9buPJI8SzVwvOYSEqsPJPNc4TC\nxuDwLNT0riVrKzQL89Io7rFUu8eAzFepjAWwiOzhGWMP12Cu8ipttvZZTG4+HT1DMvV/B/kEKRa3\n1T3PNj6igagsrDwT2YA5bebj0OCfVF9URg5yPFzVbAgoa+4daVfJvdtjajSVNOwKe57ZG2MWuSs8\nFYVvDxUA48O2yRpy/57T6z8CoGjrtjSFFyo1h601TBBZwv5K4kRkEbUK8/b2sJ2YOBUA0D9ssMop\nIVO8kPQyAODN9u/YZeOUvVCzQcze7lGOQvJh2yrmwS7B3fBNn+/xw8AfFc2P1pYL+Nshe8QFw4hI\nFaNixuL1A4vUTobkejfuix4P9eZ8ZxsRUT0Sl0b+KUshr6IwOU1CW7Qw51lOxvK3Nflfq59PUOV6\nZR6X47cu+5x8nY6rRRNpBCvPRPQ3FualwoqzbZGrd0SrlQpbpOYwd3voPXOUvPhL9hVcvHUBJ64d\nR4vaSbLF4yjTLhzlOonMwcozEdkdPvBtV07yq7hfeA/Tdk9SOymyYh61Xrug9pKFZQ8VZGvYS+Xa\n3dkd9SrXR73K9cs9R5bVtvl7BgA469hwTPbPISvPBQUFWLBgAdasWYNbt24hOTkZ06dPR/Xq1dVO\nGpFqlB5GWhyfHIW2AK+akodJysiKHA6g6Dus5umH3ed3IqNRD5VTZRlHr5BJqaz704cd/61CSmwT\n86L1HLGCbG65wMXJBWsyPkfvDV1xr/Ce3TTKEJXkkKujLF68GGvWrMErr7yC999/HxcvXsSYMWPU\nThaRqoZHjSp1bHPP7QrELP3D1cvVC8FVGkkeLimn60M90LpOCiYlTkeYX7jayZEc5zxbjxVC88xN\nKX+NCUf6LC2t0FX1qAYAcNI54fb92wavOWLFujxJgcmoX7mBxe8//FiehKkhkp7DVZ7z8/Px3nvv\n4amnnkJSUhLCw8Mxb9487N+/H/v371c7eWSB4uFZSu59aI/GxY3Hzj578XzLl5BSty0+y9go+dY9\nJcld2Eiq3VrW8ImMqaiQzpW9zcPKifUGhj2GPk36l/mat6uPxeFGVI8y+PvUsF8tDksJljYUNPQN\nxrsPf4B9Aw5JnCLtsvR3NypmLACgT5N+qOEVAACYkDDFpPcG/H0+kVY53NP7+PHjuHXrFhISEvTH\n6tSpg8DAQOTm5qqYMrLUy61mY0ric3iu5Qy1k2LzmlQLxaiYMfi081q0CpS38llceXBxkmf2yItJ\nL+Pt9Pdw+vELeLRBZ3zc6TNZ4iEqj06nw/h4w7nb1TyqoXNwV6TV66BSqmxT7AMNeVObPy95HG2D\n0gAU3Tv2Dzwqefha8HLyHDSv1RIAMD5+Enb3zUW3Rj0wr4JeaWMaVX0I/x14BABQp1Jd+LhVxutp\ny/Svj459EiFVGwMA5qcssSL11kmukwI/Dz+rGq46NuyMuj5BWPXoJwCKKoRNazTD4PAsqZKpirFN\nnyrz+LPxky0Kr3/YIJwdfglp9dKxJuNzDIvMxqiYsZjeYgY8nD306xUE+RiuiJ6TPMei+IiUpBMO\nNnZs8+bNGDNmDI4cOQJXV1f98T59+iAsLAzTp08v971XrvylRBJthr+/Dz8TstiV/11B9pYhmNL8\nOTQLiFc7OSZhnidHpJV8XygKcf3Odfi4+cDN2U3t5JARQggUikL97gNCCFWHh98vvI+7BXfh7ept\n9FxT8nx+Qb5d5cOb927Cy8ULAFBQWABXZ1cj7yB7opX7vFb4+5c/GsfhFgy7ffs2nJycDCrOAODm\n5oa7d+9W+N6qVb3g4sKVBEuqKHMRVcQfPvhm2A61k2E25nlyRFrJ9wHwVTsJ5CC0kueV4g/Hul4q\nzdHyvKUcrvLs4eGBwsJC3L9/Hy4u/1x+fn4+PD09K3zv9ev/kzt5NoWtVORomOfJETHfk6NhnidH\nwzxvqKKGBIeb81yrVi0AwJUrVwyOX758GQEBXKSAiIiIiIiISnO4ynOTJk3g7e2N77//Xn/s119/\nxfnz5xEfbxvzLomIiIiIiEhZDjds283NDf369cPs2bNRtWpV+Pn54YUXXkBCQgJiYmLUTh4RERER\nERFpkMNVngHgySefxP379zF+/Hjcv38fycnJFa6yTURERERERI7N4baqsgYn0hvi4gLkaJjnyREx\n35OjYZ4nR8M8b4gLhhERERERERFZgZVnIiIiIiIiIiNYeSYiIiIiIiIygpVnIiIiIiIiIiNYeSYi\nIiIiIiIygpVnIiIiIiIiIiNYeSYiIiIiIiIygvs8ExERERERERnBnmciIiIiIiIiI1h5JiIiIiIi\nIjKClWciIiIiIiIiI1h5JiIiIiIiIjKClWciIiIiIiIiI1h5JiIiIiIiIjKClWcb8fvvv2PChAlo\n1aoV4uLikJWVhRMnTuhf37VrFzIyMhAVFYXOnTtjx44dZYaTn5+PLl26YN26dQbHb9y4gSlTpqBF\nixaIjY3F448/jlOnThlN1+HDh9GnTx9ER0ejQ4cOWLt2bZnnCSEwbNgwvP766yZd7/r165Geno6o\nqCj07t0bhw4dMnh9z549yMzMRGxsLFJTU/HKK6/gzp07JoVNtoF53jDPHzp0CP3790dsbCzat2+P\n9957z6RwyXY4Wp4v9vnnn6N9+/aljt+4cQOTJ09GQkICEhIS8PTTT+PatWtmhU3a50j5/t69e1iy\nZAnS0tIQExODbt26YevWrQbnbNu2DV27dkVUVBTatWuHZcuWgbvK2hdHyvP5+fl45ZVXkJycjOjo\naPTv3x8HDhwwOOfs2bPIyspCbGws2rRpg+XLlxsNV1WCNK+goEBkZmaK3r17i4MHD4q8vDwxduxY\n0aJFC3Ht2jWRl5cnIiIixOuvvy5Onjwp5s+fL8LDw8WJEycMwvnrr7/EsGHDREhIiFi7dq3Ba9nZ\n2aJLly7ihx9+ECdPnhRjxowRycnJ4vbt2+Wm6+rVqyIhIUG8+OKL4uTJk+K9994TYWFh4ptvvjE4\n7+7du2LSpEkiJCREvPbaa0avd/fu3SI8PFx8/PHH4uTJk2LKlCkiLi5OXL16VQghxLFjx0R4eLiY\nP3++OH36tNi5c6do06aNmDRpkqkfKWkc87xhnj979qyIiooSTz75pDhx4oTYvn27SEpKEkuWLDH1\nIyWNc7Q8X+zrr78WUVFRIi0trdRrAwcOFJ07dxYHDhwQBw8eFJ06dRLDhw83OWzSPkfL97NnzxZJ\nSUli27Zt4syZM2Lp0qWiSZMm4vvvvxdCCHHgwAERFhYmli1bJs6dOye++uorERMTI1auXGnqR0oa\n52h5/sUXXxQpKSliz5494uzZs+KFF14QMTEx4uLFi/rw0tLSxJgxY0ReXp5Yv369iI6OFp988omp\nH6niWHm2AUePHhUhISHi5MmT+mN3794V0dHRYs2aNWLatGliwIABBu8ZMGCAmDp1qv7v3bt3i3bt\n2olu3bqV+qHdvXtXjB8/Xhw4cEB/7NixYyIkJEQcPXq03HQtXbpUtG3bVhQUFOiPTZw4UQwZMkT/\n95EjR0RGRoZo27atiIuLM+mHNnToUDFhwgT93wUFBaJdu3bijTfeEEIIMWPGDNGzZ0+D96xZs0aE\nh4eL/Px8o+GT9jHPG+b5mTNnitTUVIP8vW7dOhEVFVXhw5Bsh6Pl+du3b4upU6eK8PBw0blz51KV\n52+//VaEhoaK06dP64/t2rVLpKWliVu3bhkNn2yDI+X7goICER8fLz744AOD44MGDRITJ04UQgix\nadMmkZOTY/D6qFGjxIgRIyoMm2yHI+V5IYoqz9u2bdP/fePGDRESEiI2b94shBBiw4YNIiYmRty8\neVN/zuLFi0WHDh2Mhq0WDtu2AbVq1cKbb76JBg0a6I/pdDoAwJ9//onc3FwkJCQYvCcxMRG5ubn6\nv7/++mt07doVH3/8canw3dzcMHv2bERHRwMArl27hpUrV6J27dpo2LBhuenKzc1FfHw8nJz+yUYJ\nCQnYv3+/fojR7t27ERcXh3Xr1sHHx8fotRYWFmL//v0G1+Pk5IT4+Hj99fTu3RvTp083eJ+TkxPu\n3buH27dvG42DtI953jDPnz17FjExMXB1ddWfExYWhjt37uDw4cNG4yDtUo3ucgAAC7ZJREFUc6Q8\nDwBXr17Fzz//jI8++qjMIdu7du1CaGgo6tevrz+WlJSELVu2wMvLy6Q4SPscKd8XFhZiwYIF6NCh\ng8FxJycn3LhxAwCQnp6OiRMn6s//9ttvsW/fPrRq1cpo+GQbHCnPA8C0adPQtm1bAMDNmzexfPly\n+Pj4ICoqSh9vREQEvL29DeI9c+YMfv/9d5PiUJqL2gkg46pWrYqUlBSDY6tWrcKdO3fQqlUrLFy4\nEAEBAQav16hRAxcvXtT/PXXqVJPimjlzJlatWgU3NzcsXboUHh4e5Z578eJFhIWFlYr39u3buH79\nOqpVq4bhw4ebFG+xGzdu4H//+1+Z11NcSQgJCTF47d69e1ixYgViYmJQuXJls+IjbWKeN8zzNWrU\nKDVf6fz58wCKKiFk+xwpzwNAYGAgPvjgAwDA9u3bS71+5swZBAUFYeXKlfjwww/1n8Ozzz4LX19f\ns+MjbXKkfO/i4oKWLVsaHDt06BC+++47PPfccwbHr127huTkZNy/fx/Jycno3bu3WXGRdjlSni9p\nxYoVyMnJgU6nQ05Ojv4aL168iBo1apSKFwAuXLiA6tWrWxynXNjzbIO2bduGefPmYciQIQgODsad\nO3fg5uZmcI6bmxvu3r1rdth9+/bF6tWr0aVLFzzxxBM4duxYueeWFy9QtECAJYoX/XJ3dzc47urq\nWub1FBQUYOLEicjLyzP5ZkK2x9HzfEZGBvbv34+VK1ciPz8f586dw8KFCwEUNR6R/bHnPG+Kmzdv\nYteuXdi+fTtmzZqFnJwcHDx4EKNHj+biSXbMkfL92bNnMXr0aERFRaFHjx4Gr3l4eODTTz/FokWL\ncPz4cX1vNNkfR8nz7dq1w9q1a5GdnY0pU6boF0G7c+dOqfJPcbyWXLMSWHm2MZ999hnGjh2LRx55\nBOPHjwdQVOh+sACdn58PT09Ps8MPDg5GREQEZsyYgcDAQHz44YcAgNjYWIN/QNHN/cEfVPHfpsSd\nm5trEOawYcP0P6AHw713716pMG/fvo3Ro0dj8+bNWLRoESIjI82+XtI+5nkgPj4eM2fOxOLFixEd\nHY0+ffqgX79+AGDy0CmyHfae503h4uKC+/fvY/HixYiNjUXLli2Rk5OD77//Hj/++KM5l0s2wpHy\n/ZEjR9CvXz/4+vpi6dKlBlNyAMDLywvh4eFIT0/H5MmTsXHjRly6dMnsayZtc6Q8X7duXYSGhmLc\nuHFo2bIlVq5caTRerU7R4bBtG/LGG29gwYIFGDBgAKZOnaqfI1GrVi1cvnzZ4NzLly+XGvZRnps3\nb2Lnzp1ISUnRZ1QnJyc0atRIf7Mua7n6mjVr4sqVK6Xi9fLyMqlAHxERYRCuh4cHqlSpAi8vL6PX\nc/36dWRnZ+PkyZN466230KJFC5OulWwL8/w/19OrVy/07NkTly9fhp+fH06ePAmg6IFE9sMR8rwp\nAgICEBgYiEqVKumPNWrUCADw66+/Ijw83KRwyDY4Ur7ftWsXxowZgyZNmmDp0qUG0xAOHz6M/Px8\nNGvWTH+seKrapUuXTL5u0j5HyPP5+fnYsWMHYmJi4O/vr38tJCRE3/Ncs2ZNnD59ulS8ADSb39nz\nbCOWLVuGBQsWYOzYsZg2bZr+RwYAzZo1w759+wzO37t3L+Li4kwK++7duxg3bhx27typP3b//n38\n+OOPCA4OBgDUq1fP4F9xvLm5uQZD6Pbu3YumTZsaLDhQHg8PD4MwAwICoNPpEBsba3A9hYWF2Ldv\nH+Lj4wEUDfHIysrCL7/8glWrVrHibKeY5//J85s2bcK4ceOg0+kQEBAAFxcXbN26FbVr19anl2yf\no+R5U8TFxeHcuXP4448/9Mfy8vIAAEFBQSaFQbbBkfJ9bm4uRo4cicTERLz77rul5u+vXr0azz//\nvEG8hw4dgqurq8HieWTbHCXPOzs7Y8KECVi/fr3BuYcPH9anpVmzZjhy5IjBgr979+5FgwYN4Ofn\nZ9I1K06dRb7JHMeOHROhoaFi0qRJ4vLlywb/bt26JY4fPy7Cw8PFwoULxcmTJ8WCBQtEZGSkwTL4\nJZW1J9zTTz8tUlNTxZ49e0ReXp545plnREJCgn4ftrJcuXJFNGvWTEybNk2/J1x4eLjYs2dPmeen\npqaatKz9jh07RFhYmHj//ff1e94mJCTo97ydNWuWCA0NFdu3by/1eZRcYp9sF/O8YZ7Py8sT4eHh\n4p133hG//PKL+PTTT0V4eLhYt26d0bDJNjhani9p0aJFpbaqun37tujQoYMYPHiwOHbsmDhw4IDo\n3LmzGDhwoFlhk7Y5Ur6/e/euaN26tejUqZP47bffDK71jz/+EEII8dNPP4mIiAjx8ssvi9OnT4tN\nmzaJxMREMWfOnArDJtvhSHleCCHmzZsn4uLixJYtW8SpU6fErFmzREREhPjxxx+FEEX3+tTUVDFy\n5Ejx008/iQ0bNojo6GixevVqo2GrhZVnGzB37lwREhJS5r/ijPuf//xHPProoyIiIkJ06dJF7N69\nu9zwyvqh3bp1S7z00kuiVatWIioqSgwdOlTk5eUZTdsPP/wgevToISIiIkSHDh3Exo0byz3XnELV\nv//9b9G2bVsRGRkpMjMzxZEjR/SvJSUllft5XLhwwaTwSduY5w3zvBBCbNmyRXTs2FFERkaKjh07\nivXr15sULtkGR8zzxcqqPAshxIULF8SYMWNETEyMiIuLExMnThR//vmnWWGTtjlSvv/mm2/KvdbB\ngwfrz9u7d6/o3bu3iIqKEikpKeLNN98UhYWFRtNLtsGR8rwQQty7d0+89tprIjU1VURERIjMzEyR\nm5trcM6pU6fEwIEDRWRkpEhJSRErVqwwGq6adEJw2UoiIiIiIiKiinDOMxEREREREZERrDwTERER\nERERGcHKMxEREREREZERrDwTERERERERGcHKMxEREREREZERrDwTERERERERGeGidgKIiIhIWhMn\nTsSaNWuMnjd69GgsWbIEhw4dgru7uwIpIyIisl3c55mIiMjOnDt3DteuXdP//eGHH2LdunX45JNP\nDM6rWbMmLl68iOjoaOh0OqWTSUREZFPY80xERGRngoKCEBQUpP9769atAICYmJhS59asWVOxdBER\nEdkyznkmIiJyUIsXL0bjxo1x9+5dAEXDvQcOHIg1a9YgPT0dkZGR6N69Ow4dOoRDhw4hMzMTUVFR\nSE9Px5dffmkQ1qVLlzBhwgQ0b94ckZGR6NWrF3bt2qXGZREREcmClWciIiLSO3r0KN566y2MGzcO\n8+fPx5UrVzB69Gg8+eST6Nq1K5YuXYrKlSvj2WefxaVLlwAAf/zxB/r27Yt9+/ZhwoQJWLx4MWrV\nqoXhw4djx44dKl8RERGRNDhsm4iIiPRu3bqFuXPnIiwsDABw/PhxLF68GDNnzkSvXr0AAG5ubujf\nvz8OHz6MgIAArFy5EpcvX8aGDRvQoEEDAEBKSgoGDx6MnJwctGnTRrXrISIikgp7nomIiEjP09NT\nX3EGAD8/PwCG86WrVq0KALhx4wYAYM+ePQgODkbdunVx//59/b927drh9OnTOH/+vIJXQEREJA/2\nPBMREZGet7d3mcc9PT3Lfc/169dx9uxZhIeHl/n6pUuXEBgYKEn6iIiI1MLKMxEREVnFx8cHMTEx\nmDp1apmvFw/lJiIismUctk1ERERWSUhIwJkzZ1C3bl1ERkbq/+3duxdLly6FkxOLG0REZPv4NCMi\nIiKrDB06FK6urhg0aBDWr1+P7777DnPnzsXcuXNRpUoVeHl5qZ1EIiIiq3HYNhEREVnF398fH3/8\nMebPn4+XXnoJt2/fRu3atTFu3DhkZWWpnTwiIiJJ6IQQQu1EEBEREREREWkZh20TERERERERGcHK\nMxEREREREZERrDwTERERERERGcHKMxEREREREZERrDwTERERERERGcHKMxEREREREZERrDwTERER\nERERGcHKMxEREREREZERrDwTERERERERGfH/vU9jZ/t0ePQAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.get_highs('Flow_total',0.95,arange=['2013/1/1','2013/1/31'],method='percentile',plot=True)" ] @@ -357,22 +302,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", "start_time": "2017-05-09T11:54:57.350077+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "47 values detected and tagged as filtered by function NaN tagging\n" - ] - } - ], + "outputs": [], "source": [ "dataset.tag_nan('CODtot_line2')" ] @@ -387,22 +324,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", "start_time": "2017-05-09T11:54:57.361076+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2464 values detected and tagged as filtered by function double value tagging\n" - ] - } - ], + "outputs": [], "source": [ "dataset.tag_doubles('CODtot_line2',bound=0.05,plot=False)" ] @@ -417,22 +346,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", "start_time": "2017-05-09T11:54:57.394331+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "199 values detected and tagged as filtered by function moving slope filter\n" - ] - } - ], + "outputs": [], "source": [ "dataset.moving_slope_filter('index','CODtot_line2',72000,arange=['2013/1/1','2013/1/31'],\n", " time_unit='d',inplace=False,plot=False)" @@ -447,22 +368,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", "start_time": "2017-05-09T11:54:58.315777+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2810 values detected and tagged as filtered by function moving average filter\n" - ] - } - ], + "outputs": [], "source": [ "dataset.moving_average_filter(data_name='CODtot_line2',window=12,cutoff_frac=0.20,\n", " arange=['2013/1/1','2013/1/31'],plot=False)" @@ -470,32 +383,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", "start_time": "2017-05-09T11:54:58.363535+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "65.77546296296296% datapoints are left over from the original 8640.0\n" - ] - }, - { - "data": { - "image/png": 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Lly/H6NGjtcddtmxZsx6/Zs0aAMB3332HTp06AQCio6MxYcIEw785NdhDhIiIiIiMSxQh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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = dataset.plot_analysed('CODtot_line2')\n", "ax.legend(bbox_to_anchor=(1.15,1.0),fontsize=18)\n", @@ -505,31 +400,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", - " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2',\n", - " 'TSS_line1', 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns',\n", - " 'Turb_ns', 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es',\n", - " 'Temp_es', 'NH4_infl', 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz',\n", - " 'PO4_mixinggutter', 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST',\n", - " 'CODsol_efflPST', 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT',\n", - " 'CODsol_efflRBT', 'Cond_line1', 'Turb_line1', 'Cond_line2',\n", - " 'Turb_line2', 'Cond_line3', 'Turb_line3', 'NH4_efflPST',\n", - " 'PO4_efflPST', 'PO4_sandtrap', 'NH4_splittingworks',\n", - " 'PO4_splittingworks', 'Flow_line1', 'Flow_line2', 'Flow_line3',\n", - " 'Flow_total'], dtype=object)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dataset.columns" ] @@ -543,35 +416,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", "start_time": "2017-05-09T11:54:59.892052+02:00" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Sa56jprfDviQjoqe9l4kjMYwTCcdYfGIBV9OjTB2KEEIIIQxk99WdfH9hY4XyEE/VrEJf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w1/22F2ccUwJ5/LuyMUCBHmE8ap9FpcYBcAQHIkN1E9vnKj5jrzWKPVYMPlb7HqzHtYOuAFk76e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xSoc3uKO2q53e9cK877P56CMvpk4Jgd0jOfTV/c4aZZcFW7HNJZmckVuAw5SH0VvDIDPgZA8l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\nwoYEykIIIYQQQgghhI3/B7oONfRNgm/LAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.savgol('TSS_line3',plot=True)" ] @@ -592,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": true }, @@ -606,20 +458,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2013-01-01 00:30:00')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data_series.index[5]" ] @@ -635,58 +476,29 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "code_folding": [], "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in period 2013-01-04 00:05:00 to 2013-01-09 00:05:00, slope: 92.06687774164706\n" - ] - }, - { - "data": { - "image/png": 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xhGW7p+ftZw326dMHg8HA3LlzuXTpEgMHDsTFxYX+/fsDsHDhQsaNG8fw4cMp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BUIAPh/e3ceHtP1/wH8Pdk3S4RExK5CJLJUpEIsIUEsQcVSpGi1qkUXVZRS+9KqvfZfEZSvllBK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\n7X3oVRavxEl7H42do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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", - " period=dt.timedelta(),time_unit='d',plot=True)" + " period=dt.timedelta(6),time_unit='d',plot=True)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[Timestamp('2013-01-01 00:05:00'), Timestamp('2013-01-14 00:00:00')]]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dataset.drift_periods" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -695,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -704,20 +516,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(test)" ] @@ -749,27 +550,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4895" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(dataset.data['2013/1/1':'2013/1/17'])" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": true }, @@ -784,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": true }, @@ -827,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -835,28 +625,7 @@ }, "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:326: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n", - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:369: UserWarning: Data points obtained during a rain event will be replaced. Make sure you are confident in this replacement method for the filling of gaps in the data during rain events.\n", - " 'filling of gaps in the data during rain events.')\n" - ] - }, - { - "data": { - "image/png": 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Gpk2bxm233cYXX3xBmzZtzO09evTg+eefZ+jQoZetITU1q3Jvqhbz9Gyo90Ns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\npdl1Ty9PwzCfESgiCxn9X4xeD5lEBlJJYvXGSdiRTz0naMVBVb9I5ox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2fkAqSfySxUyP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O5zjbolLFrVqLJI02pB3CyT4uWuQjc8uGUCbtYUAwBu3LluFaNwtLdMpx+lpbKhEn8//jKvrrTW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\nA9auXYtJkyYBAKRSKZRKJW/f0tJS+Pv7t2t7T0TVq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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n", " order=3, plot=True)" @@ -872,23 +641,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", "start_time": "2017-05-09T11:55:01.063627+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py:2033: UserWarning: Data points obtained during a rain event will be used for the calculation of an average day. This might lead to a not-representative average day and/or high standard deviations.\n", - " 'representative average day and/or high standard deviations.')\n" - ] - } - ], + "outputs": [], "source": [ "dataset.calc_daily_profile('CODtot_line2',[dt.datetime(2013,1,1),dt.datetime(2013,1,8)],\n", " quantile=0.9,clear=True)" @@ -896,33 +656,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", "start_time": "2017-05-09T11:55:01.105608+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:683: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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JTJ48uUx9Wlqa+fvGjRuXOTcqKoqoqKhyz8vOzgagYcOGFnXu7u7XHbdUHyW9RERERERE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\n45kzZ8jLy6N58+Y4OTldtt1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jVm6tMTbysWJEIiIiIiJSHWr9RvbZ2dlMmTKFrl270qtX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\nLFBAgM8+7hUb0tqV8FP4QSmwyPU6sEgKP6gpnaYzygsA4DfsT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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_standard('CODtot_line2',[dt.datetime(2013,1,14),dt.datetime(2013,1,17)],\n", " only_checked=True,clear=False,plot=True)" @@ -938,43 +679,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", "start_time": "2017-05-09T11:55:01.847864+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " interactivity=interactivity, compiler=compiler, result=result)\n", - "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:3: DeprecationWarning: \n", - ".ix is deprecated. Please use\n", - ".loc for label based indexing or\n", - ".iloc for positional indexing\n", - "\n", - "See the documentation here:\n", - "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", - " app.launch_new_instance()\n" - ] - }, - { - "data": { - "text/plain": [ - "Index(['.sewer_1.COD', '.sewer_1.CODs', '.sewer_1.NH4', '.sewer_1.PO4',\n", - " '.sewer_1.Q_DWF_UB', '.sewer_1.Q_in', '.sewer_1.TSS'],\n", - " dtype='object')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model_output_ontv_1 = pd.read_csv('./data/model_output.txt',\n", " sep='\\t')\n", @@ -987,33 +699,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", "start_time": "2017-05-09T11:55:02.251053+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:817: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " wn.warn('When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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88cQT140hMTH99j5UBVanjoveh1Q66vdSGanfS2WjPi+Vkfr9n+rUcbF2CFJB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ERE\nREREREQAGCgjIiIiIiIiIiICwEAZERERERERERERAOD/AfTr6rMwE3gEAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_model('CODtot_line2',model_output_ontv_1['.sewer_1.COD'],\n", " [dt.datetime(2013,1,18),dt.datetime(2013,1,22)],\n", @@ -1032,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.917107", @@ -1040,18 +733,7 @@ }, "scrolled": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.450642327196896, 0.672153214085126)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dataset.calc_ratio('CODtot_line2','CODsol_line2',\n", " [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)])" @@ -1066,31 +748,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", "start_time": "2017-05-09T11:55:03.919697+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best ratio (2.53282188261064 ± 0.16586491872475553) was found in the range: [Timestamp('2013-01-19 00:05:00') Timestamp('2013-01-21 00:05:00')]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py:1400: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n", - "You can access Timestamp as pandas.Timestamp\n", - " if isinstance(self.data.index[0],pd.tslib.Timestamp):\n" - ] - } - ], + "outputs": [], "source": [ "avg,std = dataset.compare_ratio('CODtot_line2','CODsol_line2',2)" ] @@ -1104,33 +769,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", "start_time": "2017-05-09T11:55:03.980745+02:00" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:462: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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yduzYAuXJycnm31epUqXAuzExMcTExBT63uXLlwGoVKmSRZm7u/s9xy2lixJlIiIiIiIi\nImLJYCix5ZbFxdXVFYB33nkHX1/fAuWenp63fNdgMBASEsILL7xQoKxSpUqYTCYAUlNTLcpuJufk\n4aGllyIiIiIiIiLywLGzs7O4rlu3Lm5ubly8eBEfHx/zr7S0NObNm0dGEYcSBAQEcOrUKZo0aWJ+\nr0aNGsyePZvExETq1KmDh4cHW7dutXhv586d96VvYj2aUSYiIiIiIiIiD5ybM8i++eYbHnvsMby8\nvHj99dd57733AGjZsiVnzpxh9uzZPPbYY0XOKBsxYgT9+vVj1KhR9OnTB5PJRFRUFOfPn6dx48bY\n2NgQFhbGpEmTqFKlCq1bt2bz5s0cOXKkQMJOHmxKlImIiIiIiIjIA8dgMDBkyBD+7//+jwMHDrBp\n0yYGDBiAs7MzH330EcuWLcPNzY0uXbowZswYbGxsbllXkyZNWLFiBREREYSFheHk5ESzZs34xz/+\nQbVq1QDo27cvAIsXL2b16tW0atWK4cOHs2TJkhLpr5QMm7y8vDxrB1GaJCdfsXYIpUbVqq76HlLm\naNxLWaRxL2WNxryURRr3v6la1dXaIYhIKaY9ykRERERERERERFCiTEREREREREREBFCiTERERERE\nREREBFCiTEREREREREREBFCiTEREREREREREBFCiTEREREREREREBFCiTEREREREREREBFCiTERE\nREREREREBFCiTEREREREREREBFCiTERERERERESkxOTl5Vk7hGLxsPTjj5QoExEREREREZFS49y5\nc/Tr1w8fHx969uxJZGQkTZs2NZcbjUaio6MBiI2NxWg0kpqaek9tjh8/nu7du9/2uYsXLxISEkJa\nWhpnzpzBaDSyZcuWO24nMTGRl19++V5CLVZxcXEYjUYOHz58x+9cuHCBwYMHc+nSJYA/9R3uRFhY\nGBs2bCjWOu+EfYm3KCIiIiIiIiJyCytXruTYsWPMnTuX6tWr4+7uTlBQkLXDAmDy5Mn0798fNzc3\nXFxciImJ4bHHHrvj97ds2XJXSanS6JtvvuHrr782X3t4eNz1d7gTY8eO5YUXXqBt27a4u7sXa91F\n0YwyERERERERESk1Ll++jKenJ08++SRNmjShevXq+Pr6Wjss4uPjiY+P58UXXwTA0dERf39/3Nzc\nrByZdd2v7/Doo4/SokULFi1aVKz13o4SZSIiIiIiIiJSKgQHBxMbG0tSUhJGo5HY2NgCSy9vZ9eu\nXfTt2xdfX1/atWvHvHnzyMnJMZdnZ2cza9YsWrduTbNmzQgPD7cov5Vly5YRHByMs7MzUHDJ4fjx\n4wkLC2PFihV06NABX19fBg4cyMmTJwGIjIxk/vz5ZGZmmvsGkJmZyfTp02nVqpX5naNHj5rbjY2N\nJTAwkKVLlxIYGEhQUJC5jjVr1jBs2DD8/PwIDg5m9erVFjFfvXqVv//97wQHB+Pr68uzzz5rMRus\nMP/+97/p06cPfn5++Pn50a9fP+Lj482xTJgwAYCWLVsSGRlZ6NLL+Ph4+vfvT7NmzWjVqhXTpk3j\n6tWr5vKBAwcSHh7O3Llzad26NX5+fowYMYKLFy9axNKtWzfWr1/P5cuXb/vnU1yUKBMRERERERER\nCxkZEBeX/9+SNH/+fIKCgqhduzYxMTG0b9/+rt7fvXs3Q4YMwdPTk/nz5zN48GCWL1/Ou+++a35m\n5syZrFq1iiFDhjBnzhwSEhLYvHlzkfVmZGSwc+dOOnXqVORz33zzDRs3bmTixIm8//77/PTTT4wf\nPx6Avn378uyzz+Ls7GzuW15eHqGhoXz66aeMHj2aefPm4ejoyMCBA/n555/N9V65coVNmzYxa9Ys\nJkyYgIuLCwCzZs3CYDAQGRlJx44dmTZtGuvWrQMgNzeXV199ldjYWIYOHUpkZCQ1a9Zk6NChfPXV\nV4XGv2XLFt566y3at2/P4sWLCQ8PJz09nTFjxmAymWjfvj2hoaEALF26lL59+xaoY+fOnbz00ktU\nrVqVuXPn8vrrr/Of//yHYcOGkZuba35u/fr1HDp0iJkzZzJlyhTi4uIIDw+3qKtdu3bk5uby5Zdf\nFvndi5P2KBMRERERERERs4wMaN4cEhLAywvi48FgKJm2GzduTOXKlTl37hz+/v53/X5ERAR+fn7M\nnTsXyE+0VKxYkQkTJjB48GAMBgNr165l9OjRDBo0CMifGdWhQ4ci6927dy85OTk0bty4yOeuXr3K\nhx9+iIeHB5C/+f+MGTO4dOkS1atXp3r16tja2pr79tVXX7Fnzx6WL19Oq1atAGjbti3dunVj4cKF\n5sRRTk4OI0eOpG3bthbt1atXj9mzZ5v7ev78eT788EOee+45duzYwf79+1m6dKn5vaCgIJ5//nnm\nzp1boC6An3/+mf79+/P666+b7zk4ODBy5Eh+/PFHGjZsyCOPPAKAt7c3lStX5syZMxZ1zJs3D19f\nXyIiIsz3PD09efXVV9mxYwfBwcEA2NnZ8eGHH+Lk5ARAQkKCOcl3k5OTE/Xq1SMuLo5nnnmmyG9f\nXDSjTERERERERETMjhzJT5JB/n+PHLFuPHcqKyuL7777jg4dOpCdnW3+dXNWUlxcHIcOHSInJ4d2\n7dqZ33NycrrtYQFnz54FoHr16kU+V7NmTXOS7PfPZ2VlFfp8XFwc5cqVo3nz5uZ4Adq0acOePXss\nnq1Tp06B97t27WpxHRISwpkzZ7hw4QLx8fGUL1++QEKsa9euHD16lIxCpgsOHTqUSZMmkZ6ezsGD\nB9mwYQP//ve/ATCZTEX2HfIThUePHqVLly4W99u2bUvFihXNSzgh//TSm0kyyP9WhX2nmjVrmr9/\nSdCMMhEREREREREx8/bOn0l2c0aZt7e1I7oz6enp5ObmMnv2bPMsq99LTk7G0dERgEqVKlmU3e5U\nxStXruDo6IidnV2Rz5UrV87i2tY2f37S75cc/l5aWhpZWVk0adKkQJmDg4PFdeXKlQs88/uk3O+f\nSUtLIz09vdB+ubu7k5eXZ7Fn2E3JyclMnDiR//3vfzg4ONCgQQNq1aoFQF5eXqF9+L0rV66Ql5dH\nlSpVCpRVrlzZIjn3x29lY2NTaBvOzs6cO3futm0Xl1KTKDOZTPTu3Zu//e1v5umGu3fvZtasWZw6\ndQoPDw9effVVi/Wve/bsYcaMGfz888/4+vry7rvv8uijj5rLV61axZIlS7hy5QpdunRh0qRJ5nW8\nIiIiIiIiIlKQwZC/3PLIkfwkWUktu7xX5cuXByA0NJSQkJAC5R4eHpw4cQKA1NRUqlWrZi5LS0sr\nsm43NzdMJhMmk8mcbCsOrq6uVKlShQ8//PBPvX/p0iWL619//RXIT0pVrFiRlJSUAu8kJycDFHpK\n5dixY7l48SIxMTF4e3tjb2/Pzp072bp16x3F4+rqio2NjTmO30tJSflTJ2Omp6eX6MmipWLp5fXr\n13njjTdITEw03/vxxx8ZNmwYHTt2ZOPGjbz22mtMmzaN7du3A3D+/HlCQ0N5+umnWb9+Pe7u7owY\nMcKcpd26dSsRERFMnjyZlStXcvjwYd577z2r9E9ERERERETkQWIwQGDgg5MkAzAYDHh5eXH69Gl8\nfHzMvxwcHJgzZw4XLlygadOmODo6WiR+srOz2bVrV5F116hRA4ALFy7cU4w3Z5jdFBAQQGpqKi4u\nLhYxb9q0ybzksSg7duywuP7vf/9L3bp18fDwICAggKtXrxbYuH/z5s14e3tbLHu86eDBg3Tt2hU/\nPz/s7fPnVt18/+Zsrz/24ffKly9Po0aNLE7AvFnHlStXaNas2W379EcXL140f/+SYPUZZUlJSYwd\nO7bA9LrPPvuMRo0aMXz4cAAeffRR4uPj2bRpE8HBwaxbtw4vLy+GDBkC5J9a0bp1a/bs2UOrVq1Y\nsWIFAwYMMGeRp0yZwl/+8hf++te/mrPMIiIiIiIiIvLwCAsL47XXXsNgMNCxY0cuXbpEREQEtra2\nNGzYkHLlyjF48GCWLFmCs7MzjRo1Ys2aNaSkpJg3qS9MQEAADg4OHDhwoMjnbqdChQpkZWXxxRdf\n4OvrS4cOHfDx8WHo0KGMHDmSGjVq8Pnnn/Pxxx8zderU29b31VdfMW3aNIKDg9mxYwfbtm0zb6Lf\nvn17/Pz8ePPNNxkzZgw1atQgNjaWQ4cOsXDhwkLr8/HxYcOGDRiNRipWrMi2bdtYs2YNANeuXTP3\nAWDbtm20bt26QB2vv/46I0aMYPTo0fTu3Zvz588zZ84cmjZtarE33J24evUqiYmJDBs27K7euxdW\nn1H27bffEhgYSExMjMX9p556ikmTJlncs7GxIT09HYBDhw7RvHlzc1m5cuXw9vbmwIED5OTkcPjw\nYYtyf39/cnJyOHbs2H3sjYiIiIiIiIhYS0hICFFRUXz//feEhoYyc+ZM/P39WblypXlPrFGjRjFy\n5EhWr15NWFgYrq6uPPfcc0XWazAYaNWq1W1nnt1Ot27d8Pb2ZvTo0fzrX//Czs6O6OhoWrduzfvv\nv8/QoUPZu3cv4eHh9OvX77b1vfrqq/z000+MGDGCPXv2MHfuXPNG+nZ2dixdupROnToxd+5cXn/9\ndS5cuMDixYtvecpneHg49erVY8KECYwZM4aTJ0+ycuVKXFxcOHjwIJB/SmibNm2YPn06y5YtK1BH\ncHAwCxYs4Oeff2bEiBFERkbSvXt3li5dets93v5o9+7dODg4FHpC5/1ik3cnu7GVEKPRaHEk6u+l\npKTQuXNnRowYweDBg+nRowfPP/88AwYMMD8zevRoKlSowJgxY3jiiSfYtGkTDRs2NJe3atWKv/3t\nb3Tv3v2WMSQnXyneTj3AqlZ11feQMkfjXsoijXspazTmpSzSuP9N1aqu1g5BHlBxcXEMGzaMr7/+\nGkMpWJNqNBp56623GDx4sLVDuW+GDx9O7dq1mThxYom1afWll3ciMzOTkSNH4uHhwYsvvgjkH636\nxw30HB0dMZlM5umAtyovSqVKLtjb312G82Gm/xGRskjjXsoijXspazTmpSzSuBe5N4GBgQQEBPDx\nxx8zdOhQa4fz0Dt58iQHDhxg2rRpJdpuqU+UXblyhWHDhnHmzBk+/vhj81RJJyenAkkvk8mEm5ub\neUO6wsqdnZ2LbO/SpcxijP7Bpn91krJI417KIo17KWs05qUs0rj/jRKGci+mT5/OgAEDeO6550r0\nJMayaM6cObz55pt4eHiUaLulOlGWmprK4MGDSUlJYeXKlRYb5lWrVs18pOlNKSkpNGjQwJwsS0lJ\nMS+9zM7OJi0trcQ/sIiIiIiIiIg8HGrWrMn27dutHQYAx48ft3YI99WCBQus0q7VN/O/FZPJxPDh\nw7l06RKrV6+mbt26FuV+fn7s37/ffJ2VlcXRo0fx9/fH1tYWHx8f9u3bZy4/ePAgdnZ2NGrUqMT6\nICIiIiIiIiIiD45Smyj76KOPOHLkCOHh4ZQrV47k5GSSk5NJS0sDoE+fPuYjTZOSkpg4cSI1a9ak\nZcuWALz44ossW7aMrVu3cvjwYaZOnUqfPn0oX768NbslIiIiIiIiIiKlVKlderllyxays7MZNGiQ\nxf1mzZqxZs0aPD09iYyMJDw8nEWLFuHn50dUVBS2tvm5v27dunH27FmmTJmCyWSiY8eOjB8/3go9\nERERERERERGRB4FNXl5enrWDKE20weVvtOGnlEUa91IWadxLWaMxL2WRxv1vtJm/iBSl1C69FBER\nERERERERKUlKlImIiIiIiIiIiKBEmYiIiIiIiIhIidNOWKWTEmUiIiIiIiIiUmqcO3eOfv364ePj\nQ8+ePYmMjKRp06bmcqPRSHR0NACxsbEYjUZSU1Pvqc3x48fTvXv32z538eJFQkJCSEtLu6f2EhMT\nefnll83XcXFxGI1GDh8+fE/1/vFblTZ/jC8sLIwNGzZYMaKCSu2plyIiIiIiIiJS9qxcuZJjx44x\nd+5cqlevjru7O0FBQdYOC4DJkyfTv39/3Nzc7qmeLVu2WCTFvL29iYmJoV69evca4gNl7NixvPDC\nC7Rt2xZ3d3drhwNoRpmIiIiIiIiIlCKXL1/G09OTJ598kiZNmlC9enV8fX2tHRbx8fHEx8fz4osv\nFnvdBoN65QlnAAAgAElEQVQBf39/XFxcir3u0uzRRx+lRYsWLFq0yNqhmClRJiIiIiIiIiKlQnBw\nMLGxsSQlJWE0GomNjb3r5YS7du2ib9+++Pr60q5dO+bNm0dOTo65PDs7m1mzZtG6dWuaNWtGeHi4\nRfmtLFu2jODgYJydnQE4c+YMRqORFStWEBwcTEBAAHv37iUvL48VK1bQo0cPfHx8aNq0KX/5y184\nfvw4kL/8cP78+WRmZpr7WNjSy23bttGnTx/8/f0JCgoiIiKC7OzsO/oGGzdupEOHDvj5+TFs2DB+\n+ukni/J///vf9OnTBz8/P/z8/OjXrx/x8fHm8szMTCZOnEibNm3w9fWlV69ebN261aKO77//npdf\nfhk/Pz+eeOIJpk+fTlZWlsUz0dHRdOjQAX9/f958802uXbtWINZu3bqxfv16Ll++fEd9u9+UKBMR\nERERERERC9kZ2aTHpZOdcWeJmeIyf/58goKCqF27NjExMbRv3/6u3t+9ezdDhgzB09OT+fPnM3jw\nYJYvX867775rfmbmzJmsWrWKIUOGMGfOHBISEti8eXOR9WZkZLBz5046depUoCwqKopx48YxadIk\nfH19WbZsGbNmzeLZZ58lOjqaSZMmkZSUxIQJEwDo27cvzz77LM7OzrfsY0xMDCNHjsTX15f58+cz\nYMAAli1bxvjx42/7DbKyspg1axZhYWH84x//4Mcff2TQoEFkZmYC+cs+33rrLdq3b8/ixYsJDw8n\nPT2dMWPGYDKZAJgxYwZ79uxh4sSJLF68mHr16jFq1ChOnjwJQFJSEgMGDMDGxoaIiAjGjRvHZ599\nxujRo81xREdHM3v2bHr16sUHH3zAjRs3WLFiRYF427VrR25uLl9++eVt+1YStEeZiIiIiIiIiJhl\nZ2Szv/l+MhMycfFyoVl8M+wNJZM+aNy4MZUrV+bcuXP4+/vf9fsRERH4+fkxd+5cID8JU7FiRSZM\nmMDgwYMxGAysXbuW0aNHM2jQIABatmxJhw4diqx379695OTk0Lhx4wJlPXr0oGvXrubr8+fPM2LE\nCPNm/S1atCA9PZ3w8HCuXr1K9erVqV69Ora2toX2MScnh4iICLp168bkyZMBaNOmDa6urkyePJlX\nX30VLy+vW8aal5fH+++/T8uWLQGoW7cuPXr04NNPP6Vv3778/PPP9O/fn9dff938joODAyNHjuTH\nH3+kYcOG7Nu3j9atW/PUU08B0KxZM9zd3c0z2qKionB3d2fx4sU4OjoC8Nhjj9G/f3/i4+MJCAhg\nyZIl9O3bl7CwMADatm1Lz549OX36tEW8Tk5O1KtXj7i4OJ555pki/xxKghJlIiIiIiIiImKWeSST\nzIT82UeZCZlkHsmkQmAFK0d1e1lZWXz33XeMGTPGYonizRlLcXFxuLu7k5OTQ7t27czlTk5OBAUF\nFXni5NmzZwGoXr16gbI6depYXL/99tsApKamcurUKU6dOsX27dsBMJlMlC9fvsh+nDp1itTUVLp0\n6WJx/2bibO/evRiNxgLLRe3t81M8rq6u5iQZQIMGDahduzb79u2jb9++DB06FID09HROnTrFDz/8\nYBEfwOOPP866dev45Zdf6NChA+3bt7eYzRYXF0dISAi2trbmb+3v74/BYGD37t1UrlyZS5cuWXxn\nGxsbOnXqZD6x9Pdq1qxp/sbWpkSZiIiIiIiIiJi5eLvg4uVinlHm4v1gbDCfnp5Obm4us2fPZvbs\n2QXKk5OTzbOfKlWqZFF2uxMXr1y5gqOjI3Z2dgXKqlSpYnF98uRJJk2axL59+yhXrhxeXl7m5Fhe\nXt5t+3Fzr64/1uvq6oqjoyMZGRls2LDBvJTzppt7oP3xPYDKlStz5coVIP87TJw4kf/97384ODjQ\noEEDatWqZRHf22+/jYeHB//617/48ssvsbW1JSgoiJkzZ1K5cmXS0tKIiYkhJiamQFvJycnmPtzp\nd3Z2dubcuXNFf5gSokSZiIiIiIiIiJjZG+xpFt+MzCOZuHi7lNiyy3t1MxkVGhpKSEhIgXIPDw9O\nnDgB5M/2qlatmrksLS2tyLrd3NwwmUyYTCZzsq0wubm5hIaG4ubmxqZNm6hfvz62trasXr2ar7/+\n+o764ebmBsCvv/5qcT89PR2TyYSbmxsdOnTgn//8Z6Hvp6enF7iXkpJCw4YNARg7diwXL14kJiYG\nb29v7O3t2blzp8Vm/c7OzoSFhREWFsapU6f4/PPPiYqKYt68eUydOhWDwUBISAgvvPBCgbYqVapk\nnpmWmppqUXar75yenm7ut7VpM38RERERERERsWBvsKdCYIUHJkkGYDAY8PLy4vTp0/j4+Jh/OTg4\nMGfOHC5cuEDTpk1xdHS0SAplZ2eza9euIuuuUaMGABcuXCjyudTUVH766Seee+45GjZsiK1tftrl\nq6++snju5v3C1KlTh0qVKrFlyxaL+5999hmQv19YpUqVLPro4+NjEcORI0fM10eOHOHMmTO0aNEC\ngIMHD9K1a1f8/PzMyzVvxpeXl0dOTg7du3fno48+AvL3OAsNDcXf35/z588DEBAQwKlTp2jSpIm5\n/Ro1ajB79mwSExOpU6cOHh4eBU7K3LlzZ6F9vnjxovkbW9uDM+JFRERERERERIoQFhbGa6+9hsFg\noGPHjly6dImIiAhsbW1p2LAh5cqVY/DgwSxZsgRnZ2caNWrEmjVrSElJ4ZFHHrllvQEBATg4OHDg\nwIEin6tSpQo1a9ZkxYoVVKlSBTs7OzZu3MiOHTuA/H3UACpUqEBWVhZffPEFvr6+FnXY2dkxcuRI\npk+fTsWKFQkJCeH48eNERkbSpUsX88ywW3F0dOSNN95g3Lhx3Lhxg1mzZuHl5UXnzp0B8PHxYcOG\nDRiNRipWrMi2bdtYs2YNANeuXcPOzg5fX18WLFiAk5MTdevW5dChQ+zbt4+pU6cCMGLECPr168eo\nUaPo06cPJpOJqKgozp8/T+PGjbGxsSEsLIxJkyZRpUoVWrduzebNmzly5EiB5atXr14lMTGRYcOG\nFdmvkqJEmYiIiIiIiIg8FEJCQoiKimLBggXExsZiMBho1aoV48aNo1y5cgCMGjUKZ2dnVq9eTXp6\nOp06deK5555jz549t6z3Zj27du2iZ8+et3zOxsaGyMhI3n33XcaMGYPBYMDHx4fly5czaNAgDh48\nSK1atejWrRsbN25k9OjRjBo1qkCybMCAATg7O7Ns2TI++eQTPDw8+Mtf/sKIESNu+w1q1arFoEGD\nmDp1KlevXiUoKIhJkyaZl4yGh4czdepUJkyYgJOTE0ajkZUrVzJ06FAOHjxIixYtePvtt3FxcWHR\nokX8+uuv1KpVi7/+9a/07dsXgCZNmrBixQoiIiIICwvDycmJZs2a8Y9//MO8pPXms4sXL2b16tW0\natWK4cOHs2TJEot4d+/ejYODA23btr1t30qCTd6d7CRXhiQnX7F2CKVG1aqu+h5S5mjcS1mkcS9l\njca8lEUa97+pWtXV2iHIAyouLo5hw4bx9ddfYzAYrB3OQ2P48OHUrl2biRMnWjsUQHuUiYiIiIiI\niIjcVmBgIAEBAXz88cfWDuWhcfLkSQ4cOMCQIUOsHYqZEmUiIiIiIiIiIndg+vTprF279ranZMqd\nmTNnDm+++SYeHh7WDsVMe5SJiIiIiIiIiNyBmjVrsn37dmuH8dBYsGCBtUMoQDPKRERERERERERE\nUKJMRKTYZWTAvn22ZGRYOxIRERERERG5G1p6KSJSjDIyoHNnFxIT7WjQIIfPP89EB+KIiIiIiIg8\nGDSjTESkGB0/bktioh0AiYl2HD+uH7MiIiIiIiIPCv0NTkSkGBmNuTRokANAgwY5GI25Vo5IRERE\nRERE7tQdL7385ZdfyMzMpFatWjg4ONzyuV9//ZXk5GS8vLyKJUARkQeJwQCff57J8eO2GI25WnYp\nIiIiIiLyALntjLIDBw7Qs2dPgoKCeOqppwgMDGT69OlcuXKl0OfXrFlDr169ij1QEZHSLONGBvsu\nxpNxQzv4i4iIiIiUJXl5edYOQYpRkYmyhIQEBg0aRFJSEk888QTt2rXDxsaG1atX06tXL06ePFlS\ncYqIlFoZNzLo/El7nlofQsf/60rHTuV46qnydO7sopMvRURERETu0rlz5+jXrx8+Pj707NmTyMhI\nmjZtai43Go1ER0cDEBsbi9FoJDU19Z7aHD9+PN27d7/tcxcvXiQkJIS0tDQA1q1bR0RExD21/UcD\nBw5k2LBhxVZfXFwcRqORw4cP39V7wcHBTJs2rdjiSE5OJiQk5J7/rO63IhNlkZGR5OTksGLFCpYv\nX86HH37IF198Qa9evThz5gwDBw7kxIkTxRKIyWSie/fufPPNN+Z7Z8+e5ZVXXsHf35+nnnqKnTt3\nWryzZ88eevTogZ+fHwMHDuSnn36yKF+1ahXt2rWjadOmTJgwgczMzGKJVUTk946nHiMxLf9n4clE\nR04m5a9q12b+IiIiIiJ3b+XKlRw7doy5c+cyY8YM+vbty4oVK6wdFgCTJ0+mf//+uLm5AbBo0aJb\nrri7lzb++te/FmudpUHVqlV55plnmDFjhrVDKVKRf4Pbu3cvnTt35vHHHzffq1SpEuHh4YSFhZGa\nmsorr7zC6dOn7ymI69ev88Ybb5CYmGi+l5eXx4gRI3Bzc+Of//wnvXr1IiwszNzW+fPnCQ0N5emn\nn2b9+vW4u7szYsQIcnPzN87eunUrERERTJ48mZUrV3L48GHee++9e4pTRKQwxsqNaODWEIB6DUzU\nq58NaDN/EREREZE/4/Lly3h6evLkk0/SpEkTqlevjq+vr7XDIj4+nvj4eF588cX72k79+vWpW7fu\nfW3DWl5++WW2bt3K0aNHrR3KLRWZKLt69SrVqlUrtGzEiBGEhoaSkpLCK6+8QkpKyp8KICkpieee\ne46ff/7Z4v6ePXv44YcfmDZtGvXr12fo0KE0bdqUf/7zn0D+9EYvLy+GDBlC/fr1mTlzJufPn2fP\nnj0ArFixggEDBhASEoKPjw9Tpkxhw4YNXL169U/FKSJyKwYHA5/33cHmPv9l24DP2LY1i82br/L5\n55nazF9ERERE5C4EBwcTGxtLUlISRqOR2NjYAksvb2fXrl307dsXX19f2rVrx7x588jJyTGXZ2dn\nM2vWLFq3bk2zZs0IDw+3KL+VZcuWERwcjLOzsznWs2fPsnr1aoxGI8ePH8doNLJlyxaL9zZt2kST\nJk24dOkS48ePZ9iwYSxZsoSWLVvy+OOPM3bsWPNSTii49DItLY2JEyfSqlUrmjVrxiuvvMLx48fN\n5adOnSIsLIwnnniCJk2aEBwczIIFC+5q77Tk5GTCwsIICAigbdu2bNy4scAzt2und+/eBZaMXr9+\nnYCAAFatWgVAhQoVaNOmjXnpbGlUZKKsZs2aHDhw4Jblo0aNok+fPpw+fZpXXnnF4g/2Tn377bcE\nBgYSExNjcf/QoUM0btwYw+/+lhkQEMDBgwfN5c2bNzeXlStXDm9vbw4cOEBOTg6HDx+2KPf39ycn\nJ4djx47ddYwiIrdjcDAQUK05XDfoxEsREREReeBlZ2eQnh5HdnbJbro7f/58goKCqF27NjExMbRv\n3/6u3t+9ezdDhgzB09OT+fPnM3jwYJYvX867775rfmbmzJmsWrWKIUOGMGfOHBISEti8eXOR9WZk\nZLBz5046depkEWvVqlXp3LkzMTExGI1GGjVqxKeffmrx7qZNmwgKCqJSpUpA/uq9mJgY3nnnHd5+\n+22++eYbQkNDC203Ozubv/zlL+zcuZM33niDefPmce3aNQYPHszly5e5evUqL730Emlpafz973/n\nww8/JDAwkA8++IAvv/zyjr5ZTk4OgwcP5vvvv2f69OmMHz+eDz74gIsXL5qfuZN2evbsya5duyxy\nQ9u3b+f69et069bNfK9Tp0588cUXmEymO4qvpNkXVfjkk0+yfPly81LL8uXLF3hm+vTp/Prrr+zY\nsYPnn38eo9F4VwHcaspicnIyHh4eFveqVKnChQsXiiy/ePEi6enpXL9+3aLc3t4eNzc38/siIsUp\n40YGB8+c4M3+rTmZZE+DBjmaUSYiIiIiD6Ts7Az2729OZmYCLi5eNGsWj719yfwf28aNG1O5cmXO\nnTuHv7//Xb8fERGBn58fc+fOBaBdu3ZUrFiRCRMmMHjwYAwGA2vXrmX06NEMGjQIgJYtW9KhQ4ci\n6927dy85OTk0btzYIlZHR0fc3d3NsT7zzDPMmTOHjIwMDAYDqamp7Nq1yxwP5CedYmJiqF+/PgBu\nbm4MGzaMb7/9lhYtWli0u2PHDo4ePcrq1avN22J5e3vz7LPP8v3331OxYkUeeeQRIiIiqFy5srk/\nX3zxBfHx8QQHB9/2m+3YsYPjx48TExNj7sdjjz1G7969zc/88MMPt22nR48evP/++2zZsoV+/foB\n+UnCNm3amN+5+d2uXbtWYAJUaVFkouy1115j165drFixglWrVjF69GiGDh1q8YytrS0ffPABY8eO\nZdu2bQWWUP5ZWVlZODg4WNxzdHTkxo0b5nJHR8cC5SaTiWvXrpmvCysvSqVKLtjb291r+A+NqlVd\nrR2CSIm723GfYcqg3ZJgEg5WgKQ4IH8j/19+caVOnfsRoUjx0897KWs05qUs0riXO5WZeYTMzIT/\n//sEMjOPUKFCoJWjur2srCy+++47xowZQ3Z2tvl+u3btyM3NJS4uDnd3d3JycmjXrp253MnJiaCg\noCJPhTx79iwA1atXLzKGm8mirVu30rt3bz777DPKly9vMTPOaDSak2QAQUFBODg4sHfv3gKJsgMH\nDuDq6mqxd3zlypXZvn27+frjjz/mxo0bJCUl8eOPP3L06FGys7PveMbW/v37qVixokVi0tvbm1q1\napmvmzRpctt2KleuTJs2bfj000/p168faWlp/O9//+P999+3aO9mvWfPnn3wEmXly5cnJiaGlStX\nsm3bNtzd3Qt9ztHRkcjISFauXElUVBSXL1++58CcnJzIyLCc4mkymcxrgZ2cnAr8oZtMJtzc3HBy\ncjJf3+r9W7l0SSdj3lS1qivJycV7eodIafdnxv2+i/EkpCRA1fLgfgxSGtGgQQ4eHpkkJ9+nQEWK\nkX7eS1mjMS9lkcb9b5QwvD0XF29cXLzMM8pcXLytHdIdSU9PJzc3l9mzZzN79uwC5cnJyeYJNTeX\nQd50q3zHTVeuXMHR0RE7u6In1lSpUoW2bdvy6aef0rt3bzZt2kSXLl0sJvJUrVrV4h0bGxvc3NwK\nzaVcvnyZKlWqFNnmwoULiY6O5sqVK9SqVYumTZtib29/x3uUpaenF/gehcV5J+306tWL0aNHc/Hi\nRb788kucnZ0LzGq7mZcp7tNCi0uRiTLI78DQoUMLzCQrzEsvvUS/fv04derUPQdWrVo1EhISLO6l\npKSY/6CqVatG8h/+BpqSkkKDBg3MybKUlBQaNsw/iS47O5u0tLQCyzVFRO6Vp+sjONg6csPpKvbD\nWrPi8UO09HPTsksREREReSDZ2xto1iyezMwjuLh4l9iyy3t1c7uo0NBQQkJCCpR7eHhw4sQJAFJT\nUy0OL7zdnutubm6YTCZMJlOB1Wt/1LNnT8aNG8eJEyc4ePAgb731lkX5H9vKzc3l0qVLhSbEXF1d\nSU1NLXB/z549eHp6snfvXubNm8fkyZPp3r07rq75ieCWLVsWGeMf+/brr78WuP/7ODdu3HhH7XTo\n0AFXV1e2bt3Kl19+SZcuXcyTmW5KT083t1saFbmZf1GuXr3KgQMH2LFjB4A58+no6IiXl9c9B+bn\n50dCQgKZmb/N8Nq3b595KqCfnx/79+83l2VlZXH06FH8/f2xtbXFx8eHffv2mcsPHjyInZ0djRo1\nuufYRER+78yVn7mRmz+DNdvhEpXrJypJJiIiIiIPNHt7AxUqBD4wSTIAg8GAl5cXp0+fxsfHx/zL\nwcGBOXPmcOHCBZo2bYqjoyNbt241v5ednc2uXbuKrLtGjRoABfY9t7UtmFYJCQnBxcWFqVOnUrt2\nbQICAizKExISLOrZsWMH2dnZBAYWXN7atGlT0tPTLfIfly9fZsiQIezatYsDBw5QvXp1XnjhBXPy\n6siRI6Smpt7xjLLAwECuXLnC7t27zfdOnTplsbXWnbbj6OjIU089xaZNm/j222/p2bNngfZuHhJw\n85uWNredUfZHKSkpzJgxg23btpGTk4ONjQ1Hjx7l448/JjY2lvDwcIu1s39WixYtqFmzJuPHj+f1\n11/nyy+/5NChQ8yYMQOAPn36EB0dzcKFC+nYsSNRUVHUrFnTnM188cUXefvttzEajdSoUYOpU6fS\np0+fQg8kEBG5F+YZZbkm7G9UIjWpARnlUbJMRERERKSEhYWF8dprr2EwGOjYsSOXLl0iIiICW1tb\nGjZsSLly5Rg8eDBLlizB2dmZRo0asWbNGlJSUnjkkUduWW9AQAAODg4cOHDA4rkKFSpw5MgRvv32\nW5o3b46NjY05WRQTE8Nrr71WoK7s7GyGDx/OyJEjuXz5MrNmzaJ9+/b4+fkVeLZDhw40btyYMWPG\nMGbMGCpVqsSSJUvw8PCga9eu2NnZsXbtWubPn0+LFi04efIkCxYswMbGxrx/++20bt2a5s2b8+ab\nbzJu3DhcXFyIiIiw2Dfex8fnjtvp1asXa9eupVatWoXmhw4cOIDBYCi0v6XBXSXKUlNTef755zl7\n9izNmjXj+vXrHD16FIBy5cpx7tw5hgwZwtq1a+/69Ms/srOzIyoqiokTJ9K7d28eeeQR5s+fj6en\nJwCenp5ERkYSHh7OokWL8PPzIyoqypzN7datG2fPnmXKlCmYTCY6duzI+PHj7ykmEZHCmGeUXS9P\n9pJd9J9RW6deioiIiIhYQUhICFFRUSxYsIDY2FgMBgOtWrVi3LhxlCtXDoBRo0bh7OzM6tWrSU9P\np1OnTjz33HPs2bPnlvXerGfXrl0Ws6SGDRvG5MmTGTJkCJ9//rl5s/927doRExPD008/XaCu+vXr\n89RTT/G3v/0NGxsbevTowbhx4wpt18HBgejoaP7xj38wc+ZMcnNzefzxx/noo49wdXWld+/e/Pjj\nj6xdu5alS5dSq1YtBg8ezMmTJy1W2RXFxsaGhQsXMnPmTGbMmIG9vT2vvPIK27ZtMz9zN+34+/tT\noUIFevTogY2NTYH2du3aRfv27Qsc4Fha2OTd6Vw8YMqUKaxbt44FCxbQoUMH5s+fz4IFCzh27BgA\ncXFxvPrqq4SEhBAREXHfgr6ftMHlb7Thp5RFf2bcZ9zIoPMn7Un83g2Wxpnvb958lYCA3OIOUaTY\n6ee9lDUa81IWadz/Rpv5y58VFxfHsGHD+PrrrzHc5l/Ep0yZwvHjx1mzZo3F/fHjx/P999/zn//8\n536GalXfffcdffv25fPPP+exxx6zKEtJSaF9+/Z88sknpXZrrLuaUbZ9+3Y6duxIhw4dCi0PDAyk\nU6dOd5y1FBF5GBgcDHzedwcHW57gzR3ZnEyyp3btHDw9lSQTEREREXlYBAYGEhAQwMcff3zLAw//\n+c9/cuzYMdatW8ecOXNKOELrOnz4MDt27OBf//oX7du3L5AkA1i1ahUhISGlNkkGd7mZ/6VLl6hd\nu3aRz1SrVq3QExlERB5mBgcDbeo0Y+OGLGrXzuX0aTt693YhI8PakYmIiIiISHGZPn06a9euveUp\nmd9//z2xsbEMGDCALl26lHB01pWVlcXy5cupWLEiU6ZMKVD+yy+/sGnTJt55552SD+4u3NWMsurV\nq5v3JLuV7777zrwmV0SkrDlzxpbTp/P/DSIx0Y7jx221/FJERERE5CFRs2ZNtm/ffsvyKVOmFJok\nuum99967D1GVDi1atLA4nfOPPDw8ivx2pcVdzSjr3Lkzu3fvZu3atYWWL1++nH379vHkk08WS3Ai\nIg+SjBsZZFXeS7362QA0aJCD0agkmYiIiIiIyIPirjbzz8jI4IUXXiApKYn69euTm5vLqVOn6Nmz\nJ0eOHCEpKYlHHnmETz75hAoVKtzPuO8bbXD5G234KWXRnx335g39005Qr5w/7zfehr+3k069lAeC\nft5LWaMxL2WRxv1vtJm/iBTlrmaUGQwG1qxZQ79+/Th79iwnT54kLy+PjRs38tNPP9GzZ0/WrFnz\nwCbJRET+rIO/7Cfx4lk404KTaYmUe+w7JclEREREREQeMHc1o+z3cnJy+OGHH0hPT8fFxYW6devi\n6OhY3PGVOP0ry2/0r05SFv2ZcZ9xI4MOKzvx0+x1kNIIO49EvvnSljpVPe5TlCLFSz/vpazRmJey\nSOP+N5pRJiJFuavN/H/Pzs6O+vXrF2csIiIPpIO/7Oenky6Qkn/Ecc4vDei95Fm+ejMSg4OmlYmI\niIiIiDwo7jpRdvLkSf71r39x9uxZTCYThU1Is7GxITIyslgCFBF5IFQ9Au7H8pNl7sc4W24Lx1OP\nEVCtubUjExERERERkTt0V4myb7/9lldf/X/s3Xl8U1X6+PFPm6RrShe6QDeW7lShtAKCUMACFVBB\nEH4zbjAKKogIIzqMznwZ1MFdGEVcQB3AZWQTEUR2EJEdi4JtaUvpQiHdl7SlTdr+/kiTJk3aJjQp\nrZz36+VL7pJ7zk1u0twnz3nOLFQqlckAmZadnV27OyYIgtBVhHlGIHWqQT17EOTdBg3QxyOECK+o\nG901QRAEQRAEQRCEFjU0NIgYTjMWFfN/9913UavVLFiwgK1bt7J371727dtn9N/evXtt1V9BEIRO\nJ7ciG3WDWrOw4wNYdxD71aehRgy7FARBEARBEARL5eXl8ac//Ylbb72VSZMm8d577zFw4EDd9oiI\nCD755BMAtmzZQkREBMXFxe1qc/Hixdx9991t7qdQKEhISKC0tJTc3FwiIiL44YcfzG5HpVKxaNEi\nYrJJF5EAACAASURBVGJiGDRoEN988w0RERH89ttv7en+ddm7dy9Llizp8HZbYu5roNX8+T9w4AAz\nZsxodz8syig7d+4cEyZM4Iknnmh3w4IgCH8UgW7ByOwdUBVE6+qUZaRLSU21Jy6u/gb3ThAEQRAE\nQRC6lnXr1pGcnMzy5cvp0aMH3t7ejBw58kZ3C4AlS5bw4IMP4uHhgYuLC19//TW9e/c2+/GHDx/m\nu+++49lnn2XgwIGo1WrbdbYNa9euxcXF5Ya1b22jR4/m008/ZcOGDUyfPv26j2NRRpmjoyM+Pj7X\n3ZggCMIfUW5FNqr62qY6ZUBYWB0RESJIJgiCoKVUKTmtOIlSpbzRXREEQRA6ubKyMgIDAxkzZgy3\n3HILPXr0oH///je6W5w8eZKTJ0/ywAMPAODg4EBMTAweHh5mH6OsrAyA+++/n0GDBmFvb1FYRmjD\nrFmz+M9//kNtbe11H8OiV2T48OH89NNP1NXVXXeDgiAIfzTajDIcK5E+cQdffJPDrl1VyMXIS0EQ\nBEATJEvcOIrxmxNI3DhKBMsEQRCEFt15551s2bKF9PR0IiIi2LJli9HQy7YcOXKEadOm0b9/f+Lj\n4/nPf/5jEMdQq9W89dZb3HHHHcTGxvLqq6+aFef49NNPufPOO3FycgKMh/4tXryY+fPns3btWkaP\nHk3//v15+OGHycjI0G1fvHgxAEOHDtX9W5+p4Yd79+4lIiKC3Nxcs8/xzjvvZPXq1SxZsoTBgwcT\nGxvL3/72N5RKzd/ghx9+mBMnTnDw4EGjY+uLiIhg06ZNPP3008TExDB8+HC+/PJLFAoFjz/+ODEx\nMSQmJnLo0CGDx+3Zs4epU6cSExPDyJEjWbFihUH2nLmvwbp16xg3bhy33HILEydO5Pvvv2/h1dG4\n4447UKvVbN26tdX9WmNRoOz555+nqqqKBQsWcPr0aYqLi1EqlSb/EwRBuFnoMsoAtawEr9A0ESQT\nBEHQk1qcTFrpBQDSSi+QWpx8g3skCIIgtEWpVnO8vBxlBw8NXLlyJSNHjiQoKIivv/6aUaNGWfT4\no0ePMnv2bAIDA1m5ciWPPfYYn332Ga+88opun2XLlrF+/Xpmz57NO++8Q0pKCjt37mz1uEqlkkOH\nDjFu3LhW9/v555/ZunUrL774Im+++SZZWVm6gNjcuXOZM2cOAGvWrGHu3LkWnZsl5wjw0UcfUV5e\nzjvvvMOCBQvYsWMHH3zwAaAZQtqvXz9iY2P5+uuv8fX1bbG9V199lV69evHBBx8wcOBAXn75ZWbO\nnElsbCyrVq3Czc2N5557jurqagC+/vpr5s2bR//+/Vm5ciUPPfQQn376qUFg0JzXYOXKlbz++utM\nmDCBDz/8kGHDhvHXv/611ddKKpVy5513smPHDoufV90xLNn5gQceoKqqij179rRasN/Ozo7ff//9\nujslCILQlUR4RRHmEU5a6QXCPMLFbJeCIAjNiM9JQRCErkWpVjPozBlSqqqIdHHhZGwscqlF4YPr\n1q9fP7y8vMjLyyMmJsbix69YsYIBAwawfPlyAOLj43F3d+fvf/87jz32GHK5nP/9738sWLCAmTNn\nAprsrtGjR7d63FOnTlFXV0e/fv1a3a+yspKPPvpIF3hSKBT8+9//pqSkhODgYIKDgwGIjo7Gy8uL\nK1euWP0cAwMDAejRowfvvPMOdnZ2DB8+nBMnTvDjjz/y3HPPERoailwux8XFpc3neeDAgSxatAgA\nPz8/du/eTUxMDE8++SSgiQHNnDmTS5cuER4ezooVK5g4caJuooDhw4fj5ubGkiVLmDVrFj169Gjz\nNSgvL+fjjz9m1qxZLFiwQHecyspK3n77bcaPH99if/v168f27dupra3FwcHB4ufXoivd39/f4gYE\nQRD+6OQyObumHSS1OJkIryjkMpFOJgiCoE98TgqCIHQt56uqSKmqAiClqorzVVUM6dbtBveqbdXV\n1fz6668sXLjQYJhffHw89fX1HD9+HG9vb+rq6oiPj9dtd3R0ZOTIka3OPHn58mVAE3xqjb+/v0F2\nlnb/6upqPD09r+u89JlzjtpA2a233oqdnZ1BX5KTLc/q1q8P5+3tDcAtt9yiW6et0VZeXs7Fixcp\nLi7mrrvuMjiGNnB26tQpgoKC2nwNkpKSqKmpYdSoUUbnuXnzZnJycgzOTZ+/vz+1tbUUFhZeVxzL\nokDZ+vXrLW5AEAThZiCXyYnwiiIp/wwAMb6x4kZQEARBj1wmJ85v0I3uhiAIgmCGaBcXIl1cdBll\n0V1kZsTy8nLq6+t5++23efvtt422FxQU6DKMmgettAGgllRUVODg4IBEIml1P2dnZ4NlbbH++nrr\nTPRlzjm21Bc7OzsaGhosbtPV1dVoXfNja2knK+jevbvBejc3NxwcHFAqlZSXlwOtvwalpaUA/OlP\nfzLZTkFBQYvDRbV9q6ioMLm9LR2TOykIgvAHp1QpGf2/YWRVXAIgxCOUPdN+FMEyQRAEQRAEocuR\nS6WcjI3lfFUV0S4uHTbssr20AZ05c+aQkJBgtN3X15cLFzQ1M4uLi/Hz89Nt0wZmWuLh4UFtbe11\nD+czl52dnVFQrbKyUvdvc87xRtJmlxUVFRmsLy8vp7a2Fg8PD90+rb0Gbm5uALz//vsG+2j16dOn\nxddMG6yzZDZSfa1e7a+++iojRoxg+PDhumVz2NnZmZy9QRAE4Y/qaN4RXZAMIKM0ndTiZJE9IQiC\nIAiCIHRJcqm0Swy31CeXy4mMjCQnJ4dbb71Vtz4lJYXXX3+dBQsWMHDgQBwcHNi9ezdRUZqamWq1\nmiNHjuDSSuZcz549Abh69aquzpgtuLq6UlRURH19vS4b7fTp07rt5pyjqcCSKdrjW1OfPn3w9PTk\nhx9+MJj4QDtbZWxsLP7+/m2+BgMGDEAmk1FUVMSYMWN0x9myZQu7d+/mrbfearEPCoUCBweHNrME\nW9JqoGzt2rW4ubnpAmVr164166AiUCYIws0mpzy7aaHGFY/yEQQ6tl7oUxAEQRAEQRAE65o/fz5P\nPfUUcrmcsWPHUlJSwooVK7C3tyc8PBxnZ2cee+wxVq9ejZOTE1FRUXz11VcUFha2GgCLi4tDJpPx\nyy+/2DRQFh8fz/r161m6dCkTJkzg2LFjRpMptnWO5urWrRvJyckcP36cAQMG4OTk1O7+SyQS5s2b\nx8svv4y7uzsJCQmkpqby3nvvcdddd+n619Zr4OXlxcMPP8xrr71GWVkZ/fv3JyUlheXLl5OQkIBc\nLm8xoywpKYkhQ4a0OUy2Ja0GytatW0dAQIDBsiAIgmBsYsi9/PPIYlTVDrD6JKWFUUzZXceuXVXI\nxehLQRAEQRAEQegQCQkJrFq1ivfff58tW7Ygl8sZNmwYixYt0tWueuaZZ3BycuKLL76gvLyccePG\nMX36dI4dO9bicbXHOXLkCJMmTbJZ/+Pj41m4cCGff/45W7duZejQobz22mvMnj3bonM0x8yZM1m4\ncCGzZs1i7dq1xMbGWuUcHnroIZycnPj000/ZuHEjvr6+/OUvf2Hu3Lm6fcx5DZ577jm8vLzYsGED\n7777Lr6+vsyYMYN58+a12LZKpeL48eMsXLjwuvtv13A9ldz+wAoKrq/Y2x+Rj4+beD6Em057rntF\nlYJPdiaxYs79unU7d1YSF2edwp2CYCvi81642YhrXrgZieu+iY+P243ugtBFHT9+nCeeeIKffvoJ\nufg1vFPavXs3L730Evv27cPR0fG6jmH9AamCIAg3KT8XP+YnJhIWVgdAWFgdEREiSCYIggCgVMLp\n0/YolTe6J4IgCIJwfYYMGUJcXBxffvnlje6K0ILPPvuMOXPmXHeQDNoYejl48ODrOqidnR3Hjx+/\nrscKgiB0ZXI57NpVRWqqPRER9WLYZSelVClJyj8DQIxvrJidVBBsTKmExEQX0tIkhIWJYemCIAhC\n1/Xyyy/z0EMPMX369OueVVGwjb179yKVSnnggQfadZxWA2UilVAQBME8SpWS1OJkIryikMvluuGW\nButFMKZTUKqUjN0QT0ZZOgAhHqHsmfajeH0EwYZSU+1JS9MU1E1Lk5Caai+GpQuCIAhdkr+/P/v3\n77/R3RBMGDNmjMEMmder1UCZNV58pVJJeXk5/v7+7T6WIAhCZ6RUKUncOIq00guEeYSza9pB5DJ5\ni+uFGyu1OFkXJAPIKE0ntTiZOL9BN7BXgvDHFhFRT0hIHRkZEkJCxLB0QRAEQRA6L5vXKPvvf/9L\nQkKCrZsRBEG4YVKLk0krvQA1rqSd8yAp94LheiCt9AKpxck3sptCowivKELcQ3XLIR6hRHhF3cAe\nCYIgCIIgCILQWXT6Yv5lZWUsWrSIwYMHM2LECN566y3q6jSFsi9fvsyjjz5KTEwM48eP59ChQwaP\nPXbsGPfccw8DBgzg4YcfJisr60acgiAIf3ARXlGEOMfA6pOw5jjPPXgHSqVmfZhHOABhHuEiGNNJ\nyGVy9kz/kS2TtrNl0nYx7FIQOkBSkj0ZGZqhlxkZmqGXgiAIgiAInVGn/5aydOlSFAoFn3/+OW++\n+SZbt27ls88+o6Ghgblz5+Lh4cGmTZu47777mD9/Pjk5OQBcuXKFOXPmcO+997J582a8vb2ZO3cu\n9fUi1V8QBOuSy+S82W8PFGoCYRnpUpLO1yCXydkyeQfLR69ky+QdIhjTichlcoYHxDM8IF68LoJg\nY0olPLuoaeYpmc9FAkMqbmCPBEEQBEEQWtbpA2WHDh1ixowZhIeHc/vtt3P33Xdz7Ngxjh07RmZm\nJi+99BKhoaE8/vjjDBw4kE2bNgGwYcMGIiMjmT17NqGhoSxbtowrV65w7NixG3xGgiD8EcVEOxIS\nqtYseCfz9K/DySy7yJStE1l4YB5Ttk5EqVLe2E4KBpQqJacVJ8XrIgg2lppqT+bFprK4qgmPklvz\n+w3skSAIgiAIQss6faDMw8ODbdu2UV1djUKh4PDhw0RHR3P27Fn69etnMDNnXFwcSUlJAJw9e5ZB\ng5oKMzs7OxMdHc0vv/zS4ecgCMJNwFHJ7Pc+hVlDYPYgLqtSueebRFGjrJPSTrQwfnMCiRtHiWCZ\nINhQRES9wQ8JIf3KxFB0QRAEQRA6rU4fKFuyZAknTpwgNjaW+Ph4vL29efrppykoKMDX19dg3+7d\nu3P16lWAFrcrFIoO67sgCDcHbdBl8fEnkASdAcdKAPKrFAS5BQOiRllnIyZaEATb02Zt4qhkz+5q\ntnxXyJYd+ex56Hsx5FkQBEEQhE5L2vYuN1Z2djb9+vXjqaeeQqlU8vLLL/P6669TXV2NTCYz2NfB\nwQGVSgVAdXU1Dg4ORttra2tbbc/T0wWpVGLdk+jCfHzcbnQXBKHDWXrdX8z9XRd0qWtQ4+fqh6JS\nQaR3JAdmHCCrNIto32jkDuLGsLOIce5HL/deZJVlEekdyfDwwTf96yM+7wVrUtYqiV99JymFKUR6\nR3Jy9knu6+MNjLzRXdMR17xwMxLXvSAIQts6daAsOzubZcuWsX//fnr06AGAo6Mjjz76KNOmTUOp\nNBwqU1tbi5OTk26/5kGx2tpaPDw8Wm2zpKTKimfQtfn4uFFQIIrtCpZTqpSkFicT4RXV5bIGrue6\n97UPJsQ9lIyydABcpK5smbSdGN9YJNWu9HXsR3VZA9WI91NnoKhSMGFzAjkV2QTJg9h493c3/esj\nPu8FazutOElKYQoAKYUp7Pn9EM5S507zd0Fc88LNSFz3TUTAUBCE1nTqoZfnzp3Dzc1NFyQDuOWW\nW6irq8PHx4eCggKD/QsLC/Hx8QHAz8+v1e2CINiGokrByP/dflPVfpLL5Lw5aoVuObPsom690Lko\nVUombLqTnIpsAHKUOeQ2/lsQBOuJ8IoizCMcgBD3UJ47tIDxmxMYuyGeny7/eFP8bRAEQRAEoWvq\n1IEyX19fysvLyc/P163LyMgAoG/fvqSkpFBV1ZQBdvr0aWJiYgAYMGAAZ86c0W2rrq7m999/120X\nBMH6mgchbqbaTzG+sYS4h+qWnzu0QNwIdkKpxcnkKHN0ywHyQFE7ThBsQC6Ts2vaQXZO3cebo1aQ\nUarJuM0oS2fKt3ffND+kCIIgCILQ9XTqQFlMTAzh4eE8//zzpKSkkJSUxD//+U8mTZpEYmIi/v7+\nLF68mLS0ND7++GPOnj3LtGnTAJg6dSpnz57lgw8+ID09nRdffBF/f3+GDh16g89KEP64buYgRPOs\nsozSdFKLk1Eq4fRpe5TifrBTiPCKMghoyuxlrewtCEJ7yGVy4vwGEeMbq8su07qZfkgRBEEQBKFr\nsShQtnXrVlJSUlrd5/Tp07z//vu65cGDB/PUU09dV+ekUikff/wx7u7uzJgxg3nz5jF48GBeeukl\nJBIJq1atori4mClTpvDtt9+ycuVKAgMDAQgMDOS9997j22+/ZerUqRQWFrJq1Srs7Tt1bFAQurQI\nryj6dOurW3aQOLSy9x9PgEMkvsX3Qo0rYR7hBDr2IzHRhfHjXUlMdBHBsk5ALpPz0vBXdcuXyjM5\nmnfkBvZIELou7ayWbWWGabPLvhi7k4DSqbrPyJvlhxRBEARBELoWu4aGhgZzd46MjOTpp59uNfD1\n2muv8dVXX3H27FmrdLCjiQKXTUTBT8FSSpWSEV8N5rIyV7du59R9xPkNuoG9ssz1XveK0kpih1ei\nyg9B6pvGkQP2FGf3YPx4V90+O3dWEhdXb83uGunKEyl0lPXn1/Lsoad1yz1de3LkgdM39fMlPu+N\nbSgqYPHVbKqBAQ5OvB3Ym2hn1zYfdz22lRTxbN4lKoEQqQPLA3tzm6ttC00frijjNUUei/38GeHm\nbvHjlSoliRtHkVZ6gTCPcHZNO9jqe0iphMREF9LSJHgGKtiyPZ9o/97tOIP2Ede8cDMS130TUcxf\nEITWtDrr5ZYtW9i/f7/Buh07dpCcbDpVXqVScfz48TZnlhQE4Y8ptTjZIEgW5BZ802QM7D2Ziyr/\nNgDU+WH8nHSKSUN9CQurIy1NQlhYHRERtg+SWXLjejNSVClYdGi+wborlVdILU7uUgFdwbY2FBUw\n72rTJA9naq8x+mIKK3sEM727dScF2lZSxKy8S7rlVHUtEy5dYEn3HjzVI8CqbWkdrihjaramZtjU\n7HSe9fTmb/69LDpGanEyaaUXgKZhlK29h1JT7UlLkwBQkutHwvtTOPr8Kvq4923xMYIgCIIgCDdC\nq4GyESNG8Morr+gK5tvZ2XHx4kUuXrzY4mMcHByYP39+i9sFQfjj8nLqjtReirpejcROyqZ7t90U\ngRqlSolv70Jkvhmo8kOQ+WYwZlAgcjns2lVFaqo9ERH1yG38VFh643oz2pGxjQYME6mD3XrdNAHd\nrqwjsyX/nX/Z5Pp5V7Pp6+Rk1WyvVxSm21padJUwZxfGuXtarS2tf1w2nOn17ZJCopzl3OvZ3exj\naGe11Abm23oPRUTU4xtcTH62F3gnU+99lnu+SeTYg7/cFH8nBEEQBEHoOloNlPn4+LB3716qq6tp\naGhgzJgxzJgxg0ceecRoXzs7O6RSKZ6enshkojiyINxslColU769G3W9GoC6BjXF14r+8NkC+llc\nff7anyf8VzHx9hD8PDRDtORybD7cUsvSG9ebUVC3YKN1D/WbKW7UOzn991mQPIjv79+Pn4ufzdp7\n0TfAIKNM3zv5V/myj/UCZf/wCzDIKNP3b8VlmwTK/BwdSK6qNVj3iuKyRYEybd2x1oKXSiUGPxR8\nt7OEoSvuod77LDhWkl9VKQL6giAIgiB0Oq0GygC8vLx0/3711VeJiooiIMA2QwEEQei6kvLPGAy7\nlNpJCXQzDkr80ehncWVe+5UBA2twdW3gtOJkh9cJM+fG9WY31P8OPB08Kakt0a1zlDjewB4J5tB/\nn+Uoc5iwOYFDfzpms2u8XK1GCqhNbJvj7WvVtsrUapyBahPbXvSzzfetJT0COXjRcHKmf1i5rcP5\nFTy0J5/qD3oTUi9nz+5q+vj4cvT5VdzzTSL5VZUioC8IgiAIQqfUZqBM33333QdAQ0MDp06dIiUl\nherqajw9PQkNDWXgwIE26aQgCF2PukFNbkW2TbM+OoNAt2Bk9g6o6muR2Tvg5dRd1Am7Dh01rE4u\nk7Nl8g5GbximWzfIb/ANCWwK5ovwiiJIHkSOMgeAnIpsm2UirVFc4YXCPN2yK1Cpt91FYtFXp1at\nL1DwbH6uwbpIiYxrwCs9g2ySTQYQ7ezKgb6R/D03m6y6Gl72C7Iomww079mxG+PJKE0nxCOUPdN+\n1L1/TlVWMDX/AsQAHyaR8WQMSefVDB/iiI+LLx+O/QSAGN9Y8Z4TBEEQBKHTsfjb3q+//srzzz9P\nVlYWoAmagWboZa9evXjzzTe59dZbrdtLQRA6vRjfWHp1601W+SUAQjxCb4pMgdyKbFT1miFMqvpa\nfs776YbVCeuqxfw7ut/X6gxzd+799i7U9eou9ZzdaB09u6pcJmfTpO+446vbUNerkdk72Cxj9bXC\nKwbLlUCQzIEcVS1hDk5EODpZra1lBXlG657w9edBL2+rtdGSaGdXtoVd/2d0Uv4ZMko1EwJklKaT\nlH+G4QHxgGZ4KnaNO9oBs8+DrxqlKlzzXldcJqh6PN/PjUMu5n8SBEEQBKGTsbdk50uXLvHoo4+S\nlZXFuHHj+Pvf/86KFSt46aWXmDhxIrm5ucyaNYucnBxb9VcQhE5MaieFGld8iu7my7E/3BQBB01G\nmaYuo8xexjD/4YR5hAN0+LAiU8X8u4Lm/U7KP2PT9rTZSVraunpd6Tm7kbSBzfGbE0jcOAqlStkh\n7RZfK9K9Vqr6WnIrTNcQa6/F3j0Nln0kUl71CyTMTop9QwO/VFnvfF/w8TdYlgDBMhn3piUzICWJ\nbSVFVmvLlMyaah7MvEB08i9sKCqw6LHValODRTX+6tujaaGhAT/HZcQEhmve64rLsPokOSs2MiHR\nDWXHXD6CIAiCIAhmsyhQtnLlSqqrq/noo4/4z3/+wyOPPMJdd93F9OnTeeutt1i1ahUVFRV89NFH\ntuqvIAidVGpxMhn5V2D1SQre+47JE7w7xQ2QUqXktOKkzW7mfy1IQlWvAkBVryK9NI1d0w6yc+o+\ntkzeQWpxcocFEiK8oghxDwUgxL3rZPRFeEXRp1vTpA/PHpxv8+fstZHvECAPNFhnyyylP5Kk3Auk\nnfOAGtcODS5qJ6sA2wahZ/n1ZJm3P27AHHdvPgzozUO5F0lrUJOqqmFqdjqHK8qs0tbDPn687RuI\nJzDdzYMNwaFMzU7nWG0VV+rqmJV3yWbBssyaaoak/86eqgoK6uuZdzXb7GCZUqVk8aFnDdY52Tdl\n2t3m6sbmwCCcy5Lg1JPI6zWB8EC3YHwrE6BQ89rlZLqSdL7GSmckCIIgCIJgHRYFyo4ePcro0aOJ\nj483uT0+Pp4777yTn376ySqdEwSh64jwiqJH5VjdDdCVLHcOnL56Q/vUEZkvOeWGWS3nLxXy7QYP\nvNTRTN46nvGbExi7Mb7DgmUGw526kCp1le7fmWUXbZZVpr0mHtwxDamdlG6ybrpttsxSak5RpeCL\n5HUoqhQd0p61KJXw7ANDYc1xWH2SEOeYDgvIauvLLR+9ki2Td9g0Y3WWX08youNYGtiLDwrzjbb/\n++plq7V1n5c3X/aJ5LWA3mwoLTba/orCem3p+6rEuK1/55vXVmpxMjlKw/fKA9/fb/A553Iti+qk\nhVB1QTc0c8rWieS77kPik6HZqXsqz/0+tuM+HwVBEARBEMxgUaCsrKyMoKCgVvcJCgqiuNj4y5cg\nCF2XOVlZcpmcuwb3Bu/G7BLvZE7X/7dD+teSjhiKODo4oWmhwpc3HnichQudGTbIm4yccqCpfo+t\npRYnG9QM6irDCJPyz6Co6pigqv41kVVxiXJVuW5bT9eeHRL0UVQpiF0XzcID84hdF92lgmVJ52vI\nzHDQLBRG8VL4tg4bYq1UKZmydSILD8xjytaJNgmuKOvq+OflS4SeP8MahaZWmcEwwkbTLCx835L3\nr14mNCWJ8ZkpJF5MYYaJ2mTWno1S68+eXkbrXvQ1ry1TmZelNaW6z7kNRQU8VGiP061vgUMPXSag\n9r1X15iFCw1klKZ1mc8qQRAEQRBuDhYFynr27Mkvv/zS6j6//PILvr7WnTpdEIQbx9ysLKVKyZ6r\nm2D2IJg1BGYPYtqtd3dwbw11xFCt4mt6w6LSJqJWaT5W69QSSJto9fZa01FD0zqCp6PxTbw16D9H\nzd3e444OCfrszdplMAHE3qxdNm/TWq647DEIhl/zOtVhbZsMfCuVSE+fxBrjvJV1ddyWksRHpUWU\n08ALhXmsUVzRDCMMDsWxMU0zQCrj/3m2v9j+GsUVlhZdpb5xOa32GnZ29hzoG8ntDi70lEhY49/b\n4tkozdXH0Znjof0Y6+KGj709K3sEM727j1mPPXHlaIvbNhQVMO9qNkXANa84GPolH0/aTYxvrOa9\nVxANRZGanYsiCaoe36U/qwRBEARB+OOxKFA2duxYzp49y3vvvWe0TaVS8c4773D27FnGjRtntQ4K\ngnBjmZuVlZR/hsvKXHCshMAT4FhpNLtgR5PL5DavF6Yp5t+YYRP2A0ga6+1IavC69TigqRcW4xtr\n1XZb8vrId9gyaXuXmr2xea0wgG/Tt9ikLe018UniOuM2L37TIdldw/yHt7rcWSlVSv55Yp5BMPxi\n1dkOa795IDjSMRjPxFF4jk/AM3FUu4NlqTXXaJ4Pr50Bc4SbO9/0DqOfvQP19fXsLy9tV1v6x9ay\nByIcnYh2duU/wb25xdGFv1tQN+x69HF05p3A3ox07cY/FLmsLzDv+j+WZzpQ5unoZWL4ph0by8qg\nRs6//I+wdOBH9OmrmZQhqE8l3899r8t8VgmCIAiCcHOQWrLz3Llz2b9/P6tWrWLr1q3ExcXhwZ7F\niwAAIABJREFU5uaGQqHgt99+Q6FQ0KdPH+bMmWOr/gqC0MG0gSBVfa1Fxc79XQNueJaAUqUktTiZ\nQLdgJn8znoyydELcQ9kz/UeDGzPtfhFeUfjgZlEbmmL+muwg3K5g/9e+1KcmIonYw86Z2ym+VkSE\nV5TNbwS1mX9ppRcIkgfx/f37u8zN54HsfUbrJoVOsVl7cpmcgirj4EN9Qx17s3bxYNQjNmsbmmUh\nNi73ce/bwt6dR2pxMsU1xeCIJhgONHRg+9ogp/a96v5rMtI0TRBfmnYBaWoy6rhB1338CEcnvMAg\nWKadAfN8dSUTLl3QrZ+Vd4k10K5sr8XePXmhME+3/M/uPZBLJLoi+1rzrmpqgZmb7WUJhaqWWy/8\nplt+Nj8X0Ewy0JqWrtcvk9fzYr9Fuj4D0NDAdwcWsvO9XWRedAO86RlcyRcbyxga54Bc7tru8xAE\nQRAEQbAmizLK5HI5//vf/7jvvvsoKipi27ZtfPHFF+zdu5fS0lKmTJnCl19+iZubZTeagiB0XrkV\n2QbDxFoqdh7jG2swc6Gj1LFD+tcSpUrJ2I3xjN+cwLiNI8koa6zdVZbO0bwjBvsZDC2tNT8rRVGl\n4JEdf9Yty+xl7PvLJpY/G0fSUwfo496XQLdgvk3fYvNMJf3MvxxlDhM2J3SZAtlB3YyDryU1tq11\n6ebQzeT6YHkvm7YL4OXUHamd5ncqmb2sy8y0GeEVhZ+zYb2uEI+QDu2DXCYnzm8QcpkcdUQU6jBN\nhpk6LBx1hGWB+ea1F+USCaciY3jCozvdsGOZtz+z/DSBsg9NFPRflHeJDUUFhJw/TcD508RfOMep\nygqz29fOrqlt66kemvpgpors//1qNttKigg7fxr/86cZkfqbRW21ZG9FudG6l/Nz2V1WQj8TbWmf\ns/OFvxk9DsDd0YPp3X1Y2SMYd4CsX+GnWeScV5J5sem32SvZriw+8iQ4KjlcUcbAxrZuSzlrtRlF\nBaGzsPXs24IgCIL1WRQoA/Dw8GDZsmWcPHmSbdu28eWXX/Ltt99y8uRJli1bhqenpy36KQjCDaI/\n3ClIHtTiTb1cJucfQ5fqljPLLrZZoNmWXx6T8s/oCttfqcwz2Pb8oYW6NpsPLT2ff97sNvZm7aIO\ntW5ZVa+ipKaYB6Mewc/Fr0OLtkd4RRkMYcypyO4yBbL7+8ToAkdazx1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2U+QqVhLu3F9g2zFKvyanJRrL7JaFsx+F2nUh35TMqf2P8oVBoVqyJjZZOK83pMCkxB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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_ratio('CODtot_line2',\n", " 'CODsol_line2',avg,\n", @@ -1147,46 +793,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", - " from pandas.core import datetools\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "slope: 0.405512924986 intercept: 0 R2: 0.973774656376\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " )" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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pyGQyLl36By+vWTRvbkrnzl24dOkfrWCkUinR0Xmy3427eTOPhIR4LCzuP+1N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obE5mFXN4J6A6r9DAbTQrTaN+jU7Rz2SinbEf0z0eoVKspNRA1Od63Mmjh276VApFcI7Gxm7dq9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\n7rv06NGD1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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.get_correlation('CODtot_line2', 'CODsol_line2', [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", " zero_intercept=True, plot=True)" @@ -1201,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.016129", @@ -1209,26 +818,7 @@ }, "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:569: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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z0ksvMXTo0PIO5W8zYsQI7r77biZPnlzeodw05b40tTTOnTvHyJEjcXNz4+mn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\n2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R8ItLpS0+1Mi54BEUwPhFRaFeth0vih8Wj5YVS3VHF9HAAlBhISAk/1nMMUyTiEkFvK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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_correlation('CODtot_line2',\n", " 'CODsol_line2',\n", @@ -1247,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:06.731819", @@ -1255,26 +845,7 @@ }, "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_OnlineSensorBased.py:961: UserWarning: When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.\n", - " 'ensures the proper working of the package algorithms.')\n" - ] - }, - { - "data": { - "image/png": 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z0ksvMXTo0PIO5W8zYsQI7r77biZPnlzeodw05b40tTTOnTvHyJEjcXNz4+mn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jgZp/1dflSVZSJxTTDhCWDx4sU4efIkPvnkE1y5cgXHjx/HwoULkZeXh4CAABAEgTfffBPfffcd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\nznLNNGx7CyYxob1sTGi6lvr6XgCfT0Am4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qFWlGxMc7gy0PHIzZ2IS5fG2wzGVVdb/x2j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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.fill_missing_daybefore('CODtot_line2',\n", " [dt.datetime(2013,1,25),dt.datetime(2013,1,27)],\n", @@ -1284,25 +855,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", "start_time": "2017-05-09T11:55:06.734413+02:00" } }, - "outputs": [ - { - "data": { - "image/png": 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txnAr3zMvL++2vv+D5r5JlBWeRAGwcuXKmz7foUOHYtf2Fho8eHCRNcUiIiIi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\n57C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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = dataset.plot_analysed('CODtot_line2')\n", "ax.legend(bbox_to_anchor=(1.3,1.0),fontsize=18)\n", @@ -1338,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", @@ -1346,18 +906,7 @@ }, "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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KwocOHdL999+v4uJiDRo0SD179lRISIhOnz6trVu36sMPP9TYsWP17rvvKjw8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\nkksuqff50PSYqc97e3urd+/eNm1ff/2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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.calc_daily_average('CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,2,1)],plot=True)" ] @@ -1371,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", @@ -1402,30 +951,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RDCIiInITKMEgIiLihDaeW8+rm14EwGhWgkFERESKnxIMIiIiTuhA9H72Ru2hpn8tOlTq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BAqryyf/ItYsIi2oF3xjvwZZ5hYr4NpZ5YMXv5CgZk1B1cw+fA3xKpjGFVtNKM7jqfrz+25E+HH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\nRK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from scipy import signal\n", "data = dataset.data['CODtot_line3'][:].copy()\n", @@ -1445,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1474,52 +1002,18 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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LkoS3vj6AXw4V6p0UIopaGt8jOXsWqSVyjvp0cTU27ynAko/36p0UoohQXF6Ltd8fRm1d\ng95JCTnterBoVEetMDmsoyYPoQzUwRY3SVZ24yLS0muf7kX+mUrExcXg5jG99E5OdOHsWRSoGkuj\n5tv0F/xFzsUTRbKSiloAQFVNvc4pCa/g+kBzCFGKQnI5bZHryonIeHS7o3jvSK0IAzUREZEXazcc\nxpa9BW5L2Y+adDD/Pz/qnQQPHFaUiDTlnINVeHv5dsdxAMCoizppnx6FmKMm1Dc04mhBpa5pUFpH\nzdBNRPpTOYRokHtjoCb876eTeidBFuuoiUhMwYVeS526BrsM1ARzrXFaezJ0E1Gg9Jrm0n2t5V/k\nuLz2F/YVBerdu3djypQpAIBjx45h0qRJmDx5Mp555hlYrVYAwNq1a3HzzTdj4sSJ2LBhAwCgtrYW\n06dPx+TJk3H//fejpKREye4iRlF5Df7yxg78eqJM76T4tG3fGb2TQERkOIEOIbrrcBEA5Q8OfgP1\nihUr8Je//AUWiwUA8MILL2DGjBlYvXo1JEnC+vXrUVhYiJUrV2LNmjV48803sXjxYtTV1eH9999H\nZmYmVq9ejRtvvBFLly5VmKzI8NW2YzhdZMbSz/bpnRSvjhZUoKyqzvF6YO+2WDbrCh1TREQUIpoN\n2KDNdpQWoPsN1N26dcOSJUscr3NycjBixAgAwJgxY7B161bs2bMHQ4YMQUJCAtLS0tCtWzfk5uYi\nOzsbo0ePdqy7bds29d/EwBw/pRajv4dAfYMV8//zk8uyBycMQEJ8rE4pIiIKD+e7svrqP8G6Z40f\nPx4nTzY3NpIkyTFeakpKCiorK1FVVYW0tDTHOikpKaiqqnJZbl9XqYyMNP8rCS45OQEAYIoxCfV9\nLPWNOF1YhZ8OnPV4r3OndMTGaF8TXN402llycoLssTDFN5+K9vfLnUZIsy+rqrd6LCOiwMXE2PJr\nyUnxEX9NtW2T4vjbOUMy/R8/4PNFNyDGx73P+di0aJEIwDZ2uJJj1qq0xuW1qWl7KS0SHK99Ud2P\n2v6jAoDZbEbLli2RmpoKs9nssjwtLc1luX1dpQoL9e0upIXapqe0xkYp7N/n2JlKlFZaMLhvO4/3\nXlr9M3KPy9eblxRXhSQ9ZaXVAICamjrZY2EfxhBo/u3tn3FeViqzLBpY6hqRmMCSDtKevZ1RTW19\nxF9TxSXNcaqu3rXldWFhpc9A7XxsqqttVcGSpOzeXlbuGqilpu1VV9c5XvuiutV3//79sWPHDgBA\nVlYWhg8fjoEDByI7OxsWiwWVlZXIy8tDZmYmhg4dik2bNjnWHTZsmNrdGZuOTZT/9s6PePXjPR7L\nz5ZUew3SLVMSQp2soAVzSC11jVi35SjKqyyapSccvtyaj2mLNwnfKJFIdC73D7cqSXUDLAV5c3fb\nlb/aUdWBes6cOViyZAluv/121NfXY/z48cjIyMCUKVMwefJk/PGPf8TMmTORmJiISZMm4dChQ5g0\naRI++OADPPzww2p3Z2h1KvvKhVpppQVPLN/u9f0xgzqHMTXh99X2fHz2w1H8e12O/5UF8uW2fADA\nrkNFuqaDyOiKymv9r6SIYHXUANClSxesXbsWANCzZ0+sWrXKY52JEydi4sSJLsuSk5Px6quvapBM\nY9rS1O1JlFlplhssQGmttNKWky4sq/Gzppg4pCpRcHKPlTa/cGsBHsgASwEPyqTyYxzwJIoUlhsr\nQNU1hKZEwmjhzvlmUFBsxsZdp3RMDVGEcCtvPl1shtVtvnvJT5l00A/PnD2L3JVUuNbNXj6wE96a\nO1bYuaC/3nbMY1lwM8gK+kUVkiTgqRU78O63B3GyMDSN/ogima973dNv7sTaDYeVbkndjoOM5wzU\nYdLQaPW/UpglCt5futRgjb5CRuaeUGNpCH86iCLcjv2eXVblhbdcjoE6TBob9Slw9VV0k9EqKYwp\nISIShxZ35IBL6VTunIE6SrgH7PEjumLssC7ua4UhHcF93tiF10TiMVqbDZFo1cDTX4mr6gFPyFgk\n2IJbfUPzifDGnKsQI1jFtMktPRt3ncLxs6yHBeQfTgQdlZYoSoT3/skcdZiUm/Wtb61tGoUnMT7W\nR5DWL3i75/jf/fagy+tvth/z2wJT+b402UzY6ZnuGksDjp+N7FGropVYj+xi8X7JaVM0qLTonIE6\nTOb+2/tAI6F0sGkUMvtE5cPPz9AlHcH6cGMeDp0sj8piOq0KP77/+STe+vpAQJ99YdXPmPf2jzjr\nNIQrUbRTGmitXp6ylRadM1BHuDPFtrFtLfYctaDjRbsXfcthS+fgrPrvr9i8pyCgkgl7d7CiMq1G\ndiIyPqWB1r1/tloM1FHCnqMWvUtWSBm8jE+rhivenu6VEKxpA1HA9KxKsu+bRd8EoLkmRfQcNfmi\nbXS0BtGln3GaIsUB5+FEAxSuQZQYqCOc/cntZKGtCNzoN1qjpz8oGuUAgslRM0tNpJ2vt3uOviiH\ngTpKrFl/CACw88A5nVNCamkdGoOpL2OYJiNT/YzqZ32l1VHBPt8yUEc499zTzWN66ZQS5b7dcdzr\ne9HY6lskzFCTkel1/wi2PpyBOsK5D13aKjVRp5TYKHkCVT4wPgXCqP3IKTR4OgROaR11sMeYgTrC\nueeoY3T6xUXIiQmQhKBod0MNptW30Y9i6NXVN+KTrDwUGWxaWf6y4mKgjnCNjVYcPlXueN2xTQsd\nUyMGrUY4CxdHbNQo2cb69sbzv59O4Mutx/DqR3v0TooqPC/UU9xlkkXf5EujVcLzK7Mdr5MSOLx7\ntGOj79CqrK4HABQaZHCY6PpJdZrFMMj9MlBHkJOFVVj9v19dWvU2Bjkijmi0uKmw+DZw4eo3amT2\n00urAWpIO2ofUv39huG6Hpi9igCllRZ8tS0f3/98CgDQzmme6a+2KeunZxRa3PqMVvSttaC+P+O0\nX46bd3SfZuRM5lz4alu+4o8zUEeAf364G8fPNU8JmZQo/7NG+6hkRs9Ia5VDC2YrBj+E4cGDFDWC\nuSY/3nRE8bos+g6jYAdm98Y5SANAfKz8z/rEnUNDsn8KLXtRvWZnDyN1WDBDTXbsnmUgjcEMsiyj\nvsGKN77c77F8hcwyAOjWIU3T/ZNyNZYGlFbqOye5XTBF36yj9o9HKHoo7kfNVt/G0dCo7TP2ovd/\nwdZ9ZzTdZsgFcQgqzHXapSPMHvnnD5j12ha9kwEgyAy1DlHobEk1dh44G/4dBynKm0IIyai/CQN1\nGGl9kjj3jzY6JTfit7/J1WRfelyrIrW+D+Y8XLf5qHYJUeiJ5dux7PMcFJUZZAARg2WpxTkzjUd5\nHTW7ZxkILwlvPvg+HMOGGuwO6s7p9NGr5fruvGJd9gsA1ZYG3fYdGGNd7wa/OkJClBw4A3UY6fGb\n//Ha83XYK2lJ6+LmaO+eFmr2ekse5sjHftQhVFppQXJibNhH6dL6wk1OjEWNpdHnOlcMPg/pqYlC\nNyRj4FBGpKNkbxgZG8bB4+sarPjl10Jc2Kst4uPEzWMYtRugSOdXqGg9CI3S7bExmUpWq4RZr23B\n7KVb9U5K0C7p39Hl9Y2je2LS1X091hvUpx1ap+k7a5Yv0XCDCIbW930tnoum/+MHzHotvNfQxxvz\nsOSTvfhia35Y9xvpDPpcERiD3myiLlDbG/WYa8Nf33WmpFqT7Rw5XYGpC7/Hhl9OuSy/YVRPjLu4\nK0b276DJfsLGoBePUWmRq6ita0SFuQ5VNfW4/6UN2Hsk9HXXh07aGk8eLagI+b4oMqk98z/N8j0o\nSbiKvqMuUOtZLPXCqmz/Kynw8aY8n+/fPKYXAOASowXscNHxwSCoYn6nzwaVKw7y+y96/xfH3y+s\nykajVcIra3cHt1EFjDZ2Nmt0jO1caTW+2XFck20FeypEZR21XrS6cLt3TMOBY6Uuyzq0Tnb8nZGe\njNdmjkGSQYYMNdoNOBgSAihq1PjpMtij7XzuFRRrU0qkiEFOE07KITCFP0mNpQGni+TPbUmS0Ng0\nJobyOurgzgUGaoORJAlH3PpPXzm4M24f61o3nexlvG8RRVXOI6BI3fxRub+D2U64NDRaUVBcjS4Z\nKQHPXmac0ySqan0NJTlRWeblr2/uQEmF/EiCC9/72VENEy5RV/Rt9KDw5Iod+NXtJBk9qLNhJtwI\nx+EvqahF9sFzHstFaI2rWS5LoyJ0ZasHn+Z3vz2IZ97aqWs/7GhjlSR8uTWfdfpOenRqqWg9b0Ea\ngEuQZh21QdTWhbdR2lmZBmkxIkQgP3zlorR+ePrrmzvx2qf7cPxspbYb1oBW3zWcOepgk1zf0IjN\newsAAHkRNJqeX5KtPUk4GtrJ2X+0BJ9kHcH8//yE/fklXtczeN5FFX/XX7mPYYp3HjiLah0aIQNR\nGai1Oy2Xfb4P/7c4C4+/rm9XLwPEaT+U/yaHT5b5XaemaQSryur6IPcmBvvPq9kDTYgOwLdeGt78\n86M9odmhoBy/F2zzwYejoZ0c55HclAzRa/jbiCKBn/zLPs/xmATJqvCiPHQiuAfUqAvUWt3s9h0t\nxs4DtuLVovJabTYaoJgYY19ian6T1d8dCl1CRCXz84a1jlrhB9ZukB8Gdn9+qezySCX34HzsTCWe\nfedHnCsNT+O7gmIzDoe5HjUaHFKQUZCz/ueTQe036gK1Vo6dCX+xqrenNyMUfWvF+RC89dUB1Deo\nnzpUz6MV3IOiJPun+jSorKM2XBmEeFZ8uR/5Zyrx0UbfXSu18tSKHfguuzk4KPnJo+FXDjajpsf4\nG0AUBmq9T8Zg6jh++bXQ8felA5pHJdMuR63P0Ql0r5v3FmBbjvdpPr0FGH3PAb3PQPU3Ky3bEDg/\nU54srEJZle85uhsatZ3DXW96/fq+nuWj5zHfuI2Joy5QB3ulVFbX4cut+aitcx1jW+kNpb7B99jc\nvpRVNTd06N+jteNvg5d8q/pN3G84anLUIhwm7RqTBb4hEe5VkiTh6Td34rF/eZ+ju76hEQ8s2uh3\nWycLq/DW1wdgqQv82iISWfQF6iC9++1BfJJ1BF9tO+ayfPfhIkWfD2Ze4jqnID+iX/vmNzQr+tYn\nlKkJOprM66zjY3Uge5ZtTCazoeraehzw0bq3+bNqu2epWl3Rtpx/xxy3NJ8uMuOdb3JxrkxZ249F\n7/+CzXsKgq4HjGRZuwv0ToIQlNxr9uQpu5eHU9QF6mDr286Wyk9erzRnp2ZOXUt9o0t9ovNMWS7F\n3UYtz2li8OSrE8B3levaJreZxWt3Y9GaXfj1hO8GL+qToPwT7iPmeeN8vWz42XXM+kVrfkHW7tPY\n6DaWvTf21v119eLmqEUozSEoOpX/8aF4vRSiLlCHitKRaj7Z5HuQd7uSilpMe3kT/vNtrmOZc9DW\nsgFZ25ZJAIAWBhrNLCg6Nr7Tqsha7uHmyGnbwBbFFX5yoiGso/56+zGf71ubctL1TlVFPzu1vQCA\n8qYqHqVdX+xEeeCTe7A6VWTWISXkTpBTRLWoC9ShupjdZ7LyZpfCIvKjBbZW5c5FVp3bpgAAxgzq\nFPAwjHJm3TEY40d0xdih52m2Ta9kjn/Yb7B6Fn2HYddZu077ToPK7alZ31+LcvskBw0KSqCS4o0x\n2p47X1emVYuqmwB5a0dj1OAVCC1G2dND1AXqYJ0srAr5Pg4eL5VtDWvPhWR2TXdZHh8X3A2tQ+sW\nuH1sXySE8MZov3nVyI7kFqaLJ6K6sXk/Zgf9FX2rbvatYlUF61ZW1+HPCuaD9zUsriRJ2LbvDEor\nfbcaF80vh/Sr/3xg0UaYa+UHAQJYPC+yKCnrNI6K6jq8uPoX2ffs9Xpxsbbnq6fvHo6jpyvQOi0x\nbOkLlP0GsXXfGdz3+/46p8b4wpkxUFNcr6SO+tFXN3t9zzmQ+MpRHzhWihVf7kfblokwQaxcoXuP\nEJEcPlmOQX3a6Z0M3Rg0Qx19OWrRf6gqmWEvSypqUd/Q6AjU8XG2n61Hx5a4amiXsKYvUFU13p/k\ng/lJ6uobMf8/PykaIlEEWs0jbf8z52gJzsiM/65lGsJxzdjro527WNnPczn2euxiH5Mn6KXa4v1c\n19uq//6qdxJ0VVgm3xhYdFEXqENNkiR8s/0YTp4LrIhcrvvRn5duxYJ3sx1DNOo1MHwolFdZ8PkP\nRwP+/N4H/3FsAAAgAElEQVQjxThaUIFln+domKpQUh/15OY3fvWjPSgqr8HLH+zCk8u3a5U43Rxo\nGmZUaW5U5GFzrT6q353njdeD34aGEe7DMI0MpzVhA3VReY3qVp/KqNum1Srhw42HcUJh4M07XYEP\nN+bh6bd2BpI4NHq5yp33r/fY4oFpvrE615Eu/2K/y1CH4aBnoUow/ajdKW3A6JmG0LamDqSb1Pam\nEeYKiptLB3zt1jlQS27/681XG4AhfTPCmBJ1RDl+5EnIQP3LwXN4/PVteD8EEzCoPRl35xXhm+3H\n8YzCwOs87WX7AJ6elfTH7qlwTlVRNVolPPvOj/jnh7txzku/9FDQfBaqAGi570C76Lk/Cx4/W4nK\nau/T+6m9anxNFejNln22QO38oOrtWOUcLVHUalwvvhp2d2ijb45ajrhlE2QnZKDe2zQyjNIBDwKV\nffCc33UsKnMHzhOJJ8TFoEViHDq0aaHosw2NVkXDIF7Uq42qNImh+e5ltUrIP1OJ3XnFujTErqqp\nR4XPwBQe5tp6/JR7TpOSI/euN74e+N7730GXNMx7+0c8+upmlz77ztSmbs6ybSo/0cx5mFxfOdM3\nvzoQ8D5CzahdgEhcQgbqULD3X3S+hnYp6CrhHHi/2pavap8SbPWLCW6NYrw1/nn4lSwsVjB3rZZ9\nqMPFOZfhPANNSL+Kj/vlr8cDm64uWM438WWf52DpZ/uwY7/ChnBu38d5GFv3OZ+rfXTDsffRB4Ba\np9HuNnnpfx2uuCNJEtasby5F87Xb0FSLaaNtqySv74mbahKZ0IFaq2vxXGk17ntpg0egzT9Tic83\nH8XHm7w3MHAOJB8rGVXMOfA0pb9HxzSP9MipE7g4T0uzXmueiKFQ4XjOkSi3qSuT/775/p9mco66\njpetdCjPOB8tq9W4pH8Hl9cNjVZ8tDEPp1WMyLX3SLHLa4FjsU+9BK+a8tYOhsQlZKC2171pNQ/u\n7jzbDcA90J4qMuPzzUfx1bZjXnO5anOvcmtfc3FXVduIRIP7tA37Pnf46LKldQxQWtzpvFZsrO1s\ncR6tylLX6HVbatK8/Iv9itaLVdB6Wsl369TWtXpn54Gz+Hr7MbywKltROgCZMZYNGql9plqAr3T/\nSxux9NO9jtcCJIn8EDJQ26OdVtepc6MbbzedJ5dvx7c7jmPZ5/tcig3Vlsy6Z6hNJpNHf9BgvteV\ngzsH/mEdBTt6mje+HqS27PU+V7WWvthyFPe9uMFncbOD029fV2/L2eSdso3RXVVTj2mLN+Gl1b+4\ntI0IZ02HJEnYvv8Myp1Gxpv/n5/8f87t9ZkSWyNBs5+uhL6GrVU/2iZDjlI/HSz0mGnNeBVq0UPI\nQG3S+JQpNysbFGHthsPYeeCcS2tz1TdJmYeCjHTXlp5y4/36GhDE2a1X9laZIAq1T384CgnAkYIK\nv+vKhZLDp2wTuvyYa2vcePBEmW59o/cfK8Xydfvxlzd2OBqoKekOOHqg6wPkl1vzFe3vD9ec7/U9\nkeuhAyUBqLE0uPQO0cuiNbsiakyGSCZmoA4gTldU13kddP7Lrc2NbpRc+uUuLYKVJ8YqSR5rm0ye\nub4ln+x1eS1Jks96cmctkuIVp8eIfndpd8y/b6Ti9UVqYRsbo+By8pHelf+vuTV2aaXFoz+y2q9a\no2JKVbuisuac8AOLNqK+QVmvh9ZpiejdWdu6WSU9IIzooVey8H+Ls/ROBgBg7Qbtu8CS9gIe6/um\nm25CamoqAKBLly548MEHMXfuXJhMJvTt2xfPPPMMYmJisHbtWqxZswZxcXGYNm0arrrqKr/bVhun\naywNmPHqZnTrkIp594zwvbKCm51zUbmahwarVQroIWP261tRIuBQiFrr06UVDvuZDvTG0T2VBTwv\nfN7cXabw1j7AK6rvlVl2vtskK3a5x0sxsLe6cZn7dmnlmHL1mx3HcPMY3yUw7t243Bs0VtWEPsfV\ntX2q7IBCXyjMlYvG56kl0IMlAGQfLBR6lDeyCShQWywWSJKElStXOpY9+OCDmDFjBkaOHImnn34a\n69evx+DBg7Fy5Up8/PHHsFgsmDx5MkaNGoWEhASf23evO/HH3if2+FltZrZyCdQqPudepC1J/j/f\naLUqDtJ/vmOwitSIZ2Cvtn4Dtdog7V5a8eW2fJWp0o6yhlmey7zNaOTZF9r3Tb5r+1Q8OOFCR6v6\nUqfzytv0is4t8AHPQVQ+zVI2fzoAjOjfAXmn/Rf/211/WQ8AQLcO8oGaQs9c24C0FpFdShcJAgrU\nubm5qKmpwdSpU9HQ0IDHHnsMOTk5GDHClpsdM2YMtmzZgpiYGAwZMgQJCQlISEhAt27dkJubi4ED\nB/rc/i9OE8lnZKT5WNOmwdR8c/e3fpumOZ19SUqKc2ynlZ/g/9LDo/H4v34AALRuk4Ky2uYcXWys\nCTGxMbJp2nmwEAP7ZmDt+sN+0/PMfZdgeL8OftcT3a3jzscnfm78Sn5vZ/FOMyxlZKR5TCPovL1k\np2qDZZ/n4NrLeysKrkq1a5vqN/1t26agbSvXNgsnC82ynzPF2c5DexeqRD/VHkvnXO3yesu+M5h7\nj60a4fufjru85y2dA/pkAP9rnrhh894C2fXcZWSkYfJ1/VWNJti+ne14dcpIAxB8w78WLRJVnz+h\n0OpMpcvrMYPPQ9Yu2+BNKanNfaxFSCsAVDZNBJSUFC9MmozE3zHbfajQ5/tKBBSok5KScO+99+K2\n225Dfn4+7r//fkiS5MjdpKSkoLKyElVVVUhLa/4SKSkpqKpS9+T88XcHMWaQ75bOJU4zohQWVvpY\nEygu8r//+rpGx3Yq/Axin57cHCjOFVairKy5m1dDowTJKsmmadmnez2WeVNVWev3e0UKtd+z3qke\nt7Cw0qMEw769zXsK8LVbUWr+iRK0bOG7dEeNsrJqbC+pwnPv2rokLX54FCrMdfjbOz861rn72f9i\n6m/74fKBnVw++9n3nrMa/evDXRjauw0am9peWPy0Kpc7dvZl+afK/a4LAJWVgQ3pGsj5OahnaxQW\nVuKy/u3x0ffB15VWV1uEuE7K3RrfxTqdlFVVze+JkFZnNbX1wqXJCHwds883H8XnmwOfdMguoMrA\nnj174oYbboDJZELPnj2Rnp6O4uLmwQrMZjNatmyJ1NRUmM1ml+XOgVuJd76RH9YwlE4XOw3S4CXD\ndee4TCyffaVjbmgAaGiwutZRO5Vz+pqyzx9vjeQizZVDvHfVUeLXE2U45TbAhtUq4cjpCrz1tXZD\nTm7ZW4BpizehrMqCX080j3BmlSQs+2yf4/Vj/9qCeW//6FHc/dbXBzwGOXEeEvO6S7q5vGf//LYc\n7/3C01M9Hzgy0ptzby0SwzP1vJpi1KQE20Nuemoinn/gEsfyti0Dm19dnOpf14SYTMAfr7W1bhcn\njRQOWgRpIMBA/dFHH2HhwoUAgLNnz6KqqgqjRo3Cjh07AABZWVkYPnw4Bg4ciOzsbFgsFlRWViIv\nLw+ZmZmaJNyZmsJLJdeJ8ww+jY3yn7h6WBeXIA3YBlRx7lomOSVu3j0Xq0glkNnUwMgEW91jpPjt\nJd1ll0/+TV/cOa5vUNte+N7PHsueXL4dC96V7wccaKH3m18dgKWuET/mnnPZZ2OjpHh+5KfflJ/k\npVVqAn5/aQ/H67+986Oi7lGPTWxuv3DH2D4AgL5dbOdQYVkN3vufsnmIg+0a+ey9I3HzmF6Ydftg\n3HG179/T+aG2Y5sWeGvuWLw1dyzat1Y2Nr5R1NY1RnxvDQqtgB6zb731VjzxxBOYNGkSTCYTnn/+\nebRu3Rp//etfsXjxYvTq1Qvjx49HbGwspkyZgsmTJ0OSJMycOROJiYE9LYdSv+6tvQ65qKYv54Fj\nJRjjNiCJ/V7USUHdOABc0C0dI/p3wJWDz4MkSahvsCIhPjSDhejh1it7o1+P1nh5zS5c1KutY9jI\n3wwPzeht5/xMFL99/xmc37U1WqepPy/dT43Dp3w3lFOivKoOiQnNv/exM/6LIgf0bIMuTg9zQ8/P\nwJrvDzvS97ZbacLw80M31WKrlAT8vqmR2ICebVBSUYv//njC8f41F3d1eR0NlI6RQORNQIE6ISEB\nL7/8ssfyVatWeSybOHEiJk6cGMhuFFOVo5aJu3+87gLMdZrxJykhFpIkoa7e6rW1rJwGq1s/areP\n/v3/LsOfl271uY3HJw91/G0ymSIqSNsN6NEGLzxwCeoarB7jO4fTryfKsHzdfqQkxWHyuEwMzcxA\nXKwp4O5hJZXajFuudvpK93PUPVdc69Zlzef43hr31GmZ4lok//vLejgCtdYDG4kqNsYY39QIaYxW\n4am4CjUVZ1i+zOhR7d1GDpMk2xB7r3+2D1eoGLLTvZhcguTSfahNS++z6kSbDm1aOAbX8Offf74C\nf/r7Js3TYJ9S0VzbgBVOY2PfcXVf9OiYhtzjpbhhVE/vG3B76vvup5Oap1EJ733CbctjY01yixXl\n1oPl/tBhwInfVHP/Oeytqm3vsZKa1IuIQK3medV9VDA5kiTh9aZGQWqKMxutktugGoo/iqemDEMv\njUd2El279GT86YYB6NnJs4Fh67RElFZakJGeFLJxwr0N9OA81eLI/h3QwUud6bGz2ge6uKag2rld\niuKZp9wLfezBUHK8dv2e9uVvfKVs4o6Q8XLZRlowP3yqnBPzUFCEHEJULS0u7BtG9XD87Tw606lC\nz5vlzWN6yW6jvsHq8dDgnjZvDcN6dW5pyHmmgzWyfwfZxkP2hxZ/QVpN1YS7gmL5QOj8Kxw9XYF3\nvsmVbXnvrRX2E38YqqokxtmLD14GAJh/7wjMumMw/vHI5bLrvTK9eXlSgnYPMqE+A5VsX5Sr4O9r\nfsGSj/f4X1EF5qcpEEIG6gVNN6twsHcFuXG0fPCVM6BnG9nlDY1Wl1yQbWQy19uOtxGYojFI+9R0\nR/N3VA46dY9Sy1tRtfPNdPkX+5G1+7RjwgwlOrVNwcSr+jgeNgb3UT4MqL2blclkwoAebZDU1Eah\nZUoCHps4yLFeWot4vPjgpRiWmYE/XOOlJ4WXqCBJEqpq6nGuNLA+02p4ntaBjfoXTh98fwhzlm3F\n/vxSjwF0AhWOy7uyug6zl25x6TIYSfJOl6u6DpUa1Dv8U/CqJWSgHtTXtVXqe/9V1rUkEIEEyPPa\nubbgdp7Ryrm/rq2O2vWzcU71hf17tG5Kg+okRLzmYlvb/7+7VL5bl5zuHTyL0t+aO9ZjkBE1Ghqt\nkCQJUxd+73fd1OR4JCfG4akpw/DUXcMw7cYBmDLe+yxRztzPx4T4WCx88FK88MAl6H1eK8fyGJMJ\nGenJeOjmi9DObaQz923InV6P/PMHmSFKvaysIdHP9XOl1fh/O0+gsKy5YWCFyyQ9QQphlvrH3HMo\nrrB47cFiZBXVdXju3Wy8/tk+TF34PTbuOoXK6jrsyQu+Meqjtw3CI7cMxMAAA/ZnPxzRrL+0N0IG\nanfrf/bdSMdfsM30MumBuz/dMEDReu4tsS+/SD4AlFRYPG5M141sDjh9mm68HdtEVr9RLdgb+Nm7\ntXnrfy3H21SgU3/bD2/NHRtQemJMJtz74gZVnzGZTOjduRXi42IV5aq9pbt9ejKSE+NUF3E7Hnbc\nl/sIFlq3T/a1Na/XrY7RXC5NM17drHr+AQC4emgX9O7cEs/eOwKhfgI6croCq5oyNIHeT0Quls9z\nayv07rcH8eirm/GPD3drUoIwuG87zLhtkP8VZazbki8bqLVsOBgRjcn8Gdynndcf0/lYjuzfAcfO\nVOLbncdl1/XGvQuKM+cnc8A1R33Nxd1QVlWH60Z2c/9Y1JtweU+kpyVi1EUdAQDJKkbWMplsI8HZ\nc4z2kotgKJ3w5aJe8k/lziN2zb5jMBat2eWxjrcHPjuTyYQHbugPs8IZrbzdKMJ6Qw5j0NXivugt\ntYvW7MIr0y9HKx/XuruObVvgzqZqibMltmqGUBz7d7/NxcZdpx2vO7WNvAf/DLdSI2fF5bWAgG31\ntGzgL2yOWsvr2/e2XI+mfbCGUHEezaxFUhzuvu4CdGCO2kNiQiyuubgrUgIc0elvU0egfWvbxX3p\ngI4u7z04QVnJibP//aRskA5v/cLjYmPw+KQhWHDfSPTrId/GIc69G5WMS/p3xNXDuvhcx+N8F6i8\nWUlSREjtqAs7optbw8+ZSzbLdik8W1KNVf89iF8OFXqdCS2UP4FzkAZsPQYijf0uLddIM5hje0E3\n36Wtgdwr7CQNH8uEzVEPzcxA9sHgZx0B1NVDJyWGdoAR92FHSXud26UgPTURC/90KcqrLGiV6jrq\n2JC+/ouh7xyXqXjYTWfOvQfcXdDdd84+bMNMhrEvbziDrhbB0PnIzJs6wqNNwmuf7cMzd9uGA64w\n12HGks2O977/+ZSCHWh/7O+4ui/WrD+EB67vj4v7tQ9qPnfRxcXG4K25Y11+l9NOQz4Hq0ViHKot\nDXjyD8PQp4utanLZ5zkBbUvLn1rYQD3+4m6KA7W/C9TX2+7HMsZkwnUju+GbHeqKvydd3Rfvr/c/\nA9CoizpiZ+5Z3KSilTnZrHj8StRYGpGabAtof1/zC/bnezacSXcKzO5B2pcXHrgEp4rMGNK3HUwm\nE3KPlyo6By8Z0AHbm7pqqalLB2xd/fxN/amW/Xz3dqPwdf/QPOdncn+pbgcmKC8u1jQGeknmsTOV\nWLx2F+6+9gK/owwq2Jwmrrm4qyb9tEUoyfDGXo0jl8Yvt+Z77TLrLrNrOkb0a4/dh4ux90ix415i\nt/DBS1FaadFkfoWoKPru06UVzstoLsLZ76Mxh78D4uttuc8ObRoLWW4Wn6uGys/wNLCPshaDLZLi\n8dSU4ejvpfiTvIuNiXG5sOSCdDA6tGmBoZkZjhKYe3/XDy9Nu9Slkcmrj47Gv2aMxtN3DwdgK6J/\n4PoBuGlML/zx2vNVD/lqb+joq52Dav5afYexktpH7ywVHwofJYdm35ESfLQxL2TbJ//u+e0FLq8t\nbsPketO2ZSLGDu2Cu6+7AFcO7ow7x7l2bUxNjvcI0v+YLj+WgX9RUPQN2LpB2Qcc+fuaXQG32PUW\nyRMTYvHHay/wWN67cyv8/f8uQ3paIu5raukbYzLhuQdGem3U4G35G3OuCizNFELKIkFSQhySEuLQ\nrlUyls26AnUNVseDQo+O8bjv9/2Q2TRD1fUBtm2IjTXhtZljgpoG1RvfA4t6eU+0SKImS63pbn2f\nI41uA+10yUjF9FsuwhynOQPcNkhBcJyXTcdx9MDOuPyiTo6eGJ/+cMTvbG0AkNj0IN06LRF3ydz7\n5QT6EB3EWEwehM1RA0C+RmMRezterz92hde+c21aJrmMU5zROhkdWrfwOuxkTIwJz90/Eq88PMp1\nuUANeSKZmnmQA5EQH+tRTHbZhZ3QLt17a1RFJFuLdi3bLoh0xinp0y0UhU8qnmsp/JxoD0IG4/wA\n5Xxu/ffHE4q6Q4V1kqNoCdRajZykxcWRrKAPa6e2KarqRCk4zvVyLz80CtNvuQjLZ18Z0La8jTYX\naiG5b7tVUrs/K+o5MYSyVt8m2b/DKoDd+uwzLv4jiiE9d/9Ix9/PvPWj3/UDDdSB5Le0bPUtdKCW\nG2EqWPbRqex1jP4EOloNAMy6fXDAnyX/nIuL42JjMKRvhqKcqdxFN+KC9lomza9rR9j6zruPcqcl\nMTNv/u94ehZCKT5mcg877g3nGJs1Yw967sfUPiASAJws9D/WQWJ8gCFPxcVUbq6D1SpFR2MyAPjN\ncN/9Re385RCcn2wmjOqJt+aORY+OoZ+pihdqaMV6qYYIRJtW4Z2CdOLYPnhzzlWqBnJRyt9RCWeG\nOthrQM3ntczBqE+2yXeu2QD3AjEf7GyUnrNPrdju8/3EAHPUf5owAAkKgvy50mrMXLIZr326N3oC\n9aiLOqF7Rw1y1U0H7PeX9UDbMN6QOdFGaF09rAvapyfjoZsuDHgbL027FA9OGID+fvo4h0LIzw/7\nxCYq9qNlsAM845Pwl0QQX1/Jd9P6+EYbuWPsXPxdUFyNc6Xe+1UHGqhH9OuAZbOu9LuefQRD22Qu\nnr91z06BZRCFDtSAto2E+pwX3vmeRb8nGV1aiwQsfPBSDDs/8GLrVimJGNGvQ0Q9VNm/i9dW32zR\n5Jf608H3MY2cs0s8ndqmYO6dQx2v5/57O0orLbLrhrMx2d4jnl2KG60yE+EoIHygTvQzH7EcS30j\n3v/uEHKOlkCSJHzo6PMY3sslgu79EYu/UYgZ7AArHlxFwTqy35zPSAGxP1t6q17I7JqOm5wGPZn1\n2hZYZR5IA81RB+Lf6zxHNJOdsU4BoftRA1BUL+A8YYLVKmHay5sA2MZnfnbqiKD2H0zmI5JyaZEq\nkn8iESblCLboW9X6mn6xAGqpfSTWCOeZyEl0VBn4SOT1l/XAp06j/NnHwPiN09j4ndvpO69C1/ap\nKAhgyFPhc9RKiipe/XiP42/3J5an39r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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.data['CODtot_line3'].plot()" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "drop() got an unexpected keyword argument 'index'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n\u001b[0;32m----> 2\u001b[0;31m plot=True, period=3)\n\u001b[0m", - "\u001b[0;32m/Users/chaimdemulder/Documents/Work/github/wwdata/wwdata/Class_HydroData.py\u001b[0m in \u001b[0;36mdetect_drift\u001b[0;34m(self, data_name, arange, max_slope, period, plot)\u001b[0m\n\u001b[1;32m 1586\u001b[0m \u001b[0mnan_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1587\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1588\u001b[0;31m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnan_values\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmax_slope\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: drop() got an unexpected keyword argument 'index'" - ] - } - ], + "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", " plot=True, period=3)" @@ -1527,31 +1021,9 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in period 4 to 6, slope: 60.142857142857146\n", - "Drift detected in period 5 to 7, slope: 81.5\n", - "Drift detected in period 6 to 8, slope: 77.14285714285714\n", - "Drift detected in period 7 to 9, slope: 70.85714285714286\n", - "Drift detected in period 8 to 10, slope: 110.92857142857143\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", " plot=True, period=3)" @@ -1559,31 +1031,9 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in day 5 with slope: 454.0\n", - "Drift detected in day 6 with slope: 504.0\n", - "Drift detected in day 7 with slope: 359.0\n", - "Drift detected in day 8 with slope: 479.0\n", - "Drift detected in day 10 with slope: 258.0\n" - ] - }, - { - "data": { - "image/png": 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LJ+I/0R/TVGVhERE7U+IqIiLSiA2bOYxXV75a4/7OfCerblvFjtt2sKf9Hla/uxrXYBc3nHEDo6NHM33tdJbtX1breAJ9Amv9XE9wupwUOAusDkNERKqh4kwiIiKNmJ/D76QSs6x1WeTNymPWObP44IIPcG1wsdt7N4+e+SiTV0wu1/fgXw7Srkm7k4onyMc+o63HMjG1NY6IiI1pxFVERKSRcrqcZBVmsT9j/wn7Ldi9gMHjB3PDFzcQ+lMoYx8cy3sXvcdDQx4C4J017xD9VnSF50W+Hsnbv79dbRz70/+4v91GXE9mOx8REbGORlxFREQaqazCLAC+2v4VAC7TxUsrXqJPeB9+P/w7vVr14ufYnyl+qZgXV77IY7c9Bh0hoXkCQ9oN4fkLn2fq6qknvMeD8x8kKSeJR896lGb+zSrtEzUlCoCHBz/MoLaD6u4FiojIaUOJq4iISANUUFzA1N+mcl6H8ziz3ZlV9jnWz/t+5qlFT7mPm2c15x//+wd99/fl25hv6XlJT5KPJDP+3PHcO+BefBw+mBNMXvv1NR778bEqY/nnL//kn7/8E4CMJzPYmriVDzd8yLU9ruVAxgF3vzcuewMvw16TvWIiYngg5gGrwxARkWoocRUREWmAdqXs4vGFjzOk3RBW3r2y0j5la1tbBLTA6XLyzyX/dJ/ru68v//jfPwgoDGDBgwt4/c3Xq7zXXf3v4psd37DsQPVFmX6I/YFbv76VAmcB09dNL3cuIz+D0IDQGry6+jOi6whGdB1hdRgiIlINJa4iIiINUJGrCIBfD/2K0+XE4eUodz67MJtCZyEAKXkpeD9f/i2/Y1JHsgKymP332cz424wT3quZfzOW3rm05Fq5KcRMjyEuPa7SvluTtlZZDCo5N9l2iavT5cRluvD28tZ6VxERG7PXfB0REZFGoMhZhPGsUW6E0xP3KOMyXeXOTVw6kZAXQ7h/3v3l2kNyQ7i76G4+Gv0Rr338GjFrYvjsqc/w9/av8X1bBLZg36P7MCdUvu/ps788W2n7+HPG07VF1xrfp768sOwFfCf6Vvh3KCIi9qIRVxERkTpWlgRtSdzisXvkFee5H8dnxdO2SVu2JW2jT3gfzu9wPgAL9y509/lvl//ieMhBO0c7hjw5BO9gbzjFwc+ExxJYcWAFMREx7gJMVbmmxzWndjMRETmtVTviahjGB4ZhJBqGseWYtn8ahnHYMIwNpf+MOObcU4ZhxBqGsdMwjEuPab+stC3WMIwn6/6liIiIWCs5N5npa6fj5+0HwJfbv/TYvfKL892Pz/nwHB5Z8Ah9/9MX41mDDzd86D53Q88buOL3K4i8K5KokCj6zutbkrTWgdbBrbm257V0aNahyj4+Xj64/uFiYMTAOrmnp5hUPoIsIiL2UJN3rhnAm8BHx7W/bprmK8c2GIbRE7gJOAOIABYahtGt9PRbwMXAIeB3wzDmmKa57RRiFxERsZUH5z/I7K2z6R3e26P3KXQWlltjeijzEO+secd9/OGGD1l6x1JwQvNnmpM0L4nQy0Lp8XEPfFr4eDS2482+frat147aOTYREflDtSOupmkuBVJreL2rgM9M0ywwTXMfEAsMLv0n1jTNvaZpFgKflfYVERFpNEZ0KZmAtD1pu0fvM/W3qdw/7346NusIwPCOwwHoEdbD3efcDudybqdz8Q7xJur5KHrP6+3RpPXF4S8y8YKJDG47GICBbQby7U3fMjp6tMfuKSIip49TmSv0kGEYtwFrgMdM00wD2gKrjulzqLQN4OBx7ZVvOiciAMSlx7Fs/zJu7Xur1aGISA35OnwBuGvOXR69T3RYNAAHMw8y/+b5ZBVmsWjfIkZHj+Ya4xpu2nsT2RuzCe4bTLd3utXLqOKT55SsArqx143sSN7BqG6jPH7PujA0cihPnP0EBhp5FRGxs9omru8AzwNm6c9Xgbug0r/6JpWP7Fa6mMQwjHHAOID27dvXMjyRhm/a2mm8uPxFbulzi6ayiTQQH2z4oNzxVd3rfnJRobOQKz69AoBiVzGXd70cp8vJ59d9zsXtLiZpfBLxb8fjuM1Bj5k96v3vR5fmXejSvEu93vNUXNjxQi7seKHVYYiISDVqtR2OaZpHTdN0mqbpAqZTMhUYSkZSI4/p2g6IP0F7ZdeeZppmjGmaMS1btqxNeCKNQtnIjZJWkYbj2Cq+ACsPrjyl68Wlx9Hq5VbEpsa629bGr3U/3vfoPgAcXg6uCLqCvRfvJf7teNo91o7u73U/pXufLvKK8kjNS8U0VZxJRMTOapW4GobR5pjDq4GyisNzgJsMw/AzDKMj0BVYDfwOdDUMo6NhGL6UFHCaU/uwRRq/TUc3AejDlEgDkVmQWaEtKTfplH6HZ22eRVJuEu+vex8o2WZn6AdDAZg0fBJRzaIAyN6SzdoBa8ndmcsZX55Bl1e64OWjrdpr4vVVr9PipRYUuYqq7ywiIpapdqqwYRifAsOAMMMwDgETgGGGYfSjZLpvHPAnANM0txqGMRvYBhQDD5qm6Sy9zkPAD4AD+MA0za11/mpEGpGvd3wNlGzRoLVXIvaXnJtcabvLdOEwHLW6ZrcWJYX5m/o3BeCvP/7Vfe6vQ/94HNg9kJbXtSTy8UgCuwTW6l4iIiJ2VpOqwmNM02xjmqaPaZrtTNN83zTNW03T7G2aZh/TNK80TTPhmP7/Mk2zs2ma3U3TXHBM+3zTNLuVnvuXp16QiIiIFTqFdqq0vdhVXOtrntm2pI5hWGAYAOe0P8f905nsZPtt2ylMLsTLx4vu07oraT0Fmt1iLafLyUPzH2Jv2l6rQxERm9I8IhGb6tWqF6APUyINyW19b6vQdiqJa5BvEKOjR9O+aXucLif9WvcD4Pqc61nTfw1JXySRvTa71tcXNKPFJtbEr+Gt399izJdjrA5FRGzqVLbDEREPuqHnDWxJ3FJ9RxGxhSVxSwjwDqjQblZeRL9apmkycNpA4tLjWLxvMdFh0ZwRdgbfZn1Lk+eb4OjooM/8PgT3DT7V0EVERGxPI64iIiJ1YG38Wt5d+26F9mDfPxLLV1e+So+3eriLr51Ip6mdiEuPAyCjIIPfDv9Gwb8LaPJqE8KuCmPgmoFKWuvA+VHnM/GCiTi8arcOWepGeHA4ADeecaPFkYiIXWnEVcSmjuYcBcDL0PdLIg1BSl4K3l7ejOo2imb+zRgcMRg/bz/3+UJnIf/d9F92JO/g6Z+fZs6YP4rrr09Yj4nJgDYD3G1lSWvbkLb8eMuPPPbTY6Q3T6fNFW3o9mg3bZVVR4ZGDmVo5FCrwzjttQhowRuXvsHwTsOtDkVEbEqJq4hNhfqH4mV46cOpSAORnJtMWGAYX99YUhF87q65vLLyFa7odgUTlkzgnTXvuPvGZ5XfynzAtJKE1ZxQMq148b7FADx9ztP8ae+fSLk1hXnz5uHlrS+y6lpGfgbp+elENo3UF4UWMgyD3KJcipzalkhEKqe/0CI2te7IOlymC6fLaXUoIlIDBzIO0CKghfv4aPZRftn/C9mF2eWSVoC1CWvLHYcFhjG47WD38YUfXYhfoR/D3h5G7LhYMMGZrb8FnvCfNf8hakoUBcUFVodyWkvNS2X8z+P5Yc8PVociIjalxFXEphbsLtlNymW6LI5ERGoiOTeZCzte6D72cfgAMOrTUdU+NywwjA5NO7D68GomLJ5AdGY0H878EMfXDjpM6ECfBX3waebjsdhFrJaQVbKz4lfbv7I4EhGxK00VFhERqQNrxq0pdxzoU7Kn6rakbdU+d0fyDnYk7+CLbV+ACe/OepewvDD6LOhD80ubeyReERGRhkQjriI2NTBiIFD7rTRExFpBPkEnPH/8bAqH04FPsQ8Y8OLVL5IwK0FJq4iISCklriI2dXX01VaHICI1FJcexwUzL2Dp/qXutrDAsBM+J68oz/14zrA5vPHhGzz4/YMl1wuP44ZLbvBMsCIiIg2QElcREZFTlJCVwJK4JeQW5brbBrUdVK7PeR3OA6BPeB8+Gv2Rew1s6k+phFwTQqfETvie48uqu1dhTjDx9/avvxdwGhveaThTLpuCt5dWT1mpZVBLAO7sd6fFkYiIXSlxFbGpw5mHAfDxUkEWEbtLzUsFSraxOtbNvW92P96btpdxA8ax/M7l3Nr3VnwMH+Kei2PTpZs47HeY+8bdx8B7BnJmuzPrNfbTXUxEDI+c+Yj7iwSxRsvAlrx/5ftc1Okiq0MREZtS4ipiU838m+Ht5a19XEUagPT8dKDk9/ZYn1zzCe+MLNkK51DmId694l0KnYUs3reYlF0pHHjpAE1vasrdd97NwbCDbEncUu+xn+6Sc5PZmrhVFdwt5mV4sSd1Dyl5KVaHIiI2pcRVxKZWHV5FsauYYlex1aGISDWKXEUA+Hn7VTh3X8x95Y5/XforF868kLgmcQzaNIgmbzahwLeA+2Pu5+nznq6XeOUPH67/kF7v9Cq35ljqX2peKi8sf4HvY7+3OhQRsSklriI29fO+nwFwupwWRyIi1Wnm34x+rfudeF2qCYemHCLoiiAu3XgpuUW5BHQKYG/aXgAu6nQRvg7feopYxF7is+IBmL97vsWRiIhdqRKBiIjIKRodPZrR0aOrPJ98fzIH7ztI7NexOC52sKL7Cnchp9WHVwNwQdQF9RKrVE5bj1lLy2JEpDoacRWxqbMjzwb0YUqkocvenE3subGkz0mn0+RONP2oKdkB2e7E1d/bn7MjzyY0ILSaK4knKGESEWkYlLiK2NSobqOsDkFEaujjTR8zaPogcgpzKpzL35+PM9tJv5/70f7x9gT5BgG4+z429DGW37W8XuMVERFpaDRVWMSmCp2FVocgIjV0OPMwa+LXuEfvnPlOMpZm0PyS5oSNCiN0dyiOQAcAESERzLlpDv3b9LcyZCl1WZfLaBHQAj9HxcJaUn+aBzQH4IFBD1gciYjYlUZcRWwqMScRQB+mROrQ+oT1tH6lNYv2LqrT65ZV//bx8iFvTx7rh65n88jN5B/IB3AnrQCBPoFc0f0K2jVpR25RLl2mduGTTZ/UaTxSc71a9eLO/ndqH1eLtQ5uzefXfc6wqGFWhyIiNqXEVcSmgn2D8XP4af2VSB2avm46R3OOEpceV6dbTZVth5M+J501A9eQH5dPr2964d++8irDS+KWMHDaQDpP7cyetD14GXo7tkpCVgKrDq3S1mMW8zK8WH5gOXHpcVaHIiI2pXdKEZtafmA5Bc4CTRkWqUNp+WkA3PPdPczbNa/OrlvsKuZPC//E1mu2Etg1kIHrBtJiZIsq+z+58EnWJazjSPYRAAZGDKyzWOTkfLrlU4a8P8RdLEuskZaXxr9X/1v7uIpIlZS4itjUioMrADQKIFKHjt0ntS73TG3ftD2tWrci4oEI+i/vT0BUwAn7D2k3pNxxtxbd6iwWkYbocNZh4I89zEVEjqfiTCIictrw8fpjHeOsLbO4vOvlp3S9tMVpmMUm4y4ehznbrPHU/skXT2ZY1DC8vby1HEBqrchZxJK4JXQP6077pu2tDkdExKM04ipiU8M7DgfANLWPq0hdObbYmct01fo6pstk/wv72XjRRvY/tx/TrHnSCiWjvVdFX8XIbiMZ0XVEreOQ01t2YTaXfHwJX2//2upQREQ8TomriE1d2vlSq0MQaXR6tux5ytcoSi1i85Wb2ff3fbS6oRW9F/TmH4v/wbkfnlsHEYrUnEnj+2JThcpEpCr66yBiU1mFWVaHINLoPDj4QdbcuwaA1kGta/y8jzd9jPGsQcLeBNYMWEPaj2n4TPQh+pNovIO9OZR1iAMZBzwVtnjQqG6jmH3dbAK8T7wu2Y6yCkreJ6avm25xJKeuqV9TAB4981GLIxERu1LiKmJTmQWZQMmejyJSd0bOGglAcl5yufZfD/7KP5f8E4DtSdsxnjWYsHgChzMPc+vXt5a0s52W17TkyAdHOKf4HD7b+hkvr3iZxfsW4+9d+dY3Ym/dWnTj+jOub5D7uJZNd88pyrE4klPXtklb5t88n/Ojzrc6FBGxKRVnErEpX4cvAd4BKtwiUoee+OkJjuYcBeCjjR9xR987+HTLp4w/dzy/HvqViUsnEuAdwIQlEwB4bulzvPQbrqKHAAAgAElEQVTTSzz2w2N8PvRzhv93OPcMu8c9RXPsV2Mtey1SN/an72dXyi6GRQ1rkMkrNI5aCA7Dwexts/EyvIgIibA6HBGxIY24itjU4rjF5BXnkV+cb3UoIrb33rr3+NuPf6u2X0J2Qrnj2VtnM33ddG7+8maaBzTHaTp5ctGTFDgLAOiQ2IF3pr/DiPUj+K7rdyX3Wv8e769/v+5fhFji6x1fc8nHlzTIUcum/iXTa6/odoXFkZy61LxUZmyYwQ97frA6FBGxKSWuIja1Nn4tULLdgYic2Dc7vuGVX18huzD7hP2O3xf5xYtepEVAC/qG92X8ovHlzv3P73+8+967tCxsSd+f+tLvkX4MaDOgzmMXqa2yvYgbw1Y4hzIPAbD8wHKLIxERu1LiKiIiDc7BjIPljv981p8BmLB4Agv3LiQtL41PN39aYQplsauYHmE93OtRk3OTCQ8O54ttX5QbjZ0XNI8WT7Wg1eBWXLT9IloMbwGU3wf2eItvX1wnr02s0RCn2zpdTnwdvvh5+1Xf2ea0LEZEqqM1riI2NbLbSObumtsotzsQORWL9y3mwo8u5NEzH+XK7lfy/vr33aM1r616jddWvebu+5+1/2HGVTMI9Ankqs+uIr84H28vb7Y+sJWxX42l67+7lru2t+FN/GPxNPdqTrxXPG0fbouXzx/f8a5NWFuuv6/Dl0JnIQAxETGeesniQQYNN2HKL86n0Fl4wi9UREQaCyWuIjZ1QdQFzN011+owRGxlS+IWLvzoQgCm/DaFKb9NOWH/pfuX0mlqp3Jtfz7zz3QK7cRFHS9i1aFV7vazdp3FW3vfIvTPoTiCHET+X2SF6+14cAfJuckk5iSSnp/OyG4jCfYNdk/ZFKlPjemLzbIvEBrD6LGIeIYSVxGbSslNARrm9DURT9lwZEOV5x4e/DBXR1/NNbOvISwwjBFdRjB19dQK/V67tGRE9v5B95NXnEdqdipjF47FMcuBq5+L4oxivJtU/vbYuXlnOjfvXDcvRmzhquir6B7WnWDfYKtDOWl5RXlAyZc49w+63+JoTk2QbxBQ8nssIlIZJa4iNpVfnE+wb7C7aqSIQE5h1ZVfx587ntbBrTFNk9jUWKZcPoW7B9xN3//0dfeZd/M891q6iJAIXuj7AtvGbCN9cTpt7m1DlyldcAQ4PP46xD6imkUR1SzK6jBqpWzEtWy6ekMW1SyKZXcuo3uL7laHIiI2peJMIiLSICTnJnPfvPsI9AnkrRFvAfDcsOf408A/kTs+l9bBrQHY8dAOtj2wDYA+4X344vovAFgwdgEjuo4od81d9+8ic1Um0TOi6T6tu5LW09Ce1D18tf0rCooLrA6l1hrDlGGH4eCNVW+w4uAKq0MREZvSiKuITf209yeyC7PJLcol0CfQ6nBELLckbgkAY3qN4YFBD3BfzH14GRW/f20d3NqdxAJc2+Nadj20i64tSgoxmaaJK9eFI8hBlyldKE4rJrhPw5smKnVj/u75PPL9IyT/LbnBra9sGdgSgCu7XWlxJKcuOTeZL7d/SXhQOKOjR1sdjojYkBJXEZvakrgFKJkCpsRVBNqGtAVKCpcBlSatlTEMw520FqUXseP2HZhFJr3n9sY/0h8q1mASaRDKioKFB4dbHMmpK6sMviZhjcWRiIhdaaqwiIg0CAXOkqmc4+aOq9Xzs9ZlsXbAWlLnp9L80uY04F1QxAMa4nTbAmcBvg5fgnyCrA6lzqggoYhURYmriE1d1/M6QG/iImXKKm3nFuWe1PNM0yR+ejzrhq7DLDLpt7Qf7R5t5y7SJKe3hvz/g4z8DAqdhQ2yIrKIyMlS4ipiU2dHnm11CCK2ct0X19Xqec5MJ3H/jKPZec0YuG4gTYeoUrc0Dg1xlLgqZV8gNPFrYnEkImJXSlxFbCoxJ9HqEERs6d4B99aoX97ePFzFLrybetN/RX/6LOiDb0tfD0cnDc21Pa5l+Z3Laepnny80Wr7cEuNZg90pu0/Yr9hVDMDEZRPrIyyP8vf2B+CBQQ9YHImI2JUSVxGbyinKoalfU0IDQq0ORcQWyooxTb18arV9E2cnsqbvGvZP3A9AQFQAhqPhTgkVz2kT0oaz25+Nj8PH6lDcknOTARj+0XDavNqGO7+984T9jUawYLtr865s+NMGLux4odWhiIhNKXEVEZEG4fqe1wOQX5xfZR9XoYvdj+5m243bCOoVRJu729RXeNJA7UjewcwNM0/4/yur5BblciT7CDM2zDhhv8YwZdjby5vHFz7Ogt0LrA5FRGxKiauITS2IXUBGQQbZhdlWhyJiudjUWPq17sfSO5bSzL9ZpX3yD+az4fwNHJ56mLaPtqXfL/1KtrsROYFFexdxx7d3kFWQZXUoFdwfc/8Jz5dtEdUY9nFNyk3ixz0/smjfIqtDERGbUuIqYlO7UnYBJfu4ipzuFu5dyFOLnqJDsw5V9ilKKiIvNo+es3vS9Y2uePnqLU4aptzxuTwQ8wDDoobRObQzY3uPrbRf2T6ujWFJSdk+rpuObrI4EhGxK2+rAxARETmRpJwk7p9XMvJUNsJUxnSapH6fSouRLQgZEMJZcWfhCHJYEaY0cHaabvt97PdkFGQQ4BPAr3f/6k5Qj5dZkImPlw+h/g0/cRURqY6+jhaxqVv73ApoH1ep6N4592I8a7hH5Ru7uPQ492OH1x9JaWFSIZsu38TmUZvJWJlRcl5Jq5wkO+7jes3sa/hk8yf8tOcn/v7z3/l0y6eV9kvKTaLIVUTzgOb1HKGISP3TiKuITQ2KGMR/N/3X6jDEhtYkrAEgpzDH4kjqR2TTSJbcvsRdVRggY2UGW2/YSlFyEd3f606TIdr7URqfN39/k+TcZJr6NeW+mPuq7NeYlpS0DGppdQgiYlMacRWxqaM5RwF7jgaI1JfEnETavNqG8T+P59wO5wJw+J3DbDh/A15+Xgz4dQBt7m6j3xOptRvOuIGN92209XTbjIKMStvLZuT8a9m/6jMcj/D2KhlL+dPAP1kciYjYlUZcRWwqsyCTUP9QTQGT09rKgysBuKzzZe4272beNB/ZnOgZ0fg0s8/em9IwhQWGERYYZnUYlapuf9aydbmN4YubM1qewZ5H9hAeFG51KCJiUxpxFRFpoOxUTMZTytbx3ht0L4mfJwIQPiacXl/3UtIqdWJL4hbeWv2WLafeJ+Um1ahfY6iF4O3lza1f38rsrbOtDkVEbEqJq4hNzd01l7T8NDLyK58iJqevni17AtDEr3Gv69x8dDNPLHyCa7ddy67zdrH3qb24ClxA4xhhEntYfmA5Dy14iKxC++zjWtMR4KhmUQBc0e0KD0ZTP45kH2HlwZUs2b/E6lBExKaUuIrYVFkl1cZUdEPqxszRM8kdn0vn0M5Wh+JRnQM78791/+Oh2Q/R5KwmDPh1AF5+etuSxi/24VieOPsJ9/G1Pa6ttJ+vwxeH4SDYN7i+QvOYsn1ctydttzgSEbErfQIQEWmAnKazUU8VduY72Xb+NlrMaUH7v7en70998Q2vfC9Lkbpgl+m2LtPF1zu+dk+T7xvelyu7X8mjCx6t0DclNwUfhw+tg1vXd5h1rmwWRWP+uyYip0aJq4hNjRs4DtCbuFR039z7CHkxhC2JW6wOxWMc/g4Sz00k851MOk3shOHQ1GDxjOoKINU3p8vJnd/eydc7vgZg49GN3P7N7UxdPbVC36M5R8kvzqdVUKv6DlNEpN5Vm7gahvGBYRiJhmFsOaatuWEYPxmGsbv0Z2hpu2EYxlTDMGINw9hkGMaAY55ze2n/3YZh3O6ZlyPSePQJ72N1CGJT64+sB6DIWWRxJHXLVeRiz9/2kL48HYDJ/Sfzmv9rFkclUn/eW/cevhNPfmaBndbnnqp2TdpZHYKI2FRNRlxnAJcd1/YksMg0za7AotJjgMuBrqX/jAPegZJEF5gAnAkMBiaUJbsiUrnDmYcB8DI0MUIav4LDBWy4YAMHXzlI2k9pACRkJTSKKZBib2N6j2HvI3tpGdTS6lB4YdkLVZ6LCImo0FY2vflEz2soyka+7+l/j8WRiIhdVbuPq2maSw3DiDqu+SpgWOnjmcAS4InS9o/Mkr+kqwzDaGYYRpvSvj+ZppkKYBjGT5Qkw5+e8isQaaTS89Ntvb+gSF1JW5TGtpu34cxx0uOTHoTfXLKP45HsI0pcxeOa+DWxTYXuExXjW3HXigptjWkf136t+3H0r0dp6tfU6lBExKZqO5QTbppmAkDpz7LFFW2Bg8f0O1TaVlW7iIjUUmNY/5yxIoONl2zEp4UPA1cPdCetOYU5ZBVmKXEVj9twZAOTlk8iq8D66baPn/14led+ifulynN2KSx1KnwcPlz00UW8v/59q0MREZuq6zmIlX3lZ56gveIFDGOcYRhrDMNYk5RUs423RRqjb3d+S3JuMim5KVaHIjZzRsszABp0QZayD9pNhjSh80udGbB6AEE9g9znE7ITAAgPCrckPjl9/H74d55a9BSZBZlWh8IjZz7Ce1e8V+m5O769o0JbdFg0AKO6jfJkWPXiYMZBNiduZtmBZVaHIiI2VdvE9WjpFGBKfyaWth8CIo/p1w6IP0F7BaZpTjNNM8Y0zZiWLa1fbyJilfiskl+RIlfjKsAjp+6jqz/CnGDSvml7q0OplczVmawbvI78g/kYXgaRj0XiHVx+5cr5M86nc2hnru5xtUVRitS/Pal7+GDDBwAMihhU7f6s3l7eBHgH4Ofwq4/wPKrsPS82NdbiSETErmqbuM4ByioD3w58e0z7baXVhc8CMkqnEv8AXGIYRmhpUaZLSttEROQk5Rfnk5CV0OCqCpumyeG3DrP+nPUUJhVSnFZcZd/4rHjS89Np5t+sHiMUsVaXf3dh5cGVAOxJ20N2YfYJK8wnZCVgGAYdQzvWV4ge0xiWPoiIZ9VkO5xPgV+B7oZhHDIM425gEnCxYRi7gYtLjwHmA3uBWGA68ABAaVGm54HfS/95rqxQk4hU7tEzSzabbwxrl6RuPTjvQSJei2DT0U1Wh1JjxVnFbL95O7sf2k3oJaHErIshuE/lo0nFrmK8DC8eGPRAPUcpYh+peSUfk070e3405yi5Rbm0CW5TX2F5nN321RUR+6hJVeExVZwaXklfE3iwiut8AHxwUtGJnMbK1i6JHG/j0Y0AuEyXxZHUXNw/40icnUjHFzrS/on2GF5VfzhNzEnEZboq3f5DRCpKzk22OoRTVvYlbefmnS2ORETsShtEitjU/oz9QMkaJpFjNaStL5w5TgCiJkTRb0k/OjzV4YRJK8C+tH1EhEQ0qlEksa9b+txC4l8TbV/B+qx2Z1VoK0v2Jq2YVOFcQ3VH3zusDkFEbEqfiEVsKjUvlfCgcFoGqUiZNDzOfCd7/rKHzFWZ9F/ZH+8m3jQ7t/r1qqZpkpCdwJB2Qzg/6vx6iFROdwE+AQT4BFgdRrXm3DSnQpt7H9dGML12cNvB5I7Pxdfha3UoImJTGnEVEWmg7FrMJG9fHuvPWU/8f+IJvSQUw6fmH6rjs+K5/ovrObf9uSrMJPViTfwa/r7o76Tnp1sdCpMvmlzluXavtyMhK6HSc3b9W3AyHIXZBPyrNd/Nud/qUETEppS4itjUV9u/4mjOURJzEqvvLKeVfuH9AGjXpJ3FkVSU/F0yawesJS82j17f9KLz5M54edf8reas90umQw6JHOKpEEXK2XhkIy8sf4GsgiyrQ+Hxsx/nhQtfqPRcobOQu+fcXa6tX+t++Dp8Gdl1ZH2E51GH9y0B4IwdC6wNRERsS4mriE2l5KUANLgtT8Tzpl85HXOCabviRabTZN8z+/Dv6E/MuhjCrgo76WscyjwEQI+wHnUdnoitFbuK+T72e1LyUujeojuvXvJqhT4FzoJyx16GF/7e/ngZDf/jXHJOSYGpQmehxZGIiF1pjauIzTWGKWBSt3KLcjmceZh2TdrZYm1ewZECHIEOvJt403tub3zCfHD4O076Osazf0wpDvELqcsQRapl9d/azIJMLv/kcgCmXzGdbUnbKvS5oecN5Y4PZBzAZbro2bJnvcRYP/SeJyKVa/hf0Yk0Uk+e/aTVIYhNPTz/Ybq92Y018WusDoX0X9JZ238tux/eDYB/O/9aJa0TFk9wP84Zn1Nn8YlUxy5VuguK/xhNvfe7e3l91esV+vQJ71Pu+Gj2UbILs203+6I2yr44MBtBoSkR8QwlriI21TG0o9UhiE1tSdoCWDNCNGvzLBbuXYjpMjkw+QAbLtxAhk8GQQ8GnfR67JkbZjJjwwzS8tJ4bulz7vZAn8C6DlvE9o6fBlyZ43/ny47Lptg3BnaYRSIi9qSpwiI2tSd1D4C2BpAKyra+cJmuer/32K/GEpwXzIptK0j9LpW04WncOeROMhZkwAIwJ9Q8mb7j2zsA6N2qNwA+Xj48f8HznghbpEq39b2Nm3vfjJ/Dz9I48ovzq+2z6egmhkYOdR+X7eP68sqXuS/mPo/FVp/CAk9+bbyInB404ipiUyl5KbQJbkOroFZWhyI2Vfahtb4FFAawa/EuUh5P4dDEQ2R4Z1Taz2W62HR0E++ve5/oN6O58tMrySrIIj0/vVzsmxM3A3Brn1t54pwn6uU1iJTx9vLG39vf8inDZVOFX7n4lSr7zNlZfi/XxrSPa7/WJdXSm/o1sTgSEbErjbiKiDRQZR9ayypP+zh86uzau1J2MWrWKOaPnU+X5l1YvHcxKz9aiWEaONo6uPWRW8n3zYcfyj/vmx3fMDp6NACTlk/i7z//3X1uZ8pOPt3yKX+a+6dK7xkeHF5n8YvU1G+HfuOTzZ8w4fwJtAhsYVkcHUM7Mu/meQxoM4Bvdn7D8gPLK/Q5vuJuY0hYj3c48zBtrQ5CRGxJI64iNvXFti9IyE6ocsN5OX3FRMQA0K1Ft5Kfb3aj6aSmdXLt7UnbGf7RcLq/2Z3dqbsZNmMY6/eu56erf+LsZ8/m+j3Xs/G+jdw+5PZKn3/151djPGvw0oqXyiWtx16/Knbcl1Yav+3J2/n36n+TVWjtPq5N/JowousIWge3Zmi7oZX2OX4d7JntziQsMIxLOl9SHyF61IGMAwBkF6k4m4hUTomriE1l5JdMvyx2FVscidjNmyPexJxguhO9uPQ48orzmLR8EusT1lf5vPT89Cr3Be48tTPhr4TT8+2e/LzvZ3e7Y5+D3eft5qJNF/HhsA+ZPGUyrYJa8cCgB04Y4xMLK5/y+8Zvb5Q7jgiJ4Joe1zCi6wjG9BpzwmuKNGZHso/w7Y5vSctL4y9D/sLANgNr9LzGMuqqvctFpDpKXEVszuq9BcV+cgpzWJewjsyCTHYm73S3P7XoKQa/N7hc362JW5m1eRYAoZNDuW9u+QIusamxOJ5zsDdtb7mqwG2C2xAbHcu06dNoldeKH/7xA5O/m0xUiyigZFuOlMdTGNl1JI8PfbzWr+WCqAv48oYvmXfzPEIDQmt9HZGG7vfDvzP689HsSdtD6+DWrE1YW6HPvQPuLXccmxpLfnE+gyIG1VeYHlO27t3Qe56IVEFrXEVs6vkLnufpxU9bHYbY0EMLHmLGhhksGLuAjs3Kb5t0/Aj9rM2zeG3Va1zf83oAoppFUewq5vZvbqdnWE8yCjLKVSeeNmoadw+4Gy/Di/Rl6WSemUmPT3owrN2wCnE0D2jO3JvnYpomvVr1wjAMHl7wMIf/7zBBLwSV6/vaJa/x5fYvWXFwBQDDOw4ntyiXF4a/UBf/SkQavLJpwCeqbly2PKDM0eyjZBVm0baJVoWKSOOnxFXEptqEtLE6BLGpHck7gJIRig7NOpQ79+iZj2KaJok5iYQHhxPgE0B+cT5JuUkAJOcm4/N8xSJOl3S+hF4tezEyeCRHph8hYlwEzc5tRr8l/aqttmoYBrf2vRWAW/rcUuF88TPFOLwcdGvRjVGfjmLaqGncO/DeCv1ErOBleOHtZf3HobLtcPy9/avsc3zNg7IZOd/s+IYA7wDO7XCu5wL0sLLXEuQTVE1PkYbNNE2KXcV1WlDxdKGpwiI2VZacnOhDjJzeXKaLrILyBWWm/DaFp39+mtavtuZI9hGWxC0B4LrZ1wEwdfXUcv27NO/CX876Cz/c8gPjzfHsHbqXPX/bQ2FiSfXSU90iZO6YuTi8HACM7DaS3+75jXsG3HNK1xSpS7f1vY2iZ4qIahZlaRxl2+H4eVc94ro7dXe547Lpte+seYfzZpznueDqgcMo+TvRQvu4SiP3zY5v8J3oy7L9y6wOpcGx/itGEalUSm4K7Zq00z6uUiUTk8Vxiyu0T/ltCgA7k3eyaN8iAHfyCNC+aXumXzHdPUJjOk32/WMf+yfuJ6h3EGf87wx8W/meUmyzr5vNzpSdjOw2slz74LaDq3iGyOmtbKrwib6sXHlwZbnjxlQDoWwf1xC/EIsjEfGsfen7ANiWtK1Bz5KwghJXEZEGpqyKqMt0serQqgrny6Y9zts9z902OGIwC8Yu4EDGAXq27OluN02TLaO3kDI3hdZ3tKbrW11xBDoqXPNkXX/G9ad8DZH6sOLACt5b/x6TL5ps6ReFV0dfzRktzyDUv+oiZdmF2eWO7TDFua4dyDhIe6uDEPGgG864gcd+fKzcF8pSM43vL55II/HZ1s/ILcrlYMZBIptGWh2O2MhZ7c7i10O/MqDNAK767Kpy55457xm6NO/Cmvg1vLzyZYBya0qPTVqhZCpw2OgwwkaH0eZurauW08+etD3M2DCDZ857xtLEtU1Im3K1DfqE92HT0U3l+uQct8fp0Mjy+72apnnK0/utsidtD52BvKJcq0MR8aiyddzHfxEl1VPiKmJTeUV5ADhNp8WRiN28dulrvHbpa+71bQAH/nyAIN8gmgc0B0rW7TX1a8rEZRMrrCk1TZNDbxzCL8KPVje2UsIqYgPrE9azLWkbY/uMBSj3+11m1aFVvLTiJR4/u/ItqEzMBruva1peGgBF2rtcGrl/r/438EdBNqk5FWcS8ZCPNn7EuoR1p3ydyj68yOmtoLiApfuX8tX2rwB46pyniGwa6U5ayzx/4fMc/r/D5UZgijOK2XrdVvb83x5S5qXUa9widmb139rZW2dz57d3uo83J26utN8TC59wP96auJUQ3xD3uthjt7YSEXvKLszG1+HLk+c8aXUoDY4SVxEPuevbu/hy25e1fv6rl7xah9FIY/L0z09z/ozzue6LkkrBd/S7o8q+ESER7sfZG7NZG7OW5G+T6fxKZ6JnRns6VBHbs8sIZX5xfo2qyI/pNcb9ODEnsWQf15C2FD5d2KDXvFr9xYGIp+UX5/P5ls95eeXLJ9yvWaqmxFXEQ5ymk6zCrOo7VuH40TORMgnZ5fdybN+0+lImefvyWHfWOpy5Tvot6UfkY5ENdi2cSF3ydfgS6h9q+e9DgbOg3FY4K+5aUWk/L+OPj25lVYX3pO3h400fezbAehLiG2x1CCIeEfivQG768iYAsgqzeHfNuxZH1PAocRXxoLJ1DLWxJXELAEG+2oxdyjt2VObRMx894ShN2ShGQMcAOr/SmZj1MTQ7p5nHYxRpKG7sdSOpT6TSKbSTpXEcP+IaHhReab9PNn/ifnzsKOVdc+4itwEXNvJx+ADQIrCFxZGIeMbx21eVbVcnNafEVcSmkvOSad+0vfZxlQreX/8+ACO7juSNy96osl/u7lzWDVlH1vqSkf+2D7Y95f1ZRcQzCpwF5aYPbjiyodrnHP9BuLgBFzYq28e1rOKqSGPTzL/8l8aqKnzylLiKiDRQx+7Terykr5JYG7OWvN15FKc23A+zIp62bP8yrv/iehKyEqrv7EGThk9izpg57uM+4X0q7Tei6wj34+PXyTWG4kwHMg6c9HNmbZ7F3F1zPRCNSN1YfXg16fnp7uNWQa2UuNaCElcRDxrbe2ytnztr8ywOZBxgb9reOoxIGpPD/3e4QpuryEXs/8Wy9dqtBEYHErMuhtDhoRZEJ9IwHMg4wP+2/c/yD5GRTSPL7bPctUXXSvsdm5ye2+HcKs81NDuSdwCQV5x30s8d+9VYrvj0iroOSaTOHJu0Avh4+ZxSHZTTlRJXEQ8J9g0+pWm+ZVO+VGlRjufr8OWJs58oVzG4TPy78Rx6/RBtH2pL/6X98e9QfZVSEbHeh+s/ZNHe8mveKiu89n3s91Vew+lquPt+Z+RnAA37NYhU5ZLOl9CleRcAArwDuKjTRfg6tHTnZDXcuukiNuYyXUSERFSaWJys49cwyenLZboodhXjMl0EH1d505njxBHkIOK+CAK7BtL8UlWlFjkZVv+tfWrRU4zsOpLhnYa728b0GsPkFZOrfM66hHU08WtCuybtSMxJxOHlqI9QPax2/x3O73B+HcchUneW7V/G0+c+zW19b7O8gnlDphFXEQ9wupzsStlFfnF+ra/xzsh36jAiaQy+j/2e0MmhnB15NgPbDATAdJnETYxjdY/VFCYW4uXtpaRV5CTY4UPkvF3zOJpzlNCA8tP6K9tj9uHBD7sfJ+UkkVmQidPlJOlvSQ16G7WyLw7MWuyr+/aIt3l22LN1HZJ4wG+HfiMuPY418WusDqVefbL5Ex5f+HiVf2+W7l/K0z8/fUqfG08HSlxFPKDsDTgpJ6nW1wj0CayrcKSR2Jq4ldyiXGZdO4vLu15OUUoRm0duJu6ZOJqd3wxHUGMYbRGpX0E+QbRv2h5vL+smob35+5sAjOo2qlz72e3PrtDXYfzxe172XrMzZSevrnwVKKlUev0X1zN/93xPhetRTf2bnPRzYiJiSMxJ9EA0UtfOm3EeHad0ZND0QaxPWG91OPUmpyinXMXsOTvnMPqz0cSmxvJL3C8s27+Mfy37V7l9mqUi/dsR8YCyAhlTV0+t9TXWJawDoInfyb+JS+NT6Czk8YWPA9A6uDWZv2Wypv8a0n5Oo9u73Yj+KFqJq6Q4a/oAACAASURBVEgtXBV9Ffv/vN+yfVzn7ZrH97Hf06FpB4ZFDSt3blS3Ue51cWXe+O2PLbAOZ/5RoO2vP/2V+Kx44rPi+d+2//HEwic8GnddK9vDtnnAye3jWlBcwGM/PsYtX9/iibCkDhU5iyh0FlodhiVyCnMI8v0jcd2fvp9vd35L1393ZdjMYczZVVJRfHfKbqtCbBCUuIp4QF0UVErOTaZjs47ax1UAeG/dewD0COuBl+HF/hf2YzgMBqwcQMS4CFtMdxSRkzfq05JR1tX3rq70fP/W/YkOi670XFx6XLnjguIC9/tPQ0sQyvZx9T9ui5/q7Enbw7IDyxrc6z0dZRZkljvu36a/RZHUv9yi3HIjriF+IeXOrz5c8vvf651e9RpXQ6PEVcQD6qLIh1n2P1UVPu3tTN6Jw3DwbMyzrLxyJQDRH0QzcN1AQgaGVPNsETmRJXFLuOzjyziYcbDe711QXOB+XFU13S+2feHeKgZgaORQ9+MWgeVHJxvydjhlDmWe3H+HY7cZ+fXgr+QU5tR1SFJHdqbsdD9uHdya3KJcC6OpXzlF5UdcWwe3rrLvocxD9RFSg6TEVcQDyqY83dz75lpfY9bmWcSlx7E7VdNGTndzd83l/9k77/ia7jeOv0/2IkuIEWLv2FuNUrXVrl2qRVFUW1o/JbZWUXvVrFGb2qNW7S1GxApBEhLZe5zfH9c9yZUg497c3Ov7fr28cs8533POc697zznP93mezzNj1Qyaf9ecR70fIcsy5s7mmDua69s0gcDg8Y/w5+CDg3p5iJ5wfILyutDs9FXona3f7pyOrDvyrdsMjZsvbgIQk5C5Pq5B0UHK6/or6/PVP19p1S6B9lBPwEz7eBo25jbU+7Me++7t+yBaIP3d5W9WdVilLH9a8lON7al7OLvNcePWi1s5ZpshIRxXgUAHmEgmuNi4kNdC1KcKso/3cm8Wr1iMHCbj7uku0oIFAiPhzdTJ9PAe5o3vCF9l+dzTcxrbU4u5nHpySrk+GJo+gvqzSJIz58S8KYLo9cJLazYJtEuLki3Y0X0Ho+uPpnGxxtwIvEGbDW2YdGJStsQsDYFCeQpp9GWWJIkd3XfwZbUvAdXvuFL+lDThSosr4R/hn+N25naE4yoQ6ID4pHhcbF0o7Vw628cSqcIfLkmxSVzuf5leq3sRVDKImldr4tjE8f07CgSCTKOPPq7hceEUsC0AQGmn9O8X+WzyUcyhmLKcxyKlPGDkgZFUKVCFvlX6Aiphl3w2+fiy2pfMbjFbh5Zrn6x++mo14SlNpwCan48gd1EkbxE+K/cZFqYWGq2bJp2cRP5Z+emyuYvRtoOZdmoaZ/zOaKz7rNxnrGi/gmuDrrGy/Uq8hnhpiLEVml2IE74nctrUXI1wXAUCHRCbGMvtl7ez5XSu/WytFi0SGCJyvEzg0UA2NNxAw5MNsSyYOdESgUDwfvSZwXD/1X1iE2NJ/iUZn+E+7xw7os4IAH5s8KOyzuuFF9bm1kz9eCoABfMUxMnaiRXtV/BRsY90Z7guUO6Xmfv/aFe2HSvbr2RUvVFs6ryJea0ypua/++5uhu0blkkjBdlh9bXV+ASrvuc1C9VMs33bnW2cenwKgDsv73D5+eUctU9XRMZHMu7fcZx8fDLd7VVcq1CrcC0Azgw4Q2+PFIXs036nc8RGQ0E4rgKBDlA7rI/DHmf5GKKX14dLyNEQkmKSMMtrxn8L/uPxoMe453PXt1kCgVFib2lPBZcKWJha5Oh5o+KjOP/sPGFxYRlynv84/wcAicmJyrrwuHB8gn1wm+OmbEtISqDlXy3ZdHOTbgzXMQ5WDpkaXyl/JZqVaMaqq6voub0ntZbXytB+u+/uZtW1Ve8fKNAK2+9sp/+u/qy6qvrMO5XvpGxLnUKrzi6osKgCNZendW4Nka5bugJQzL7Ye0aCi60LC1otUJZvBN7QmV2GiHgyFgh0gFogY/6F+Vk+xtmnZwFwtBapoR8KyYnJPBj7gOvNr+P3u0pZc1r7aRzsfVDPlgkExkur0q249c2tHO/j+jDkIQCeTTwztZ/nCU+OPToGQERchIZ4U2JyIg9CHnDwwUEmHp+oNVtzAjsLOwCcMnnPuxF4gy23tjBs/7BMiVOZmZiJtOIcpPPmzkCKk2phasHLH14S+VOkhlJ2GecyerFPlxy4fwAAdwf3DI23t7JXXv9962/R6ikVwnEVCHSANmqlgmOCKeVUSvRx/UCI84/jerPr+M30o+Cggrh970ZCUoKocRYIjJQqS6oA0K1it0zvu/r6akAVcU0t6GLIqsJVXFWfh7lJ5tTSfzz8I98f/l5jXUaum7vu7iIwKjBT5xJkje13tiuvC+YpqLzOZ5NPo0XM8X7HFZXor6t/bTTPP3eH3eX3Fr8r6cAZYXGbxcrrS88v6cIsg0Q4rgKBDtCGs5GQlEB4XPgHIRP/oRN2NoxL1S4RcSmCcmvLUXZJWUytTFl1bRXlF5bnVcwrfZsoEBgtRx8epcHKBjwKeZRj5/QK9FImOMs6l830/jUK1gBUdYIfFf2IobWGcrzfccY2HKtVO3MWVbp0ZntYxibGYmNuo7EuJvH9LXUCIgMydR5B1lFHW0Glrvsmy9st58rXV2iypgkuv7kgeUpsvbOVsNiwnDRTJ5zxO0PXLV1pVrwZZiZmGd5vcM3BeBTwAGDb7W3EJcaJiWyE4yoQ6ARHa0dszW2zNJOuZuvtrbyIeiH6uH4AmDubY1XUiurnq+PaJ6Up+Y3AGwREBmioLwoEAu0SHBPMGb8zGXJ2tEGynKzUrV0YeCHD4lA3h9xU0ijVjtruHrsZUXcEC1ovoLF7Y90YnENc8VcJ8cRkUlU2NjE2jTP0rjZDcpLmw79wBnSPqWSqvHa1c02z3c7CjmoFq9G+bHtl3auYV0xuOpkVV1bkiI26wi/MjxuBNzLltKpR93qdfW42VlOtWHtdiHYKx1Ug0AEmkgn5bPJhZWalb1MEuZSEkAT85vghyzI2ZWyofr46dpXsNMb4hvpmuCZGIBBkj2Q5maTkJJ1nuezx2cPM0zMJGxuWqdTBivkrKq8lJDbf2kyNZTUIjQ0FYPCewWy+tVlxxFxsXbRruI6Jio8CIDmTfVxjE2Op6FKRko4lAXgy8gkuNmnfe+TNSG60ucHDsara4sbFGr8+n+GmVxsK6u9iBZcKGkJMb7KiXYqT2sCtAaeenGLRxUU6t0+XvIxW9afNyu9xWrNpNC/RXFnec28Pj0NVop8rr65E8pRYeGGhdgw1EITjKhDogMj4SPJY5qG6a/UsH0OtcClmg42P8EvhXK5+mYdjHhLlpXpYSy/qIhxXgUD3qDMa1l1fh9lkM1pvaK3TXpILLizA64UXCUkJmd5XXc/qG+pL963dueJ/hfH/jgdg3Y11XHx2ESdrJ0bWGcm8lhlrC5NbyIw2RFJykpJGGpsYi5WZlRIxd7N301Bejn0ai/cAby5VuUTY6TAsi6jairUo2UJ1rEw6yoLMo07L3ttz7zvHudi6EDY2jGG1hrHr810kJidyNeAq3x38jsj4yJwwVeu8jHqJhKQhopZRzEzM2Nl9J21Kt2FT501svb2V+ivrc8L3BLdf3gZg2P4Pq6WTcFwFAh0QGR/JzRc3s9VeYc1na7RokXEjyzLHfY/n+plzWZZ5tuQZVxtcRU6SqXqyKnYedumOjYqP4t6re5TLVy6HrRQIPiyK5C0CwK9nfgXg0INDSvsKXXD44WEg821fANqXUaVSTjk1RVn3VY2vANVDbkJyAgXsCjCn5RyqFaymBWv1wbtTp2f+NxOzyWY4zHQgOiGaRW0WMbreaO4Nv4fPMB8kTwmrqVYU+r0QgesDuVD6AoHrAykysgh1H9SlyAjV/7da1Tm1kyvQPrIsY25izpgGYzI0EZvXMi/zW8/H2caZ/lX7AzDn3Bxm/DdDx5bqhsCoQJysnTA1MX3/4HSwtbBlT889dK/UHYDnEc9psqYJ673WA1DRpeIHpTosHFeBQAeoo6R3gu7o2ZIPA79wP+aem8sZvzP6NuWd3B9xn3tD7uHQ1IEaV2pgX9f+rWPjkuLoVbkXHcp2yEELBYIPj3L5yrG3516+q/udsm6Pzx78wvw49/Sc1rNeHK1U7V6y8iDrH+mvsdyxXEdFwMXMxIzE5ESSkpOouaymwaZYvq2mPzQ2lNXXVjP2aIoAle00W2oVqkWtwrWwMbfB2twas0Qz8kblxT/SH7tqduTrnI/ad2tT6vdSmDunKBY/j3hOzUI10wg7gSqi22xtMyRPSWQ9ZZPYxFgSkhOwt3z7/e5tdCzfEVtzlerw1FNTDVKsMr9tfnpW7qn14wZEBjCz+Uwehz3GcoolA3cP/CAEx4TjKhDoAHXKU3b6uJ54fAJQycUL3k1YbBi77u7K9Rdtp1ZOuE9yx2OfBxb53h2Nd7J2YmWHldRzq5dD1gkEHy6tS7dmVotZ7OmxR1n3zb5vqPdnPbbf2c4J3xNaO5f3MG+ufH0lS/v+dPQnjeW5Lecqr81NzElMTuTmi5tc9r+crfuPPlBHoB3TiUQ7zXTCcaYj/Xf1T7Mt74y8yLKMnCxjsd+Cw2sPM2rvKAAOSgep8FcFrN2t0+yXmJz4VsGcwrML8++jfwGRSpxdwuJUKd2pe5NmFDMTMxoVa6QsXw+8zlm/sxpjEpMT8Qr0Unql5jYmNZ3EvFbaSdu//c1thtQcAsDC1gv5scGPSgr1n1f/pODvBdngtUEr58qtCMdVINAB2pihDYkNoYxzGYMT2NAHzyKeAaoZ9NTIskxcYhynn5ymw6YOxCXGMfH4RNptbJdjtgVuCsRvjh8Azq2ccR/vjmTy7lS4yPhIemzroYgwCAQC3SNJEm3KtOHiVxcpkrcIe3xUTuyA3QNosqaJ1h6Mp5+arlyzMsuO7jtwy+tGCccSABpRLEdrR5ZeXsox32NasTOnUUeOTaS0keiQ2BDldTXXavzS6Bd+++Q3ZV3oiVCu1LnC7c9vY+tgy/VG1wFVG5a3pQIffniYc0/PEREXobE+PC5co79rXGJc1t+UnkhISiAwMpANXht4Fp6175q2UNciZyXiCrCn5x6mNJ1CA7cG1FhWg/or62M3zY7e23sTmxhLpUWV8FjiQav1rVh8cbHGvuFx4ZRfWJ4Lzy4QnRCd7feib8q7lGdRm0X4jfLjm1rfALCs7TIARXG8c/nOGvtMPTkV88nmbL29NWeN1RHCcRUIdEBmRCbeRkxCDM/Cn2VJwONDQy0RP+LACI4+PEpEXARLLi2hw6YOVFqsEjPZfXc3VlOt8DzhyR6fPRy4f0CjZcLmW5u15iiuu74O5ynO3B12lzs97hC0MyhNC4Z3sebaGjbd3MSqa6u0Yo9AIMg4NQvVxG+UHwVsCwAprVXSa+ORWfzC/Jh7PutlDZ+V+4wno55wY/ANzn15TiOKdWaA6phXA65m2059cuvlrTRRtdSMrjcaz6aefF//e2LGxfDI5RHXm14nPjCecmvKUf1ydXa67lTGTz05lXnn52E3zY5P1n2C5ClpRG6jElQCeVf9rzJ4z2COPdJ0/A2xfnD8sfG4/u5Kr+292H9/v15tsbeyZ3yj8VRxrZKl/U0kE8Y1Gsep/qfIb5sfUP2frfdaz5e7v+Ru8F1lrFqwSM21gGt4B3lTZ0Ud8v+Wn6DooKy/kSwQHheO00wnll5aqtXjquvyQVXjHj42HJ9gH9wd3Lnw7ALTTk0jOiGa4n8U53/H/kdiciIvo15q1QZ9kfmmQgKB4L0UzlMYdwd3pUl8VvjH5x+S5WR8gn002iAI4Fn4M4rMKcK+nvtoVKwRG29uVLY1X9ecPh59WHdjHU7WTryKeUXDVQ3THKPV+lZUKVCFK4Ou8Cz8Gd23dqeAbQECvs9+uvHCnQuZsnwK/s/8KTK6CCWml0AyzVivRoBDDw9hIpkwvtH4bNsiEAiyxpVBVyg8u7Cy7BXoRXGH4llKeVTTeLWqBUtFl+xd020tbKlTpI7GOrWDncciT7aOrS/OP7tAHSAhOYF5F+ZplEnIE2RexbxiyskpdK7QmdinsSSGJmJXyY5CXQthEm1C4aGFMbU21VCfLeNchoknJirLRx4eAWD1tdXKusF7BnPwwUFFSXrpZU0nIy7J8CKuqZ25ukXq6tES1YTPpKaTsn0cSZIIGB1AVEIUnTd35tCDQxppsXUK16FH5R7cDbpL2XxlOff0HMHRwcr2qIQopp+aTqfynWhQtEG27ckIhx4cIiQ2ROfdAdSK2kXyFqHRalVq9bh/x2mMcbN306kNOYWIuGqBuivqsv7Gen2bIchFSJKElZkVJpL4iWWX7w99j+Qpcf7peWXd5deN6ltvaE2/nf3S7LPl9hYADWGjCwMvAFCvSD0lMnE98Drnn56n6FxVX7nU6WHpEZ8Uz5pra94ZBU+MSGT6/Om4BblhssSEUrNKYWKe8e9BQlIChx4cordH7yyrEAoEguxTKE8hjeW+O/viMNOBwMhAbr+8TWR8JJeeX8rw8ZLlZKXn6kfFPtKqraCqcQNYeFHV1/Fd/TJzIzEJMcrrTTc3ARCdEE2Z+WXov6s/TtZO/Fr3V56Pf86F0he4N+QeABYuFhT9viim1qrrpZ2FHcm/JCNPkNPViOhWsZvG8q67u4hNjKV7xe7KOre8KQ/5hhhxTZ1NpM4c0BdB0UEERgZqRVhJkiTsLOw42Pugsm547eG4O7iztO1SGqxsQLmF5Tjy8Aj1/qxHp82dlHG/Nv+V2edm03BVQ513IHge8RzJU1LUyXU9eZDfNj/XB19nb8+9LG+3XFmv/v4f7H2QNqXb6NSGnEI8VWeTpOQkzj87T+8dvfVtiiAXERwdjLmJOU3dm2b5GOp6EG2kHRsyNwJvACkCD6ApeqWuOwsfG86f7f+kj0cfZXaza4WubOy8kR/q/0CtwrXY23Mv6zquo0ahlEh4/ZX1Nc4XnxTPuKPjCIoOIiAygGpLqyF5Slx+fplpp6bxxa4vsJhiwb3ge9wLvqfUM6v/muUxw8HTgcFfD+ZJvSeZfr9bbm8hNjHWaG4yAoEhM7re6DTrXH93pe6KugzaM4hay2tlOAXvbtBdQmJDsLe010j10xYuNil6COMbjTe4Pq7pMXTfUO69usf6S+t5+sdTzpU8x5MZT3Dp4kK5dW9vFabui72j+w6+rPYlVwddJemXJOQJMgtbL9QYO6WpqrVQQGQAtQvXprhDcZ6MekL8/+KJ/jlaw4k1BGRZ5uKzi3Qq34k+Hn3IPys/Iw+M1Js9k09MpuDvBbVeY3ruy3N8UfUL5racy6MRj3C0dlS2fbLuEwDalmkLqCKRZ5+q0s+H1x7O2CNjlQmJhKQE9vrsJSQmBG1x1T8lXd/e0j5bWRoZxaOAB3kt8zKw+kCuD77O8nbLefnDS5J/SaZFyRbp9oo3RITjmk3U4fnspIQKjI/wuHC8XnhhbZ5WyTCjLGu37K3bAiMDkTwlo1ePg7SO+6QTk5R0L1Cl//iO8CWPZR4GVBvA2o5r2dtzL09GPqFV6VZ8Xulzfv1E1Z+xdenWlHQqiYWpBSFjQngyMsWx/N9H/yNmXAz1/6zPtP+m0fKvlhT8vSDXAq4B0Gt7LzxPeCrjvYO8KbOgDBtvbiT+RTxXW1zl5T8veRL2hN9cf+OZ8zOehGXecQ2PC6dvlb40L9E80/sKBALtMqvFLELHhKZZHxEfoVx/Lzy7QGDku7M1IKWWcl3HdTrJxlGLtbQr045JTSdRNl9ZrZ9Dl8hoRsHO+p1VUnpnhM7g/sj72FW1o8blGpRfVz5dpeA3yW+bnxXtV1DVtarymeezyUf42HAlMtWpvCoqV9+tPkf7HuXGENVkqbmpOdbm1gb3wP80/CkR8RE0L96c7+qpWjxtv7Ndb3oZ6lTZPJbaTWGvU6QOqzqsUv5fXe1cqVmopkZ0c9fnu/AZ5oPfKD8OPlBFabfe3spvZ36jwsIKXHx2EYspFrTd2BanX524F3xPqzbmt83Pn+3/1OoxM4JHAQ8GVh8IYHDf3/chHNdsop5B+qLqF/o1RJCrUDtbmUkjywzq/rDLLr/duc0MT8Ofcvn5Za0cS5fIssyE4xMA2NR5ExamFrQq1YpiDsU0xpVwLPHeeg4HKwfc7N24/PVlzn55lskfT2bGfzOUNGT1XzXqmqFFrRcR/GMwa66vAeDi7otcrHaRl8df0nN1T4rNLcbGmxupnL+y8iCZUaovrc6QvUOoUqDKW3sZCgSCnMXeyp4/Wv5B+7Lt093edmNbXH9/v3CT+nnB1sJWq/apMTUxpal7UwKjAik1rxRTTk7RyXlyim/Gf0Ndn7r09ujNiN9G4HHYgyqHq5CnevYdoDyWefii6hfcHHKT8i7lkSfITGs2DTsLO+ws7ADVJOKI/SP4dv+3OvssI+IiNFKkM8NO751pxIhA1e+2SoEqVC5QmaquVelZuSd+4X5YTLFg2eVlb+16EJ8Uz7LLy96qwpxVIuIjlM9Ul1iYWnDxq4v80+Mfpn08jbj/xWEimVDauTQAJ744wYxmMzjz5RlqFarFg5AH1F5RW+MY/Xf110pKc0JyAlZmVhzte5TOFTq/fwdBhhGOazYxMzGjj0cfKrhU0LcpuZpkOZmtt7fqvK4gszyPeE7XLV01xBy0gfp9qmuNssLB+6rZwfSULM1NVE3U3yz4335nO01WN9Gob8kI1ZdWp+bymlkzNAdJHcHsXqk7kT9F8k+Pf7J1zOoFqysztOom4XM+nUOl/JWUMT0r92RknZFs67aNIbWGkNcyL3eD7tL1TFfajm/Li6QXDB04lCMeKZHgzV03c+7puTT1UV6BXhT/ozhbbm1R1sUlxtFmQxtFDbRZ8WbZek8CgUC7fFvnW7Z03cLvLX7P8jFmn50NgI25jbbMSoOrnSsXnl3gQcgD1nvlfu2NuefmUndFXc4/PY+lqSUAdkl2TF8/nTlr5tDrVC9Wd1iNqZUpTs2dtBo9MjMxe6fwoYlkwrwL85h/YT7jj+lGKC/vjLyUW/j2lOe3cen5JTr+3ZEhe4ek2Va5QGWuDb5Gw6IqUUKJlM9s0J5BXHx+Mc0+W29vxXaaLYP2DNJ6JldkfGSOOK5q8tnk46ePfsLCVLNXes1CNRnTcAzuDu7s76WptKy+95/2O829V9mPun5W7jNixsVoPEcItEO2HFdJknwlSfKSJOmaJEmXXq9zkiTpsCRJ917/dXy9XpIkaZ4kSfclSbohSVJ1bbwBfeNk7YTXCy8WXVykb1NyNUsuLaHrlq6supq72nv8dvo3tt7eyk7vne8fnAm00cc1LC6M8vnKpysuYWVmBaSkOKnpvrU7Jx6fSNOX7n28jFbVaKVW4MstlHBQ9St0tnZmh/cOAAZWU6XAmJuaa1XAqIxzGeQJMiPrjsRriBd3h91lUetFrGy/kjkt5yift5mJGUfcjvDNoW/wr+dPn/59iCoVRciYEPb02IM8QcYvzC9NX7lXMa+YcXoGvqG+dNvajSdhT5BlmWJzi7Hv3j5AJSCR1bYBAoFAd1iYWvBdve/wHuqtsb5S/krvVQmWZZldd3cB4Gjl+M6x2cGjgIdyfzAERh0cxfln5xl9aDSe21WlGEUCiuDx1INnQ58RsCRAbyJ1tuaakXFtROLSQz0hO//8fGotrwXAf0/+e2v6+dPwp8q4N+t102NJ2yX83PBnANqUbsOqq6uQPCVO+J4AVJHWrlu6KpHWfjv7IXlKWnmOgZx3XDOCs40zL39IqU1f+9la5XWfHX3wDvJObzdBLkAbEdemsixXlWVZHa4ZCxyVZbk0cPT1MkAroPTrf18Di9McyUC5FnCNbXe26duMXM3ziOcaf3MLHct3BKCgXUE9W5KWiPgI7gbfVST6U1MuXznOfnlWmVFVo77RqGupMsrnlT4HwCfYhzsv7xAVn7n9dcnSdkuRJ8h4FPBg7JGxDK89nKXttNsT7W2UcS7DkFpDsDSzVNYlRakeXvK3zU/lPZVJWpBElFUUdYvUxcHKgTZlVKJKtQurUpD23NsDqBQzG61qpDGbXWxuMT5e+7GiZty3St9MpxcLBIKcJXXtaAnHEhS1L0p8UjxhsWH4BPukGR+TEKMhKFfSqaTObBvbcCyHeh/S2fG1RVhsGJJnSiTwtN9p3F+6A2BewJzmfs3ptaAXU1rrL91ZkiSN1kKpBQK1TVB0EL6hvtx5qSoD+mjVR+mmJ196fgm3OSllMMP2DUPylDhw/wA1ltVg9tnZDN83nC6buyhj7CzsmNpsKvIEmVUdVrHk8hJAJXy1x2ePIoD4JiGx2hErmtB4Aj82+FErx9Im+WzykTg+keRfkjE1MaVX5V6A6jMuv7B8to69484O+uzoo7e6YmNGF6nCHYA1r1+vAT5LtX6trOIc4CBJUu7zFjJJ6ibik05kv0+VsVLKqRRArqvbU88CqmtGtUVp59JUda1KuzLtsnyMQw8OKX1c38TWwpYe23qw/PJyjfXq2lrfUF+NtOx99/a9s4Z1erPpdC7fGTMTMyosqkCXLV3SHRcVH0VobKjWZmIzw6A9g4hLiqNO4Tp6azPk/6c/54qfI/puNJIk4dzGmaIOqpYT39b+VmOsvZU9xR2Kc+ThESRPCbvpdtx6eSvNMY/7HsejgAfdKnZjZfuVOqt/EwgE2kMdwapesDr77u1DRsZhpgNlF5TV6BEK8PWerxlxYAQAZwacwczETKe2nfY7DZCro0Y3X9zEPNGczuc60+Ws6n5zzf06ANZFrbBwtnjX7jlGeReVA1PcobhOU7wDIgOwNLNUesY6WDmke59TCxNWyl+JoB+COPFYFTVttb4VV/yvMPrQaP71/fetQQIX2xTl6Vsvb9FuYzv+OP8HNuY2LGq9iAFVByjbyy8sr5VWQC1Ltcy1YoOmJqZK+vmqDquUsqHs2nvF/wrrb6zXCDsCbgAAIABJREFU+W/9QyS7T38ycEiSpMuSJH39el0BWZb9AV7/zf96fWHAL9W+T1+vM2hSy3tnt6G4MdO8RHOG1RpGfFI8kqeUrjOW03gFelFjmUoNevj+4XTd0lWrDpmEpLNWNpeeX8I31BfvYNWDiX+EP3VX1FWc1U//+hTTSaZ8u/9b/r75N202tHlnDautuS1rO66lXD5VrU1q1d7ULL60GMeZjlqvCX4X3+7/FslTYtU1VZp5VdeqOXZuNUnRSXj39+buwLvYVbHDzDHlZqS+MSXJadPIxjYcq7xOlpOxNktRwUxdx3qq/yn+7vK36NsqEBgIU5tNJWB0AFtvbwXg/qv7yrb+u/prjPUN9QVUfVXrudXTuW0/Hf1Jef049LHOz5dZDvocZObYmaxesJphB4bh8dgD5NzZ+k3dIsXByoF553XXXuis31mm/zedxOREyswvQ2hsKM8jn3Pg/gEevHrA3SCVOOCyy8sol68c1wdfx9nGmU9KqNq+qPuUA9x+eVujNcybRP0cxcr2K5XlWy9uMaTmEIbUGkLLUi2V9S+iXih9h7PDyccnld9Absbc1JwzA84Q/XM0JR1LYj/DnkvPL3H5+WVkWc7U82FUQhS2FrZGp+ibG8iu49pAluXqqNKAh0qS1OgdY9P730vzLZAk6WtJki5JknTp5cuM9UbTJyuvqn78Zwacwc3eTUnzEGjiaufKrBazyGuZF4Bn4c9y5LzxSfHsvrtb44Kz/PJyFl9cjMcSD42xW29vxeuFF6DqwXX4weEsn/d5xHMSkxPpWK5jlo+hFmV682IZGBmo1LeopduXX1nO+Wfn0xxj/oX5fL7tc2X5bRfeWstrMXjP4PemBoXFhiEh5WjEM7VqYn23+jkuhBbtE82VulcIWBNAsV+K4XHAA4v8KdEA9Wfx4+G0qVB9q/SlXpGUB1V1+6xGxRpxpO8RXv34irNfnlV+FwKBwHB4Vy3pkkuqdMzAyEDO+qn6R46qOypH7EpdQ6vWL9A2j0Ie0XdH30wr4oZfCse3iS/fbfyOaMtoLs+4TK8zvVjWbpmG85VbONznMBs6beBqwFXGHBnD0/CnWjt26vtx6pIgtTjQ1ttbabW+FaXml6LcwnLYTrPlUegjelXupdx39vTcQ8DoAGoWqknsuFhOfKGKwKod3fSwMbfhi6pfsOYzVXLkmAZjmNViFqCZFTf307lsu70t26KaLda1UH4PuR1JkjA1MWXp5aWEx4VTa3kthuwdwmd/f5ZGgfhdRMZHpqmRFmiHbD19yrL8/PXfF8AOoDYQqE4Bfv33xevhT4HU/SmKAGlyGWRZXibLck1Zlmu6uLi8uTnXsfHmRkCValpnRR0qLBLqwumxwWsDVlOtFLVbXabcpGbE/hF02NSBC89UN0SvQC++3vM13+zTrCO8O0x1ka+ypAp3g+5SfVl1WvzVgm5bulFuQTlCYkK44n+Fpmua4hfml+Y8bxIaG4rXC69sCRIsaLUg3fUR8SnCS6f9TiN5SoroB6iid782V/UtfbO/8MqrK4mIi+DYo2Nsu72Nxqsb03t7byLiI1h3Yx11V9RldYfVnBlwRmO/pOQk/ML8CIsLQ0Zm0J5BWX5fmeH80/McfXRUWT494HSOz2A+W/SMuOdxVN5XmeKexZFMNc+vTu1N7wHRysyKM1+eob5bfdqWaUv3it1Z3GYxuz5/LdJi7ajRc04gEBgOqWvf32TI3iFInhKuv7sq2Rgj647MEbtSZ6XEJcbp5ByeJzxZd2OdEnF+H3LyaydNAvtoe6Z1nEbvJ70ZPWY0JRxL8FWNr3RiZ3Yp5lCMHpV7KMvpqfxnFXVaMMC5Z+feO16d4Zf6vm5hakEBuwJIkoSlmSW1Cqkmtb+q/u7PU5IkelTqwZgGYzRqtmsVrkXLUi25M/QOEfERfLPvG8YdHZep95WauMQ44pLiNGqFczsWphZM/Xiqsnzx+UWOPjzK49DHGc42C4gMEBPSOiLLydeSJNkCJrIsR7x+3QKYBOwG+gEzXv9VP1HvBoZJkrQJqAOEqVOKjYGMXrw/VNRpIjNPzwTQSvrJ+0iWkxURgubrmtO2TFs23dykMaZGwRpMajqJ0k6lsTC1ID4pXkOafsttVcuSen/WU/p4rruxjp8/+plkOfmtkUf1DOWxR8foVrGbVt9XKadSPB75mGJzU3qXXvG/AoD/aH9c7VxpWLQhPzT4geO+x5n+33QG1xhMp82dGPjPQAb+M/Ctx/aP9CcmMYb1XuupvaI2K9qtYEC1AZhN1rxUrPdaz8DqA2ni3kSr7+1N1t1Yp7z2bOKp03O9jRIzSuA22g0rt/SjK1Vdq1LaqTQ1C709Ffv0gNO6Mk8gEOgJKzMr1nVcR58dfd47tkjeIjlgkQpr85SShLC4MGITYzn95DTNSmivzZbaEQmOebcSfaxfLL6/+IIE5VaWI7FCIr2+7UWyaTJWFm9eU3NfqvCbdN/anW3dtCPGmTrK+rYWNOXylVNqlYvZF2Nz182K8F96WJtbkzA+IUO1leam5sxoPkNjXV7LvEqrGHX529oba5nefPp7j5ceL6JUsasCdgWytL++KO1UWmM5KiGKqIQoqi+tzp2hqsxK31DftwqtRSVE8WnJT3Vu54dIdiKuBYD/JEm6DlwA9sqyfACVw/qJJEn3gE9eLwPsAx4C94HlgMFLZ158ltILq6xzyozVqAM5kw5kSKgdPLV6qjbTbdLjZdRLmq9NKa6PjI/UcFr/7vI3Tdyb8E+Pf2hdujWSJBH3v5TZz29qfkPbMm2Z++lcAMVpBZh8cjLdt3an9PzSSJ5Suqpx6hQgteOcFdRR1DcfeL47+B3HfY+zvdt2WpVqpax//t3zNLPBTdybcLD3QUU9OSMM2TuEP87/AcDAfwZiMin9y8QXO78AVClj3bd2JyAygOpLq9N7e+8Mn+t9VM5fmU7lOxH0QxC/NP5Fa8fNDKZWpm91WtW8axJDIBAYL709evNR0Y/eO+7K11dywBoVqUWZWpduzdgjY2m+rrnGfdcn2Cdb0Vh1tLl92fbpbk8ITeDB2AdcKHOBwA2BmOczR5ZlWqxrQbJpMue+fH+EMTehdla239muNY2Ht6VZT2qSIvR5qv8pHo14xL3h9/Ad6ftOp1WNtgSBOpbvSB+PPiTLycozjV+Yn9I2JyOoM6by2+Z/z8jcRXqaFaBK4zabbIbZZDNKzS/F+H/T7+97tO9R5reen+42QfbI8pOWLMsPZVmu8vpfRVmWp75eHyzLcjNZlku//vvq9XpZluWhsiyXlGW5sizLl7T1JvTF7HOqZuInvzhJGecyyvq55+fqrN+XoSK9UeKcXm/S7PLHuT/ot7MfRx8eJf+s/BzzPQbA6HqjlTFrP1tLwvgEulXsxrF+xyiYR1PYWq0kN/vT2fzT4x9G1B1Bfbf6gCqydmfoHVqXbs3mW5t5GPJQ9V5+y8fX/3zNrDOzlONoQ2QiIj6CSvkr4WzjrKwLjg5mzrk59NvZj47lO7Kv1z78RvnxeOTjNO/lTULGhDCj2QwSxicwr+U82pRuw6LWKf2Hrw26pvH/4u7grrH/inYr6Fm5Jz81/IlmxZsxsclEJE+J7lu7s/nWZpZeWsrVgKus91rP3HNzsy109TT8KQOqDWBbt20an0FuwzvImwchDzQUxgUCwYeDOvX/Xejinvc2vq/3vfL6iv8VTCQTrMysKJxHpYcZFhtG2QVl+WZvSvxg2+1tWE+11tAUeBfjPhrHnaF3KOFYIs22V0decb7kefxm+uHSxYXad2tT8teSPI94zmV/lbp9nSJ10h5UD2r1GSV1cEKt0eEX5seCCwuYc3ZOlu53VmZWjKwzkmkfT+OXRqqJ2a+qf8X4xuORJ8jIE2Ty2eTD3cFd6cyQ01R1rUpAZABPwp4QnxRP0blFmX4q49FXda1to2LvksDJfXSv2J0zA87weORjXGzeXrY45dQUJE+J7Xe288m6Tzj/9Dy/n/mdVutb6aX7woeA0GnOBus7rWdYrWE0KNpAuRiriUqIEvntqVBHowraFcQ/0p/KBSpr/RwjD6rqh9ZeT2kkfbD3QT4u/jEj6ozAzd7tbbsq7P58NzGJMRq1S0f7HiU+KV75/3xTkCM2MZblV1RtaX44/AP3ht9j/Y312X4/ITEh3Hxxk+iEaKUm+M+rfwJoTJRkNAXNwcqBMQ3HADC8znCG1xkOgNcLLz4p8QlVXKvw8oeXhMWGcTXgKo2LNebk45PsvrubWS1mIUkSX1b/UjmeugffxeeqzIOJJyYq20YdHEVT96ZUca2SxXcPM/+byY0XNxSxidyKuu5Im7VPAoHAcHC0dmRL1y1svLmR7Xe2pzsmJ2vzU0861lhWA1c7V+oWqavYsOiiasJy5bWV/NjgR6ISohiwewCxibEceXiEko4lMTc1f2cWiaO1I95B3lwPuE4V1yrIyTKJIYmYO5tjW94W+/r2uE9yJ0+1lNrGpZdzpv+2LpAkiSN9jtB8XXP8I/0p41yGZmubKUJKrUu31qgVzQiO1o7MaTlHWfZsqp9ymHcxqIZKz8Leyp7jvscB0p1IlmWZJDkpTbT3l8a/8GX1L3GwctC5rdpEkiRFAdzF1uW9ImedN3cGNDsyLL+ynK9rfP22XQRZROS2ZQMTyYQGRRsA0LhYYwAGVlPVD0bFR+nNrtyI+nNSo820yvNPzyN5SrQt01ZZ92N9lcJrWeeymJmYZchpBVV9yJu9Zq3MrDQmITqU7QDAod6HCBsbxtfVNS9Mg/cM5tczKnGkko5ZbzSv7s+WWh0wJCaEqq5VFTEpbbCozSKNVGJ7K3uauDdBkiQauzfm909/T/ehS/1wtPaztfzvo/+RzyYfztbOFLRTRX6vBlzNtIS8GlmWOfTwkEa7qdyKOpsgNzZYFwgEOUOXCl3Y1m0b4z5KEbIp7lCcQTUGcX3w9Ry15VHoI43lgMgAjvsep9GqRkieEj//+7Oyrd/Ofnyx8wtFODFZTman907+9+//3nmO9TfWU39lfWacnkHIsRAu177Mzc43kWUZy8KWVP6nMkHuQbjNccMr0ItB/wxi8snJAFwddPUtR83dEap6bvW4M/QOtQrVouKiiorTCppCSxklMTmRqPiobKv26hJbC1sq5a+E40xHPv1LVbP576N/WXFlBX+c+4M80/PwMuolow6OwnyyeZr7vbW5td6ixdri9svblHQsSaX8lTK1X3a6SgjejnBctUS1gtWQJ8iKWE1O9rk0BBoVa8TPDX9WFFhPPj6plePKsszCiwsB+LTkpzwa8YikX5KY1mwa/qP9KeZQ7D1HyDzdKnYj8PtAPin5CXkt89K8RHNG1BlB3yp9AfjvyX/K2LfVSWSV6c2n52it1LvY32s/vT16071Sd4bUGkLXCl25MugKp/qfAmDg7oGUWVCGYfuGZfrYE49PxCfYJ8db32QF9SSMSAsSCARTPp6iTGY+Cn3EkrZL8Cjg8Z69tMvb2vScenIqzbrzz84rbeBAlUHjecIzjfP7JgsuLqB4YHGaT2rO9Y+vk/AigYIDNMtV7r+6z9Pwp+y6u4tlV5Yp6/XRi1sb2JjbUC5fOYKig7gTpNn6sHL+zGeRFZ5dGLvpdhx9ePT9g/XIm+0Lt93Zxlf/fMXIgyOJjI9k1plZmEqm2Jpr9i2NTojm2/3fcvn55TcPaVAE/RDEjSE3aFKsCQDH+h0jYHQA6zque+s+05tNx8U293dGMUSE46plulXsRtz/4ijtXPr9gz8gIuMj+a7ed8xuoaoL1kYa9dxzczGZZKIoz265vQV3B3dMJBNMTUx1mrqZWmigQ7kOzG05l5XtV1KjYA3WdVzHxs4bGVV3FFM/nkrDlQ2puawma66tydQ51J+RjMzwfcNxneVKRFxErmloXS5fOdZ1XIeFqQWF8hRiUZtFFLUvSkmnkvzS6BeS5CTuv7qPhanF+w+WirXX1zLppEqc4ueGP79ntP5RO65jj47VsyUCgUAAA6oNoIJLBTwKeOBk7ZTu/bZ8vvLp7rvBawN3gu5wLeDaO89R5EIRVixeQcH7BSnxWwlq+9TGta+rxv0pPikeQCNz5vzAtP3GDYWEpATc5rjh/od7mm0Z0bVITE5UlIR9Q30Vxd3cXmbSr2o/+lftD5CuUm4eyzzMPjebqARVpuG229uYd34efXf0Zf6F+RriloaIs40zNuY2/NHqDyJ/iqSJexMK2BWgt0dvgn4ISnefsQ3F84CuEI6rljE3Nc/0g/qHwNSTU8n3Wz4l4qoN1bt55+cBKgevbZm27Oy+M9vHzA6mJqZc+voSXSt25fNKn/PrJ7/Sa3svTvud5rL/Zb7Y9UWGo3K/nv5Vo2XQgosLCIwKJO+MvJlS9NMX6lpaUKkKZrT5eEBkAP129lMd443+crkV9XdaCLIJBAKAxW0X079qf3Z/vltvNlz5+gr7eu6jUbFGFLQryNkvzzLt42k8GfmEY/2OseazlInUA70OcKDXAQCmNJ0CqETnPI978iLqhZLKmhCaQOR1VTbZhWIX+KvRX/Qd0Zc8Q/NgamWaxgZ1VHL6fyoxn4ZFG75bFTeXZ62Ympim2xFhRJ0ROP/qTP9d/Zl2atpb9+/0dyesp1qz484OPBanROEzm4Ka05hIJsxoPgO3vG5pHNfeHr1ZdW2VsrzBawNdtnRhxIERbLujahvUsGjDHLVXV5hIJsr9Xo2zjTN/d/lbTxZ9mAhxJh0w8sBISjiW4Ns63+rblFxDQrKqZUz7jSrpfG3UAPer0o+JJybSs1JPFrddnO3j5QTzzs9jRN0R7xyTlJzEmCNjNNZ1LNeRHd47+Kr6V1qTutclNuY2LGi1gDXX13Dx+UWG7B3Cw5CH/PrJr2/d50XUCwr+rko1W95uOQOqDcgpc7NFCccS5LfNryhSCwSCDxsTyYSVHVbq1QZLM0sK5y3M6HqjiYyPpG6RutQtUhdA0XyQJ2g6iurlTbc2cfPFTSaemMjEExMxTzRnR+IO7JfZY+5sTq07tQhIDuB2n9sEPwvm5OOTdCjXIY0Nb7aKe1v7F0PBRDJhcZvFDNk7RGN9bGIsobGhrL62GkcrR35q+FOazKjg6GD+8fkHUIkXRsRH4D3Um2IOxXJNFtW7yG+bnyejngBQMX9Fmro3JVlOxtLMUhFqBOi1vZfGfvNazqOofdEctTWn6VqhK93pDsDNITeN/v3qGxFx1QEnHp/g0IND+jYjV6G+galTSSLiI7J1vDsv77D08lIG1xjMrBaz3r+DHjAzMUOeIOM7wpcJjScAKS2U3kVcUpxGiuxV/6scfniY29/cZlm7Ze/YM3cxtPZQdn6eEgX/7cxv6Y4LiQkhMj6Sz7d+rqwrYFvAoPqiyrJsUPYKBIIPg4ZFG9KyVMtM7XPpK1W3QilZ4mOvj1mzYA22M2xJrJBIhb8rEJkYSXxSPG1Kt+HiVxcxNzVPN+PkTYfsQO8D7zlz7o64AjR1b4qdhR1jGoxRsrxSqyWHxIZQbmG5NPs9Dnus8bq0U2nK5iv71nrk3EyLki0wNzVXui8sb7dcY3vqmm519wJjRpIkgn8MJv5/8VTMX5E8lnnev5Mgy4gnLR3glteNU09OpZlt/JBRR1zVFLPPumiST7APFRZVwD/Sn9H1R6dJ3chtFHMoxsQmExlScwhPwp5o1Ptsu72NS881WxrP+G+GhmNvZ2FHZHykVnrD5jSF8hQi+ZdkHo98zNVBV/E87klMQgyxibHIssyRh0dw+tWJPNPzKH13m7o3pUXJFnq2POM8CXvCy+iX7Lu3L8fOGRgey8Jj94UglEAg0DqWZpZs6LSBVXlWMX7beFwKuPBD7x9o2rwpA+4PIDg6mJAxIXxb51si4yNps6ENVZakbX3Wvmx7jeWc7GWrK0o5leLb2t/yff3vMTVJSY9O3ZbOJ9iHgbsHauznH+GvsZy6C4KhM7D6QOQJMs+/e84/Pf7h377/srL9ylwjJJkTOFk7YW5qrm8zPgiE46oD2pRuQ3hcOFcD3ib5blycf3qeZZffHQl804lPt/l4Bjn39BwA1maGJbOurn3+88qfSp2quhZETXRCNJNPTsbazJoGbqoWQuo2Beq2K4aGJEkUtS9KtaXVmHhiIjbTbLCeas2sM7P4ZN0nyrhahWrh7uDOkb5HNPro5nbU3+2MtlzSBsM3XOW3g3fxDshe5oJAIBCkJvJGJC+3v6RH5R70/a4vFXdUpNntZsyfPp+GRRuy9fZWSswrwYuoF9hb2eOWV3Xdu/XyFknJSSQmJ/I84jmAxv15cI3B7z+5AUzEmZqYMrXZVPLZ5KNFyRZ0rdCVw30OU9ZZU49B3XNdzfeHvwdgU+dNVHOtZpStUgrmKUjbMm1xtnGmf7X+VCtYTd8mCYwQ4bjqgHZl2wFw+slpPVuiW+KT4ll8cTF1/6zLoD2DuBF4gyarm7DRa2OasZ3Kd9JYNpXSCjlkFAtTC8o4lyHg+4AsH0MffFPrGwC+PfAt5pPNKbugLNZm1tQvUp9kOZmo+CgWXFgAqNronPZTfX/UinyGUAeTGdSCHWoO9TnEoxGPDC7lVm2vuodzThAZp5r4SErO/Q96AoEg9xPrF4t3f28uVb3Eg+8fICfJSCYSLp+5IJlI1C1Sl0lNJinjzz9VqQOXcCzBmAYqTYYuW7pQ7896FJ5dmLN+Z6mxrAagag2yqM2inH9TOsbC1ILNXTfTvERzRtUd9dZxt17cwjvIG1A9C10ZdIWPin2UU2YKBEaFYT0hGgiF8hSiU/lORpEW8ybqNFdZlrGcYsk3+75RtlVZUoUTj0/Qc3tPFl5YqNHLtlXpVuzpsUd5yN90c1OWzj/33FwauDXg7rC7Wmmpk5OUcS7D+k7rlWWfYB9iEmOYdXYWppNMsZtup/SkVdfEQkp/UEONuKo53OewxnJIbAgAi1ovInZcLA5WDvowK9uoJxRycxN5gUAgSI+E0AQe/vSQC2UuELghELfRbtS4VAPJNO39prF7Yxa1XsSTkU/oXkklRiNJElM+noKDlQM7vXcqpS/1V9bn5oubAHxf//sMTrwa7kRcmzJt8BrilWa9T7APlRarVIMvf31ZpJMKBNlEOK46YkjNIfTd2Re/MD99m5ItkpKT6Ph3R6osqcLHaz7Gdpot62+sZ9fdXcqY/b3206pUK439hu0fhuNMR4btG8bpJ6cJiAygqmtVzn2pSvPNSlRt/739jDo4Kk2kzpB4X3rQk7AntC3TVqO4v6RTSb6q/pXBOnZqmpdozuYum9nUeZOGIIW1ubVBpQa/ifq7PP7Y+Bw7p5EF3wUCgZ6IvhXNk5lPcOniQu27tSn5W0nMndJ3rkwkE4bUGoKbvZtG2z8zEzNCxoQQPjacFiVb0KpUK4bWGgpAfbf6BqGErw0q5a/Eqx9fETsulhffv6DSokrs8dmjbK/mKlJnBYLs8mFcTfTAiisrADj88DBVClTB3cEdZxtnPVuVOfzC/Ljw7AI7vTX7ow7YPYASjiUA8B/tj6udKy1LtUSWZRKTE7nif4W6f9YlMTmRhRcXYiKZcPbpWWISYpjeTOV0ZiU61XpDawAmNpmYvTemR6zNrdnZfSef/f1Zmm0N3Bow5eMpNCrWSGN97cK1lTRjQ6drxa6Aqk7o2/3f4h/pb3CpwW9ibWYNgL2VvZ4tEQgEgsxh38CeOvfrYF3COtvHymOZh4O9DyrLC1ovyPYxDQ1Ha0cAbj29xa2Xtxh9aDSV8lfir45/GV25j0CgD4TjqiPuvboHwM9HfyYwKpCqrlW5OshwxJpkWabx6sY8Cn2UZlt8UjzeQd782f5PXO1clfWSJGFuak6dInUIHRNKz+098Y/wZ/6F+YCqxUn7TSqVwSQ5rXT+uwiJCVFe57fNn5W3lGvoUK4Dj0c+xjfUl6quVQ0u5VkbRMRF4B/pT7sy7SiXL23rAEPCxdYFazNr2pRuk+PnNgAtE4FAkMvRhtOqFYzkgrbl1hbWe6WUBS1us5gqrmlVlwUCQeYRjquOONT7EF/s+oKYhBgCHwVyLeCavk3KEH/d+Iuzfmf5y+svRc3W1c6Ve8Pv4RPsQ2BkIMlyMnWK1Hmnw2VvZc/ennsB6LCpA7vv7qa+W312eO8AVM6vmtjEWKzMrIiIi8DG3AZTE1MO3j9I9YLVcbF1AVT1kHkt87Ku4zpdvfUcpah90Qw3qT7rd5Zay2txf/h9SjqV1LFlOYO6jcCcT+cYxXtKlpMNPnIsEAgEguzz76N/Ncqp6rvV16M1AoFxIRxXHeFs48ykJpOovqw6oIo2yrKca1NFvtr9Fflt8zPtv2nKusbFGjOq7ihqFqqJnYUd1QtWz9Kx/+7yN4suLqKJexPFcR2+fzi3X97m4IODPAx5qIytV6QeI+qM4PNtn9Ozck9FzKiEYwluf3Pb4NKttYF6giC3fneyglpVOjoh2uCdvqDoIOKS4ljvtZ4ZzWfo2xyBQCAwUIwj4pq6vytkTdNDIBCkj3BcdUjqlNbAqEBexbzKdY6XLMvMPjubFVdXpNl2rN8xrThLVmZWfFfvOwAufXWJpZeXsvzKchZfWpxm7NmnZzn79CwAztaan1XhvIWzbYshUbtwbS48u6Co7xq6qnBq1Dd2jyUebO6yWal9NUTUqs/qum+BQCAQfLgExwTr2wSBwGgR00A6xNXOlS4VujC/1XxmNp+ZZhZOX8QkxFBqXilG7B9BvT/rKY2x1Xxa8lOeffdMJxG+GoVqsKzdMiWN2NbclgWtFjC2wViNcc2KN+NG4A0239pMty3dkDwl9t3bp3V7cjMXnl0A4G6Q8fVxtTRNURE29Nlotf1dynfJsXMa0VdBIBAIVBhJjau63d8vjX4hfGy4nq0RCIwLEXHVIaYmpmzpugWA4OhgYhNj9WyRihZ/teBByAPmXZinrLM2s8Z7mDdWZlY5In7UunQEEeI3AAAgAElEQVRr/u37LyUcS1DMoRgAPzb4keuB14mMj6RFyRZYTrHEwcpBqRUxdBGfrCJjHH1cU9OxfEeuDbpG1aVVDd5x1WcfV9lIUusEAoHA2Fh2ZRmDag7SaG8nEAiyh2E/MRoQ+X7LR8HfC3Ly8Um9nP/is4sU/6M4ow+O5r8n/ynrGxVrxJkBZ4geF01R+6I5qtjbtHhTxWkFlYx8E/cmtC3TFgtTC2zMbRSn9bdPfvtgUzErulRkVN1RRqc+rHb0DN5xfT2hMPnkZD1bIhAIBIaMcUzEjftoHAABkQHcf3Vfz9YIBMaFiLjmMDvu7EjTpzMnmHhiIr6hvsw+NxsA76HelM1XNsftyAzRCdGASiRqSM0herZGf9QuXJvhdYbr2wytcj3gOj229QAM33E1NzUHwN3BPcfPbUxReIFAIDAGkpJT2v2phQgFAoF2MOwnRgOiUJ5CACy/slwv51fXFNqa2zKmwZhc77QC5LFQpdcc/+I4tha2erZGf8QnxRMRF6GXVFRd4R/pz93guzRxb0Jp59L6NidbWJlZAdChbIccP7dIFRYIBMaHYV/X1N0TAMxMRHxIINAmwnHNIa4NUvVxTUxOxD/CP8fPb25qTqE8hYj8OdJgWnb4DPfh0YhH+jZD75x7eo68M/ISEBmgb1O0hnoWenLTyQZfu6xWFc7JyLGItAoEAqPDSMSZUk/G5hZRToHAWBCOaw7hYuvC0FpDiUuKo9DsQjl23jGHxyB5SgREBjDn0zk5dl5t4Grnqpf0y9yCOuKsFnYwJmdFfTMPjAwkPilez9Zkj7ikOAD+vvV3jp/bSJ7zBAKBwGioW7iu8lpEXAUC7SIc1xykmH2KEJE20j4fvHqA5Clx6MEhHoc+TrM9JiGGF9EvALgReINuFbtl+5yCnKOUUykAXkSp/g+NqR2O+mbeZUsXvQmWaQs7CztWtFvBnp57cuycRvRVEAgEgtcYx0ycd7A3AOcHnjf4jCKBILchHNccZFS9UfzS6BcAjvsez/bxHoQ8AODTvz7F/Q93IuIilG3fHfwOm2k2nH5yGoBahWpl+3yCnOVqwFUA7ga/7uNqRBFXdV0oGL44E8CX1b+kqH3RHDufiLQKBALjxbDvdUceHgFUAYPU9zqBQJB9DP+J0YAwMzFjWO1hALTZ0EapjcsqfXf01Vi2n2HP8H3DCYoOYs45VVpwvyr9WNl+JRs7b8zWuQT6Q61QaEwR19qFa3Os3zHAOBxXgUAgEGQTI5mR29ZtGwALLiwgNDZUz9YIBMaFSL7PYVxsXehSoQsRcRFExEdkqTenX5gfpiamBEYFaqyXkVlwcQELLi4AVCmM4xqN04rdAv1Ro2ANyjqXxdbcuJSVjaWPqz4wojkMgUAgMCoK2hUE4HrgdT1bIhAYH8Jx1QNbum7J1v4t/mpBbGLse8ft7L4zW+cR6BczEzMSkxOpWagmTYs31bc5WuVRyCPabmgLCMc1OxhHfEIgEAjAWK5oqe9pDlYOerREIDA+xBOjnth3bx+NVjXKkkiTd5A3vqG+gMq5+avjX3gP9aa3R2/K5StHm9JteDrqKc1KNNOy1YKcRC1gFBkfiX+Ev1H1cY2IjyAmMYYaBWt80MrRWUUdcM1uuYFAIBAItItogSMQ6A4RcdUT++/t59STU+z12Uu7su2yfJzwseFYm1sDsK7jOm2ZJ8gFqKPq1wKu0X5Te179+ApHa0c9W6Ud1H1cf2zwI0XyFtGzNYaH/MZfQe7k/osICjvYYG0hHmQFgoxj2Fc2azNrfZsgEBgtIuKqJ2a1mAVA+03tlehpZmni3kRxWgXGx4NvH3Bn6B3sLOwA4xJnUs9I3391n7jEOD1bY7iIgGvu40VELMtOPiA2IYnms08y6u9r+jZJIDAMjOSC5mzjTMtSLalduLa+TREIjA4RcdUTlmaWyuvU/V3fhzo10N7Snn/7/qt1uwS5B1tzW1WP3jBVj15jaoejToMe9+84Pi35KTUK1dCzRYaFSBXOvYzYeI2zD4MpX1AlvHfpcYieLRIIBDnNnh57iE+K17cZAoHRISKueuTkFycpYFuAyScnZ2h8TEIMrda3Io9FHlZ2WGlUEThBWnxDfRmydwg3X9wEjCviamx9XPWFcFtzH2ExCQC8ilI9tAZFiowCgSBjGMcV7VXMKwrPLswGrw36NkUgMDrEE6Me+ajYR+SxzMOE4xNYfW21sn7TzU0cfnBYWX4c+pjQ2FBKzS/FwQcHsTSzpFP5TnqwWJCTqB1VtSiTMUVci+QtwvZu2wHhuGYJ9Xcj2Tge9IwJ9fyS2oEVCAQfHoFRgcy/MF/fZggERodIFdYz5fOV5/6r+/Tf1Z8elXpgYWpBj209AJAnyNx6cYtKiytp7LOnxx59mCrIYdQO3UdFP6J16dYa6eXGgOjjmn2E25r7uPU8HIBfdt3SsyUCgYFhJBc0tfig6OMqEGgf4bjqmTWfrWHUwVEMqz2Mn4/+rNQzAvzv3/8x9dRUZfmnhj/xNPypKPj/QFA7dDUK1aB92fZ6tka7hMSE0GVLFwAhMJYNRIlr7iIhyXhaVgkEBkVysirdIReU1IjJWIFAdwjHVc84Wjuy+rPVPA1/yuxzszW2pXZaH498TFH7ojltnkCPqFODg6KD8An2oZRTKaO5ISbJSQBUda1KSceSerbGcBHiTLkLUc8qEGSHbFzPfi0ODm4w+D/tmZNFjOU+LRDkRsSvK5dQJG8RNnRSFfJXL1idkDEpSpQBowOE0/oBUt6lPD7DfHgU8oiyC8oaVdsYdSpVvyr9jEp0KqcRbmvuIjhSqIgKBNknC/eE2FAI8NK+KVnA3NQcgNJOpfVsiUBgfIiIay6iR+UedK7QGQtTCwDCx4ZjZWalXAQFHxZWZlaUdi6NjbkNYFyqwuo+rtcCRI/LrKD+JiSLiGumiY5PZOmJhwxpUhIrc1OtHlutJCwQCLKDYV/XLEwtsLOwo1vFbvo2RSAwOoTjmstQO60AeSzz6NESgb4Jig5i1dVVeAd7A8alKqyOuK65vobVn63WrzEGjNpvDYtJIDlZxtHW4t07CPj1wF1Wn/HFPZ8NHasV0eqx3+a4JifLmJgYz+9XINAJRjQRF/FThL5NEAiMEuG4CgS5lBdRL/jxyI94FPAAjCviamYiLj3aQP2YV3XSIWQZfGe00as9hsDTkBgAbCy0/x0MfovjmpgsYyEcV4FANxhRGY1AIHg3osZVIMilqAUekpJVQkbGFHG1NLOkqXtTGhZtqG9TDJpkWSYpWTamQMV7CY2OZ9C6S7iP3cvVJyFKL9vEpGRiE5Leu7+5qep3tPeGP/GJ2lUBfhUVh2k6DmqS6LcrEGSALP5OYsO1a4ZAIMi1CMdVIMilqB3XlqVasqTNEqUu1Fh4FfOK/7d33/FRVXkfxz9nUgk1SJVeREClo4CgYqOI/UFl7T5W0F3bg4jKIsiK5bVrF9u6rr1hpYkiAtJ77z30lgSSEJLc5497ZzKTTOpMMpPJ9/16zSt37ty5czJnIPO753d+p2ZczVA3o2Kz4M/Nhzx3/9h4kAMpGSFsUNn7Y+NBpq3ZD8AT366k5cjJbNiXym3/XkjbZ6YW+fzoKPvf1Y8r9vCvXzcGtW1HTmSSmJA/XTsrR8vkiJSZkwpcRSoLBa4iYcoduHZq0In7ut0XcSX2V+xfwbZj20LdjAotx7LI9hpuvf3fC7nhnXkhbFHZO5CSmxa4cf9xAGasP8DcLYcBSMk4Vejzo71GRJOctOFg2bAvldP8zDNW3CpSDKVNHclIDm47RCRsRdY3YZEI4k4NTkpJYsmeJRG3Zuf3N37PtFumhboZFZJ7urNlgSvP3Ofth9NC0KLysWFfKuMmr8u3/4Wp6z3bS7Yfzfe4N3eqcFlYsyeFTk1q8dnd5/HZ3ed59pfliOvuo2lkZUduZJyWmcWLU9cXKw1cKqn0I7nb2Vmha4eIlDkFriJhqlmtZux9bC9H0o/Q7b1uEVWcCeDqtlfTuEZwq7pWNhawZk/lGW3YuL/oSp0/rdzDjsMnCnzcnSoMuRcAgiE9M5uTWTk0q5NAr9Z16NW6juexsprjeiA1g94v/M74KeuLPriCenfWVt6auYVP5u8IdVOkzJXy38nxg7nbH/aHnfOD0xwRCTsKXEXCVLQrmgbVGhAXHRfqpkiYccdb2TkWL07dENK2lCd/hY/ymrg0iQtfmlnwObyi1WBeCjqWblcUrlUlN1X4hevPAXxHwTfsS2Xq6n1Bec3Dx+3XnL3pUBFHBkd2jsWi7UeKPjCITjmjyemZGnGVApzwClx3L4Lvh4auLSJSphS4ioSp5IxkRv0+iiV7l0RURWEJXFy0XairoPRJ77TyjFPZBa4vWtF4x63bnh/I+rH9efaqs/we++bvm/nH5HWsTvIdkfaeExzMLIajJ+y5tYkJMZ59US77T+wN78zj3VlbWLc3hX6vzOL+T5Ywa+NBv+cpCfdIbnmtEfvGjM0MnjCPhdvKPng9fjKLIycyPRcasvKMWv+0Yg+Hj2sZlKBJPwo/PQyZFXCqwYkDvvfjVfRPJFIpcBUJU6mZqYydNZbJmyZHXJqwBCYh1g5cCwpIP56/g94vzCA14xR3f7SYLmOnl2fzysywz5Z5to0xxMdEcXuv5mweN4A/R1zMuGvP9jz+0rQNvDtrK4Nen0NaZu68N+/5oEEdcU1zRly9qgrHRef+if3H5PUMeHW25/5t/14Y8Gu6Y/B1e1M4cbLguX3fL0ti+6GC06eLa+MBO1V7fxlXrp64dDdn/30aXcZO9wT/3unWyWmneOjzZVz1xp8s2n6Ek1kajQ3YrJdhyYew7OPQtaG0dRxO5Mk4iI4PvC0iEpYUuIqEKe8qwjlW5BZfkZKr4gSuY35eC8AlbetxR6/m1K1up5WP+mENu4+ms+XgCeY4y+Xk5Fg8+uVypq/dH5pGB0FBc0Wjo1w0qlWFG7o14eXBHfM9vmZP7nIZ3iN3eUfxAnEs3R5xreU14lolpmyXsMr0CsIPFTL6+PCXy7ny9TmBv2A51Yd79KsVnm33NTvvvjrhXIhIOpbO4AnzGOv8OyiuY2mZ3PHhwkLfs0olaSkseMfezqmAFwFO5pn7vnNuaNohImVOgatImPJOD36l3yshbEnltjopOeyCvfg8AdE/rjuH0VedxSs3dvLZv+dY7nIvxzOzmLgsiXv+u7hc2hhs3iOl13Zu5PeYmCgX/9O1sWcubPfmiQAMnjDPkz7tHfz+uGJPsQo+FUeaMwezamy0Z597ZDwQ2TkWny3Y6Znr6e2kV6p4QUG4+31LLWREtqTKMgEkJ8/v4c4q8O7/vCnya/eUbB3PTxfsZOaGg3z4p5bjIu0IfH0H5BS+jFT5KOWVkazIXrtaRHIpcBUJU94jrpe3ujyELancBr0+h3v+u5iZGw4UfXA5+WbJbs92w5rx1HNGWnu0PI3/63cmA85uAMCuI7nz1eZuPuxzjvlbD5OZVXFG8vccy/1y+uhlbQo99s8nLmbK3/rw0V3neva1eHIywz5dmi+9evmuY0FpX7az5E2U13I78UEIXD9fuJOR363iP39u99l//GSWz+egoOJFmRVsqZxNB4773N+bbF98eX/ONlKdNXozTvn+ToVNpcjJsfh2yW6/SwZV+toBmWnw4QA45lWxOScL1k+CrCJGo9OPwrw3w2OR4qyTEJV//WQRiTwKXEXClPeXse/WfxfCllRe3kWOvli4i5wciyU7juYbFQqlyX/t4/msRLkMw/q25q2bu1C/RhzPey2Tcv8nSzzb09fu56Z35/OfuRVnxGnrITugaVmnKg1rFj6HrUHNeNo1rEFCbDR39Gru2T9p1V5mbzrEmfWre/btOHyCvi/PZMmOwAoOncq2PxMxXoWSgjHimuykIB/OE3A//d0qJi5L8twfMXGl389lMC9OWOWQK3zcGRl2j5YneWUNvPPHVgAySjCndeKyJB77egUfzMn9rEfamtilFpsAp3f23Tf9GfjiL/DbmMKfO+M5mDYSNk4NXnvc/bJnGUwdWfBxGSkwoTfsmGffP5UOsdV8jwmHgFpEgk6Bq0iYqpOQuw7kUzOeCmFLKq9Dx3ODhalr9tFy5GSuf3sunywIzZqSJ7OyeeKblZ77f724NYlV8480GGN4+or2BZ7HnS68L9keVTmWlkly2ilP0BBulu86xh0fLgLgq/t7+qzFWpSnr2hHo1pVfPbtT82gQQ07+H3z9y1sO3SC0T+uZeLS3QyeULr5ce4UZO8lexonJhT7+QNfnc1FL/2eb7/LuSjhHWz9vHIP3y/f43Pc6qQUZm06yJu/byYnx8KyLNIys8pkVL0sRyrdacCDuzUBYPfR3MDVXewqb6pwYa1xVx7WfNYCnHWd//0pe/zvd8t2UotTkgo/rrTmv+l7/7exMLomLPsUxjeBfavsNVvBHnGtmWf6wKnAi5GJSPhR4CoSpgyG+lXrh7oZlVqOZXFVx9Pp2fI0n/2jfljDhn3BmRtZEuv2pvLl4l2e+49efmaBxw7q0JBBHRpye89mbB9/Be/d1o3BXRv7HPPvP7fRfMQkOo2ZTscxv3D++BlA+I1IXfPmn57t0/wE6oWJjnJxV+8WPvsGntOQ6Y9e4DNye+REJo9+tYJF24+Was1Q9xxU76C6Wlw0C0deUuBzvNNX1+5N8VnvFWDJjqOs3G2nMnv3yINe1ZW93fHhIl6atoElO4/S4snJtB81jVd+21TSXyWk3O99EyfoP5aWO/dy2lp7/duTeVKFF+84SnKa/zma7vfNO4PF/fE2xk6Zv/L1OZW3MnGby+GeGbn36zuVuddMzA1O/XE52QQ5wbzYVcj/O7Nftn/+kGeN1pQ99hzX087w3X/SN+VcRCKDAleRMGWMYd/j+0LdjEqtfo14XhvSmc/v7UHnprUAaF3PTkm7+s055Zoy/Mn8HXwyP3ek9/aezQo93hjDG3/pwrNX219EL2tfn5cGd2TjcwOY8diFPiODbsnpp2g+YhItnpzMm79vZtyktT5LyYSCd3DXtkH1Ui0NdWP3Jtx3QUs+uL0bX9zbg9FXnkX1+Bj6tq3nOeak18jkS9M28PK0DQx5dz6Lthcvhdg94hqd532tVyOeH4ad7/c5GUWMhl7/9lymrLb/D3h31lZycqx867++PqQzc0dc7LNv8IR5nu3PFuz0bO85lk7GqWz++vkyVu32Xd+2OMrjeka6M5pap1r+CxSrk+wiTCkZ+QMq71R4sFOkp6zay+xN9vvl3SvefT3yu1WsSkpmx+EKuH5psDTqCrf/BLdMhAdyLxKRWsjfH3el+4ySFcYKun+2s+fbxuTJbshbaVhEIkJ00YcElzGmP/AqEAW8b1nW+PJug0hFcsNZN7B83/JQN6PS+/b+XqxMSqZTk1r87Ytl/LB8D93H/cpPD/Xm9DypqMF2LC2Tp79f7bk/9KJW3NazeanOFRvtomXdasx5oi8fzN7G+3P8z3N9adoGwA7e7+7T0rM/PTMblwviost2qRe3BdvswLFFnapMffiCUp2jWlw0Tw5sl29/yzpVPdveqaT/9qo2O3jCPOY9eTENaxbex+6qvtFR+QPrjk1qebbXjunHq79t4p0/tpKemU21ON8/wxmnsvNVjXabu+UwQz9d6rm/4u+XU7OKvfzO2GvO5hmvz4g/vcbnjqxtO3SCv5zXlJu6NynxxYCyrCrsHnGtUsD84Ce+WcnZjWsC8N+7zmXe1sO8PXML87Ye9vy7vLVHMz6e75vOf/hEJv3+NYstB497+mrZzmNsPWinlIZZkkH5a+Hn39Z/BsJdv0CNhvkfc49oZgRY3Oy9iyF5N9z/Z8HHJC0t+DGAtENQrx08ssYuLDVluAJXkQhVriOuxpgo4E1gANAeGGKMKXgilojwxfVfsG7YulA3o9JzuQydnADkcSdF9/CJTD71M981LTOLdXuDNxKxJs9yH8P7t6VBEQWKitKwZhWeHtSe8dedwxf39ijwuE8X7PSkUf6+4QDtRk1l0GtBWBO0GHJyLE9RndeHdC7i6JIbcm7TYh03/JuVrNubwqNfLfe7LA1AVrZ7xLXwP6sJsdG0rmuP2uedqwkwb+thOo35hRu8Rk3dxk9d51O8yB20Agzp3oRRg9rz+OW5FZdv69mMxon+A+5VSck8OXEVPZ+f4VNpuaC1csvLDyvsOZN1qsX57J9wS1cAvly8iyXOKPi5LWrzRP+2uc915v3mDVrBrsS9YX+qz7JB7jWOIbhFrCq8R52ibsd2wj/bwubf8h/jDlgzSj5yD9jB6outIGkJHN8Pb/Wwt/OaMQ7e62tv3/ajXUxq6Hx45pDvcR1ugJqNocE59v33L1a6sEgEKu8R13OBzZZlbQUwxnwBXA2UbPVwkUrEGKNlG8JM48QqdGhck5W7k9l7LP8ago99tYIpq/ex5tl+VI0L/L/ZOZsP4TLw7FVneYrWBMtNTvC2adwAciyLQ8cziXYZXp+xia7NEnnkyxUMnjCPB/u25t6P7S+Wmw4cL3RkMFgmrdrLjPX2MkRnnV4j6OevGhfNtucHsiopmaveKHjEZ/amQwx4dTYAN5/XlK7Nauc7JisnB2Pwm4IN8MOw8z2PuUcT0/0Ernc6RagWOsHZx/97Ln3OqMvjX6/wWf7mhes7+DzPey5v7zPq0rZBdeJjohhztZ1yvGTHUb/t2peSQdfnpvM/XRoTF+Pik/k7mT28L4lVY9mXnOFJjYeyG5Xcn5JBfEwUHZ/9xbMvPiaKe/q04L3Z27isfX0uOrOu5zF3Yapgfv5OhDglPqzUaAi1W8GRLfb9T66DoQugXu5FAtKP+f4siQPr4a3zfPelHYI5//Ld9/F1sMUraG7eG+6dmXt/dDIseh9iq0M1J+2/hleRpucb2ceISMQo78C1EbDL6/5u4LwCjhURCUvGGL4bej73fbyYicuS6Nu2HrUSYpi96RBPDmjrCRL2p2TQsm61Is5WuLTMLN6euYU61WK5tZTpwcUR4xQVclfgfe6ac5z5rStYuTvZE7S6TV+7nys7nl5m7QFYvcf+0vnPGzqWam5rcRhj6NC4FrOH9yUuxkVcVBRLdx6lb9t6HEw9Sfdxv/ocf+SE/4I1WTkWMYWMtnqnC1dxAi73iGthc6X7nGEHbC9c34Hlu46x+cBxHrusTaGfq05erwXwnzu7cyD1JCdP5XDd239y/4WteOXX3KJNlgVfewXFK3cn89jXy8k4lcPqZ/vlS2cOZk9MXb2X+z/xnwr61BXtubtPS2rExxAfE8Uzg9oz9uf817m/G9qLl6ZtYO6Ww0wc2guApTuOclvP5sRGu3jo82X8tKLwKrmpGQpcffzvL3BkG3xwqX3/rR7w5G6Icz53pR1xzcnJH7TmFRUH2Sd9g9YH5uUWhPLW/W7f+4nNoOeDMO+NkrVLRCqE8i7O5O/vnc9fbGPMvcaYxcaYxQcPHvRzuIhI6EW5DNd2tqv0PvT5Mm79YCHvztrKO7O2er7o70vOPxpbUh/+uR2AW3oUXoypLCTERvPzQ72Jj8n/p+Khz5fRfMQkWj45iZvenVcmlYgPpJykcWIVruvSuOiDA9SkdgL1qsdTMyG3aFPd6nEseupSOjpzKgEe+GQJe7zWFnXLys4pcLQ1L3fg6p7PWdC6pN5Vj6NchumPXMAPw87ngYtaFe+XclSPj6FV3Wq0P70G68cO4OFL27Dhuf75qmW7DftsKRlO5d6z/z7Nkz4c7HVcN+5P9Ru0PnxpboXY+jXiPSPUbRtUz3csQOemiXx2Tw/mP3kJXZom0qVpInf3aUmss3zOKzd24q+XnMGvj17A+rH92fb8QM9zz29tvwf3/HdxqapJR6yqdaBJd/i/LdD1TsCyRzDdc0fTnRH8ki6HM/P5/PtqemWRnDMYGnXxfbzjEKhfglll7a/J3T5ScdaqFpGilXfguhvwznNrDPhcBrUs613LsrpZltWtbt26iIiEqwFnN8i3b/yU9Ww9ZBd82RuEwHXhtiO0a1iDhy9tU/TBZeDsRjVZN6Y/H97ZncVPX8qWfwz0eTzHgvlbj5CSnhX0JUX2p2RQr3pc0QeWobrV4/jwznN55cZOgD2y2mv8DJqPmESH0dM8Fyeyciy/hZn8ic+TKpyWJ2AaNcj+kv7Dg77ViI0xdGxSq0Tr2BYkLjqKz+/twfqx/YmNctGgRjyzh/elVkJMvmMHvjqbo17zYIMlbyXfC9vUZfv4Kwr8rPdqlRtou4NSbwXN+45yGR69rA2t69np08YYxlx9Fl2bJfLp3bnzu/1doKn0qtaBQV4pvO9dbA/Ru0daj26DoyVY13rD5NztUUdh1BG44PHcff1fgCtfs9eXvX8O3DEZrp1QsjbH5hZd47VOJXuuiIS18v5fehFwhjGmhTEmFrgJ+LGc2yAiEhQul+HvV9pBxhn1qnHFOb7VN/elBB64btyfWuBIU3kxxtD3zHrUqRZHlMsw4ZYuXNquns8x9jqwvwf1dfenZFC/RmBFqIKhdtVYrunciAf7tvbZn5KRxXfL7BGnU9k5+ZbCKUjeVGHvkb4z6lXjrt4t2D7+CupVL/vfPT4mijVj+vHH8ItoUjuBKX/rQ4M87/mG/an0Gj+DjfvtYjfBGnfddcQ3cG3hVeXZH2MMG58bwPJRl7HoqUsDeu3bejbn2wfstOLvh53PTw/2LrN09ArPGLj3D3v70EbYtcBev7XTzfa+pMX+n5ecBG90h2PODLGsk7DfqXx98zfgctnpv13vgL8fswsuVT0N6raBwR/ahZaa+19KqlAxeQqSpRVvSSsRCX/lGrhalpUFPAhMA9YBX1mWtaY82yAiEkxt6ttB5YHUkzwzqD1XdGjoGQ3yl1JaEquTktmbnEH7hsEvTBSI/mc35Kbu+Svyei8pEwwHUk+GReDq9ni/M/Pt+9f0jXwyfwd7j9MDC14AABFmSURBVGUUu1iQJ1XYCVw37LPTL0cObFvqJX8CERPl8ixv1LBmFeaPvITt469g6z8GcpUzjzn9VDbbnEyCnCClhX88fwcJsVG86BSaqh5fdNmN2GgXtRJifSoqB6pTk1qc45UOLn6c3gmucuaN/ruf/bNlX4it5r/qMMCyj+1A9+dHYHRNeDk3BZwzLvM91hiIClKf1mpmpxy7Hdmm9Y5EIkS558VYljXZsqw2lmW1sixrXHm/vohIMLmLGlmWRYOa8bz5ly5sfG4AXZslFljJtbh+XrmXmCjDjecGt5JwMFzavj5jrzm7zM6flplFakYW9WqENlW4KJnZOTz9/Wp+W3/Ab/qqP/GeOa72PNKVu+1CN385r1mx58mWB5fL8JqfZYh+XrGXcZMCWwyg+YhJbDt0gi5NE7m2SyOG9z+ToRe1LvqJEjrNe/veb3oeVG8Iyz+FdT/5eYLzWd483f5Z2qVzSsrlguvfhzun2PffvxjGnAZzXoGsTLtAlIhUSJrQISISgILijF6tTmP9vlS2O6NUpbHj8Ama1E6gRnzwRpeCqWfL/MvCDJ4wl8NBGHl9Y8ZmgHJJly2JqzsVXEk575zNgtSoYo8sHku3541uPnicFnWq5qveGy4+u8e3CuzUNft4b/Y21u4JfK3iv5zXlJgoF0Mvau0pwiRhqnYLGL4NrnvfnvdaqynUdJaf+X5Y/uNNAV8xhy0quzZ6q+lV1M3Khl//Ds/VhTGJKtokUkEpcBURCUBB8+J6OoVkpq7ZV+pz7zicRrPaCaV+fllrXa8668f2Z+zVZ3n2Ldp+lGveKnhN1OJ6a6a9hmSruoXPeyxvr97Uma15ClSVVEJsNNXiojmQYgf4Ww+eoGUR8ztDqVerOvzzho759g98bXapzuddgbppGH++xY+E2tBhMHS7y75/46f2z5PJcCrPnH5//zXeN9uew1oeajXNHXXNS0WbRCokBa4iIgEoeMS1Do0Tq3jSQEvqQGoGmw8e54z6oS3MVJT4mCja5pmDu+tIumc+ZGl4Bzbtwmx+L9gptC9cf05A52hVrxr/mbud5iMmsX5fKi3DLEDP66zT/c8B/W7Zbr/7C+Neagfsqs1SgcVVg2vftbfH1YeUvbmP+RtxbdihfNrl1qwXPLIGTmsNQ770XSonK7hz8kWk7ClwFREJgMsZcfVX+uPM+tWZvGpfqdZzfXnaBrJzLK4vhzVMA9W9eW2+faCnz76+L89k5HeraD5iEuv2ppCScarY5xv9o12z774LWha74FF5u7F7U7aPv4IRA9qW6vkN8szdbVGnWjCaVWbObFA9X8owwCNfruCrxbuKfZ7th04w8rtVnvunVY0NSvskhDrckLv9z7bw2Y3w6+j8c1pv/7lcm+VRszE8tATO7A83fAQ3fW7vf7WjijaJVDAKXEVEAlDYChpdmiUC0OP531idlEzGqWzW70th2GdLOXEyq8DnnTiZxVeLd9Or1WmcGeKlcIqra7PanvVH3T5bsBOAAa/OpsPoX/Itf1KQj+bZ60J2bFIruI0sA/df2IpGtezlNx66uPjFhW4+r5nP/bNOD7+R5bx6tarDzw/1zrd/+DcrWZ1UvMI7wz5b6llC6LUhnYOyJq2EmDEw0mukdeNUmPMv+PNV3+Na9CnfdhWkWU+okgind4bs4l9QE5HQ018MEZEAuEdc/cWv3qOlg16fQ9tnptL/ldlMWrmXm99f4JMS6+392XbhkOIsDxJO7urdgi3/GMhX9/X0+3ifF38vMoU4LTM3oO/ePH/xp3D0oBOwPnBRq2I/54I2dVk48hLP/YoQpAOc3agmo69sn2//oNfnFPnczKwc0rzWrG1UK7wKb0kAYhPg6QNQpYB/s5eH0SISVRLh8c0w5HOI1oi/SEWiwFVEJADuEVd/IWiDmvH5Umjdlu86xvYCqtBO+MMuTPTcNYHNowyFKJfh3BYFB5y7j+b+zr+s2ccveYpXtR81DYAJt3StMPMfh5xrpw0nxJbsQkO9GvFM+Vsfvn2gVxm1rGzc1rO53/3pXkGpPxfkuXDRtkH4jzJLCUTHwfCt8OBiaHdV7v4ndkCvB0PXLn+iKtZFQRGxKXAVEQmAq7BcYewU2oIs3HaYnBzfkPfzhTtJP5XNrT2aUTsC5//d+sFCmo+YxJIdR7n34yXc+/ESwA7km4+Y5Dmuh5+ldiJRu4Y16OqklFcULpfh07vzz3dtN2oqxwtJgd+XkjvXO9plqBqmy/9IAIyBOmfAwJehVjPofjdUqRjZBCIS/vRXQ0QkAEUFrgA/PdibPcnp3OcEaW5PfLuKWRsP8chlbRg/ZR0xUS6mrLZHIO/p07JM2hsurn97rmf784U7eXLiKp/Ha1YJz7VrxXZ+6zrce0FL3p211Wf/ueN+ZfXofrgKKrftaBPm1bIlQNXrw8MrQ90KEYkwGnEVEQlAMeJWzmlck8vb12fUoPbUSojhxm5NPI9NWrWXS//5B7+uO+AJWgGanlax17e8tF19n/uFjSrmDVpXP9uvwPVxJXyMHNiO7s19+zUtM5s/Nh0s8rnv3Nq1rJolIiIRSiOuIiIBKGJgycMYw129W3BX7xYA1Koawzt/bPV77JS/hUn1zQC8f3s3z3Z6ZjZVYqP4YM42xv68ttDnNa2dQDWlkFYYX9/fi7dmbubFqRs8+6Jdhqmr91KvRjxdmua/YHFrj2Y0qV2xL8yIiEj504iriEgASjsy+OSAdkx9uA+Xt6/P0mcuA6BBjXi2PT+Qdg0jq2hNlVh7LdaigvweLWsza3jfcmiRBNPQi1rz3dDcAlMvT9vA/Z8s5bq35vKfP7exYV+qz/HdmlesOb0iIhIedFlbRCQAxZnjWpC2DWrw7m32yOSyZy7DZUxEp8gO7taE5buOcUO3JoyYuJJdR9J9Hk9MiLxiVJVFZ6+R1RW7c9d0Hf3TWuJjXFzbuZFnX0mrL4uIiIACVxGRgBQ3VbgoiRFYQTivanHRvHpTZwD+eLwvb/+xhUvb1SchNoqP5m7n3gsjuyBVZZVxKofPF+7y3E9wRuBFRERKQoGriEgADJE7QlqWXC7DsL6tPfefHtQ+hK2R8lRR1ucVEZHwojmuIiIBiODMXpESefH6DlzV8fRCj3lqYDsthSMiIqWiwFVEJABFrVcpUlnc0L0Jrw3pXOgxQ85rWk6tERGRSKPAVUQkAIpbRXz5G3WNjXbx66MXaqkjEREpNQWuIiIB8FQVtkLbDpFw8cqNndg0boDPEjkbnxtA63rVQtgqERGp6HTpU0QkABpwFfHlchlcGDo3TWTNs/10TUdERIJCgauISAA8664qghXJp6pSg0VEJEiUKiwiEgDPHFcNK4mIiIiUGQWuIiIBcGk9HBEREZEyp8BVRCQAClxFREREyp4CVxGRQChuFRERESlzClxFREREREQkrClwFREJgDKFRURERMqeAlcREREREREJawpcRUSCwOXS0KuIiIhIWdHK4CIiAageF83Qi1pxVafTQ90UERERkYilwFVEJADGGIb3bxvqZoiIiIhENKUKi4iIiIiISFhT4CoiIiIiIiJhTYGriIiIiIiIhDUFriIiIiIiIhLWFLiKiIiIiIhIWFPgKiIiIiIiImFNgauIiIiIiIiENQWuIiIiIiIiEtYUuIqIiIiIiEhYU+AqIiIiIiIiYU2Bq4iIiIiIiIQ1Ba4iIiIiIiIS1hS4ioiIiIiISFgzlmWFug0FMsYcBHaEuh3iVx3gUKgbIeVKfV75qM8rH/V55aM+r3zU55VPuPd5M8uy6hZ1UFgHrhK+jDGLLcvqFup2SPlRn1c+6vPKR31e+ajPKx/1eeUTKX2uVGEREREREREJawpcRUREREREJKwpcJXSejfUDZBypz6vfNTnlY/6vPJRn1c+6vPKJyL6XHNcRUREREREJKxpxFVERERERETCmgLXCGGMaWKM+d0Ys84Ys8YY8zdnf21jzHRjzCbnZ6Kzv60xZp4x5qQx5nGv88QbYxYaY1Y453m2kNe83TnvJmPM7V77xxljdhljjhfR5q7GmFXGmM3GmNeMMcbZ/5IxZr0xZqUx5jtjTK1A359IFEl97vX448YYyxhTp7TvSySLtD43xjxkjNngtOHFQN6bSBVJfW6M6WSMmW+MWW6MWWyMOTfQ9ycSVdA+93ucMSbOGPOl81lYYIxpXrp3JbJFWJ8/aoxZa+zvcL8ZY5qV9n2JZJHU516P/4+xv8OVXfViy7J0i4Ab0BDo4mxXBzYC7YEXgRHO/hHAC852PaA7MA543Os8BqjmbMcAC4Aefl6vNrDV+ZnobCc6j/Vw2nO8iDYvBHo6rzkFGODsvxyIdrZfcLdZt8jtc+exJsA07LWb64T6/Q3HWyT1OdAX+BWIc7c11O9vON4irM9/8doeCMwM9fsbjrcK2ud+jwOGAhOc7ZuAL0P9/objLcL6vC+Q4Gw/oD6P/D73+h1mAfOBbmX1vmnENUJYlrXXsqylznYqsA5oBFwNfOQc9hFwjXPMAcuyFgGn8pzHsizLfSUlxrn5mwjdD5huWdYRy7KOAtOB/s455luWtbew9hpjGgI1LMuaZ9mf+P96te0Xy7KynEPnA42L+TZUKpHU545/AcMLeG0h4vr8AWC8ZVkn3W0t5ttQqURYn1tADWe7JrCnGG9BpVPR+ryI47zb/A1wiXsEXnJFUp9blvW7ZVlpzl19hytAJPW5Yyx20J1R1HkCocA1AjmpOJ2xr7rUd3/InJ/1ivH8KGPMcuAA9od8gZ/DGgG7vO7vdvYVVyPnOUU9/y7sK/ZSiIre58aYq4Aky7JWlOB8lVpF73OgDdDHSR/8wxjTvQTnrZQioM8fBl4yxuwCXgaeLMF5K6UK0ueF8ZzbuSCdDJwWpHNHpAjoc2//i77DFami97kxpjPQxLKsn4NxvsIocI0wxphqwLfAw5ZlpZTmHJZlZVuW1Qn7Ktm5xpiz/b2Uv6eW4GWKfL4x5ikgC/i0BOetdCp6nxtjEoCngFElOFelVtH73PkZjZ2u1AP4P+ArjcQULEL6/AHgEcuymgCPAB+U4LyVTgXq88KU5bkjToT0uf0CxtwCdANeCuZ5I01F73NjjAs7Y+6xQM9VHApcI4gxJgb7w/+pZVkTnd37ndQtdwpXsdPxLMs6BswE+htjzjN2QY3lzujYbuw5iW6NKSTty301yLmNcZ7vnT7i83xn0vgg4GYn3Uz8iJA+bwW0AFYYY7Y7+5caYxoUt92VSYT0Oc5jE500p4VADqCiXH5EUJ/fDrjb/zWg4kwFqGB9XhjPuY0x0dgp4keK2+7KJIL6HGPMpdgXpK+ynOkgkl+E9Hl14GxgpvMdrgfwoymrAk1WGExQ1i3wG/aVlP8Cr+TZ/xK+k7xfzPP4aHwnedcFajnbVYDZwCA/r1cb2IY9YpLobNfOc0xRk7wXOR9wdwGPgc7+/sBaoG6o39dwvkVSn+c5ZjsqzhTxfQ7cD4xxtttgpzCZUL/H4XaLsD5fB1zkbF8CLAn1+xuOt4rY5wUdBwzDtzjTV6F+f8PxFmF93hnYApwR6vc1nG+R1Od5HptJGRZnCnnH6RakjoTe2EP+K4Hlzm0g9lyS34BNzs/azvENsK++pADHnO0aQAdgmXOe1cCoQl7zLmCzc7vTa/+LzvlynJ+jC3h+N+c1tgBv4Hxpdc63y+v3mBDq9zccb5HU53mO2Y4C14jvcyAW+MR5bClwcajf33C8RVif9waWACuw53J1DfX7G463Ctrnfo8D4rFH1zdjV5tuGer3NxxvEdbnvwL7vX6PH0P9/objLZL6PM8xMynDwNX9x0REREREREQkLGmOq4iIiIiIiIQ1Ba4iIiIiIiIS1hS4ioiIiIiISFhT4CoiIiIiIiJhTYGriIiIiIiIhDUFriIiIiIiIhLWFLiKiIiIiIhIWFPgKiIiIiIiImHt/wHnYfNdfHQKkQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=180, \n", " plot=True, period=1)" @@ -1591,30 +1041,9 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in day 5 with slope: 454.0\n", - "Drift detected in day 6 with slope: 504.0\n", - "Drift detected in day 7 with slope: 359.0\n", - "Drift detected in day 8 with slope: 479.0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,10)], max_slope=180, period=1, \n", " plot=True)" @@ -1622,30 +1051,9 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", @@ -1655,32 +1063,11 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.data['CODtot_line2']['2013/1/9':'2013/1/12']+= line10.values[::-1]\n", "dataset.data['CODtot_line2']['2013/1/5':'2013/1/8']+= line10\n", @@ -1693,36 +1080,9 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in day 5 with slope: 449.0\n", - "Drift detected in day 6 with slope: 499.0\n", - "Drift detected in day 7 with slope: 354.0\n", - "Drift detected in day 8 with slope: 474.0\n", - "Drift detected in day 9 with slope: -317.0\n", - "Drift detected in day 10 with slope: -70.0\n", - "Drift detected in day 11 with slope: -303.0\n", - "Drift detected in day 12 with slope: -176.0\n", - "Drift detected in day 13 with slope: 157.0\n", - "Drift detected in day 14 with slope: 158.0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,15)], max_slope=68, \n", " plot=True, period=1)" @@ -1730,35 +1090,9 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in day 5 with slope: 449.0\n", - "Drift detected in day 6 with slope: 499.0\n", - "Drift detected in day 7 with slope: 354.0\n", - "Drift detected in day 8 with slope: 474.0\n", - "Drift detected in day 9 with slope: -317.0\n", - "Drift detected in day 10 with slope: -70.0\n", - "Drift detected in day 11 with slope: -303.0\n", - "Drift detected in day 12 with slope: -176.0\n", - "Drift detected in day 13 with slope: 157.0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,14)], max_slope=68, period=1, \n", " plot=True, drift_type='B')" @@ -1766,30 +1100,9 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'g--', label='data with drift')\n", @@ -1799,30 +1112,9 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "\n", @@ -1837,30 +1129,9 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in period 4 to 7, slope: 90.5\n", - "Drift detected in period 5 to 8, slope: 103.42857142857143\n", - "Drift detected in period 7 to 10, slope: 98.71428571428571\n", - "Drift detected in period 8 to 11, slope: 99.28571428571429\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, \n", " plot=True, period=4)" @@ -1868,32 +1139,11 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in period 4 to 7, slope: 90.5\n", - "Drift detected in period 5 to 8, slope: 103.42857142857143\n", - "Drift detected in period 7 to 10, slope: 98.71428571428571\n", - "Drift detected in period 8 to 11, slope: 99.28571428571429\n" - ] - }, - { - "data": { - "image/png": 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A/kFWcabTAGZkt9RmApgJ4AyAxwD+yd6WMVaAo0ePwtvbmy9QGdMTvr6+0nOxCjCQVcFXtHDhQgiCILWovk6bNm2wdOlSAIBCoYCnpyeUSiUAYNOmTZoK+43Hn7G6ISkpCZs2bYK3N18eMsbyV5wqxPnNXr7zNdv/BuC3fNa7AHAp6nEZe9OZmprCyMiIL64Y0xPPnj2DiYkJ0tPTce/ePWm9IAjYtWsXoqOj8eTJEwDA3LlzYWdnJ23z7bffIjY2Fo6OjgAAHx8f+Pv7AwD8/PxQtWpVqFQqvPXWW3B2dpbGwzLN4RZYeXl7e+Obb75B9+7dcfv2bbnDYYzpIE1UIWaMadGIESNw9uxZZGZmSvMzMsZ017lz52BhYYHff/8dRARfX18cO3YM4eHhsLW1Vdu2QYMGasvr168HACxatAjW1tZ4+vQpAGD+/Plo2bKltF1YWJiWz+LN061bN3z99df8OcsYYzqOE1jGdNzZs2cBZBWA4QsrxnRbZmYmwsPDMWnSJHz00UcAgEOHDsHd3R3169fPs31oaCiIKE8PizZt2kjPu3btiuXLl2s3cIZBgwZh0KBBcofBsnGvI8ZYQTiBZUxPcLc2xnSfkZERwsPD1SoOF1ZJPyUlBVWqVAGQVV145MiR0ms1atSAh4cHX8yXAaVSCZVKxUM2GGNMx+lPfXvGGGNMT+ScPqZhw4YFbte7d2+kpaVJyz179sTevXtx4cIF+Pj4ICwsjJOpMrJmzRpUqFABqampcofCwDdtGWMF4wSWMT3BX+aM6T43NzeMGjUKr169ktaZm5vDyckJmzdvRsWKFXHy5EkQEYgI7u7uMDMzA5BV/MnKygrVq1dH//790bZtW1SsWFGuU3lj8WetvFq1aoWRI0fit9/y1AFljDEAnMAypvMGDhwodwiMsSLy8/PDsWPHpGluRGPGjMHXX3+N1NRUaWxsbsePH0dgYCAaNWpUFqGyXHgeWN1gZmaGo0eP8ncfY6xAnMAypuMOHz6Mly9fokKFCnKHwli5kZKSgnXr1iEjI0Oj+xW7A1eqVKnQbQMDA2Fubo5jx44ByKpe3L59e7W5Y1nZ4a7auiE+Ph6LFy/GnTt35A6FMaajOIFlTMeJxWD44ooxzVm8eDG+++47/Pzzz2oFl0pLHD9ZlK6/1apVg5+fHy5cuIBHjx7h4sWL+OCDDzQWCysZboGV15MnT/DHH39gxowZcofCGNNRnMAypuOsra3RpEkTKBQKuUNhrNwYOnQoAOCPP/7AqlWrNLZfsQXWxMSk0G1r1aqFmjVrYtOmTejYsSOUSiUGDBigsVhY8fTo0QMLFiyAsbGx3KG80fhmLWOsMDyNDmM6zt3dHQCQkZHBF1aMaUinTp2k561atdLYfk1NTdG6desiX4SvXLkS06ZNw4IFC/DWW29JiTUre3379kXfvn3lDuONxy3gjLHCcAssYzqOv8wZ07xnz55Jz+vVq6ex/c6bNw9Pnz4t8vZTp05Famoqli9fjjlz5sDQ0FBjsbDiSUtLQ0xMjEa7lJeVxMREtG3bFo6OjnKHojHcEssYKwgnsIzpCU5kGdMcJycn6fmOHTsA/Pd/7OnTp9i7dy9UKhUuXryI3r174/Dhw7h//z5SU1MRFBSEsLAwJCUlYceOHUhISEBkZCQ2bNiA8+fPF7swFE+Voxu2bt0KMzMzJCYmyh1KsRERnjx5gujoaLlD0Rj+zmOMFYS7EDOm4wRBkOaMZIxphjhWFQB2796NIUOGYMyYMdi/fz8WLFiA4OBgTJkyBZmZmQCAa9eu5dmHiYkJ0tPTMW3atDyv8f9X/aWPv7vY2FgAwF9//QU7OzuZoykdCwsLzJgxA2PHjpU7FMaYjuIWWMZ03PDhw+UOgTGdlpqais2bNyMjIwMhISHIzMyEr68vvL29kZmZibVr12Lt2rVqiUlaWhqqVq0qLQcHBwMAbGxspOdi8rp27dp8j5uenq6tU2Iy0Od5YMVuz8nJyTJHUno1atTApk2b0KtXL7lDYYzpKG6BZUzHHThwAKmpqTA1NZU7FMZ0Rnx8PH755RfY2trit99+w99//42ZM2fm2c7a2hr//vsvAMDIyAhEhDlz5qBu3bpo3LgxbGxskJGRgYsXLwIAGjVqhICAAHTr1g0JCQlwcnJCt27d8O233wLISm4EQUDLli0REBAgHcfc3By+vr7SMZn+4TGXuiE2NhbfffcdJk+ejH79+skdDmNMB3ECy5iOi4uLQ1xcHGrWrCl3KIzpDBcXF6ll9XXE5BUATp48iXPnzgEAXr16hVGjRmHu3LkAssbBnjp1Cnfv3sXFixcxfPjwfAsqiUnO1atXERcXB4VCgZCQEPTt2xdVqlTR1OkxGeljC6yBQVaHuhYtWsgcSen5+vrC0dER3t7euH37ttzhMMZ0EHchZkzHDR8+HG3btkVqaqrcoTCmE8LCwjBu3Di1dfPmzQMADBgwACEhIVi9ejVWrlyJDRs2gIigVCoxefJkafszZ85gw4YN0vLUqVNBRKhRowZGjhxZaDXg+vXro23btujUqROGDBnCyWs5YGVlhWXLlqFSpUpyh1Js4hRro0ePljkSxhjTPm6BZUzHeXh4AMgab6ePF1aMadrVq1cBAL1794aJiQlmzJiB4cOHY8WKFVILae5CNgYGBhgzZgwaN26M3r17c3dRlkf37t3RvXt3ucMokYoVK+Kzzz4rFy2wjDFWGG6BZUzH6WN3Nsa06ebNmwCAmjVrws3NTWoxLSwpNTAwQJ8+fTh5ZflKTEzEy5cvoVQq5Q6l2KpVq4br16/Dx8dH7lAYY0zrOIFlTE9wIstYFnHcK8+fyjRp9+7daNq0qTQljS4YMGAAKlSogEePHsHb2xuhoaEFbhsSEoL4+PgyjI4xxuTBCSxjeoITWMayii8BQKdOnbB161aZo2HlkS591p4/fx4KhQLr169Hhw4d0LBhw3y3E6d+2rdvX1mGpxVt2rTBsmXLYG9vL3cojDEdxQksYzqOJ3NnBQkODsbPP/+sUxfc2rZx40YAwPbt22FmZiZzNKw80eV5YMWxrX379s33dXEeWIVCUWYxaUu1atWwZMkSvP3223KHwhjTUZzAMqbj9u3bB4VCwdPosDxGjx6NpUuXwsvLS+5QyoRKpcLq1asBAF27dpU5Glbe6OLYaDGmwMBAmJubF9gCq4tJd0lFRUXhww8/hKurq9yhMMZ0FFchZkzHvXz5EtHR0ejWrZvcoTAdk5KSAgB6WXSmuBITE3HhwgVs3rwZycnJMDLiry+mHbqUDN68eRM9e/bEsWPHEBUVVeAYVzHmDh06lGV4WhEQEICzZ88iNjYWQ4YMkTscxpgO4hZYxnSctbU1LC0tkZiYKHcoTMeYm5sD+G8OyPJsy5YtGDFiBCIjIzFr1iy5w2HlUO/evbFhwwaYmprKHYrE0dERQFarJPDfGPDcxASW54FljL0JipzACoKwSxCEV4IgPMqxrpYgCOcEQfDN/rdm9npBEISNgiD4CYLgJQhC1xzvmZS9va8gCJM0ezqMlT8PHz4EAKSlpckcCdM1+/btQ2JiItq3by93KFrl6uqKhQsXonbt2pg/f77c4bByqlOnTpg1axaqVKkidygAspLS3IXKCqqJULVqVUyZMgUtW7Ysi9AYY0xWxWmBdQQwONe6hQDOE5E5gPPZywAwBIB59uNLAFuBrIQXwE8AegLoAeAnMelljL2eLnVrY7qhQoUKMDU1hYFB+e5Mc/LkSQCAi4tLuT9XJp+YmBh4e3vrRCEkIso3joIKl7311ltwdnbGmTNntB0aY4zJrshXAkR0BUBMrtXWAHZnP98NYESO9Xsoy00ANQRBqA/gQwDniCiGiGIBnEPepJgxlgMnrqwgtra2EAQBAQEBcoeiVV5eXujduze6d+8udyisHDt06BA6dOggddeV05o1a2BiYpJn/aZNm1C7du183xMfH4/U1FRth8YYY7IrbRWMekQUBgBEFCYIQt3s9Q0BBOXYLjh7XUHrGWOF4ESW5Xbv3j0AQGxsrMyRaNfq1au5Cz0rM7rwWevh4VHga9HR0XnWeXt7IyMjA4cOHcLy5cu1GZrWtW/fHjt37kSvXr3kDoUxpqO01Rcrv1r09Jr1eXcgCF8KguAhCIJHZGSkRoNjTJ9Mnz5d7hCYjtLFaT807dy5cyAi9OvXT+5QWDmnS/PADh06tFjbi/PAlgdVqlTBlClTYGFhIXcojDEdVdoENiK7azCy/xXL4wUDaJxju0YAQl+zPg8iciAiSyKyrFOnTinDZEx/bdu2DUSEevXqyR0K01G6cMGtLYMGDcK7774rdxjsDaBLCWxxhwXoQsyaEhERgU6dOuHw4cNyh8IY01GlTWBPABArCU8C8G+O9ROzqxG/AyA+u6vxGQCDBEGomV28aVD2OsZYAZ48eYIrV67IHQbTQbp0wa0NGzZsAAA0bty4kC0ZKz1d6tGwdOnSYm0vtsD26NFDG+GUqYCAADx8+BArV66UOxTGmI4qzjQ6TgBuALAQBCFYEARbAMsBDBQEwRfAwOxlAHABEADAD8B2AF8DABHFAPgVwJ3sxy/Z6xhjBRgxYgT69etX7sc5suJ7++235Q5Ba4gIc+bMAQAcOHBA5mjYm6BPnz7YuXMnatbU/ckRct+0EpfHjBkjRzgaVV5vyDHGNKfIRZyIKP/Jx4AP8tmWAMwoYD+7AOwq6nEZe9P5+voCAFJSUvTiwoqVHQcHB2zfvl3uMLQiJCQEDg4OiIiIgJWVldzhsDeAhYWFToy7VKlUEAThtYnc+vXr8e2330rLderUwezZs9GiRYuyCFGrOIFljBWGJ9RjTMfxlzkriC51edQkKysrNG7cGI8fP8b3338vdzjsDfHq1SvcvHkT6enpssaRnJxc4Oe+sbExgKyppXJq1KgR9u7di507d2o9PsYYkxsnsIzpCU5kWW6fffYZBEHAs2fP5A4F/v7+2L59O3x9feHv71+s97q4uMDFxQVhYWG4ffs2bty4AQCYMGGCNkJlLF8nT57Eu+++i4iICFnjSEhIKPA1hUIBAHnmRFYqlUhOTkZmZqZWYysL/F3HGCtMaeeBZYyVEf5SZ7m9fPkSABAaGorWrVtr7TjR0dEwMzMDkPV3OHv2bCiVStjb22PQoEGws7PDF198gbCwMOk9ycnJqFSpEgRBgFKpxI0bN5CUlASlUglPT08sWLAAxsbGICJ89NFHAIAKFSogIyMDAPDee++ha9euWjsnxgoi92dt5cqV8ccff2DRokUFbiMmsiIPDw+kp6fD1dVV2+FpXbdu3eDq6or27dvLHQpjTEdxCyxjOm7BggUAAAMD/u/K1IldiJVKJQBg//79sLe3L9U+ExMTkZKSgrCwMMybNw+CIKB27dqIiIjA4cOHYWBggD///BMPHjyAUqmEq6srBgwYoJa8AllzOQ4bNgzfffcdBg8ejD59+mDIkCEYNmwYfvzxR5iYmMDAwACGhobSe8TkdefOnbh48WKpzoOx4tKVLvk1a9bEwoULkZycXGAvhNOnT6sty510a1KlSpUwePBgrj7OGCsQXxEzpuP++OMPEBEaNmwodyhMR6lUKqSnp2PChAn46quvYGpqirVr16ptEx4eLnXN/fHHH+Hu7o6MjAysWLECkZGRAIDHjx+jWrVqaNOmDY4ePYrVq1dL769RowYmTpwoLa9atQpLliyBra1tgXG5uLhg3bp1cHNzK9b5WFpaFmt7xjRJ7mQwMTERz58/h5GREbp06ZLvNpMmTVJbljtmTQoKCsJbb72FPXv2yB0KY0xHcQLLmI7z9PTMc7edsZyUSiU8PDyk5eTkZNjZ2QHIuhgGgJ49e8LKygppaWn49ddf0bdvXxw7dgwLFy5E3bp1YWhoiHbt2gHIuoDs3r07Pv30U7Ro0QKRkZEwMTGBq6srvvrqK6SmpuLdd9/FsmXLsGPHDqSnp2Pnzp148eIF3n//fWn6m6ioqEJjHzp0KLp164bly5eDiEBE6NSpk6Z/RIwVSldaYM+cOYMWLVrg6dOn+N///pfvNrl75IjzwL7//vtaj0/b/P39ERERgU2bNskdCmNMR3ECy5iO++STTzBkyBC8evVrdS7SAAAgAElEQVRK7lCYjhEvVg0MDODn56f2mpmZGRYvXoxq1aohODhYGi8rJpdAVhEoUc2aNWFjYwMAGD9+PDp06IBDhw7B398ftWvXBgD069cPW7duRcWKFdWOVaFCBUyZMgVNmjTB+fPnsXbtWqSnp0vjZgHA2toarVu3RmJiImJiYjBs2DCEhITg1KlT8PDwkLrKMyaX9957D//88w/q1KkjaxxiEadq1aqhcuXK+W7j7u6utiy2wO7YsQM9e/bUboBaVp5akxl7HZVKxX/vJcRFnBjTcWLikZKSInMkTNf8/vvv+P333wH813p08eJF2NjYIDAwEH/88QcA4NSpUwCA6tWr448//oC9vT1Gjx4NAwMD+Pj4ICQkBFZWVqhcuTJ27Sr9NN2CIKBChQoAgDt37iA4OBgjRoxQ28bZ2bnUx2FMk5o1a4ZmzZrJHYZaAgtkjSdPTk5W2yY4OFhtOed40du3b2s5Qu3iC3r2pli7di0WLlyIuLg4mJqayh2OXuEWWMa0zMnJCQEBASV+P3+Zs4IoFAqp8FHTpk0BAH379sWJEydgYGCAZs2awc3NDUOGDEHFihVx6tQp1KxZE0SEAwcOAADatm2LAQMGFNjSU1qWlpZ5klfGdFFYWBjOnTsn+83C3Alsy5YtpdfERLVWrVpq7xH//4vELsX6iL/z2JsiPj4eSqWSixaWALfAMqZl48aNQ6tWreDr61uq/fCXOstt0qRJcHJyQq1atXD69GkYGhrCwMAAHTt2RFpaGoyNjaVtU1JSdGaMH2O6yM3NDRMnToSfn59a0ljWEhISUKVKFalCd3p6uvRaUFAQAGDXrl2oVKmSNE5UvJElyszMlHpBMMZ0y7Vr13D27FksW7YMAHDhwgV8/PHHMkelXziBZUzLTExMMGrUqFLvhxNYlptYPTgmJgbr1q3D33//Lb2WM3kFdKdADWO6Tu7P2lGjRsHCwkJanjVrFmbMmJFnO29vb+l57jGxCoVCbxPY3r17w8vLiyvvs3Krd+/eastc46T4uAsxY1pmaGhYqu5c4jhGIyO+38TU5ZyeZtWqVTJGwpj+05WbPFZWVpg2bZq03L59+3y3u3TpkvQ8d9ItdxJeGiYmJujYsWOebtJFsXv3bhw7dkwLUTGmGTlnDBBFRETIEIl+4wSWMS1SqVRISUnBzp07S7yPBQsWgIjQpEkTDUbG9JlKpcLGjRul5StXrnBrBWMaInfyd+XKFURHR0vL/fr1Q9euXV/7npwxt27dWq8Lwvj5+cHQ0BBbt24t9nsnT56skR5PjGlLfg0aoaGhMkSi3ziBZUyLxA+q2NjYEu/j2rVrOHr0qKZCYuXAjRs3MHv2bMyZMwdHjhxBnz595A6JMb2nCy2wt27dQr9+/bBo0SK19YVN7ZMzgbW3t9dKbGXF398fKpUKf/31l9yhMKZxPXr0QHBwMK5duwaVSoUZM2ZI87WL4uLikJmZKVOE+oETWMa0SExgc1+MFMfnn3+OTz75BCEhIZoKi+mxH374QRo/895773FrA2Ma0r9/f7i6uqJBgwayHP/atWsYMGAAgKyWxJw+//zz175XrFwMZJ1HaGgoUlJSsHbtWjx48EDjsWpTaVrAbW1tZfv9seLp0qULBEGAr6/vG5WsPX78GMnJybCysoIgCFi/fj08PT0xYsQIBAcHY8qUKahZsyZsbW3lDlWncQLLmBYplUoA/02HUBLh4eEAeB5YlnVDRJw/tVOnTrC2tpY5IsbKjwYNGmDw4MGoUqVKmR87NTUVvXv3RlJSEszNzWFlZaX2+pQpU7B37161dfPnz5ee566anJaWhlevXsHOzg7r1q3TXuBaUJoEtnXr1mjWrBlu3ryZZ+5cpjtCQkKkGyutW7fG7NmzZY6o7MyaNQuTJk2Slo2MjNCrVy/8+++/aNy4sdTzYM+ePdy1+DU4gWVMi8QW2JMnT5Z4H3KPx2K6w8DAAJ6envD09MT9+/flDoexciUoKAhHjx7N052vLOQc75mamprvNuPHj4enp6e0PHToUOn522+/rbatu7s70tLSAAA3b97UZKhaV9LvPGdnZxw4cAArVqzAu+++i5kzZ2o4MqYpua+JnJ2dERISgqioqHKftEVGRqoNCSCiAqdZbNiwISZNmgQ/P7+yCk9vcALLmBaJY6o00YWLE9k32/nz57FlyxYAWRerujBej7Hy5Nq1a7IM1yAiODg4SMtmZmb5bicIglqiKlaoBwAfHx+1bY8fP67hKHXfgwcPcO/ePakA1oULF4r0voCAALi6umozNJaLu7s7atasCVdXV9jY2CAoKAiNGjVCnTp10LBhQwiCgLCwMABAWFgYAgMD5Q1YQ1QqFZ4/f65WdFEQBPj4+ODo0aNYvnw5fvzxR/zzzz/S63v27IG5uTlsbGzkCFln8bwcjGlR5cqVUb9+fQwbNqzU++IE9s22fPly+Pr6YvLkyahcubLc4TBWbpX1Z21ERASePn2KjRs3Ytq0aUWeMu3MmTNISkqCqakpJk2aBDMzMyl5O3LkCJ49e6bNsLVm0KBBiIyMLHYl5adPnwL4b7zwy5cvi/Q+CwsLZGZm8ndsGUlPT8ehQ4dgY2ODwYMHo0WLFvkW7Dpz5gwmT54sjWkuD7+fXr16ISEhAe+++67a+rZt26Jt27Zq6+Lj47Ft2za0adMG1tbWcHR05MJmOXALLGNaZmBgII2FLYmtW7fC2NhYbyelZ6UXFBSEy5cv49NPP+XklTEtEXs1lOWFckJCAurXrw8AsLS0RMWKFYs15/f3338PIGsaDktLSwBA165dYWCgv5d3RkZGqF27NipWrFis90VFRQEAMjIyACDPmOGCvEkFhOQWHR2NihUrIiMjAx988AGArDGwQUFBiI2NxZAhQ6RtbWxscO/ePblC1QobGxtUr14dAwcOLHTbatWqYf78+Rg+fLi07urVq1CpVOUimS8t/f2EY0wPxMXFISQkBPv37y/xPqZPn46MjAw0b95cg5ExffL3339DoVDgq6++kjsUxsqtsu6WT0RSsaa2bdvmaZUpyJEjR6Tn8fHxCAkJQWhoKLp37w4HBwc8ePAA69evR4sWLQBA74q9eXt7QxAErFmzpljvi4uLQ9OmTaXl8ePH5zvnZm6dO3cudoysZGrXri09z9kdvlGjRqhRowYOHjyIW7duSZX2xfmPb9y4gWXLlpVtsBoWGBiIzp07IyoqSrppVVRilfE+ffrA0NAQBgYG+O2335CWlgYigqOjo1Tw803BCSxjWiTeCU5PTy/xPs6dO1eqBPhNU5qfta46cuQILC0t0apVK7lDYazcEqsPX758GYIgQBAEeHh4aO14f/75J7y9vTFq1Kg8Y1hfJ+fUWY6Ojli7di2ArIvcadOmwdjYGKGhoTA2NkZMTAyWLl2q8di1yd/fHwBw8ODB124XEhICLy8vODo6AgC6d++uNk7QwMAA9erVK/R3OG7cOHz55ZelC5oVycqVKwFkTfWU3/dZ1apV0aNHD5w4cQLNmjUDAGzatAmnTp3CDz/8gF69euH58+dlGbLG7N27F++88450XVgcVatWxapVq9TWff/99xgyZAh8fX1hY2OD+vXrS+OG3whEpPOPbt26EWP6KCwsjADQxo0bS7yPOnXqEAAKCAjQYGTl0759+wgAeXl5yR2KxigUCho0aFCp/oYYY4WLi4ujK1eu0Pvvv08ACABZWVmRg4MDTZw4kdLS0kilUmnseACoV69eJXrvpk2bpBgB0MCBAykwMJCIiGrWrEkzZ86kiIgI+vnnn8nHx0djMZeFf//9lwCQpaVlntfu3LlDEydOpI0bN6qdPwDpPF+8eEGHDx9Wey0pKanA4wUGBtLJkycpIyMjz2tXrlzRu5+frkpPTydTU9NifZelpaUREdGFCxek3+VPP/1Ex48fz3f7jIyM1/6u5fT+++9T69atS7WPiIgIiomJIScnJ+nnUaNGDfrggw+kZWdnZ5o+fTqdOnVK7fMqMjKSLl++XNrT0DoAHlSE3FD25LQoD05gmb4KCQkhAGRvb1/ifRgYGBAA8vb21mBk+kelUlFYWNhrt7G2tiYAtGvXLmldcnIy+fr60sWLF2ns2LFUv3598vLyokqVKtGIESPy7CMlJYWUSqVGYk5MTKS7d++Wej8pKSmUmZmpgYgYY4VJS0ujH3/8kWbOnElBQUF5EqWHDx+W+hgqlYoA0MyZM0u8j+DgYCmm8PBwaX2DBg0IAK1fv54AkK2tbanjLUvHjx+XzuvatWtqr+X+XeR8/P3339J2KpWKbG1tpdcEQaD09HRSKBT06NEjUqlU5O/vT35+fvT7778TAIqKiiKirJuGAQEBlJycrLb/1NTUMv05aMrDhw/p66+/pvv378saR0BAAAGgnTt3luj9AwcOJADUp08ftd+Lq6srHTlyRHodAB04cCDP+9PT0+nx48d0/Phxjd6IKgqVSkXVq1enr7/+WmP79PPzo3bt2tGff/5JCQkJZGxsTACoZ8+e0s/Bzc2NiIiePn0qNYa8fPlSYzFoAyewMggMDKSUlBS5w2A65OXLl6W6y070XwL76NEjDUamf9auXUsA6OnTp2rr3d3daf369UREtG7dOgJAH3zwAU2bNo1q1qwpXSTm/MKrW7eu9LxLly4EgM6ePUsKhaLUF5U5jRs3jgBQZGRkid4/Z84cWrRokUZiYYwVX0ZGBjVp0kTt86NWrVp05coVevz4MZ0/f75E+x0+fDgBoB9++KFU8QUEBKjdsCMisrKyIgDUu3dvAkAWFhalOkZZO3bsmNrPW/To0SNpnYmJCU2bNo3S0tLIzc2N3Nzc8tx4DA8Pp4YNG0rvERNVADR9+nT67rvv1I4TEhJCoaGh0vI///yj9vqrV6/K+kdRahEREVL8PXv2lDWWy5cvEwA6c+ZMid6vUCgoMjKSUlNTqX379tJ51atXL8/NjFq1apGfnx8plUpydHSkbdu2qb0+YcIEOnr0qIbPMC+VSkWHDx+mn376iQDQ1q1btXashIQEun//PvXq1Us6zy+++IJu3rwpLa9bt05rx9cUTmDLmHg39aOPPpI7FKZDwsPDCQBVrVq1xPsQE9iC7vrfv3+fFApFifevL8S7q66urkSU1bq5efPmPBc6VatWzfNl5u3tLT1/8uQJhYWF0eDBg6ly5crUpk2bfO/mOzs7U0xMDKlUKnJycqL58+fTkCFD6NmzZ5ScnEzXr18na2tr2rBhAx09epTS09OJiCgpKYkePHhACoWC2rZtK+2ruOzt7QkATZkyRXM/RMZYsalUKoqMjHxt619h3VRzE99TWK+SklixYoWUuAKgNm3aaPwY2pQ7gVWpVOTq6kq7d+8udgu4mECcOHGCunbtKu3zww8/pA8//FDtOBUrVpQ+s8XHJ598Ij3Xt2E8KpWKZs2apdYKLWcjyw8//EAAyNfXV2P79PLyoo8//pgAUP/+/enBgwdST6xq1aoV+n8WAAUFBRER0aVLl2jt2rX08uXLfLuTl4SHh4d0HGNjY4qLi9PIfguiUqnozz//pPv379OuXbsoOTmZmjdvLl2H6sO1YpkmsAACATwEcF88MIBaAM4B8M3+t2b2egHARgB+ALwAdC1s//qQwMbFxeW5W8gYEVH79u3pk08+KfH7X5fAPnnyhADQwoUL1dY7OTlRmzZtNNYVVheICezp06eJ6L8LwCpVqkjP4+Li6Nq1a3T27Fm6c+cOTZ06VUoeo6Oj6cmTJ3n2q1KpaO/evdI+Vq9eLT3/7bff8v3CMzY2piFDhqits7e3l8Y8i4m22BJy8uTJIp9nUlKS2nFDQkI08wNkjJXK9evXadWqVQVeCHfu3Jm++uqrQvdz584dunTpkla7dHbt2pVq1KihNwlsenq6lDRER0eTp6enWmua+PzSpUslPkZKSgo5OjpKNxpUKhXt2bOHli1bRgDI1NSUWrRoQatXr6bTp09LLXQnT54kADR69OgSd38tjL+/f4luZogJen7XB/fu3SMAtH37dnry5AmdP39e+jn++uuv+SYzSqVSuhmraWPGjKF69eppfL8pKSl07949afnVq1c0YMAAOnLkiHS+7dq1IxcXF/L19aX4+Hjy8/OTXqtcubJa67wmby6J47nt7e2lRLmsxcTEUGhoqCzHLgk5EtjaudatBLAw+/lCACuynw8F4JqdyL4D4FZh+9eHBDYwMJAT2CIQW8LySyTKq44dO9LIkSNL/P4DBw5QkyZNyM/PL89rd+/eJQDk6Oiotn7s2LEE/Dempzzw8PCgyZMnU3BwsFpBB3t7ezp58iTdvHmzVPuPj4+n2NhYIsrqliwIAv3yyy9SK4Z4ISXeUIiNjZXGOAOgjIwMsrS0lJZjYmLI2to63y6GYWFh9PDhQ3JxcaH27dtTYmIixcbGUr9+/QiAVJChKBfDjLGyFRkZSZ999lm+SWydOnUKfb+4rTaHhXzxxRfScfQhgRVvRIrXCDVr1qSIiAgaNGiQdB7Tpk3T2vHFYkH5SUxMVPsda4O479wtpO7u7hQREZHve65fvy69b+7cudLYRrFewuLFi8nIyEjq+pyzoQUALVu2jARBoAcPHhBRVvIqvmZpaUmdO3em7777TmPneOzYMXJwcNDY/gpz584dOnLkSIGvi93P3333Xem869evLz23sbEpdQzbt28nAPTixYtS7+tNoQsJ7FMA9bOf1wfwNPu5PYCx+W1X0EMfElgioo0bN5KTk5PcYei0JUuWEABaunQpRUdHyx2O5MCBA2RpaUnXr1/X6OB+sWiBtr70rl69SoD6mJKMjAzav38/AaDbt28Xa3+zZs2iBg0akIeHx2u3CwwMpNmzZ5d5gqxUKmnFihW0f/9+GjduHM2aNeu1Fx6aEh8fX2D3QKVSKd0FB0CNGjXKs434JdalSxcKCwujDh06qF1IDB06VG15z549OltJkTGWJSkpiSZPnqz2f3fgwIG0dOlSSkxMzPc94lh+AJScnKy12NasWSMdp0OHDlo7TmnZ29tLXYPzuxFw48YN2rNnD926dUvWOD/99NMySWCPHTtGN27coOPHj0sJZ0FjmDt37iy9r3bt2tK24t/hyJEjqV27dmrvuX37Ni1atIhMTU2lFlogq0t1fpWdK1asSKdPny5Xvblyy8jIoBUrVtDu3bspPT1d6nbdu3dv+vzzzykqKooePnxI8fHxxd632LLL9XGKrqwT2OcAPAHcBfBl9rq4XNvEZv97EkDvHOvPA7B83f71IYG9d+8enTx5khwcHGjt2rVyh6Oz9uzZQwCoR48eBIDu3LlTJsdVKBTk7u6utu78+fN09erVfLuJPn78mFQqFZ06dUrqmhIVFUVKpbJYlQgfP35c6i+9Y8eO0Y4dO/J9zdXVlQDQzz//TCqVim7duqV2Hg0bNpRa9QDQiRMnaPPmzdK0APHx8ZSQkCB1lWnWrBkBoDlz5lC3bt3ol19+yfe44o2Isvxbd3NzUzs3bXVzKqkrV64QABowYECe13799Ve1C9z8Wm46duxIo0aNKnB6AMaY7nn8+LFaUprzkZaWRpmZmZSSkkIhISFqn8XaLggUHR1Nzs7ONGnSJAK0N34zPj6eHBwcSpTgiLGJDysrKzIyMiLoaC+tnLFquiJxp06dCAA5ODhIQ08EQVA75oYNG+j333+nmTNnUmhoKF2+fJkGDBhAqamp9OrVK7Vthw0bRkBWt/bXyVmx9p9//qF169bR/fv3acuWLQRkFTxs0KCBRlojvb29S5QEljWlUklJSUl09uxZtZ9p27Ztaffu3VIdjqLYsmULvfPOO1qMtvwp6wS2Qfa/dQE8AND3NQnsqXwS2G757PNLAB4APJo0aaLVH5YmiH/gvXr1InNzc7nD0Vn+/v4EgAwNDQlAgYmZJqSnp1NycjJFRkbS6NGjCQD9+OOPRET07bff5nvBIT4WLVpEX3/9tbR86NAhArIGwRsaGtK9e/do69athX5pi92hDh48WOLzqF69upRU55b7wqldu3Zqyy1btszT2ic+fHx8qG/fvtLy0aNHpediIaQOHTpI45JWrVpFrVq1otTUVGl6gq1bt+Y7R55KpdJoS3bOaRUAaLRbk6Zcu3aNgP/G6OakUqmku9utWrWiqlWrkpmZGbm4uFBoaKjsrQuMsZJLTU197fdJ7kdZTeHx7NkzatmyJQGgy5cvU2ZmZqHTsalUqmJ1bRYL8+Q3bcnriBXfgawhMOLPJC0tTWMFdDTtxYsXUswXLlzQ6L7Fcb7Lly8v8t9R7u+N8PBwWrNmDUVERFBKSgoBWWNdCzunNWvW5OlNFRwcTNWrV6cDBw5I3btnz55d4vNLSkoiIKvbsr7I+Tea+3H27FnKyMigtLQ0rlOhYWWawJJ64vkzgLl4g7oQ//3339IftdidqHbt2tSjR48yn2tK18XGxtLcuXOlqnHF/dIrznFyTn2Qs5Lg4sWLafDgwdKypaUlzZgxQ2ppXb16NY0cOVLtwyrnWBMANGPGDOn58ePHydjYmKZMmZKny66Xl5eUAJeUOOYyZ5EC8YNVnCZGfEyfPp0aNmxIK1eupLCwMIqLi5MuSJYuXarWpdnOzk6aNyznI+cYEHE5Z6Kbc0yS2LL45Zdf0q+//koqlUoqvjFt2jSNzIEaGxsrHa958+bk5eVV6n1qg9j6/bqCTeI8hIyx8sXd3Z3eeeedQpOOrl27lllMn3/+uXTcOXPm0Lx58wgA+fv7E1FWS9PcuXPVElaxO29+N+LyI97cXb58eZHjSk9Pp6ZNm0rfQ/pEnHon91jO5OTkEvcoK6jC9a5du6SxzB9//DFNmjRJ+n5u0KBBodeXMTExpZo/XNx/zoJax44dK9G+xBbdspi6RpOcnJyKdDPBzs6O/vzzT3rx4gUdPnyY/Pz86Ndff6V9+/bJfQp6p8wSWABVAFTN8fw6gMEAVkG9iNPK7OcfQb2I0+3CjqHrCWxUVBRZWFjQli1baOXKlWp/1AkJCXKHp1PEFkOxTL4m72KmpKTQzJkzadKkSbRz506134OzszPZ2trSW2+9RW5ubpSRkUHBwcEF7svJyYmmTp2q1k1o9+7d1KlTJwoKCqL4+PgCP8i6d+9OAGjq1KlS66eVlVWJz0tsrfb09JTWiXe9kX03f82aNUXuepqcnEw7duyguLg4OnPmDM2ePZt8fHzo9u3bZGFhQUFBQdKFDgDatm2bdLHxzjvv0KNHj2jv3r106dIl2rFjh7Rd3759ycbGJs/PY9GiRaW6kfPNN98U6U6y3MSCWs2aNZM7FMaYDOLi4sjT01Oqapv7YWNjU6qEorjee++9fOP49ddfqU2bNmpTiN28eVOtp8uCBQto1qxZRUrKatSoQTNmzCjwdYVCQdu2baPY2FhaunSp2k1QffTnn39Kxflu375Nnp6e9NVXXxFQsilifHx8CMgacynODwxkFQZUqVR5bnqmpKSU+ZjKGzduUL169ej06dPS937//v1p0aJF5OvrSzt37iSlUkm+vr55ikoSEY0aNYoAzXe91jaVSkXXr1+n6OjoPD3BivrQZk/D8qgsE9gWyOo2/ACAN4Al2evNkNU92Df731rZ6wUAmwH4I2vqndeOfyU9SGBzio6OliYPB7RbZVAfiV/sERER0odzaYWHh5Ojo6NaJbl//vmHgoKCaM+ePTRw4ECt3Ejw9/en5s2b0+3btyktLU2assDc3DzfD7ErV65QeHh4scuZi+NgxAQ2JiaGjI2NqWfPngUWCilLf/zxBwGgc+fO0YEDB6TzdXd3l56fOnWq2PuNioqi4OBg8vb2pi+//FLni0iIFYnlniyeMSa/devW5fkOKOuidznrO9StW1d63qVLl0IvupcvX04mJibSTdKCJCYmUpcuXWjo0KEFbrNp0yYCIO0vZ9Ksj4KDgwnIGjIm9kISf74TJ04s1r7S09Olm9Ti2MpTp05pZX5gTTh9+vRr/27Ec0lMTKSIiAgKDAyUpvv75ptv5A6/VJRKpdr1nZmZmVQ863WPyMhIuUPXK2WWwJbFQ58SWNG9e/fI2tq60Gqub5rFixcTkDXfWrVq1Sg8PLzU+xQ/JMTutIsXL5al67ZCoaC0tDRSqVQ0ffp0mjFjBgUEBNCIESPyfKAVdT4wcUJ6ANKdXrH1tTiFBLQtZzXNnMWVfv75ZwL+m1Dc3Nxc6r72OlOmTJHOW9cT15zq169PU6dOlTsMxpgOOHjwIDVs2JBOnTpV7IrwmpBzWpSQkBBavHgxPXjwgJydnaX1zZs3V/tuatmyJQ0YMIDS0tLUbgi3bNmSVq5cmaf4VMOGDal69eqvHVu7fv36PN+BV69e1fbpa03On03OR69evQjIGrL022+/Ffj+zZs3U+XKlSk1NVUaZgRoZt5RbRNnPijskbM3olhfI3chTX2VlJSkNu+uvb09LV68WBrulbPRQhcaGfQNJ7A6gMe/5jVnzhy1/+Bnz54t8b6USiX98ssv0r48PT11ssu22D2ocuXKUqxvv/222jYJCQl5poO5ceOGtH21atXoxx9/lO5iynExVFILFy6kVatW5Ungc3fhvnTpEp06dUrqiis+ymKaHE3IzMwkAwMDmjx5styhMMYYEWXNFfq6bq1KpZLCwsIoICCAbGxs1LoM/+9//yMAebpEd+/enZRKJSUnJ5MgCLR06VJKS0sjOzs7taEuoqCgIAL+q6qriRvXcsuvhT33QxCEPAV+bt++LRVmBED79u2jGzdu5FsMUVf5+vqSUqkkR0dHevbsGalUKsrMzFTrBSe2ugOgevXq6dU1S0mpVCr68MMPqUGDBjzvaylwAqsDVCoVXbp0SWvl63VJTEwM2dnZUVRUFAUEBNDdu3fzHaMxffp0tQ/40kxq7eHhIe3HxcWlNOGXGbGL7aZNm2jnzp3k7Ows3SXv1KkT2dnZ0bmzQXMAACAASURBVMuXLykqKko6N/FOn1hc43VFgnSVWEgq95isTZs2STd6cq5fsmQJffXVV3qTvBJlVfwEQDVq1JA7FMYY0wiVSkVKpZKmTZsmTbliZ2dH8+fPlz6vnZ2d6a+//pKWV61aRWFhYRQZGUnTp0+X5iW3tLSkRYsWyX1KGqFQKOijjz6iyZMn06VLl6Rzz1kwEsgqtiTKWcVYfJSXnwdRVsukhYWFNJXc06dPaf78+XTp0iWZI2P6hBNYHRAVFUVGRkblakxcWloahYWFkaurKymVSlKpVNS1a1fpw7hjx45qH85ixbro6GhKTU2VKhaWJoFNS0uTqtv++++/dOPGDY2eY1kQJ8oGsuYNzf2lduHCBQKy5gz95JNPaNy4cVI1PF2cH684cs/nCoDWrFkjPTc3N6ekpCS5wyw2scJz//795Q6FMca0QpzXVpzLvV+/fpSenk7h4eHSOgD01VdfqRUbBKA21KS8SUhIkG7ai0Uqgaw57wMCAujq1atq4yWTk5PJzc1N74oaMaZtnMDqCPHD6vz583TmzJkij33UFbGxsXT69Gnq1q0bzZ49W20A++zZs6XpZoyNjalOnTp5kpMFCxbQ1KlTpeWDBw+qdSctSQLbuHFjAqDXXVJytrDm/MIXnw8bNkyqVimuE+94P3v2TOboS0elUtHcuXOlolfiw9HRUa/Gu+b28uVLArjiIGOs/CtobN+VK1fom2++oWfPntHKlStpzJgxUiL3JlGpVLR06VJycXGRvuMmTJhALVq04KnUGHsNTmB1hJjgiXOStmzZUu6QiuTgwYMUGhpKVlZWakmGvb292h3Ew4cP0+LFi/NNPNLT0yk1NZX69++vtg9vb29pehmxtSo5OZkuXrwodTX18/OjlJQUUqlUamOJxfnI6tWrp/djjGNjY2njxo108+ZNWrduHaWnp5OXlxfduXOHYmNjpe3En9uGDRto4sSJ5WL8kMjBwUFtLJA+E6sQb9u2Te5QGGOMySz3kKlbt27JHRJjOo8TWB2hUCjo2rVrah9iuph4ubq60rJly+jChQtqc7SJk2h/9NFHUsGluLi4ElXLO3PmDA0fPpyOHTtGGRkZ1Lx5c2lsjPgQKwmLj82bN1Pt2rWl440fP56MjY31olqfppTnL799+/ZJ53fgwAG5wymViIgIAkC1a9eWOxTGGGMya926tdr1DGOscEVNYI3AtMrIyAjvvvsuOnfujAcPHuDZs2do0aIFzp49C3Nzc1lj27JlCxQKBVxcXHD27FkAwODBg6XnALBnzx7s2bNH7X3Vq1dH9erVi328QYMGYdCgQdKyl5cXTExMMH78eGmds7MzxowZg+DgYACAm5sboqKiYG9vjz59+mD//v2wtbXFW2+9VezjM91TqVIl6bmRkX5/HJmamgIALC0tZY6EMcaY3J49eyY9t7OzkzESxsof/b5i1BOCIOD+/fsAgFGjRiEwMBDr1q3Dli1bZInn5cuXaNq0aZ71TZs2xaBBg3Do0CF4eXmhUaNGWo1DvOD38/NDZmYmLCwsAADPnz9HaGgo0tLSYG5uDgMDA/z888/4999/sW/fPlhbW2s1Ll31119/oU+fPvDz80Pjxo3lDkcjRo0ahbt376Jbt256n8CKyXjPnj1ljoQxxpjc2rRpgydPngAAPv74Y5mjYax8MZA7gDeNj48PAMDR0RHx8fFldlyVSgU/Pz+MHTtWLXldtmwZUlJSkJmZicDAQHz77bcwNTWFlZUVmjRpUiaxtWzZUkpegayWuCZNmqB169YQBAGtW7cGAERHR2P8+PFS4vum2L17Nzp27IiqVasiIyMDBgbl679t27Zt4e3tjf79+8sdSqlkZmYCABITE2WOhDHGmNwUCoX03MTERMZIGCt/yteVsB64fv06pk2bhoYNG6JGjRoa2adSqcT69euRlJSU78VzeHg4DA0NYW5ujgMHDmDQoEHw8vICEWHJkiWoVKkSDA0NNRKLNly5cgXPnz+HjY2N3KHIYuLEifDy8kLLli0BoFwlsI8fP4atrS2ICNWqVZM7nFLJyMgAAFy+fFnmSBhjjMlt8ODB0nNOYBnTrPJzJawnatWqBQcHB3z66acAgPXr15d4XxkZGVAoFPjf//6Hb7/9FlWrVkW1atVw5coVAMCtW7ewbt061K1bFy1atJDe5+TkhI4dO5buRMpQvXr10KxZM7nDkM3QoUPRt29fqFQqAOUrgf0/e/cdHkX1NXD8e9NDIBB6r4L03hEUqSKIFEVEKQpKURQFRKXj77WgYkUEBAtSFOm9ifQWCJ2EGgiBAIE00pP7/rG7QxYCpLKbzfk8zz7ZmZ2ZPbuz2Z0zc++5oaGhLFiwgA8++MDo95xTeXl5sXLlStauXWvrUIQQQtjY22+/bdyXBFaIrJWzO53lYCNHjuSzzz5jxIgR1K9fn1atWqV7GwUKFKBUqVKcOXPGav6TTz7JG2+8wcyZMwHw9fXlzJkzzJ8/n2eeeYaCBQtmyWsQj4YlIbL0SXakBNbyo7569WrOnTuX7f2us1vnzp1tHYIQQgg74OrqatyX4y4hspbjHAnnMIUKFcLX1xeADh06cPjwYdO4RulQpEgRzpw5Q9GiRe95zJK8gqn6nVKKPn36yJdoDlalShWGDRtmVbk3p3NzczPu5/QiTkIIIYSF5Tdt2rRpMnKCEFlMjhhtqH79+sydO5cSJUrQtWtX5syZw9NPP52mdbXWxMXFAabiMTExMYDpilZISAixsbHky5ePQoUKZVv84tFwcXEhMTGRFi1a0K5dO1uHk6VSJrApz1YLIYQQOZklgXWkk85C2Au5Amtj/fv3p169egQGBjJ8+HCio6ONx2JjY43CMCkFBATg5ORESEgIJUuWxM/PDw8PDzw8PFBKUbx4ccqXLy/Jq4PYvn07YOrzHB8fn+4r9fbMkcaBFUIIISwsXWQGDx5s40iEcDySwNqBokWLUqlSJY4fP069evW4evUqiYmJeHp6Ur9+fSIiIjh//jx169Zl2rRpVkPOHD161GHGBBWps1SI/vLLL3F3d+f27ds2jijrlC9fnsWLFwOSwAohhHAchQoVonr16vTo0cPWoQjhcCSBtRMrV640+rSuWrWK6tWrA3D8+HGqVq1KxYoVOXz4sJG8NG/enPj4eOnTmgsULVqUAQMGUKJECcCxijgBPPPMMwQGBlqdmBFCCCFyurCwsCwbMlEIcYdjHQnnYNWqVePatWvcunWLgQMHMm/ePAB+/PFHfHx8jOU+/vhjtNbs3LlT+gzmEuXKlWPOnDnUrl0bcKwE9vbt2/Tq1QtfX1+r/rBCCCFETpaQkEBwcDDr1q2zdShCOBxps2dnvL29AWjcuLHR13Ho0KFERERw+fJllFK2DE/YkCOOA6uUYtWqVRw8eJBu3brZOhwhhBAiS1h+q7t06WLjSIRwPJLA5hDe3t5GcityF39/f2rUqEHVqlUBx0pgLVddg4ODbRyJEEIIkXWcnZ2JjIyUKsRCZANJYIWwc05OTiQlJdG4cWO6dOniUAmspUBV+fLlbRuIEEIIkcXy5s1r6xCEcEiSwAph5yzVeZ988kn69etn42iyllKKvXv3UqVKFVuHIoQQQgghcgDHuZQjhIOyXKXs378/N2/etHE0Wa9x48ZSpVEIIYQQQqSJJLBC2LmU46MWKlTIhpEIIYQQQghhW5LACmHnpA+NEEIIIYQQJjZLYJVSHZVS/kqpM0qpMbaKQwh75+3tbfQRlbF/hRBCCCFEbmaTBFYp5Qz8CDwDVAd6K6Wq2yIWIXKC5cuXA6aB0YUQQgghhMitbFWFuDFwRmt9DkAptRDoCpywUTxC2LWqVasycuRITp06ZetQhBBCCCGEsBlbJbClgEsppoOAJjaKRYgcYerUqbYOQQghhBBCCJuyVR9Ylco8bbWAUm8opQ4opQ5cv379EYUlhBBCCCGEEMJe2SqBDQLKpJguDQSnXEBrPVNr3VBr3bBIkSKPNDghhBBCCCGEEPbHVgnsfqCyUqqCUsoNeAlYYaNYhBBCCCGEEELkADbpA6u1TlRKvQWsB5yBOVrr47aIRQghhBBCCCFEzmCrIk5ordcAa2z1/EIIIYQQQgghchaltX74UjamlLoOBNo6DpGqwsANWwchHinZ57mP7PPcR/Z57iP7PPeRfZ772Ps+L6e1fmjxoxyRwAr7pZQ6oLVuaOs4xKMj+zz3kX2e+8g+z31kn+c+ss9zH0fZ57Yq4iSEEEIIIYQQQqSLJLBCCCGEEEIIIXIESWBFZs20dQDikZN9nvvIPs99ZJ/nPrLPcx/Z57mPQ+xz6QMrhBBCCCGEECJHkCuwQgghhBBCCCFyBElgHYxSqoxS6l+l1Eml1HGl1Dvm+QWVUhuVUqfNf33M86sqpXYrpeKUUiNTbMdDKbVPKXXYvJ1JD3jOfubtnlZK9Usx/39KqUtKqaiHxNxAKXVUKXVGKfWdUkqZ509VSp1SSh1RSi1VShXI7PvjiBxpn6d4fKRSSiulCmf0fXFkjrbPlVJvK6X8zTF8kZn3xlE50j5XStVVSu1RSvkppQ4opRpn9v1xRDl0n6e6nFLKXSm1yPxZ2KuUKp+xd8WxOdg+f08pdUKZjuE2K6XKZfR9cWSOtM9TPN5TmY7hsq/asdZabg50A0oA9c338wEBQHXgC2CMef4Y4HPz/aJAI+B/wMgU21FAXvN9V2Av0DSV5ysInDP/9THf9zE/1tQcT9RDYt4HNDM/51rgGfP89oCL+f7nlpjl5rj73PxYGWA9prGfC9v6/bXHmyPtc6A1sAlwt8Rq6/fXHm8Ots83pLjfCdhq6/fXHm85dJ+nuhwwFJhhvv8SsMjW76893hxsn7cG8pjvD5F97vj7PMVr2AbsARpm1/smV2AdjNb6itb6oPl+JHASKAV0BX4zL/Yb8Lx5mWta6/1Awl3b0Vpry5kVV/MttQ7THYCNWuubWutbwEago3kbe7TWVx4Ur1KqBOCttd6tTZ/831PEtkFrnWhedA9QOo1vQ67iSPvcbBow+j7PLXC4fT4E+ExrHWeJNY1vQ67iYPtcA97m+/mB4DS8BblOTtvnD1kuZcyLgTaWK/LiDkfa51rrf7XW0eZJOYa7D0fa52ZTMCXfsQ/bTmZIAuvAzE106mE6C1PM8mEz/y2ahvWdlVJ+wDVMH/a9qSxWCriUYjrIPC+tSpnXedj6r2E6gy8eIKfvc6XUc8BlrfXhdGwvV8vp+xyoArQ0Nyv8TynVKB3bzZUcYJ+/C0xVSl0CvgQ+TMd2c6Ucss8fxNi2+cR0OFAoi7btkBxgn6f0OnIM91A5fZ8rpeoBZbTWq7Jiew8iCayDUkrlBf4B3tVaR2RkG1rrJK11XUxnzRorpWqm9lSprZqOp3no+kqpj4FE4M90bDfXyen7XCmVB/gYGJ+ObeVqOX2fm/+6YGrG1BQYBfwlV2buz0H2+RBghNa6DDAC+CUd2811ctA+f5Ds3LbDcZB9bnoCpV4BGgJTs3K7jian73OllBOmFnTvZ3ZbaSEJrANSSrli+if4U2u9xDw7xNyky9K0K83N9LTWYcBWoKNSqokyFd7wM18tC8LUZ9GiNA9oDmY5O2S+TTavn7JZidX65s7lnYE+5mZoIhUOss8rARWAw0qpC+b5B5VSxdMad27iIPsc82NLzM2f9gHJgBTvSoUD7fN+gCX+vwEp4nQfOWyfP4ixbaWUC6am4zfTGndu4kD7HKVUW0wnpp/T5m4i4l4Oss/zATWBreZjuKbACpVdhZy0HXRgllvW3TCdWfkd+Oau+VOx7gz+xV2PT8S6M3gRoID5viewHeicyvMVBM5juoLiY75f8K5lHtYZfL/5g24p9NHJPL8jcAIoYuv31Z5vjrTP71rmAlLEyeH3OTAYmGy+XwVT0yZl6/fY3m4Ots9PAk+Z77cBfG39/trjLSfu8/stBwzDuojTX7Z+f+3x5mD7vB5wFqhs6/fVnm+OtM/vemwr2VjEyeY7Tm5ZvEPhCUxNAY4AfuZbJ0x9TTYDp81/C5qXL47pbEwEEGa+7w3UBg6Zt3MMGP+A53wNOGO+DUgx/wvz9pLNfyfeZ/2G5uc4C/yA+eDVvL1LKV7HDFu/v/Z4c6R9ftcyF5AE1uH3OeAGzDM/dhB42tbvrz3eHGyfPwH4Aocx9fVqYOv31x5vOXSfp7oc4IHpavsZTNWpK9r6/bXHm4Pt801ASIrXscLW76893hxpn9+1zFayMYG1/JgIIYQQQgghhBB2TfrACiGEEEIIIYTIESSBFUIIIYQQQgiRI0gCK4QQQgghhBAiR5AEVgghhBBCCCFEjiAJrBBCCCGEEEKIHEESWCGEEEIIIYQQOYIksEIIIYQQQgghcgRJYIUQQgghhBBC5AiSwAohhBBCCCGEyBEkgRVCCCGEEEIIkSNIAiuEEEIIIYQQIkeQBFYIIYQQQgghRI4gCawQQgghhBBCiBxBElghhBBCCCGEEDmCi60DSIvChQvr8uXL2zoMIYQQQgghhBDZwNfX94bWusjDlssRCWz58uU5cOCArcMQQgghhBBCCJENlFKBaVkuzU2IlVJzlFLXlFLHUsybqJS6rJTyM986pXjsQ6XUGaWUv1KqQ4r5Hc3zziilxqT1+YUQQgghhBBC5G7p6QP7K9AxlfnTtNZ1zbc1AEqp6sBLQA3zOtOVUs5KKWfgR+AZoDrQ27ysEEIIIYQQQgjxQGluQqy13qaUKp/GxbsCC7XWccB5pdQZoLH5sTNa63MASqmF5mVPpDliIYQQQgghhBC5UlZUIX5LKXXE3MTYxzyvFHApxTJB5nn3my+EuI9rt6+x4ewGW4chhBBCCCGEzWU2gf0JqATUBa4AX5nnq1SW1Q+Yfw+l1BtKqQNKqQPXr1/PZJhC5Fytf2tNh3kdSNbJtg5FCCGEEEIIm8pUAqu1DtFaJ2mtk4FZ3GkmHASUSbFoaSD4AfNT2/ZMrXVDrXXDIkUeWk1ZCId14rq0sBdCCCGEEAIymcAqpUqkmOwGWCoUrwBeUkq5K6UqAJWBfcB+oLJSqoJSyg1ToacVmYlBCEc3oukIALROtbGCEEIIIYQQuUaaizgppRYATwGFlVJBwATgKaVUXUzNgC8AbwJorY8rpf7CVJwpERimtU4yb+ctYD3gDMzRWh/PslcjhAMq6FkQJ+WETr21vRBCCCGEELmGyglXdRo2bKgPHDhg6zCEsIn/LvzH7qDdjHlChk0WQgjh+C5HXKZEvhI4qayoNSqEyCmUUr5a64YPW06+GYSwcxvPbeTjLR/bOgwhhBAi210Kv0TpaaWZuHWirUMRQtgpSWCFsHOrT68mWSeTmJxo61CEEEKIbBUcaartuf7sehtHIoSwV5LACmHnDl89DEBScpKNIxFCCCEejZzQxU0IYRtpLuIkhLANKd4khBAit6hauCpDGw7lpZov2ToUIYSdkgRWiBxCElkhhBCOLr9Hfn589kdbhyGEsGPShFgIOzeu1ThAmlMJIYRwfKHRoQxYPoDtgdttHYoQwk5JAiuEnSvoWRBvd29bhyGEEEJku7O3zvKr36+8v+F9W4cihLBTksAKYeeqFq7KO03ewdPV09ahCCGEEEIIYVOSwAph57ac38LUXVNtHYYQQgghhBA2JwmsEHZuZcBKYhNjiU2MtXUoQgghhBBC2JQksELYOf8b/gAkJCXYOBIhhBDi0ZDK+0KI+5FhdISwc/IjLoQQIreoVbQWU1pPoX2l9rYORQhhpySBFSKHkERWCCGEo/N09WRsq7G2DkMIYcekCbEQdu6Ltl8AMg6sEEKI1CXrZM7fOk9YbJitQ8m0K5FX6DCvA5vObbJ1KEIIOyVXYIWwcwU8ClAqXymUUrYORQghhB26FH6Jit9VpHqR6hwfetzW4WTKpYhLbDi7gVsxt2hbsa2twxFC2CG5AiuEnSuUpxC9a/bG293b1qEIIYSwQ8k6GYDohGgbRyKEENlPElgh7Ny2wG3MPDjT1mEIIYSwc9LVRAiRG0gCK4SdW+G/goi4CCLjIm0dihBCCCGEEDYlCawQdu5C2AUA4pPibRuIEEIIkc0UUu9BCPFgUsRJiBxChtERQgiRmnzu+QAY2miojSPJvPol6jOz80walWpk61CEEHZKElgh7JyzkzOJyYnSt0kIIXKZmIQYXJ1dcXF68OFa4TyF0RMc4zfC2cmZQQ0G2ToMIYQdS3MTYqXUHKXUNaXUsRTzCiqlNiqlTpv/+pjnK6XUd0qpM0qpI0qp+inW6Wde/rRSql/WvhwhHM83Hb4B5AqsEELkNnn+Lw+uU1z5atdXD1xOa82RkCNcjbr6iCLLPoFhgdT+qTZrTq+xdShCCDuVnj6wvwId75o3Btista4MbDZPAzwDVDbf3gB+AlPCC0wAmgCNgQmWpFcIkbqCngWpWrgqzsrZ1qEIIYSwgU93fMrTvz3NyA0jU338QtgF6syoQ6u5rR5xZFkv5HYIR68dZeLWibYORQhhp9KcwGqttwE375rdFfjNfP834PkU83/XJnuAAkqpEkAHYKPW+qbW+hawkXuTYiFEConJibSt0JZCeQrZOhQhhBA2oJTiYvhFrkRdSfVxyziwicmJjzIsIYSwicxWIS6mtb4CYP5b1Dy/FHApxXJB5nn3m38PpdQbSqkDSqkD169fz2SYQuRcu4N2s/D4QluHIYQQ4hErl78cACObjeTsrbPMPzo/1eWki4kQIjfJrmF0UquBrh8w/96ZWs/UWjfUWjcsUqRIlgYnRE6y3H85N6JvcCP6hq1DEUII8QgFvB1A4rhEqhSq8sDlLFdghRAiN8hsFeIQpVQJrfUVcxPha+b5QUCZFMuVBoLN85+6a/7WTMYghEOzFOWQcWCFECL30FpT6ItC+Hj4cCni0gOXdaQE1lLv4WGVl4UQuVdmr8CuACyVhPsBy1PM72uuRtwUCDc3MV4PtFdK+ZiLN7U3zxNC3IcMnyOEELlPYnIiUfFRD01eAfK758fDxYMPWnzwCCLLXg1KNmBBjwXM7DLT1qEIIexUmk9vKaUWYLp6WlgpFYSpmvBnwF9KqdeBi8AL5sXXAJ2AM0A0MABAa31TKTUF2G9ebrLW+u7CUEKIFLzcvIiKj5JEVgghcon1Z9az9szaVB+r8n0VAt4OsJpXIl8JYj6OeRShPRIv1XzJ1iEIIexYeqoQ99Zal9Bau2qtS2utf9Fah2qt22itK5v/3jQvq7XWw7TWlbTWtbTWB1JsZ47W+jHzbW52vCghHMnX7b8GpEiHEELkFpvPb+bbvd9azXu28rMAnL55+p7ltdbsCdrD+VvnH0l82SkgNIAiU4uw9ORSW4cihLBT2VXESQiRRYp6FaVRyUa4OrnaOhQhhBCPwMqAlVbT+dzycTPm/g3WjoQcodkvzWjze5vsDi3bWYoWfrrjU1uHIoSwU5LACmHngiODqVOsDsXyFrN1KEIIIR6BUzdOWU1HxkeyO2j3fZe3tNBRKrXBHnIW6S4jhHgYSWCFsHO+V3xZc2aNrcMQQghhJyzV6S0sVYgdIfmT7jJCiIeRBFYIO7fcfznBkcEERwbbOhQhhBDZLCk5ybg/oukIEsYl3LPMa8tfs5p2hMTVwpFeixAPcvL6SXot7sXliMu2DiXHkQRWCDtn6fck48AKIYTjc3Zy5tII09A5UfFRfLbjs3uWSUxOtJq2XIF1hCbEbs5uVn+FcFRzDs3hr+N/sezUMluHkuPIKNFCZLNFxxbRqFQjKvpUzNR25Ky0EELkDqW9S9OqXCtmHZyV6uOVfCpZTRfLW4wqhaow6alJjyK8bNWkdBPWvLyGCj4VbB2KENmqdrHaAETERdg4kpxHElghstlL/7zEgLoDmNN1TobWL+RZiOvR17M4KiGEEPbI/4Y/4/4dx9uN32Zb4LZ7Hm9cqjHfPmM9xE7Z/GXxf8v/UYWY7Z6p/IytQxAi2yw8tpAFxxaw9cJWvFy9CLkdYuuQchxpQixENvNw8aBIniIZXn9qu6mAFLYQQojcIDA8kL9P/E3xvMUZ0XSEMb9BiQYA7Lu8j+iEaKt1EpMTWX5qOdP3T2fi1omPMtwsdyTkCGqSYsHRBbYORYhs0fuf3qzwX0FEXARPln+Sxwo+ZuuQchxJYIXIRlprYhNjuRhxMcPbKJGvBE9XeBoPF48sjEwIIYQ9CosNA6CARwE+b/u5Md/3iq9x/91171qtsydoD88vep5ha4Yx6b+c3Yz4RvQNAL7e87WNIxEi++V1y8tbjd+ydRg5jiSwQmSjJG2qJrnw2MIMb+Pw1cOUyleKkvlKZlVYQggh7NStmFsA+Hj44OrsmuoyAaEBVtOOVCPBkV6LuL9pu6ex8ezGe4aEyg1GNx9t3B/bcqx85jNAElghspGlMmRmHL12lO0Xt2dBNEIIIezdrVhTAlvAo8B9l9kdtJvp+6cb03f/1mTFb4+tWLrLKNJfUXl74HZ8g30fvqCwKa017214j/bz2lPiqxL4XfWzdUiPzOiNozlz6wxb+m4hbmwc2wK34TTZiVEbRjFwxUC2XtiKmqT4Yd8Ptg7VrkkCK0Q2sozn17R00wxvY7n/ci6EXeBC2IUsikoIIYS9cnVypYx3GfK45gEgfmzqQ6gNWzPMuJ8yYQ37IAwnlXMP7zIzJNDzi57P8U2oc4Nzt85ZTc8+ONtGkTx6flf9CIoIonWF1rg5u5HXLS8AX+7+kl8O/ULr31oD8PbatwmNDrVlqHYt537DCZEDWH6Iu1ftnuFtRMVHATIOrBA51a2YW8bJLJExp0NP8/Xur7l2+5rV/OPXjhMZF5mhbR6+ehg1SbH/8v6sCDHLvN/8fS6OuGgkcOvOrHvoOikT2Hzu+bIttkfBy9ULIN11H7TWRCdEU7lg5ewIS2ShwnkK83nbz1n8wmK6VOnCxnMbSUpOYunJpWw+rjngfwAAIABJREFUt9nW4WWrK1FXKJG3hDHduUrn+y5beGphHvtOCjylRhJYIbKRpQ9sXFJcprclfSTE3f488ieVv69sFD0R9iMoIohJWycRnRBNwS8KMmrjKFuHlCMdCTnCpnObOBB8gPc3vM/NmJvGY+Gx4dT8qSaFpxZGTVIsPbn0vttJTE7k2z3fEpd457t4VcAqAJaeWorWmoSkhOx7IZlQNn9Z436VQlWM+5YrNwAVfCoYiZvzZOccPa5ki7It2DFgB3OeS9/Qc1ejrhKbGMvlyMvZFJnICudvnScuKY7RLUbTo3oPmpRqQkBoAC5TXOj+V3fa/tGW0RtHc+rGKQAWn1jMLwd/sXHUWWNb4DaOXTtmdZKlUJ5CBL8XzOHBh/mr51/8/vzvHHrzkJHYnr11lta/teaVJa/YKmy7JAmsENnI08UTgA1nN2R4G6W9S2dVOMLB3Iq9xZmbZ2wdhkjFO+veYeJ/E/nzyJ8ArPBfYeOIcp5lp5ZRZ0Yd2v3RjitRVwBT8SLLyTxL8RdL65Spu6bed1tzD83l3fXv8tXur4x5FXwqAPBSzZf4bu93uH3iZpUg28qA5QP4cNOHxnSd4nX48IkPKehZkIDQAArnKUzXx7sS+eGdK88VfSryVfs7ry0mIYbYxFhKf12a1QGrH2n8WaFF2RZUKlgpXeucvXUWgEXHF1F3Rl22nN+SHaGJTPC76kfF7ypS7MtiBIYFAjCi2Qj61OpjtdzUXVOp/3N9/j7+Ny/8/QIDVw60RbhZ7tu9pvGb21dqbzW/RL4S1C5WmxdqvMCrdV6lbvG6rOy9kvAx4QBsvbCVP4/+abxnQhJYIbKVq7Mrpb1LZ2qMr8/afAbIOLDiXsv9lwNwJfKKjSMRd9t1aRcAb6x6A7hzcO0IbsffZvr+6dnaKuRS+CW6LepmTG+9sBWArgu7suTkEuDOcCtDGw4FTFdZ73boyiFazW1lFIlJTE5k/+X9lPumHG+tMQ1dMXbLWN5dbxqW5mJ4xoc8ywrf7/2eX/1+xc3ZzWr+/7X5P9a/sh4wve7l/sutWl6cu3WO5xY+Z0zHJ8VzIewClyMv896G9x5N8FlkT9Ae1CTFr36/pmu9IyFHjPuHQw4zfO3wLI5MZFa9n+sZ9y0npfK45mFe93noCZrtA7bTu2ZvSnuXJiYxhhcXv2gsn97Pgz2a1mEac56bQ5uKbdK0vLe7NxteuXMBZP7R+Ty/8Hme+fMZNpzdgNaamIQYFhxdQGxibHaFbZckgRUiG8UlxhEUEURgeMbPmpX2Lk3Xx7taNRcTAjAKPEj/aPtyKfySQw8N8ceRPxi2Zli2Vg49ffM0gHFlZu2ZtcZj+4NNfVaDI4MBeKHGC4DpqoaapIzb0ZCj1J9Zn+0XtzP9gKli74StE2g8uzEXwy8a1X5XBqw0tj3Td2a2vaaHGbJqCMPXmZKut5u8fc/jjWY1spouMrWIcX+W7yyrxxKSEyjkWQiAzpXv38fOHlmugv+4/8d0rVe1cFWKeRUzpkvkK/GApe/wu+rH/KPz0/VcImN8PHyM+w1KNLjn8SfKPsH8HvOZ1cX0eV7y4hIWv7AYN2c3niz3JENXD+Xw1cOPLN6sNO/IPCb/N5lX67yariJr7Sq149xwU9GrEc1GsNx/OevOrKPDvA78e+Ff8vxfHl5e8jKe//PMVd2JJIEVIhtZfogz05Rp07lNeLh4SFNicY+MVOkU2S/llaCUHKWQk6XIjre7d7Y9h6VY00ctP6JkvpJWV1cDQgOY6TuTfy/8y57X99CiTAuiPoy6p8BTyv+PAXUHUKdYHYY1Gsb7zd7Hx8OH+iXqG48Pqj8IgJ8O/JRtr+lBFp9YzAzfGQDkd89P4TyF71kmZf/XuwVHBVtNxyTE4Olq6sJSPG/xLIw0+2X0yv7TFZ7m7xf+Nqb/7P5nmtb7bMdn9FnS5+ELikyJS4wjLDaMV2u/StzYuPuOcQzQ8bGO6AmabtW60aN6D+LGxrHh7AZ+OvATdX+uy9e7vwYgJCqEHRd3GOuFRIXgd9XPLmuG/HLoF/4L/A8XJ5d0r1vBpwJ6gsbDxYNDbx4y5l+/fd1quVeXvkrLuS3ptbgXP+77MdVWKY4i/e+iECLNsmIsPv9Qfw5dPfTwBUWuU7lgZQ5eOZijh8xwRE1LN+Wp8k8ZzV4tYhNj8XLzyvLnS0hKIDgymKPXjj6woqWapJjw5AQmPjUxU89nuXKZkJz1RY/iEuMYsnoIf5/4GzdnN8rlL0fHSh05dPUQa/qsoZhXMT7c/CFvrnoTgNCYUBb1XISrsyvDGg2jVtFaHA45zKdtPqVQnkJcH3U91WTwy/ZfAqbm95cjL9OwZENalm3Jiesnsvw1pcWcQ3cKFlkSz7vtG7iPmMQYKnxb4Z7mgpcj7i1cZFkmpw3Blp7uMqHRoRTKU8i4b0nWy3iXYeyWsUxpPYVieYs9aBMsOr4IMDUvz0hyYc8uR1zm0x2fEhwZzOgWozM1pF9mXQy/iEbTtmLbe5rIp4VlWCm4M0Zw8a9M+7tzlc4MbjCYzgtM339ft/+aEc1GGMuHRIVQ/Kvi9K/bn7yuefmqw1cZiiGjkpKT2Hd5n3GiLDPqFq/LgUEHOH3zNF2rdsXvTT/KFyhPWGwY765/16haXsyrGJFxkcQnxfPd3u/4YtcX9K/Tn6GNhlKvRL2HPIv9c6z/VBub8t8UOlXuRIOS9zaLELmTpQpxp8qdMryNFf4riEmM4dSNU1QtXDWrQhMOoGf1niw6vsjhDroehcNXD9N/eX9W9l6Z5a0bCuUpxL/9/jWm1STTwVZ2JbCHrh6iyewmAISPCU/1yqil+u6k/yZlOoHdFrgNMF1pzorvpPikeD7f8TmDGgxi0MpBrApYRe1itRlQdwBebl5Mf3Y6UfFRRqIypOEQPt/5OQDfdPjG2E6d4nWoU7yO1bZTS15TKpGvhNHU9NU6r2b6tWREYnIinat0pk6xOrzV+C3cXdxTXS6/R37yk98qeV1/Zj0dHutAYHggnat0ZvJTk1kVsIqIuAhjO7ZMWjLCGAeW+7cw0Vrzxc4vGLN5DB+3/JgBdQfQ7o92NC7VmIvvXuTE9RN0/LMjsw7OImRkCEW9ij70eeMS43Bxs/4u/Wn/T4THhVPQsyCD6g/Kca1eWv3ayhhztVnpZjb9LFjiqOhTMUPrv1rnVXpW70mSTsLL1Yvb8bfxcPEgNjGWVQGrrAqwHbhygK4Lu9KzWk8+2vIRL9d8GbjTj/b3I7/jrJxZ/fJqmpVpZqyXrJOz5YTws/OfJTohmpZlW2bJ9hqUbGDkGpbvvPwe+fm87ecsO7WMXjV6Ma3DNHyv+Bq/DQB96/SldrHaWRKDrWXJXlJKXVBKHVVK+SmlDpjnFVRKbVRKnTb/9THPV0qp75RSZ5RSR5RS9R+89ZwhLjGO8VvH0/SXnPVDIbKX5Yf4heovZHgbliF4UmsKkpCUwOWIy1nWBzIhKSHDYyqKR6+YVzFal29tdWZapM3E/ybid9WPnRd3Zul2YxJi+GDjB1b9Qy1F3KZsm8LUnVP5Zs8391v9vmITY5m4dSLRCdFW8y+EXeDFv+8UOpm6cyrRCdHsDdoLwKkbp1h6cqkxnnRWelCCkR6rA1bz+c7P6bKgizG0zdZ+W3m3qamwkruLu5G8ApQrUI7bH90m9uPYNPdzTIuQqBBOXj+ZZdtLi2PXjuE6xZXzt87zadtPKeVd6qFJd0rzj80nWSdzMfwi1QpXo07xOozfOp51Z9YZTdadnZyzK/xsUcCjAJD6lehv9nyDmqSo8G0FxmweA8D/tv+Px75/jPNh52lRpgVl8pehUak7/YWLfVmMnn/1RGuN1poT10+w4ewGFp9YbLXtyPhIbsffZv2Z9Uz+bzIHgg8wdM1Q44r/7YTb2fiqs8fyl0yF/p4s9yTvNH2Hyf9NZt/lfVbDSaUmtSv6mdWmYhvODT+Xat/XtPJ09SSvW16UUni5eRHzcQxzu86lSakmbHhlA3Oem8O6PutoXro5K/xX0HdZX4Iignil9iu8XOtlqhauyvl3zjOk4RBuxd6i+ZzmvLb8NS5HXEZNUjhPdmb8v+PZcXFHlnT52H95P53+7MT6s6YCbN2qdXvIGplTpVAVzr9znjld5+Ds5Gwk9Z4unsx4dgYty7XMcd8H95OVpxlaa63raq0bmqfHAJu11pWBzeZpgGeAyubbG4BtOpxkMTdnN/K55ePZys/aOhS7FhYbxuvLX8/RY9Slh+UL8FbMrUxvK7U+HQGhAZSeVvqB4x+mx/C1w/H+zDtLmj6L7Lf38l5OXD9hDAeS26QctzM8Njxd6z7mY0oqO1XulKX9pfxD/fli1xecDj1tzJvbdS5gGkJh9KbRjFg/wig0dHcz4/uZ6TuTSf9NMvp+Acw4MINXlrxiVSTuk+2f4PV/XjT9pSmvLHmFaj9Wo/tf3Sk81ZQUpSx0tPPiTip8WwE1SdH+j/YkJSfx++HfGbZ6GO3+aEdEXATzj85n0IpBVs1Qnyj7BECWXPnXWvPh5g9RSjGk4RAA/nnxH3w8fR64Xh7XPPe9UplRX+3+igYzH10LKt9gX2r9VAuAoMigtK/3hq9xPyA0APdP3Hmr0Vv0qtELJ+WEp4sn0QnRRgugZaeWZW3g2axVuVYcGXyExiUbG8NQgemzMmK9qVmo5TM/rNEw4/FOlTsxpJHpM1TQsyABbwUYj/1z8h/6LutL5wWdqTG9Bh3mdeCFv1/g2LVjxjJ1Z9Ql76d56fhnRyZsnXDPiab0fsc8CtEJ0by3/j0m/Dsh1e+xmkVroidotvbfykebP2LC1gk0md0Ej/95MO/IPF74+wWrKva1f6qNmqQoPa00cw7N4Te/37IsVhcnFyr4VLhvE/mM6l+3P3sG7sHLzYsB9QbQ4bEO9K3Tl2cee4ZaRWux+uXVPF74cf7s/icnh52kfIHyfNrmU75s9yWeLp7M9ZvLlG1TjO1N2TaFlnNbUn16daNQXEadvHGStWfW0rJsS04NO/VIuvuUL1DeOKlt6Usc/XE0bzZ8M9uf+1HKzneyK2D55P8GPJ9i/u/aZA9QQCmVdadQbUQpRWxiLMv9l1t1KBfW5hyawxy/Ocz0nUlQRNp/sLNbZFwke4L2sO7MuoeemUwPy9WB+ccyXuHwQYU7wuNMP6j5PfIb88Jiw3hz5ZuoSYrVAauNSrUAOy/uxP+G/32392T5JwHYcXEHg1cNfuBn2VEK0uRkkXGRhNwOyZV9YPcG7cXtEzdKfV2KQSsG0W1RN9afWc/sg7OtDkrvx9Kiwfszb+b6zc2yuCz/Xymb1loSvtQ8t+A5q+lRG0bx+vLXreb95vcbsw/OBuBn359JTE5k0bFFDFk9hJ2X7lxBXvLiEqv17r4yv+blNVYnWZ+Y+4SRmG48t5Fx/46j37J+TD8wnU3nNnEj+gZ9lvRh9qHZVPi2Aievn2TD2Q1UK1ztYW9Dmr2z7h38Q/0Z02IMr9V7jSvvX6F7te5Ztv30ikmMwTfYl4NXDrI9cHu2nsxrOMt0vv/Ldl+yoMeCNK9Xv0R9/N40XeHfE7SHxOREXJ1djSaFnq7mBNb8HX2/omL2rFaxWny5+0teWfqKMW/Sf5MA0wH6Xz3/4uAbB/mh0w/oCRo9QbP65dVWJ1UeK/gYn7b5lAF1BwCmQllVCt75Pe36eFdqFKmBp4snH7f8mNEtRhuP/dz5Z5557BlmPDuDUc1HAaZijGtOr8mW15v/s/yoSeqellZrT69N9VgpJiGG1r+1Zvja4aw7s47J2yaT5//yEBkXydGQo3y/93tm+c7ih30/GOukTPY9XTwpma8ki08spuTXJRmwfAAJSQkcvXbUWOb1Fa/Tf3l/Ptj4QZb83g9fO/yeatnZJZ97Ptb0WcORIUfoVLnTPf1dlVK83/x9oj+OZkvfLbzR4A361+3Pf/3/o15xU//QgNAAakyvkakTnJaTA2v6rOHxwo9n/AWJe2RVxykNbFBKaeBnrfVMoJjW+gqA1vqKUsrSAaEUcCnFukHmeTl6IMOvd39tFLTwv+GPp4un9IVNRduKbQHTMBNlppXh/DvnKV+gfLY/b1hsGIuOLWJQg0E4KSe01vyw7wcqF6rMiPUjOHXjlNXym/tupmnppny46UMqFaxE/7r98Xb3Jjw2nKCIIBKSE6hTrM5D+8Pkcc1DveL1MlUF8pPWn9Dz7573FLaIjIukxZwWgOmgNr97fqLio2g/784A2ZaCBlfev8LL/7zMvxdM/fLix8bj6uzK9sDtNC3dlCtRV3BxcqFzlc54uniy4OgCfvb9mWu3r9G0dNN7rrQsP7Wcnn/35L/+/9G8TPMMvzaROdsumvoi+gb75rrvm32X9wGmoVRmH5ptKvbzZ0cAulfrjv8NfxqXaszs52az8+JOfDx9qFm0prF+TGKMcX/L+S28Vu+1LInL0rqkoGdBq/lTWk9h3L/j7lk+Mj7SqnjMl7tNxYVmdJ5BQGgAsYmx9F/e31g+KCKIJ399khJ575z3TR6fbHwXJY1PYtmpZbQu35okncQHLT6gWN5iaK3J556P9sntKeBRgD+O/MHsLrM5e+ssv/r9SiWfSsQmxuKknGhYsiED6g6wqurbp1Yfjl47Sq/FvYx5n2z/hJkHZ7K2z50hbtIqNDqUNafXsOncJgAGNTAVN7FlxVxL94mlp5ay8NhCzt46i7uzO4cHH+ZSxCXaVGiTpX0g32/2PisDVvJ+8/fTvW6d4nUIHR1KoS9MTatHNL1TsKZwnsJMPzCdluVM/e1yWr/NLee30OZ30ziZr9d7nen7p/PWmrdY98o63mnyDl+0+yJNBXiUUox5wtT47//a/B+eLp5oNFPbT7X6TYv++E6z/MoFK1O7WG3KFShnzLt2+xpTd02l77K+gPX/W1axfG8sO7WM5x5/joNXDpLPLR+d5neibP6y7HxtJ16uXiTpJArnKcywNcPYemErxfMWZ3PfzZT8uiSxibH0WdKHpqWb8vGWjwGoV7webzU2jXdsqWRrkZScRCWfSsZ3wKdtPiXw3UD+OfEPtYvVpu0fpuO1Zf7LeL/5+2nqR/wgfxz5g1dqvfLwBR+x1hVaA3dayux8bSdz/eby2Y7P+KztZ3RZ0IU9QXs4OuQoJfKV4OzNs1QqWClN274ceZl8bvlkGMRskFUJbAutdbA5Sd2olDr1gGVT+6+/5/SGUuoNTE2MKVu2bNZEmU0SkhJ4f4PpB8jvTT/q/lwXwOqLQpjkdzddKbSM8Zcd/bIsToeepnKhykz5bwrjt44HTP1JN5/fzAr/FQC8Vve1e5JXgIXHFjJk9RACQk1NkG7G3OTFGi9SY3oNwHRmd+/Avbg4uTD+3/G0q9iOZ6vc23w8Ii6CQ1cPpauq4t3K5i9Ln1p9jH5BFqExd66sLju1jGWnltG4VONUt/Hh5g+N5BVMZ/4fdFbeMpzD0lNL6TivI8teWkbN6TUJDA+kSJ4itKnYhsTkRJaeXCoJrA3djjf1ybq7X6SjOxB8wBgv06KCTwWjWeHxa8fxD/Xn+PXjVldXD75x0Ki++FX7r/jZ92cA/jz6J/97+n84OzlTKl+pTB2cWvbF3Vc/x7YaS1hsGF/t/uqedW5E37BK3Ip5FaPx7MZW/WhLe5fm8ODDTP5vMn3r9KVe8Xr86vcr3ap1s4rXSTlZXcG8uz+ls5Mzfev0pW8d08F4uQLleLrC04Dpt+x/bf5ndUU/YVyCcbA/5b8pVts6eOUgYGp2OLbVWB4r+JjV0DQPMmD5AGP81fGtxmf64DgrWKorF8lTxOg7FpcUx/B1w9lwdoOxXMBbAVQuVDnDz6O1pv289tQuWhv/t+7fIuZhCnoWJPbjWE7fPG1Vafexgo8REBpg/M49Ueb+LQDsxYKjC5h/bD4jm42k/7L+xvxfDv3CL4d+AUwFeOb3yFhrprSeGOnyeJd75hX1KoqPh4/x+dh+cTutyrXKUBypSXmFLzgymFJfl7Iaz/Ni+EXKTCtjTH/e9nPm+s2lVblWzO4yGy83Lw6+cZDxW8ez+MXF7L+831j2qfJP3fd5nZ2c2fX6LlYFrGLxicVcCr9Eo1KNGNFsBDeib9C0dFOGNRpG7WK1CYkKYcaBGXzU8iNcnFyIT4pPVxXfc7fOERYb9kguWGSWp6snQxsNZWijofzq9yurT68GoOTXJalbvC5+V/14r+l75PfIT5sKbdgfvJ++dfrec9IS4N8L/xo1EETWypIEVmsdbP57TSm1FGgMhCilSpivvpYALKdyg4AyKVYvDdzTyNx8FXcmQMOGDe06E0w5CHpuO5BML8tg9JbEMKuKgFhorXln3Tt8v+97wHRCwZK8guls7jvr3jGm+9ftT8DNAN5s8Cadq3SmgEcBdlzcwakbp4wDllL5SpHXLS89/+pprNeuYjsuhV8yTlZ8u/dbtvXfRpvf25CQnMC4VuP4oMUH/HX8L1McKQ5E02v+0flEJ0RTNv+dEzl+V/04EnKExHGJvLvuXX7Yb2omtPyl5cQkxBAaE0qDEg3QaJyUExFxEfSu2ZuiXkW5HX+bDvM6GNtqVa4VDUo0YNqeaQAMrDeQ2YdmG4+fDzvPu+veNZKD69HX6VmtJwuPLeTL3V/yZsM35QvaRiyJiyOP9ZaaRrNMBVq8XL249YHpoDJJJ7Hj4g7yuOahbP6yTNw60TjwbVKqCXsv76X+zPrkd89PeFw4HR/rSOcqnY2iQUevHaXLgi5MemoSXq5efLrjU66Nusbt+NvsvbyXMt5l0tQE7H4JLJiGbuldszdhsWG0qdiGxScW88LfL5iGeMhb3OjTm6yTje+MLlW64OzkzJIXl6CU4puOd/rlDag3IKNvYapSG5cx5ZWqcU+Oo2+dvrT7ox3tK7Xnx/0/AlhdmR3eeDjfPvPtQ5/rjQZvGL+do1qMyorws4y3uzc3P7hJUEQQs3xn8UrtV6jyw52mp1V+qELMxzEsObmEE9dP0LxMc+KT4nm+6vMP2OodF8MvsuncJjad28R7zd6jlHepDMfq7uJu1bIAYEGPBdT/uT5R8VHkdctLPvd8Gd5+dgsMCySPax5eXmKqEmv5fwTTCeY5fnMom78sfWv3tWri+6gdHnyYqj9WJTohmid/fTLTJzFSSnncOGbTGKvWIWD6PKasG/LBpg8AWNRzkVHVvF6Jeqzsbfp/alG2BYnjEjl27dgDuyCBKTl/rd5r97RAKZynMLtf321Mv7f+Pabtmcbpm6f5uv3XlPumHHWL1+WvF/6yquKelJxETGLMPVccLU2HH9Sdwh41KtnIatryvfz1HlMtgglbJwAYfbNHNB3BkZAjDKo/iC92fUEBjwI8/3javhdE+mQ6gVVKeQFOWutI8/32wGRgBdAP+Mz8d7l5lRXAW0qphUATINzS1Dineu7x55jffT49q/dk+8Xtxvzha4cz/snx6aoo6OgsZ7XP3joLwO6g3dQoWiNT20xISuBSxCVK5C3B8LXDrZKvkvlK8lLNl2hVthWDGw5GKcWWvlvYH7yfkc1H4qSc2D5gu9X2nij7BBUKVKBBiQZWY2W9VPMlgiKCqFKoCj4ePkafNDCVJo+MjzSakU/ZNoVS+UoxePVgY5mYhBg8XDzSfXUnMDzQuGJtGdPr5X9e5trta7St2JbvO33PtI7TUCijupylqI/lBIG3uzftK91pWnxj9A3cnd2Jio8iPC6cYl7F8PHwoUHJBjzz2DN82PJDbsXc4t8L//JW47fYFriNfG75eKvxWxTLW4y8bnnZ1n8b/Zf3x83ZjY82f0RCUgI+nj680+SdbBkq5HToaT7Y9AHze8zHw8Ujy7efEzUs0ZB9l/cZxVpyg5RXK9pWbGskXa64Gl0UAGY/N5vJrSdzM+YmNYrU4KvdXzFq4yij3/i6M+s4O/wsrcq24nbCbXZfMh2sWQ5IANr/0Z7N5zcb04cHHzb1Rz00m+D3glP9nI95YgzvNXvvvlcnUjb1LuZlumpmaaobGR9pJNsA54afs7sCXeUKlCPgbdMJyPIFyjNqo3Xy+d2+7yjgUYDGpRrTqXKnVL/vPt3+KR9t+QiASU9Nspvmdd2rdWfR8UVGzKW9SzOptanfZYUCFTgfdt5YtsK3FbgaddVq/Z87/0z5AuWtvmtTcyXKdMjTr06/TCWv9+Pt7k3T0k3586ipAFLK1jr2ZJbvLN5Y9YbVvGalm7E7aDf/vPgPz1d9njFPjMmyRDEzyuQvQ9SHURy6eogGMxtQ5YcqRlechKQEQmNCiYqPytDJXMvV1pdrvcya02uMBPb40ONM2TaF/O75mdF5BhvPbuSfk/8wsP5AFOqBV5WdnZzvGVIqMyY9NYm/T/zNvCPzmHdkHmA6fuv9T2+jVsartV9Fo5l3ZB4J4xJwUk4oFMk6mfnH5vNE2SdoUrrJg57G7tQoWsNoTek6xfWhJ4stFwIsvxu7XttlNUyPyEKWsuIZvQEVgcPm23HgY/P8QpiqD582/y1onq+AH4GzwFGg4cOeo0GDBjonuRp5Va89vVYzEf3zgZ9tHY5dGbdlnFYTlX5t2WuaiehZvrMyvc2GMxtqJqKZiF50bJGu81MdveXcFh0RG5EFEd9fcnKyPnX9lNW8yxGXda+/e+mfD/ysE5MSjdgsr9dyS6uFRxca6xy6ckg3nd3UmJ66c2pWv6QM+WDjB1av7dCVQ5qJ6A5/dNAJSQlZ9jztfm+nmYhed3qd1fyLYRd1cnKy1lrr8NhwHZcYl2XPae92XtxpvCdJyUl66s6p+lbMLVuHlW0CbgToFr+00OO2jNNPzHlCxyTEpHsbp0NP65JfldQd53XU129fN+Y3mdVEMxE9esNoq8+z5dZ0dlP99pq3jen/2/Z/+vrt63re4Xl6lu+sDH3WT10/pZmI/uPwH1bzk5OTdVJyUrq3ZwvHQo7pqTunpvqeHbl6RPsw4pLoAAAgAElEQVRd8dNjN481/kdPh542Hv92z7c2jt7auZvnNBPRvx769Z7HouKidEhUiO6/rH+qrzUt3+/Jyck6LjFO/+b3m2Yi2jfYN9tey0/7fzLi2XR2k/H+24uk5CQjvl5/99JnQs/o6PhonZScpAPDAm0d3n3FJcYZcR+4fECvPb1W1/ixhjEv4EZAurfpd8VPO01y0stOLtNJyUm628Ju+q9jf2VD9Jlz5OoR7fGJh378+8f18lPL7/nct/61tdVnznK/+6LuevSG0Xql/0pbv4RMCQwL1NsubNM7Anfo8VvG6z2X9uiLYRd1x3kdtctkl1S/C/YG7bV12DkOcECnIf9UOguHD8guDRs21AcOHLB1GOmitabol0WpXaw2m/tufvgKucR7699j1sFZ9KrRi18O/cKsLrMYWH9ghrd3O/42eT+9c/Y+O4orZIW745zw5ASerfysMVZdcGQwXq5eVtWEN5/bbBRRANPQCT/s+4G5fnPpUa0HC3osSLXJ36PmG+zLG6veoFXZVgRHBfNuk3dpPudOv9jQ0aGp9g1Jr3Z/tGPTuU2sf2U97Su152bMTZaeXMrAlQOZ3mk6gxsOpvr06py6cYpvO37LgLoD7LrpXFbwv+HP6E2jGd9qPGGxYcbn5dzwc7g5u2XL1R1bSUxOxHWK6fO++/XdNC2dtWNuh0SFsPPSTrpV7caFsAvcir1FraK1SNbJxCfF4+7izh+H/2DI6iEkJCew6dVNVv+f7zd7n0NXD1HauzTl85c3rtw97DWFxYYRFhvG8lPLaVepXY4dZP506Gmi4qOYc2iO0aXhYTa9uok2Fdtkc2Rpdzr0NCsDVtKnVh+rPqWp+X7v9xy/ftzoR53ST8/+xJDVQ5jffT69a/U25k/bPY33NrxHn1p9CI8LZ1mvZdk2JmN8Ujzun9wZZujE0BNUK/Lw6tFaa7YFbqNVuVZp+i29HX+bfsv68XzV53mldtoK9ETFRzFqwyhm+M7g5Vov82f3Px++kh0JCA3g8R8e55sO3/Du+netHnvu8eeY8ewMPF0976lbYfH74d9pXb410QnRNJ/TnIU9FtKmYhu01nY/RmdSchLzj86nw2Md+HzH57xU8yXTcDw1XsDTxdOorP1jpx8ZtsZU9biYVzGujrz6oM06hH2X96G1ZvzW8bxc82XcXdx5qeZLtg4rx1FK+eo7Q7LefzlJYLPPgOUD+NXvV4dvQpCQlEBcUhxLTy7l+33f07JsS1ydXelXp989P5hvrnyTmQdnUq94PQ5dPcTCHgvpVbPXfbb8YEERQZT2Ls3uS7sJuR2S5v5HthQZF8naM2uN/mJjW45lzBNjyPtpXvK45uHpCk8zoukIqhWuRsu5LY2m1gB7B+6lTrE6uDm72WWSbpGsk3lrzVv8dOAnmpRqwp6Bezhz8wxFvYri7e6d4e0OXT2Unw78xNnhZ6noU9FIaME08H1YbNg961wbeY1CeQoREBpAsk6mepHqGX5+ezRi3Qj+OfkPF0dcZPmp5Ty/yPp/wFEKycUkxNB1YVc2ntsI2P5EVXBkMB9t/ojfDt9/jMT0vPfOk52N4VrmPDcny/u2Pmpq0sP3TRnvMhwfetyuTjJ9vPljvtj1BQnjEh6+sNmuS7tYeGyhUXfhbmeHn2X3pd1cCLvAlG1TiEuKo3qR6hwfejyrwr6vjWc3GlXp5zw3B09XT3r/05vro65TOE9hknUyozeO5uVaLxvFt2YcmMGQ1UNY1stUDfdh/2fnbp2j0neV+KDFB3zW9rNUl4lLjOPLXV/St05fuv/VnQPBpuO5nJi8gimJuxJ1hZ8P/Mwn2z9JdZmCngW5Pur6PUOcXQq/RNlvrAuTti7fmi39tmRbvI9St0XdyOOah+cff57bCbc5EHyA7tW6G4XihHiYtCawWVWFWKTi247fstJ/JZO3TWZRz0WZOni3tWu3rzFp6yTC48Ipma8kg+oPIjA8kNjEWLos6MKLNV6kbYW27A/ez/5gUwW8usXrsuTkEs6Hnad43uK4ObsZVxgPXT0EmM4Sp9euS7uM4WPGthzLlKenPGQN+5HPPR/NSjfD3dmduKQ4Ptn+ifEDGJ0QzaqAVUYRi5ujb7I7aDfj/h3HwSsHWXRsEU1mN+HUsFN2PZ6Yk3Ji+rPTmf7sdG7H38ZtihsJyQmUyFuCoPeCMjxm6fRnp/NZ28+Ytnsau4J2GeNtDm88nO/2fQeYDgRO3jhp9E0bs2kMc/zmGNuY89wc2ldqz69+v+Ls5Eyjko3s6gpQesUmxhKbGAvcGVYmpX/P/2sMEbD05FJiEmPoXbM3TpOdGNlsJFPbT+VK5BVjvGJ7tencJjae20gBjwJcff+qzU/glMxXki5VuhAYHkiPaj14e+3bGd5WUnISTUs3ZdelXYDpCk5Ot6DHAi5HXGbkxpH3PNa7Zm/aV2pPvzr9bL4f72YZ0ujcrXNU9KmYpnWal2lOw5IN75vAVvru3uE2Znaemak40ypl3/jXVrzGyGam/TFp6yTebfouj31v6q/51e6vaF6mOVv7bTXGrfx6z9c8v+j5h/7GDlppGv7IP/T+1ZTnHJrD2H/Hstx/uZG8gmloqZzI2ckZL1cv47c7j2seohOimdJ6ChFxEUzdNZWbMTdxnuxMveL1OPimqVp3SFTIPcnrYwUfY9XLq+55jpxqaa+lVtP96/a3TSDC4UkCm4283b35sv2XDFg+gKJTi3JxxEW7GCogrcJjwxm7ZSyVClbiu73fWRWwyO+en7H/jjWmS+UrxcD6A0nSScw6OItuVbvRq0Yvqv1YzeqH7f1m7/NHtz94demrgGmw8vRIOfbpK7VfYWijoZl5iTZRJn8Zoj+OZvCqwbg5uxmVPLf220rHPzsSmxjLtA7T8PH0oVPlTiTrZLos6GIcjGQ0AbSFPK558HLzIiw2jCtRV3h16asZOuN+IPgAozaOIjIuEt8rvgD80e0PIuIiGFR/EOOeHIeniydebl5orYmKNxXbWHdmHQB53fISFR/FayteY8mLS6w+u49qLOLscDP2Jtejr6MmKYrkKUK3qt0okqcIMw+aDpCn7ZlmJEaW19xnSR/ANN5oRZ+KDF0zlEH1BzGk4RDKFSjH9P3TqV6kOt2rdWd74HaKehXl6LWjdKvazap5W0hUCB4uHlbN3rNLl8e7cG3kNZRSuLu4P3yFR6BH9R70qN4DMFUK9nT15J8T/+B31S9NTTUtnJ2c2fnaTm7F3CI2MZZCeQplV8iPjKXZ3LNVnmXJySU0LNmQURtH8Ub9NxjWeJiNo7s/SzGtXZd2pTmBBXBzduPayGv4ePoYzdzvZ9/AfUbXkex2KfyS1bSl+N0P+3+wGj0BTK/Z7ZM7hce6Ve3GtsBtfLL9EwbWH8i6M+toVqYZlXwqWRUvOx1qKjC46dwmzt86n2rRsSJeRUzxRJji6VS5E3/1/Ctbiv09Kj6ePmzpuwUfTx8q+lTk2z3fMqr5KNyc3Zi6a6qx3KGrhzgScoRrt69x5uYZY37EmAj+C/yPpys8nWrFciHEg0kT4kfg+73fG2MW2lufH4urUVe5dvsag1cN5pXar+Dm7GacWf3wiQ/ZH7yfTec28UXbL6hVrBZ1i9fl2fnP4qScWPLiEsrkL5PqdpOSk1h/dj03Y24ycMVADg8+TAWfCkbfnNHNRzNtzzQSkhPI65aXBT0W0POvnsQlxdGmQhtGtxjNrIOzmNdtHu4u7ny24zM+3PwhX7f/mhHNRqT6nDnNlcgr5HO//0DXDWc2xPeKL5OemsSErROMJrQ5SWBYIM8tfI6t/bZyMfwiZfOXxcfTJ03rHg05Su0Z1v0CB9YbyMwuM9N1Bcf/hj/+of60r9Qez/95AtCjWg96Vu9J739689OzPzG44WDWnF5DveL17P6qJMDry183rjCXyFuC8++cx93FnaTkJOYcmkPnKp3pMK8DR68dBbAaNmbHgB30WdLHGB4JTFcWgyNNo5r97+n/se7M/7d359FRlGnfx79XFgJhDYjIvgjIIKIoKpujAiK4oQ6OILjhK686Km4oyozigguKj8vg6+jBIzo+Io6oDOqwgwuoOIALIBAWWQwY1oBAIMn9/tHVbcCkA2Sp7urf55w6qa6urr66Lkj6qrqX/0RGVr+g1QVM+vMkdu3fRZdxXVi5bSXDuw7n0e6Pkl+Qj5kd0byAIrFmwHsDmPDDBN687M3D7s95qEVZi9i4ayNVU6vS/Y2Dm00+1fOpCp0KJr8gn5RHQ/cpeh3fiw7HdaBRjUbc9slt3N/tfjK3ZTLirBHMWD3joLvlJ9c7mRnXzKDu06HC85WLXjlotOBTjjuFeYPnsW7nOtqMbUP/dv15d8m7DGo/iNcvfT2yn3MOM2PcwnH8n3//NtZFULo2RJObl8vpr57ORa0v4onPnwBg6JlDefTcR0lPTY/5vq4iflEf2BiSk5tDzSdDdyl6Hd+LOWvn8NMdPx32xNrl5R/f/IObPrqJd694l5FzRrIku+g+OTuH7yQtOY3U5NQyu/v31YavmL56On+b/bfItua1mvPF4C9o8GyDyLaZ18ykxxs9eOjshxjcYTB7Duzh5W9ePmguxKAL9yd77ZLXGDx5cFzfMSw8EE+fln146cKXaFyj8e/+mG/M2ciq7av4acdPXPPBNUBoioqeLXrStGbTMv3jf8arZ0SavRfmdz/LaHJyc/jzu39m6qqpQGie0/8M+k+Rg4bk5OZw19S7yKicwVPnPcXWPVupk16HJEsic1smS35ZwjPzn+HRcx/l2fnPRu7MbBm2hZFzRkYG5Lmhww08e/6zkd9lAMO6DOOV/77Cztyd1K5Smxs63MDNHW8u06lfzn79bD796VNmXzubc5qdU2bHFTlU/3/1550l7/DPy/7JwPYDS328p794mntn3MuEP02gaqWqXNT6ojKI8sis2raKmpVrsmXPFj796VOGnDak2H1z83LZsW8HSZZE3ap12Ze3j027N1Gvaj1OfOlE0lPT+XHLj+S7fGZdMytSoH9+/ed8kvkJl5xwCSu3rmTQ+78V/6N7jmb0vNGRqWIm9pvIFSdeUb4fOkbkFeQx9uuxkYGe9DtMpGQqYGNM9q/Z5BXkRYqzcP8zPyzLXkbbl34bzGZMrzFMXTWVaaum0bVxV+7qHPqyC0T6z5UX5xwfrfyInNwcBrQbgJmxIWcDb3//Ns0zmnP5Hy4n+ZHfipW9I/Ym3Byg4QL21Ytf5cZ/38i6O9YVe8c7HlR9vOpBE7d3a9KNFhkteOPbN3jknEdYu2Nt5K7ivV3uZfS80eX6pafAFfDLr79Qf0zojmvD6g3ZuGsjw7sOZ2inob5faDrUX2f9lVGfjYo8vqXjLYy9cGyZHf9A/oFiR7fevHszPd7owf78/TzW/TEuan0R3cd3j8xbWjimJ3s+WeoBeqasmMLFb18ceu97NsdVFwyJP+8tfY9+7/bjrcvf4qqTrvI7nJi1afcm0lPTGTZtGKccd0pkjnWAK/91JROXTIzse+3J15KalMqCnxew8P8ujKsuMKUVbjEGkJKUckSDg4kkKhWwMerc8ecyZ+0cxl86nhYZLejWpFuFvO/S7KWMnDOSd5e+yz2d7+GZ+c9QNbUqD579IHd1vouUpNjtDn3x2xczZcUUzj/+fD4e+HFC/QGE3wrYF3q/QNbuLO7rel+F9DssL845Bk4ayIWtLjzoSj2ERqX83+//N/J4xtUzaFWnFU1qNjn0MGUuNy+XJEti9/7dnPzyyazPWc85zc7hqnZXMWTKEE6tfyon1zuZJ3o8Qb1q9cjJzanwgdmcc1z2zmV8uPxDAKYNmka3Jt2oklqlQuMobGPORq7815Us27KMbXu3RbZ3adyF+7reR5tj2tAiowVz187lzEZnkpKUclgXoZxzJD0S+r/+et/XufaUa8vtM4hAqJtBm7Ftfjf9jRy57F+zqZFWg7SUNApcAQWuIKa/Z5SH7uO7M3vtbADmXjeXPzb9o88RicQ+FbAxan/+fhZlLaLTuNAchjvu21HqYiRrVxZdX+tKn5Z9SLIknuv9HMlJyeQV5LEvbx/VKlWjy7guzN8wH4A7O93JmF5jYrZ55KH2HthLXkFeTE23UJHCBewXg7+gS+MuJewdXzqP68yXG74EQgOMLL91OfkF+WzctZHWdVr7dsdt0+5NnPfmebSu05pODTtx74zf91tLSUph6S1LaVWnVYXEtCFnAw2rNyTf5TNz9Uy6NO4SM/8nwn9HJi6ZSP/3fpv3rknNJqzbGRq8LjxADkD35t2Z2G9iZMCi8HRIPVv0ZPX21YyYNYIJP0zgyxu+ZNX2VfRv1z/hLlxJxVuWvYyPV37M4A6DD7uPvkhx+k7oy+Tlk4Fg/v0WKQ8qYGPc29+/zVWTrqJKShWy7s46qiL25W9eJiUpJTLYUljvlr3ZsW9HpDBYcOMCXl/8OmMXjGXcJeMY3GFwmXwGqRjhAnbqoKmcVv80MqpkBObL/KRlk7jug+vo0aIHD3R7oMJG5zwSu/fv5obJNzBxyUSGnjmU5796HoCzmpzFp9d/yozVM7hr6l280++d340+O2ftHMYuGMuEP00odb/d7uO7c0GrC7iny++nJoklew7sYfqq6Xyw/APObno2138YfU7Tvif0jdxRrl+tPlm7syLPPXf+cwztNLRc4xUJGzlnJA/PfTghBhmS8ld4PuSFQxbSoX4HH6MRiQ8qYOPA/TPu58kvnqRZrWasun3VERclh04Wf1aTs/hs3Wdc3f5q3vzuzcj253s/z+1n3l4mMUvF6/VmL6avns5lbS7j/R/fZ8uwLYGYaiNe7di3gxmrZ/CnP/yJHft2cNyY4yLzGX/Y/0Nu/+R2svdkM/OamQx4bwBrd6xlwY0L6NigxN/HxQpf8BrVfRQPnPVAWX2UCnHo76nDdXqD0/n6xt/PbStSXoZ+MpQXvn6BFbeuqLCWFRJcbce2ZdmWZQAsvWXpEU2vJZKoDreATawOCTHm8R6PM2/DPLo27sq+vH045w57XrQCVwBA50ad+XHLj3x6/ae0O7ZdZNj6S9tcSnpqOmc0PIPaVWqX58eQcvbg2Q8yffX0SJEUlLuvYfkF+eTm51I5pXJcfLZalWvRr20/IDQX4Pc3f8/ri1/nic+foO+EvpH9Mrdl0qVxF9buWMusNbNKVcCOWzSO5rWax/zd16JMGTCFycsnc1K9k1i3cx2juo9ixuoZPDDrARZvWhzZLy05jdz8XAafMphxfcf5GLEkqk2/bgJgYdZCFbBSarOvnc1xY0KDADao3qCEvUXkSKiA9ZGZMfe6uWzfu51qj1fD4Q4aYXbVtlWkp6ZH5qPcvX834xePp3fL3jw892Eg1Fx43g3zDjomwOV/uLyCP42Ul0Y1GjGsyzBycnOAYBWwC7MWcs7r57Br/y6mDJjCha0v9DukI9a6Tmse7/E4TWo24eaPbqZ2ldp8d9N3NKzRkIEnDeTfy//NP/77D4Z1GXZU/c4f+/QxZq6ZyUNnPxSX86xe2PrC3+W1T6s+9GnVh9y8XPJdPpOWTaJf234YRlpKmk+RioQ4Yr9lmsS+etXq8Xzv5/l+8/dxPfCiSCxSARsDMqpk0LFBRxb8vIAmzzXh3i73cneXu2n5YksgNOn3oqxFnPrKqUBoapEDBaHh2OPxjowcmWa1mjH6vNE8M+8ZgEBNgJ6Tm8Ou/bsA4n6Eyps63sTq7atpUL0BDWs0BEIXlObfMJ866XXYm7eXtOS0SP4O5B9g9trZnNfivKiF7YacDfRo3oMRZ42okM9RkcLF6qD2g0rYU0QkvuzP30+zWs24sFX8XZgViXXBuZUT56ZfPZ3RPUdTv1p9Rs8bfdA8mVNWTOGvs/8aedygegPeuvwt3EOO9NR0P8KVCpRfkM/OfTvZl7cPCNYd2LTk3+62FTf3aLzYtncbp9Y/lSvaHjxf7YnHnkjtKrWp+nhVUh5NwR42Tvj7CVR6rBLn//N8Pl/3+e+OtXXPVsYtHMeKrSs4t9m5vHrxq3F/fkRiXf8TQyNoJ1twLhKKf3bv303fCX2ZsmKK36GIBE583/IIkJqVazKs6zCGnDaE+Rvm07RmU249/Vb+vuDvLPllCVe0vYLjqh6nvmEJaGn2Utq/3J67Ot3F490fJzUpOIVM4eai8f651mxfw4D3BjC5/+TIHdiw/IJ86lWtx+ZfNwPQqnYrVmxdAcCg9wexbuc6AK45+RrmrZ9H5rbMyGs1IqpIxWhZO9TqKUgXCcU/4b9pd0y9Q6Opi5QxFbAxpmblmvRu2RuAFy94kRcveDHy3HWnXOdTVOKncNPa0xueTv92/UvYO74E6Q5sOE9Dpgwh64Ssg56rklqFTfds4u3v3yYlKYU+rfrQ7qV2nNX0LOavnx/Zr2P9jrzx7RtA6C7QR1d9VHEfQCTBmRljeo3hvOPP8zsUCYB4/5smEstUwIrEuHBhtGXPFtbuWEvTmk2PajCgWFQ9rTppyWm0rduWRjUa+R1OqYTzFG7qXZQBJw2IrK8eupokS2L73u0s2rSIz376jJs63kSrOq2okVZDk96LVLDJyyczYtYIbj3jVr9DkQBIS06jfb32DO863O9QRAJHBaxIjAsXRrd9chu3fXIbBQ8W+BxR2WlSswn7/lp8wRdPwnk63Lm1w80UM6pk0L15d7o37w4QaYEhIhXrl19/AWD5luWcVO8kn6OReGdmfHvTt36HIRJI6ughEuMKj85rWGDuvoat2b6GlVtX+h1GqcX7KMoiiS5rd6jp/5LsJT5HIiIi0egbl0iMq1W5VmQ9aPMT7j2wlxYvtADif7CicBPovm36+hyJiIiISHD5dgfWzHqb2XIzyzQzdRAQKUb1tOp+h1BugjSnbVpKGrefcTsXt77Y71BEREREAsuXAtbMkoGxQB+gLTDAzNr6EYtIPBjedTi1KteK+6lmDhWkz7Mvbx/tjm1Hm2Pa+B2KiByFgScNBA4eHV1ERGKPX3dgzwAynXOrnXP7gQmA2t2JFOOJnk+w+Z7NdG7c2e9QypSZMbHfRH664ye/Qym1PQf2MGTKEGatmeV3KCJyFMLdAILUMkREJIj8KmAbAusLPd7gbRORYlRKrsTc6+b6HUaZu+LEK2hSs4nfYZRaeFThv83+m8+RiMjRSLZkxvQaQ4/mPfwORUREovCrgC1qGNWDRnAxsyFm9o2ZfZOdnV1BYYmIHB0r8teaiMSLaaumcfe0u/0OQ0RESuBXAbsBaFzocSPg58I7OOdecc51dM51rFu3boUGJyJypKqnVafvCX354MoP/A5FRI7Cxl0bAfhxy48+RyIiItH4NY3OAqCVmTUHNgL9gat8ikVEpNSSLIkP+qt4FYlX4XlgM7dlclqD03yORkREiuNLAeucyzOzW4GpQDLwmnNOM4eLiIiIr4I237aISND4dQcW59zHwMd+vb+IiIiIiIjEF7/6wIqIiIjEjPA8sNUqVfM5EhERiUYFrIiIiCS8Y6seC0BKkm+N00RE5DCogBUREZGEVzmlMmN6jeGPTf/odygiIhKFClgRERFJeHPWzuHuaXeTm5frdygiIhKFClgRERFJeGu2rwE0D6yISKxTASsiIiIJLzwP7Pqc9T5HIiIi0aiAFREREfE4p3lgRURimQpYERERERERiQsqYEVERCThXd3+agAyqmT4HImIiESjAlZEREQSXs3KNUm2ZM0DKyIS41TAioiISMKrVqkao7qP4syGZ/odioiIRKECVkRERBLelxu+ZPjM4fx64Fe/QxERkShUwIqIiEjCC8//qnlgRURimwpYERERSXjheWA37d7kcyQiIhKNClgRERERERGJCypgRUREREREJC6ogBUREZGEd037awCoV7Wez5GIiEg0KmBFREQk4VVPq06tyrWolFzJ71BERCQKFbAiIiKS8KpVqsZfTv8LJx57ot+hiIhIFCpgRUREJOEtylrEqM9GkZOb43coIiISRakKWDMbaWYbzWyxt1xQ6Ln7zSzTzJab2fmFtvf2tmWa2fDSvL+IiIhIWfhu83cALN+y3OdIREQkmpQyOMb/OOeeKbzBzNoC/YETgQbADDNr7T09FjgP2AAsMLPJzrmlZRCHiIiIyFHZ/OtmALbu3epzJCIiEk1ZFLBF6QtMcM7lAmvMLBM4w3su0zm3GsDMJnj7qoAVERER3znn/A5BRESiKIs+sLea2Xdm9pqZZXjbGgLrC+2zwdtW3HYRERERERGRqEosYM1shpn9UMTSF/h/wPHAKUAWMCb8siIO5aJsL+p9h5jZN2b2TXZ29mF9GBEREZGjcXX7qwFoUrOJz5GIiEg0JTYhds71PJwDmdmrwBTv4QagcaGnGwE/e+vFbT/0fV8BXgHo2LGj2vOIiIhIualWqRqNazSmckplv0MREZEoSjsKcf1CDy8DfvDWJwP9zSzNzJoDrYCvgQVAKzNrbmaVCA30NLk0MYiIiIiUVtVKVbn8D5fTtFZTv0MREZEoStsHdrSZfW9m3wHnAncCOOeWABMJDc70H+Avzrl851wecCswFVgGTPT2FREREfHN0uylPP/V8+zct9PvUEREJIpSjULsnLs6ynOjgFFFbP8Y+Lg07ysiIiJSlr7e+DUAK7auoHlGc5+jERGR4pTFKMQiIiIicS17T2jAyJzcHJ8jERGRaFTAioiIiHhc0ZMjiIhIjFABKyIiIgkvyfSVSEQkHui3tYiIiCS8q9pdBUDL2i19jkRERKJRASsiIiIJr3paddrWbUuVlCp+hyIiIlGogBUREZGEl56aTqeGnciokuF3KCIiEoUKWBEREUl4q7ev5rXFr2kUYhGRGKcCVkRERBLe3LVzAVi5daXPkYiISDQqYEVERCThbd27FYA9B/b4HImIiESjAlZERERERETiggpYERERSXiVkisBmg9WRCTW6be0iIiIJKVH1bEAAAbYSURBVLzL2lwGwAnHnOBzJCIiEo0KWBEREUl4tavU5syGZ5Kemu53KCIiEkWK3wGIiIiI+K1Hix70aNHD7zBERKQEugMrIiIiIiIicUEFrIiIiIiIiMQFFbAiIiIiIiISF1TAioiIiIiISFxQASsiIiIiIiJxQQWsiIiIiIiIxAVzzvkdQ4nMLBv4ye84pEjHAFv8DkIqlHKeeJTzxKOcJx7lPPEo54kn1nPe1DlXt6Sd4qKAldhlZt845zr6HYdUHOU88SjniUc5TzzKeeJRzhNPUHKuJsQiIiIiIiISF1TAioiIiIiISFxQASul9YrfAUiFU84Tj3KeeJTzxKOcJx7lPPEEIufqAysiIiIiIiJxQXdgRUREREREJC6ogA0YM2tsZrPNbJmZLTGzod722mY23cxWej8zvO1tzGy+meWa2T2FjlPZzL42s2+94zwc5T2v9Y670syuLbR9lJmtN7PdJcR8mpl9b2aZZvaCmZm3/Wkz+9HMvjOz982sVmnPTxAFKeeFnr/HzJyZHXO05yXIgpZzM7vNzJZ7MYwuzbkJqiDl3MxOMbMvzWyxmX1jZmeU9vwEUZzmvMj9zCzNzN7x/i18ZWbNju6sBFvAcn6XmS210He4mWbW9GjPS5AFKeeFnu9noe9w5TfasXNOS4AWoD5wqrdeHVgBtAVGA8O97cOBp7z1Y4HTgVHAPYWOY0A1bz0V+AroVMT71QZWez8zvPUM77lOXjy7S4j5a6Cz956fAH287b2AFG/9qXDMWoKbc++5xsBUQnM/H+P3+Y3FJUg5B84FZgBp4Vj9Pr+xuAQs59MKrV8AzPH7/MbiEqc5L3I/4BbgZW+9P/CO3+c3FpeA5fxcIN1bv1k5D37OC32GT4EvgY7ldd50BzZgnHNZzrmF3vouYBnQEOgLjPd2Gw9c6u3zi3NuAXDgkOM451z4ykqqtxTVYfp8YLpzbptzbjswHejtHeNL51xWtHjNrD5Qwzk334X+5b9RKLZpzrk8b9cvgUaHeRoSSpBy7vkf4N5i3lsIXM5vBp50zuWGYz3M05BQApZzB9Tw1msCPx/GKUg48ZbzEvYrHPO/gB7hO/LymyDl3Dk32zm3x3uo73DFCFLOPY8SKr73lXSc0lABG2BeE50OhK7C1Av/Y/N+HnsYr082s8XAL4T+sX9VxG4NgfWFHm/wth2uht5rSnr9YEJX8CWKeM+5mV0CbHTOfXsEx0to8Z5zoDVwltescK6ZnX4Ex01IAcj5HcDTZrYeeAa4/wiOm5DiJOfRRI7tXZjeCdQpo2MHUgByXtgN6DtcieI952bWAWjsnJtSFseLRgVsQJlZNeA94A7nXM7RHMM5l++cO4XQVbMzzKxdUW9V1EuP4G1KfL2ZjQDygLeO4LgJJ95zbmbpwAjgwSM4VkKL95x7P1MINWPqBAwDJurOTPECkvObgTudc42BO4FxR3DchBNHOY+mPI8dOAHJeegNzAYBHYGny/K4QRPvOTezJEIt6O4u7bEOhwrYADKzVEL/Cd5yzk3yNm/2mnSFm3YddjM959wOYA7Q28zOtNDAG4u9u2UbCPVZDGtElOZg4atD3vKI9/rCzUoOer3XufwiYKDXDE2KEJCcHw80B741s7Xe9oVmdtzhxp1IApJzvOcmec2fvgYKAA3eVYQA5fxaIBz/u4AGcSpGnOU8msixzSyFUNPxbYcbdyIJUM4xs56ELkxf4rxuIvJ7Acl5daAdMMf7DtcJmGzlNZCTi4EOzFrKbiF0ZeUN4LlDtj/NwZ3BRx/y/EgO7gxeF6jlrVcBPgMuKuL9agNrCN1ByfDWax+yT0mdwRd4/9DDA31c4G3vDSwF6vp9XmN5CVLOD9lnLRrEKfA5B24CHvHWWxNq2mR+n+NYWwKW82XAOd56D+C/fp/fWFziMefF7Qf8hYMHcZro9/mNxSVgOe8ArAJa+X1eY3kJUs4PeW4O5TiIk++J01LGCYVuhJoCfAcs9pYLCPU1mQms9H7W9vY/jtDVmBxgh7deA2gPLPKO8wPwYJT3HAxkesv1hbaP9o5X4P0cWczrO3rvsQr4O96XV+946wt9jpf9Pr+xuAQp54fssxYVsIHPOVAJ+Kf33EKgu9/nNxaXgOW8G/Bf4FtCfb1O8/v8xuISpzkvcj+gMqG77ZmERqdu4ff5jcUlYDmfAWwu9Dkm+31+Y3EJUs4P2WcO5VjAhv+YiIiIiIiIiMQ09YEVERERERGRuKACVkREREREROKCClgRERERERGJCypgRUREREREJC6ogBUREREREZG4oAJWRERERERE4oIKWBEREREREYkLKmBFREREREQkLvx/CHH7lLZwqSoAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, period=4, \n", " plot=True, drift_type='A')" @@ -1901,30 +1151,9 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 143, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2']['2013/1/1':'2013/1/15'],'g--', label='data with drift')\n", @@ -1934,20 +1163,9 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1967,30 +1185,9 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,10))\n", "ax.plot(asd, 'm--')\n", @@ -2037,30 +1234,9 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 146, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", "detrended_values = signal.detrend(dataset.data['CODtot_line2']['2013/1/5':'2013/1/8'])\n", @@ -2081,26 +1257,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "749.1368773481072 513.0807872140786\n", - "-0.20490980046356652\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,10))\n", "ax.plot(asd, 'm--')\n", From 867802704e0ab91e131a237c8d263bd564a0136e Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Mon, 3 Sep 2018 13:47:22 +0200 Subject: [PATCH 38/42] improve detect_drift: shorten code and improve updating of drift periods #303 --- Showcase_OnlineSensorBased.ipynb | 324 ++++++++++++++---- wwdata/Class_HydroData.py | 170 ++------- .../Class_HydroData.cpython-36.pyc | Bin 62789 -> 62902 bytes 3 files changed, 282 insertions(+), 212 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 214b8a1af..1fdc96e82 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -52,8 +52,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import wwdata as ww" @@ -68,9 +70,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'0.2.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ww.__version__" ] @@ -84,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -92,7 +105,29 @@ }, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Time', 'TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", + " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2', 'TSS_line1',\n", + " 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns', 'Turb_ns',\n", + " 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es', 'Temp_es', 'NH4_infl',\n", + " 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter',\n", + " 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST',\n", + " 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT',\n", + " 'Cond_line1', 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", + " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", + " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1', 'Flow_line2',\n", + " 'Flow_line3', 'Flow_total'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "measurements = pd.read_csv('./data/data_example.txt',sep='\\t',skiprows=0)\n", "measurements.columns" @@ -107,13 +142,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", "start_time": "2017-05-09T11:54:55.589786+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -133,12 +167,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", "start_time": "2017-05-09T11:54:55.671616+02:00" }, + "collapsed": true, "scrolled": true }, "outputs": [], @@ -155,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -177,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -200,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -222,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -237,14 +272,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", "start_time": "2017-05-09T11:54:56.050129+02:00" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x\nb/Ih/DD5kH5RUbIpns6d4GivLo/FbkXUHPiwoOXIZXK8N2qVOH21OA2Pfjse2WVZ6Nq+K5YM+7cV\nW0dEZFuMBjasbdeuXQgODjb479q1a3jrrbf05q9fv158/alTpzBu3DiEhoZixowZyMzMtN6HoWal\nfUPHpxttn6ASMGbHQ8i+3Z2CwSw1TXX9peHLJPOXhi/D/rgjULgq8IBiIIMaNkxQCXj0m3GorlU/\nRGC3ImoOfFjQsnp4BIsZGn5yP/HclnszF9P2xmH09hEMLhERmcHkcK/Z2dn4/fffzdpQVpZlL67G\njBmDBx98UJyura3F7Nmz4efnBx8fH6SmpmLBggUYP368uI5crr5gv379OubMmYO5c+ciIiICH3/8\nMebOnYvvvvsO9vatNpZDjcTh0u4uKYXJyBayxemucl8Gs26Ty+To49VXMq+PV1/uE22E7m/fAQ7M\n2CCL0zws0AwdzeNr05ga4URQCZi0JxbZZVnwk/thx4TvMH3vFDGwBKizOJLyEjG864iWbjoRkU0x\nGdj48MMP8eGHH5q1obq6OtjZ2VmkUQDg7Ows6Qrz1Vdf4fr162JWRnp6Ou6//354eXnpvTY+Ph69\nevXCrFmzAADLli3DsGHDcOrUKYSHh1usjdR6yGVyDl95l9D0SdbUk5DZy6zcotalh0cwZPYyqGpV\nkNnL0MMj2NpN4tCFFuLr5g872KMOtQCAGtTg9/wkjOaQr2RBfFhgOZpuPZogkW5XQO3smGwhG4W3\nbmB/3BHE/7EVC4/NF9erqK5o8bYTEdkao4ENTVCgNRAEAR999BFefvlldOzYEfn5+SguLkZAQIDB\n9c+fP4+BA+/c5Lq4uKB379747bffGNggsnFymRz/GLIYM/fPAAD8WZrBp1m3CSoBBzP3i6OiqGpV\nSC1KseqQoPVd2JP5Tl8/JQY1NLJL2RWlrbJmQJAPCyzDULce7e/VUHaMXCZHhH+kZDtvHFuAoT7D\neOwkIjLBaGBj/vz5xha1uG3btsHJyQlxcXEAgLS0NDg6OuKDDz5AQkICPDw88PTTT2PSpEkAgPz8\nfHh7e0u20alTJyiVyhZvOxFZlqAS8I9jr0vm8WmW+nsZvX0ErhanwdHOEdV16joMrx99BQfiEqx2\nQVzfhT2Z71j2Ub15fTuHWqEl1Ny0A4J+cj/se/RnqwYozXU3Z2cZ+uz1desxlh1z4tpxyXp/lmbw\n2ElEVA8rx1AlAAAgAElEQVSTXVG01dTUIDU1FXl5eairq4NCoUBQUBAcHc3eRKPU1dVh27ZtmD59\nOmQydcp5eno6AKBXr16YMWMGTp8+jbfeegsuLi6IiYlBRUUFnJycJNtxcnJCVVWVyffy8HCFo6ND\n83yQNsrLy83aTaC7zMWMs1CW/yWZ597Btc38Fhv7OS5mnMXVYnX3HE1QA1D3z/6z8g9E+ERYpH0N\nNbzjIPTs1BNXblxBz049MbznIMidbPuGR6gScCnvEnp7927Rz9Ktk/4Q7D/mfgtPT3mLt8WWNGaf\nstbfWCM957Kki8KYXQ/h8guXW/XfWKgSMOK/D+GPgj/Qq3MvnJl1plW315KMffYa4Sb+d+jLCPAI\nwIhuIwx+Hy5VdsirbQ+vzm7i8sddJmPB0Xli9l2QZ1CrOna2lfMtUWvC/arp6o1KFBcX44MPPsAP\nP/yAkpISybIOHTrg4Ycfxv/+7//C09OzWRp46dIlZGVlYcKECeK8qVOnIjY2Fu7u7gDUAY7MzExs\n3boVMTExaNeunV4Qo6qqSlzfmKKicst/gDbMy8sN+fll1m4G3WWKS/T308ryujbxW2zKPpWce1Uy\n3dm5MwpuFQAAZn37P2LWRks/URVUAmqqb9eEqK5FfkEZKmR1zf6+zcWaT9J7drhfb97as2ux+vRq\ndvMxojH7lHb2U3f3IKtkPHnb+6Nr+67IvZkLAMguzcaBy0dbdZe7c8oz+KPgDwDAHwV/4PiV03dN\nhoGhz+7r5o/+G++DqlYFBzsHnJh6DgEdAyWvU5YrMWZnJLLLsiT7sAPa4/gTZ7Dh4hd44J6BiPCP\nREVJHSpg/fMcr/2ILI/7VcMYCwKZHCLkwoULGDNmDLZu3Yp77rkHTz31FF5//XUsWrQIM2fOREBA\nALZt24Zx48aZPXpKQyUkJCA0NBQKxZ0LRzs7O70gRWBgoNjVRKFQID8/X7K8oKDAYKFRIrItPTyC\nYY87mVV+bv4I8+5vxRYZJqgEnFOeaZFh+jJK0vHCoTt1kRztHcWgBqDO2vgmbReU5UpEbx+FmJ2R\niN4+qkXallKYLBZ6vVqSZvNDR+oW+/vb9pEtNhTjUJ9huLeDtLaU5okuh+W0nKS8RDH7STMiRUuT\ny+R4d9SqFn/fpvB184fMXp0tK7N3uqtG7DE07Pzeq9+K+2dNXQ3G7IiUHCsMDV2u+a0py5V49Nvx\nWHN+NZadWoqkvEQO+UpEVA+jGRuFhYWYM2cOnJyc8OWXX2Lo0KEG10tKSsKrr76KF198EXv27LF4\n5oZuIVAAWL58OTIyMvDpp5+K85KTkxEYqI6Eh4aG4uzZs+KyiooKXL58GXPmzLFo24io5aUWpaAW\nNeJ0TW2NibWto6ULZm5N/koyXV1bLZmW2csw7/CL6Cr3Ra6QA6Dl6l1obnZUtVVt4mYn2DMECpd7\noKxQd4e6fvMaTl77pcVGJnGwUwf17GGPWq1CojJ7mVW+W2W5Egcz9yOqW7RN1IAwx3XhmmS66Fah\nVdox1GcYAjoGIqMkHQEdA1tlABe4U1uioroCqlp1tqyqtgo5ZVlt5jdRH0O1MtycpE8Ub1TekBwr\ndIdvBtQ1kfZM/EEd8Li97GpJGiZ9M5ZZWURE9TCasbFlyxaUlZXhiy++MBrUAICwsDCsX78eZWVl\n2Lp1q8UbmJqaiqCgIMm8iIgIJCQkYOPGjcjKysJXX32FPXv2YObMmQCAyZMn4/z581i7di3S0tLw\nxhtvwMfHx+TnICLraUh2Q9GtIsn0tZu5re5JtaGCmc1pQtAkybSv3E/8f892nuJTw1whB13lvgBg\nsJBdc8gpy9K72bF1ms+j0VIjk2hnv9TqjI6iqlW1+HerLFei/8bemHf4RfTf2BvKctsv0C2oBLxx\n7O+SeX/csN7xxd7OXvLf1kYTxI3ZGYnXj7yC7u7q67WWOr60ZimFKXrztAsAa2d5aFwtTsPBzP16\nAQ+AWVlERPUxeqb86aefMG7cODELwhR/f39MmDABP/30k0UbB6i7kOh2Oxk8eDBWrFiB+Ph4xMbG\nYsuWLfjPf/6DAQMGAAB8fX3x4Ycf4ptvvsHkyZNRUFCANWvWwN6+dV4YEN3NNP3ZY3ZGYvT2EfUG\nN3LKpBd89nYOrS4LQDct2dO5EzYnb2y2G79rt/vhazzZ+1nx/wsrpU+b3x25Ej9MPtRiT/6CPUPE\nm52uct9W97dqqJPXftH7TltqZBJDN0IadrBr8e9WPbTwnaDVwcz9Lfr+zSEpLxHFVTrBUyHXyNrN\nK6UwWdIlJqUwuUW7uJlDO4h7tSQN741c1aLHl9ZCO8ATvX0UlOVK/Pf8Gr311l9cJ/7tNFkeuyZ8\nL9be6O4ehKhu0eJ+3rV9V3EZg0VERKYZ7YqSk5ODqVOnmr2h3r1749tvv7VIo7QZq90xZswYjBkz\nxujrRo4ciZEjR1q8PURkWYb6s5sqkBfk0UMyXVtXg9/zk1qsK4A5bqpuYmaf5+HXwR9B7j0wfOsg\nqGqr4GDniBNTz0oKyGkX8/RCI0ZvUAl49fBLknl2Out0ae+D6zevwcvZC0HuPfQK2DW32lp1dkGu\nkIOJe2KsOvxsU6UVperN2526AwO6DGr299bcCP0zYSE2p2yULKtDHRKyDyMu+PFmb4dGuM9wk9O2\nyFC3ky5y/dFoWoLuUKG+bv4W6eJmTgFhc4sMa3c1c7SToaK6AmHe/W12/24s3Sy9904tQ0Wt/jDk\nt2orcDjrIMZ1nwhAvU+HefeHveY5Yx3QXtYe++OOiPU2engEI6cs664cQpfIUu7moajvJkZTGBwd\nHaFSqczeUGVlJVxcXCzSKCJqe8x90lhRrX8xqC3IvQdc7KXHGnO6AjT2SWdDX6csV6LfhhAsPDYf\nT+59HN+m7RafatfUVWPc7mhxW7pP+YSqhj+FTcpL1Bv+1kfeFTJ79fDYMnsZ1v1tIxztHZF/Kx/D\ntw5q0S4DKYXJyChNF6c1T55tlW5gDQC+Td/TIk/QNRdmnVwNF8J++ec5zfa3NbQfXCyQPnjYnvJ1\nq8kkaKycshy9ef0ULVvbQvNdA8BXsfGYG/oyFg7+J1KLUprcxU3vmGPg7yWoBIyOv51FF286i067\nq1l1nQrT9sa1WGHi1sTXzR8OWs8KN/7xpdF1j2UnSKZ1CyxrAhp/P/oqJn0zFpP2xMLXzV/M2KGG\naW1ZTtTyGpoZTLbLaGAjKCgICQkJxhbrSUhIQPfu3S3SKCJqWwSVgMj44YjZGYmhm/vjQOZ+8cQS\n5t0fAR3uZBC89csioycdZbkSw7YMlDwJc4ADYruPr/f9GzMaSGNet+vKdlTXqYt31qAGHyeulizP\nK1eKF6jfpO2S3KhcyrtkVru0GQoEFVQUiHU1VLUq7E7bKRYUbekuA57OnSTT/m7dbDqduq9XGOx0\nTp3K8r/wQ/r3zfq+2r/FLZc3GFynpq4Gu65st/h7Z5SkY8jmfnr7wbm/zkrWe//sckRsC7fpi0Zf\nN1/JtLerAkN9hrXY+2v/nSO3DcewLQOw5vxqzNw/A/MOv9jkGhbm1P9Jyks0eKNtiKHuUXdjLYic\nsizUoLr+FQF0dZNmAPm6+Yu1jwBg3uEXsenSesnfSXP+1D0P2cJN+6WCi5h94DlsT9nW4u3kDS0B\n+pnBJ6/9YuUWUXMxGtgYP348jh8/joMHD9a7kX379uHYsWN47LHHLNo4ImobTl77BRkl6qf2yvK/\nMG1vHB68nTkgl8mxIuLOzb+pJ/oHM/ejuk6aSaZofw/ay9qbfP/GFvNszOv+unldMl2skvbX93ZV\niCnl8w6/KA6P2MO9J3p79zarXfXxdfMVbzYCOgRi3YVPJctbssvAiWvHJdM3VTcl07ZwYa4tpywL\ndTqFOwHghUP/g68ubWi2z6H9WyyoLICdXocjtX+d+KdFszaU5UqEb3kAebe3qb0fGOr2kln6p01f\nNDo7SrPBFg/9fy36pFz775xRmi4GSQH1d2uohoWyXGl2DR9zhmTVDZaayqLTrhNxNxcODfYMgZ/c\nvBo3UVrdJgWVgEl7YsXRqgD133nxiX9IXmNo/2tswL4lXSq4iIj4cOxKjccLh2Zh+JaBLdrO1jB0\nM7U+C47Oa5X7CzWd0cBGXFwcwsLCMG/ePKxZswZFRUV66xQVFWHlypVYsGABwsPDTda8IKLWo6Vv\nJi8VXNSblyvk4OEdERBUAsK8+0uKbRq7KI7qFg1HO5lk3rWbuTh57ReTn0f7qaKf3E+8mK/ve9At\nAlrfxXpGSTrWnv/Q6HIHOOC7R/YjpyxLvHlR1VZhZcRH2DVxLy7lXWrw38TFUb8LoIezJ/bHHcEP\nkw/h+dAX9EbQKLx1o0Hv0RRR3aLv9B8HcONWgXhxaQsX5rqcHYx3uXz16EvN9lRQfUN6p3vR3kcO\nwNNJf3j1GtRg71XL1bvadWU7auruDKncQdZB3H9u1Ri+4TVUh8RW/fvU0kb/PhtznNU+5gR0CISj\n3Z3uDZohXx9QDJQENfptuA/zDr+IsPW9xACyMalFKZKCr6lF+iN3NPSzyGVyDO86AgfiEu7KwqGA\n+juY0fsZs9ZNyDki/r92IMscfm7+4nmopUffaoxVZ9+TTGvO143VkCAekUYPj2BxqHRAff3ZGvcX\najqjgQ0HBwd88sknGDRoEFavXo1hw4bh4YcfxowZM/DMM89g3LhxGD58OD799FOMGDECH3zwAezs\nDD9BIqLWo6VvJgWVgC8v/NfgslwhB0l5iZDL5Ng1ca94g2/soljhqsAvU89g1v2zoXC9R5w/fe8U\nk/3BNdv3c/NHtpCNSXtioSxX1vs9aJ5GmnuxvjX5K5PLu7r5wsvVWy9gEtUtGpP2xGLIuiEN/pv0\n8AiGA+6csLt1uFcs3hfsGQK/Dv6Sm6N7OwS06NPU9rL26OwirQmheQJsCxfm2gSVgMe+m2hyneaq\nIaK+Ib3TvehW7S2cfeoiYu4dq7euXwfLjY5SWVMpmS5VlWLsrtFQlitRUV0BN8cOeq/p7NLZIu8t\nqAQcz03A8dyEFgt66QYKNSMOaX6fxm7wddva2OOs9jHn0GPHcSAuAZN6TMHHkf/FoSnH9Y5Be69+\nK2ax1aAGY3ZEGn0vQSVg3s8vSua98vMLeusbCpaa81nkMrkk6HK3MXYF7OYoLQr9YeJK8Tv0dfOX\n3HCZ4u2qwL7Jh8Tvt6GBd2vo4dlLb97xbPO7uWvvVxkl6Q0eXjrMuz+6d7w9Kld7X/TwCDa/8dRm\n5JRlSQL0gH43WWobTI5/2rFjR6xbtw5r1qxBVFQUKioqkJiYiNOnT6O0tBQPP/wwPvvsM6xZswZy\n+d15IiOyNUl5iS16M5lSmIzr5deMLq+orhDTcecdfhGT9sSavDCfvncK/nvxE0kWQB3qAJjuD55T\nloXsMnWR0dTiKziYud+s76EhF+tPhEw3uTyrLBO7r+yQBHK+io03uy2GpBaloAZ3TtjLHnwPcplc\nrGsybW8cFO3vQad26pO4sS4MzSWlMBl5FdILUM2Nky1cmGtTf5Y8k+v4yf2b5XPojtZRdKsQcpkc\njwZP0Vs3yF2/wGljdXfXr52VWfon/rZ9BCZ9MxZl1aV6yzNKMpockBBUAiK2hauLJ34zFsO2DGiR\n4EaYd3/J8MTa6mrrDBbV1OxrmraO/HpIk46zmmNOfnkeoraPwK7UeLxyeK5eNy4AYrcSjRuVN3Dy\n2i8GAzBJeYnILPtTsn5WWabeE/Qw7/7iyEkBHQPh4ugi+Szvn14uqZNEaoEG9hUA+HbSfnRudyfY\nV3ArH4ez1N28U4tS9G64DOns3BkrIz6SdLtsaODdGp66/1m9eYnKswbW1KcpYqvZr8buGt3g4aXl\nMjn2PPID/Nz8kXszx+T1BbVdwZ4h8HbxlszT7SbbEKYy2Gyte21bYzKwofHQQw9h9erVOHr0KC5d\nuoSLFy/i6NGjWLFiBUaMMD4sIxG1LoJKwOtHXhGnu8p9DfaxtqT6tn88J8HsmwDtJ/ymgiUa2icY\nXzd/+N1uiyZLwtI31QEdA/FxpOHsFI35R1/G/zuxBIM29cW8wy9i6Ob+mHf4RfGpXUPbklGcIZku\nvlUMADicdUhMS88VcnCjUt39JKM0vUX7GQd7hkiKwzrAQXxqZgsX5trMecKTLWQhv9x08KMx0ouv\nGpz2cNbvjtKUCzZzXdepJaPt/bNvizcjje2ac/LaL8gs/VPr/a5hW/KWxjS1wf417G0sf3CFpBYC\nAHx6/mODRTWT8hIlXUCyy7JwPi+pSccXQSUgZsdDqKnTFP1VYcPFL/TW++OGfsHhvVe/E4tNmvP9\n1zeqVA+PYEmB0DXnV2Pa3jg8tG0YL961GNoXD085gd6d70ds0ATJ/FPXTgKofxQwQJ3x4ezogml7\n4/SyElt7lozCVYHFQ/8tmZd847JZvxvtIrYAkF+RD0d7dfahzN5Jb/80RvehRmvPDCTLk8vkWP+w\n9PwR5tW40a5MZePZYvfatsaswEZ1tbTSs6bLSVZWFsrKyizfKiJqFtrDygHqG97mfoKRU2b6onnt\n+Q/xwsH/MavwnHbhO3sjhy/NU1btE8zo+BEYvzsa2WVZkMvk+CBiDRSuima5qXZ3dq93nQ+T/oOK\n2/UJNPUvaupq0Mmlk8muOLoElYAlv0iLzCUpz0FQCfj7kXkNbHnzkMvkeG3gQnG6BjX4PT9Jsrw1\nX5hrO5x1SDLdQdbR4Hqrz/7H4u/t5NDO4HSYd38xYKdRW61f3LSxDA1/2hCN7ZpjqE7HFxc/a1Jb\n6qOd5bTw2Hy9kW66dQyQTF8XjAdXl558E4uH/l+jji+CSsCmS+tRWCnN0ll57l1J+r2gEtDRwPFm\nyx8bxUCLdsHEHh7BBjO2IvwjJdPagZqMknSkFqVgf9wRvNL/Ncl6f5ZmsBijFu1sHy8XL/w6LQm9\nO98PABh0z2DJur1un+MMdfvReDpkJuxgh7LqMuQI2QDqH6WmNXrq/mfQ3v5OpklpdQn+e/4To+tr\nup/MP/KyZH539yD88sRZrIz4CIlPXoLCVWHW+9taZmBbZo3uhRpnlKcl079eP9mo7ZjqQmtr3Wvb\nIpOBjZqaGqxcuRIRERGoqqrSW/7+++/jwQcfxHvvvWdwORG1LtYYmi/YM0QvpVvX9ZvXMPP+5/FK\n/9fwVWy80ZuAnLIsMRVVtyCmhubmU/sEc7UkTbxQF1QCxuyOwrHso/gmbRd83fwtdlMtqAS8eezv\njX79jYob2HBxndkn/MNZh1BWLQ0uD+kajpTCZBRUFhh8TVe5L8K8G/ekojHUwZc3JPPqe0LcWnm5\nSmuFLA7/f3C2078x2XrlK4sXt3s4YIzBablMjoWD/ilZNv/Yy3j317ctcvGoO/xpY9TV1jX4NUEe\n+t1pUouv1Fscsyl0My/yKpS4x7ULAHXRRrmTtFbCC4f+B9+m7oGzvbPB7U3/YQrSi9UZUg0ZYjpi\nW7jeqBiAOvipSb/XBG7fP7vcrO0C6m4Pmm572gpv3ag3fVoukxvsamdOxsHdQi6TiwVUf51+XuzO\nAwC3qm9J1n3nzL/FwtmGzo9+bv7o3N7b4N/L1shlcni4SLNZNl360uC6mt/1pG/GSvbFpeHLcCAu\nAV6u3ujlGVLvSGja20spTMauiXttJjOwrcooScegr0KbnM3XGIJKwNokaWF3L1dvI2ubZipQxiCa\n9RkNbFRXV2P27Nn49NNP0a5dO+Tn5+ut079/f/j4+GDdunWYPXs2amst95SIiCxPLpPjs7+tl8wL\n6BjY7Adfp9tZFo6QGV3nzeN/x6rE9zF860CjN4XaJw3d/pIabk5uOKc8A183f70gjrbJ340TRxK4\nVHDRIn0ik/ISkVHatBuv988ux4CN95t1Y3ws+6hkWu4gR4R/FII9Q8SCabq+GmM8cGRpynIlVp/7\nD/JvSc8fuk+INVp739Rb1dJCms6OLni6z3N669XW1ZrV/7shdEey0Z6+VHBBb/33z6m7g0RsC2/S\n96k7/GljjNkdhe0p2xrUDp/2XQ3Or69AryV1lfviwJQE7JrwPWrrarHs16V66zx34EmM2R1ldBsv\nHJqFSd+MxcBNfc0Kyuh2wdGlSZ+ubzQNTWZG945BYiBTt04LADjYOcDTuZMkfbqHR7B4/NB+fUuO\nptTaGTtWGctAO5Er7R6WV65ESmEy5DI5JnSfJFnmJuuAfZMPoaSyWO99tbvy2ZIl4dLuKOWqcoPH\nA+1uqdrWX/oc+eV5GPn1ELPT/LWzNiftiUWwZwiDGi1It/Br+JYHUFBx51qguQptG5JSmIy/yqXd\nJz2cPRq1LVNdaG2te21bZDSw8dVXX+HYsWN46aWXcODAAXTtqn+R8fTTT+P777/Hs88+i5MnT2Lr\n1q3N2liitsQaN3HKciXG7ZL2Sx0X+EizHny1b/arocKy4e8ZXE+TgaGqVRm9edE+aayNWmdwnWW/\n/gsxOyMxcXcMlgz7N2b1mWOyfTWoQUR8OGJ2Rpp982GIslyJ53/SL5TWGIWVhRi5dUi9v43OOhkE\nz/Z9HnKZXP3kcEoCPo7UT91vbPplQ6mHoQzBqsT39ZZdLPhdb54t9E3VDSBcKriAB/0M15kyNFpI\nUwR7hohp7t3dgyTBSEMF+jQyS/9s9PCKgkrAW8cXmbWuK0w/QX3h0CxExg836+8qqAQ8sjvW4DLd\nrAFLHke1i2Z2ae+DHx89DIWrAteFa8gVmtYl58atAgzd3L/egGV92UyaoUJ93aSjHemqQx3uce2C\nPY/8IB7fdeu0AOoskBPXjkvSp1OLUnBgijrz4MCUBMkoHF3bS7MLTHWlaKsac6x6sf8revM0mUy6\n++/yESvQXtYeU0Nm6L1GtyufrRjfYyKeDL4zHG5h1Q3svrJDso5uDTDPdneyPDJK0jF21+gG1cpg\ntwDr0S2o/PCOhwwWyTU1fLolqY+Xdx6sebsoxML1jaEZdU4zUpbuMlvpXtsWGQ1s7NmzByNGjMAL\nL7xgchhXe3t7LFiwAGFhYdi5c2ezNJKorRFUAkZvH2F2cTdLvefD20dB0Om68N/f1zRrhXvdVOVu\nHe+tt8Dmmt9WG02j15w0juUe1VtmB3vxBuRqSRqm7Y3Dfy+sNbutN24VYMjmfg3+PjQn8fx6Rsxo\niMJK/Qs/Xf0U0i4lg32GiP8vl8n1nhIClh0K1JQPzr6P6rpqg8tm7n9SL4BkCxehujcgT93/LIb6\nDJOknGvMPTjL4vtUbV2t5L8aAR0DsXPcd0Zfd/raKQDqm4Nlp/5ldvBOtybPy/1eNbruc/1m4/PR\nG01uL6Mk3awgy8lrv6BYVSSZ16dTKH6dliR+15qngZrjaPT2UVCWK3FOeUb8b2O+f03tHldHVzHd\n/efMgw3ejiG1qK034yS2+3iTIxfdqFBnTfyen2R0/9L4q/w6TmsFMitr9LsMawopa/+GNbUNdC/O\n5TI5fow7LHad6O4e1KLd2lqLxhyrene+H8O7PCiZtypxBQD1/vvrtCQ8HTITnZw744VDsxC9fRSK\nKvUzbADg9SOvtMrAb33SSqV1c3ZeiZdM6x5vdGvM5Gs97e/s7FVvYXJ2C7Ae3W59xn7L21O+bpH2\n5JRlicNiA+puhtP2xjX6+tsWHsTcrYwGNjIyMho04klkZCTS05uv7ytRW5KUl4irxber6xe3TDGw\nlMJk5N7M1ZtfUVOBaXvj8ODWQRavCwAAt3QCG7eqKxATGIvOLl5GXgEUVxVh0jdjTZ4wDPX3rkOt\nyaeY5qhDHabtjcOwLQPM/j4OZx1EnhnrtndU3yQ427vAy0hXGm3zj75s8ia0r1cYHKD+vA5wRF+v\nMHGZoBKw58ouyfouDq6SdZrL2eun8fnFT02uszZR2t812DNEMsRka7wI1dyAvNL/NfEmWy6T49CU\n45jT9yXJulV1lXjv9NtNusnWplvQUfeY8aDfSLwcajjwsPq3/+DzpE8xeHMYViW+j8Gbw3D2+mmD\n62pTF+tVP+WS2csw7b4njXZxcnOSY3yPiTg85QQGeA0yus1Xfn6hUaN0vDJgviSooemHrzmOphZf\nES80+2+8784FZ5X537v2jdXVkjtp0oaethvT0dF08eD6Mj8Urgr8POUXo8WRV/+2Ahkl6fhNad45\nY9VZ9fqCSsBXl9dLli0NX4b9cUfQXtZe8j0Z+n1pt+/YE6fV2RxxCXflU0ntrn7dOwaZfazSLT57\n8vovkn1hY/KXuHFLXRtJEzjpaKBA8bWbua0y8FufAToFVAtvFeJSwUVx2tO5k8nzt8L1HvH/C27l\nY/zuaJPHEnYLsB5ThZW1PXDPwGZuifp8UVFdIWY8amtsdxhbeBBztzIa2HB2dkZdnflFi1xdXSGT\nGe8/T0TGVVRX4HhuAg5k7m+2atGG0oi15Qo5GLMz0uLvnXxDesDPKctRj0zy0Jp6X5tafAXrfv/U\nYJsCOgZiXfQmvfn1PcU01/Wb1zBi62CzghsJOfrZIwBwj0sXyXSoVxh+mHwIl2dexa/Tk9RF5qYl\nmQxyjNkZZfRvklOWhRqoP28NqiUj0Jy89gtu1kpfV1FTjjE7HmqWABagvoA4kLnfZM0BjY3JX0ra\ncVN1E1m3b2izSrNwU3XT4u1TliuxOXljkz6/l6s3ogNiJIXH5DI5hhvokrL2/Ifos74HYnZGIuLr\ncIsFOYzx6WC4LkUd6vCPE69L5o3ZHVVv5kZqUQpUteqnXKpaFXKFHByYkoDNsdv1sgruuz36Q+/O\n9yN+4h50djYcuMyvyMPhLNMZELHdx0tucHzlfojwv/ObMlZf4trtwK2mzanFV3Ap75LZ3VWCPUPg\n79YNAODv1k28Ye3d+X4cnnIC47s/gid66ncP0PBy8cargxaYfI++nUNNLte83/mnU7Ay4iMsDV+m\nt3xt4oe4LugHqQ25cOM8Bm8Ow+4rOyV9zB3sHDCpZxzkMjkOZx3SyzYzVI9Dg6nWgPjzN55co+e5\nvulhvmcAACAASURBVLMl02VVpWJh2dgdUZKC2O7tPNDDIxizQufqbcenfddWGfitz6zQ2eKw5gDw\nR9FlRMSH41LBRQgqAZP2jDV6/u7uHoTn+jwvmZdRkl7vDSV/qy1PUAl465h5XRgDO3bXe60lz5Ha\nQXDUAR9HfgYPmbS2RmOKW2t3De0q9603e4hajtHARkBAAJKSzO/Hl5iYaLAOBxHpH6zDvPvDT64+\nEHZu1xnP/fgUJn0zFtP2xmHSN2MxYGMfZJSkW/wmqExlenjm7LIsfJO2y2LvqSxX4v2zb0vmaUZZ\nGOozzOjNj7Z//7oUI7YONtimQV2GiBkLgLqwWn0jsDREUWUhIr4eWu/34e6k/5TWHvZ4f9QHknlv\nDlkiXmRpLrgCOgbi1+lJWDFytcFt37hVYPTizdfNX5IWrn2xa6yvfraQ3SwBLM0FxLS9cWatX4ta\nPLJ7DOb9/CIuFVzE/51YjJrbF7U1ddX42sJFIjNK0tFvQwjmHX4R/Tfe16jghqn00/pqDWSW/YmH\ntqlruQzbbH42kEYPj+A7f+uOhrsAxHYfD3s46M03JnbX6Ab/DuQyOUZ3i8apab+hk3NnAEBAh0AM\n9RkmWefw4yfg6uBqcBuzfnrG5OdXuCrw21PJWP7gCmyO3Y6EJ36V3Jhop5ibCtb2cO+Jbu7dzE4Z\nziz5E1llmQCArLJMZJb8KS7r3fl+fB69AR9EfSx2G/Bs1wkA4GzvjLeHv49fpydhUk/Tv/8VZ98x\n2Abdc4TCVYFpIU/e3p707nlj8pfYl/G93jZCPHobfd9FR6VDtWpGWBFUAs79dUZv/fxy/YLxpJZS\nmCzJuDT3ae2tGv0RZCqqK5CUl6g3ilVxZREm7YlFXPBjcNDZp98btUrcH1p7wWVtClcFPhil3zX0\no8RVtzNK9bOZAjoGYteE73EgLkEMnmrzdO7ULG0l0zQPMb648F+9Y3lKYTJuVJlXaPjRb8eLv93m\n6N6hOzreyz/PQZFON8cxu6MadT2gGTAjV8jBxD0xNrEP3g2MBjbGjx+PH3/8EefOnat3I4mJifjx\nxx8RFVX/Uzqiu43uwTqjJB2rzq5AtqC+8SyoLEBFTbnkNYWVNzBkcz+LHuCT8hJRWlVich0HOwfM\nO/yixep+GOpP7uGsLggml8nxUv95Zm0nR8jGD+n6F/LaGQsAsDH2awy6Z4jeetoOTzmB1wYsQnS3\nMfB08jS5LgAU3CrA18mbTa7T3kn/adB7I1fhbwEPY98jBxHlH419jxzEgC6GU/TlMjlm9H4aJ581\nXNgzueCy3t9DUAkYu2u0mNquW3dBfZNr+BCfXZZl8dTJ+kZpMCStJBWb/9iIiPhwbLuyRbIst8y8\nJ9LmEFQCxuyIEp8GqmpV2HVle4O3Yyr9NMy7P7xdFSZfr+kjfr38GkZtrT9gpt3+iXtikCvkoKvc\nV1IQUpvCVYHzT/+BfwxejPao/wllQUW+yd9BD49gseCao51MMhpDQMdAnJnxO36YfAiHHjuu1x6F\nqwKHHz9hcLu1dTXYcPELk21TuCrwbJ9ZGN0tWm/b2inmP8Ydhp/cT+/1yx9cgf1xR5BZnCn5m5kK\n3H6UuMrktEZAx0C8G7ESZ5+8cDsDKx0z+/4P5DI5FK4Kk/VOrt3MxaZL6yVtMFVzSeGq0CsCXIta\nvT7rdrDDCp1AqrYqSEf00Rzro7ePwtjA8ZJljnaOiO0undfWXCq4iJcOzZF0haiPJoigPeJWQ2o3\nBHuGoIurj9nvl1p8BYW3buDglGNipoPMXiZ2J7S1fv6CSsC8Iy/ozX+om/5IXt063ItdE77HoSnH\nMbzrCMhlcgz1GSYpKArcGd6dWo6gEhC5bTim7Y3DwmPz0Wd9D/zfyaVibbKGZC/cuFUgdntrju4d\nusVJDRUwBYA1iYYfLBmTUpgsGQGvJUd4IdOMBjYeffRRBAcH47nnnsMXX3yB0tJSvXVKS0vx5Zdf\n4vnnn4dCocD06fp93qn52VLE/m6ke7AesrkfVv+2ot7Xacavt9QB3lRqsYbmoH+1OE3v4rsxrun0\nJ+8g6yB50jypZ5zYh78+Lx56Xi+qrlscLMi9B3anmS64eaumAgsGLcKm2K9x9qmL+DjyM7R3MH0T\n+I/jrxtN2xdUAjZcko7QYg97/C0gBgAwoMsgbBm73WhQQ9sQvyF4ud98vfmvHn1JrwaK7rCQumm5\nClcFTk5LFGuZaD/Jl9nLLJ46qf230OUv74bxgY80aHs9PS03pGFKYTJu6DwRvS5cN7K2caYyZDS1\nNlztDWcp6LpRWX/ATONw1iHxCXGukIPUohSj6ypcFXjlgflYEP6PerfrYOdg8negXXCtuk4l6eoE\n1J/mHdAxEIenGA5urDi7vNEjEGm/t8JVgX2P/izJ1AroGIgpvZ6AXCZHb+/e4u9SZu8k3swbOrY9\n1C3K5LSxNuh+/gf9RmLfIwfhbOds8HWLT/xDMgxvfTWX3J1N1+0AgJ+n/IIBXQaZDKpo0xzrU4uv\n4PeC85Jln/7tCyjqCdLZsksFF9XB1JTNiIgPx7unltV7rlOWKzF0c3/E7IxE7M4o7Jq4t8G1G+Qy\nOd6PkAafXBxdEObd32D/f0CdkXCrpkL8e6lq7+yHttbPPykvESqtAo4AIHdwQ0zgWHEkr10Tvseu\nCd/j8GMnxICGuK5MjvdGSYONLVUMm+7QvakH1LV/pu2NQ8S28AaP2qMpMG/JYq/KciW+uPBf/PP4\nQrPW/+LCZw263i2vkj6M7Cr3tcnuYW2R0cCGk5MT1q5di+DgYLz77rsYMmQIxowZg6eeegozZszA\nmDFjMGTIELzzzjvw8/PD+vXr4e5e/8mXLMvWIvZ3I+2DdWfnzmLAoiF+U/5mMOWvIa4aGOrPlMUn\n/oGwDSENeqKlq49Of/KFg/8puVBRuCqQ+ORlrIz4CEuG/lv35RJ1qMNGnae8usXBfszYZ3Ib93YI\n0LsZjQt+HBeevWJ0GFqNZSeXGty/Tl77Ra8gYC1q9W4CzTUrdLbB+blCDh7eESG2Qffv0sm5s96J\nNaBjIE5PP4+VER+hFneeVGhfHFuKXCbHrol70cFJWuzO3ckDR544iTeGLm7Q9nLKsi3WNkPpyqlF\nfzRoG4JKwMTdMUYzZIDbWQpPGL6RN+Qfx1/HqnMrTI7CoyxXYuZ+aV2HjOKMercd5NGj3nW0uyMY\noi4e6gRAHRRoTDBMU59CVx3q8PCOhyxyztIUtNTcFB2acieDRO6kPkasjPgIqlr1qCDGbgJH+EXA\n7vZlkR3sMcIvotFtGtBlEC4/l47XBxjua96QYXh1CzAbXOd2N4cH/Ubi12lJGHrPMJPra2qJ9HDv\nCTcn6dDEzm18CNcPf5PeHL+fuBzhmx8w+lsUVAKGbxkAZflfANTdlBKyjzSqdsNQn2GSYZvDvPur\nb+rjEgwOTf5jxj6jN3xtYdSP7ybvv7OvyuQY3nWEXkBDW4R/lFhEuFuHe+Hi6MLr3hYW7BliNGib\nWfon1v++zuAyjZFdDR9XLVXsVVmuRNj6ECw8Nh/HryWY9ZrKukqDWcHGrD3/kWS6h3sw67i0EkYD\nGwCgUCiwdetWvPfeexgxYgQEQcC5c+eQlJSEiooKPPzww1i5ciV27twJPz/9VFBqfrYWsW8tNFku\nzV3MD5AerKcGP9mobfzj+GtYeGw++m0IaXRtgC8ufFb/ijpKq0oQER+OnzJ+bPBrASCtWDq8m6ao\nnzZNX/In738Gbo5uJre3+8p2vdojmqemAJBeYjh4MyHwEayL3oSfH/vF4MlHLpPjub7P49dpSZjS\n4wmD2/gmfTdGx+t30UkrStVbV/dpfkMoXBV4zcjNUK6QI94MtXNoJ1n2fOgLRj/bhKBJCOhwZzhH\nRztHi2dsCCoBBzP363V3+v/snXlcVFX/xz8zMCDDhREEJlFBFkWEEvfcIzTcNRW0R1N/ppVpZo/1\nlFmplUulbZotVk+ZPRqm5Za5ILmLyuaGC4iAiCwiywDKwMzvD5px7tx7ZwaYGWD4vp+Xr5577nIO\n986595zv+X4/3+khs8BIGPjJ/BHpO8Lk60UFTTFb2/jclQ9nH6pTX9JPRSgkXCckaivEyvjl2pUu\nvvfQocz9nLLDWQeNXref9wCT9GZejZuPiJiBvHXfKsvSGgOUqqp6G8NCPEJ5PZHuPSgy2zfL0KSI\nkTDo7z2QVcZn7LpVlgX1PwKO6gYYJ3Xr7ddO2MDw2t8LoFAqalfsdbJs6OunGNO7cG/VhvW+8ZP5\n45cx2wy+T18KW4B9E2OxY/xeLD/5til/js0Q4TOMU3anIpd3YqNQKrDsxNso0XuvHTBiRBdCY8TQ\nzyrDSBgs6Plv3lS/QhO+5pb1I8yrB+edxKc7YgiNZ9yOcXtgL7bXZk+zxliOqIWRMBjQfpDg/oPZ\nD8eLfO8gXSFoALij4z1pDrHXvem7WCHKpjIv9nmTxwQzQ55jbesL2xKNh0HDBgCIRCKMGTMGX3/9\nNY4ePYqLFy/iwoULiIuLwyeffIIRI0ZAJKqDLDRhVmzBYm9tdL1cemwKEfR2MWeIDyNhEOQejK9S\n1hk/2ADV6mqjsel8JOcnshTxRRBh1cA1Jp8/bV80vj//bZ0ytlwqvMj5ezXCoXwwEgaHJh/jHdhp\nSCtNQ99fwjjPTPNM9UNCgNpVnU8jvsSYgHFGP5Z+Mn+sH/YN4qJPcgTbgFrxKX03cf2V8SV9lzY4\nDaKfXlpAXTSToQmdo2D/TxiPvVjCm/5WAyNh8MGgD7Xb1epqg+EMdUWhVCAiZiBejZvP2dfG6eEE\n8s2+75h8zVl/TWtw39NkQXFx4A6u1FBjY8rXAEzr6/ox4IaMV+E+EYKu5UJklt7kTbE51DeSUxbQ\n2rg3BiNhcOyZM1ja7wOjx2aU3ODNVKKbfrGh4Ut9vbnaN26O7lb7Zukbt/iMXe6t2sBerPl76+eh\nok+YVw9BkeTc8lwk5yeCkTD44+l9+DR8Pa9+yqiAsQbfi/smxvIac2Y9+jzv8RoNjZ7y3jhfkIz8\nSstkSWqqjPAfBQeRI6dcV3NDoVTgeM5RhP/aH5suc7+5mlDD+iA0edOk+tXV09CI0Qqd05yyfjAS\nBn9NikOHf/pVfcesjISBk70TK9XzyO0R5LlsRd7ut9yk49RqNUfrK0fPG/P1IwvNmqlNVYeMnvpM\n2x1lNERSoVTgjaPs1OqvH11Iv7smglHDBtG0aYoWe3MZBCylHaLr5SLkmmyJEJ/k/EQowfVYAABn\nOwYLui/C95GbILPn5q3XZc25VTiXe6ZOdZ/VO14NNXxkvvB17WjyNRYffw0Tdo4WXFnW5+uULzll\nGuFQIfxk/jg/8xpWD1qL7yM3ccIadNF9ZkLCla/1ehNxk0/WuV+EeITii4iveffpa5V4O7OzQY0N\nfLrB/bCsSjh7TW55Lq4WpcJZ4oy2zrXpZNs6t4WzxNngNY1l7agvCqUC353/RnAwoJslQhOWMNJv\nDKZ2mY5ZXdkTLwke6q1klN4wyVVf6D2RV5GnzYLycuyLvEKqXyStxbncMxi0pQ+vcKMumsmnJlOH\nIeOVZlV2x7g9sNfJ2mOMXMVtTlmteORGVhmfkUCoHfO6L0Bc9ElMDpqKuOiTmB3Kv7L0Whx7YFab\nfnEUS3C1Icawft4DtBMaoNa4+tekw1b7ZunH4utva9NNqjR/b/09VHQxJpJ8736RVhz21bj5vOr6\ncqkcp6cm8eq3vNJ9kdY1X58+Ar8TmWNr7fsiKY9rTLPUu6KpwEgYrBrMNeyrUIPwmP54escohP3Y\nBRN2jmbpGGloJXLCCP/RFmlbiEcokmdcwafh65E4/bLNaZ3IpXIcmXK6wWNW/cxI2f/0VfJctg4h\nHqGYHcofNquLokaBjZE/aoW1O7XujDB5T9YxKqjqLOYt9N1XKBVYddo0owsfKXeT0feXMGy7+qvg\nWCA5P5GTwSe3/LbJoYWEZWmyho09e/YgKCiI9e+ll2rzeefk5GDWrFkICwvDiBEjcOTIEda5p0+f\nxpgxY9CtWzc8++yzyMzMbIw/oUViLoOAJbVDdD+Imvhx/ZUDS4T4nM9P4ZS1Y9pjx7g9uDDrGt7u\ntxRjAsbj+LRzcJUYNm6M/H2oycJ7GSU3sOrMe5xyJ3snxE0+qY1LN1V0ztTY8Be7sdXP2zMdeFNU\n6qPJhjAmYDwORh0RPE73mbV38eH1sOjfbmC9B04j/EfxpqtcfPR1lqdI9K5xrP3mUGk3lNFEoxNy\n6vYJ7WAuuyzL6DPp5BakFWqViNkZLuqLQqnAsJjBWBnPP5AIcuvCGZiHeITixxG/4NMn1+PtAcu0\n6To9W3libvcFrGMvG9F30dSvSaGqq1Xx+bk12km56p//8TH+95Fa3Yz04jTB+6iZ6L95bBGWnVhi\nsF3Aw9CIX8f8zip3tRPu2/oGSA2DOzyhNaD5ydipVU0hxCMU6yK+QohHKDq4+vIec6+qiOW1UZt+\nkZ2Z5t79e/qnmQwjYXBkymn8MmobVg9ai/MzrwlOyC1BP+8B2nAs/fS0AHewak4xuOF+IwX3PX/g\n/7Dvxl6D4qHAP0KsPPotfBmZNDzmGcb7HtFNIV3yoJi1T+YgM+k93dx5uvNEuDm48e47cecYSpVc\nwXwN+6K4HjLmRBOeaWtGDQ3m8DLRLOr9Mmob693u69oRldWVtHpuBV7pxQ0v1EcsskOftv1wemqS\n1pjVyp7rLfWg5gHP2fwYmh9cLUpFWbXwwpCpzIudI7jQUSmgecQXlmxNKJFELU3WsHH9+nUMGzYM\nx48f1/5bvXo11Go1XnrpJbRu3Rq//fYbnn76aSxYsADZ2bWuTbm5uZg7dy7Gjh2L7du3w8PDAy+9\n9JI237CtoUm7NGJ7BCJ+5Y+TtibmMghYUjtE18slcfol3pUDXdE8O5G9WXKl/36dna0jUNYZx545\nw4kJl0vlODH1HDydvAxeb+2ZDw3u1/BdCtfzQObQWitapolL14jOyQxMvDT8dP57o781X1lHdGBq\nV0W9nOTYV4/VWT+ZPzaPiOHdt3rQWu31atO+stN4tXX2btAAnZEwmK4XRwkA+ZV52snv1aJUFNxn\nx7+bQ6VdLpVjY+RPvPumBtfqtCTlsVNxG/uo1uol1HoMmUs8VF93Qp8ZIbMNns9IGBz71xnsmxiL\n+GdTwOhN0h7UVBk8Pzk/UVt/bsVtTN0bhWHbBuNc7hl8d/Ebk/6GKrDr0IT66FPfd5ImQ4Ym5W/y\nrFQM9B7Me+yuG9xUpBqDyu3yHHRgOmDX0/sbNCHQ9aDR53DmQ6NcexcfrZCmhoKK/HrXC9Q+72G+\nkZj16ByrT9oYCYPYyce16WkBsAaB+oPV9wasNNvktej+XcF9NeoavHmE7dYslMHKT+bP8d4J8QgV\nvPatsixeg55uGNXsx9gePH+M508lbGswEgZH/3WG5SUmxGu9FuO1Xosx59G5iJ+abPCeE9blP3+/\nitzyh55ut8puaXU3Gns8bOvIpXKsHWI4vFqlrsH1e1dZxiw+zSBT9KA0GPoWt3fxQRtHD5Ou84i0\nLd7qIyxqLpTCVcijzVCotaWhRBIPabKGjfT0dAQFBcHT01P7z9XVFadPn0ZGRgbee+89BAYG4vnn\nn0f37t3x22+1k8aYmBh06dIFc+bMQWBgIFauXInc3FycPn26kf8iy3Dq9glt2iVTXbctibk0Pyyt\nHaIrOJmSn4xTt0+wxKd0RfNq1NWYtGssFEqFNma/rvGACqUCOQp2XOGy/h8IDiDlUjnipyXjy4hv\n4STiTx+5J2OnSYJZMp5UgfO6v8Jbt5/MH0mzUvF95CbMDH4Obg78oSMHsv/C45u7G7wPV4tSka2o\nnTznV+bVeyItdeD/+6fvm6L9u4Pcg9FW6q3dZwc7/DH+zwYP0NsybXnL42+f1tbbQS8OP9AE/QNT\nCPeJwCNSbv0r4pdj0JY+WHuObdgy9lHVN87p53evD2qVcCyri70rpgT/y+g1dAc8Aa0DWPt+STWc\ncphv5SS9OA3vnjCe6lQITaiPPg15J+mm/GUkDNaGf8F7XNH9Iuy7sZdVpjuIy1ZkN9ggxRfaomHL\nlZ+1nmC6QppAbWrYUQFjG1R3Y6P73jc2CDRnZpAg92B4OQkbcvRXGG+V3RI4staTTOPpYsx7J8g9\nGJ56+h5zHp3LCqPylHpp32EdXHzgK+to8G+xJeRSOQ5EC3sFaujfbgD+02cxVgz60KpeRoRh+EIC\nav7x0qOQFOvwdOeJ2pBmZ4Hxln6IJd93pLDSsECyLkLfYoVSgZHbI1ip3R3Ftd4hnk5eWp0iO9jh\nl1HbcHJqAmZ3e0FwnAtw07oCEEzPXFBR0GgGBUok8ZAma9hIS0uDnx9XQC8lJQVdu3YFwzzsQD17\n9kRycrJ2f+/evbX7nJycEBISgqSkJMs3uhHQj4/li5e1JhpviB3j9uDDIZ806Dqfh29AT48+cLF3\nwR/Xtpv9haGJwX/z2CJM3RuFR3/spI2z15/0aVz9e2wKwatx89FjU0idjBunbp9A4f1CVlkbqWEv\nEE0q0kuz03hTkVZUV2D4b+FGLbRt9TQg7ER2RoUmxwSMx0fhn+I7Aa8BoNZYMXJ7hEVTRRqivLpc\na8jLLLmJ3IqHH88a1HAystSHCZ2jeEX7frr0nfbvLq8qZ+0zRygK8I9OQ/RRSO242hk5iluctMHG\n9Ev02xW9e3yD+pRCqcCk3eME9x+aXHcBVf2/QcjIYAjPVp68rvwaHMGfpk4XPoONOfWM/GT+iJ+a\njFEdx3D2LT66iPVcLGHkFTLYqaDCqB3DoFAq/um/tavZYohxKOqYzbjG8w0C9VfhzKkzUat18orJ\nxxsTWY6N/sfzRCetrdCxeyYeZAnALuj5b9Y5+iFthvqOLaLR/XEA1z0eMD2EkmhatHX2NvuYg+DC\nSBjETT6JfRNj8dFg/jF/cj57/sVnXPdwMs3LQlMn37c4OT9R+y7TMLHz5FqP0GnJOD/zGj4NX4/k\nmVcwzDcSjIT5x3MrHq72rnxVYeLusZywb42G1veRm1jlbx5b1GjeEpRI4iFN0rBRVVWF7OxsxMXF\nYdiwYRg6dCjWrFmDqqoqFBQUwMuL7aLfpk0b3LlTm19caH9enm2qfhdWsl2DC42khbMWbxz5d53d\nATXxYRklN/Du8SUY+ftQJBSeQWJhAv595GWE/dgFnyesbbB68qXCi3jx4GwsilugjcHXJb04jZN5\nxMPJE9ml7NSHfGkYhYi/fYq1XZdsAJpUpHyrrBptACELrUKpwIrTy1hlr/b8j8kTlEEdhuC7YZsE\n92eXZQlOPK/fu2qWVJFhXj1Y3his+ktrr7nm7GrBfQ1BLpXjrb7vcspLqkqQnJ+I5PxEFD1gu5mb\nIxRFt/69E42n9pRL5UYH3/rtKqjMN8loIBS3GZd1CBXV5bznfB+5qV4rm3zuqEJhYAqlAu8e56bF\nLbhfgGoDqd4e4D5cJfyDGA2fJ6w10tKG4yfzx7ph30Bmz/aoKlWWstJOWkIgOsyrB9o68/epwsqC\n2on/vava0CUVVLj3gD88ojnCNwg0lnK1oUzoHKU1MBjDmLdIXTQK/GT+SJqRyitGqVAq8Foc2+Ai\nFD9uy4R4hCJh5kXtvRFBhP6PDMSXERtx9Jn4FhGa0xzRXTnX15LJLb/NK8RLmB/N+2iE/2heQfrH\nvftxygZ3eIKli/Zy7IvajER1qVO3b57NjeccJwK0xwlp18ilcpyYliCQHlutNfbr119axdXhaSxv\niaaYSKKxEPzKjhwpLHYlhEgkwt69e40faITMzExUV1dDKpVi3bp1yMrKwooVK1BeXo4HDx5AImHH\nRDo4OECprB2AVVZWwsHBgbO/qspwrDYAuLlJYW/PFSBsqiiqFPg7h70Ke+R2LJxkIk6suqXw9OS+\nCG7cusxaDctXZcHPs6/B6yiqFOj/zWCkFQnH65cqS7EifjlWxC/H0sFL8WLvF/EI80id2nv+znmE\nx/Q3etyWyz+ztlWowZBO/YFjD8vGhA6Hpzvfi5BLfC47RKhf+8fh582/airEdNkUvHHkVSiquR/q\nQPdADOzch/PcL2ac40y8wzsN5H1uQjzn+Sx6+3dDt2+6cfa5Orjy1quoUuA/WxdqtyViCcI6doUn\nY3q9GjzhgreHLMG8ffM4+yaFjYOnuwvauHDDbYZ06l+nv1OI/v59AO73EjlVGWitF+bjJfXC2MeG\nN6j/6bf5Cc9+eLn3y1h3VjiWdc1Ta4z+nsbKhqPjiY64WXwTgPBvRhdFlQIDv30C1+5eQ+c2nZHw\nfIL2+JRz53jP8XbxRnSPp+t1D3Zlc695tug4+gRyf3s3bl02qO9hiLWRazFnzxzB/cdvH+W8RxVV\nCgze+CSuFF5BF48uODvnbIPfs55wQWTnpxBzma0j83Lsi4js+iQC3GtDc2oU5ci5k4Ew1/r1Ib56\nE19MQPdvuuOO4g5rnxhihHXsihNZ7HeWyuG+WfpTY6Dfbk+4IHFuAi7lX4KH1AN/pu2An5sfjs8+\nhsziTIR4hZj9G+oJF2T/Oxuv7HuF87z1adumjVnvtSdcEOrLdZ2+cesyy9PNEnU3FzzhgrRX0nAp\n/5JFnr+t0RR+I55wQfLcJFzKv4Rbpbcwadsk1v704jR8l7oeL/d9uc5jRaLueMIFF+ddwNmcs5i1\naxZuFt+Ev5s/73jgxq3LLF00FVQIj+mPtJfTUFhRWOc+qKhS4OOzqzjlw7sMM+m36gkXJM1NQuA6\n7nuysLKAdx4zxm44Xo1jH9u5TWej4yqD7WhAv/KES53nFbaIoGGDYRiIRMJ50y1Jp06dcPr0abi5\n1SpWd+nSBWq1GosWLUJUVBQUCvbErqqqCq1a1boXOzo6cowYVVVVaN2aO/HR5949bixVUyYh76x2\nkqIhozgDx6+d0cYRWxJPTxcUFHDVh73EPujUujOuF19Dp9ad4SX24T1Ol4OZ+w0aNfRZfnQ5Gd8k\n9wAAIABJREFU3j/6PlJmXq2Te/TKvz8y6bgHYCs0F1UWod8PbKtzTNLvHOE1Pg5k/IX4O+yZcVe3\nbkbvCR8j/ccg5toWTnlJZSkKCstQKWG70OfeZRs1vJzkCGa617nutnZ+2D5mNybuZrvOl1aV4tiV\nePRq24dVnpB3lvU8lSolTqUlYGA7ftFEYwyWPwU72KNGbyX++u1MuNZ4YUjbodh0nu1Z8nPCFgS0\nCqlXfboEM93h5SRHfiXbU+jNQ4sR1Xkyq2xkx7GoLFGjEvVT5ebrUwqlApuShb1mAOBGfrbRZ6pQ\nKiBSP1zVUlZX4+DlI1oRWYVSgatFqQhyD9Za+4/nHMW1u7VGymt3r+Hg5SPaZ+jrxNUS8XTywv6J\nR+p9D/q2GQIRxCxtByeVTPA9EyALrJdx425JKaRiKSpU/O/88upybDqzBVFBU7RlCXlncaXwCgDg\nSuEVs71n54Yu5Ex0VVCh//cDcHpqEsqV5eixKQRKVRUkYgckTr9klpAQOzhjQ8R3mLCTnbZSBRX+\nd24b7pTnssrjMxIw2POpBtdrbYS+UwDgXNMGQeu6aN8rbZ29cSCq/r9fY9jBGeP8ogwaNuxFEniK\nO9Tr+1BXKsvYwqI+Lr7o6NjFKnU3Vfwdu1rs+dsKhvpUY+Dv2BVerX3gJ/PnhA2sPL4SH534GEkz\nbC91blMllOmFw1EnteMJvv7kJfaBZysvFNxne533/rYP7j0oQjumPcYFTMCM0FkmeX8ezNzP64Ht\nrHYz+bfqCi/ERZ/kXfwsKlKgwJF9net53IybNTUq3My9g1tlWayxlCk0tX7V1BEyAgmGosTExODX\nX3+t8z9zoTFqaAgICIBSqYSXlxcKCtjhFoWFhfD0rBXIksvlBvfbEnwpLv1k/k0iturDIZ9gx7g9\nJrlEKZQK/Ha17r8dFVT4+DTXQmuIGV3/r871CPHW8dfxwanlrBSTfKzgSYU5I3RWveqMFEgbWFCZ\nb5Jw7KrBH9fbRU1IxHPU78M44UHtXXw4rqENcXGWS+VInpmK13othp2o9jevq9sR7jMUbVqxYzR7\nPtKr3vXpwkgYrBrM1TgpVypQVMl2zx/UoX6GG0NcLUpFibLE4DGmxKdeLUplDfoyS29iws7RGBYz\nGAcz92PYtsEcvRb9Z6a7ratEDwBPB0YhflpygwaPcqkca4Z8plfKL1DKSBi8N9B4/x/jP54lDiYR\nSzAqYCx+G7fL4HkHMvaxtoPcg1mijeZ6z4Z4hGJ9+Lec8vyKPPx86UfsTd9V7xA4Y4R59dCmQNVl\n0ZEFyCy9ySorbkCq16bKltTNLGNpbvltg7pB5qCf9wB4Gegj1WrzZCwyhkKpwKQ/2Ibq6KB/tWgX\nZqL5otGe2TFuD34ZtQ1zQl/U7qtWK7E33fD7njAvxsLlGAmDGaHcrHOakMccxS1sSPkCfX8Jw4GM\nv7Dy9Hsco5UufFnhfF071jmkUKO5o8/EnWNxPOeo9tugUCpQWV3JERFNL07DyO0RlJ2kETGrxkZ6\nerpZrnPgwAH079+f5Xlx+fJluLq6IiwsDFeuXEFFxcOVtoSEBISFhQEAunXrhsTEh+JXlZWVuHz5\nsna/LXEm9xQnxWWNqkbgaOugUCowLGYwJuwcjdf/XmjS8X1/DsPvab8ZPZaPTVd+wNJjS0x+edxX\n3a9XPUJ8kbQWU/dG4Ymt/QTbEN3pGdb2yv4f13vyF+4TAVc7fn2AY9lcdff7ZoyXDnIPhq9LR065\nGmrsuLaNVXb93lVOmsGGivHJpXJE+A5Fjbr2N66r28FIGPw95RTk0lp3U1/Xjgj3Gdqg+nQREubc\ndeN37f/v4OJj1jo1BLkHa2P/hSirMm7lD3IP5p3EppekYereKKQX13o+6MaICgkqKpQK/HCBnU41\nzKu7WSZF+p4C+zP2sQYUumSVcFdM9BnfaQISZlzEL6O2YfWgtVqdgV5t+yAu+iQmB03F9jG7Mcb/\nadZ5HlI5q85yZTmyS2szG2WXZqNcya8vUh90Vdx1WXryLXwYvwJirTFPgqG+kWar11CGFn170r+6\nTjdbvU2BvIo8rIp/n1NuSDfIHGgmYC4CYnUuDq5WWZy4WpSKu1Vsj76SB8UWr5cgLIUmfX0/7wFo\nr6cpZU7tK8I8ONg5GD8IwLR90fgscY3WyKFQKnA85yhrXKAvuLyg+78RN/lkvcYk92u44+ZKVYV2\nISivIg+R256o9XZUA2uHfKFdcLMT2WsFTK2ptyGkhdYSMdmwUV1djXXr1iE6OhqjR4/GyJEjtf8i\nIyMxcOBAjB492viFTKB3795Qq9V49913kZGRgb///hsfffQRnnvuOfTp0wfe3t548803cf36dXz7\n7bdISUlBVFQUAGDixIlISUnBV199hbS0NCxZsgTe3t7o148rXtPcOXqLO5HNKsts1JSvyfmJWtfw\n9JI0owrrv6b+j+OKVle+urAOYT8FG/WcAGpDdfh4pfsi9JcPrHcbssoyEZd1iFtfyQ0sj3+bVebk\nWP8JPiNhsHPiX7z7ku4kcMr084Xz5Q+vS91xU05i7mMvc/Z9GP8By5qun97L08nLLGJ8hpSf5VI5\nTk1NxL6JsfX+oAlhSGxRw+rBay2y2mnMM8FeZG9SGk5GwuCDQR8aPU73vuqu6Pu5+mufYa1o6kNv\nFTuRHSZ0jjJ6bVMo1ptcxVzbUjug2DaY07/3Z7K9KvSRSx9BuM9QMBIGw3wjMevROSyjYohHKNZF\nfIVBHYag1yPssJLvL36Nvpu7ab2RDmXuR7W6VsupWq00q+eEIe5VFUH1jzGv2gKG6zCvHpBJZJxy\nV0d2Gd9gzxJYa4B2KHM/K+RJg53I3uLZFORSOQ5NPsq778fIX6ziNRHkHgwPPS+3Ie3DLV4vQViS\nvIo8DNn6OJaefEs72TSWFploHEI8Qut8zrR90Qj7IRgTdo7GhJ2jEREzEAqlgiO43Ne7X73fo/pZ\nEXVJL0nD3vRdWh3B9JI0vH5koXbBrUZdrU2fba3sJAqlQutxO2hLnwYnWGjumGzYWLduHb788kvk\n5OSgpqYGGRkZcHZ2xv3795GZmQmFQoHXXnvNLI1yc3PD999/j5ycHEyYMAHvvPMOpkyZghdeeAF2\ndnbYsGEDioqKMGHCBOzcuRPr169H+/a11rr27dtj3bp12LlzJyZOnIjCwkJs2LABYnGTTADTIPgy\nCAD8K/dNEaGsBrqsD/+WE9LAR2lVCabujeKd/OjWt/zkEk65j4svXum1CJvHxnAGenVh4eH5nLq3\npG5mbYtF4gavuApNMNJKr3PqD/eJMLhdVxgJg4E84RYVNRV4/JfuWlVrfXXr8QETzDJYN6b8XJds\nAXWt90DUEbg5uAkek1ly06x16mLI22Ve2CsmewDdrzbusdTZLYj9t4jY/1UoFTh35yzrnC+e/Mps\n8ctCujXpxWmc1Y//9OK+PzSD2Q5MBxyKPmbybyHQjasZUlBZgL4/d8Pu9J0I82Qb5vp7198Qqo+p\nRiE1VBzvqIbCSBisHLyGU/79xYceOQGtA602QIvc9oRV3HiH+kZCDK5YeI26GtfvXbVYvRr8ZP5Y\n0H0Rp9zcXoVClCvLOSnId9/YaZW6CcISKJQKjPztSe2KeY26Bl5SOXY9vZ9CrJogj3mGQYS6azmW\n1jwMzc0ouYHk/ESzpuvembbD4H5Pqad2gc3doQ3LO7lNKw/8OTHWqtlJkvMTtR63OYpbLT4ExuTZ\n/p9//omePXvi77//xn//+1+o1WqsXr0ahw8fxrp166BUKiGTcVd96kvXrl3x888/IykpCceOHcP8\n+fO1Yqa+vr7YvHkzLly4gL1792LgQPYAc8iQIfjrr7+QkpKCTZs2wcfHNl3QngmextHYAIBLhRca\noTW16KbfCmhtOGXevht7oYSSd5+bozvipyYjOngKUmZexafh6xEXfRJTuxh2h+ab/Gg4dfsESpXs\n9EyrBq7B31NOafNZn3n2PL6P3ISnAybByY5fU0KIMr00jQDwlO9w1vam4VsbPAHU9VrQ5e79Qo63\nTloxO+5Qkx62IQh9MNRQIyJmIA5m7kdXd7YlfrjfqAbXq8FSxgtjyKVyPGdALPbtE29YzFIe5tUD\nnk78OkElDwzrb+hSUGHcO2pvxm6Ex/THpcKLSM5P1HriZJTcwKnbJzAsZjBW6unGPKh+wHepeuEn\n88f3kT/z7tNP/dpB5ovozv9ilW0auRX7JsbiyDPxdepr/bwHoI0j17BZUVOB5/Y/i2f2TGSVm6Mv\naZBL5XiNx0hjLUb4j4K3cztWmVonFuW9Aaus0t+uFqWyMmpZ0o1XLpVjz9P8XjfWSnna1/txThlf\nrLgl4DOQvdiNm3mKIJoLV4tSka3IZpXlV+RZRbOGqDu3yrJY35n6UlldiTCvHtpUs/XR1tDlmeBp\nBve3snfC/qi/sWPcHo4cQHVNNZwlzlYdo+p7SN8uzzHqLW/LmGzYuHPnDoYPHw6JRIJHHnkE7u7u\nWi2LYcOGYdy4cdi6davFGkpwqRVUvMJZ9VnY0zyeM/WBkTA4GHUU+ybG4mDUUYMde0sq/+Tly4iN\nSJh+USvUp8k9HeIRivcHreaI9egTm3GQ11qpP2B8rddiPPfY86w2MhIGYwLG45vIH3BpVhr2TYzF\nhZnXtfH5QhMuDfNjX2BNbvVXwFKLLhk83xR0vRZe15sM6f6NCqUCrx6ez9p/7z5b7LI+hHn10Lra\n6aOCClP3RmF+3POs8mM5zcOLyDjCqwsqtcpi4QmMhMGeCQd5vZfqIljat63pIXlreFKnZZdm8WYh\nic06aPJ1TSHcJwLujm045XFZD9Nb51XkofumYMRc+5+2rFPrzujnPaBegwpGwiDCd5jg/jsVbO0P\nc09+u8uND8TEEJst5EcXRsLgfQPhTtYSDm3v4gOJuDbuWlcc2FKcL0zhLW+oHpCp9PMewDFYtnfp\nYPF6FUoFvk5ezypbOfDjermGE0RTQXfRx15Um/TRWuEARN0RWqSrK072TihXliOnrHaxIafsVoM0\nsPxk/oifmoyI9vzjAY12XWbpTZRUsUNnS5TF+PnSj1b1mND3kAasZ5xviphs2HB0dISjo6N228fH\nB1evPnTX7N69O7Kzs/lOJSyIXCrHwl6L0M75YVjKf4692qhuSKasqF8qvIjjt9kxxj5MR8RFn0RU\n0GSDSsoHo45ix7g98HLiX41dk7gaQ7Y8zlIv/vnSj1h5kr3K/GQHw2EZmr9DLpVr4/PDfSIMpp7S\nFdLMKLmBr1LWsfbnlJpnlVfTti5t2B9sDycPbXx6cn4iJ0VpQzQ2dOs+MuU0x6hiiHGBExpcb1PA\nxUE4x7g5wowM4Sfzx8bIH1llcqm8ToKlyQWmW/EdxI7o5Bak9Qqzg13t759HgDS4TcPT6upSUJGP\nogd3OeWe0tpJYF5FHl6JfQnVqocZLaZ2mW4G18/GSXEO1E5yNStOQkzqNNliKQtvlQm/m/gGTpZp\nQxYrA4ylV1r5BAXbMx3MogdkCoyEwWdPbmCVubUSDnczF1eLUpFb8XCVz05kjzGB4y1eL0FYEt1F\nn6QZqVYNByDqju7zujDzulGPbD403hnfpXytTfdara5ucBYcP5k/No74CS723DHfrbLacI9X4+aD\nb8yw9ORbVg0HqW+WRVvFZMNGUFAQjh8/rt329/dHSsrD1Y6CggKo1Q13KSLqztWiVOSUPxyUGgrH\naCp8lsCN6Y7sONykFSON8vXpaUlYOZCbhhMAshW1yvYKpQKDtvTBoiML8ABsd/lfUjfVud26KcW+\nj9zEyaQAADeKale0v0j4hLNvkM+QOtdpCH3NhP8ceRUjtkcgImYgRyhVDLFJIpOmwEgYTK/Dy9Ra\nwoOWxtBq+fywVy026dSgn53lk/D1dRq01UUXYmf6DmxM/lrralmDGqQVX+cIkIogMvuH9aeLP/CW\nv39qKTJKbiDsxy44nM32EimoLGjwAJZPZ0MIc6/qMxIGcZNPYse4PVjQ/d+8x0T686d7NgeG/vYI\nH2FPFnNiSBzYEvTzHgA3R3afsvZKVz/vAdqsRwEyw+Gb5iLIPRgdmIeeITXqanLXJ2wC3QWpxghZ\nJeqG7vN68/F3eMPr3+q7FK/1epNTvqzfCsRNPonMkpv4PGkta585suAwEgaHJh/TKxUh0K2TNmRS\nKB29NTOi+Mn88WXERlaZtbwOmyImGzaeeeYZHDhwADNnzoRCocDw4cNx4cIFLF26FJs2bcJPP/2E\n0FByY2wM9NM4SsQSi7vwNhSpvTOnbHa3F3mOFIaRMJj92AuCsemt7JyQnJ8oGAt/70H93Ks1hpUx\nAeMxoB13ovjTlR9wqfAifr/KTmHrJJaaPR1ocn4Sa7u8utb9LqPkBv68wbZYT+o8xawTb1MF9lwd\nZDbjCiqXyjHn0bmcchFEmFPH32990NewqavSe9F9rheEECqo8EUye7CQlJfISSG8ZsjnZjfoCIWb\n3SzNwOcJn3DiWoFaIbKGIqRbpA8jcbHIBFTzblnY6zXOhNvN0b3B4r+G6Oc9AB6t+HVcrBVKZkwc\n2BL1zQ1jZ3m6e7/QqgsDjITBweh/wjejDYdvmrPOPycdtrp6P0EQhBCa8Pq3+i7V6mn5yfwx+7EX\n8FL3Bejo6gcAcBQ7YvuY3Xip+8tgJAy+TvmSdR1ne8ZsWXD8ZP7YPma3Tokabg5u2jGKkJelm6O7\nVedhgzs8wfKu7eQWZLW6mxomGzZGjx6Nt99+G7du3UKrVq0wePBgTJo0Cb/++itWrlwJR0dHvPHG\nG5ZsKyEAI2GwNvwL7bZSpWzSqy95FXnYcpWtVRHd6RmDIR6GEFotXnNmlUFNidd7N1ysT8gDYtmJ\nJahQV7DKxgSOM/ug9XFvYc2EB3reHD6uvmat21RWDVpjU6smfFk7vov8yeLeGkDdNGz4CHIPRlsp\nO23t9C7/ByeRaUK5a8+tRko+W5fAEu6Wdw0YYH6/yp8VxBxeI3KpHCenJsDLyLN8q+9Si/6mGQmD\nvyYdht0/ceJ2Ijv8Nemwxev8PGID7z5rhpJZWxz4meBprOwofjJ/q0/yG0MQWS6V48iU0+SuTxBE\nk0EulWNhz0U49+wF7JsYi9jo41px/8OTT2DfxFikPpeBQR0eej/P6Pp/rGtsGrHFrO+zmGts/cg1\n5z6ESl2bCUUsEvNmt7r3oAgT/hhltXCU8wXJLO/aM7mnrFJvU6ROOVCnTZuGQ4cOwd6+drD1wQcf\n4K+//sLWrVtx4MABBAW1XAtRY6Of+lU/e4ClyKvIwy+pm1iCmQqlQqvzwAefm/miPvU3ismlcsRF\nn+SU7725G6/HLeQ9Z+2QL8wilCaXyvHn04c45Udy4jhlkX4jGlyfPuE+QyEVyN5yPJftQhfcxryD\n9TCvHrx6C7o4SxiM8DdfRpSmgJ/MH3HRJ9HasTYWPqB1oNk9cQzRkEkQI2FwIPoI2jrXGjf8ZP5Y\nNmgFLs1Ow/eRmyCBg8Hz1VDjy6TPONc0N452joL7KtXcUIERHUebzbDkJ/PH6alJ2DFuDzwEMtGI\nRZbX4vCT+SN5Rio+DV+P5BlX6m34rQtCXi+2EkrGh1wqR8rMK1g9aC1+GbVNO5BuCTRWhimCIAhD\n8L2bhN5XuXrC3uZOma2fLepw9kFWtrhuXt20aeZ1uV58zWrZSTjJEeIWttiUryYbNubMmYP4+HhO\neceOHREWFob4+HhMmGAbAoHNEd1sAXzbluD8nfN47MfOeDVuPh79sRN2Xf8DCqUCw2IGY8T2CAyL\nGczbsVL1hOiGeIc3eNAe4hGKzSNiOOVFVVyPjYDWgXi686QG1adLr7Z9EOlr2GghEUksMvllJAzW\nDf3apGP19RnMUXfs5ONYPWit4DHjAyba5KA5xCMUidMv1dtzojGRS+U48a9znNWQMQHjcXzqGaPn\n64eBXLGA2/6EzlG8AwUh5AJCwvVFExLy+ZNcDwZ7kb3ZtGqMockIZQ1vIAC8nn7tmPY2H6Ygl8ox\n69E5GOYb2az6MkEQREtGoVTgtbgFrLLkPPMaE0I8QjGkXbjgfrdW7tg9nj8j3qK/F1jFwNDehb24\nfa+qCPtu7BE8nm9R2lYQNGxUVVXh7t272n/Hjh3DjRs3WGWafwUFBTh27BjS0rhpAAnroMkWILRt\nbvIq8tDtm26sHNSzD07HZ2fWaNNBppek8Vor/VuzRerMJYhXcD/f6DFTu0y3yET0lR5cVzRd3nrc\ncq7r4T5D4WrvavAYJzupxTQBors8oxW/02dBz1fNXmdToTmvdgq13U/mjwszr2NS4BSTr2UoHKq+\nyKVynPxXAtq08jDp+Lk9XjZ+UD3QFXaUOz2C5f1XImlGqtUMDdamvYsP7EUS7fYj0rb4a1Jcs/yN\nE9bHmLcmQRCEOblalIp7VWy9PEukJw8USEvrJ/NHmFcPnM3jXxTKKLlhFc0mvoXLJcf/w/suzii5\ngW4/BmkXpVecWm5TBg57oR0lJSUYPnw4KipqdQJEIhHee+89vPfee7zHq9Vq9O3b1zKtJIzSSk8B\nV3/bXFwqvIilJ5bgUuF53v1fpHAzgeifvy6ZfYxSpTRL20xJtdnZvYtFBukisbBreiuRk0XTMTES\nBquGrMW82DmCxwz3G2mxyYlG/O7U7RNYFLcAdypy4eogw87x+6ziPk+YF7lUjue6zcFvaVuNHutq\nL7NYGI6fzB9nnz2PN48sQsy1LYLHrR3yhcV+Z5rf9tWiVAS5B9v8BP9WWRaq1Q/fxxuGbbRZIw5h\nXhRKBSK3PYHrxdfQqXVn0u0gCMLi8IXd1zURgSmIxYYDHKpqHgjuu1F8w+LjhzCvHvBo5YHC+4Xa\nsuIHxbhalIqe8t7aMoVSgSe3DIAKKm3Z50lr8WXy5zazaCNo2PD09MSHH36IlJQUqNVqfPfdd3ji\niSfQqRM3JZxYLIa7uzvGjrWOey5hnKS8BPTzHmDWjnQu9wxG/m76JMZeZM9R5uVL81qXFIuGkEvl\nmN5lFjZd4U8VCRhO19kQgtyDIbWToqKmgrNv7ZOfW3yAN8J/FHzPdkRm6U3e/aMt7DrPSBgM843E\nyakJLWYSaMto0m5eL74GO9jxZiEBgEHtB1tc0HJcpwmCho1WYiezhpUJtUF3YGDL6D73Tq07WyX1\nKGEbXC1K1aZA1KQ6bCn9hiCIxmFX2u+s7bmPvWyRhY7Zj72AjRe+4pRrPDK6GtDsmxc7BwEJgRYN\nW2YkDJ7vNg8r45dry8QQcww/+27sQbmqnHN+tboaW1M345Wehr3PmwOChg0AGDp0KIYOrZ3I3r59\nG9OmTUOPHjTQaYro5yxec241tl+PMZsQmkKpwPg/6iYCWa2uxvV7V1kWQE+9dIIu9q5mS8sEAAHu\n/CERADCw7SCLWSMZCYPfxu7iGH7k0kcwwn+0RerUrz9u8knEZR3Cc/uns/Z5O7ezmrhlS5oE2jKa\ntJsaI1XSnQRM3D2Gc9xrfRqeWcgY/bwHwNeV32g3wHsgGdDMiP5zp3tLmIq+UczWdVkIgmh8bpbc\nZG2XVpVapB4/mT/e6rMUK88sZ5WLRXZo7+JjNLVrenGaRY29fCEnKqgwaddYHJlyWvst1zcE6ZJf\nbhvhKAYNG7p88snD8IErV64gJycHEokEbdu25fXiIKwLX85ijSXRHB1pQ+I6VKmFXa2EyFXcBlDb\n6a4WpaJUyX7pTOwUZdbB84TOUVh2cglL+0PD+4M+NFs9fPRq2wd/Pn0Ik/dOQFlVKTowHfCnhVM0\n6qIRgIyfmoyvEtdBqVbiSd9hCPeJoAkKUWd0jVSDOgxBXPRJrEv6DEFuXXCt6Arm91holsxCprQj\nbvJJ/H7tNyw6whYJe7v/coGziPpCxkmiPpBRjCAIa9OWYaev95V1tFhds7u9gE/OfoT7OpnZVOoa\nXtFtfRg7F6PGj/qiGwaoT3ZZFpLzEzGwXW0yh1O3TghexxIhPI2ByYYNADh+/DiWLVuGnJwcVnm7\ndu2wdOlSDBo0yKyNI0xHqGOZI+3rsewjWJOwSnC/I1rhAfjTK82LfR4/pGzEleJUlFdzLYrmFv2T\nS+U4PTUJI7cPxd37hbCDPQa1H4yl/T+wyiSsV9s+SJlxpVEHd34yf3wU/qnV6yVsmxCPUHw97LtG\nqZuRMEgvZotTTw2abpU+TRCEaZBRjCAIa6BQKnDq9gn898JGbZkYdngmeJrF6mQkDCYGReGXK5u0\nZTIHmdY7zc/VHxmlN/jbW1OGEb89iaPPxJt9XqAbBsjHvIPP48TUc4jLikVpDXtxuZ1ze/Rt2w9v\n9F1iM5p4Jhs2kpKS8OKLL0Imk2HevHnw9/eHWq3GjRs38Ouvv2Lu3Ln43//+h8cee8yS7SUECHIP\nhrujO4oesNObxmXFwu/R+v9Yz+We4XVB1+Wv6MOQSqSYvOtp3CzL4OxPKDzLe95rvRZbpCNpRAcb\ny7hAgzuCMC8KpQK70/9gldnK6gJBEARBEKahUCoQvrU/MstussrbOLnDWeJs0boX9Pw3y7Dxx/h9\n2jlG7OTj2HdjD+bHvsDrNX5LkY2tqb9g9mMvmLVNQe7BCGgdiPTiNNiLJCwBcADIrbiNram/4FZZ\nNufcdUO/xsB2g83ansbGsMyrDuvXr4dcLseePXswf/58jBw5EqNGjcLLL7+MvXv3om3bttiwYYMl\n20oYgJEwWPI41y27g2v9XZ+OZR8RFAv1cPTAgj4LED81GSEeofCT+ePXscKxW3wEt7FcDG5zTsVJ\nEASbq0WpyFawvdLu11QKHE0QzRtrpE2l1KyWge4rQViWU7dPcIwaAFBQWWDx1Kp+Mn/ET03Gwh6v\naec/GhgJg6igKTg9NQkeTp685791/HUcyz5itvacyz2DKTsn4k5ZLgDAWSIVrLerO9vDta2zt00K\nhJts2EhKSsLkyZPh5ubG2SeTyRAVFYXExESzNo6oG0pVFacssHX99E+OZR8R9NQY4zeQEp0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'.g')\n", @@ -285,7 +331,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", "start_time": "2017-05-09T11:54:56.761091+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -307,7 +354,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", "start_time": "2017-05-09T11:54:57.350077+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -329,7 +377,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", "start_time": "2017-05-09T11:54:57.361076+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -351,7 +400,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", "start_time": "2017-05-09T11:54:57.394331+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -373,7 +423,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", "start_time": "2017-05-09T11:54:58.315777+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -388,7 +439,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", "start_time": "2017-05-09T11:54:58.363535+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -401,7 +453,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.columns" @@ -421,7 +475,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", "start_time": "2017-05-09T11:54:59.892052+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -459,7 +514,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "data_series.index[5]" @@ -467,48 +524,123 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sign(0)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "code_folding": [], "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No drift detected\n" + ] + }, + { + "data": { + "image/png": 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vjh1bmTp1EjY2BWnRwpU3b97g5+fLyZPHmDVrgVYeUvKp8peSVau8WbRoHqVK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m78cmUNz+Goa9ocpXZES3JekkPOXx4wUS6jHqbn4dDZeChUGoNlTAqSy5a5H0EMKY4e8sfV2JMO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\nruPHj6u6utq6vri4WDdv3rRrO3nyZA0dOlTZ2dkqKSmx23by5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", - " period=dt.timedelta(6),time_unit='d',plot=True)" + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=100,\n", + " period=dt.timedelta(5),time_unit='d',plot=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'OnlineSensorBased' object has no attribute 'slopes'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslopes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'OnlineSensorBased' object has no attribute 'slopes'" + ] + } + ], + "source": [ + "dataset.slopes" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[Timestamp('2013-01-03 00:05:00'), Timestamp('2013-01-09 00:05:00')],\n", + " [Timestamp('2013-01-07 00:05:00'), Timestamp('2013-01-13 00:05:00')]]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dataset.drift_periods" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "test= [[1,4]]" + "np.sign(-1) == np.sign(-10)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "test.append([3,5])" @@ -517,7 +649,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "len(test)" @@ -551,7 +685,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "len(dataset.data['2013/1/1':'2013/1/17'])" @@ -623,6 +759,7 @@ "end_time": "2017-05-09T09:55:01.060520", "start_time": "2017-05-09T11:54:59.898063+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -646,7 +783,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", "start_time": "2017-05-09T11:55:01.063627+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -661,7 +799,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", "start_time": "2017-05-09T11:55:01.105608+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -684,7 +823,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", "start_time": "2017-05-09T11:55:01.847864+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -704,7 +844,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", "start_time": "2017-05-09T11:55:02.251053+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -731,6 +872,7 @@ "end_time": "2017-05-09T09:55:03.917107", "start_time": "2017-05-09T11:55:03.905461+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -753,7 +895,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", "start_time": "2017-05-09T11:55:03.919697+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -774,7 +917,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", "start_time": "2017-05-09T11:55:03.980745+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -794,7 +938,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.get_correlation('CODtot_line2', 'CODsol_line2', [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", @@ -816,6 +962,7 @@ "end_time": "2017-05-09T09:55:06.016129", "start_time": "2017-05-09T11:55:05.261370+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -843,6 +990,7 @@ "end_time": "2017-05-09T09:55:06.731819", "start_time": "2017-05-09T11:55:06.018568+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -860,7 +1008,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", "start_time": "2017-05-09T11:55:06.734413+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -904,6 +1053,7 @@ "end_time": "2017-05-09T09:55:07.830400", "start_time": "2017-05-09T11:55:07.433945+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -952,7 +1102,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from scipy import signal\n", @@ -1003,7 +1155,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.data['CODtot_line3'].plot()" @@ -1012,7 +1166,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", @@ -1022,7 +1178,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", @@ -1032,7 +1190,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=180, \n", @@ -1042,7 +1202,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,10)], max_slope=180, period=1, \n", @@ -1052,7 +1214,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1065,6 +1229,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "collapsed": true, "scrolled": true }, "outputs": [], @@ -1081,7 +1246,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,15)], max_slope=68, \n", @@ -1091,7 +1258,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,14)], max_slope=68, period=1, \n", @@ -1101,7 +1270,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1113,7 +1284,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1130,7 +1303,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, \n", @@ -1141,6 +1316,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "collapsed": true, "scrolled": false }, "outputs": [], @@ -1152,7 +1328,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1164,7 +1342,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", @@ -1186,7 +1366,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,10))\n", @@ -1235,7 +1417,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", @@ -1256,7 +1440,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,10))\n", diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 37ee42454..a887a5577 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1580,6 +1580,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, from scipy import signal data_series = self.data[data_name][arange[0]:arange[1]].copy() drift = False + slopes = [] #removes NaNs, infs and other values that signal.detrend can't analyse from the dataset data_series.replace(0,np.nan) @@ -1588,7 +1589,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, if plot: fig = plt.figure(figsize=(16, 6)) ax = fig.add_subplot(111) - ax.plot(data_series, 'g', label='Data') + ax.plot(data_series, '.', label='Data') ax.set_xlabel(self.timename, fontsize=20) ax.set_ylabel(data_name, fontsize=20) ax.tick_params(labelsize=15) @@ -1614,92 +1615,23 @@ def detect_drift(self, data_name, arange, max_slope, period=None, slope = _get_slope(line_segment,arange,time_unit=time_unit) if abs(slope) > max_slope: drift = True + slopes.append(slope) print('Drift detected over the whole data range, slope: {}'.format(slope)) else: print('No drift detected.') - if plot: + if plot and drift: ax.plot(line_segment,'b',label='Detected drift') ax.legend(fontsize=20) + # If the period given is shorter than the range, the period window is + # shifted else: - #if type(period) is not int: - # return ValueError('the period must be a integer') - - #if period < 0.5: - # return ValueError('period must be larger than 0.5') start_index = data_series.index[0] end_index = data_series.index[-1] - #end_index = 0 - #new_index = start_index drift_periods = [[start_index,end_index]] - -# if period == 0.5: #Need a solution -# do = 0 -# # print('Not yet possible with period = 0.5') -# # pass -# -# elif period == 1: -# count = 0 -# # day_list = [] -# for value in data_series.index.day[:-1]: -# count += 1 -# if value < data_series.index.index.day[count]: -# end_index = count - 1 -# day_list.append([start_index, end_index]) -# start_index = count -# -# for value in range(len(day_list)): -# start_index = day_list[value][0] -# end_index = day_list[value][1] -# detrended_values = signal.detrend(data_series[start_index:end_index]) -# line_segment = data_series[start_index:end_index] - detrended_values[:] -# slope = (int(line_segment[-1]) - int(line_segment[0])) / 1 -# if slope > max_slope: -# n += 1 -# print('Drift detected in day {} with slope: {}'.format -# (data_series.index.day[start_index], slope)) -# #combines the indexes where the slope was larger than the max_slope over a longer period -# if m > 0: -# list_value.append([start_value, end_value, 'm']) -# if n == 1: -# start_value = data_series.index[start_index] -# end_value = data_series.index[end_index] -# else: -# if n > 0: -# list_value.append([start_value, end_value, 'n']) -# n = 0 -# -# if -max_slope > slope: -# m += 1 -# print('Drift detected in day {} with slope: {}'.format -# (data_series.index.day[start_index], slope)) -# if m == 1: -# start_value = data_series.index[start_index] -# end_value = data_series.index[end_index] -# else: -# if m > 0: -# list_value.append([start_value, end_value, 'm']) -# m = 0 -# -# if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: -# list_value.append([start_value, end_value, 'n']) -# if data_series.index.day[end_index] == data_series.index.day[-1] and m > 0: -# list_value.append([start_value, end_value, 'm']) - -# else: # The first while-loop makes sure that the calculations of the last - # period is right and that it doesn't overextend. The second while-loop - # finds the indexes for the right period length(could be improved). + # period is right and that it doesn't overextend. while start_index + period <= data_series.index[-1]: - #checked = False - #while data_series.index.day[end_index] < (data_series.index.day[start_index] + period): - # if data_series.index.day[end_index] == (data_series.index.day[start_index] + 1): - # if checked is False: - # new_index = end_index - # checked = True - # end_index += 1 - # if end_index == len(data_series)-1: - # break end_index = start_index + period detrended_values = signal.detrend(data_series[start_index:end_index]) line_segment = data_series[start_index:end_index] - detrended_values[:] @@ -1707,88 +1639,40 @@ def detect_drift(self, data_name, arange, max_slope, period=None, # store the indexes where the slope was larger than the max_slope. if abs(slope) > max_slope: - #n += 1 - drift = True + slopes.append(slope) print('Drift detected in period {} to {}, slope: {}'.format - (start_index, end_index, slope)) + (start_index, end_index, slopes[-1])) # firstly, if your start index is larger than the end index - # of a previous drift, then a new drift has been detected: - # add a new array with start- and endpoints - if start_index > drift_periods[-1][1]: + # of a previous drift, or if the sign of the newly detected + # is different from the previous one, then a new drift has + # been detected: add a new array with start- and endpoints + if start_index > drift_periods[-1][1] or (drift and np.sign(slopes[-1]) != np.sign(slopes[-2])): + print('new period') drift_periods.append([start_index,end_index]) - if start_index > drift_periods[-1][0]: - drift_periods[-1][0] = start_index - if end_index < drift_periods[-1][1]: + + else: + if not drift: # indicating that this is the first detected drift period + drift_periods[-1][0] = start_index drift_periods[-1][1] = end_index - #if n == 1: - # start_value = data_series.index[start_index] - #end_value = data_series.index[end_index] - #else: - # if n > 0: - # list_value.append([start_value, end_value, 'n']) - # n = 0 - - #combines the indexes where the slope was larger than the max_slope over a longer period - #if data_series.index.day[end_index] == data_series.index.day[-1] and n > 0: - # list_value.append([start_value, end_value, 'n']) - - #start_index = new_index - #end_index = new_index - - start_index = start_index + dt.timedelta(1) + # Indicate that at least one drift has been detected + drift = True + start_index = start_index + dt.timedelta(1) + # if drift hasn't changed value by the end of the while loop, no + # drift has been detected. if not drift: print('No drift detected') - #Makes sure that list_value don't have two values in the same index - #for l in range(len(drift_periods) - 1): - # if drift_periods[l][1] > drift_periods[l + 1][0]: - # ind = len(data_series[:drift_periods[l][1]]) - # drift_periods[l + 1][0] = data_series.index[ind-1] - - if plot: - #detrended_values = pd.DataFrame() - #fig = plt.figure(figsize=(16, 6)) - #ax = fig.add_subplot(111) - #ax.plot(data_series, 'g', label='Data') - + if plot and drift: for driftperiod in drift_periods: detrended_values = signal.detrend(data_series[driftperiod[0]:driftperiod[1]]) line_segment = data_series[driftperiod[0]:driftperiod[1]] - detrended_values[:] #df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:driftperiod[0]])-1:len(data_series[:driftperiod[1]])]) #detrended_values.append(df1) - ax.plot(line_segment, 'b',label='Detected drift') - ax.legend(fontsize=20) - #ax.plot(df1) - """ - detrend = signal.detrend(data_series[value[0]:value[1]], type='constant') - df2 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) - detrended_values.append(df2) - - b = df2.iloc[-2][0] - a = line_segment1[0] - slope = (b - a) / len(df2) - f = [a] - s = df2 - s[:] = a - for val in range(len(df2)-1): - a += slope - f.append(a) - - ds = pd.DataFrame(f, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) - ds = ds[:] + s[:] - ds = ds / 2 - ds = ds.squeeze() #from dataframe to data_series - - if value[2] == 'n': - #ax.plot(data_series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') - data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - line_segment1 + ds #data_series[value[0]:value[1]] - (line_segment1 - line_segment1[0]) - elif value[2]=='m': - #ax.plot(data_series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') - data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-(line_segment1-line_segment1[-1]) - #ax.plot(data_series, 'k--') - """ + ax.plot(line_segment,label='Detected drift \n({})'.format(driftperiod)) + ax.legend(fontsize=16) + self.drift_periods = drift_periods def drift_analysis(self, data_name, arange1, arange2=None, plot=False): diff --git a/wwdata/__pycache__/Class_HydroData.cpython-36.pyc b/wwdata/__pycache__/Class_HydroData.cpython-36.pyc index 272adf0a7066d875c2887f2b6ae1e9dc690765bf..9fcd46be32343fd98852c4d1fb7cd1ee5c2f5ba3 100644 GIT binary patch delta 1362 zcmaJb~}4_clI~?mt?axN$pA$YE0EMMMF!_prvYU&`@LXu4!iLww)bp z67RAPK?LcR)%zgyp;ah_ifGG-uu-H4V)7sgBDAqpDE_q8h8hDtD8_Rq+ZuiFVb6Z& z|9sz_Pk&ZFxTezHV33Zq)kaU8I!m{mFyR8w5v;IPIEu`r9&05ckk78UDV z=BWfrY%0Yyt}r!C&_bG|ND<>Bq%^D%J6xn%9jvhEIXhSlB09#`nK7>80S4E(!J;hs zvLc%*Av?}PT;U#;sHv5(-NM5&z}2Gc-;b+&oSF$417asx(o9J#y^@Z8N+Ry9tJaW9 zEX2|*bBkD7(x}SY>ri@!>t;q?T8X&mHbhY#;n8tr94fIoOnTANlElrfPm?0l73kJ}yxCjwJ~+9L$F`F_&sO41CE`*`9+!u%QRF{SRA%d%w{E^s2l9^3 z0KU`e2j{+%`%KEAi?}|<$yk;}pCI|XGwB@l^jlx!2R=g#ZLpEfU9SUH5t^WFV);oh=_`R4|;~A1~|PcEFpmV&`WK=oWNt zgKDKfT>m~T9+^w{J2APSvb|GQPP{O;8Pek9++#Oe{+R284$(2+j^VBIJE6-tIzMB; z10s6<3+NG-&%Y0Y;*ASW!+Xxsg?=4|ojWgoqrjuW_rssC%klnbLb4kd=5Zu|mpj~HEgF}Di~7CiRU!NQo8!PTp#Rie{q z9umJUos8_3MLw&rm+hY@70T9#IQ3gE^oa$udxWu^jE-SN9(LrxdP<_C^|-iq`L!0g a-g-t3d+X*zYfM~P9`hZhJ-Vug^~B#$=xOr+ delta 1266 zcma)4Z)jUp6u;-aBz<{FUS3|Zf~F*dtZv}d!8V})(23NSBCR=Jf>TclCCUCVX=S#RM=q72(Ny%yxMbtFRZ5i=ETV;u^9fPK6X5Eh6 z6^|WlsJ+a!hLe{1>L%AP%3&1aCXY=h6HvFNE@13SkuG;lnpB~!u-7lwu&*mxc1y86 z@xAPSnowBdNY6z-y1-#{qIUJ0+@kq3kxz(j6}F38PDXZ2DC_)2=JiU+bU|X4d1D|hT{rQY3%B@}GiO1{l zp4Yfs$Kz7eRi6J<{ipZ;{0o$W(RXmQ#mRV3!@x=7pR%b@s4*zoZ_N(3YS|7)B5~<}#2FV{;?ot+|IF zBmS7XDR>A8k_$slY@6SDr>SU408B9ZbkJ4(j-;-E1vVI}q4*#GVV?yCR>Jbc64_&P+$W}%{)FA$^WQp<8o~tyKf4WoM%XLpabyHvZIAfRU8bGqvR3D56Zf From 6ad5bde9eef7f76e3a3d0bff62cfb00e3b7b88a8 Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Mon, 3 Sep 2018 14:06:03 +0200 Subject: [PATCH 39/42] add calc_slope function based on _get_slope function --- ...howcase_OnlineSensorBased-checkpoint.ipynb | 324 ++++++++++++++---- Showcase_OnlineSensorBased.ipynb | 18 +- wwdata/Class_HydroData.py | 93 ++++- 3 files changed, 337 insertions(+), 98 deletions(-) diff --git a/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb index 214b8a1af..1fdc96e82 100644 --- a/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb +++ b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -52,8 +52,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import wwdata as ww" @@ -68,9 +70,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'0.2.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ww.__version__" ] @@ -84,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -92,7 +105,29 @@ }, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Time', 'TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", + " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2', 'TSS_line1',\n", + " 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns', 'Turb_ns',\n", + " 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es', 'Temp_es', 'NH4_infl',\n", + " 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter',\n", + " 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST',\n", + " 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT',\n", + " 'Cond_line1', 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", + " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", + " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1', 'Flow_line2',\n", + " 'Flow_line3', 'Flow_total'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "measurements = pd.read_csv('./data/data_example.txt',sep='\\t',skiprows=0)\n", "measurements.columns" @@ -107,13 +142,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", "start_time": "2017-05-09T11:54:55.589786+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -133,12 +167,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", "start_time": "2017-05-09T11:54:55.671616+02:00" }, + "collapsed": true, "scrolled": true }, "outputs": [], @@ -155,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -177,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -200,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -222,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -237,14 +272,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", "start_time": "2017-05-09T11:54:56.050129+02:00" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'.g')\n", @@ -285,7 +331,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", "start_time": "2017-05-09T11:54:56.761091+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -307,7 +354,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", "start_time": "2017-05-09T11:54:57.350077+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -329,7 +377,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.391744", "start_time": "2017-05-09T11:54:57.361076+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -351,7 +400,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:58.312987", "start_time": "2017-05-09T11:54:57.394331+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -373,7 +423,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:58.360928", "start_time": "2017-05-09T11:54:58.315777+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -388,7 +439,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:59.889452", "start_time": "2017-05-09T11:54:58.363535+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -401,7 +453,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.columns" @@ -421,7 +475,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:59.895406", "start_time": "2017-05-09T11:54:59.892052+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -459,7 +514,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "data_series.index[5]" @@ -467,48 +524,123 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sign(0)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "code_folding": [], "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No drift detected\n" + ] + }, + { + "data": { + "image/png": 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vjh1bmTp1EjY2BWnRwpU3b97g5+fLyZPHmDVrgVYeUvKp8peSVau8WbRoHqVK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m78cmUNz+Goa9ocpXZES3JekkPOXx4wUS6jHqbn4dDZeChUGoNlTAqSy5a5H0EMKY4e8sfV2JMO\nKwjkTPpzNL1kYu2yJ3oFgfQrvRosRWI2mTSVdBZu1x8O+3//fYyE3PtoG9kW41tPMCoIJJEUSUDF\nIuy5sxPv7hkCASoI0OiqcH7b4TsMbfqu0ftTqpWYffJHzI6ZAgA41/MaKvlUwsmkODQPaWm3O9+2\nUkE7h04mlkEQBMw4XngH29LQtaXnzPcQlNafVzc4LFEu6M635ibT0UEn0eC3WhbblGb5pdIwNdfx\n7Z2v42HeQ2y8uk63zVyiXDQh/uTgRwbPf+m2BL4yP7yx7VUA2mW7shSZeHJlCzzMSzZoO6Lp6BK9\nB6LyrmedXhi/b5TJfTczrsNX6luqZK5oAcNMeQaG7xqi6wWWmJiWYel3n5/MDwq5YaJctEdZLahM\nnrekxBDbtKazpaWa8lV5uscKtcJsO1sl5SZh8ZkFGNfqA4T5hdntvKaIHn0/TJQ9h6NuxMj1/g2o\nNer/Z+++45q43ziAfxJC2MgQEHAgKCqggOLErbj33tVq3dpqf622WltH1Wqt1bpH3avWrdVq3eLE\ngQsH7sFS9kxI7vdHyOUui7AEwvN+vXy9krvL5Zuc5O657/N9vjARftoieKUBBcql1JUH0Tgc+lLn\nejcEAwBiooEYJAAomoJAIvVgUUdPpq4qtNqXa+kVFfIrz37qQj+Se48RI30IqXUF+Ho4GPQakSkD\nuUDzRPwk4bHGsszsTATvCsKblNfssueJkdj45j8svfkrWlRshb3dDml9n3sf7qKmfS2YmpjiVkwY\nKli5ws3a3cBPljt27mEI8DwpEr+GqVJqs2SZOgNl9V6H/Jha7xtMrvs1PNbnbzqo/GAvPgwYF+xg\n7pjrNrrG6RU1bSnSFiJ+976/UyDC425DKpNCxshgLjJ8rug6zgF4ncwfK37s+RGNIPl0v0uo5eCT\nh5YTUnZYi21wtOcpdjwuV+f9IbA3s8fjkXmrybD5/kb4OPqhnpaxy1sfbsaZ1/+xz9WDXED/XL1W\nptYaWTTqc8xny7O17je/hAJhnkpl60tH594YLIxzlNKMS9/ibtwdxKTHYEP7LYW2X22EUATKNGVQ\n0UjIjMfUc5MxPmAS6ldoWNzNAVB01c6zOBkt16OvorFbcL73tf/pXmy4uxZ/dTsIa1PrwmheiVDi\nA+XIyEh07txZY/mOHTsQFBSES5cuYfHixXjx4gWqVKmC//3vf2jRQpVW+PHjR8yZMwehoaEwNTVF\nr169MGXKFIhEJf6j69WxURX4eDhAIBBoDTrrbK0ORpCNEbVHYXrD72Aptsh3QSCimvtX29gqXRgd\nd8CztOxj9pWZvCAZAKacm8iOIz3/9iycV9kielwihAIhEjLjYWlqhfNvzmDIP/0xzOdzfOY7Ah32\ntUZ7j47Y1mmPwe3MjalQEQhL5VKkc+7GA0CmLAvWsCm091LXpko7WJpq5m7rKlBTGJQXH4YWz9jY\nfhtG/jtU5/riCJTfpbzF86RnvDaIhCJYiFQFt8pbOMHDtirC425j8plx2Pf0L9z97DEqWLkC0H+x\nvKrteniW80JcOj8onnxmHO/5535fwM+xNlVmJUQPMxOxznUJWQmQM/JcM1yuvr+MEScGY127zfj2\ngmJs8ZVBN9n1yr/9bLUq29oK/en77atoUwnvUt8CAIb6DEf9Cg2x6f563jbZjCxf887rIhAINVJP\n4zPj8SblNVsXgUtfT2sKp3haXs7nuXmf+g7Ap0lhpd/TojXv6k849vwwotOicLz36eJuDoCi7FFW\n/Q10P9hRb/0WXdKl6YhMfIKxp0YCAI5EHkRjt2BUsqmMAUd7oUWl1pjdbmYueym5Sny0+OTJE9jb\n2+PIkSO85XZ2doiMjMS4ceMwfvx4tGvXDkeOHMGECRNw4MABVK9eHQAwadIkCAQCbN++HTExMZg+\nfTpEIhGmTJmi7e1KDZGJEF7u5XSulwiTAADr7i9DvCQaq9qu17kt0S8q9T37WH08mT667gDuerQd\njd2CMaDmYHbZjejrGtspg2Su0HcXUcfJHzX+9EDziq3g7xSQs89tqFrOEwDw78vjBrfREMoLnmy5\nFNky/kVWVh6+D11sxeVwtNdJNN/Nv3O7oNli9m7uowmP8N2/M3Agcl9OWwoWfOqbFoTJQ+o1oEid\n1Ccs5jp+vjobMxr9mLdGFkDgNn4Pbpo0FeXM7CASqnr/q5bzhHPO2GFlKnb97XVwtOdJ+DsHsttx\nbxwoH/fxVqRblzPT/Rv0Y+N5mBA4uRA+DSHGTfnbrctfj3fxzhdcyqB43tWfkCxJYodCAMDxF/+w\nj73tFUW47sTe5r3eVmzLPt7ScRcyszMg1hO4r2+3GT9fnY25wQtgZ24PANgZwZ/ZQCYv5NRrgVAj\n+E2WJKHeNj+tF/b60rR5Pcpq57OCUE63peztLUpltUjrpxLxUdExYmZiVswtUSmqNHttHTd5NenM\nWBx5dpB9rhxK0q/GQJx/exbn354t1YFyif9re/LkCapVqwYnJyfeP1NTU2zduhUBAQEYN24cvLy8\n8NVXXyEwMBBbt24FANy+fRs3b97EwoULUbNmTbRo0QLffvsttm3bBomk8FJuSrp/nh8t7iYUyNuU\nN5h4egxi0qKL5f0TshLYx5kywwNDfT9s3J43hmEgFuq+MOG6HXsL3hurAAAuvD2LgzmBo1QuRWx6\njMFtywvlRZNELkU2ww9QuWnYXHmppGwiEKKGvWYl1a5ePdnHNcrXwBd1VN+Zeq9IXuk7Nnkp5mWo\nZbeWQFqIF2UAcPDpPo3xh4BmFV1AUVBMIpMgnXNcajn4ahTZypJlIeTvFpDKpOx3dDfuDhiGYS/O\n9nZVDQEoJ9YeKM8NXoBxARPz/qEIKYNszcohZlwSWlRspXX95DPj4LzKFhNPj9G6/tsLU5AsUdwc\nT89OZ5dzzwkSmQRrwlfgnxf8Tgd7cwfs7rIPkwOnooNHJ/Ss3kdvWytYueKPNmvYIBnQDCi0FfOa\nHDgVIVXa6903F7e2g1AgzFOPmr7CRxlS1fdTkB7lI88O4QTnRkR8ZjyAogtiuZ//UwTjZZmyvoed\nmX0uW35KRdWjrLqmtTbNX3YgN0jm+uvxLvZxYV//fEol/q/t6dOn8PTUfrc1LCwMDRo04C1r2LAh\nwsLC2PXu7u6oVEk1xUKDBg2QlpaGiIiIoms0KVRfnZ2Ivx7vwqzQ74q7KXkq/mHoiX3i6TEIi9Hs\nUdZmz6MdvP1y07U/Zn4wuG15oeyFlMokkMn5hal2RGzV+hpur0ZuGPB7d1e33YCYcUlsb6eSuchC\n/aX5Zsj0IYV9wdNsdwMsv7W0UPYVn/kRo0+NQO9DXTXWxaRFaSwb8k9/TD4zjjddS5sqIXDWUXSm\n4tryOP36lGqf6dGQMTK0q9IBLSqpLua5F8tKIVXaY4z/BOr1ICQPBAIBtnbajeY6gmVAceHZeGdd\nXH53yaB9vkx+AQAwNzFHTHoUZoV+r7GNvbkDWlcOwczGP+U7pTda7TcnJj0acWq1ChTzxxvey6yc\nJxrQ3qOspC27SN94ThnnZm9WPot5XXkfipH/DsWw41qmDyzitGgBBPTbWsSUY9fzUkCuqBXFGOV1\n4asw49I09nmmLKPIxkJHxpfeqadK/F/b06dP8f79e/Tr1w/BwcEYPnw47t69CwCIjo6Giwv/Qs/Z\n2RnR0Yqex5iYGDg7O2usB4CoKM2LSWNw4sU/qLvVl7estI9nSc4pHMIdW1RcsgqpR1npyLOD2Ptk\nd67bVbRW3OxRL5TE9TFDESgXZhEVABBzxigbUvxEzsi19nTqouwBrWZXHW5W7uhVva/W/7PmnIIx\nBe3t1XcClDN5G6MMwKCCH8+TnmHe1YKlX8elxyEs+jre5wwH0HYzRtffyf6ne/Ex8yMAwM3KHU3c\nglHZtgq73pFTmEx9v10PKHqCypnZ8ZZbiCywqcMOnOkXiptD72Nzh53Y0XlvPj4ZIcRCZIF2ufS6\nPkuMxICjvQza34kXxyAUCBHs3ozt8eTytq9RKNMjPU54pLEsRZKM3V32sc+FAhOIBIYHyjGc3nAh\ndAfK2lJH9f2+Z3Nu9krzWcyr+8GOOtclaPmeC5PixrLq0p1hGCRnJeHhxwdF+r5libJDJC+ZcUWt\nKFKvZ4ZOZx87mjsiW56Nv5/krb7N25Q3Bm13J/pOnvZbkpToMcqZmZl48+YNHBwc8O2330IsFmP7\n9u0YMmQIDhw4gMzMTIjF/JRVsViMrCzFD2dGRgbMzPgpQaamphAIBOw2+tjbW0IkKrml0g8/Pozw\n6HD80OIHdtmeU9vwNpX/H1coEMDJyQbZ8mxky7PzVNm2JBCZKo6B2EwEJydVagj3cVFyZFTV+6SM\nxOD3tYnS/z2nm8Zj5L/DtK6bUH8CVt5YCQC4Puo6olKj0H13d14auLrkbMUNBQYMype3LrQbJNaW\nimJacoEMZlaa+3ycEY6mlZuyz0cfGa2xjUgo0rjz361GNxx+fBiVy1WGk5MNHk58AIlMAiux9gs3\ndxfVfKKZsswCfUZHRytYmGrvobbLUCy3sjIz+FgfHXIYLr8aNiWI+j6vvr2K+RfnY3uv7bA1s4VE\nJsGhR4fQwqMFnK34N/q8f6mMxMxEbO2h6snvfbQzzg8/j213t8HBwgEPEhQXTEFuQWhSsQmWX1/O\n20d7r/Y4NugYTIQm8HR3x7QP0yASijCv9Twsu7oMX/37lUabXyW/BAC42jlrtH+40yD2cV1P/k26\ngvpUf+Ok5Cjrx/z7Nt9CZA5MPz1d5zbahgCVMyuHpKwkjeXOVs4IqlSXlyGilJadWijft5mJmdaA\ntb5nAPvY1toSjlI7jW10yZCrgpRytpaws9f+e21jZwpHS/5ncHCwhJON9s9l/UF1zWhhbVLo/9/q\nV6xX4H0yDAMGDK/n2NxcccNaKBTA1lr1XQRsq4moVEXHz50xd+Bfwb9A7/2plOS/8/TsnP97Qlme\n2pktz0Z8Rjx73o5KiUIF6wp5uk7Jys5CiiQF5S3586fbxKiuJ9Xb9OTjEzhbOcPO3PC/L3WNKzfG\n0SdHMeH0aIxv+oXBr2u1tx/7eHKDybC3sMfs87PZZTOazcDPF3/G3xF/Y2DtgfluX3Eq0YGyubk5\nbty4AbFYzAbECxcuxIMHD7Bz506YmZlBKuXnvUskElhYWLCvVx+LLJVKwTAMLC01K+mqS0hIz3Wb\n4tR9t6KI0CCvEbDNKaojkmsrPiBAXFwKgrbVxuuUV/mqalecpFLFHWBJVjY7JdSnnB4qPl51wk6X\nZBj8vonJ+u9GXn+u+w6bs6lqeicPcU1E5fQE6mIpskRMiqLappyR41VUTKH0FACAXKr4kX+Z+BJ3\n3zzUWN9sUzNcHHAdi28swOFnB7Tu48qgW3iX8hYx6dHYdH8DrkZdxrS6s+Bbzh89q/fhfafp0Px+\nnZxskJnM7+V8F/Mx38U2YuKSYGWqvSBYfKLiuGVmSA0+1gJYoI5TAO7G5X7XVH2fLTa1gEQuwfTj\nMzE7+GdsefAnvjn/Fbzta+DSwBu8bRMzFTdDrr+6xS67+PoivjvxA365/jNv2y4ePVHXJQjLwQ+U\n7U3LI/6j6rfta/8ZbLusGP6J1trUBqlSVXuPPj6GmUHzcv2MheFTTwFHih8dc4XulftjOhSB8rzg\nhbgVG4ZJgVPR6q8mWrcfWXs0ZjaajYcf72PxjQVo6NqY/T2oauuFlhXa4Rf8AgBoUKERGDC4EX0N\nsWmxhfJ925s7aKRfz2w0G0kJqoC+kpkXXsneGrzPlExVrYXU1CzEx2vWXgCAdzEfILfmd5h8+JAC\n00ztnys+UbU8LiEpz5+fW9xTG7lUWKDvNEWSjJC9LfAy+QVej45la4SkpitmnBAwQqSnq657lUEy\nAFx/fhtuJvoLw5UERf13zjAMpHKp3qJ0uvz78jg+Ziiut9KzDL/eA4AfLk3H2rurcGHANbxKfomh\n//THUJ/h6OzZDS0qtsp1juLotCiMPTUSl99fwoPhz+Bk6cSuS0pWnbO5bUrMTCnMCWYAACAASURB\nVECNP2sAAK4NvpNrYUBdRtUaj6NPjsLNyl3jM6dKU7Hs5hJMDPxSI6vMWqQoBjiz0WxMrqsoLPg+\nPgbr761BZ89uGOczBfZCZ9SuVLPE/7bruilS4lOvra2teb3GQqEQ1apVQ1RUFFxdXREbyy/FHxsb\ny6ZjV6hQAXFxcRrrAWikbJc23HEE3OkI1IstAaoU0tcpeZuLsaQpCSnkeam2nNtYD+V0Etp423vz\nntvqKJrEbdeHDNUY5c77QwptrAm3UvLK28u1bvMo/qHOINnNyh1VbD3QxL0pelbvgz1dD+DSgBuo\nbu+NKUHfwKNcVYPaYW/ugHUhm9gTwe5HO/JdsZHRO0Y5f8W8Ap3rAQBG1R6DJS21f0+A5nFXprOv\nDv8DABCWUwH9ScJjnelW6hdr6kEyoEjjVP4/6uKpqsztWc5LZ9ucLFQ92F/W/Ron+5zD7i772WXD\nfD/X+VpCSOHgVpMf7jcKa0L+hG95PzwY/gyj64zT2L5lpTawMrVC/QoN8VfXg+xsCICi2nWAU132\n+a4uf+OP1qthKbLCD41na+wrP8RqNyxr2NfE5LpTeHOpetvX0JjHXR/1Yl55Sr3W8/vOvUZSFvPa\n8uBPjPx3WK7nzMTMBPhv1Sw8yW+P6uaATC5jgy7d22fhx9AZbL2ROltq4nnSM8gZOW/mi9ScITXW\nYhudWYEl4RqpJBj6T39UXFve4Joycelx6LivDf56vItXX0Vq4PWenJHjXlw41t5dBQA4+fIEO8Z+\n28PNGHC0l87Cp0oMw6DOlhq4/F5Rf+CP20vxLuUtjj47zK7X5kb0NfZxwx0BWrd5m/KGN12kNuXM\n7OBq5QaRianGuh9DZ2DZrSWYcnaSxjqpTAKxUIyJgV+yy+Y1/QWx45OxqcN2iE3EGOY7Au282ul9\n/5KsRPco379/H8OGDcPWrVvh5+cHAJDJZHj06BE6dOgAR0dH3LjB73G5du0agoKCAAD16tXDr7/+\nygbVyvVWVlaoWVP/j11Jxy2U0XhnPRzqcRyN3YJ5Ux8o0Y9nwXBPunJGpmdLvtyKeX11doLOdX5O\n/hjuOxJBFRTF6tyt3fVeLEjkEt744Ycf7+Nu3B3eND/5xZ1/W1cREX3jx0/34xeesRBZwNuhRr7a\n0qN6bxx9fhgvkp7jm/Nf4XF8BOY3W5zn/RhSzCuvRVl+ajIP9VyC0L1aL1iILPD1Oe1TIzXZGYST\nfc7B1syWdxMCAMacHMFOgQUAyVlJWgtmKadz0sfZ0gUO5o54NuotzE0s4L5WMQbZRqw7lcy3vB88\nbKtijP8EjKytSKG3N3eAm5U7XK3dMM6fKlkTUtREQhGWtlyBDxlxvF4xJ0sn1HJQDW+4OugWUqWp\nqOPEv0A249Rz8CtfGyZCE6wJ2Yg0aRpsxLawEdvi2ai3ufZwGYr7S+nvFIhTfc8DAKw4gbKbtRtv\nHvfcqN/MLLwxyqrgZ+nNX7Hw2jw8TXwCAEjIiocDp1aD+vusv7cm13ZzK2kP/ac//nt9Eg9HPEd5\nC34qrTKA3v5wM1aH/4H/Xv2Lf/ueYysuA0DLPY3xee0vsKDZr+w51lZsC7lc+3VIYc7UUJqdfHUC\nAJCUlcTrldXlr8e7cDPmhsZNbKkBgfbyW0s1ao/EpkfDVO3cvvfxbmx/uAWdPbtiYfMlGvtRryFw\n/8NdNN/TCCmSZPzS/DdeQMy14d7aXNvYeX8IotLe49bQB6hoU0nrNuYiM2TJMhGV9h7vU9/BzVqV\n1fg+Z950bvFYhmHw8OMDvE55Dbec61MlY4s5SnSPcs2aNeHu7o5Zs2YhPDwcT58+xXfffYeEhAQM\nGzYMQ4YMQVhYGJYvX45nz55h2bJlCA8Px2effQYACAwMREBAAKZMmYIHDx7g/PnzWLx4MUaMGKEx\ntrm0CY+9xXve/WBHxKXHIVpLWpD6f9miqmpnrLhVMrPzECjnp/jCtAYzcHngTbhYumBRi6XoV0Mx\npsPO3B6PRrzQ+pqm7s3Zx9y0m3AD0oANwf3/kqZl6iEAiEx8qvP1jhbaLzzyi3s3/eSrf/O1D0Om\nh8rrFBxWplYYUHMwezG4qcMOrdulZ6eh6e76qLOlBhbfmM9bxw2SAfCyBPJiTJ3x6Fi1MwDARmwL\nUxNTHO15Cs0qtkSv6n11vq6cmR2uDwlng2RAcfzufBaB471PG90JkJCSarDPMHxZ72uN5XVdFB0B\nFiILeNpV0wiSAf5vZJCL4mZrr+p9MdRnOLu8sIJkgB+gta7chn1syumdshbboE2VEN7rvO113zDl\n36CW67zxrK3Apt7fd866+x/uskGyYh2DxMwE9D7UFVejrvBet+7uaiy+sUDnflXtkWDPo5344dJ0\n/Pf6JACgwXZ/vEl5jekXvsb2h1twOPIAam2qihZ7GrF1R96kvEas2hSYDBhsvLcOMrmM7QSxFdvy\netu5BAZUw34UH2FwAaayQvl/KyrtPSxEFvB3CoSThXOuxUuTshK1Fuhcd3c1Nj/YyFv2OuUVYtKj\n8ef99Xib8gZTzk7E85yMgfNvzqLNX01523/M+MDOUjHtwlSdRbbUsw13P9qBNeErVJ+NYRCVpogL\n1oav1PlZzEzM2WD9f+e+5K1T/n0rv6ekrEQMONoLrf5qgg8ZcTqDb2NRonuURSIRNmzYgEWLFmHs\n2LHIyMhA3bp1sX37djg6OsLR0RErVqzA4sWLsX79enh6emLNmjXw8lKkFgoEAqxYsQI//fQTBg8e\nDCsrK/Tt2xcTJujuySstFmn5we51qDNep7yCh21VdloIQPGfnDvfn5yRF3plZGPGnRIpLzcZ8npD\nQiwU4+ugaTrX25nbY1fnv/Em5Q3mX5uNxKxEWJvaoKZDLbbKdLsqHdDJsyu6H+xYJCdDXcXEVt3R\nnWpc2Li9JfpSqPXR1+OgPG4FDQo7e3bF4Z7/Yu/jXTj35gzvbqzSpvsbAAATAr7E9ogtSMqp8K7U\nZFc9vTUF3o75gIiPDxDydwve8jnBCzTa38C1IfZ1O5zfj0MIKQFqOfrg8sCbGtPncXFTnNV7MosC\ntzfJRE9l63ou9RE+7BGcLJ0hEorwNuUN6m7TXgCQGxgzDKMz+M3MzmK34W6vi77hU1KZBFsfbcfF\nd+dx9dBlvBv7kd3fuTdndL6OKys7E5POjOUtS5WmoN42P41tY9Nj2HNnpiwTEfGKaUur23nzAvhU\naQo7T7aFyBIyHd+FIdNGNd+tmKFB23lFIpPgQ0YcrzexNJNpGYqodTvO/4mM7AyYmZjBVGiaa+r1\n+P80i16VM7PTOI+rU/6f1zW9JgBExGvWg1FKlaTAOiczLCY9GgII4GhRHh8y4jD5jGJoxme+I2Eh\nsmD/3wDA21R+jQBzE3O2MKDYxAye5bzwPOkZJHJ+7Sf1v8XqGyvz1vs7FTxzsSQr0T3KgGIs8ZIl\nS3DlyhXcuXMHf/75J7y9VeM3W7ZsiWPHjuHevXs4dOgQmjThF7twcnLCypUrcefOHYSGhmLq1KkQ\nCkv8x87VstarNJY9TniELFkW2nt05KV2JGQl8Ob70zaOuSQzdD7iosK9e6veFoZhdJ6U8zIHX89q\nvfFvn3O5btemSjsM9xvJ9jTLGTnszFSpuY4W5eGSMzduLGd6DV2+PT8FvQ510TsZfEnLPzDPZwEv\nLplcT6CMwptHuZFrYyxpuRw3h96Hj6PmhZLSrMZz8HTkaxzs/g/+bL8dK9usY9c9T9Q+/+D5/lch\nNhHD3zkQmzrsQOvKbdl2U88vIcarmn11toCnNlU4077ZmzsUeXu4vzcitbmSbw99iHufqYI+V2s3\ndpuKNpVwYvAJjPHX7LyQc86rMkbGe86l7FHm9UAbmHqtLlOWCWlOkMA97y+/9Rsuvj2nsb2LZQXV\nNq1XK9qTzymnAGDSaUWA3SEnG0gpWZLM9i4yYHD/w12try/oOevrc5MRsLUW6m+vg3cpqqDqetQ1\n9D/SE4mZCbgbdwcDj/ZGfC4FRgHFmO57ceEFalNBGDJG+U3Ka/x8jT9WP0uWBVMTU72p1xEfH+KU\nWkbbWP+JONrzZP4amwfK7ILEzARcjboMR4vyGoVNu+xXjAl+z8kyjU7jZ5wKczrMOnh0gqO5I/Z1\nOwIAGoVgM7IVheTuxt3Bu1TNgnzdvHoU5OOUeKU/YiyjfBx9NcZ+KjlZOmO470idr5XKdQdFJVlx\njb/hplur39Wed/UnuKwuh7h0xZjxR/ER2P1oh0YA3bFqF0SPS8STz1+hRcVWvH380+s/rG23Cb7l\ndQdS6pQ/ZOnZaXDgXAhVtKkE55xAOSY9WutrlZ7EP8bmBxtx6d0FXgaCuoKk6ivnfy5M5S1UY45S\ntIzJN4T+eZTzV8wrNw1dG2ld7m1fg73QbOLeFF28uqGjZxd2/dB/BvDapWxbLUcf9nlnz67Y3WU/\nno16i6cjNXuuCSFlh43YFlPq/Q/zghfmq/JvXs1qPJd9rB4ou9tUhItVBfWXsNpXa4+5wZoZcnlN\nveb3eun+fdfXyyiRSdg6JNz3Vw+kAKCbV08c6H6MfT6g5mAIBUKN9Om8UM4w0KNaL1S3U3UIfcz4\nwKbFXn5/UWcabm6Bcm7n8j2PdwJQTAeo7JkEgL5HuuHsm9PY/GAjOuxrjdOvT+Gvx7v07uvC23Pw\n/rMK2uxtpjWb6lPI1nGtm5mdicxsxf+bzfc3aqxf1npVTo+y5utlchmi06LQcZ9qiMHUoG9xtt9l\nzAmejxoONRE9LhHbO+3Bgma/4t/eZ/G5n6LnuYNHJ73tHVRzKB59/gKDag5ll3nZVcNQnxG87VIl\niiFwyuJhzpYuGOHH792+9yEct2Nu4nbsTXZZNOf/pkwuUwwDc2+OrZ12w0RoggpWilpOsenR7P9/\nhmHwiNO7/VvYIo12B7rU0/u5SjsKlEsxBzNVgLQuZBP7uK5LEIQCISYGas6HCvDTTEjuuIUz1O8w\n/nF7KQDg35eK1PapZydh8plxuPjuPPtDU8mmMpa2+gNCgRB25vYaPYv5meIoIKfCcqtKbXjFnrzt\na8BabANLkRVi9PQop0pT0XR3ffa5vjRtbRcohgRjzdxb4GROUZfCxE0LS8xKzFMlciV9F1LK41YY\nPcpcc7RcDAKad28BwNrUGmf6hQIAniY+wao7f6DKOlWl/gsDtBf2UBbqIYSUbd81nIXR/uM/yXt1\n5PSA6ku91ufq4Nu859xAdf612Tp/szOzsyCTy3jngWwdxa4A/lAqdRJZFq/n+kXSc53bN3RtpDFj\ng5yR43HCI537N1R1+xoIHRSGb+p/BwBo93dLdp2+uhX6MtwAqH1H+s+b3MxDZVCZkZ3Ovk5XD79S\nn8Pd2MeJmdqHbAGKaxFlR0Nh05U6XfNPDwTkVC/XltLv4+gLU6FYa6C88Po81NlSQzXXMoDpDWby\nOjqEAiHaeXTEyNqjEehSDwubL0Hs+GQ0cVeNQz7X/wpaVVIF2z2q9cKvLZfBwdyRzRi0N7PHlUG3\nUNeZH4h+eXYckrIScSZnbvQ5wfMxScv1/ogTQ/Ak4TEAxc31GE4ArLwpY80p7qmsW3AzJgw/XFJM\nT5cmTeUVGtsesQWAop7OouZLsb7dZo33NTYUKJdi7jYVsaDZYvzb+yx6VO+N5a1XY1bjuWxxJ265\ndi5DS94TBW4KlkQuYU+cG++p0mOnnpuEdGk6wmIUU/t8zPjABpg/NJrNq6QZ7M4v2qA+tYYhOlbt\njN1d9mN9u82w56ReK9OwXaxccP/DXUz4b7TWE+eRyIO85/2P9oTzKlveWHZ91OfSU3eu/xXs636k\nSMbHOamNzUvMZTyQEvd70FfsRZmulZ/joo/6DRHPcl6o5eCLHxrP0bq9X/naqGSjGAv00+UZbHXX\n1W03oIZD6a7aTwgxTqJ8FgmrbFOF95w7w0R8ZrzOIWNPEx7DdY09m7YMAMee667FoK8gZ6Ysk3e+\nv//hLuZcmQVAUUPEUqS6qdnYrSlEQhGaujfHhADt11q5qeXgAxfLCjjTLxR3P3vMLlcWY3O3rpin\n/f1y/Wf0OtQFnfa11bqeW5wqXZqmdRul2zE3cfb1aRyK3M9eyyjHUAPQmDdbKSY9RqMQWpYsC2nS\nNK3XIm3+agrfzV75uuGdG+Xnlcqk2P90L7JkWfjz/nqkZ6ezwZ96R8CqtusBAGITsdbU62W3+BWr\nudMn5qZFxdawNrXBupBN8HH0xZ6uB9jin/Vc6rPZGI3dgrGs1Soc730agOZwyZsxYZh2YSruxN5G\ngFMgmldsCYFAgAfDn+FUn/Ns8Po+7R3i0hXTx9ZxCuBNI6pM37Yx1T4LhrLCe1JWktb1FSxdMdxv\nJLpX62Xw5y+tKFAu5UbWHsOmPQyoOZgXHIt03Nnl9igzDIM3Ka+pErYe6j/gHzM/4lXyS3x38X+8\n5R7rVelladI0VQqv2njRtlXa84p25Tc1rnXltrA1K8frUVbOv6mcD3fvk91YcWeZxmdYclORPjOq\n9hje8omn+c8VtP/fCB0YprNtlYqwCqKrlRvvuc8mT6RKU3PS5rQHwCNODEHV9a7sc32BsjTn5CoW\nas4nWJg6e3bD+QFXeFXL1Q33G8V7PrPRT+jt3a9I20UIIfmlnnqd39dlqlWznndFs7owACy4rkj7\nPvRMFbAsvD4PsemxWq9rlOdCbZk3m+5vQEZ2Ovv8wNN97Pz2ErkEp/qex1j/iXj5RTT8ytcGAOzv\nfhQ/NpmrsS99BtYcgquDb+P8gKu4N/wJ/MrXRgUrV5zvfxUXB1xnt2tTmV8l/M4wVaA6KXAKvqrL\nvwZ58PEeQt9fZG/Yq+MGfspxp0rqvb6Zskz0P9oTX5wczi5Tzg0MAGvCVyD03UXeaxZcm4Pam6uj\n24H2vOVhMddRdb0rav7pwVueJk3Di6TnAMAGdHkhkUmQkZ3BTrXFMAzGnlINO7yTMzvMjEvfYuyp\nkeh1qAumX1BVkk+TpiE5JxDsX2MQvO1roJtXTwCKqvLp2emI0jKTjJKHbVW2Noghajn64Nmot+hR\nvTe7bFfnfejs2Q2DfT5jlwkEAgysNQSedtUAAL29+6FD1c7Y1+0IvHKW7X/6NxgwCOZcPzhZOsHf\nORDdvHqyf097n+yGmYkZ6jj5AwBicm5wKKcb0zdd5PaHW5CUUwxMfThns4ottL3EKFGgbMSsxTZa\nq1tzg6ZNDzag3jY/bH24SWM7oqA+d3KdLd5Il6br2FpBkdaivSiUUCBE84ot2efmnCrO+cFN5Vae\n/F+nvGKXzb0yC/879yUmnR6LVGkq7sTewuvklwCAUXX41Tm1pVnruomia77Jbl49izT9t5ajDzZ1\n2IF5wQvZZYuvL0DFteXhs8lTY/tUSQqOPT+MdM4FkL5AWdlzW9g9ygAwv6lqfI8hc4pODPgSnap2\nZZ9Prju10NtECCGFJb+p1wDwv6DpOtfd4oy1NITf5moYc4o/tnPD3TXsdD4b2m3B6rYbcLjHCXb9\n30/28DLFjj4/xD5e0WYtqtt7Y07wfFiaqqqK61LOzA6Daw1jh9BwNXYLhmc5L43ltRx9eNlCLlYV\ncH1wOG4NfYCYcUlws3ZHxIgX2NZpD35oPBvfN5qF92PjEWBg1WFuNeO0bH6P8q1Y3Te+del5qDOm\nnp2EBtv9Mf/qHCy9+avW7WaFfg9AUVj2XcpbrL6xGteirvJuXuvqoeZ6k/IamdmZeBQfgeMvjqHi\n2vKoss4FtTZVxYMP91FhtR32P93Lbv/1uckAgF2PtgMAHn58wNtfh79b4UjOMZ7VeC4uDbzBdlwo\na7z4b+Vnb9lxsunO9Nc8trlR7zhp4t4UmzpshzVnznF11qbW2NpxF5pVbIErg25hQM3B7DplAKz+\nHmPqqArkDa41DFVsFcMElN9zVM6c0cqaNkqbOuxg/29OPTcJ68IV46DtOR0yoQPDUNmWnwFizChQ\nNmJCgVDrfHvKMQsAsP+J4kflcOSBT9auvCru3m7171DOyNFij/bCTEqpklQ2RVug5c+MGxwXNCCz\nEFng9tCHON33IhuUD6o5hLfNzkfbsOfxTniud8Oi64q5e1tUbAU3K/40ENqKY+kqomIm0t7uT5GK\n09mzK3pU78M+V47Vic+M17gz/i7nhMClLVC+GnUFV99fZiuAF0URHO6NCe4ULroIBAJ0r6a4w61e\nCZUQQkqKzp6KMakBzvmfKuab+t/xijUW1MHI/byOge8vfcs+NheZo7d3P3Ze6twox43qo0yVBYCm\n7s2xtNUK+JWvjUauitlYApwCsb3THvSvMcjQjwCPclVR0aYSG2A5WjiivUdHdr1IKNLIPAK0Xzdx\ni1tlSPk9ytc46dIdqnbmFRPTZ3vEFrxMfoHfb2kPktW1+qsJxv8zHl0PtOMtj86l+Ojld5dQb5sf\nKq9zRvPdDfHZcf7xGPffSD0F3xQ3v9OkqbzljxMesVM52Zrxb+5zO0OuR13DmJMj8Cr5JRKzEuFq\n5Yaz/S7rDW6LUu/qqqyy2uU1A2UAvDnLpzWYgQo5BfWi0qKwM2IbBh5TXD+p37Dp7NkVu7rsY5/v\nfLQNAFDOzB7/9b2Aoz1Pobq9Yf83jAUFykZOPbUWUIxH7bSvrUbqTUlXXFPe6CsMokuqNJW9eys2\n0UzhNef0JpoVQkDmblMRtTl3Fr+p/z32dTuikZoFAP+9VkxfsKrtBpiLzHGi9xmM858EQHEiX3h9\nHrw2VMTvaneHB6oF39am1lp7wwtyoZQXzpbOuDFEMU0Gd37AlJwiFQAw/+octgeBS1vV624H2qPb\nwQ5YE74CQNEEylwWprn3KANAj2q9sbH9Nqxpq1mdkxBCSoJ1IZtwddAtBDjXzfc+BAIBdnT6q0Dt\nmBQ4hfec2zHApcx6Kszf+Xou9dlrrnouqmKZynGeLlYV0M6jY6Ffy3Cz1pQzTWgLGrnTJaWr9Sg/\nS3wGALg59D62dtyF0/0u6Uwrrm7nrXPd5MCpaOauSMv9tcUyjfW6aopEcaYuksqkeBQfgZi0aDxP\nUrQrt6zHRznjp50snNGggqojI1WSouslLAuRhUYNkarlVNlpXQ6E4EDkPtTfXgcA0NWre55mKSls\n9SooxjN72FaFp51mZgKguFHzfNQ7xI5Phr25AztkLTotCl+dVfU2cz+nvmXWptao4xSABq4NC+lT\nlB4UKBu5+c0W4/dWKzWWh8Vcx8mXx1U/pjTvqk7aeuVzk5AZjzlXfgCgvceYm3ZbFCm+JkITNKvY\nAt83mqWzerOTpeLOfV2XIEzIGdt+J+42fgtbhBRJMuZfm4N3KW/ZO9Nj/CegVaU2vAuZiM/500qd\n6nOeLUD1KVS2qQJXKzfeneLkLEWv+Iuk5/j91q/49+VxjdepV1Dl3n2PyJkKwVRYtIGymdCw4y4Q\nCNDVq7tB6X6EEFIcTE1M2TGVBeFXvg4aujbWuq6RaxOsCVHdMFTW4rDMyc5p6t4cfbz7885BymmO\n1HtYbTnDg5Q3igvDD43nYFunPRhTR1VxfGYjxc3aL+t+retlBRJSpQPG+k/EpQE32Erc3M+bkBmP\nV8kveVWc1TtK7sTdRnmL8mygbS4y5w0VWtR8Kft4Ut0p2N1lP3wdFeO0lenIdmZ2mNn4J/zd7TBC\nB4ZhqM9w9vqzi2d3jTRfQJX6e/rVSWTLszHx9Bi4r3VE890NUXuLN5rvaoiTL4/zUqprOfhonS2k\ni2d3PBgRiaO9TqKmQy0AgOcGd43t1GnrNNJVDBeAxnRNn5q1qTXChtzD4Z4n9M7Owa1orZz6SX3a\nUPXK7Urqs7OU5esPCpTLgEG1hmpdnpiVyP6YFtccxaVBfqbTUo6JARTVMtVxA2XTIi4a9W19xfgg\nZfqXNsoiYOqi06PYmykWIgvs6XoAIR4d2PVWplaY2Ug1x6T/J+pNVhIIBOji2Y237Njzw7j49jyO\ncwqPqONOdwCopkr4lPJzA4YQQoyZqYkp9nU7wj4XC8U40fsMfmw8D391PYhe1ftieoOZONrzFFa2\nXQcfRz9cGHANS1uuwJaOO1HL0Qc3h96HrVhxTnPL6UlTn1aJGyj3yaVA4pzg+Qa330JkgfYeHWHK\nySQL8eiA2PHJCKrQwOD95IWjhSPmBM+Ht0MNCHICp7Nv/sOXZ8bjl+s/I2BrLYTsbc7rXeWmFktk\nErxOfgkHc0deb3fFnBsOrSq1wXC/kQhyUbTfI2e868Eex3BhwDWc6ReKVpXaYGsnxfzOAoEA1e29\nIRAIMKjWUJzuexEb2m+Bi6XmfNpf1FYMRzr16l+4rXHQmJ9ZIpdgyD/9ASgK1j4c8Rzn+l/Bs1Hv\nsLLNOlS29WC3dedMHdnVq0eu35u+bdp5dMTSlis0ln8dNK1EzDrhZu3OBr+GcM3ZVn1Oa10zmJzu\ne5E3o4qyyFlZlP+qC6RUGes/EWvCV2B12w0Y959iPEuSgdPqlHUFDWhMtaRecwPlok4pn1LvGwz2\n+Qwuli7YFbEdX57VnFvTzMQMAU6BuBPHn8vyeeIzXHmvKFih62aKttTyT2lWk7nIlGVhW05q1q9h\nC7Vut7jF71gS9gui06LQeX8I7g+PBMPI8c35r9j0Lq4PGUUzt+OSlsux7cEmduwxIYQQFbGJGK9H\nx0IkFLHVe7ljiacGqcYan+t/GQAw2GcYbx9rQzZi4LE+8MlJkY1K49eq4Pa22eq4UQwAnap2xeg6\nn2Y+6sKgPE8POtaXtzwjOwP3P97jPFcEyqdensDgfxQ3CtRTbsUmYrwaHcPezN/V5W/cib2NRm6K\nm+7lzOzYQGtPV911bpTDwjpW7Yx7H8KxOGQx+lUdhgtvz6FN5RD8fG02b3u/8nVw/8Ndjf00cWvK\nTjkpNhGjb40B6FtjAOZe+RF/3F7KK5L6ddA0tK3cDoOO9YG9uQPO9r+MSmsVWXSHehxHkEsDZMoy\ncPrVKa1DFAFFJ9PsKzPZdPHxAZMxrcEMnZ+zJLMW28DK1BoRHx+yy/5oW2SUJgAAIABJREFUvUbn\n9iZCE3b44FCfEUU+FK0ko0C5jJgTPB+zm/ysGAMUsRWX3l3Aq+SXvGkEXie/wqlX/+Jzvy+KbTyw\nNroKNHwqBQ2UtfUoF2VVaHUCgQAuOelNyhMWdwyP0vbOe/Hw4330O6K6yzrh9GjefrSpbOMBAAh2\na1ZYTc4TMxMzLGm5DF/V+xr1tmmOG5obvABx6XH4zPdz9PHuz1bavPj2HELfXcSJl6q5o2c2ms2O\nae7r3b9I2jvUZziG+gwvkn0TQogxUM4lnF/KWRl+C1uEYLdmCH3Pn8qIOx2Vg7mDzv1YmlrqTW8t\nafRduSmrQAOqHmVlkAxA63mJe1O/nJkdWlRqle+2Tao7BU3dm6NznRB8/JDGFiV7O+YDkrKScPT5\nIViILDCg5mBsefAn1oavRGTiUwBAPZcgnUXQvm84C4NrDeWl/gsFQgS61MPDEYrpp7jXL43dggEo\nOjFefPFe57WNQCDAyjbr8PO1OdjYfgu87Krn+7OXBL6OfrgefRWAIvjtX1N/Ubk+3v2x5cFGdKza\n6VM0r8SiQLkMUf4YLG21AvW318G2h5tV6wB02t8WsekxqGxTmZdeW9bJ8lHMi8tUy504gUAAU6Ep\nb8zQp+BXvjaO9TrFzsXH5WzpDGfL1jjTLxR34+7wCj7o096jIza234q2VdrnvnERUp9fGQAmBn6F\nMf6qz2FlaoVtnfZg6D/92cwKJWdLF0yuOwV9vPvB2dIl3/OBEkIIKV7cgpm9D3fVs6XixvXZfpcR\nmx6D/kcVmT5ioRgSuYRXKbo0MLSTI0mShJg0/njVJm5Ni6JJLDMTMzRya6Jx40FsIoaTpRNGcKp3\nf+b7OT7z/Ryh7y7C1cpV7/h3E6GJzvXc72NNyEZkZWfpXK9NiEcHo7kerudSnw2UG+moA8A1J3g+\nhvp8hjpOAUXdtBKNrgTLIMec1BWu82/Pso/Vx/KUdXnpUV7ZZh2vFxbQXdX68chXQDFMfVW/gv6q\nhX7la8PJ0lljua7UaxOhiUHjgYqaSCjC6b4XcfbNaUwI+BImQs05xAHAREfvgHK8j5t17sU/CCGE\nlFxWplZ52t63vB+qSD3Y5/UrNETo+4sGTx9VUhhab+anyzPwz3PVWPCQKu156eglRbB74WWq9are\nN/eNjFg9zv9lXdWyuSxEFmU+SAYoUC6TrER5O4GUFMVVcMyQQHmoz3AsaPYrxCZijUBZVyxcXHPw\nGcLZQkugXILS8XWp7eTPmyZLG11pdK0raZ/ughBCSOlS2bYKdnX+m50vVmlW47loUzlE62usTa2x\nqcMOXH53EVOCvsXpVyfRt8aAT9HcQsM9T89o+COcLJ3hYumCYPfmGHC0F3pW74NfbyxETHo027s4\nIeBL/NhkbnE1mXwiHat2YR8rq5uT3JWegRek0HB/SLnjT7StJ0B2TtXrzmrVle8Nf4r2Hh1xqMdx\nLGm5nC12EMJJQTY3MS+VPZQCgQAXBlzjpWIZT2V0/ufY2nE3fm+1Ev+rP72Y2kMIIaSwtanSDrUc\nfHjLxtQZj1qOPjpeAXT27Iqfmy1CeYvy6F9zUKkanwzwz9MCgRCDag1FmyrtYC4yx8Ee/+Az38/Z\n6ZyUfmg8W303xAhxC8tqyxok2lGPchl397PHyMjOQJ0tNYq7KTqpz3/4qclzepSV01I0262YIsHF\n0gXbcqZD4NrReS/kOfP0lraTLFdNh1qoYuuBy+8vFXdTChX3mLSs1BodynihCkIIMVYmarUmtM1C\nYUy45zdd1x+bO+5A4531ct2OGJ/DPU4gPjOejnkeUKBcRq1uuwGRiU/Z8v4+jn54+PF+cTerRFKm\nXpsITOBZzgtuVu7o6tVd72uM5UeId3faSHqUuZ/DWI4TIYQQTT6OvuxUQ/OCtU8daEy45zcTgfY6\nHV521bG7yz4MONobWzru0roNMU7Kqb2I4ShQLqN6e/fjPbcUWbKPjSUgKizZOVWvRUITmJqY4s5n\nEcXcok+HG0gaS0o+7447jT4hhBCjtbDZr6hazhNj/SfmucBXacQ9Twv1nLNbVw7BmzFxMDMx+xTN\nIqTUokCZAOBXiCyxAVExtUvO6VEua7j/F4zlBgo3UNZVGZsQQkjpZy22wddB04q7GZ9MXjKmKEgm\nJHfUnUIAAFYluAJzfnHnP74bdwfLbi4xeE5kmVyGH0K/w5X3oexrhGUwUFYvfGUM1IudEEIIIUaB\n16NcFq9ZCClcdJVIAADuJawy8/vUd8jMzgQAMMh7Ma85V2bBdY09Oyd0273N8fO12Zh79Uc8/PgA\nSVmJel9/I+Y61oavRPeDHZHNKKpei4RlLwGDH1QaR9BMqdeEEEKMEdXgIKRw0V8RAQD4OwcWdxNY\nCZnxCNhaCx33teEtP/HiGKLTogzax4rbvwMAbseE8ZZvf7gFLfc0RvWNlTH4mO7J55/EP2Ifyyj1\nWvHYWHqXOZ+JUq8JIYQYCwqUCSlc9FdEAAAulhXYxymSlGJsCRCdFg0AePDxnsa6g5H78rQv5YnC\nRmwLAEiWJLHrTr36F+9S3vK2T5WkYP3d1fjf+S/ZZRKZBIDmNBNlAa8YiLH0KIN6lAkhhBgffjEv\nOr8RUlD0V0QAANXsqrOP5175sRhbkr9Ua10EAgEYhkFWThq3usBtPtj/dC8AYP3d1fDc4I4Zl/iF\nP1bdWQ4AsDezL7R2lRZG04vMwY339VUFJYQQQkoTXrHKMpgFR0hho0CZAADcbSpicK1hAAAG8mJt\nC8MUXqAMCJAiSYZELuEtXdh8Cft47KmRuPfhrkaArCRnFN+Hs6VzIbardDDG1GveGGW6kCCEEGIk\nKPWakMJFf0WEtbTVCnjZVQPDMMjIziju5rAKEjjHpEXjduwtAMDgWsPQxbM7xvhPwAjfUXg68jW7\nXc+DnXPdVzkzu3y3o7TiFfMylkCZm3pNFxKEEEKMhDGeswkpTnSVSHiaurdApiwTJ18eL7Y2yDk9\n2jK5jNfDfeX9ZSwNW8wGz6mSFDxLfMp7PTew/vLsePQ90h0A4OPoiz87bMPc4AUQCAQoZ2aHfd2O\nAFCNXR5VewzChz2CunufPTGaqs95YYxVr2kMFyGEEGPEPU1TsUpCCo6uEglPgJOi+vXp16eKrQ3Z\nMin72HWNPR7FR7DPj784igXX52LM0TEAgDZ7m6HxznpIzExAXHocFl6bi9uxN7XuN9i9ucayALVq\n38HuzSE2MWOfrwnZiNjxyXCxqqD+0jLBGANJGsNFCCHEGFHqNSGFi/6KCM/AWkMgFoqx+9EOpEpT\ni6UNErk0123W31qPF0nP8SLpOQDg8vtQTLswFb/dXIwO+1prbN/Vqwd8HH01ltuIbTG6zjhMazAD\nx3ufRmfPrjATqQLlMh9IGeEYZbqQIIQQYox4GVN0iU9IgZW9+W6IXkKBEJ52XngUHwHvjZWxq/M+\nBFVoACtTq0/WhmwDAmUAaLgjgH08/MQg3jqhQIgHw5+hy4EQPEuMxMSAL9VfzprX9BfeczOhmY4t\nyx5+6nUxNqQQCTjBsYACZUIIIUZDdaKm1GtCCo6uEomGmY1+AgBky7PR90h3VF3vitOvTn6y95ca\nGCjrI2fkcLRwxP5uR7Gv2xEEutQz+LUiznzJhVuBu/QxxsIg3M9hQoEyIYQQI8HNkhLQJT4hBUZ/\nRURDO4+O8Hfij90deKxPkb/v25Q3aLe3Bfod6cEu6+zZDc+/eI9fWywzaB9Tg74FAJS3KA8AcLV2\nQ7OKLfLUDm7qUmHO6VwaGUsBLy7+9FD0E0gIIcQ40NAiQgoXpV4TrbZ32oP51+Zg16Pt7LJ3KW/h\nblOx0N9LJpfh4rvzWHR9Pu7E3eatczB3hLWpNYb5jsAw3xF4lvgUcelxCPFtgQ1XtmDSmbHsto8/\nf4lyZnawN7NHU/e8Bce6lPlA2Qh7lClQJoQQYoy4N7cp9ZqQgqOrRKKVi1UFLGu9CrHjk9HeoyMA\n4PCzg4X+PlKZFK5r7NHvSA+ExVzXWF/FtgrvuZdddTRyawJzkTn61RjIW2dv7gChQIgx/hPgW96v\nUNpX1lOveWlcRtK7zA34z745XYwtIYQQQgoP/+Y2IaSgKFAmuVrQ7FcAwI+Xv8fld5dy3V7OyHPd\n5uTL43BeZQv3tY5a19ub2aOaXXUM8flM5z4EAgEG1RwKAHjxRVSu75kf1KNs3D3KzxIji7ElhBBC\nSOHh3tA2FYqLsSWEGAcKlEmuKtpUgl/5OgCAmaHT9W77Q+h3qLDaDqmSFJ3bvE15gyH/9Ne5flOH\nHXj0+UtcHnQTDubaA2ml31uvROz45CKryl3We5SNpReZi/uR2lQOKb6GEEIIIYWIe0NbbEKBMiEF\nRYEyMch/fS+gkk1lPIl/hIzsDJ3brQ1fCQCITHzKW77j4Vb8cv1nAMDgY/00Xnd10C3Mb7oIYUPu\nobNn1xIToBnSO27M+NNDlYxjUlDcKaGWtFxejC0hhBBCCg/3nE09yoQUHBXzIgYRCoTo5tUTK+8s\nwz/Pj6C3t2awqw3DMJhydiJ2PtoGALAR2yIi/gEA4PdWK9G9Wi+kSdPgbOkMT7tqRdb+/Crzqdec\n2NhYUq+5n8NSZFmMLSGEEEIKD3dokamQLvEJKSjqUSYGa+fRAQAw7r9ReJH0XO+2UrkUDMNge8QW\nNkgGgJ8uzwAA9K7eD4NqDYWVqRWcLZ2LrtGkQLi9rzCSHmXuhYSILiQIIYQYCd4YZUq9JqTA6CqR\nGKyGQ0328byrP2Fj+63IzM5E5XWKQHdj+63s+s77Q2BtaoNMmfY07R+bzC3axpJCYSy9yFzcQNmE\nAmVCCCFGgzNGmVKvCSkw6lEmBnMwd8RQn+EAgFfJL3H/wz3U316HXT/y32G87VOlKciWZwMAtnXa\ng8M9TqCeSxBWtlmHClaun6zdBUFjlI2v6jX3c4gEFCgTQggxDrwxyiamxdgSQowDXSWSPFnScjme\nJUbi8vtLaP1XsEGvqV3en52L+XjvM0XZvELH7X0s64wlUOb3KJsUY0sIIYSQwsNNvTYR0PmNkIKi\nQJnkWceqnXH5veZ8ymP9J6KPdz/4OtaGUCCEQCBAZMJTlDOzK4ZWFsz+7kexLnwVulfrVdxNKVZy\nRsY+Npqq15yAn26EEEIIMRbcLLhsJrsYW0KIcaBAmeRZV68e+CH0OwDA5MCpCHZvhibuTWFmYqax\nbTX76p+6eYWiqXtzNHVvXtzNKHZyI5xHmoJjQgghxihFksQ+drGsUIwtIcQ4UKBM8szN2h3PR72D\n2MSMJrQ3cjJuj7KRpF4bS/VuQgghhOtg5H72sZWpVTG2hBDjQIEyyRdrsU1xN4F8AjIjTL0WUg1D\nQgghhBCSC7piJIToZIw9ysYS8BNCCCGEkKJDgTIhRCe5XJb7RqWMtal1cTeBEEIIKXSmQsWUUKPr\njCvmlhBiHChQJoToZIw9yuYi8+JuAiGEEFLopHIpAMBWXK6YW0KIcaBAmRCik4wz1QSlLBNCCCEl\nHxVaJaRwUDEvQohOciPsUQaAF19EgeHcBCCEEEKMhVjLdJ2EkLyjQJkQopPMCMcoAzRtBiGEEONF\nQ4wIKRyUek0I0ckYp4cihBBCjNHhHifQwaMT+tcYVNxNIcQoUI8yIUQnOXeMshGlXhNCCCHGppFb\nEzRya1LczSDEaFCPMiFEp4zsDPYx9SgTQgghhJCyggJlQohOSZKk4m4CIYQQQgghnxwFyoQQnZKy\nEou7CYQQQgghhHxyFCgTQnSaEDC5uJtACCGEEELIJ1cmAmWZTIYlS5agadOmCAwMxOTJk/Hhw4fi\nbhYhJV7P6n2KuwmEEEIIIYR8cmUiUP7jjz9w4MAB/PLLL9i+fTuio6MxadKk4m4WIYQQQgghhJAS\nyOgDZYlEgq1bt2Lq1KkIDg6Gr68vfvvtN9y6dQu3bt0q7uYRQgghhBBCCClhjD5QfvToEdLS0tCg\nQQN2WcWKFeHu7o6wsLBibBkhhBBCCCGEkJJIVNwNKGrR0dEAABcXF95yZ2dndh0hRLd5wQsREf+w\nuJtBCCGEEELIJ2P0gXJGRgaEQiFMTU15y8ViMbKysvS+1t7eEiKRSVE2jxSAk5NNcTehTJjRdlpx\nNwEAHe+yiI552UPHvOyhY1720DEve0rrMTf6QNnc3BxyuRzZ2dkQiVQfVyKRwMLCQu9rExLSi7p5\nJJ+cnGwQF5dS3M0gnwgd77KHjnnZQ8e87KFjXvbQMS97SsMx1xXIG/0YZVdXVwBAXFwcb3lsbKxG\nOjYhhBBCCCGEEGL0gXLNmjVhZWWF69evs8vevn2Ld+/eoX79+sXYMkIIIYQQQgghJZHRp16LxWIM\nGjQIixYtgr29PRwdHTF79mw0aNAAAQEBxd08QgghhBBCCCEljNEHygDw1VdfITs7G9988w2ys7PR\nrFkzzJo1q7ibRQghhBBCCCGkBCoTgbJIJML06dMxffr04m4KIYQQQgghhJASzujHKBNCCCGEEEII\nIXlBgTIhhBBCCCGEEMJBgTIhhBBCCCGEEMJBgTIhhBBCCCGEEMJBgTIhhBBCCCGEEMJBgTIhhBBC\nCCGEEMJBgTIhhBBCCCGEEMIhYBiGKe5GEEIIIYQQQgghJQX1KBNCCCGEEEIIIRwUKBNCCCGEEEII\nIRwUKBNCCCGEEEIIIRwUKBNCCCGEEEIIIRwUKBNCCCGEEEIIIRwUKBNCCCGEEEIIIRwUKBO9Pnz4\ngGnTpqFp06YICgrCyJEj8eTJE3b9pUuX0L17d9SpUwddu3bF+fPnte5HIpGgW7duOHToEG95cnIy\nZsyYgcaNGyMwMBBffPEFnj17lmu77t27hwEDBsDf3x/t2rXDwYMHtW7HMAxGjRqFVatWGfR5Dx8+\njPbt26NOnTro168f7t69y1t/+fJl9O/fH4GBgWjVqhV++eUXZGZmGrTv0oKO+V2d286ePRutW7c2\naL+lCR1z/jFPTk7G999/jwYNGqBBgwb4+uuvER8fb9C+Sws65vxjHhERgaFDhyIwMBAtWrTAokWL\nIJFIDNp3aVHWjrnSsWPHEBISorH81atXGDlyJHvMN2zYkKf9lgZ0zPnoGq7sHXOufF3DMYToIJPJ\nmP79+zP9+vVjwsPDmadPnzKTJ09mGjduzMTHxzNPnz5l/Pz8mFWrVjGRkZHM0qVLGV9fX+bJkye8\n/aSkpDCjRo1ivL29mYMHD/LWjRkzhunWrRtz+/ZtJjIykpk0aRLTrFkzJiMjQ2e7Pn78yDRo0ICZ\nM2cOExkZyWzdupXx8fFhLl68yNsuKyuL+e677xhvb29m5cqVuX7e0NBQxtfXl9m9ezcTGRnJzJgx\ngwkKCmI+fvzIMAzDREREML6+vszSpUuZFy9eMBcuXGBatGjBfPfdd4Z+pSUeHXP+Mee6cOEC4+3t\nzbRq1SrX/ZYmdMw1j/nQoUOZrl27Mnfu3GHCw8OZLl26MKNHjzbk6ywV6Jjzj3liYiLTqFEjZtas\nWczLly+ZixcvMk2aNGEWLlxo6Fda4pW1Y6505swZpk6dOkzbtm019te2bVtm0qRJzNOnT5nDhw8z\n/v7+zJ49ewzed0lHx5x/zOkaruwdc678XsNRoEx0evDgAePt7c1ERkayy7Kyshh/f3/mwIEDzA8/\n/MAMGTKE95ohQ4YwM2fOZJ+HhoYybdq0YXr27KnxB5eVlcV88803zJ07d9hlERERjLe3N/PgwQOd\n7VqzZg3TunVrRiaTscumT5/OjBgxgn1+//59pnv37kzr1q2ZoKAgg/7gPv/8c2batGnsc5lMxrRp\n04ZZvXo1wzAMM3fuXKZPnz681xw4cIDx9fVlJBJJrvsvDeiY84+5UkJCAtO0aVNmyJAhRhco0zHn\nH/MrV64wtWrVYl68eMFuc+nSJaZt27ZMWlparvsvDeiY84/5mTNnGG9vbyYlJYXd5pdffmG6dOmS\n675Li7J2zDMyMpiZM2cyvr6+TNeuXTUuoI8cOcIEBAQwqamp7LI//viDadeuXa77Li3omPOPOV3D\nlb1jrlSQazhKvSY6ubq6Yu3atahatSq7TCAQAACSkpIQFhaGBg0a8F7TsGFDhIWFsc/PnDmDHj16\nYPfu3Rr7F4vFWLRoEfz9/QEA8fHx2LJlC9zc3ODp6amzXWFhYahfvz6EQtV/3wYNGuDWrVtgGAYA\nEBoaiqCgIBw6dAg2Nja5fla5XI5bt27xPo9QKET9+vXZz9OvXz/MmjWL9zqhUAipVIqMjIxc36M0\noGPOP+ZKP/74I9q0aYPGjRvnut/Sho45/5hfunQJtWrVgoeHB7tNcHAwTp06BUtLy1zfozSgY84/\n5g4ODgCAnTt3Ijs7G+/fv8f58+fh5+eX6/5Li7J0zAHg48ePeP78OXbt2qU1HTMsLAx+fn6wsrLi\nve/Lly/x4cMHg96jpKNjzkfXcGXvmCsV5BpOlOdXkDLD3t4eLVu25C3btm0bMjMz0bRpUyxbtgwu\nLi689c7OzoiOjmafz5w506D3mjdvHrZt2waxWIw1a9bA3Nxc57bR0dHw8fHReN+MjAwkJCTAwcEB\no0ePNuh9lZKTk5Genq7189y7dw8A4O3tzVsnlUqxefNmBAQEwNbWNk/vV1LRMecfcwA4dOgQHj58\niEOHDmHz5s15eo/SgI45/5i/fPkSlStXxpYtW7Bz5072e/j2229Rrly5PL1fSUXHnH/M/f39MXbs\nWCxfvhy///47ZDIZgoKC8OOPP+bpvUqysnTMAcDd3R07duwAAJw7d07r+zo7O2u8LwBERUWhfPny\neX7PkoaOOR9dw5W9Yw4U/BqOepSJwU6fPo3ffvsNI0aMgJeXFzIzMyEWi3nbiMViZGVl5XnfAwcO\nxL59+9CtWzdMmDABEREROrfV9b4A8l18RVnMwczMjLfc1NRU6+eRyWSYPn06nj59avCPSmlU1o95\nVFQU5s+fjwULFhhNb2JuyvoxT01NxaVLl3Du3DksXLgQCxYsQHh4OCZOnMje+TY2Zf2YZ2Zm4vXr\n1+jWrRv27NmDFStW4N27d0YVKKsz5mNuiMzMTI3/E8r3zc9nLg3K+jHnoms4FWM+5oVxDUeBMjHI\n/v37MXnyZHTs2BHffPMNAMWFh1Qq5W0nkUhgYWGR5/17eXnBz88Pc+fOhbu7O3bu3AkACAwM5P0D\nAHNzc40/LOVzQ947LCyMt89Ro0axJ0z1/UqlUo19ZmRkYOLEiTh58iSWL1+O2rVr5/nzlgZl/Zgz\nDIPp06ejV69eCAoKyvPnK43K+jEHAJFIhOzsbPzxxx8IDAxEkyZNsGDBAly/fh0PHz7M82cu6eiY\nAxs3bsSTJ08wb9481K5dGyEhIViwYAEOHjyIx48f5/kzl3TGfswNoe99jfGmKB1zFbqGKxvHvLCu\n4Sj1muRq9erV+P333zFkyBDMnDmTHe/g6uqK2NhY3raxsbEaaR26pKam4sKFC2jZsiV7YhIKhahW\nrRpiYmIAQGv5+AoVKiAuLk7jfS0tLQ0a1+Dn58fbr7m5Oezs7GBpaZnr50lISMCYMWMQGRmJdevW\nGeWYVYCOuYuLC96/f4+rV6/izp077FgdqVSK7OxsBAYGYv369UYVQNMxV3weFxcXuLu7w9raml1f\nrVo1AMDbt2/h6+tryMcuFeiYKz5PeHg4atWqxRs/pxyD9/r1a9SoUcOQj10qlIVjbogKFSrgxYsX\nGu8LwODPXFrQMVeha7iyc8wL6xqOepSJXuvXr8fvv/+OyZMn44cffmD/2ACgXr16uHHjBm/7a9eu\nGRw8ZGVlYcqUKbhw4QK7LDs7Gw8fPoSXlxcAoEqVKrx/yvcNCwvjpUFeu3YN/2/vTkOi+OM4jn+k\nrOjSLk06KII2ScvKKMsowyQ7JHMp0kSpJ9lNp2JkWtJpmhFREZliGEmJGFlREhEhWiIombVh9wNJ\nyrQHHc7/gbTtZvK3svJ4v2AfzMxvfzu/+bIyH3dmfhMnTrQ70WlOjx497Pp0dXWVg4ODJkyYYDee\nhoYGFRUVafLkyZIaLx1ZuXKlnj9/royMjA77B5aaN9bc1dVV165dU25urnJycpSTk6OwsDC5uLgo\nJyenQz3oh5p/+557e3vr2bNnevv2rbXNo0ePJEnDhw9v0ZjbA2r+reaDBw+2m2dU+lbzr/vWEXSW\nmrfEpEmTVFZWZvcQp8LCQo0cOVIDBgxoUR/tATX/hnO4zlXz1jqHIyijWRUVFUpOTlZISIiWLFmi\n6upq6+vDhw9avny5iouLlZqaKovFoiNHjqi0tFQREREt6n/AgAFauHChDhw4oLt37+rx48eKiYlR\nbW2tIiMjm32f2WxWTU2N4uLiZLFYlJGRoby8vJ++/OZ7kZGRysnJUWZmpiwWi3bu3Kn379/LbDZL\nko4cOaKKigrt27dPLi4udsejoaHhtz67raDm32retWvXJn/wnZycrOt/5r/YbRk1t/+eBwYGys3N\nTRs3blRFRYVKS0u1Y8cOTZkyRe7u7r/12W0FNbev+bJly/TkyRMlJCSoqqpKhYWFiomJkZ+fX5MH\nALVXna3m/2fOnDlycnLS5s2bVVlZqby8PJ0+ffqXHijUVlFze5zDda6at9o53E9NJoVOJSkpyRg9\nevQPX1/nNysoKDDmzZtneHh4GEFBQcadO3ea7e9HE5fX19cbiYmJhq+vrzFu3DhjxYoVxqNHj/53\n30pKSoyQkBDDw8PDCAgIMPLy8ppt6+fn1+KJy7Ozs43Zs2cbnp6extKlS42ysjLrtunTpzd7PF6/\nft2i/ts6am5f8+8dO3asw82jTM2b1vz169fGunXrDC8vL8Pb29uIjo423r1716K+2wNq3rTmRUVF\nRmhoqDFx4kRj5syZxu7du+3m2G3vOmPNv0pNTf3h/KoWi8UIDw83PD09jVmzZhlpaWk/1W9bR83t\na845XOer+fd+5RzOwTA66GM8AQAAAAD4BVx6DQAAAACADYIyAABsP6fNAAAFH0lEQVQAAAA2CMoA\nAAAAANggKAMAAAAAYIOgDAAAAACADYIyAAAAAAA2CMoAALQz0dHRMplMevDgQav1mZiYKJPJpMLC\nwlbrEwCA9qrrv94BAADwc/z9/TVkyBANHDjwX+8KAAAdEkEZAIB2xt/fX/7+/v96NwAA6LC49BoA\nAAAAABsEZQAA2hnbe5RfvHghk8mko0eP6saNGzKbzRo3bpx8fHy0Y8cO1dTUNHl/dna2goKCNH78\neAUEBCgrK6vZz3r69Km2bNmiadOmycPDQ4GBgTpx4oQ+ffpkbZObmyuTyaTFixeroaHBuv7t27fy\n9fWVl5eXqqqqWvUYAADwJxGUAQDoAAoKCrR27VoNGjRI4eHhcnV11YULF7R69Wq7dikpKYqNjVVd\nXZ3MZrPGjBmjhIQEXblypUmf5eXlCgkJUX5+vqZOnarIyEg5OTnp8OHDioqK0pcvXyRJQUFB8vPz\nU3l5uTIzM63vT0hIUHV1tbZt26YRI0b80fEDANCauEcZAIAOoLy8XCkpKQoMDJQkbdy4UcHBwSop\nKZHFYtGoUaNUVVWlU6dOyd3dXenp6erbt6+kxpAdFRVl159hGIqOjtbHjx+VlZUlDw8P67a9e/cq\nLS1NWVlZCgsLk9QYihcsWKCUlBTNnTtX9+/f1+XLlzVjxgyFhob+paMAAEDr4BdlAAA6gGHDhllD\nsiQ5OjrKx8dHkvTy5UtJUn5+vj5//qxVq1ZZQ7Ik+fn5ydfX166/0tJSVVZWymw224VkSdqwYYMc\nHR118eJF6zoXFxfFxMSorq5O8fHxSkhIkLOzsxITE1t9rAAA/Gn8ogwAQAfwo0ub+/TpI0n6+PGj\nJKmiokKSmgRfSZowYYJu375tXS4vL5ckPXv2TEePHm3SvlevXnr48KEMw5CDg4MkKTg4WFeuXNH1\n69clScnJyXJ1df2NUQEA8G8QlAEA6AC6devWZN3XAPtVbW2tpMaQ+z1nZ+cftr19+7ZdgP5efX29\nevfubV0OCAjQrVu35OjoKE9Pz5YPAACANoSgDABAJ/H1cuu6ujr169fPblt9fb3dcs+ePSVJiYmJ\nMpvNLeq/pqZGSUlJcnJyUm1trWJjY3X27NkmgR0AgLaOe5QBAOgkxo4dK0m6d+9ek21lZWV2yyaT\n6YfrJenTp0/at2+fMjIy7NbHx8erpqZGcXFxCgkJUWFhoc6dO9dauw8AwF9DUAYAoJOYN2+eunfv\nruPHj6u6utq6vri4WDdv3rRrO3nyZA0dOlTZ2dkqKSmx23by5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", - " period=dt.timedelta(6),time_unit='d',plot=True)" + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=100,\n", + " period=dt.timedelta(5),time_unit='d',plot=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'OnlineSensorBased' object has no attribute 'slopes'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslopes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'OnlineSensorBased' object has no attribute 'slopes'" + ] + } + ], + "source": [ + "dataset.slopes" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[Timestamp('2013-01-03 00:05:00'), Timestamp('2013-01-09 00:05:00')],\n", + " [Timestamp('2013-01-07 00:05:00'), Timestamp('2013-01-13 00:05:00')]]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dataset.drift_periods" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "test= [[1,4]]" + "np.sign(-1) == np.sign(-10)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "test.append([3,5])" @@ -517,7 +649,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "len(test)" @@ -551,7 +685,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "len(dataset.data['2013/1/1':'2013/1/17'])" @@ -623,6 +759,7 @@ "end_time": "2017-05-09T09:55:01.060520", "start_time": "2017-05-09T11:54:59.898063+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -646,7 +783,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", "start_time": "2017-05-09T11:55:01.063627+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -661,7 +799,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:01.844129", "start_time": "2017-05-09T11:55:01.105608+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -684,7 +823,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:02.248297", "start_time": "2017-05-09T11:55:01.847864+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -704,7 +844,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:03.902986", "start_time": "2017-05-09T11:55:02.251053+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -731,6 +872,7 @@ "end_time": "2017-05-09T09:55:03.917107", "start_time": "2017-05-09T11:55:03.905461+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -753,7 +895,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:03.978297", "start_time": "2017-05-09T11:55:03.919697+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -774,7 +917,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:04.632959", "start_time": "2017-05-09T11:55:03.980745+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -794,7 +938,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.get_correlation('CODtot_line2', 'CODsol_line2', [dt.datetime(2013,1,1,0,5,0),dt.datetime(2013,1,31)],\n", @@ -816,6 +962,7 @@ "end_time": "2017-05-09T09:55:06.016129", "start_time": "2017-05-09T11:55:05.261370+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -843,6 +990,7 @@ "end_time": "2017-05-09T09:55:06.731819", "start_time": "2017-05-09T11:55:06.018568+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -860,7 +1008,8 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:07.431337", "start_time": "2017-05-09T11:55:06.734413+02:00" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -904,6 +1053,7 @@ "end_time": "2017-05-09T09:55:07.830400", "start_time": "2017-05-09T11:55:07.433945+02:00" }, + "collapsed": true, "scrolled": false }, "outputs": [], @@ -952,7 +1102,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from scipy import signal\n", @@ -1003,7 +1155,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.data['CODtot_line3'].plot()" @@ -1012,7 +1166,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", @@ -1022,7 +1178,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=34, \n", @@ -1032,7 +1190,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=180, \n", @@ -1042,7 +1202,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,10)], max_slope=180, period=1, \n", @@ -1052,7 +1214,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1065,6 +1229,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "collapsed": true, "scrolled": true }, "outputs": [], @@ -1081,7 +1246,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,15)], max_slope=68, \n", @@ -1091,7 +1258,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.remove_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,5),dt.datetime(2013,1,14)], max_slope=68, period=1, \n", @@ -1101,7 +1270,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1113,7 +1284,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1130,7 +1303,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90, \n", @@ -1141,6 +1316,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "collapsed": true, "scrolled": false }, "outputs": [], @@ -1152,7 +1328,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", @@ -1164,7 +1342,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", @@ -1186,7 +1366,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,10))\n", @@ -1235,7 +1417,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dataset.data['CODtot_line2'].update(data['2013/1/1':'2013/1/14'])\n", @@ -1256,7 +1440,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,10))\n", diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 1fdc96e82..13770c7f5 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -575,23 +575,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'OnlineSensorBased' object has no attribute 'slopes'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslopes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'OnlineSensorBased' object has no attribute 'slopes'" - ] - } - ], + "outputs": [], "source": [ - "dataset.slopes" + "dataset.time_unit" ] }, { diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index a887a5577..df9a93cf7 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -831,9 +831,10 @@ def tag_extremes(self,data_name,arange=None,limit=0,method='below', def calc_slopes(self,xdata,ydata,time_unit=None,slope_range=None): """ - Calculates slopes for given xdata and data_name; if a time unit is given as - an argument, the time values (xdata) will first be converted to this - unit, which will then be used to calculate the slopes with. + Calculates slopes at every index value for given xdata and data_name; + if a time unit is given as an argument, the time values (xdata) will + first be converted to this unit, which will then be used to calculate + the slopes with. Parameters ---------- @@ -871,9 +872,9 @@ def calc_slopes(self,xdata,ydata,time_unit=None,slope_range=None): slopes = self.data[ydata].diff() / self.data[xdata].diff() self.time_unit = time_unit except TypeError: - raise TypeError('Slope calculation cannot be executed, probably due to a \ - non-handlable datatype. Either use the time_unit argument or \ - use timedata of type np.datetime64, dt.datetime or pd.tslib.Timestamp.') + raise TypeError('Slope calculation cannot be executed, probably due to a ' + 'non-handlable datatype. Either use the time_unit argument or ' + 'use timedata of type np.datetime64, dt.datetime or pd.tslib.Timestamp.') return None elif time_unit == 'sec': slopes = self.data[ydata].diff()/ \ @@ -898,6 +899,52 @@ def calc_slopes(self,xdata,ydata,time_unit=None,slope_range=None): return slopes + def calc_slope(self,data_name,arange,time_unit=None): + """ + Calculates the slope, based on first and last point, of a given + data series + + Parameters + ---------- + data_name : str + name of the column containing the data to get the slope for + arange : 2-element array + can be either int or or timedelta values + time_unit : None or str + in the case of datetime index, the time unit to calculate a slope with + is needed; options: 'd','hr','min','sec' + + Returns + ---------- + the slope of the series + + + """ + data_series = self.data[data_name] + date_time = isinstance(data_series.index[0],np.datetime64) or \ + isinstance(data_series.index[0],dt.datetime) or \ + isinstance(data_series.index[0],pd.tslib.Timestamp) + if date_time: + if time_unit == 'sec': + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]).seconds + elif time_unit == 'min': + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]).seconds/60 + elif time_unit == 'hr': + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]).seconds/3600 + elif time_unit == 'd': + return (data_series[-1] - data_series[0]) / ((arange[1] - arange[0]).days + (arange[1] - arange[0]).seconds/3600/24) + else: + raise ValueError('Could not calculate slope with time index. ' + 'Please make sure you entered a valid time unit for ' + 'slope calculation (sec, min, hr or d)') + else: + try: + return (data_series[-1] - data_series[0]) / (arange[1] - arange[0]) + except: + raise ValueError('Could not calculate slopes, most likely due to an ' + 'an unrecognised index. Currently avaible are ' + 'datetime and integer indexes.') + def moving_slope_filter(self,xdata,data_name,cutoff,arange,time_unit=None, clear=False,inplace=False,log_file=None,plot=False, final=False): @@ -1548,7 +1595,7 @@ def get_correlation(self,data_1,data_2,arange,zero_intercept=False, return slope, intercept, r_sq def detect_drift(self, data_name, arange, max_slope, period=None, - time_unit=None, plot=False): + time_unit=None,clear=False,plot=False): """ This function calculates the slope of the data in a certain given period by fitting a line through it and compare it with the maximum @@ -1569,6 +1616,9 @@ def detect_drift(self, data_name, arange, max_slope, period=None, if None, it is assumed that the index value can be used as is for slope calculation. In the case of time indexes, the time unit is needed for this. Allowed: 'd','hr','sec' + clear : bool + if True, the tags added before will be removed and put + back to 'original'. plot : bool if true, a plot is made of the orginial data, detrended data and slope @@ -1577,14 +1627,27 @@ def detect_drift(self, data_name, arange, max_slope, period=None, ---------- information about the drift """ + if clear: + self._reset_meta_valid(data_name) + self.meta_valid = self.meta_valid.reindex(self.index(),fill_value='!!') + + if not data_name in self.meta_valid.columns: + # if the data_name column doesn't exist yet in the meta_valid dataset, + # add it + self.add_to_meta_valid([data_name]) + from scipy import signal - data_series = self.data[data_name][arange[0]:arange[1]].copy() + # copy the data for function operations + # Make temporary object for operations + data_series = self.__class__(self.data[data_name][arange[0]:arange[1]].copy(), + timedata_column=self.timename,experiment_tag=self.tag, + time_unit=self.time_unit) drift = False slopes = [] - - #removes NaNs, infs and other values that signal.detrend can't analyse from the dataset - data_series.replace(0,np.nan) - data_series.dropna(inplace=True) + + # Remove NaNs, infs and other values that signal.detrend can't analyse from the dataset + data_series.data.replace(0,np.nan) + data_series.data.dropna(inplace=True) if plot: fig = plt.figure(figsize=(16, 6)) @@ -1623,8 +1686,9 @@ def detect_drift(self, data_name, arange, max_slope, period=None, if plot and drift: ax.plot(line_segment,'b',label='Detected drift') ax.legend(fontsize=20) + # If the period given is shorter than the range, the period window is - # shifted + # shifted iteratively and drift is looked for in each separate period else: start_index = data_series.index[0] end_index = data_series.index[-1] @@ -1672,7 +1736,8 @@ def detect_drift(self, data_name, arange, max_slope, period=None, #detrended_values.append(df1) ax.plot(line_segment,label='Detected drift \n({})'.format(driftperiod)) ax.legend(fontsize=16) - + + self.drift_periods = drift_periods def drift_analysis(self, data_name, arange1, arange2=None, plot=False): From 9b8ec98142ceb5725a0d9e850e7032045369e1bf Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Mon, 3 Sep 2018 14:21:52 +0200 Subject: [PATCH 40/42] improve integration in package, add tagging functionality #303 --- Showcase_OnlineSensorBased.ipynb | 407 +++++++++++++++++++++++++++++-- wwdata/Class_HydroData.py | 37 ++- 2 files changed, 406 insertions(+), 38 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 13770c7f5..cafe1a651 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -326,15 +326,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", "start_time": "2017-05-09T11:54:56.761091+02:00" - }, - "collapsed": true + } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Eq20TEREREZHLUakMiIrSAwCiovRQqRg4WyMrS2oKnAEgL09m6oV2Vux5JiIiIiIil6NU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Pjw4fJ82xUemUyGIU21q/LMKriQMD9zLHxhCQBu+JExrL2hvtrkNd79clWpTQ2Y8Nd2W9xWrHpz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "dataset.get_highs('Flow_total',0.95,arange=['2013/1/1','2013/1/31'],method='percentile',plot=True)" ] @@ -349,15 +359,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", "start_time": "2017-05-09T11:54:57.350077+02:00" - }, - "collapsed": true + } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47 values detected and tagged as filtered by function NaN tagging\n" + ] + } + ], "source": [ "dataset.tag_nan('CODtot_line2')" ] @@ -544,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": { "code_folding": [], "scrolled": false @@ -554,14 +571,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "No drift detected\n" + "Drift detected in period 2013-01-04 00:05:00 to 2013-01-09 00:05:00, slope: 92.06687774164706\n" ] }, { "data": { - "image/png": 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vjh1bmTp1EjY2BWnRwpU3b97g5+fLyZPHmDVrgVYeUvKp8peSVau8WbRoHqVK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m78cmUNz+Goa9ocpXZES3JekkPOXx4wUS6jHqbn4dDZeChUGoNlTAqSy5a5H0EMKY4e8sfV2JMO\nKwjkTPpzNL1kYu2yJ3oFgfQrvRosRWI2mTSVdBZu1x8O+3//fYyE3PtoG9kW41tPMCoIJJEUSUDF\nIuy5sxPv7hkCASoI0OiqcH7b4TsMbfqu0ftTqpWYffJHzI6ZAgA41/MaKvlUwsmkODQPaWm3O9+2\nUkE7h04mlkEQBMw4XngH29LQtaXnzPcQlNafVzc4LFEu6M635ibT0UEn0eC3WhbblGb5pdIwNdfx\n7Z2v42HeQ2y8uk63zVyiXDQh/uTgRwbPf+m2BL4yP7yx7VUA2mW7shSZeHJlCzzMSzZoO6Lp6BK9\nB6LyrmedXhi/b5TJfTczrsNX6luqZK5oAcNMeQaG7xqi6wWWmJiWYel3n5/MDwq5YaJctEdZLahM\nnrekxBDbtKazpaWa8lV5uscKtcJsO1sl5SZh8ZkFGNfqA4T5hdntvKaIHn0/TJQ9h6NuxMj1/g2o\nNer/Z+++45q43ziAfxJC2MgQEHAgKCqggOLErbj33tVq3dpqf622WltH1Wqt1bpH3avWrdVq3eLE\ngQsH7sFS9kxI7vdHyOUui7AEwvN+vXy9krvL5Zuc5O657/N9vjARftoieKUBBcql1JUH0Tgc+lLn\nejcEAwBiooEYJAAomoJAIvVgUUdPpq4qtNqXa+kVFfIrz37qQj+Se48RI30IqXUF+Ho4GPQakSkD\nuUDzRPwk4bHGsszsTATvCsKblNfssueJkdj45j8svfkrWlRshb3dDml9n3sf7qKmfS2YmpjiVkwY\nKli5ws3a3cBPljt27mEI8DwpEr+GqVJqs2SZOgNl9V6H/Jha7xtMrvs1PNbnbzqo/GAvPgwYF+xg\n7pjrNrrG6RU1bSnSFiJ+976/UyDC425DKpNCxshgLjJ8rug6zgF4ncwfK37s+RGNIPl0v0uo5eCT\nh5YTUnZYi21wtOcpdjwuV+f9IbA3s8fjkXmrybD5/kb4OPqhnpaxy1sfbsaZ1/+xz9WDXED/XL1W\nptYaWTTqc8xny7O17je/hAJhnkpl60tH594YLIxzlNKMS9/ibtwdxKTHYEP7LYW2X22EUATKNGVQ\n0UjIjMfUc5MxPmAS6ldoWNzNAVB01c6zOBkt16OvorFbcL73tf/pXmy4uxZ/dTsIa1PrwmheiVDi\nA+XIyEh07txZY/mOHTsQFBSES5cuYfHixXjx4gWqVKmC//3vf2jRQpVW+PHjR8yZMwehoaEwNTVF\nr169MGXKFIhEJf6j69WxURX4eDhAIBBoDTrrbK0ORpCNEbVHYXrD72Aptsh3QSCimvtX29gqXRgd\nd8CztOxj9pWZvCAZAKacm8iOIz3/9iycV9kielwihAIhEjLjYWlqhfNvzmDIP/0xzOdzfOY7Ah32\ntUZ7j47Y1mmPwe3MjalQEQhL5VKkc+7GA0CmLAvWsCm091LXpko7WJpq5m7rKlBTGJQXH4YWz9jY\nfhtG/jtU5/riCJTfpbzF86RnvDaIhCJYiFQFt8pbOMHDtirC425j8plx2Pf0L9z97DEqWLkC0H+x\nvKrteniW80JcOj8onnxmHO/5535fwM+xNlVmJUQPMxOxznUJWQmQM/JcM1yuvr+MEScGY127zfj2\ngmJs8ZVBN9n1yr/9bLUq29oK/en77atoUwnvUt8CAIb6DEf9Cg2x6f563jbZjCxf887rIhAINVJP\n4zPj8SblNVsXgUtfT2sKp3haXs7nuXmf+g7Ap0lhpd/TojXv6k849vwwotOicLz36eJuDoCi7FFW\n/Q10P9hRb/0WXdKl6YhMfIKxp0YCAI5EHkRjt2BUsqmMAUd7oUWl1pjdbmYueym5Sny0+OTJE9jb\n2+PIkSO85XZ2doiMjMS4ceMwfvx4tGvXDkeOHMGECRNw4MABVK9eHQAwadIkCAQCbN++HTExMZg+\nfTpEIhGmTJmi7e1KDZGJEF7u5XSulwiTAADr7i9DvCQaq9qu17kt0S8q9T37WH08mT667gDuerQd\njd2CMaDmYHbZjejrGtspg2Su0HcXUcfJHzX+9EDziq3g7xSQs89tqFrOEwDw78vjBrfREMoLnmy5\nFNky/kVWVh6+D11sxeVwtNdJNN/Nv3O7oNli9m7uowmP8N2/M3Agcl9OWwoWfOqbFoTJQ+o1oEid\n1Ccs5jp+vjobMxr9mLdGFkDgNn4Pbpo0FeXM7CASqnr/q5bzhHPO2GFlKnb97XVwtOdJ+DsHsttx\nbxwoH/fxVqRblzPT/Rv0Y+N5mBA4uRA+DSHGTfnbrctfj3fxzhdcyqB43tWfkCxJYodCAMDxF/+w\nj73tFUW47sTe5r3eVmzLPt7ScRcyszMg1hO4r2+3GT9fnY25wQtgZ24PANgZwZ/ZQCYv5NRrgVAj\n+E2WJKHeNj+tF/b60rR5Pcpq57OCUE63peztLUpltUjrpxLxUdExYmZiVswtUSmqNHttHTd5NenM\nWBx5dpB9rhxK0q/GQJx/exbn354t1YFyif9re/LkCapVqwYnJyfeP1NTU2zduhUBAQEYN24cvLy8\n8NVXXyEwMBBbt24FANy+fRs3b97EwoULUbNmTbRo0QLffvsttm3bBomk8FJuSrp/nh8t7iYUyNuU\nN5h4egxi0qKL5f0TshLYx5kywwNDfT9s3J43hmEgFuq+MOG6HXsL3hurAAAuvD2LgzmBo1QuRWx6\njMFtywvlRZNELkU2ww9QuWnYXHmppGwiEKKGvWYl1a5ePdnHNcrXwBd1VN+Zeq9IXuk7Nnkp5mWo\nZbeWQFqIF2UAcPDpPo3xh4BmFV1AUVBMIpMgnXNcajn4ahTZypJlIeTvFpDKpOx3dDfuDhiGYS/O\n9nZVDQEoJ9YeKM8NXoBxARPz/qEIKYNszcohZlwSWlRspXX95DPj4LzKFhNPj9G6/tsLU5AsUdwc\nT89OZ5dzzwkSmQRrwlfgnxf8Tgd7cwfs7rIPkwOnooNHJ/Ss3kdvWytYueKPNmvYIBnQDCi0FfOa\nHDgVIVXa6903F7e2g1AgzFOPmr7CRxlS1fdTkB7lI88O4QTnRkR8ZjyAogtiuZ//UwTjZZmyvoed\nmX0uW35KRdWjrLqmtTbNX3YgN0jm+uvxLvZxYV//fEol/q/t6dOn8PTUfrc1LCwMDRo04C1r2LAh\nwsLC2PXu7u6oVEk1xUKDBg2QlpaGiIiIoms0KVRfnZ2Ivx7vwqzQ74q7KXkq/mHoiX3i6TEIi9Hs\nUdZmz6MdvP1y07U/Zn4wuG15oeyFlMokkMn5hal2RGzV+hpur0ZuGPB7d1e33YCYcUlsb6eSuchC\n/aX5Zsj0IYV9wdNsdwMsv7W0UPYVn/kRo0+NQO9DXTXWxaRFaSwb8k9/TD4zjjddS5sqIXDWUXSm\n4tryOP36lGqf6dGQMTK0q9IBLSqpLua5F8tKIVXaY4z/BOr1ICQPBAIBtnbajeY6gmVAceHZeGdd\nXH53yaB9vkx+AQAwNzFHTHoUZoV+r7GNvbkDWlcOwczGP+U7pTda7TcnJj0acWq1ChTzxxvey6yc\nJxrQ3qOspC27SN94ThnnZm9WPot5XXkfipH/DsWw41qmDyzitGgBBPTbWsSUY9fzUkCuqBXFGOV1\n4asw49I09nmmLKPIxkJHxpfeqadK/F/b06dP8f79e/Tr1w/BwcEYPnw47t69CwCIjo6Giwv/Qs/Z\n2RnR0Yqex5iYGDg7O2usB4CoKM2LSWNw4sU/qLvVl7estI9nSc4pHMIdW1RcsgqpR1npyLOD2Ptk\nd67bVbRW3OxRL5TE9TFDESgXZhEVABBzxigbUvxEzsi19nTqouwBrWZXHW5W7uhVva/W/7PmnIIx\nBe3t1XcClDN5G6MMwKCCH8+TnmHe1YKlX8elxyEs+jre5wwH0HYzRtffyf6ne/Ex8yMAwM3KHU3c\nglHZtgq73pFTmEx9v10PKHqCypnZ8ZZbiCywqcMOnOkXiptD72Nzh53Y0XlvPj4ZIcRCZIF2ufS6\nPkuMxICjvQza34kXxyAUCBHs3ozt8eTytq9RKNMjPU54pLEsRZKM3V32sc+FAhOIBIYHyjGc3nAh\ndAfK2lJH9f2+Z3Nu9krzWcyr+8GOOtclaPmeC5PixrLq0p1hGCRnJeHhxwdF+r5libJDJC+ZcUWt\nKFKvZ4ZOZx87mjsiW56Nv5/krb7N25Q3Bm13J/pOnvZbkpToMcqZmZl48+YNHBwc8O2330IsFmP7\n9u0YMmQIDhw4gMzMTIjF/JRVsViMrCzFD2dGRgbMzPgpQaamphAIBOw2+tjbW0IkKrml0g8/Pozw\n6HD80OIHdtmeU9vwNpX/H1coEMDJyQbZ8mxky7PzVNm2JBCZKo6B2EwEJydVagj3cVFyZFTV+6SM\nxOD3tYnS/z2nm8Zj5L/DtK6bUH8CVt5YCQC4Puo6olKj0H13d14auLrkbMUNBQYMype3LrQbJNaW\nimJacoEMZlaa+3ycEY6mlZuyz0cfGa2xjUgo0rjz361GNxx+fBiVy1WGk5MNHk58AIlMAiux9gs3\ndxfVfKKZsswCfUZHRytYmGrvobbLUCy3sjIz+FgfHXIYLr8aNiWI+j6vvr2K+RfnY3uv7bA1s4VE\nJsGhR4fQwqMFnK34N/q8f6mMxMxEbO2h6snvfbQzzg8/j213t8HBwgEPEhQXTEFuQWhSsQmWX1/O\n20d7r/Y4NugYTIQm8HR3x7QP0yASijCv9Twsu7oMX/37lUabXyW/BAC42jlrtH+40yD2cV1P/k26\ngvpUf+Ok5Cjrx/z7Nt9CZA5MPz1d5zbahgCVMyuHpKwkjeXOVs4IqlSXlyGilJadWijft5mJmdaA\ntb5nAPvY1toSjlI7jW10yZCrgpRytpaws9f+e21jZwpHS/5ncHCwhJON9s9l/UF1zWhhbVLo/9/q\nV6xX4H0yDAMGDK/n2NxcccNaKBTA1lr1XQRsq4moVEXHz50xd+Bfwb9A7/2plOS/8/TsnP97Qlme\n2pktz0Z8Rjx73o5KiUIF6wp5uk7Jys5CiiQF5S3586fbxKiuJ9Xb9OTjEzhbOcPO3PC/L3WNKzfG\n0SdHMeH0aIxv+oXBr2u1tx/7eHKDybC3sMfs87PZZTOazcDPF3/G3xF/Y2DtgfluX3Eq0YGyubk5\nbty4AbFYzAbECxcuxIMHD7Bz506YmZlBKuXnvUskElhYWLCvVx+LLJVKwTAMLC01K+mqS0hIz3Wb\n4tR9t6KI0CCvEbDNKaojkmsrPiBAXFwKgrbVxuuUV/mqalecpFLFHWBJVjY7JdSnnB4qPl51wk6X\nZBj8vonJ+u9GXn+u+w6bs6lqeicPcU1E5fQE6mIpskRMiqLappyR41VUTKH0FACAXKr4kX+Z+BJ3\n3zzUWN9sUzNcHHAdi28swOFnB7Tu48qgW3iX8hYx6dHYdH8DrkZdxrS6s+Bbzh89q/fhfafp0Px+\nnZxskJnM7+V8F/Mx38U2YuKSYGWqvSBYfKLiuGVmSA0+1gJYoI5TAO7G5X7XVH2fLTa1gEQuwfTj\nMzE7+GdsefAnvjn/Fbzta+DSwBu8bRMzFTdDrr+6xS67+PoivjvxA365/jNv2y4ePVHXJQjLwQ+U\n7U3LI/6j6rfta/8ZbLusGP6J1trUBqlSVXuPPj6GmUHzcv2MheFTTwFHih8dc4XulftjOhSB8rzg\nhbgVG4ZJgVPR6q8mWrcfWXs0ZjaajYcf72PxjQVo6NqY/T2oauuFlhXa4Rf8AgBoUKERGDC4EX0N\nsWmxhfJ925s7aKRfz2w0G0kJqoC+kpkXXsneGrzPlExVrYXU1CzEx2vWXgCAdzEfILfmd5h8+JAC\n00ztnys+UbU8LiEpz5+fW9xTG7lUWKDvNEWSjJC9LfAy+QVej45la4SkpitmnBAwQqSnq657lUEy\nAFx/fhtuJvoLw5UERf13zjAMpHKp3qJ0uvz78jg+Ziiut9KzDL/eA4AfLk3H2rurcGHANbxKfomh\n//THUJ/h6OzZDS0qtsp1juLotCiMPTUSl99fwoPhz+Bk6cSuS0pWnbO5bUrMTCnMCWYAACAASURB\nVECNP2sAAK4NvpNrYUBdRtUaj6NPjsLNyl3jM6dKU7Hs5hJMDPxSI6vMWqQoBjiz0WxMrqsoLPg+\nPgbr761BZ89uGOczBfZCZ9SuVLPE/7bruilS4lOvra2teb3GQqEQ1apVQ1RUFFxdXREbyy/FHxsb\ny6ZjV6hQAXFxcRrrAWikbJc23HEE3OkI1IstAaoU0tcpeZuLsaQpCSnkeam2nNtYD+V0Etp423vz\nntvqKJrEbdeHDNUY5c77QwptrAm3UvLK28u1bvMo/qHOINnNyh1VbD3QxL0pelbvgz1dD+DSgBuo\nbu+NKUHfwKNcVYPaYW/ugHUhm9gTwe5HO/JdsZHRO0Y5f8W8Ap3rAQBG1R6DJS21f0+A5nFXprOv\nDv8DABCWUwH9ScJjnelW6hdr6kEyoEjjVP4/6uKpqsztWc5LZ9ucLFQ92F/W/Ron+5zD7i772WXD\nfD/X+VpCSOHgVpMf7jcKa0L+hG95PzwY/gyj64zT2L5lpTawMrVC/QoN8VfXg+xsCICi2nWAU132\n+a4uf+OP1qthKbLCD41na+wrP8RqNyxr2NfE5LpTeHOpetvX0JjHXR/1Yl55Sr3W8/vOvUZSFvPa\n8uBPjPx3WK7nzMTMBPhv1Sw8yW+P6uaATC5jgy7d22fhx9AZbL2ROltq4nnSM8gZOW/mi9ScITXW\nYhudWYEl4RqpJBj6T39UXFve4Joycelx6LivDf56vItXX0Vq4PWenJHjXlw41t5dBQA4+fIEO8Z+\n28PNGHC0l87Cp0oMw6DOlhq4/F5Rf+CP20vxLuUtjj47zK7X5kb0NfZxwx0BWrd5m/KGN12kNuXM\n7OBq5QaRianGuh9DZ2DZrSWYcnaSxjqpTAKxUIyJgV+yy+Y1/QWx45OxqcN2iE3EGOY7Au282ul9\n/5KsRPco379/H8OGDcPWrVvh5+cHAJDJZHj06BE6dOgAR0dH3LjB73G5du0agoKCAAD16tXDr7/+\nygbVyvVWVlaoWVP/j11Jxy2U0XhnPRzqcRyN3YJ5Ux8o0Y9nwXBPunJGpmdLvtyKeX11doLOdX5O\n/hjuOxJBFRTF6tyt3fVeLEjkEt744Ycf7+Nu3B3eND/5xZ1/W1cREX3jx0/34xeesRBZwNuhRr7a\n0qN6bxx9fhgvkp7jm/Nf4XF8BOY3W5zn/RhSzCuvRVl+ajIP9VyC0L1aL1iILPD1Oe1TIzXZGYST\nfc7B1syWdxMCAMacHMFOgQUAyVlJWgtmKadz0sfZ0gUO5o54NuotzE0s4L5WMQbZRqw7lcy3vB88\nbKtijP8EjKytSKG3N3eAm5U7XK3dMM6fKlkTUtREQhGWtlyBDxlxvF4xJ0sn1HJQDW+4OugWUqWp\nqOPEv0A249Rz8CtfGyZCE6wJ2Yg0aRpsxLawEdvi2ai3ufZwGYr7S+nvFIhTfc8DAKw4gbKbtRtv\nHvfcqN/MLLwxyqrgZ+nNX7Hw2jw8TXwCAEjIiocDp1aD+vusv7cm13ZzK2kP/ac//nt9Eg9HPEd5\nC34qrTKA3v5wM1aH/4H/Xv2Lf/ueYysuA0DLPY3xee0vsKDZr+w51lZsC7lc+3VIYc7UUJqdfHUC\nAJCUlcTrldXlr8e7cDPmhsZNbKkBgfbyW0s1ao/EpkfDVO3cvvfxbmx/uAWdPbtiYfMlGvtRryFw\n/8NdNN/TCCmSZPzS/DdeQMy14d7aXNvYeX8IotLe49bQB6hoU0nrNuYiM2TJMhGV9h7vU9/BzVqV\n1fg+Z950bvFYhmHw8OMDvE55Dbec61MlY4s5SnSPcs2aNeHu7o5Zs2YhPDwcT58+xXfffYeEhAQM\nGzYMQ4YMQVhYGJYvX45nz55h2bJlCA8Px2effQYACAwMREBAAKZMmYIHDx7g/PnzWLx4MUaMGKEx\ntrm0CY+9xXve/WBHxKXHIVpLWpD6f9miqmpnrLhVMrPzECjnp/jCtAYzcHngTbhYumBRi6XoV0Mx\npsPO3B6PRrzQ+pqm7s3Zx9y0m3AD0oANwf3/kqZl6iEAiEx8qvP1jhbaLzzyi3s3/eSrf/O1D0Om\nh8rrFBxWplYYUHMwezG4qcMOrdulZ6eh6e76qLOlBhbfmM9bxw2SAfCyBPJiTJ3x6Fi1MwDARmwL\nUxNTHO15Cs0qtkSv6n11vq6cmR2uDwlng2RAcfzufBaB471PG90JkJCSarDPMHxZ72uN5XVdFB0B\nFiILeNpV0wiSAf5vZJCL4mZrr+p9MdRnOLu8sIJkgB+gta7chn1syumdshbboE2VEN7rvO113zDl\n36CW67zxrK3Apt7fd866+x/uskGyYh2DxMwE9D7UFVejrvBet+7uaiy+sUDnflXtkWDPo5344dJ0\n/Pf6JACgwXZ/vEl5jekXvsb2h1twOPIAam2qihZ7GrF1R96kvEas2hSYDBhsvLcOMrmM7QSxFdvy\netu5BAZUw34UH2FwAaayQvl/KyrtPSxEFvB3CoSThXOuxUuTshK1Fuhcd3c1Nj/YyFv2OuUVYtKj\n8ef99Xib8gZTzk7E85yMgfNvzqLNX01523/M+MDOUjHtwlSdRbbUsw13P9qBNeErVJ+NYRCVpogL\n1oav1PlZzEzM2WD9f+e+5K1T/n0rv6ekrEQMONoLrf5qgg8ZcTqDb2NRonuURSIRNmzYgEWLFmHs\n2LHIyMhA3bp1sX37djg6OsLR0RErVqzA4sWLsX79enh6emLNmjXw8lKkFgoEAqxYsQI//fQTBg8e\nDCsrK/Tt2xcTJujuySstFmn5we51qDNep7yCh21VdloIQPGfnDvfn5yRF3plZGPGnRIpLzcZ8npD\nQiwU4+ugaTrX25nbY1fnv/Em5Q3mX5uNxKxEWJvaoKZDLbbKdLsqHdDJsyu6H+xYJCdDXcXEVt3R\nnWpc2Li9JfpSqPXR1+OgPG4FDQo7e3bF4Z7/Yu/jXTj35gzvbqzSpvsbAAATAr7E9ogtSMqp8K7U\nZFc9vTUF3o75gIiPDxDydwve8jnBCzTa38C1IfZ1O5zfj0MIKQFqOfrg8sCbGtPncXFTnNV7MosC\ntzfJRE9l63ou9RE+7BGcLJ0hEorwNuUN6m7TXgCQGxgzDKMz+M3MzmK34W6vi77hU1KZBFsfbcfF\nd+dx9dBlvBv7kd3fuTdndL6OKys7E5POjOUtS5WmoN42P41tY9Nj2HNnpiwTEfGKaUur23nzAvhU\naQo7T7aFyBIyHd+FIdNGNd+tmKFB23lFIpPgQ0YcrzexNJNpGYqodTvO/4mM7AyYmZjBVGiaa+r1\n+P80i16VM7PTOI+rU/6f1zW9JgBExGvWg1FKlaTAOiczLCY9GgII4GhRHh8y4jD5jGJoxme+I2Eh\nsmD/3wDA21R+jQBzE3O2MKDYxAye5bzwPOkZJHJ+7Sf1v8XqGyvz1vs7FTxzsSQr0T3KgGIs8ZIl\nS3DlyhXcuXMHf/75J7y9VeM3W7ZsiWPHjuHevXs4dOgQmjThF7twcnLCypUrcefOHYSGhmLq1KkQ\nCkv8x87VstarNJY9TniELFkW2nt05KV2JGQl8Ob70zaOuSQzdD7iosK9e6veFoZhdJ6U8zIHX89q\nvfFvn3O5btemSjsM9xvJ9jTLGTnszFSpuY4W5eGSMzduLGd6DV2+PT8FvQ510TsZfEnLPzDPZwEv\nLplcT6CMwptHuZFrYyxpuRw3h96Hj6PmhZLSrMZz8HTkaxzs/g/+bL8dK9usY9c9T9Q+/+D5/lch\nNhHD3zkQmzrsQOvKbdl2U88vIcarmn11toCnNlU4077ZmzsUeXu4vzcitbmSbw99iHufqYI+V2s3\ndpuKNpVwYvAJjPHX7LyQc86rMkbGe86l7FHm9UAbmHqtLlOWCWlOkMA97y+/9Rsuvj2nsb2LZQXV\nNq1XK9qTzymnAGDSaUWA3SEnG0gpWZLM9i4yYHD/w12try/oOevrc5MRsLUW6m+vg3cpqqDqetQ1\n9D/SE4mZCbgbdwcDj/ZGfC4FRgHFmO57ceEFalNBGDJG+U3Ka/x8jT9WP0uWBVMTU72p1xEfH+KU\nWkbbWP+JONrzZP4amwfK7ILEzARcjboMR4vyGoVNu+xXjAl+z8kyjU7jZ5wKczrMOnh0gqO5I/Z1\nOwIAGoVgM7IVheTuxt3Bu1TNgnzdvHoU5OOUeKU/YiyjfBx9NcZ+KjlZOmO470idr5XKdQdFJVlx\njb/hplur39Wed/UnuKwuh7h0xZjxR/ER2P1oh0YA3bFqF0SPS8STz1+hRcVWvH380+s/rG23Cb7l\ndQdS6pQ/ZOnZaXDgXAhVtKkE55xAOSY9WutrlZ7EP8bmBxtx6d0FXgaCuoKk6ivnfy5M5S1UY45S\ntIzJN4T+eZTzV8wrNw1dG2ld7m1fg73QbOLeFF28uqGjZxd2/dB/BvDapWxbLUcf9nlnz67Y3WU/\nno16i6cjNXuuCSFlh43YFlPq/Q/zghfmq/JvXs1qPJd9rB4ou9tUhItVBfWXsNpXa4+5wZoZcnlN\nveb3eun+fdfXyyiRSdg6JNz3Vw+kAKCbV08c6H6MfT6g5mAIBUKN9Om8UM4w0KNaL1S3U3UIfcz4\nwKbFXn5/UWcabm6Bcm7n8j2PdwJQTAeo7JkEgL5HuuHsm9PY/GAjOuxrjdOvT+Gvx7v07uvC23Pw\n/rMK2uxtpjWb6lPI1nGtm5mdicxsxf+bzfc3aqxf1npVTo+y5utlchmi06LQcZ9qiMHUoG9xtt9l\nzAmejxoONRE9LhHbO+3Bgma/4t/eZ/G5n6LnuYNHJ73tHVRzKB59/gKDag5ll3nZVcNQnxG87VIl\niiFwyuJhzpYuGOHH792+9yEct2Nu4nbsTXZZNOf/pkwuUwwDc2+OrZ12w0RoggpWilpOsenR7P9/\nhmHwiNO7/VvYIo12B7rU0/u5SjsKlEsxBzNVgLQuZBP7uK5LEIQCISYGas6HCvDTTEjuuIUz1O8w\n/nF7KQDg35eK1PapZydh8plxuPjuPPtDU8mmMpa2+gNCgRB25vYaPYv5meIoIKfCcqtKbXjFnrzt\na8BabANLkRVi9PQop0pT0XR3ffa5vjRtbRcohgRjzdxb4GROUZfCxE0LS8xKzFMlciV9F1LK41YY\nPcpcc7RcDAKad28BwNrUGmf6hQIAniY+wao7f6DKOlWl/gsDtBf2UBbqIYSUbd81nIXR/uM/yXt1\n5PSA6ku91ufq4Nu859xAdf612Tp/szOzsyCTy3jngWwdxa4A/lAqdRJZFq/n+kXSc53bN3RtpDFj\ng5yR43HCI537N1R1+xoIHRSGb+p/BwBo93dLdp2+uhX6MtwAqH1H+s+b3MxDZVCZkZ3Ovk5XD79S\nn8Pd2MeJmdqHbAGKaxFlR0Nh05U6XfNPDwTkVC/XltLv4+gLU6FYa6C88Po81NlSQzXXMoDpDWby\nOjqEAiHaeXTEyNqjEehSDwubL0Hs+GQ0cVeNQz7X/wpaVVIF2z2q9cKvLZfBwdyRzRi0N7PHlUG3\nUNeZH4h+eXYckrIScSZnbvQ5wfMxScv1/ogTQ/Ak4TEAxc31GE4ArLwpY80p7qmsW3AzJgw/XFJM\nT5cmTeUVGtsesQWAop7OouZLsb7dZo33NTYUKJdi7jYVsaDZYvzb+yx6VO+N5a1XY1bjuWxxJ265\ndi5DS94TBW4KlkQuYU+cG++p0mOnnpuEdGk6wmIUU/t8zPjABpg/NJrNq6QZ7M4v2qA+tYYhOlbt\njN1d9mN9u82w56ReK9OwXaxccP/DXUz4b7TWE+eRyIO85/2P9oTzKlveWHZ91OfSU3eu/xXs636k\nSMbHOamNzUvMZTyQEvd70FfsRZmulZ/joo/6DRHPcl6o5eCLHxrP0bq9X/naqGSjGAv00+UZbHXX\n1W03oIZD6a7aTwgxTqJ8FgmrbFOF95w7w0R8ZrzOIWNPEx7DdY09m7YMAMee667FoK8gZ6Ysk3e+\nv//hLuZcmQVAUUPEUqS6qdnYrSlEQhGaujfHhADt11q5qeXgAxfLCjjTLxR3P3vMLlcWY3O3rpin\n/f1y/Wf0OtQFnfa11bqeW5wqXZqmdRul2zE3cfb1aRyK3M9eyyjHUAPQmDdbKSY9RqMQWpYsC2nS\nNK3XIm3+agrfzV75uuGdG+Xnlcqk2P90L7JkWfjz/nqkZ6ezwZ96R8CqtusBAGITsdbU62W3+BWr\nudMn5qZFxdawNrXBupBN8HH0xZ6uB9jin/Vc6rPZGI3dgrGs1Soc730agOZwyZsxYZh2YSruxN5G\ngFMgmldsCYFAgAfDn+FUn/Ns8Po+7R3i0hXTx9ZxCuBNI6pM37Yx1T4LhrLCe1JWktb1FSxdMdxv\nJLpX62Xw5y+tKFAu5UbWHsOmPQyoOZgXHIt03Nnl9igzDIM3Ka+pErYe6j/gHzM/4lXyS3x38X+8\n5R7rVelladI0VQqv2njRtlXa84p25Tc1rnXltrA1K8frUVbOv6mcD3fvk91YcWeZxmdYclORPjOq\n9hje8omn+c8VtP/fCB0YprNtlYqwCqKrlRvvuc8mT6RKU3PS5rQHwCNODEHV9a7sc32BsjTn5CoW\nas4nWJg6e3bD+QFXeFXL1Q33G8V7PrPRT+jt3a9I20UIIfmlnnqd39dlqlWznndFs7owACy4rkj7\nPvRMFbAsvD4PsemxWq9rlOdCbZk3m+5vQEZ2Ovv8wNN97Pz2ErkEp/qex1j/iXj5RTT8ytcGAOzv\nfhQ/NpmrsS99BtYcgquDb+P8gKu4N/wJ/MrXRgUrV5zvfxUXB1xnt2tTmV8l/M4wVaA6KXAKvqrL\nvwZ58PEeQt9fZG/Yq+MGfspxp0rqvb6Zskz0P9oTX5wczi5Tzg0MAGvCVyD03UXeaxZcm4Pam6uj\n24H2vOVhMddRdb0rav7pwVueJk3Di6TnAMAGdHkhkUmQkZ3BTrXFMAzGnlINO7yTMzvMjEvfYuyp\nkeh1qAumX1BVkk+TpiE5JxDsX2MQvO1roJtXTwCKqvLp2emI0jKTjJKHbVW2Noghajn64Nmot+hR\nvTe7bFfnfejs2Q2DfT5jlwkEAgysNQSedtUAAL29+6FD1c7Y1+0IvHKW7X/6NxgwCOZcPzhZOsHf\nORDdvHqyf097n+yGmYkZ6jj5AwBicm5wKKcb0zdd5PaHW5CUUwxMfThns4ottL3EKFGgbMSsxTZa\nq1tzg6ZNDzag3jY/bH24SWM7oqA+d3KdLd5Il6br2FpBkdaivSiUUCBE84ot2efmnCrO+cFN5Vae\n/F+nvGKXzb0yC/879yUmnR6LVGkq7sTewuvklwCAUXX41Tm1pVnruomia77Jbl49izT9t5ajDzZ1\n2IF5wQvZZYuvL0DFteXhs8lTY/tUSQqOPT+MdM4FkL5AWdlzW9g9ygAwv6lqfI8hc4pODPgSnap2\nZZ9Prju10NtECCGFJb+p1wDwv6DpOtfd4oy1NITf5moYc4o/tnPD3TXsdD4b2m3B6rYbcLjHCXb9\n30/28DLFjj4/xD5e0WYtqtt7Y07wfFiaqqqK61LOzA6Daw1jh9BwNXYLhmc5L43ltRx9eNlCLlYV\ncH1wOG4NfYCYcUlws3ZHxIgX2NZpD35oPBvfN5qF92PjEWBg1WFuNeO0bH6P8q1Y3Te+del5qDOm\nnp2EBtv9Mf/qHCy9+avW7WaFfg9AUVj2XcpbrL6xGteirvJuXuvqoeZ6k/IamdmZeBQfgeMvjqHi\n2vKoss4FtTZVxYMP91FhtR32P93Lbv/1uckAgF2PtgMAHn58wNtfh79b4UjOMZ7VeC4uDbzBdlwo\na7z4b+Vnb9lxsunO9Nc8trlR7zhp4t4UmzpshzVnznF11qbW2NpxF5pVbIErg25hQM3B7DplAKz+\nHmPqqArkDa41DFVsFcMElN9zVM6c0cqaNkqbOuxg/29OPTcJ68IV46DtOR0yoQPDUNmWnwFizChQ\nNmJCgVDrfHvKMQsAsP+J4kflcOSBT9auvCru3m7171DOyNFij/bCTEqpklQ2RVug5c+MGxwXNCCz\nEFng9tCHON33IhuUD6o5hLfNzkfbsOfxTniud8Oi64q5e1tUbAU3K/40ENqKY+kqomIm0t7uT5GK\n09mzK3pU78M+V47Vic+M17gz/i7nhMClLVC+GnUFV99fZiuAF0URHO6NCe4ULroIBAJ0r6a4w61e\nCZUQQkqKzp6KMakBzvmfKuab+t/xijUW1MHI/byOge8vfcs+NheZo7d3P3Ze6twox43qo0yVBYCm\n7s2xtNUK+JWvjUauitlYApwCsb3THvSvMcjQjwCPclVR0aYSG2A5WjiivUdHdr1IKNLIPAK0Xzdx\ni1tlSPk9ytc46dIdqnbmFRPTZ3vEFrxMfoHfb2kPktW1+qsJxv8zHl0PtOMtj86l+Ojld5dQb5sf\nKq9zRvPdDfHZcf7xGPffSD0F3xQ3v9OkqbzljxMesVM52Zrxb+5zO0OuR13DmJMj8Cr5JRKzEuFq\n5Yaz/S7rDW6LUu/qqqyy2uU1A2UAvDnLpzWYgQo5BfWi0qKwM2IbBh5TXD+p37Dp7NkVu7rsY5/v\nfLQNAFDOzB7/9b2Aoz1Pobq9Yf83jAUFykZOPbUWUIxH7bSvrUbqTUlXXFPe6CsMokuqNJW9eys2\n0UzhNef0JpoVQkDmblMRtTl3Fr+p/z32dTuikZoFAP+9VkxfsKrtBpiLzHGi9xmM858EQHEiX3h9\nHrw2VMTvaneHB6oF39am1lp7wwtyoZQXzpbOuDFEMU0Gd37AlJwiFQAw/+octgeBS1vV624H2qPb\nwQ5YE74CQNEEylwWprn3KANAj2q9sbH9Nqxpq1mdkxBCSoJ1IZtwddAtBDjXzfc+BAIBdnT6q0Dt\nmBQ4hfec2zHApcx6Kszf+Xou9dlrrnouqmKZynGeLlYV0M6jY6Ffy3Cz1pQzTWgLGrnTJaWr9Sg/\nS3wGALg59D62dtyF0/0u6Uwrrm7nrXPd5MCpaOauSMv9tcUyjfW6aopEcaYuksqkeBQfgZi0aDxP\nUrQrt6zHRznjp50snNGggqojI1WSouslLAuRhUYNkarlVNlpXQ6E4EDkPtTfXgcA0NWre55mKSls\n9SooxjN72FaFp51mZgKguFHzfNQ7xI5Phr25AztkLTotCl+dVfU2cz+nvmXWptao4xSABq4NC+lT\nlB4UKBu5+c0W4/dWKzWWh8Vcx8mXx1U/pjTvqk7aeuVzk5AZjzlXfgCgvceYm3ZbFCm+JkITNKvY\nAt83mqWzerOTpeLOfV2XIEzIGdt+J+42fgtbhBRJMuZfm4N3KW/ZO9Nj/CegVaU2vAuZiM/500qd\n6nOeLUD1KVS2qQJXKzfeneLkLEWv+Iuk5/j91q/49+VxjdepV1Dl3n2PyJkKwVRYtIGymdCw4y4Q\nCNDVq7tB6X6EEFIcTE1M2TGVBeFXvg4aujbWuq6RaxOsCVHdMFTW4rDMyc5p6t4cfbz7885BymmO\n1HtYbTnDg5Q3igvDD43nYFunPRhTR1VxfGYjxc3aL+t+retlBRJSpQPG+k/EpQE32Erc3M+bkBmP\nV8kveVWc1TtK7sTdRnmL8mygbS4y5w0VWtR8Kft4Ut0p2N1lP3wdFeO0lenIdmZ2mNn4J/zd7TBC\nB4ZhqM9w9vqzi2d3jTRfQJX6e/rVSWTLszHx9Bi4r3VE890NUXuLN5rvaoiTL4/zUqprOfhonS2k\ni2d3PBgRiaO9TqKmQy0AgOcGd43t1GnrNNJVDBeAxnRNn5q1qTXChtzD4Z4n9M7Owa1orZz6SX3a\nUPXK7Urqs7OU5esPCpTLgEG1hmpdnpiVyP6YFtccxaVBfqbTUo6JARTVMtVxA2XTIi4a9W19xfgg\nZfqXNsoiYOqi06PYmykWIgvs6XoAIR4d2PVWplaY2Ug1x6T/J+pNVhIIBOji2Y237Njzw7j49jyO\ncwqPqONOdwCopkr4lPJzA4YQQoyZqYkp9nU7wj4XC8U40fsMfmw8D391PYhe1ftieoOZONrzFFa2\nXQcfRz9cGHANS1uuwJaOO1HL0Qc3h96HrVhxTnPL6UlTn1aJGyj3yaVA4pzg+Qa330JkgfYeHWHK\nySQL8eiA2PHJCKrQwOD95IWjhSPmBM+Ht0MNCHICp7Nv/sOXZ8bjl+s/I2BrLYTsbc7rXeWmFktk\nErxOfgkHc0deb3fFnBsOrSq1wXC/kQhyUbTfI2e868Eex3BhwDWc6ReKVpXaYGsnxfzOAoEA1e29\nIRAIMKjWUJzuexEb2m+Bi6XmfNpf1FYMRzr16l+4rXHQmJ9ZIpdgyD/9ASgK1j4c8Rzn+l/Bs1Hv\nsLLNOlS29WC3dedMHdnVq0eu35u+bdp5dMTSlis0ln8dNK1EzDrhZu3OBr+GcM3ZVn1Oa10zmJzu\ne5E3o4qyyFlZlP+qC6RUGes/EWvCV2B12w0Y959iPEuSgdPqlHUFDWhMtaRecwPlok4pn1LvGwz2\n+Qwuli7YFbEdX57VnFvTzMQMAU6BuBPHn8vyeeIzXHmvKFih62aKttTyT2lWk7nIlGVhW05q1q9h\nC7Vut7jF71gS9gui06LQeX8I7g+PBMPI8c35r9j0Lq4PGUUzt+OSlsux7cEmduwxIYQQFbGJGK9H\nx0IkFLHVe7ljiacGqcYan+t/GQAw2GcYbx9rQzZi4LE+8MlJkY1K49eq4Pa22eq4UQwAnap2xeg6\nn2Y+6sKgPE8POtaXtzwjOwP3P97jPFcEyqdensDgfxQ3CtRTbsUmYrwaHcPezN/V5W/cib2NRm6K\nm+7lzOzYQGtPV911bpTDwjpW7Yx7H8KxOGQx+lUdhgtvz6FN5RD8fG02b3u/8nVw/8Ndjf00cWvK\nTjkpNhGjb40B6FtjAOZe+RF/3F7KK5L6ddA0tK3cDoOO9YG9uQPO9r+MSmsVWXSHehxHkEsDZMoy\ncPrVKa1DFAFFJ9PsKzPZdPHxAZMxrcEMnZ+zJLMW28DK1BoRHx+yy/5oW2SUJgAAIABJREFUvUbn\n9iZCE3b44FCfEUU+FK0ko0C5jJgTPB+zm/ysGAMUsRWX3l3Aq+SXvGkEXie/wqlX/+Jzvy+KbTyw\nNroKNHwqBQ2UtfUoF2VVaHUCgQAuOelNyhMWdwyP0vbOe/Hw4330O6K6yzrh9GjefrSpbOMBAAh2\na1ZYTc4TMxMzLGm5DF/V+xr1tmmOG5obvABx6XH4zPdz9PHuz1bavPj2HELfXcSJl6q5o2c2ms2O\nae7r3b9I2jvUZziG+gwvkn0TQogxUM4lnF/KWRl+C1uEYLdmCH3Pn8qIOx2Vg7mDzv1YmlrqTW8t\nafRduSmrQAOqHmVlkAxA63mJe1O/nJkdWlRqle+2Tao7BU3dm6NznRB8/JDGFiV7O+YDkrKScPT5\nIViILDCg5mBsefAn1oavRGTiUwBAPZcgnUXQvm84C4NrDeWl/gsFQgS61MPDEYrpp7jXL43dggEo\nOjFefPFe57WNQCDAyjbr8PO1OdjYfgu87Krn+7OXBL6OfrgefRWAIvjtX1N/Ubk+3v2x5cFGdKza\n6VM0r8SiQLkMUf4YLG21AvW318G2h5tV6wB02t8WsekxqGxTmZdeW9bJ8lHMi8tUy504gUAAU6Ep\nb8zQp+BXvjaO9TrFzsXH5WzpDGfL1jjTLxR34+7wCj7o096jIza234q2VdrnvnERUp9fGQAmBn6F\nMf6qz2FlaoVtnfZg6D/92cwKJWdLF0yuOwV9vPvB2dIl3/OBEkIIKV7cgpm9D3fVs6XixvXZfpcR\nmx6D/kcVmT5ioRgSuYRXKbo0MLSTI0mShJg0/njVJm5Ni6JJLDMTMzRya6Jx40FsIoaTpRNGcKp3\nf+b7OT7z/Ryh7y7C1cpV7/h3E6GJzvXc72NNyEZkZWfpXK9NiEcHo7kerudSnw2UG+moA8A1J3g+\nhvp8hjpOAUXdtBKNrgTLIMec1BWu82/Pso/Vx/KUdXnpUV7ZZh2vFxbQXdX68chXQDFMfVW/gv6q\nhX7la8PJ0lljua7UaxOhiUHjgYqaSCjC6b4XcfbNaUwI+BImQs05xAHAREfvgHK8j5t17sU/CCGE\nlFxWplZ52t63vB+qSD3Y5/UrNETo+4sGTx9VUhhab+anyzPwz3PVWPCQKu156eglRbB74WWq9are\nN/eNjFg9zv9lXdWyuSxEFmU+SAYoUC6TrER5O4GUFMVVcMyQQHmoz3AsaPYrxCZijUBZVyxcXHPw\nGcLZQkugXILS8XWp7eTPmyZLG11pdK0raZ/ughBCSOlS2bYKdnX+m50vVmlW47loUzlE62usTa2x\nqcMOXH53EVOCvsXpVyfRt8aAT9HcQsM9T89o+COcLJ3hYumCYPfmGHC0F3pW74NfbyxETHo027s4\nIeBL/NhkbnE1mXwiHat2YR8rq5uT3JWegRek0HB/SLnjT7StJ0B2TtXrzmrVle8Nf4r2Hh1xqMdx\nLGm5nC12EMJJQTY3MS+VPZQCgQAXBlzjpWIZT2V0/ufY2nE3fm+1Ev+rP72Y2kMIIaSwtanSDrUc\nfHjLxtQZj1qOPjpeAXT27Iqfmy1CeYvy6F9zUKkanwzwz9MCgRCDag1FmyrtYC4yx8Ee/+Az38/Z\n6ZyUfmg8W303xAhxC8tqyxok2lGPchl397PHyMjOQJ0tNYq7KTqpz3/4qclzepSV01I0262YIsHF\n0gXbcqZD4NrReS/kOfP0lraTLFdNh1qoYuuBy+8vFXdTChX3mLSs1BodynihCkIIMVYmarUmtM1C\nYUy45zdd1x+bO+5A4531ct2OGJ/DPU4gPjOejnkeUKBcRq1uuwGRiU/Z8v4+jn54+PF+cTerRFKm\nXpsITOBZzgtuVu7o6tVd72uM5UeId3faSHqUuZ/DWI4TIYQQTT6OvuxUQ/OCtU8daEy45zcTgfY6\nHV521bG7yz4MONobWzru0roNMU7Kqb2I4ShQLqN6e/fjPbcUWbKPjSUgKizZOVWvRUITmJqY4s5n\nEcXcok+HG0gaS0o+7447jT4hhBCjtbDZr6hazhNj/SfmucBXacQ9Twv1nLNbVw7BmzFxMDMx+xTN\nIqTUokCZAOBXiCyxAVExtUvO6VEua7j/F4zlBgo3UNZVGZsQQkjpZy22wddB04q7GZ9MXjKmKEgm\nJHfUnUIAAFYluAJzfnHnP74bdwfLbi4xeE5kmVyGH0K/w5X3oexrhGUwUFYvfGUM1IudEEIIIUaB\n16NcFq9ZCClcdJVIAADuJawy8/vUd8jMzgQAMMh7Ma85V2bBdY09Oyd0273N8fO12Zh79Uc8/PgA\nSVmJel9/I+Y61oavRPeDHZHNKKpei4RlLwGDH1QaR9BMqdeEEEKMEdXgIKRw0V8RAQD4OwcWdxNY\nCZnxCNhaCx33teEtP/HiGKLTogzax4rbvwMAbseE8ZZvf7gFLfc0RvWNlTH4mO7J55/EP2Ifyyj1\nWvHYWHqXOZ+JUq8JIYQYCwqUCSlc9FdEAAAulhXYxymSlGJsCRCdFg0AePDxnsa6g5H78rQv5YnC\nRmwLAEiWJLHrTr36F+9S3vK2T5WkYP3d1fjf+S/ZZRKZBIDmNBNlAa8YiLH0KIN6lAkhhBgffjEv\nOr8RUlD0V0QAANXsqrOP5175sRhbkr9Ua10EAgEYhkFWThq3usBtPtj/dC8AYP3d1fDc4I4Zl/iF\nP1bdWQ4AsDezL7R2lRZG04vMwY339VUFJYQQQkoTXrHKMpgFR0hho0CZAADcbSpicK1hAAAG8mJt\nC8MUXqAMCJAiSYZELuEtXdh8Cft47KmRuPfhrkaArCRnFN+Hs6VzIbardDDG1GveGGW6kCCEEGIk\nKPWakMJFf0WEtbTVCnjZVQPDMMjIziju5rAKEjjHpEXjduwtAMDgWsPQxbM7xvhPwAjfUXg68jW7\nXc+DnXPdVzkzu3y3o7TiFfMylkCZm3pNFxKEEEKMhDGeswkpTnSVSHiaurdApiwTJ18eL7Y2yDk9\n2jK5jNfDfeX9ZSwNW8wGz6mSFDxLfMp7PTew/vLsePQ90h0A4OPoiz87bMPc4AUQCAQoZ2aHfd2O\nAFCNXR5VewzChz2CunufPTGaqs95YYxVr2kMFyGEEGPEPU1TsUpCCo6uEglPgJOi+vXp16eKrQ3Z\nMin72HWNPR7FR7DPj784igXX52LM0TEAgDZ7m6HxznpIzExAXHocFl6bi9uxN7XuN9i9ucayALVq\n38HuzSE2MWOfrwnZiNjxyXCxqqD+0jLBGANJGsNFCCHEGFHqNSGFi/6KCM/AWkMgFoqx+9EOpEpT\ni6UNErk0123W31qPF0nP8SLpOQDg8vtQTLswFb/dXIwO+1prbN/Vqwd8HH01ltuIbTG6zjhMazAD\nx3ufRmfPrjATqQLlMh9IGeEYZbqQIIQQYox4GVN0iU9IgZW9+W6IXkKBEJ52XngUHwHvjZWxq/M+\nBFVoACtTq0/WhmwDAmUAaLgjgH08/MQg3jqhQIgHw5+hy4EQPEuMxMSAL9VfzprX9BfeczOhmY4t\nyx5+6nUxNqQQCTjBsYACZUIIIUZDdaKm1GtCCo6uEomGmY1+AgBky7PR90h3VF3vitOvTn6y95ca\nGCjrI2fkcLRwxP5uR7Gv2xEEutQz+LUiznzJhVuBu/QxxsIg3M9hQoEyIYQQI8HNkhLQJT4hBUZ/\nRURDO4+O8Hfij90deKxPkb/v25Q3aLe3Bfod6cEu6+zZDc+/eI9fWywzaB9Tg74FAJS3KA8AcLV2\nQ7OKLfLUDm7qUmHO6VwaGUsBLy7+9FD0E0gIIcQ40NAiQgoXpV4TrbZ32oP51+Zg16Pt7LJ3KW/h\nblOx0N9LJpfh4rvzWHR9Pu7E3eatczB3hLWpNYb5jsAw3xF4lvgUcelxCPFtgQ1XtmDSmbHsto8/\nf4lyZnawN7NHU/e8Bce6lPlA2Qh7lClQJoQQYoy4N7cp9ZqQgqOrRKKVi1UFLGu9CrHjk9HeoyMA\n4PCzg4X+PlKZFK5r7NHvSA+ExVzXWF/FtgrvuZdddTRyawJzkTn61RjIW2dv7gChQIgx/hPgW96v\nUNpX1lOveWlcRtK7zA34z745XYwtIYQQQgoP/+Y2IaSgKFAmuVrQ7FcAwI+Xv8fld5dy3V7OyHPd\n5uTL43BeZQv3tY5a19ub2aOaXXUM8flM5z4EAgEG1RwKAHjxRVSu75kf1KNs3D3KzxIji7ElhBBC\nSOHh3tA2FYqLsSWEGAcKlEmuKtpUgl/5OgCAmaHT9W77Q+h3qLDaDqmSFJ3bvE15gyH/9Ne5flOH\nHXj0+UtcHnQTDubaA2ml31uvROz45CKryl3We5SNpReZi/uR2lQOKb6GEEIIIYWIe0NbbEKBMiEF\nRYEyMch/fS+gkk1lPIl/hIzsDJ3brQ1fCQCITHzKW77j4Vb8cv1nAMDgY/00Xnd10C3Mb7oIYUPu\nobNn1xIToBnSO27M+NNDlYxjUlDcKaGWtFxejC0hhBBCCg/3nE09yoQUHBXzIgYRCoTo5tUTK+8s\nwz/Pj6C3t2awqw3DMJhydiJ2PtoGALAR2yIi/gEA4PdWK9G9Wi+kSdPgbOkMT7tqRdb+/Crzqdec\n2NhYUq+5n8NSZFmMLSGEEEIKD3dokamQLvEJKSjqUSYGa+fRAQAw7r9ReJH0XO+2UrkUDMNge8QW\nNkgGgJ8uzwAA9K7eD4NqDYWVqRWcLZ2LrtGkQLi9rzCSHmXuhYSILiQIIYQYCd4YZUq9JqTA6CqR\nGKyGQ0328byrP2Fj+63IzM5E5XWKQHdj+63s+s77Q2BtaoNMmfY07R+bzC3axpJCYSy9yFzcQNmE\nAmVCCCFGgzNGmVKvCSkw6lEmBnMwd8RQn+EAgFfJL3H/wz3U316HXT/y32G87VOlKciWZwMAtnXa\ng8M9TqCeSxBWtlmHClaun6zdBUFjlI2v6jX3c4gEFCgTQggxDrwxyiamxdgSQowDXSWSPFnScjme\nJUbi8vtLaP1XsEGvqV3en52L+XjvM0XZvELH7X0s64wlUOb3KJsUY0sIIYSQwsNNvTYR0PmNkIKi\nQJnkWceqnXH5veZ8ymP9J6KPdz/4OtaGUCCEQCBAZMJTlDOzK4ZWFsz+7kexLnwVulfrVdxNKVZy\nRsY+Npqq15yAn26EEEIIMRbcLLhsJrsYW0KIcaBAmeRZV68e+CH0OwDA5MCpCHZvhibuTWFmYqax\nbTX76p+6eYWiqXtzNHVvXtzNKHZyI5xHmoJjQgghxihFksQ+drGsUIwtIcQ4UKBM8szN2h3PR72D\n2MSMJrQ3cjJuj7KRpF4bS/VuQgghhOtg5H72sZWpVTG2hBDjQIEyyRdrsU1xN4F8AjIjTL0WUg1D\nQgghhBCSC7piJIToZIw9ysYS8BNCCCGEkKJDgTIhRCe5XJb7RqWMtal1cTeBEEIIKXSmQsWUUKPr\njCvmlhBiHChQJoToZIw9yuYi8+JuAiGEEFLopHIpAMBWXK6YW0KIcaBAmRCik4wz1QSlLBNCCCEl\nHxVaJaRwUDEvQohOciPsUQaAF19EgeHcBCCEEEKMhVjLdJ2EkLyjQJkQopPMCMcoAzRtBiGEEONF\nQ4wIKRyUek0I0ckYp4cihBBCjNHhHifQwaMT+tcYVNxNIcQoUI8yIUQnOXeMshGlXhNCCCHGppFb\nEzRya1LczSDEaFCPMiFEp4zsDPYx9SgTQgghhJCyggJlQohOSZKk4m4CIYQQQgghnxwFyoQQnZKy\nEou7CYQQQgghhHxyFCgTQnSaEDC5uJtACCGEEELIJ1cmAmWZTIYlS5agadOmCAwMxOTJk/Hhw4fi\nbhYhJV7P6n2KuwmEEEIIIYR8cmUiUP7jjz9w4MAB/PLLL9i+fTuio6MxadKk4m4WIYQQQgghhJAS\nyOgDZYlEgq1bt2Lq1KkIDg6Gr68vfvvtN9y6dQu3bt0q7uYRQgghhBBCCClhjD5QfvToEdLS0tCg\nQQN2WcWKFeHu7o6wsLBibBkhhBBCCCGEkJJIVNwNKGrR0dEAABcXF95yZ2dndh0hRLd5wQsREf+w\nuJtBCCGEEELIJ2P0gXJGRgaEQiFMTU15y8ViMbKysvS+1t7eEiKRSVE2jxSAk5NNcTehTJjRdlpx\nNwEAHe+yiI552UPHvOyhY1720DEve0rrMTf6QNnc3BxyuRzZ2dkQiVQfVyKRwMLCQu9rExLSi7p5\nJJ+cnGwQF5dS3M0gnwgd77KHjnnZQ8e87KFjXvbQMS97SsMx1xXIG/0YZVdXVwBAXFwcb3lsbKxG\nOjYhhBBCCCGEEGL0gXLNmjVhZWWF69evs8vevn2Ld+/eoX79+sXYMkIIIYQQQgghJZHRp16LxWIM\nGjQIixYtgr29PRwdHTF79mw0aNAAAQEBxd08QgghhBBCCCEljNEHygDw1VdfITs7G9988w2ys7PR\nrFkzzJo1q7ibRQghhBBCCCGkBCoTgbJIJML06dMxffr04m4KIYQQQgghhJASzujHKBNCCCGEEEII\nIXlBgTIhhBBCCCGEEMJBgTIhhBBCCCGEEMJBgTIhhBBCCCGEEMJBgTIhhBBCCCGEEMJBgTIhhBBC\nCCGEEMJBgTIhhBBCCCGEEMIhYBiGKe5GEEIIIYQQQgghJQX1KBNCCCGEEEIIIRwUKBNCCCGEEEII\nIRwUKBNCCCGEEEIIIRwUKBNCCCGEEEIIIRwUKBNCCCGEEEIIIRwUKBNCCCGEEEIIIRwUKBO9Pnz4\ngGnTpqFp06YICgrCyJEj8eTJE3b9pUuX0L17d9SpUwddu3bF+fPnte5HIpGgW7duOHToEG95cnIy\nZsyYgcaNGyMwMBBffPEFnj17lmu77t27hwEDBsDf3x/t2rXDwYMHtW7HMAxGjRqFVatWGfR5Dx8+\njPbt26NOnTro168f7t69y1t/+fJl9O/fH4GBgWjVqhV++eUXZGZmGrTv0oKO+V2d286ePRutW7c2\naL+lCR1z/jFPTk7G999/jwYNGqBBgwb4+uuvER8fb9C+Sws65vxjHhERgaFDhyIwMBAtWrTAokWL\nIJFIDNp3aVHWjrnSsWPHEBISorH81atXGDlyJHvMN2zYkKf9lgZ0zPnoGq7sHXOufF3DMYToIJPJ\nmP79+zP9+vVjwsPDmadPnzKTJ09mGjduzMTHxzNPnz5l/Pz8mFWrVjGRkZHM0qVLGV9fX+bJkye8\n/aSkpDCjRo1ivL29mYMHD/LWjRkzhunWrRtz+/ZtJjIykpk0aRLTrFkzJiMjQ2e7Pn78yDRo0ICZ\nM2cOExkZyWzdupXx8fFhLl68yNsuKyuL+e677xhvb29m5cqVuX7e0NBQxtfXl9m9ezcTGRnJzJgx\ngwkKCmI+fvzIMAzDREREML6+vszSpUuZFy9eMBcuXGBatGjBfPfdd4Z+pSUeHXP+Mee6cOEC4+3t\nzbRq1SrX/ZYmdMw1j/nQoUOZrl27Mnfu3GHCw8OZLl26MKNHjzbk6ywV6Jjzj3liYiLTqFEjZtas\nWczLly+ZixcvMk2aNGEWLlxo6Fda4pW1Y6505swZpk6dOkzbtm019te2bVtm0qRJzNOnT5nDhw8z\n/v7+zJ49ewzed0lHx5x/zOkaruwdc678XsNRoEx0evDgAePt7c1ERkayy7Kyshh/f3/mwIEDzA8/\n/MAMGTKE95ohQ4YwM2fOZJ+HhoYybdq0YXr27KnxB5eVlcV88803zJ07d9hlERERjLe3N/PgwQOd\n7VqzZg3TunVrRiaTscumT5/OjBgxgn1+//59pnv37kzr1q2ZoKAgg/7gPv/8c2batGnsc5lMxrRp\n04ZZvXo1wzAMM3fuXKZPnz681xw4cIDx9fVlJBJJrvsvDeiY84+5UkJCAtO0aVNmyJAhRhco0zHn\nH/MrV64wtWrVYl68eMFuc+nSJaZt27ZMWlparvsvDeiY84/5mTNnGG9vbyYlJYXd5pdffmG6dOmS\n675Li7J2zDMyMpiZM2cyvr6+TNeuXTUuoI8cOcIEBAQwqamp7LI//viDadeuXa77Li3omPOPOV3D\nlb1jrlSQazhKvSY6ubq6Yu3atahatSq7TCAQAACSkpIQFhaGBg0a8F7TsGFDhIWFsc/PnDmDHj16\nYPfu3Rr7F4vFWLRoEfz9/QEA8fHx2LJlC9zc3ODp6amzXWFhYahfvz6EQtV/3wYNGuDWrVtgGAYA\nEBoaiqCgIBw6dAg2Nja5fla5XI5bt27xPo9QKET9+vXZz9OvXz/MmjWL9zqhUAipVIqMjIxc36M0\noGPOP+ZKP/74I9q0aYPGjRvnut/Sho45/5hfunQJtWrVgoeHB7tNcHAwTp06BUtLy1zfozSgY84/\n5g4ODgCAnTt3Ijs7G+/fv8f58+fh5+eX6/5Li7J0zAHg48ePeP78OXbt2qU1HTMsLAx+fn6wsrLi\nve/Lly/x4cMHg96jpKNjzkfXcGXvmCsV5BpOlOdXkDLD3t4eLVu25C3btm0bMjMz0bRpUyxbtgwu\nLi689c7OzoiOjmafz5w506D3mjdvHrZt2waxWIw1a9bA3Nxc57bR0dHw8fHReN+MjAwkJCTAwcEB\no0ePNuh9lZKTk5Genq7189y7dw8A4O3tzVsnlUqxefNmBAQEwNbWNk/vV1LRMecfcwA4dOgQHj58\niEOHDmHz5s15eo/SgI45/5i/fPkSlStXxpYtW7Bz5072e/j2229Rrly5PL1fSUXHnH/M/f39MXbs\nWCxfvhy///47ZDIZgoKC8OOPP+bpvUqysnTMAcDd3R07duwAAJw7d07r+zo7O2u8LwBERUWhfPny\neX7PkoaOOR9dw5W9Yw4U/BqOepSJwU6fPo3ffvsNI0aMgJeXFzIzMyEWi3nbiMViZGVl5XnfAwcO\nxL59+9CtWzdMmDABEREROrfV9b4A8l18RVnMwczMjLfc1NRU6+eRyWSYPn06nj59avCPSmlU1o95\nVFQU5s+fjwULFhhNb2JuyvoxT01NxaVLl3Du3DksXLgQCxYsQHh4OCZOnMje+TY2Zf2YZ2Zm4vXr\n1+jWrRv27NmDFStW4N27d0YVKKsz5mNuiMzMTI3/E8r3zc9nLg3K+jHnoms4FWM+5oVxDUeBMjHI\n/v37MXnyZHTs2BHffPMNAMWFh1Qq5W0nkUhgYWGR5/17eXnBz88Pc+fOhbu7O3bu3AkACAwM5P0D\nAHNzc40/LOVzQ947LCyMt89Ro0axJ0z1/UqlUo19ZmRkYOLEiTh58iSWL1+O2rVr5/nzlgZl/Zgz\nDIPp06ejV69eCAoKyvPnK43K+jEHAJFIhOzsbPzxxx8IDAxEkyZNsGDBAly/fh0PHz7M82cu6eiY\nAxs3bsSTJ08wb9481K5dGyEhIViwYAEOHjyIx48f5/kzl3TGfswNoe99jfGmKB1zFbqGKxvHvLCu\n4Sj1muRq9erV+P333zFkyBDMnDmTHe/g6uqK2NhY3raxsbEaaR26pKam4sKFC2jZsiV7YhIKhahW\nrRpiYmIAQGv5+AoVKiAuLk7jfS0tLQ0a1+Dn58fbr7m5Oezs7GBpaZnr50lISMCYMWMQGRmJdevW\nGeWYVYCOuYuLC96/f4+rV6/izp077FgdqVSK7OxsBAYGYv369UYVQNMxV3weFxcXuLu7w9raml1f\nrVo1AMDbt2/h6+tryMcuFeiYKz5PeHg4atWqxRs/pxyD9/r1a9SoUcOQj10qlIVjbogKFSrgxYsX\nGu8LwODPXFrQMVeha7iyc8wL6xqOepSJXuvXr8fvv/+OyZMn44cffmD/2ACgXr16uHHjBm/7a9eu\nGRw8ZGVlYcqUKbhw4QK7LDs7Gw8fPoSXlxcAoEqVKrx/yvcNCwvjpUFeu3YN/2/vTkOi+OM4jn+k\nrOjSLk06KII2ScvKKMsowyQ7JHMp0kSpJ9lNp2JkWtJpmhFREZliGEmJGFlREhEhWiIombVh9wNJ\nyrQHHc7/gbTtZvK3svJ4v2AfzMxvfzu/+bIyH3dmfhMnTrQ70WlOjx497Pp0dXWVg4ODJkyYYDee\nhoYGFRUVafLkyZIaLx1ZuXKlnj9/royMjA77B5aaN9bc1dVV165dU25urnJycpSTk6OwsDC5uLgo\nJyenQz3oh5p/+557e3vr2bNnevv2rbXNo0ePJEnDhw9v0ZjbA2r+reaDBw+2m2dU+lbzr/vWEXSW\nmrfEpEmTVFZWZvcQp8LCQo0cOVIDBgxoUR/tATX/hnO4zlXz1jqHIyijWRUVFUpOTlZISIiWLFmi\n6upq6+vDhw9avny5iouLlZqaKovFoiNHjqi0tFQREREt6n/AgAFauHChDhw4oLt37+rx48eKiYlR\nbW2tIiMjm32f2WxWTU2N4uLiZLFYlJGRoby8vJ++/OZ7kZGRysnJUWZmpiwWi3bu3Kn379/LbDZL\nko4cOaKKigrt27dPLi4udsejoaHhtz67raDm32retWvXJn/wnZycrOt/5r/YbRk1t/+eBwYGys3N\nTRs3blRFRYVKS0u1Y8cOTZkyRe7u7r/12W0FNbev+bJly/TkyRMlJCSoqqpKhYWFiomJkZ+fX5MH\nALVXna3m/2fOnDlycnLS5s2bVVlZqby8PJ0+ffqXHijUVlFze5zDda6at9o53E9NJoVOJSkpyRg9\nevQPX1/nNysoKDDmzZtneHh4GEFBQcadO3ea7e9HE5fX19cbiYmJhq+vrzFu3DhjxYoVxqNHj/53\n30pKSoyQkBDDw8PDCAgIMPLy8ppt6+fn1+KJy7Ozs43Zs2cbnp6extKlS42ysjLrtunTpzd7PF6/\nft2i/ts6am5f8+8dO3asw82jTM2b1vz169fGunXrDC8vL8Pb29uIjo423r1716K+2wNq3rTmRUVF\nRmhoqDFx4kRj5syZxu7du+3m2G3vOmPNv0pNTf3h/KoWi8UIDw83PD09jVmzZhlpaWk/1W9bR83t\na845XOer+fd+5RzOwTA66GM8AQAAAAD4BVx6DQAAAACADYIyAABsP6fNAAAFH0lEQVQAAAA2CMoA\nAAAAANggKAMAAAAAYIOgDAAAAACADYIyAAAAAAA2CMoAALQz0dHRMplMevDgQav1mZiYKJPJpMLC\nwlbrEwCA9qrrv94BAADwc/z9/TVkyBANHDjwX+8KAAAdEkEZAIB2xt/fX/7+/v96NwAA6LC49BoA\nAAAAABsEZQAA2hnbe5RfvHghk8mko0eP6saNGzKbzRo3bpx8fHy0Y8cO1dTUNHl/dna2goKCNH78\neAUEBCgrK6vZz3r69Km2bNmiadOmycPDQ4GBgTpx4oQ+ffpkbZObmyuTyaTFixeroaHBuv7t27fy\n9fWVl5eXqqqqWvUYAADwJxGUAQDoAAoKCrR27VoNGjRI4eHhcnV11YULF7R69Wq7dikpKYqNjVVd\nXZ3MZrPGjBmjhIQEXblypUmf5eXlCgkJUX5+vqZOnarIyEg5OTnp8OHDioqK0pcvXyRJQUFB8vPz\nU3l5uTIzM63vT0hIUHV1tbZt26YRI0b80fEDANCauEcZAIAOoLy8XCkpKQoMDJQkbdy4UcHBwSop\nKZHFYtGoUaNUVVWlU6dOyd3dXenp6erbt6+kxpAdFRVl159hGIqOjtbHjx+VlZUlDw8P67a9e/cq\nLS1NWVlZCgsLk9QYihcsWKCUlBTNnTtX9+/f1+XLlzVjxgyFhob+paMAAEDr4BdlAAA6gGHDhllD\nsiQ5OjrKx8dHkvTy5UtJUn5+vj5//qxVq1ZZQ7Ik+fn5ydfX166/0tJSVVZWymw224VkSdqwYYMc\nHR118eJF6zoXFxfFxMSorq5O8fHxSkhIkLOzsxITE1t9rAAA/Gn8ogwAQAfwo0ub+/TpI0n6+PGj\nJKmiokKSmgRfSZowYYJu375tXS4vL5ckPXv2TEePHm3SvlevXnr48KEMw5CDg4MkKTg4WFeuXNH1\n69clScnJyXJ1df2NUQEA8G8QlAEA6AC6devWZN3XAPtVbW2tpMaQ+z1nZ+cftr19+7ZdgP5efX29\nevfubV0OCAjQrVu35OjoKE9Pz5YPAACANoSgDABAJ/H1cuu6ujr169fPblt9fb3dcs+ePSVJiYmJ\nMpvNLeq/pqZGSUlJcnJyUm1trWJjY3X27NkmgR0AgLaOe5QBAOgkxo4dK0m6d+9ek21lZWV2yyaT\n6YfrJenTp0/at2+fMjIy7NbHx8erpqZGcXFxCgkJUWFhoc6dO9dauw8AwF9DUAYAoJOYN2+eunfv\nruPHj6u6utq6vri4WDdv3rRrO3nyZA0dOlTZ2dkqKSmx23by5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bw6VLF/D0XIu5eervRly/fi1Hjx6mTJnUg95du7YzY8ZULC2L0KJFK+Lj4/Hx\n8eb06RPMnbtYqwzp+VDlS8/atStZunQhpUvb4eramTt3gti4cT3Xr19j/vyl5MqVS2cdlUqF/mt9\nLM9aZimvpKQkJkwYy6lTJ6hTpx7Ozo05ffoks2fPICTkMUOHjtRK//vv09m1aztVqjhQr159rl69\njKfnEoKCAlm6dHGG+b148RwPj368ePGcpk1bYGZmho/PAb79dii//DILR8cGWulv375F8eK2qd6U\n2ttXyDC/ixfP8+23QzE3t6Bly1a8evUKHx9vLl70w9NzrVJ3ANy7d5fBg/uiUiXTtKkLenp6HDjg\nxeDBfVm4cFma3/l8+fLRs2dvZs36lbVrN6X5MCM9dnZldeq8Y8d8CQoKxMWlFVZW1jrrWFvb4O7e\nn4oVK2c5v49t0aJ5rF+/hpUr12V3UbIkPDycefN+Y+DAoZiYmPDyZYROmrt37zB4cB9iYmKoV68+\nX3xRjICAm+zYsZUzZ06zfPlqrUA1vd/Q//3fjFTLkZiYyM8/j9d6oPy2adMmc+DAPuztK9ChQyce\nP37Ijh1bOHXqOJ6eazMdLH+o8qUlK3VQePgLBgxwJyTkERUqVKJJk2YEBPjz22+/cunSeSZNmqYT\nWL8tK3VQbGwsI0cOISgokIYNG1OkiBVHjx5m4sQfiIgIx9W1c4b7l5U66O7dOyQlJVG7dh0qVqyk\ns628eTM+h1m5frzr9a1WrS+pVKkKv/8+nWnTZirLW7ZsTbVqNZS6S+N91JHvSgJlIcRnIzExkRkz\nptK0aQudp6JGRkZaLWBvt4Z5ee3m2DFfqlWrkWZLWWZb0LJbREQ4ycmfX/fY/fv3cuXKJdat26Is\nMzY2BiB37twAbN68gaCgQHr27MnAgSOUdBcvnmfkSA9+++1XVq/eAEBMTAy//z4DG5uirFy5DlNT\nMwBq1drJr79OYfXqFTqBWlJSEkuXLmD9+rVpljM6+hVz5szC2lq9XTMz9XZdXTvRv38v5s37HU/P\nNRnu74cqX3qePAnB03MJlSpVYcGCZUoLjqfnElat8mTXrm2p3pypUFHoQiFyxeoG0ek5dOggp06d\noGvXngwZoj5f/fsPZvToYWzcuA4Xl1aULq3u8XL16mV27dqOs3Njpkz5FT09PVQqFVOnTmL//r0c\nOXKESpVqppvf8uVLePr0CdOnz6ZePScAunXrRd++Pfjtt+nUrl0HIyMjAEJCHhMdHU2rVm3f6bed\nnJzMzJkID21yAAAgAElEQVTTMDbOjafnGiwtiwDQrJkLo0YNYeHCOVo3/nPnziI2NgZPzzVKz4f2\n7V0ZMKA3v/02XfnOvP2dB+jYsTObNq3njz+W6XwnMsPOrpxOb4snT0KUQLl6dd3jam1t89nUeeHh\nL7K7CO9k8eJ5mJiY4uLSCkj93M+fP5tXr14xdeoMGjRopCxftcrzn9/tckaO/B+Q8W/oxIljyu9C\nIzLyJRMn/sC5c2fSLOfZs6c5cGAfzs6NmDJluhIs7tixlVmzfmHdutXK7zs9mSlfu3Yts1y+9GSl\nDlq0aB4hIY/o2LEzI0Z8p+znokVzWb9+LV9+WZeWLVunm19W6qDNm/8iMNCfUaPG4OraCYDevfsx\ncKA7ixfPp1Gjpun2LMpqHXT79i0AOnToqPPQMDOycv3IyvUtte/9gAEe9OnTQ+s7qzn2mrorpX9b\nR74rGaMshPhsHD16iIcPH9CxY5fsLorIIpVKxbp1q/nyy7p88UUxZbmtbUny5cun3Cz8/fdh9PT0\nGDlS+0JYrVoNHBxqcPt2EKGh6snrfHy8iYqKpHPnbspFGqBVq7YUL27Lvn27tbqgBQT407dvT9av\nX5tuS3xQ0C0KFSqEq6ubEiQDlCljR8mSpQgIuMnr168z3OcPVb707Ny5jaSkJHr2dNfq5tizpzum\npqbs3r0z1fXi78eR73Y+Xtm8ylJ+27dvwsDAgJ493ZVlhoaG9O8/GJVKxZ49b/Lbtm0zAH369Fdu\nUPX09Bg0aCh6enps3rw53bxiYmLw9t5LuXLltYKBQoUK07FjF0JDn3H69EllueamsXRpuyztk8b5\n82cJDr5Pq1ZtlRtUgJo1a1Or1pccO+artBA+eBDMuXNncHJqoBWwlipVhmbNXPD3v8GtWwEAlChR\nEoCSJUsr6YyNc/P1123ZuXMrkZGR71Re8Wl59uwp3t5etG/vpvwWCxQoiIVFXkqUKAWoh5mcP3+W\ncuXKawXJAD169MbIyFjrO53Rb2jPnh1a2zh4cD/du7tx7tyZVLsFa9y7d4cCBQrSo0dvrRbVpk2b\nA+quv5nxocqXnszWQYmJiRw9ehgLi7wMGjRMaz/V3YpN2bgx/WEuWa2Dtm/fQoECBWnXzlVZZmJi\nSq9efYiLi+Pgwf3p5peVOgj+fZ2XletHVq5vpUuXRk9PT/neA5Qta0/lylVZu3ZlpsqWXXWkBMpC\niM/Gxo3rsLUtgb19+Q+yfUfHmvTu3U35e8WKpTg61uTBg2AWLZpL27YtaNy4HoMH98Hf/wbJycms\nW7caN7c2NGniSP/+vbhwwU9nu8+fhzFr1q+0b9+Shg3r4ObWhkWL5hETE62VLjExkT/+WMY333Sh\nSRNHXFwa8e23Q/HzO6ukmTp1kjIWcd6833F0rElIyGNl/U2b/mLAgN40b94AZ+evcHVtxcyZ0wgP\nD9fZ119/ncLFi+fx8OhH48b1aNu2OUuXLiQpKYm7d+/w7bfDaNq0Pu3auTB79gzi4uKU9S9c8MPR\nsSa7d+9g27bNdOrUlsaN6/HNN13x8trN206fPsG9e3dp3txFa3mpUqUpVerNGPu2bV0ZMMBDK0DV\nMDJSt3bGxsYAcPmyeixTtWq6rWXVqtXg5cuX3LlzW1l2/Lgvjx49YPDgYcycOVdnHY2qVauxadNO\nunTpobU8Pj6eJ0+eYG5ukWr35bd9qPKln+dFZfspGRsbU7FiFYKCAnn1SjsYjouLI2J/ODGWMUSW\nzvwNSEJCAjduXMfOrhwWFhZan5UvX5HcuXNz6dJ5rbLly5dP63yD+iazWLHinDt3TlkWEvIYR8ea\ndOz4pnXnxo1rJCQkpNo6qjnGKfMLCsr8TaOX124cHWsydeokZdmlS+pjmVZ+SUlJyvjn9M61Zv2L\nF9VpNAHy28ehWTMXYmNj2blza4blfR80v+G5c39Tlg0dOoDOndvx5EkIEyaMo0ULZ1q0cGb8+LGE\nh4cTFRXF9OlT+frrxri4NGLs2FFK/ZNSQIA/338/mpYtG9OoUT169+7Gjh1btGZXBwgLC+OXX36m\nc+d2NGpUl7ZtWzBlygQePnygpOnYsTX79u0BwN29u9Z3IiIigoUL59K9e0caN65H48b16NGjE2vW\n/EFiYqLOvnp7e7Fr13a6d+9Io0Z16dbNFW9vL0D9++vTpweNG9ejS5cObN26SausmuvB7dtBzJkz\ni1atmtC8eQNGjPBIdRz8li0bSE5OplmzFlrLS5YspbRwJierGDx4GJ07d9dZ38DAAAMDA6W+g4x/\nQ5rvmMbOndswNjZm+vTZWoHk2zp16sauXd46wxHu378HQIEC6c+n8KHLl5as1EERERHExsZQqlRp\nrZZNUNePxYoV586dIKKj035YmJU66NGjh4SGPqNKFQcMDAy00r5dJ8C/r4MAgoKCMDU11eqOnZap\nUyfh6FhT63qdletHVq5vdnZ2WFnZ6HSZbtbMhWvXrmT6QczHriNBul4LIT4Tjx495ObNG7i5df3o\nef/00zgiIyNp0qQZT58+5ejRQ4wePYx69epz8uRxnJ0bk5AQj7e3F2PHjuKvv7ZRqFBhAJ48eYKH\nR19CQ59Rr54TtrYluXUrkPXr1+Dnd4aFCz3JkycPAHPmzGTHjq04OFSnQ4dOREe/4tChA4wePYzZ\nsxdSvXpNnJycefUqimPHfJVxSGZm6nFAkyb9wNGjh6lSxYE2bTqQkBDP2bOn2blzGwEB/jrdha9f\nv4q3txd16jjSrl1HfH0Ps3btSsLDX3D06GHs7cvTvr0rp06dYOtW9VP74cO1J1nZvn0Lt2/fomHD\nJlhYWHDsmC/Tpk0mJOSxVrdOHx9v9PX1dVoN+vUbpPV3q1ZtUz0HERERXL58iTx58mBlpb4JePTo\nEQBFixbVSa9J8+BBsDL5Vr169WnfviMFCmRtHHpCQgK3b99i6dKFREa+ZMiQzHX7+ljl087zIQUK\nFEx1DJe1tfU/ed7XGi+7dOlCkl4l8dTlKUYRRpnO68mTEJKSklLdPwMDAywti/DgQTCgPobPnj2l\nQgXdcXOgPh7BwfcJDw8nf/78yjwDKce4PXr0EEj9eL7Zt2Bl2e3bQejp6XHlyiWmT59CcPB9zM0t\ncHZuTN++A7UexmjG+KZsDX6Tn+7kUW/n9+Zc66ZNea41aY4f132gVqJESSwti+Dj4/1OQcP7Eh0d\nzeDBfSlc2JI2bdpz+fIljh49xMuXEcTExJCQEE+LFq24e/cOJ04cIywsDE/PNUoL3alTJ/jxx/9h\naJiLBg0akj9/fs6cOcWsWb8SEBDA2LE/AuoHT8OG9ScgIIAGDRrRsGETHj16iI/PAc6cOc369Vuw\nsMhLp05d8fLaQ1BQIG3bdlCG3bx69YoBA77h6dMnODrWx8nJmYiIcHx9j7Bs2SIiIyN1umhu2PAn\nDx8+pEmTZlSvXot9+3YzZcpP3LoVyNatG2nYsAkODtU4cGAfs2fPwNLSEicnZ61tTJ06icePH9Gs\nWQtiYmI4csSHESMGM336bK2eID4+Byhd2o6CBQtprb9w4XLl32ZmZjoP5DTOnTtDbGyM8pvJ6m8I\n1PNuVKpUBWNj41Qf4qYlOvoVFy9eYO7c38iVK1eaZUwps+V78eIFkOtflU8jK3WQ5kFrWr2BoqNf\noVKpePr0iU6gr5GVOii9+qNgwUIYGRlr1Vf/tg4CuHMnCEvLIixdupAjR3x49uwpNjZFadOmA25u\nXbRa0Z2cnLGystaanDIr14+sXN86duxIgwbNddJpfi8+Pt5UqlRF5/O3ZUcdKYGyEOKzcPGi+iL6\noVqT0/Pq1StWrfpLuWmfNOlHfHy88fU9zLp1W5Sg2MrKmj/+WMaxY760b6+eTfe3334hNPQZ06fP\npm7dN7M1bt68gblzZ7Fy5TI8PEYQHf2KXbu24+BQnQULlinpWrduR79+vdi2bTPVq9ekfv03gfJX\nX9WhUyd1C/i1a1c5evQwzZq58NNPU5T1ExMT6du3B/7+NwgOvk/x4rbKZ3fv3mH48G+VbbRt255u\n3TqyZ4+6NVVzk/nNN33p0OFrDh701gmUAwP9mTLlVxo2bAKob3wGDnRnzZo/aNbMhWLFigPqJ+fW\n1jZYWOR9p3OwaNFcYmKiadeuozL+6+XLCIyMjDA2zq2TXhMEpWwdeJfvTmJiIk2aOCpjwt3cutK1\na8Y3jR+rfG+LjHyZZmuCpntcyhbla9eusHXrRsyczHlt/jpLgXJk5EsA5UFNavnFxd0nMTFR6SqX\nVtqUxyN//vyYm5vrjJ9NLz/N+in37fbtW6hUKlasWIKzc2OqVq3OpUvn2bz5L86fP8vixSuUY5La\nGN83+en2bnj7WGq6P6ZXtvRaqjTs7Stw7NhRXr6MyNTEOx9CREQ49es3ZOrUGejp6ZGYmEjnzu24\nePE8lStXYcmSP5QeFcOGDeTixfPcv3+PEiVKEhcXx9SpkzA1NWPZslXKd3HQoGH89NP37N69nfr1\nG1CnjiN+fme5ceOGMvu3xvr1a1m0aC4HD3rj6tqJTp26cetWIEFBgbRr56qcp+3bt/D48SPGjh1P\n69btlPXd3fvTtWsHDh7crxMo37lzm6VLVym/tTJl7Jg5cxobNvzJjBlzlDraycmZYcMGcvCgt06g\n/OjRA/74Y50SvLRv74aHR19mzfqFDRu2o6+vz6NHD3n27Ok7D6GIi4tj/vzfAWjTpj1Aln9DADVq\n1Mpy3n5+Zxk50gNQB5uTJk2lcuWqGa6X2fJFRUVhYlLgncunnWfm6yALi7xYWxfl1q1AHj9+hI3N\nmwDvzp3bPH6sDvze7nGT2fzeroPSqxPUZTPVqhP+bR30/HkY4eEvCA9/QXx8PI6O9YmNjePUqePM\nm/cbt24F8OOPk5T169d3pn59Z538Mnv9yOr1LTVFi36BhUVeLl48n266lD52HSldr4UQn4WAAM34\nvlIZpHz/XFxaabVsaW4amjRprgTJgPIkXdMVMSwsjNOnT1KnTj2tIBnUE0NZWhbBy0vdpTA5WfXP\n0+ynPH8epqSzt6/Axo07mDRparpltLS05McfJ+kEF4aGhlSurJ6h+e3u10ZGRrRv76b8Xbx4CWVm\n05TBoKmpGba2Jf+5AMdpbaNy5apKkAyQP38BevVyJykpicOHDyr5Pnv2VBmbmVWrVnni5bUbKytr\nBgzwUJYnJial2QVaszwhIf6d8tSIiYmmffuOdOzYGRubomze/BfTp0/V6UKamo9RPt08E8mVK/Vg\nV/OAISEhQfn/r79OoUwZO0xqmr5TXkCm8tOk1bTqvC0zxyO9/DTLNPuWnJyMmZk5dnZlWbt2E+PG\nTWDEiNGsWPEnbdt24M6d2/zxxzKd7WQ2v7ePZXr7l5VzXbJkKVQqFYGB/hmm/ZBStj4ZGhoqPRBc\nXTtrfaffrvOOH/clIiKcrl17at1w6+vrM2jQUAD27lV39VT9877uoKBbxMe/OTYdOrixdeseOnR4\nUzel5ssvv+K7775XJsrSKFLEChubokREhOusU6WKg9YDKU1dXry4rVYd/fZ+peTq2lmrha9iRfXs\nyY8fP1K6kAYEqM/fu9R5r1+/ZsKEsdy9ewcnpwY0btwUSP87Bu+vTjEyMqJbt560bNma3LlzM2nS\nj6kOp3lbZsuX8lz/W1mpgwC6dOlOQkI848Z9y5Url4iJieHy5UtMmDBWmXAqvao9K3VQZo5HRucq\nK3XQixcvKFmyFM7Ojfnzz80MHz6asWN/ZO3aTdjbV2Dfvj0cP+6bYX6ZPZbv6/pWokRJ7ty5nal5\nP+Dj15HSoiyE+CxoZj3NjlaWlJNPAUpX6befvGouJJoKPzDQH5VKxcuXL1mxYqnOdnPlysWzZ08J\nDX1G4cKWNGrUlEOHDuDq2orKlavy1Vd1qVvXiZIlM344YGlZBBeXViQmJhIQ4E9w8D0ePXrIrVsB\nyhjn5OQknXXevtDlzp0HY+NYne6Cby6Sr7WeIDs4VNcpS/ny6ptMzRjRf3PuNLNt5s2blxkz5miN\nQzM2Nub168RU19Ocg9y582Q5z5QsLPIyatQYQN0q9t13w9m9ezu1an1Jo0ZN0l33Y5QvtTwTE1O/\n4dDc4Gi+vytXLufBg2CWLVvN9/7fvVNeQLr56enpkTt3buXm+N8cj/Tye/1ae9/09fVZtmyVTjp9\nfX2GDBmJt7cXPj7eDBv27Tvl9/ax1PwmUtu/rJxrzW8kPFz3NUIfU9p1nnY3y7frPE2AGBBwM9U6\nz8DAQJnNtmbNLylWrBjHjh2lTZtm1KxZm6++Uj9ULFLEKsMyli1rT9my9sTExHD9+lUePnzAgwfB\n3Lx5gwcPglN9l2xm90tz7lO7ea9WLbU6ryLe3vsICrpFlSoO71znxcbG8uOPYzh79hTly1dgwoSf\nUynTh61TqlRxUF5/16fPAPr168nMmdOoWbO21oRSb8ts+TTH/H3ISh0E6ocwDx8+YMuWDXh49FPS\nNWvmQrVqNdixY6vO+OXM5vd2HfSmTki9bK9fv87wXGWlDtI8FHybubk5Hh7DGT58EAcPeqc7G3ZW\nrh/v6/qWN2++f+6TIrQaHtJLDx+vjpRAWQjxWdB04UnvIvahpFXZa24S0/LqVRSgHgt8/frVNNNF\nRkZSuLAlEyb8jL19Bby8dnHx4nkuXjzP4sXzsbevwNixP+p0y3rbjh1bWbXKk7CwUEDd5atixcrY\n2pbkxo1rOq2gae1XZiaq0ihcWPe9u5oxtppz9i7nLikpiV9/ncKePTvJn78Av/++gFKlSmulMTc3\nJyEhnoSEBJ1zoekellqXtXeVO3duBgzwwMOjH8eP+9KoURO8vHbrtDrZ2ZWjfn3nD1a+TZvWExUV\npbWsWrUaVK9eE3NzizS7DmrOg6mpGYGB/qxfv4bOnbtRrpw9vMPDeXNz9UOL9PLLk8cEfX19zMzM\n0NfXT7MrXmaOR3r5aZaZmmbcMm5iYkKxYsW5dSuQ+Ph45WY0vfzeHjf+5lia/pPWXGt5amXLzLnW\n3IRGRWXvzNdp13np1w2aOu/QoQNpptF00c2dOzebNm3i99/ncfjwQXx9j+DrewR9fX3q12/ImDE/\npDtUIz4+nmXLFrJz5zZlosHChS2pWrUa+fLl1+qZ82/3K6VChVKr89QPFv9NnRceHs6YMSO4efMG\nFStWZtYs9aulNN7HbyirrKyscXPryvLlizlz5hStW7dL9QFI/frOlChRKlPlMzc3J5ONh4C6q/am\nTbqzUbds2TpLdRCoZ+AeMWI0rVq1xc/vDCqVCgeH6tjbV2D8+LFA+hOXZaUOelMnROuk1SzPaJK0\nrNRB6Slb1h5IvYfE2/ll5vqhTvt+rm+aOi8yMjJTgfLHriMlUBZCfBY0LYnR0a+U7sGfOk2F3rt3\nP51Jq1JjaGhI16496Nq1B0+ePMHP7zSHD/tw9uxpxowZxebNu7Re2ZDS4cM+zJr1C6VL2zF69FjK\nlrVXWmVmzfqFGzeuvb8dSyG1bnSam2XNk1/NzW5mxmiC+sn1kCH/48iRI1hb2/D77wuUsc4pFStW\nnKtXL/PkyWOKFy+h9VlIyKN/0tjqrJeRR48eEhDgT7VqNZSxfhpWVuoJTSIi1E+zvbx2c+mS9kyu\nLi6tqF/f+YOVb9Omv3jyJERnefXqNSlWrDiXLl0gPj5OZ+xYSMhj9PX1KVasGH/99SdJSUmsX79W\neWdzWd5M6rJy5XJWrlzODz9MTPO9olZW1uTKlSvVm6+kpKR/uture0PkypWLIkWslf1+W0jIIwoU\nKJBuYKT5DqSWn2aZ5jhHRUVx794d8ubNpzUuXyM+Ph59ff00f09v5/f2Nt7OT5NWM87x7X1Tp8n4\nXGsegKQVvH/qNHXe3LmLMzX+tECBAowYMZrhw78lKOgWZ8+eYv/+vRw9egh9fX1+/vmXNNddsGAO\n27dvxtm5MR06uFGmjJ3y/enevWOqgfL7kLk67831KjOePAlh5MghPHwYTO3aXzF16kydltfM/Iby\n5cv/TvNA+PurW+GbNm2h89nbdd7Klct10lhb22BnVy5T5cuXLx+hoVGppknNq1dRqeZZrVoNqlRx\nyHQdlFLp0mWUmcc1AgJuYmZmlurDX42s1EGa33tqdUJYWBgJCfEZ1glZqYOePXvKw4cPKFWqjM49\nkmbIVEYP9zN7/dCkfR/XN81vJ7N13seuIyVQFkJ8FjRdgSMiIlKdAfJTpHktjb//jVQ/X7FiKUZG\nxnTp0p3Q0Gfs3r2DSpWqUK+eE1ZWVrRq1Y5WrdoxYsRgzp8/x+PHjyhe3FZr5koNzfsYJ078P52W\n13v37r7nPXvD3/+6zjLNOD3NOL835+5lhttTqVRMnvwjvr5HKFmyFLNnL0zzKXOVKg54ee3m4sUL\nOhfqixfPY2Zm9k5jBH18vFm+fDEjR36n885uTXdyzXcw5cRrH6t8W7akPV6wShUHLlzw4/LlS1oT\nCcXHx3P9+lVKliyFiYmpzus/Nviv42HUA3JF5sIi2AIHh+pUq1ZDa0bUtxkaGlKhQiVu3rxOTEy0\nVuvXzZvXiYuLo1KlyinKVhVvby+dSeXCwkJ58CCYhg0bprvf5cqVx9jYWOfBBKBMBqPJLzDQnxEj\nBlOvnhPTp8/WShsWFsbjx4+wsyun89qWlDTdTy9dusCXX9bRyU9fX18Zu5sybcp3pmqXLeNZXTUT\nABUpknYX109Zyjrv7UA5MvIlK1d6Ym9fnubNW3Lp0gXOnDlGq1auFC36BXZ2ZbGzK4ura2dat26m\nvKoGSLPOy5+/AFOm/Kr1eXx8nPIgSaVSpbruv+Hvf11n4r2067yMu4dGREQoQXLjxk2ZMGFKmg9w\nMvoNpXy3b1YsWbIAP7+zlCqlG0C+XeelNmP7hyyftbVNunlmpQ6aOPEHLl++yNate7R++4GB/oSE\nPNaabyM1WamDrKysKFLEiqtXL5OcnKy0aqvT+mmlTUtW6qBdu7azapUnQ4eO1JmlXPMKqbdf/5Va\nfpm5fmjSvo/rW0REBPr6+uk+oEjpY9eRMpmXEOKzoAn+7t69nUHKT4eNTVEcHKpz+vRJjhzx0fps\n//69rFy5nDNnTpIrVy6MjY1Zt241np6LlbFAoB7r8/x5GEZGRhQsqO56ZWBgqHymoXlSrBkbp7Fv\n3x7lop7yvaLvi6/vES5ffvMex+fPw1i9+g/y5MmjjOE1MzPD0rJIps7dli0b8fU9gq2tLfPnL0u3\nK1b9+s6YmJiyfv0aZXZQgD17dvLgQTCtWrXTujnJLGfnxujr67N+/VqtG92IiAgWL56Hnp4eLVu2\nSmcLH7Z86WnatAUGBgb88ccyre/R2rUriY6OVmbQrV69Jn37DlT+M6lnyvMqz4myVT+tr1atBn37\nDsywu3+LFl+TkJCg1R0zMTGR5cuXANC6dXuttADLli1UZhFXqVQsWbIAgM6dO6ebV548eWjQoBHX\nrl3RmpQmLCyULVs2UKhQYerWVd+IV6niQMGCBTl9+qTWTe3r16+ZPXs6iYmJGU4W5eBQnSJFrNi5\nc5tWC5Kf31nOnTtD/frOSo+DokW/oHLlqhw9ekjrwdidO0EcOLAPe/sK6i7uGdD8RsqUSfsBxaes\nfv2GmJqasm7dGoKD72t9tmjRPDZv/kt5R/Lz589Zu3Ytf/31p1a6Fy+ek5AQr7RkQup1nrGxEQkJ\n8VrDEJKSkpgz5zel1fdD1Hnr168lLOxNa/XVq5c5eHA/5cqVp0wZ9YMCzeuF7t69k+H2ZsyYysOH\nwTRo0JCJE6em28sho99QmzYd3mmfGjVSTxi2ZMl8rbHd/v432bZtEwUKFKROnXoZbudDlS+jPDNb\nB9naliAsLBQfH29lWVxcHHPmzAKge/dv0s0rK3UQQPPmLXn27KnWO7ljYqJZs+YPjI2Nad7863Tz\ny0od5OzcGD09PTZsWKd1HxAWFsbSpYswNDRU6v+0ZPb6Ae/n+pacnMz9+3cpXtw2w9ZujY9dR0qL\nshDis/DVV47KO1HTetfup2jMmB/w8OjPhAnj+OqrupQqVZrg4PucPHkcC4u8jB49DlC3QLi5dWXj\nxnX06tWZOnUc0dfX48yZU9y7d5fevfspY4MKF1YHjzt2bCUyMhI3ty40b96SQ4cO8MMP39GkSXNM\nTU25ceM6ly5dIH/+AoSHv1CexL5PuXPnZuTIwTRs2AQTE1OOHTvCixcvGDPmR60JwerUqcfOndt4\n8uQJVlapT9STkJDA6tWeAJQrV46tWzemmq5dO1cKFiyEhUVePDyGMWvWr/Tu3Y1GjZoSGvqMI0d8\nKFasOL16vdt7Fm1tS+Du3p8VK5bSs2cnGjZszOvXiRw/7kt4+AsGDhyq9R7itHyo8mVU9i5derBu\n3Wr69OlO3bpO3Lt3h5Mnj1O5clWtm8aUMjOLd2patmyNl9cuNm5cz+3bQZQrV54zZ04RFBRI1649\ntVqnatX6ksaNm3Lo0EEGDnSnevWaXLt2hcuXL+Ls3BhnZ2fCwtRdVTXjEs3NzZXXlwEMGDCEs2dP\n8+OPY2jSpDn58uXDx8eb8PBwpk2bqYyvz5UrF2PGjOeHH75j5EgPGjVqioVFXvz8znDv3l0aN26m\n1aX81q0A/v77qDK+HNQTT40ePY7vvx9Nv349adrUhdjYGA4e3E/evPnw8BihdSxGjPiOoUP7M2zY\nQJo1c0Ff34ADB7xQqVSMHj02w2OpUqm4evUKpUvbkT9/gXSPw6fK3NycsWMnMHnyj/Tp05369RtS\nqFAhLl68wM2b1ylfvgJdu/YE1Dfa1apVY8eOLdy5E0SlSpWJjo7m6NFDAPTr92YGf02dt2DBHGrW\nrE2fPgNo1qwlf/21ln79euLk5ExSUhJnz54iOPg++fLlJyIinJcvX1KoUCHdgv4LkZEvlX2LiVGX\n19jYmDFjflTSFC36BcWL23LlyuV0txUQ4M/ffx9BT08PKyvrVLsYGxkZ07NnbyDj39Dbb1fIrK+/\nbosNkBYAACAASURBVMORIz6cOnWCPn26U6vWV4SGPuPvv49gYGDAxIn/l6lJuD5U+dKTlTqoc+du\n7Nu3h19++ZmzZ0+TP38B/v77CI8fP6Jfv0GZekVfZusggO7de3H4sA9z587i0qXzFC36BUePHubx\n40eMGvU/raE9/7YOKlPGju7dv+HPP1fRs2dnGjZswuvXCRw//jcREeGMHj1Oq5X/77+PcutWAPXr\nOysPRLNy/Xgf17fbt4OIjo7GxeXLDNNC6nXkhyaBshDis1CoUCHs7Svg53dWpxvTp6x48RKsWLGW\nVatWcPr0Cc6fP0fBgoVo3rwlvXv30+pG7uExnGLFirFr1w727dtNUlISJUqU4scfJ2m9AsXBoTod\nOrjh7e3Ftm2bqFmzNnXrOjJ58jTWrVvNgQP7MDbOjY1NUb79diyVKlWmT58enD59ItUxaP9Gixat\nKFy4MFu3biIy8iV2duUYN+4nndYHR8cG7Ny5jXPnTmu98zSl+/fvKi24Bw6kPRlQ/frOShDerl1H\nzM0tWLduDdu2bcbCwoIWLb5mwIAh7/zOZlC/i7V4cVs2blzPnj27MDDQp1y58owbNyFL3Qc/VPnS\nM2jQUCwti7B9+xa2bNlAgQIF6dy5G+7uAzL91D6zDAwM+O23+axYsZTDh324cuUyRYsWZdSoMcq7\nxFOaMGEKJUuWxstrN5s3/4WlpRX9+g2iW7deWl1kNeMSraystQJEKysrli5dyeLF8zlx4hjJycmU\nKWPH+PGTqVVL+5219eo5sXChJ6tXe3Ly5DESEhIoVsyWUaP+R/v2blr53boVyMqVy5Xx5Rp16zoy\na9Y8Vq5czp49O8iTx4S6dZ0YOHCI1ntYQf0e7IULPVm6dCEHDuzH0NCQihWrMGDA4DS7PKbsGuzv\nf4OoqEi6d++V4XH4lDVq1ARLS0vWrl3J6dMniYuLw9ramt69+9G1aw9MTEwA9cOMpUuXMnfuQo4d\nO8rWrZswMjKmUqXK9OzprnQ7BejQoRNXr17m8uVL3Lt3ly5dejBggAcmJiZ4e3uxffsW8uXLR4kS\npRg58n/cu3eXefN+4/Tp47RqlXp9865GjPiOK1cu4+Pjjb6+PnXrOtKv32CdbqaOjg1Yv34NDx8+\n0JltW+PyZXVvB5VKxcaNuhNWgbpHjiZQhsz/hrLCwMCAGTPmsG7dary9vdiyZQOmpqY4OjbA3b2/\nznCe9HyI8mVU9szWQaamZixevILFi+dx/vw5YmJiKF26NEOGjKBBg0aZyi8rdZCpqRmLFi1n6dKF\nnDhxjDNnTlG8eAkmTZpKkybNtdK+jzpo0KChlCxZii1bNrB3r3pOE3v78nTv/o1O1+1jx46yb98e\nZXx5ym1k9vrxb69v586dBsj0fUlqdeSHpqd618fIOUBWJhsQH1fhwuZyfnIQzfn28fFm0qQfmT17\ngdYFqWPH1rx6FcX+/Uezr5A5zIULfgwfPgg3t66MGDE6w/QqlYqe/8/efUdHUX4NHP/upncCJJCE\nHqqiVFEEIYgoipUmKCIoigVFLLx2UUFUxIJKUfiBAqI0KUpRehEIHekQCDU9kLrZOu8fkSVLkk02\nbEv2fs7hnN2ZZ565kw2bufO0J/oTEhLClCn/K7O8/B93rgd+v4cdSdsA+KjTJzzXaoTTY6jKn3l8\n0g6OXTrCEzcMMW87nHGIuN868uPds3iocW+++OJT/vprJQsXLrdYBu3EieO8//6bzJu32AWRO1Zl\n+sxnzJjGzJk/8sknX1gkMqVJSUnm0UcfZuDAJxg+/EXHB1hJVKbPXNhHaZ/5oEH9CA0NY/Lk6Rbb\nx40bw8qVfzBz5lyLJL6070h7xViSytEkI4QQFI6jqlu3HsuWLXF1KMJGKpWKJ54Yyr//HijXuD3h\nXEWfmRv/G1voCY5kHOb1Da9QYChw6Hnu/70Hr214mUsFV8cOxv1W2MLzxsZX0Gg0rFmzmkce6Vvs\nBnDNmtWVdsyyJ6tVqzY9e/Zi1ao/HTJWWojK7MCBwt4hgwc/Va7y1r4jHUkSZSFEpaFWq3n55dfY\nuHGdeSbOK65M5lHSGo/CPfTo0ZObbrqZGTOmujoUcQ2Fq4myCc9JlHsv7cXPh//H7MMznXK+DE0G\nAGn5aeZtl7WX+fXXOfj7+zNo0BCL8rm5uRw/fpQRI15xSnzCvoYNe56CggKWLl3k6lCEcCszZkyj\nY8dO3Hbb7eZtK1YsZ8aMaZw4cbxY+dK+Ix1NEmUhRKXSsWMn7r33fqZO/dZiu06nM6/9KtyTWq3m\nrbc+YNu2rRw65Jh1nUXFFG1RVhTPSZQzCgoT18yCzDJK2keaJpXJ+77l3S2jzdu8CryYN28Ob7zx\nNiEhlt3/goOD+eqr781roovKpWbNmowc+RqzZs0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4nqIoTusG/MWuT82vCwwlL8HkLFqd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TAAAg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\nhNJCDGVCkSxpuxwA8POtH3Hr/Y0i28vksiLbnH9zB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -569,17 +586,377 @@ } ], "source": [ - "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=100,\n", + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", " period=dt.timedelta(5),time_unit='d',plot=True)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "dataset.time_unit" + "dataset.meta_valid['2013-01-01 00:05:00':'2013-01-01 00:45:00'] = 'fake'" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CODtot_line2
Time
2013-01-01 00:05:00fake
2013-01-01 00:10:00fake
2013-01-01 00:15:00fake
2013-01-01 00:20:00fake
2013-01-01 00:25:00fake
2013-01-01 00:30:00fake
2013-01-01 00:35:00fake
2013-01-01 00:40:00fake
2013-01-01 00:45:00fake
2013-01-01 00:50:00original
2013-01-01 00:55:00original
2013-01-01 01:00:00original
2013-01-01 01:05:00original
2013-01-01 01:10:00original
2013-01-01 01:15:00original
2013-01-01 01:20:00original
2013-01-01 01:25:00original
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23:35:00 original\n", + "2013-01-30 23:40:00 original\n", + "2013-01-30 23:45:00 original\n", + "2013-01-30 23:50:00 original\n", + "2013-01-30 23:55:00 original\n", + "2013-01-31 00:00:00 original\n", + "\n", + "[8640 rows x 1 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.meta_valid" ] }, { diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index df9a93cf7..2cde5cd15 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1637,11 +1637,10 @@ def detect_drift(self, data_name, arange, max_slope, period=None, self.add_to_meta_valid([data_name]) from scipy import signal + # copy the data for function operations # Make temporary object for operations - data_series = self.__class__(self.data[data_name][arange[0]:arange[1]].copy(), - timedata_column=self.timename,experiment_tag=self.tag, - time_unit=self.time_unit) + data_series = self.data[data_name][arange[0]:arange[1]].copy() drift = False slopes = [] @@ -1678,8 +1677,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, slope = _get_slope(line_segment,arange,time_unit=time_unit) if abs(slope) > max_slope: drift = True - slopes.append(slope) - print('Drift detected over the whole data range, slope: {}'.format(slope)) + drift_periods = [[data_series.index[0],data_series.index[-1]]] else: print('No drift detected.') @@ -1704,8 +1702,6 @@ def detect_drift(self, data_name, arange, max_slope, period=None, # store the indexes where the slope was larger than the max_slope. if abs(slope) > max_slope: slopes.append(slope) - print('Drift detected in period {} to {}, slope: {}'.format - (start_index, end_index, slopes[-1])) # firstly, if your start index is larger than the end index # of a previous drift, or if the sign of the newly detected # is different from the previous one, then a new drift has @@ -1713,32 +1709,27 @@ def detect_drift(self, data_name, arange, max_slope, period=None, if start_index > drift_periods[-1][1] or (drift and np.sign(slopes[-1]) != np.sign(slopes[-2])): print('new period') drift_periods.append([start_index,end_index]) - else: if not drift: # indicating that this is the first detected drift period drift_periods[-1][0] = start_index drift_periods[-1][1] = end_index - # Indicate that at least one drift has been detected drift = True - start_index = start_index + dt.timedelta(1) - # if drift hasn't changed value by the end of the while loop, no - # drift has been detected. - if not drift: - print('No drift detected') - if plot and drift: - for driftperiod in drift_periods: + if drift: + for driftperiod in drift_periods: + print('Drift detected in period {} to {} /n'.format(driftperiod[0],driftperiod[1])) + self.meta_valid[data_name][driftperiod[0]:driftperiod[1]] = 'filtered' + if plot: detrended_values = signal.detrend(data_series[driftperiod[0]:driftperiod[1]]) line_segment = data_series[driftperiod[0]:driftperiod[1]] - detrended_values[:] - #df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:driftperiod[0]])-1:len(data_series[:driftperiod[1]])]) - #detrended_values.append(df1) - ax.plot(line_segment,label='Detected drift \n({})'.format(driftperiod)) - ax.legend(fontsize=16) - - - self.drift_periods = drift_periods + ax.plot(line_segment,label='Detected drift') + ax.legend({'.':'data','-':'Detected drift'},fontsize=16) + else: + print('No drift detected') + + self.drift_periods = drift_periods def drift_analysis(self, data_name, arange1, arange2=None, plot=False): """ From 208cd232a346bca65ae50ab8d709804f99228771 Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Mon, 3 Sep 2018 14:52:29 +0200 Subject: [PATCH 41/42] add _filling_function_check instead of copy-paste code in each filling function --- Showcase_OnlineSensorBased.ipynb | 642 ++++-------------- wwdata/Class_HydroData.py | 6 +- wwdata/Class_OnlineSensorBased.py | 391 ++--------- .../Class_HydroData.cpython-36.pyc | Bin 62902 -> 64598 bytes 4 files changed, 193 insertions(+), 846 deletions(-) diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index cafe1a651..37d3ab318 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ "'0.2.0'" ] }, - "execution_count": 3, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -123,7 +123,7 @@ " dtype='object')" ] }, - "execution_count": 4, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -257,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", @@ -284,7 +284,7 @@ "data": { "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -326,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", @@ -335,14 +335,15 @@ }, "outputs": [ { - "data": { - "image/png": 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89dZbWLp0KRQKRZMAV6vVQhAEeHl5QaFQAGgaBNfV1cGzPj+5uW0YX3s6Y2I+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Pjw4fJ82xUemUyGIU21q/LMKriQMD9zLHxhCQBu+JExrL2hvtrkNd79clWpTQ2Y8Nd2W9xWrHpz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_highs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Flow_total'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2013/1/1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'2013/1/31'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'percentile'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" + ] } ], "source": [ @@ -359,22 +360,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", "start_time": "2017-05-09T11:54:57.350077+02:00" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "47 values detected and tagged as filtered by function NaN tagging\n" - ] - } - ], + "outputs": [], "source": [ "dataset.tag_nan('CODtot_line2')" ] @@ -516,52 +509,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)]\n", - "data_series = dataset.data['CODtot_line3'][arange[0]:arange[1]].copy()\n", - "data_series.replace(0,np.nan)\n", - "data_series.dropna(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "data_series.index[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sign(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": { "code_folding": [], "scrolled": false @@ -571,14 +519,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Drift detected in period 2013-01-04 00:05:00 to 2013-01-09 00:05:00, slope: 92.06687774164706\n" + "Drift detected in period 2013-01-04 00:05:00 to 2013-01-09 00:05:00 /n\n" ] }, { "data": { - "image/png": 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bw6VLF/D0XIu5eervRly/fi1Hjx6mTJnUg95du7YzY8ZULC2L0KJFK+Lj4/Hx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\nNiEhlt3/goOD+eqr781roovKpWbNmowc+RqzZs0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4nqIoTusG/MWuT82vCwwlL8HkLFqd\nkXr5D/J/U//hr53nqBbsx7MP3kB2g9mkex8wl+sc06XYsT/ePcuJkQohhHA2mxLlS5cuUbduXatl\natWqRWZm5nUFJYQQQgjnsbVF+VzOWZ77+ymSci+W+xiNQcOTKx9j7pGfi2zLtylOezGaTGzaf5Fm\nKe8QnfMgXmo1A7s3Ydwzt3HbDbX58Z5ZFuV71O/JsJuGm98ffeo0DzXubbd4ZDIvIYRwPzYlyrVr\n1zaPSS7NgQMHqF279nUFJYQQQgj3NWr9Syw+sZD3tr5ltVzC5RMoisKMf3+g/g+1WHn6D4v94+PH\ncu+i7jSbUZ/Tl0ru4rw8YUm5EvIs7eUyyyiKwt4TaXzwv53MWnkUL1Mgl8I28OnwjvS4pS4+3oW3\nRXVC6pL6QjYxwXUACPAOIMgnGCic9Ky6f40yz1Ue0vVaCCHcl02J8j333MO2bdv49ddfS9w/c+ZM\ndu/ezV133WWX4IQQQgjheIqigA1dr3N0WQDk6nNKLbMrOZ6Ov7Tj/za9ylubXy+xzIG0fexO2ckl\n7SUemPdAsf17U3bz9OrB9FjY1Wo8vxyZTZMZ9VhyYpF5m6IovLFxFCPXvYDBZODkhSw+nbuHbxf9\nS3JGPl1bR7M1bCSaiM0E+pc8t+mSh1cwqt3rDGj+OANbDALgy7hvrcZSETKZlxBCuB+bZr1+7rnn\n2LhxIx9++CFz587FZDIB8Oabb3Lo0CFOnjxJvXr1eO655xwSrBBCCCHsr7DrtQ3l/0vsrLWI7kvd\nA8CsQzPKVefxjOPFtiXlJQGQmp9i9dif/jvHvKNzeLhJHwB2p+zkp0MzCDJGUyN5C4lnC+9Z2jSp\nSd+4WGpXD+SNKcn4eceWWm/90Aa8dev7ADQKiyX1BftOPibLQwkhhPuyqUU5ODiYefPmMWDAAC5c\nuEBCQgKKorBkyRLOnDnDQw89xLx58wgNDXVUvEIIIYSwM1vHKF9p/7R2jK3jbvUmfYXrUKsK10Fe\nf24tX+76HIDd5w9zk+Y5uuZ9S+JZE41jwnhrUFte6nMzUTWC0P43+7Z/keWhhBBCiCtsalGGwmT5\ngw8+4N133+X06dNkZ2cTGBhIo0aN8PX1dUSMQgghhHAgW2e9Lk8Cq7Eyo3U1v2pU96/BqawEi+3b\nL/5D7aAoBq8cwMi2r1mscWyN13+JMsAXOyZyObERF0/FUN9Ujxz1ObKqr2XGoJ8srrHAoAHAz9v1\nibJM5iWEEO7Hphblory8vGjcuDFt27alefPmkiQLIYQQlZhNLcr/db1Wq0q/jcjX55a6b88Th7iv\nUfExyQ8u6UmHua04mnmE59cMK/HYPH0exzOPATBpz1e8vO554pO3o1K8aaDtRbfcqZw/WR0tuRzw\n/55NQSPRBp8g4fJJIieHsvL0nwBFWpT9ynfRDiCTeQkhhPuyuUU5ISGBpUuXcuHCBXQ6XYkTUKhU\nKr791v6TXQghhBDC/mxt0bxS3lordI6u9Im+ArwDaVytifn9I4378PvJRcXKjd70SrFtg1cMYPOF\njfSofw9/n1kNiopoQ2eaFQwiSKmNnnyO+s3htO9yjKrCZPhI5iE+3zkOgCdXDrSoz987wMqVCiGE\n8FQ2Jcrx8fEMGzYMvV5vdYZGmZxCCCGEqDxs7Xp9xerElaXuy9aVPvGVl9rLYoml6gElL7eUrkkv\ntm3zhY0A/H1mNTUMN9OiYDDVTI0xoeeU73JO+M5Hr76apMfVvZMN59axI2l7iefwVbuwRVnul4QQ\nwm3ZlChPmjQJg8HAK6+8QteuXQkODpYveSGEEKKSs3kyr3IsZ3RtohzkE0xeke7YLWrcYH6tNWjL\nfW6AEGMDWhQMJtLYFoAL3hs55v8L9WvWQJ+ZQ0RAJGmaVAB6N+nHhnPrSMoreS3mYN9gm84thBDC\nM9iUKB88eJD77ruP4cOHOyoeIYQQQjhdYaLcrW531p9bW2Zpk2Iss0zuNV2v64c2YMY9P+HvFWB+\nHxlYC38vf2oGRJRZ3/d7J9G3wVO01rxCjL4LKtSkee3jiP/PZHudIioomj/7rAFFIT55OwP+6IOf\nlx+1AmtbrdeVY5SvkHWUhRDC/diUKPv5+RERUfYfMyGEEEJUHle6Xs+7fxH3LerOntTdVstr/psx\nujTHMo+au0hf0TS8KbFFxiUD7Br0LwAp+clM3j8JnVFHo7BYetS/h2kHJpvL+ZhCmL8+gZ26f6hD\nHFnqUxzx/wmfsDSy8y4S6hvG/iePmsvfERPHF12/4a76d+NfwqzWfZr0Z9GJ+QBojTqr1yKEEMIz\n2TTrdefOndmyZQtGY9lPkoUQQghRuahVauqE1LPYtq7/1mLlykqUP972vsX7RmGxfBlXfJJPf29/\n/L39qR/agPQ30vmo0ycse2Q1/Zs/VhiP4kustjd35k4lVvcQBaoM9gZ8yeag10j33k+/pgOYfd9v\nbB6ww6JeHy8fBt84lOjgGIux0Fe0jmxDlzrdAAj1DbV6Lc4gy0MJIYT7salFefTo0Tz22GO88sor\nDBkyhIYNG5a6LFRwsIz5EUIIISqDomOUA30CLfZFlNAtumiinKvPJdjH8m9+uH91i/cvtH6ZYN8Q\nqzGE+IXwXKsRAAR7h9DZ/2n80joSoNREp8rmkO8MzviuxKQymI+5sWZL7mlwb5nXN/6OLzibfYbe\nTfqy5uxfDG35DAObD2L6v9N4ptXzZR7vKLI8lBBCuC+bEuXHHnuM/Px8/v77b9asWVNqOZVKxeHD\nh687OCGEEEI4XtFZr6ODYyz2BZUw2VVBkUS5x4IubHtsDwA7krZTw78GQT5BlvWXs8VUURT2n8xg\n0cYEqqU/gNpL4ZjXQhL8FmNQ5Rcr3yS8WbnqffqmZ82vW0W2AcDXy5dX248u1/GOJmOUhRDC/diU\nKEdHRzsqDiGEEEK4SNEW5RFtXiElL5mtFzYzoevXBHkHFSvfOrIt8cmFyy0lXD5p3v7A73dblJt0\n5xR+ODCFO2K6lBnD0TOZ/Lj4AMfPZ6FSQZdWUTzUuRHNZj8CwLjOnzFl33eE+IaSmp9MRkEGsdUa\nV/ia3YGsHCKEEO7LpkR59uzZjopDCCGEEC5S2J5ZmLQF+wTzVbfvrJaPDKxVbJveqC+2rXeTfgxo\n/rjVupIy8li86RS7j6UB0LpxTfrExRJTszBBX/LQCgyKgTtiujL4xqdQoSJLm8Vl7SUCvAPKvjgh\nhBCiAmxKlIUQQghRNZW3dTM5LwmDyTIpztfnF9sGhd2bS5OVq2Xp1kQ27buISVFoXj+chzs3pGnd\nahblbo/pbH7t999SThGBEUQEVp1VOGQyLyGEcD9WE+Xx48dzxx130LlzZ/P78lCpVLz55pvXH50Q\nQgghHM6WMbI3/1R8XPCGc+toV6t9uY7XaA2sjj/L6vhzaPVGalcPpE/XWO7p1JD09Nxyx1EVyGRe\nQgjhvqwmyj/99BMhISHmRPmnn34qV6WSKAshhBCVR9ExyhWRrcsiz5BntYzBaGLjvoss23qanHw9\nYUG+PNq9MXfcHIWXWu3R43WrYotynj6PX4/OpV/TRwn1C3N1OEIIYTOrifLPP/9MTEyMxXshhBBC\nVC1FZ70uSb2Q+pzNOVPq/pfXPc+Ht39ise2tDu+Z6955NJXFG0+RelmDv68Xj9zRkLtvqYefKjbY\n8AAAIABJREFUr5d9LqCSqsoPBz6LH8fU/d+xO2Unk+/60dXhCCGEzawmyh06dLD6XgghhBCVX1kt\nyuv6b+H5NcP4+8zqUst88M/b5tf1Qxswqv0bHDlziQXrT5KYnIOXWsVd7epwf6cGhAaWPnZZVA2n\n/psN/filYy6ORAghKkYm8xJCCCE8XFktyqF+YexKji93fcb8UL6cv4+DpzIB6NAikt5dGhEZHnjd\nsVZFso6yEEK4H5talMtLpVKxY8eOCh0rhBBCCPfz1q3vM3rTKKtl/E01aaZ9jLr6bhzMzKRF/XD6\nxsXSMCrUSVFWLjKZlxBCuC+riXJwcLCz4hBCCCGEy5Q9mded9e4qdZ+PEkxjbR8a6HrhhS91IoPo\nH9eYGxtWr9LjcO3FHSbz+vPUctrX7kCtEtbIFkIIT2Q1UV63bt11nyA3N5fs7Gyio6Ovuy4hhBBC\n2F/hGGXrAn2Cim1TK7401PUiVtsHX4LJV6XS/EYN7/cagloS5DK5y0OEnck7GLrqceoE12XP4EOu\nDkcIIdyC2tEnmDVrFt27d3f0aYQQQghRQWWNUQYI8A4ocoCaOro76ZY7mRbaJwn2DSK66QVS601h\ncOc7JEmuZJJyLwJwPveciyMRQgj3IZN5CSGEEB6uPOsoB3gHgAKRhnY01w4m1FQfI1ouh21iztB3\nCPT3AZ5wTsBVjEzmJYQQ7kcSZSGEEEKU2aJ8OimHwX4LyMzxQcHIWZ81HPebR8PwWgT6j3FOkFWM\nTOYlhBDuSxJlIYQQwsMZFSOqUkZjpWTms2hjAruOpQE+hNS8zHLNe+R6naNrnW58cPtY5wZbBbnD\nZF5CCCEsSaIshBBCeDCTYsJgMuDn5WexPStPx7Itp9m0/yJGk0Kj6FD6xcWyPGUauTsLx7IueHCp\nK0KuMtylRVkSdSGEKE4SZSGEEMKD6U16AHy8fADQaA2sjj/L6vhzaPVGalUPpE+XRrRrFoFKpeKP\n1MLkLtA70GUxCyGEEI4mibIQQgjhwfRGHQC+Kn/W7j7P8q2nyc7XExrkS/87G3PHzVF4e13tlv10\ny2fZkbSNNzu866qQqxxXt+g6omXb1dckhBDXSxJlIYQQwoNpjVqi9Lfjc/xR5h48jp+vFw93bsjd\nHeri71v8NqGafzjzH1jigkirHndZR1mSWiGEKE4SZSGEEMJDHT1ziV/WHaedZjRgonvbOjzQqQGh\nQb6uDs2jVMXlodxl/LUQQlSUJMpCCCGEB7mQc57D5y9y6KAfBxIyALjovYVGLbJ4/O6JLo7Os0gy\nKYQQ7qvktSCEEEIIUeVkZBUw5Icp/LI0mwMJGTSvV42hvSPZE/gF/oE6V4cnhFVao5ZfjswmS3vZ\n1aEIITyATS3KS5YsoXnz5jRv3rzUMrt372b79u28+OKLAHTo0OH6IhRCCCHEdckr0PPntjOs2XWe\nusbuZKsTefn+LtzRIpajmUeAq7NeC1eoel2vHWHKvm/5ZMdHrDnzF//rOdvV4QghqjibWpTffPNN\n1q5da7XM33//zQ8//GB+36FDB0aMGFGx6IQQQghRYXqDkVU7zvLm1G2s2nGW0CAf9vp/zaagV6lZ\nS4tKpUJv+m/W62vWURaO5y6TeVUWJy+fAOBA2j4XRyKE8ARWW5QXL17MunXrLLb9+eefHDlypMTy\ner2eHTt2UK1aNftFKIQQQgibmEwK2w4l8/vmU2Rmawny96Z/t8Z0bxdDzA8bAEjXpPFp/FjWnPkL\nAF+1TODlKlVxMi8hhKjsrCbKd9xxB2PHjiU/Px8ofPJ56tQpTp06Veoxvr6+vPzyy/aNUgghhBBl\nUhSFf09lsHBDAufT8vD2UnPvrfW4r2N9gvwtu1YvPfk7sw/PNL+XrteuIC3KQgjhrqwmyhEREaxZ\nswaNRoOiKNx11108+eSTDB48uFhZlUqFt7c34eHh+PjIH1shhBDCmU4nZbNg/UmOnr2MCuh0U20e\nuaMR1UP9SyxfNEkGaVEWQgghiipzMq/q1aubX48fP54WLVoQExPj0KCEEEIIUT4pl/JZtPEUu46m\nAnBzbA36do2lTmSwuYyiKOxP28uqxBWl1uPjJYmyqygymZcQQrgdm2a9fuSRR4DCP7i7du3i6NGj\naDQawsPDady4MW3atHFIkEIIIYSwlJ2nY9nW02zcdxGjSaFhVCj9u8XSrF54sbIbzq3j0T8esVqf\nr1p6gzlbVV5HWZJ/IURlZ1OiDHDgwAFGjx7NmTNngKsTUKhUKurXr8+ECRO46aab7BulEEIIIQAo\n0Bn4K/4cK+PPotUZqRUeQJ+usbRrFmExi/LulJ0sPfk779z2AfHJ20usK8Q3lBxdNgDekii7jEzm\nJYQQ7semRDkxMZGnnnqKvLw87r77btq1a0dkZCTZ2dnEx8ezatUqhg0bxsKFC6lbt67Nwbz//vsY\njUbGjRtn3rZlyxYmTJjA6dOnqV+/Pq+//jpdu3Y178/IyOCjjz5i69at+Pj40Lt3b0aNGoW399VL\nmzVrFj/99BOZmZm0bduWDz74gAYNGtgcnxBCCOEqBqOJzfsvsnRrItl5OkIDfegXF0uXVtF4exVf\n7XHkuhc4fukY1fyqoTVqS6wz2CfYnCjLWr7O5y7LQzkiUXdEa7k8UBBCOJNN6yh/9913aDQapk2b\nxjfffMPgwYPp2bMn/fv354svvmDy5Mnk5OQwbdo0m4JQFIVvvvmG3377zWL7yZMnef755+nZsye/\n//473bt358UXX+TEiRPmMi+99BLp6enMmTOHTz/9lMWLF/Ptt9+a9y9YsIBJkybxf//3f8yfPx8/\nPz+GDRuGTqezKUYhhBDCFRRFYdfRVN6bvoPZfx1HqzPyUOeGjB/ekTvb1ikxSQbI0mYBsD3pH1ae\n/gOASXdOIfn5y/RrOgAAterqsQWGkpNpIYQQwhPZlChv27aNbt260aVLlxL3d+nShTvvvJMtW7aU\nu85z584xePBg5s2bR3R0tMW+n3/+mdatW/P8888TGxvLK6+8Qps2bfj5558B2Lt3L7t37+bTTz+l\nefPmdO3aldGjRzN79mxzIjx9+nSGDh1Kz549adasGRMnTiQjI4PVq1fbculCCA8078gc5h+b5+ow\nhAc7dvYS42bvZvKSg6RnFdCtbQyfPteRhzo3JMCv5E5hiqLw86GZpOQnA4XjkxMun6RLnW4MaP64\nRXIcE1yHoS2HAdCiRgvHX5AokavH87pLy7YQQrgTmxLlrKysMrtU161bl8zMzHLXuWfPHqKioli+\nfDl16tSx2Ldr1y46dOhgse3WW29l165d5v0xMTEWMXXo0IG8vDyOHDlCRkYGiYmJFnUEBQXRsmVL\ncx1CCFGaketfYMTa4a4OQ3ig82m5fLNgP5/9spdTF7Np3zySscNu5Ym7mxEWZDk7taIonM85B8DK\n039Sa0oYr28cWazOWoG1zK9Htn2N2GqNea7VCD7pPIEdj+/jzno9HHtRohh3mcxLujQLIURxNo1R\njoqKYu/evVbL7N27l8jIyHLX+dBDD/HQQw+VuC85OZlatWpZbIuMjCQ5ufApeUpKSrFzXXmflJRk\nHqdsrQ4hhBDCXWRmF7Bk82m2HkxCUaBZ3Wr069aYRtGhpR4z7cD3vL/1bX69fzGT9ky02NcgtCGJ\n2acBqBkQYd7etHoztj22x/y+YVgjO1+JsIWrW5QrC2n5FkI4k02Jco8ePZg5cybffvstL730ksU+\nvV7Pt99+y/79+xk6dKhdgisoKMDX1/LJua+vL1pt4TgqjUaDn5+fxX4fHx9UKhVarRaNRgNQrEzR\nOqwJDw/E29vrei5BOFBERIirQxBO5MrPW37XXMOTfu65Gj0L1x5n+eZT6Awm6tcOYcj9N9KueWSJ\nyYHOqCPmyxgea/kYM/fNBGDAH72JCo6yKHdz1E3mRLlhRF23/5m6e3z2Vi0jEIDgIH+XXntoSoD5\ntb3i8P1vaIC3t9pqnbacz++/OtVeKo/7XalK5LPzPJX1M7cpUX7hhRdYt24dkydPZsmSJbRr146Q\nkBBSUlL4999/SUlJoWHDhjz//PN2Cc7Pzw+9Xm+xTafTERBQ+IXu7+9fbFIuvV6PoigEBgbi7+9v\nPqa0Oqy5dCn/esIXDhQREUJaWo6rwxBO4urPW37XnM/Vn7mz6A1G1u6+wJ/bEskrMBAe4kfX1t4c\nVy2nbo127D99lP8d/JFBNzxJgHcA1f1r4Ovly7mcs6TnpzMpfpJFfUm5SRbva/hc7XXlawxy65+p\np3zmRWVlFz7Qz80rcOm1Z/8XB9jv+06nNQBgMJhKrdPWz7ygoPCe0GRUPO53parwxP/nnq4yfOal\nJfI2JcrBwcH8+uuvfP7556xYsYJly5aZ9/n5+dG7d2/eeOMNQkLs89QgKiqK1NRUi22pqanmrtS1\na9dm48aNxfZDYXfrqKjCJ+tpaWnUr1/fokxsbKxdYhRCVE0yZk/MPjyL+KTtTLpzit27fJpMCtsO\nJbNk8ykysrUE+nnTr1ss3dvWoc6P4QDcWa877255kyOZh/h271cA+Hn5MeWuGWw6v77Uul9tP5pZ\nB6eTWVA4X0jfpo+y8Phv3FTzZrteg7Cfqvh1I93JhRCVnU2TeQFUq1aNTz75hJ07d7Js2TJ++eUX\nli5dys6dO/nkk08IDw+3W3Dt2rVj586dFtt27NhB+/btzfvPnTtHUlKSxf6goCCaN29OjRo1aNCg\nAfHx8eb9eXl5HDx4kFtuucVucQohqh69SV92IVGlvbbhZX479gsag6bswuWkKAr/nspgzMydzPjz\nCFl5em5sATurv0FonbPsSb/696rAoOFI5iGL47VGLU+tHsSsQzMstg9v9eLVuNv9n/m1ChWfdZnI\n1oG7uCmild2uQ9iHu0zmJdxbSl4yfZY9yIG0fa4ORQiPYlOL8uDBg+nduzcPP/wwPj4+NG3atFiZ\n2bNnM3fuXFatWnXdwQ0aNIg+ffowadIkevXqxR9//MH+/fsZM2YMAG3atKF169aMGjWK9957j/T0\ndCZMmMDQoUPNY5uHDBnC559/Tv369WnSpAlffvklkZGR9Oghs3sKIUq35sxf5teKosgkMh7MXp/9\n6aRsFm5I4MiZSygotGsRzsC4G2gxt7B79CNLe1mUf3xF/3LVWye4Lh/d/gn1QupR3b8GPl4+FvtD\nfEMJ8S19MjDhelWx9VUeAtjPxF2fsfn8BgavGMi+J4+4OhwhPIbVRLmgoACDoXCMiaIoxMfH06ZN\nG3Jzc0ssr9Pp2Lp1KxcvXrRLcM2aNeO7775jwoQJ/PjjjzRq1IipU6eau02rVCq+++47xowZw+OP\nP05QUBD9+vXjxRevPlkfOHAg2dnZjB8/nry8PNq2bcv06dOLTRImhBBFDVn1mPm13qTH10u+MzzV\n9XbDT72Uz+JNp4g/Ujg0SB90mn/4Gm//dvT1Hn/d8Y1o+woqlYpnbrbP/CDCeeQBXAV52M/NqJgA\nMCgGF0cihGexmigvWrSIsWPHWmz74Ycf+OGHH6xW2qpVxbp3zZ49u9i2uLg44uLiSj0mIiKC77//\n3mq9w4cPZ/hwWQtVCFE+OuM1kwRKouzRKtral52nY/k/iWzYewGjSaFhVAh94xrz4o73yUk/w7FL\ngYxY+5zVOhY/9AdB3kF8Gj+WZ29+nt0pu/hi16cMavEkH3f+lCztZaKCoisUnxCOYjQZ+evM9fcs\nFEIIV7KaKA8cOJCdO3eSkZEBwK5du4iKiiImJqZYWZVKhY+PD5GRkXab9VoIIVwhX59n8V5v1GHy\nDkCtsnlaB1EFKP+15pSXVmdk9c6zrNxxFq3OSGS1AHp3bcQtzSPJ0l4mR5cNwNHMIxzNtN6NsnNM\nFwB+e+B3ALrXv5vRHd427w/yCbIpNuGeXD15oL27fi9PWGLX+kTV65ovRGVgNVFWq9V8/fXX5vfN\nmzend+/ejBgxwuGBCSGEq+QbLJeGu3dxdxIun2TrwF00CS8+N4Oo2kzlTJQNRhNbDiSxdMtpsvJ0\nhAT60LdrLF1bR+PtpeavxJUMWvFoicc+1vwJzuWeY/P5DXaMXLi7qtqBODU/xbE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TAAAg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\nAAAA7FAoAwAAAABgh0IZAAAAAAA7/w/YH5vcYEqxpQAAAABJRU5ErkJggg==\n", 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E73//e/bs2UNWVlaLdQ0NDYwZM4b09PQW+27atIkNGzbw/vvvM3jwYEaOHMmdd97JggUL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\nrFR1VJS+fcJMEkmrc5KikbGLEnOTBhB3xrAQ2zmLj7PZz2aSuerQYhE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sNixYsACNjY24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\nMVEmIpvCr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2013-01-01 02:15:00original
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2013-01-01 02:25:00original
2013-01-01 02:30:00original
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2013-01-30 21:35:00original
2013-01-30 21:40:00original
2013-01-30 21:45:00original
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2013-01-30 21:55:00original
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\n", - "
" - ], "text/plain": [ - " CODtot_line2\n", - "Time \n", - "2013-01-01 00:05:00 fake\n", - "2013-01-01 00:10:00 fake\n", - "2013-01-01 00:15:00 fake\n", - "2013-01-01 00:20:00 fake\n", - "2013-01-01 00:25:00 fake\n", - "2013-01-01 00:30:00 fake\n", - "2013-01-01 00:35:00 fake\n", - "2013-01-01 00:40:00 fake\n", - "2013-01-01 00:45:00 fake\n", - "2013-01-01 00:50:00 original\n", - "2013-01-01 00:55:00 original\n", - "2013-01-01 01:00:00 original\n", - "2013-01-01 01:05:00 original\n", - "2013-01-01 01:10:00 original\n", - "2013-01-01 01:15:00 original\n", - "2013-01-01 01:20:00 original\n", - "2013-01-01 01:25:00 original\n", - "2013-01-01 01:30:00 original\n", - "2013-01-01 01:35:00 original\n", - "2013-01-01 01:40:00 original\n", - "2013-01-01 01:45:00 original\n", - "2013-01-01 01:50:00 original\n", - "2013-01-01 01:55:00 original\n", - "2013-01-01 02:00:00 original\n", - "2013-01-01 02:05:00 original\n", - "2013-01-01 02:10:00 original\n", - "2013-01-01 02:15:00 original\n", - "2013-01-01 02:20:00 original\n", - "2013-01-01 02:25:00 original\n", - "2013-01-01 02:30:00 original\n", - "... ...\n", - "2013-01-30 21:35:00 original\n", - "2013-01-30 21:40:00 original\n", - "2013-01-30 21:45:00 original\n", - "2013-01-30 21:50:00 original\n", - "2013-01-30 21:55:00 original\n", - "2013-01-30 22:00:00 original\n", - "2013-01-30 22:05:00 original\n", - "2013-01-30 22:10:00 original\n", - "2013-01-30 22:15:00 original\n", - "2013-01-30 22:20:00 original\n", - "2013-01-30 22:25:00 original\n", - "2013-01-30 22:30:00 original\n", - "2013-01-30 22:35:00 original\n", - "2013-01-30 22:40:00 original\n", - "2013-01-30 22:45:00 original\n", - "2013-01-30 22:50:00 original\n", - "2013-01-30 22:55:00 original\n", - "2013-01-30 23:00:00 original\n", - "2013-01-30 23:05:00 original\n", - "2013-01-30 23:10:00 original\n", - "2013-01-30 23:15:00 original\n", - "2013-01-30 23:20:00 original\n", - "2013-01-30 23:25:00 original\n", - "2013-01-30 23:30:00 original\n", - "2013-01-30 23:35:00 original\n", - "2013-01-30 23:40:00 original\n", - "2013-01-30 23:45:00 original\n", - "2013-01-30 23:50:00 original\n", - "2013-01-30 23:55:00 original\n", - "2013-01-31 00:00:00 original\n", - "\n", - "[8640 rows x 1 columns]" + "[[Timestamp('2013-01-04 00:05:00'), Timestamp('2013-01-09 00:05:00')]]" ] }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.meta_valid" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[Timestamp('2013-01-03 00:05:00'), Timestamp('2013-01-09 00:05:00')],\n", - " [Timestamp('2013-01-07 00:05:00'), Timestamp('2013-01-13 00:05:00')]]" - ] - }, - "execution_count": 13, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -980,48 +558,6 @@ "dataset.drift_periods" ] }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sign(-1) == np.sign(-10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "test.append([3,5])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "len(test)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1049,11 +585,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2013/1/1'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'2013/1/17'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" + ] + } + ], "source": [ "len(dataset.data['2013/1/1':'2013/1/17'])" ] @@ -1118,16 +664,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", "start_time": "2017-05-09T11:54:59.898063+02:00" }, - "collapsed": true, "scrolled": false }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n\u001b[0m\u001b[1;32m 2\u001b[0m order=3, plot=True)\n", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" + ] + } + ], "source": [ "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n", " order=3, plot=True)" @@ -1143,15 +700,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", "start_time": "2017-05-09T11:55:01.063627+02:00" - }, - "collapsed": true + } }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dataset.calc_daily_profile('CODtot_line2',[dt.datetime(2013,1,1),dt.datetime(2013,1,8)],\n\u001b[0m\u001b[1;32m 2\u001b[0m quantile=0.9,clear=True)\n", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" + ] + } + ], "source": [ "dataset.calc_daily_profile('CODtot_line2',[dt.datetime(2013,1,1),dt.datetime(2013,1,8)],\n", " quantile=0.9,clear=True)" @@ -1412,16 +980,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", "start_time": "2017-05-09T11:55:07.433945+02:00" }, - "collapsed": true, "scrolled": false }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_daily_average\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CODtot_line2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2013\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2013\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" + ] + } + ], "source": [ "dataset.calc_daily_average('CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,2,1)],plot=True)" ] @@ -1435,15 +1014,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", "start_time": "2017-05-09T11:55:07.833046+02:00" - }, - "collapsed": true + } }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dataset.calc_total_proportional('Flow_total',\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Flow_line1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Flow_line2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Flow_line3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'TSS_line1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'TSS_line2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'TSS_line3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 'TSS_prop')\n", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" + ] + } + ], "source": [ "dataset.calc_total_proportional('Flow_total',\n", " ['Flow_line1','Flow_line2','Flow_line3'],\n", @@ -1466,11 +1056,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CODtot_line3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdetrended_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CODtot_line3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2013/1/5'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'2013/1/8'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mline_segment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CODtot_line3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2013/1/5'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'2013/1/8'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdetrended_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline_segment\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mline_segment\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" + ] + } + ], "source": [ "from scipy import signal\n", "data = dataset.data['CODtot_line3'][:].copy()\n", diff --git a/wwdata/Class_HydroData.py b/wwdata/Class_HydroData.py index 2cde5cd15..55e5f3fb8 100644 --- a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1645,8 +1645,8 @@ def detect_drift(self, data_name, arange, max_slope, period=None, slopes = [] # Remove NaNs, infs and other values that signal.detrend can't analyse from the dataset - data_series.data.replace(0,np.nan) - data_series.data.dropna(inplace=True) + data_series.replace(0,np.nan) + data_series.dropna(inplace=True) if plot: fig = plt.figure(figsize=(16, 6)) @@ -1725,7 +1725,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, detrended_values = signal.detrend(data_series[driftperiod[0]:driftperiod[1]]) line_segment = data_series[driftperiod[0]:driftperiod[1]] - detrended_values[:] ax.plot(line_segment,label='Detected drift') - ax.legend({'.':'data','-':'Detected drift'},fontsize=16) + ax.legend(['Data','Detected drift'],fontsize=16) else: print('No drift detected') diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index d1e3fea6f..697c26944 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -320,53 +320,7 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index',order=N ### # CHECKS ### - self._plot = 'filled' - wn.warn('When making use of filling functions, please make sure to '+ \ - 'start filling small gaps and progressively move to larger gaps. This '+ \ - 'ensures the proper working of the package algorithms.') - if clear: - self._reset_meta_filled(to_fill) - self.meta_filled = self.meta_filled.reindex(self.index(),fill_value='!!') - - if not to_fill in self.meta_filled.columns: - # if the to_fill column doesn't exist yet in the meta_filled dataset, - # add it, and fill it with the meta_valid values; if this last one - # doesn't exist yet, create it with 'original' tags. - try: - self.meta_filled[to_fill] = self.meta_valid[to_fill] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill] = self.meta_valid[to_fill] - else: - # where the meta_filled dataset contains original values, update with - # the values from meta_valid; in case a filling round was done before - # any filtering; not supposed to happen, but cases exist. - try: - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - if not to_fill in self.filled: - self.add_to_filled([to_fill]) - - # Give warning when replacing data from rain events and at the same time - # check if arange has the right type - try: - rain = (self.data_type == 'WWTP') and \ - (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) - except TypeError: - raise TypeError("Slicing not possible for index type " + \ - str(type(self.data.index[0])) + " and arange argument type " + \ - str(type(arange[0])) + ". Try changing the type of the arange " + \ - "values to one compatible with " + str(type(self.data.index[0])) + \ - " slicing.") - - if rain : - wn.warn('Data points obtained during a rain event will be replaced. '+ \ - 'Make sure you are confident in this replacement method for the '+ \ - 'filling of gaps in the data during rain events.') + self._filling_function_check(to_fill,arange,clear) ### # CALCULATIONS @@ -395,25 +349,11 @@ def fill_missing_interpolation(self,to_fill,range_,arange,method='index',order=N ### # FILLING ### - # Use the .interpolate() method to interpolate for the nan values just created - # the limit argument makes sure that only the values that can be filled by - # interpolation are filled; needed to prevent other, already present NaN values - # from also getting filled!! - - #if method is 'polynomial' or 'spline': - # order = int(input('Please specify an order:')) - # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method, limit=range_, *kwargs, order=order) - #else: - # self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,limit=range_,*kwargs) - self.filled[to_fill] = self.filled[to_fill].interpolate(method=method,order=order, limit=range_, *kwargs) # Adjust in the self.meta_filled dataframe self.meta_filled.loc[indexes_to_replace[0],to_fill] = 'filled_interpol' - # Set all points still tagged filtered in the self.filled dataset to NaN - #self.filled.loc[self.meta_filled[to_fill] == 'filtered'] = np.nan - if plot: self.plot_analysed(to_fill) @@ -456,54 +396,7 @@ def fill_missing_ratio(self,to_fill,to_use,ratio,arange, ### # CHECKS ### - self._plot = 'filled' - wn.warn('When making use of filling functions, please make sure to '+ \ - 'start filling small gaps and progressively move to larger gaps. This '+ \ - 'ensures the proper working of the package algorithms.') - if clear: - self._reset_meta_filled(to_fill) - self.meta_filled = self.meta_filled.reindex(self.index(),fill_value='!!') - - if not to_fill in self.meta_filled.columns: - # if the to_fill column doesn't exist yet in the meta_filled dataset, - # add it, and fill it with the meta_valid values; if this last one - # doesn't exist yet, create it with 'original' tags. - try: - self.meta_filled[to_fill] = self.meta_valid[to_fill] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill] = self.meta_valid[to_fill] - else: - # where the meta_filled dataset contains original values, update with - # the values from meta_valid; in case a filling round was done before - # any filtering; not supposed to happen, but cases exist. - try: - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - - if not to_fill in self.filled: - self.add_to_filled([to_fill]) - - # Give warning when replacing data from rain events and at the same time - # check if arange has the right type - try: - rain = (self.data_type == 'WWTP') and \ - (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) - except TypeError: - raise TypeError("Slicing not possible for index type " + \ - str(type(self.data.index[0])) + " and arange argument type " + \ - str(type(arange[0])) + ". Try changing the type of the arange " + \ - "values to one compatible with " + str(type(self.data.index[0])) + \ - " slicing.") - - if rain : - wn.warn('Data points obtained during a rain event will be replaced. '+ \ - 'Make sure you are confident in this replacement method for the '+ \ - 'filling of gaps in the data during rain events.') + self._filling_function_check(to_fill,arange,clear) ### # FILLING @@ -563,54 +456,7 @@ def fill_missing_correlation(self,to_fill,to_use,arange,corr_range, ### # CHECKS ### - self._plot = 'filled' - wn.warn('When making use of filling functions, please make sure to '+ \ - 'start filling small gaps and progressively move to larger gaps. This '+ \ - 'ensures the proper working of the package algorithms.') - if clear: - self._reset_meta_filled(to_fill) - self.meta_filled = self.meta_filled.reindex(self.index(),fill_value='!!') - - if not to_fill in self.meta_filled.columns: - # if the to_fill column doesn't exist yet in the meta_filled dataset, - # add it, and fill it with the meta_valid values; if this last one - # doesn't exist yet, create it with 'original' tags. - try: - self.meta_filled[to_fill] = self.meta_valid[to_fill] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill] = self.meta_valid[to_fill] - else: - # where the meta_filled dataset contains original values, update with - # the values from meta_valid; in case a filling round was done before - # any filtering; not supposed to happen, but cases exist. - try: - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - - if not to_fill in self.filled: - self.add_to_filled([to_fill]) - - # Give warning when replacing data from rain events and at the same time - # check if arange has the right type - try: - rain = (self.data_type == 'WWTP') and \ - (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) - except TypeError: - raise TypeError("Slicing not possible for index type " + \ - str(type(self.data.index[0])) + " and arange argument type " + \ - str(type(arange[0])) + ". Try changing the type of the arange " + \ - "values to one compatible with " + str(type(self.data.index[0])) + \ - " slicing.") - - if rain : - wn.warn('Data points obtained during a rain event will be replaced.' + \ - ' Make sure you are confident in this replacement method for the' + \ - ' filling of gaps in the data during rain events.') + self._filling_function_check(to_fill,arange,clear) ### # CALCULATIONS @@ -677,66 +523,7 @@ def fill_missing_standard(self,to_fill,arange,only_checked=True,plot=False, ### # CHECKS ### - self._plot = 'filled' - wn.warn('When making use of filling functions, please make sure to '+ \ - 'start filling small gaps and progressively move to larger gaps. This '+ \ - 'ensures the proper working of the package algorithms.') - - # several checks on availability of the right columns in the necessary - # dataframes/dictionaries - if clear: - self._reset_meta_filled(to_fill) - self.meta_filled = self.meta_filled.reindex(self.index(),fill_value='!!') - - if not to_fill in self.meta_filled.columns: - # if the to_fill column doesn't exist yet in the meta_filled dataset, - # add it, and fill it with the meta_valid values; if this last one - # doesn't exist yet, create it with 'original' tags. - try: - self.meta_filled[to_fill] = self.meta_valid[to_fill] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill] = self.meta_valid[to_fill] - else: - # where the meta_filled dataset contains original values, update with - # the values from meta_valid; in case a filling round was done before - # any filtering; not supposed to happen, but cases exist. - try: - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - - if not to_fill in self.filled: - self.add_to_filled([to_fill]) - - try: - if not isinstance(self.daily_profile,dict): - raise TypeError("self.daily_profile should be a dictionary Type. \ - Run calc_daily_profile() to get an average daily profile for " + to_fill) - except AttributeError: - raise AttributeError("self.daily_profile doesn't exist yet, meaning "+ - "there is no data available to replace other data with. Run "+ - "calc_daily_profile() to get an average daily profile for " + to_fill) - - # Give warning when replacing data from rain events and at the same time - # check if arange has the right type - try: - rain = (self.data_type == 'WWTP') and \ - (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) - except TypeError: - raise TypeError("Slicing not possible for index type " + \ - str(type(self.data.index[0])) + " and arange argument type " + \ - str(type(arange[0])) + ". Try changing the type of the arange " + \ - "values to one compatible with " + str(type(self.data.index[0])) + \ - " slicing.") - - if rain : - wn.warn('Data points obtained during a rain event will be replaced. '+ \ - 'Make sure you are confident in this replacement method for the '+ \ - 'filling of gaps in the data during rain events.') + self._filling_function_check(to_fill,arange,clear) ### # CALCULATIONS @@ -813,55 +600,7 @@ def fill_missing_model(self,to_fill,to_use,arange,only_checked=True, ### # CHECKS ### - self._plot = 'filled' - wn.warn('When making use of filling functions, please make sure to start filling small gaps and progressively move to larger gaps. This ensures the proper working of the package algorithms.') - - # several checks on availability of the right columns in the necessary - # dataframes/dictionaries - if clear: - self._reset_meta_filled(to_fill) - self.meta_filled = self.meta_filled.reindex(self.index(),fill_value='!!') - - if not to_fill in self.meta_filled.columns: - # if the to_fill column doesn't exist yet in the meta_filled dataset, - # add it, and fill it with the meta_valid values; if this last one - # doesn't exist yet, create it with 'original' tags. - try: - self.meta_filled[to_fill] = self.meta_valid[to_fill] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill] = self.meta_valid[to_fill] - else: - # where the meta_filled dataset contains original values, update with - # the values from meta_valid; in case a filling round was done before - # any filtering; not supposed to happen, but cases exist. - try: - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - - if not to_fill in self.filled: - self.add_to_filled([to_fill]) - - # Give warning when replacing data from rain events and at the same time - # check if arange has the right type - try: - rain = (self.data_type == 'WWTP') and \ - (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) - except TypeError: - raise TypeError("Slicing not possible for index type " + \ - str(type(self.data.index[0])) + " and arange argument type " + \ - str(type(arange[0])) + ". Try changing the type of the arange " + \ - "values to one compatible with " + str(type(self.data.index[0])) + \ - " slicing.") - - if rain : - wn.warn('Data points obtained during a rain event will be replaced. '+ \ - 'Make sure you are confident in this replacement method for the '+ \ - 'filling of gaps in the data during rain events.') + self._filling_function_check(to_fill,arange,clear) ### # CALCULATIONS @@ -955,62 +694,7 @@ def fill_missing_daybefore(self,to_fill,arange,range_to_replace=[1,4], ### # CHECKS ### - self._plot = 'filled' - wn.warn('When making use of filling functions, please make sure to '+ \ - 'start filling small gaps and progressively move to larger gaps. This '+ \ - 'ensures the proper working of the package algorithms.') - # index checks - #if arange[0] < 1 or arange[1] > self.index()[-1]: - # raise IndexError('Index out of bounds. Check whether the values of \ - # "arange" are within the index range of the data. Mind that the first \ - # day of data cannot be replaced with this algorithm!') - - # several checks on availability of the right columns in the necessary - # dataframes/dictionaries - if clear: - self._reset_meta_filled(to_fill) - self.meta_filled = self.meta_filled.reindex(self.index(),fill_value='!!') - - if not to_fill in self.meta_filled.columns: - # if the to_fill column doesn't exist yet in the meta_filled dataset, - # add it, and fill it with the meta_valid values; if this last one - # doesn't exist yet, create it with 'original' tags. - try: - self.meta_filled[to_fill] = self.meta_valid[to_fill] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill] = self.meta_valid[to_fill] - else: - # where the meta_filled dataset contains original values, update with - # the values from meta_valid; in case a filling round was done before - # any filtering; not supposed to happen, but cases exist. - try: - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - except: - self.add_to_meta_valid([to_fill]) - self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ - self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] - - if not to_fill in self.filled: - self.add_to_filled([to_fill]) - - # Give warning when replacing data from rain events and at the same time - # check if arange has the right type - try: - rain = (self.data_type == 'WWTP') and \ - (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) - except TypeError: - raise TypeError("Slicing not possible for index type " + \ - str(type(self.data.index[0])) + " and arange argument type " + \ - str(type(arange[0])) + ". Try changing the type of the arange " + \ - "values to one compatible with " + str(type(self.data.index[0])) + \ - " slicing.") - - if rain : - wn.warn('Data points obtained during a rain event will be replaced. '+ \ - 'Make sure you are confident in this replacement method for the '+ \ - 'filling of gaps in the data during rain events.') + self._filling_function_check(to_fill,arange,clear) ### # CALCULATIONS @@ -1389,6 +1073,69 @@ def check_filling_error(self,nr_iterations,data_name,filling_function, # turn warnings on again wn.filterwarnings("always") + def _filling_function_check(self,to_fill,arange,clear): + """ + Function that executes the necessary checks when using a filling function. + + Parameters + ---------- + to_fill : str + name of the column containing the data to be filled + arange : array of two values + the range within which missing/filtered values need to be replaced + clear : bool + whether or not to clear the previoulsy filled values and start from + the self.meta_valid dataset again for this particular dataseries. + """ + + self._plot = 'filled' + wn.warn('When making use of filling functions, please make sure to ' + 'start filling small gaps and progressively move to larger gaps. This ' + 'ensures the proper working of the package algorithms.') + if clear: + self._reset_meta_filled(to_fill) + self.meta_filled = self.meta_filled.reindex(self.index(),fill_value='!!') + + if not to_fill in self.meta_filled.columns: + # if the to_fill column doesn't exist yet in the meta_filled dataset, + # add it, and fill it with the meta_valid values; if this last one + # doesn't exist yet, create it with 'original' tags. + try: + self.meta_filled[to_fill] = self.meta_valid[to_fill] + except: + self.add_to_meta_valid([to_fill]) + self.meta_filled[to_fill] = self.meta_valid[to_fill] + else: + # where the meta_filled dataset contains original values, update with + # the values from meta_valid; in case a filling round was done before + # any filtering; not supposed to happen, but cases exist. + try: + self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ + self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] + except: + self.add_to_meta_valid([to_fill]) + self.meta_filled[to_fill].loc[self.meta_filled[to_fill]=='original'] = \ + self.meta_valid[to_fill].loc[self.meta_filled[to_fill]=='original'] + + if not to_fill in self.filled: + self.add_to_filled([to_fill]) + + # Give warning when replacing data from rain events and at the same time + # check if arange has the right type + try: + rain = (self.data_type == 'WWTP') and \ + (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) + except TypeError: + raise TypeError("Slicing not possible for index type " + \ + str(type(self.data.index[0])) + " and arange argument type " + \ + str(type(arange[0])) + ". Try changing the type of the arange " + \ + "values to one compatible with " + str(type(self.data.index[0])) + \ + " slicing.") + + if rain : + wn.warn('Data points obtained during a rain event will be replaced.' + \ + ' Make sure you are confident in this replacement method for the' + \ + ' filling of gaps in the data during rain events.') #============================================================================== # LOOKUP FUNCTIONS diff --git a/wwdata/__pycache__/Class_HydroData.cpython-36.pyc b/wwdata/__pycache__/Class_HydroData.cpython-36.pyc index 9fcd46be32343fd98852c4d1fb7cd1ee5c2f5ba3..a9438e28e1fa22cabd1707acca090a3f25c3d6b5 100644 GIT binary patch delta 3956 zcmai14RBP|6@K@<-OcXH?q-whX0!R@g#^Nq5GWAJAb%>2_!kjqfkcT*&a&(#?uNTB zF~PU48f2zuhA>x|@oy?PQ)#QBv(z7KZT;h?Rco~fS(*A%e?X8@MQa_UJ?CvmfI98l z-1EMB&bjBFd(OG{zC3kYdHZWco#Aq+2kQpg_HBDoeO6%)iIn1Q@fSPwIy2~hyJ*lr1=lQ0kF zW4{t~SO5zVRY4D23X2epgI@R%G$YdBCb$e1BdUfzxE!uPG#>ilO1KJ94Qzm`p#@Pb 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zrm6NGg6lN4Tko%v*l`i3&r$_{9@y}X^)}8*4!+wdvAxlm?9O{Hu$iZ2GQbWT^;&}T zCY~oRp}X)!KVbt$`}`F(w0TR^1A%CuDH;Gzczb|)qFV!=9b3b#0S`aG9@^Lz43v#s z*9$y5W8Jp;um>KnE2lTmXSccLuAlU9cNshIn}YZRQ~EJiQ`!PMJnaG69ER@9Z~5xB zKj_7yls4~RkoZ{LaSySu_T#x) zI*AAZd;a(|lUawC7Y|p+GZJ?4_}m(WSVbAXIaXrLP>3Qa3tfs#Y{W&(k{Q29I3mv@ zGO=oP#7K;iCex1EvaHi7B!_q;JF(>@m%i_mL~}-R;|CYzsXQK9f>7(#()qmOeJBxV+E6HEjy1@W`>y8Y1 z4P|`Hgl^~%`*QFJqGyi|Z6Zt9t3y8WcJjv10-4k$XAc*OQi_zb4I@4*^5GHB!cWiy z1K72nti?Hge*-QS&?fA*5t@V=hzf_ACBlDGpaSe9a-7r^#2C7IAol z!wUApFFFdA@$Z?^iLC)!hf=Bdm4(Q*VT%iKfg}sR?A18_M_%}w9-tm?m=#vp^9G5yw|5`a*(q5mOQrYdZk#YPrh~i91-X1le_*sLdaa!`ptIFT2x7C zqh0L*|HF>nL~qNPcMX<_Va{E5NsZ4MqR-OPn=aNx63Kf8L{ Ouiv4R$&zf9v;PYzAa4o) From de2699a3f14001a7363738b72fca170f82f184bd Mon Sep 17 00:00:00 2001 From: cpdmulde Date: Mon, 3 Sep 2018 16:22:03 +0200 Subject: [PATCH 42/42] finalise remove_drift function and minor testing --- ...howcase_OnlineSensorBased-checkpoint.ipynb | 282 ++++-------------- Showcase_OnlineSensorBased.ipynb | 269 +++++------------ wwdata/Class_HydroData.py | 172 ++++------- wwdata/Class_OnlineSensorBased.py | 19 +- .../Class_HydroData.cpython-36.pyc | Bin 64598 -> 63789 bytes .../Class_OnlineSensorBased.cpython-36.pyc | Bin 45442 -> 40323 bytes 6 files changed, 189 insertions(+), 553 deletions(-) diff --git a/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb index 1fdc96e82..955d223e8 100644 --- a/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb +++ b/.ipynb_checkpoints/Showcase_OnlineSensorBased-checkpoint.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -70,20 +70,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.2.0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ww.__version__" ] @@ -97,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -105,29 +94,7 @@ }, "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Time', 'TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", - " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2', 'TSS_line1',\n", - " 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns', 'Turb_ns',\n", - " 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es', 'Temp_es', 'NH4_infl',\n", - " 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter',\n", - " 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST',\n", - " 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT',\n", - " 'Cond_line1', 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", - " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", - " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1', 'Flow_line2',\n", - " 'Flow_line3', 'Flow_total'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "measurements = pd.read_csv('./data/data_example.txt',sep='\\t',skiprows=0)\n", "measurements.columns" @@ -142,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -167,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -190,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -212,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -235,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -257,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -272,25 +239,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", "start_time": "2017-05-09T11:54:56.050129+02:00" - } + }, + "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x\nb/Ih/DD5kH5RUbIpns6d4GivLo/FbkXUHPiwoOXIZXK8N2qVOH21OA2Pfjse2WVZ6Nq+K5YM+7cV\nW0dEZFuMBjasbdeuXQgODjb479q1a3jrrbf05q9fv158/alTpzBu3DiEhoZixowZyMzMtN6HoWal\nfUPHpxttn6ASMGbHQ8i+3Z2CwSw1TXX9peHLJPOXhi/D/rgjULgq8IBiIIMaNkxQCXj0m3GorlU/\nRGC3ImoOfFjQsnp4BIsZGn5yP/HclnszF9P2xmH09hEMLhERmcHkcK/Z2dn4/fffzdpQVpZlL67G\njBmDBx98UJyura3F7Nmz4efnBx8fH6SmpmLBggUYP368uI5crr5gv379OubMmYO5c+ciIiICH3/8\nMebOnYvvvvsO9vatNpZDjcTh0u4uKYXJyBayxemucl8Gs26Ty+To49VXMq+PV1/uE22E7m/fAQ7M\n2CCL0zws0AwdzeNr05ga4URQCZi0JxbZZVnwk/thx4TvMH3vFDGwBKizOJLyEjG864iWbjoRkU0x\nGdj48MMP8eGHH5q1obq6OtjZ2VmkUQDg7Ows6Qrz1Vdf4fr162JWRnp6Ou6//354eXnpvTY+Ph69\nevXCrFmzAADLli3DsGHDcOrUKYSHh1usjdR6yGVyDl95l9D0SdbUk5DZy6zcotalh0cwZPYyqGpV\nkNnL0MMj2NpN4tCFFuLr5g872KMOtQCAGtTg9/wkjOaQr2RBfFhgOZpuPZogkW5XQO3smGwhG4W3\nbmB/3BHE/7EVC4/NF9erqK5o8bYTEdkao4ENTVCgNRAEAR999BFefvlldOzYEfn5+SguLkZAQIDB\n9c+fP4+BA+/c5Lq4uKB379747bffGNggsnFymRz/GLIYM/fPAAD8WZrBp1m3CSoBBzP3i6OiqGpV\nSC1KseqQoPVd2JP5Tl8/JQY1NLJL2RWlrbJmQJAPCyzDULce7e/VUHaMXCZHhH+kZDtvHFuAoT7D\neOwkIjLBaGBj/vz5xha1uG3btsHJyQlxcXEAgLS0NDg6OuKDDz5AQkICPDw88PTTT2PSpEkAgPz8\nfHh7e0u20alTJyiVyhZvOxFZlqAS8I9jr0vm8WmW+nsZvX0ErhanwdHOEdV16joMrx99BQfiEqx2\nQVzfhT2Z71j2Ub15fTuHWqEl1Ny0A4J+cj/se/RnqwYozXU3Z2cZ+uz1desxlh1z4tpxyXp/lmbw\n2ElEVA8rx1AlAAAgAElEQVSTXVG01dTUIDU1FXl5eairq4NCoUBQUBAcHc3eRKPU1dVh27ZtmD59\nOmQydcp5eno6AKBXr16YMWMGTp8+jbfeegsuLi6IiYlBRUUFnJycJNtxcnJCVVWVyffy8HCFo6ND\n83yQNsrLy83aTaC7zMWMs1CW/yWZ597Btc38Fhv7OS5mnMXVYnX3HE1QA1D3z/6z8g9E+ERYpH0N\nNbzjIPTs1BNXblxBz049MbznIMidbPuGR6gScCnvEnp7927Rz9Ktk/4Q7D/mfgtPT3mLt8WWNGaf\nstbfWCM957Kki8KYXQ/h8guXW/XfWKgSMOK/D+GPgj/Qq3MvnJl1plW315KMffYa4Sb+d+jLCPAI\nwIhuIwx+Hy5VdsirbQ+vzm7i8sddJmPB0Xli9l2QZ1CrOna2lfMtUWvC/arp6o1KFBcX44MPPsAP\nP/yAkpISybIOHTrg4Ycfxv/+7//C09OzWRp46dIlZGVlYcKECeK8qVOnIjY2Fu7u7gDUAY7MzExs\n3boVMTExaNeunV4Qo6qqSlzfmKKicst/gDbMy8sN+fll1m4G3WWKS/T308ryujbxW2zKPpWce1Uy\n3dm5MwpuFQAAZn37P2LWRks/URVUAmqqb9eEqK5FfkEZKmR1zf6+zcWaT9J7drhfb97as2ux+vRq\ndvMxojH7lHb2U3f3IKtkPHnb+6Nr+67IvZkLAMguzcaBy0dbdZe7c8oz+KPgDwDAHwV/4PiV03dN\nhoGhz+7r5o/+G++DqlYFBzsHnJh6DgEdAyWvU5YrMWZnJLLLsiT7sAPa4/gTZ7Dh4hd44J6BiPCP\nREVJHSpg/fMcr/2ILI/7VcMYCwKZHCLkwoULGDNmDLZu3Yp77rkHTz31FF5//XUsWrQIM2fOREBA\nALZt24Zx48aZPXpKQyUkJCA0NBQKxZ0LRzs7O70gRWBgoNjVRKFQID8/X7K8oKDAYKFRIrItPTyC\nYY87mVV+bv4I8+5vxRYZJqgEnFOeaZFh+jJK0vHCoTt1kRztHcWgBqDO2vgmbReU5UpEbx+FmJ2R\niN4+qkXallKYLBZ6vVqSZvNDR+oW+/vb9pEtNhTjUJ9huLeDtLaU5okuh+W0nKS8RDH7STMiRUuT\ny+R4d9SqFn/fpvB184fMXp0tK7N3uqtG7DE07Pzeq9+K+2dNXQ3G7IiUHCsMDV2u+a0py5V49Nvx\nWHN+NZadWoqkvEQO+UpEVA+jGRuFhYWYM2cOnJyc8OWXX2Lo0KEG10tKSsKrr76KF198EXv27LF4\n5oZuIVAAWL58OTIyMvDpp5+K85KTkxEYqI6Eh4aG4uzZs+KyiooKXL58GXPmzLFo24io5aUWpaAW\nNeJ0TW2NibWto6ULZm5N/koyXV1bLZmW2csw7/CL6Cr3Ra6QA6Dl6l1obnZUtVVt4mYn2DMECpd7\noKxQd4e6fvMaTl77pcVGJnGwUwf17GGPWq1CojJ7mVW+W2W5Egcz9yOqW7RN1IAwx3XhmmS66Fah\nVdox1GcYAjoGIqMkHQEdA1tlABe4U1uioroCqlp1tqyqtgo5ZVlt5jdRH0O1MtycpE8Ub1TekBwr\ndIdvBtQ1kfZM/EEd8Li97GpJGiZ9M5ZZWURE9TCasbFlyxaUlZXhiy++MBrUAICwsDCsX78eZWVl\n2Lp1q8UbmJqaiqCgIMm8iIgIJCQkYOPGjcjKysJXX32FPXv2YObMmQCAyZMn4/z581i7di3S0tLw\nxhtvwMfHx+TnICLraUh2Q9GtIsn0tZu5re5JtaGCmc1pQtAkybSv3E/8f892nuJTw1whB13lvgBg\nsJBdc8gpy9K72bF1ms+j0VIjk2hnv9TqjI6iqlW1+HerLFei/8bemHf4RfTf2BvKctsv0C2oBLxx\n7O+SeX/csN7xxd7OXvLf1kYTxI3ZGYnXj7yC7u7q67WWOr60ZimFKXrztAsAa2d5aFwtTsPBzP16\nAQ+AWVlERPUxeqb86aefMG7cODELwhR/f39MmDABP/30k0UbB6i7kOh2Oxk8eDBWrFiB+Ph4xMbG\nYsuWLfjPf/6DAQMGAAB8fX3x4Ycf4ptvvsHkyZNRUFCANWvWwN6+dV4YEN3NNP3ZY3ZGYvT2EfUG\nN3LKpBd89nYOrS4LQDct2dO5EzYnb2y2G79rt/vhazzZ+1nx/wsrpU+b3x25Ej9MPtRiT/6CPUPE\nm52uct9W97dqqJPXftH7TltqZBJDN0IadrBr8e9WPbTwnaDVwcz9Lfr+zSEpLxHFVTrBUyHXyNrN\nK6UwWdIlJqUwuUW7uJlDO4h7tSQN741c1aLHl9ZCO8ATvX0UlOVK/Pf8Gr311l9cJ/7tNFkeuyZ8\nL9be6O4ehKhu0eJ+3rV9V3EZg0VERKYZ7YqSk5ODqVOnmr2h3r1749tvv7VIo7QZq90xZswYjBkz\nxujrRo4ciZEjR1q8PURkWYb6s5sqkBfk0UMyXVtXg9/zk1qsK4A5bqpuYmaf5+HXwR9B7j0wfOsg\nqGqr4GDniBNTz0oKyGkX8/RCI0ZvUAl49fBLknl2Out0ae+D6zevwcvZC0HuPfQK2DW32lp1dkGu\nkIOJe2KsOvxsU6UVperN2526AwO6DGr299bcCP0zYSE2p2yULKtDHRKyDyMu+PFmb4dGuM9wk9O2\nyFC3ky5y/dFoWoLuUKG+bv4W6eJmTgFhc4sMa3c1c7SToaK6AmHe/W12/24s3Sy9904tQ0Wt/jDk\nt2orcDjrIMZ1nwhAvU+HefeHveY5Yx3QXtYe++OOiPU2engEI6cs664cQpfIUu7moajvJkZTGBwd\nHaFSqczeUGVlJVxcXCzSKCJqe8x90lhRrX8xqC3IvQdc7KXHGnO6AjT2SWdDX6csV6LfhhAsPDYf\nT+59HN+m7RafatfUVWPc7mhxW7pP+YSqhj+FTcpL1Bv+1kfeFTJ79fDYMnsZ1v1tIxztHZF/Kx/D\ntw5q0S4DKYXJyChNF6c1T55tlW5gDQC+Td/TIk/QNRdmnVwNF8J++ec5zfa3NbQfXCyQPnjYnvJ1\nq8kkaKycshy9ef0ULVvbQvNdA8BXsfGYG/oyFg7+J1KLUprcxU3vmGPg7yWoBIyOv51FF286i067\nq1l1nQrT9sa1WGHi1sTXzR8OWs8KN/7xpdF1j2UnSKZ1CyxrAhp/P/oqJn0zFpP2xMLXzV/M2KGG\naW1ZTtTyGpoZTLbLaGAjKCgICQkJxhbrSUhIQPfu3S3SKCJqWwSVgMj44YjZGYmhm/vjQOZ+8cQS\n5t0fAR3uZBC89csioycdZbkSw7YMlDwJc4ADYruPr/f9GzMaSGNet+vKdlTXqYt31qAGHyeulizP\nK1eKF6jfpO2S3KhcyrtkVru0GQoEFVQUiHU1VLUq7E7bKRYUbekuA57OnSTT/m7dbDqduq9XGOx0\nTp3K8r/wQ/r3zfq+2r/FLZc3GFynpq4Gu65st/h7Z5SkY8jmfnr7wbm/zkrWe//sckRsC7fpi0Zf\nN1/JtLerAkN9hrXY+2v/nSO3DcewLQOw5vxqzNw/A/MOv9jkGhbm1P9Jyks0eKNtiKHuUXdjLYic\nsizUoLr+FQF0dZNmAPm6+Yu1jwBg3uEXsenSesnfSXP+1D0P2cJN+6WCi5h94DlsT9nW4u3kDS0B\n+pnBJ6/9YuUWUXMxGtgYP348jh8/joMHD9a7kX379uHYsWN47LHHLNo4ImobTl77BRkl6qf2yvK/\nMG1vHB68nTkgl8mxIuLOzb+pJ/oHM/ejuk6aSaZofw/ay9qbfP/GFvNszOv+unldMl2skvbX93ZV\niCnl8w6/KA6P2MO9J3p79zarXfXxdfMVbzYCOgRi3YVPJctbssvAiWvHJdM3VTcl07ZwYa4tpywL\ndTqFOwHghUP/g68ubWi2z6H9WyyoLICdXocjtX+d+KdFszaU5UqEb3kAebe3qb0fGOr2kln6p01f\nNDo7SrPBFg/9fy36pFz775xRmi4GSQH1d2uohoWyXGl2DR9zhmTVDZaayqLTrhNxNxcODfYMgZ/c\nvBo3UVrdJgWVgEl7YsXRqgD133nxiX9IXmNo/2tswL4lXSq4iIj4cOxKjccLh2Zh+JaBLdrO1jB0\nM7U+C47Oa5X7CzWd0cBGXFwcwsLCMG/ePKxZswZFRUV66xQVFWHlypVYsGABwsPDTda8IKLWo6Vv\nJi8VXNSblyvk4OEdERBUAsK8+0uKbRq7KI7qFg1HO5lk3rWbuTh57ReTn0f7qaKf3E+8mK/ve9At\nAlrfxXpGSTrWnv/Q6HIHOOC7R/YjpyxLvHlR1VZhZcRH2DVxLy7lXWrw38TFUb8LoIezJ/bHHcEP\nkw/h+dAX9EbQKLx1o0Hv0RRR3aLv9B8HcONWgXhxaQsX5rqcHYx3uXz16EvN9lRQfUN6p3vR3kcO\nwNNJf3j1GtRg71XL1bvadWU7auruDKncQdZB3H9u1Ri+4TVUh8RW/fvU0kb/PhtznNU+5gR0CISj\n3Z3uDZohXx9QDJQENfptuA/zDr+IsPW9xACyMalFKZKCr6lF+iN3NPSzyGVyDO86AgfiEu7KwqGA\n+juY0fsZs9ZNyDki/r92IMscfm7+4nmopUffaoxVZ9+TTGvO143VkCAekUYPj2BxqHRAff3ZGvcX\najqjgQ0HBwd88sknGDRoEFavXo1hw4bh4YcfxowZM/DMM89g3LhxGD58OD799FOMGDECH3zwAezs\nDD9BIqLWo6VvJgWVgC8v/NfgslwhB0l5iZDL5Ng1ca94g2/soljhqsAvU89g1v2zoXC9R5w/fe8U\nk/3BNdv3c/NHtpCNSXtioSxX1vs9aJ5GmnuxvjX5K5PLu7r5wsvVWy9gEtUtGpP2xGLIuiEN/pv0\n8AiGA+6csLt1uFcs3hfsGQK/Dv6Sm6N7OwS06NPU9rL26OwirQmheQJsCxfm2gSVgMe+m2hyneaq\nIaK+Ib3TvehW7S2cfeoiYu4dq7euXwfLjY5SWVMpmS5VlWLsrtFQlitRUV0BN8cOeq/p7NLZIu8t\nqAQcz03A8dyEFgt66QYKNSMOaX6fxm7wddva2OOs9jHn0GPHcSAuAZN6TMHHkf/FoSnH9Y5Be69+\nK2ax1aAGY3ZEGn0vQSVg3s8vSua98vMLeusbCpaa81nkMrkk6HK3MXYF7OYoLQr9YeJK8Tv0dfOX\n3HCZ4u2qwL7Jh8Tvt6GBd2vo4dlLb97xbPO7uWvvVxkl6Q0eXjrMuz+6d7w9Kld7X/TwCDa/8dRm\n5JRlSQL0gH43WWobTI5/2rFjR6xbtw5r1qxBVFQUKioqkJiYiNOnT6O0tBQPP/wwPvvsM6xZswZy\n+d15IiOyNUl5iS16M5lSmIzr5deMLq+orhDTcecdfhGT9sSavDCfvncK/nvxE0kWQB3qAJjuD55T\nloXsMnWR0dTiKziYud+s76EhF+tPhEw3uTyrLBO7r+yQBHK+io03uy2GpBaloAZ3TtjLHnwPcplc\nrGsybW8cFO3vQad26pO4sS4MzSWlMBl5FdILUM2Nky1cmGtTf5Y8k+v4yf2b5XPojtZRdKsQcpkc\njwZP0Vs3yF2/wGljdXfXr52VWfon/rZ9BCZ9MxZl1aV6yzNKMpockBBUAiK2hauLJ34zFsO2DGiR\n4EaYd3/J8MTa6mrrDBbV1OxrmraO/HpIk46zmmNOfnkeoraPwK7UeLxyeK5eNy4AYrcSjRuVN3Dy\n2i8GAzBJeYnILPtTsn5WWabeE/Qw7/7iyEkBHQPh4ugi+Szvn14uqZNEaoEG9hUA+HbSfnRudyfY\nV3ArH4ez1N28U4tS9G64DOns3BkrIz6SdLtsaODdGp66/1m9eYnKswbW1KcpYqvZr8buGt3g4aXl\nMjn2PPID/Nz8kXszx+T1BbVdwZ4h8HbxlszT7SbbEKYy2Gyte21bYzKwofHQQw9h9erVOHr0KC5d\nuoSLFy/i6NGjWLFiBUaMMD4sIxG1LoJKwOtHXhGnu8p9DfaxtqT6tn88J8HsmwDtJ/ymgiUa2icY\nXzd/+N1uiyZLwtI31QEdA/FxpOHsFI35R1/G/zuxBIM29cW8wy9i6Ob+mHf4RfGpXUPbklGcIZku\nvlUMADicdUhMS88VcnCjUt39JKM0vUX7GQd7hkiKwzrAQXxqZgsX5trMecKTLWQhv9x08KMx0ouv\nGpz2cNbvjtKUCzZzXdepJaPt/bNvizcjje2ac/LaL8gs/VPr/a5hW/KWxjS1wf417G0sf3CFpBYC\nAHx6/mODRTWT8hIlXUCyy7JwPi+pSccXQSUgZsdDqKnTFP1VYcPFL/TW++OGfsHhvVe/E4tNmvP9\n1zeqVA+PYEmB0DXnV2Pa3jg8tG0YL961GNoXD085gd6d70ds0ATJ/FPXTgKofxQwQJ3x4ezogml7\n4/SyElt7lozCVYHFQ/8tmZd847JZvxvtIrYAkF+RD0d7dfahzN5Jb/80RvehRmvPDCTLk8vkWP+w\n9PwR5tW40a5MZePZYvfatsaswEZ1tbTSs6bLSVZWFsrKyizfKiJqFtrDygHqG97mfoKRU2b6onnt\n+Q/xwsH/MavwnHbhO3sjhy/NU1btE8zo+BEYvzsa2WVZkMvk+CBiDRSuima5qXZ3dq93nQ+T/oOK\n2/UJNPUvaupq0Mmlk8muOLoElYAlv0iLzCUpz0FQCfj7kXkNbHnzkMvkeG3gQnG6BjX4PT9Jsrw1\nX5hrO5x1SDLdQdbR4Hqrz/7H4u/t5NDO4HSYd38xYKdRW61f3LSxDA1/2hCN7ZpjqE7HFxc/a1Jb\n6qOd5bTw2Hy9kW66dQyQTF8XjAdXl558E4uH/l+jji+CSsCmS+tRWCnN0ll57l1J+r2gEtDRwPFm\nyx8bxUCLdsHEHh7BBjO2IvwjJdPagZqMknSkFqVgf9wRvNL/Ncl6f5ZmsBijFu1sHy8XL/w6LQm9\nO98PABh0z2DJur1un+MMdfvReDpkJuxgh7LqMuQI2QDqH6WmNXrq/mfQ3v5OpklpdQn+e/4To+tr\nup/MP/KyZH539yD88sRZrIz4CIlPXoLCVWHW+9taZmBbZo3uhRpnlKcl079eP9mo7ZjqQmtr3Wvb\nIpOBjZqaGqxcuRIRERGoqqrSW/7+++/jwQcfxHvvvWdwORG1LtYYmi/YM0QvpVvX9ZvXMPP+5/FK\n/9fwVWy80ZuAnLIsMRVVtyCmhubmU/sEc7UkTbxQF1QCxuyOwrHso/gmbRd83fwtdlMtqAS8eezv\njX79jYob2HBxndkn/MNZh1BWLQ0uD+kajpTCZBRUFhh8TVe5L8K8G/ekojHUwZc3JPPqe0LcWnm5\nSmuFLA7/f3C2078x2XrlK4sXt3s4YIzBablMjoWD/ilZNv/Yy3j317ctcvGoO/xpY9TV1jX4NUEe\n+t1pUouv1Fscsyl0My/yKpS4x7ULAHXRRrmTtFbCC4f+B9+m7oGzvbPB7U3/YQrSi9UZUg0ZYjpi\nW7jeqBiAOvipSb/XBG7fP7vcrO0C6m4Pmm572gpv3ag3fVoukxvsamdOxsHdQi6TiwVUf51+XuzO\nAwC3qm9J1n3nzL/FwtmGzo9+bv7o3N7b4N/L1shlcni4SLNZNl360uC6mt/1pG/GSvbFpeHLcCAu\nAV6u3ujlGVLvSGja20spTMauiXttJjOwrcooScegr0KbnM3XGIJKwNokaWF3L1dvI2ubZipQxiCa\n9RkNbFRXV2P27Nn49NNP0a5dO+Tn5+ut079/f/j4+GDdunWYPXs2amst95SIiCxPLpPjs7+tl8wL\n6BjY7Adfp9tZFo6QGV3nzeN/x6rE9zF860CjN4XaJw3d/pIabk5uOKc8A183f70gjrbJ340TRxK4\nVHDRIn0ik/ISkVHatBuv988ux4CN95t1Y3ws+6hkWu4gR4R/FII9Q8SCabq+GmM8cGRpynIlVp/7\nD/JvSc8fuk+INVp739Rb1dJCms6OLni6z3N669XW1ZrV/7shdEey0Z6+VHBBb/33z6m7g0RsC2/S\n96k7/GljjNkdhe0p2xrUDp/2XQ3Or69AryV1lfviwJQE7JrwPWrrarHs16V66zx34EmM2R1ldBsv\nHJqFSd+MxcBNfc0Kyuh2wdGlSZ+ubzQNTWZG945BYiBTt04LADjYOcDTuZMkfbqHR7B4/NB+fUuO\nptTaGTtWGctAO5Er7R6WV65ESmEy5DI5JnSfJFnmJuuAfZMPoaSyWO99tbvy2ZIl4dLuKOWqcoPH\nA+1uqdrWX/oc+eV5GPn1ELPT/LWzNiftiUWwZwiDGi1It/Br+JYHUFBx51qguQptG5JSmIy/yqXd\nJz2cPRq1LVNdaG2te21bZDSw8dVXX+HYsWN46aWXcODAAXTtqn+R8fTTT+P777/Hs88+i5MnT2Lr\n1q3N2liitsQaN3HKciXG7ZL2Sx0X+EizHny1b/arocKy4e8ZXE+TgaGqVRm9edE+aayNWmdwnWW/\n/gsxOyMxcXcMlgz7N2b1mWOyfTWoQUR8OGJ2Rpp982GIslyJ53/SL5TWGIWVhRi5dUi9v43OOhkE\nz/Z9HnKZXP3kcEoCPo7UT91vbPplQ6mHoQzBqsT39ZZdLPhdb54t9E3VDSBcKriAB/0M15kyNFpI\nUwR7hohp7t3dgyTBSEMF+jQyS/9s9PCKgkrAW8cXmbWuK0w/QX3h0CxExg836+8qqAQ8sjvW4DLd\nrAFLHke1i2Z2ae+DHx89DIWrAteFa8gVmtYl58atAgzd3L/egGV92UyaoUJ93aSjHemqQx3uce2C\nPY/8IB7fdeu0AOoskBPXjkvSp1OLUnBgijrz4MCUBMkoHF3bS7MLTHWlaKsac6x6sf8revM0mUy6\n++/yESvQXtYeU0Nm6L1GtyufrRjfYyKeDL4zHG5h1Q3svrJDso5uDTDPdneyPDJK0jF21+gG1cpg\ntwDr0S2o/PCOhwwWyTU1fLolqY+Xdx6sebsoxML1jaEZdU4zUpbuMlvpXtsWGQ1s7NmzByNGjMAL\nL7xgchhXe3t7LFiwAGFhYdi5c2ezNJKorRFUAkZvH2F2cTdLvefD20dB0Om68N/f1zRrhXvdVOVu\nHe+tt8Dmmt9WG02j15w0juUe1VtmB3vxBuRqSRqm7Y3Dfy+sNbutN24VYMjmfg3+PjQn8fx6Rsxo\niMJK/Qs/Xf0U0i4lg32GiP8vl8n1nhIClh0K1JQPzr6P6rpqg8tm7n9SL4BkCxehujcgT93/LIb6\nDJOknGvMPTjL4vtUbV2t5L8aAR0DsXPcd0Zfd/raKQDqm4Nlp/5ldvBOtybPy/1eNbruc/1m4/PR\nG01uL6Mk3awgy8lrv6BYVSSZ16dTKH6dliR+15qngZrjaPT2UVCWK3FOeUb8b2O+f03tHldHVzHd\n/efMgw3ejiG1qK034yS2+3iTIxfdqFBnTfyen2R0/9L4q/w6TmsFMitr9LsMawopa/+GNbUNdC/O\n5TI5fow7LHad6O4e1KLd2lqLxhyrene+H8O7PCiZtypxBQD1/vvrtCQ8HTITnZw744VDsxC9fRSK\nKvUzbADg9SOvtMrAb33SSqV1c3ZeiZdM6x5vdGvM5Gs97e/s7FVvYXJ2C7Ae3W59xn7L21O+bpH2\n5JRlicNiA+puhtP2xjX6+tsWHsTcrYwGNjIyMho04klkZCTS05uv7ytRW5KUl4irxber6xe3TDGw\nlMJk5N7M1ZtfUVOBaXvj8ODWQRavCwAAt3QCG7eqKxATGIvOLl5GXgEUVxVh0jdjTZ4wDPX3rkOt\nyaeY5qhDHabtjcOwLQPM/j4OZx1EnhnrtndU3yQ427vAy0hXGm3zj75s8ia0r1cYHKD+vA5wRF+v\nMHGZoBKw58ouyfouDq6SdZrL2eun8fnFT02uszZR2t812DNEMsRka7wI1dyAvNL/NfEmWy6T49CU\n45jT9yXJulV1lXjv9NtNusnWplvQUfeY8aDfSLwcajjwsPq3/+DzpE8xeHMYViW+j8Gbw3D2+mmD\n62pTF+tVP+WS2csw7b4njXZxcnOSY3yPiTg85QQGeA0yus1Xfn6hUaN0vDJgviSooemHrzmOphZf\nES80+2+8784FZ5X537v2jdXVkjtp0oaethvT0dF08eD6Mj8Urgr8POUXo8WRV/+2Ahkl6fhNad45\nY9VZ9fqCSsBXl9dLli0NX4b9cUfQXtZe8j0Z+n1pt+/YE6fV2RxxCXflU0ntrn7dOwaZfazSLT57\n8vovkn1hY/KXuHFLXRtJEzjpaKBA8bWbua0y8FufAToFVAtvFeJSwUVx2tO5k8nzt8L1HvH/C27l\nY/zuaJPHEnYLsB5ThZW1PXDPwGZuifp8UVFdIWY8amtsdxhbeBBztzIa2HB2dkZdnflFi1xdXSGT\nGe8/T0TGVVRX4HhuAg5k7m+2atGG0oi15Qo5GLMz0uLvnXxDesDPKctRj0zy0Jp6X5tafAXrfv/U\nYJsCOgZiXfQmvfn1PcU01/Wb1zBi62CzghsJOfrZIwBwj0sXyXSoVxh+mHwIl2dexa/Tk9RF5qYl\nmQxyjNkZZfRvklOWhRqoP28NqiUj0Jy89gtu1kpfV1FTjjE7HmqWABagvoA4kLnfZM0BjY3JX0ra\ncVN1E1m3b2izSrNwU3XT4u1TliuxOXljkz6/l6s3ogNiJIXH5DI5hhvokrL2/Ifos74HYnZGIuLr\ncIsFOYzx6WC4LkUd6vCPE69L5o3ZHVVv5kZqUQpUteqnXKpaFXKFHByYkoDNsdv1sgruuz36Q+/O\n9yN+4h50djYcuMyvyMPhLNMZELHdx0tucHzlfojwv/ObMlZf4trtwK2mzanFV3Ap75LZ3VWCPUPg\n79YNAODv1k28Ye3d+X4cnnIC47s/gid66ncP0PBy8cargxaYfI++nUNNLte83/mnU7Ay4iMsDV+m\nt3xt4oe4LugHqQ25cOM8Bm8Ow+4rOyV9zB3sHDCpZxzkMjkOZx3SyzYzVI9Dg6nWgPjzN55co+e5\nvulhvmcAACAASURBVLMl02VVpWJh2dgdUZKC2O7tPNDDIxizQufqbcenfddWGfitz6zQ2eKw5gDw\nR9FlRMSH41LBRQgqAZP2jDV6/u7uHoTn+jwvmZdRkl7vDSV/qy1PUAl465h5XRgDO3bXe60lz5Ha\nQXDUAR9HfgYPmbS2RmOKW2t3De0q9603e4hajtHARkBAAJKSzO/Hl5iYaLAOBxHpH6zDvPvDT64+\nEHZu1xnP/fgUJn0zFtP2xmHSN2MxYGMfZJSkW/wmqExlenjm7LIsfJO2y2LvqSxX4v2zb0vmaUZZ\nGOozzOjNj7Z//7oUI7YONtimQV2GiBkLgLqwWn0jsDREUWUhIr4eWu/34e6k/5TWHvZ4f9QHknlv\nDlkiXmRpLrgCOgbi1+lJWDFytcFt37hVYPTizdfNX5IWrn2xa6yvfraQ3SwBLM0FxLS9cWatX4ta\nPLJ7DOb9/CIuFVzE/51YjJrbF7U1ddX42sJFIjNK0tFvQwjmHX4R/Tfe16jghqn00/pqDWSW/YmH\ntqlruQzbbH42kEYPj+A7f+uOhrsAxHYfD3s46M03JnbX6Ab/DuQyOUZ3i8apab+hk3NnAEBAh0AM\n9RkmWefw4yfg6uBqcBuzfnrG5OdXuCrw21PJWP7gCmyO3Y6EJ36V3Jhop5ibCtb2cO+Jbu7dzE4Z\nziz5E1llmQCArLJMZJb8KS7r3fl+fB69AR9EfSx2G/Bs1wkA4GzvjLeHv49fpydhUk/Tv/8VZ98x\n2Abdc4TCVYFpIU/e3p707nlj8pfYl/G93jZCPHobfd9FR6VDtWpGWBFUAs79dUZv/fxy/YLxpJZS\nmCzJuDT3ae2tGv0RZCqqK5CUl6g3ilVxZREm7YlFXPBjcNDZp98btUrcH1p7wWVtClcFPhil3zX0\no8RVtzNK9bOZAjoGYteE73EgLkEMnmrzdO7ULG0l0zQPMb648F+9Y3lKYTJuVJlXaPjRb8eLv93m\n6N6hOzreyz/PQZFON8cxu6MadT2gGTAjV8jBxD0xNrEP3g2MBjbGjx+PH3/8EefOnat3I4mJifjx\nxx8RFVX/Uzqiu43uwTqjJB2rzq5AtqC+8SyoLEBFTbnkNYWVNzBkcz+LHuCT8hJRWlVich0HOwfM\nO/yixep+GOpP7uGsLggml8nxUv95Zm0nR8jGD+n6F/LaGQsAsDH2awy6Z4jeetoOTzmB1wYsQnS3\nMfB08jS5LgAU3CrA18mbTa7T3kn/adB7I1fhbwEPY98jBxHlH419jxzEgC6GU/TlMjlm9H4aJ581\nXNgzueCy3t9DUAkYu2u0mNquW3dBfZNr+BCfXZZl8dTJ+kZpMCStJBWb/9iIiPhwbLuyRbIst8y8\nJ9LmEFQCxuyIEp8GqmpV2HVle4O3Yyr9NMy7P7xdFSZfr+kjfr38GkZtrT9gpt3+iXtikCvkoKvc\nV1IQUpvCVYHzT/+BfwxejPao/wllQUW+yd9BD49gseCao51MMhpDQMdAnJnxO36YfAiHHjuu1x6F\nqwKHHz9hcLu1dTXYcPELk21TuCrwbJ9ZGN0tWm/b2inmP8Ydhp/cT+/1yx9cgf1xR5BZnCn5m5kK\n3H6UuMrktEZAx0C8G7ESZ5+8cDsDKx0z+/4P5DI5FK4Kk/VOrt3MxaZL6yVtMFVzSeGq0CsCXIta\nvT7rdrDDCp1AqrYqSEf00Rzro7ePwtjA8ZJljnaOiO0undfWXCq4iJcOzZF0haiPJoigPeJWQ2o3\nBHuGoIurj9nvl1p8BYW3buDglGNipoPMXiZ2J7S1fv6CSsC8Iy/ozX+om/5IXt063ItdE77HoSnH\nMbzrCMhlcgz1GSYpKArcGd6dWo6gEhC5bTim7Y3DwmPz0Wd9D/zfyaVibbKGZC/cuFUgdntrju4d\nusVJDRUwBYA1iYYfLBmTUpgsGQGvJUd4IdOMBjYeffRRBAcH47nnnsMXX3yB0tJSvXVKS0vx5Zdf\n4vnnn4dCocD06fp93qn52VLE/m6ke7AesrkfVv+2ot7Xacavt9QB3lRqsYbmoH+1OE3v4rsxrun0\nJ+8g6yB50jypZ5zYh78+Lx56Xi+qrlscLMi9B3anmS64eaumAgsGLcKm2K9x9qmL+DjyM7R3MH0T\n+I/jrxtN2xdUAjZcko7QYg97/C0gBgAwoMsgbBm73WhQQ9sQvyF4ud98vfmvHn1JrwaK7rCQumm5\nClcFTk5LFGuZaD/Jl9nLLJ46qf230OUv74bxgY80aHs9PS03pGFKYTJu6DwRvS5cN7K2caYyZDS1\nNlztDWcp6LpRWX/ATONw1iHxCXGukIPUohSj6ypcFXjlgflYEP6PerfrYOdg8negXXCtuk4l6eoE\n1J/mHdAxEIenGA5urDi7vNEjEGm/t8JVgX2P/izJ1AroGIgpvZ6AXCZHb+/e4u9SZu8k3swbOrY9\n1C3K5LSxNuh+/gf9RmLfIwfhbOds8HWLT/xDMgxvfTWX3J1N1+0AgJ+n/IIBXQaZDKpo0xzrU4uv\n4PeC85Jln/7tCyjqCdLZsksFF9XB1JTNiIgPx7unltV7rlOWKzF0c3/E7IxE7M4o7Jq4t8G1G+Qy\nOd6PkAafXBxdEObd32D/f0CdkXCrpkL8e6lq7+yHttbPPykvESqtAo4AIHdwQ0zgWHEkr10Tvseu\nCd/j8GMnxICGuK5MjvdGSYONLVUMm+7QvakH1LV/pu2NQ8S28AaP2qMpMG/JYq/KciW+uPBf/PP4\nQrPW/+LCZw263i2vkj6M7Cr3tcnuYW2R0cCGk5MT1q5di+DgYLz77rsYMmQIxowZg6eeegozZszA\nmDFjMGTIELzzzjvw8/PD+vXr4e5e/8mXLMvWIvZ3I+2DdWfnzmLAoiF+U/5mMOWvIa4aGOrPlMUn\n/oGwDSENeqKlq49Of/KFg/8puVBRuCqQ+ORlrIz4CEuG/lv35RJ1qMNGnae8usXBfszYZ3Ib93YI\n0LsZjQt+HBeevWJ0GFqNZSeXGty/Tl77Ra8gYC1q9W4CzTUrdLbB+blCDh7eESG2Qffv0sm5s96J\nNaBjIE5PP4+VER+hFneeVGhfHFuKXCbHrol70cFJWuzO3ckDR544iTeGLm7Q9nLKsi3WNkPpyqlF\nfzRoG4JKwMTdMUYzZIDbWQpPGL6RN+Qfx1/HqnMrTI7CoyxXYuZ+aV2HjOKMercd5NGj3nW0uyMY\noi4e6gRAHRRoTDBMU59CVx3q8PCOhyxyztIUtNTcFB2acieDRO6kPkasjPgIqlr1qCDGbgJH+EXA\n7vZlkR3sMcIvotFtGtBlEC4/l47XBxjua96QYXh1CzAbXOd2N4cH/Ubi12lJGHrPMJPra2qJ9HDv\nCTcn6dDEzm18CNcPf5PeHL+fuBzhmx8w+lsUVAKGbxkAZflfANTdlBKyjzSqdsNQn2GSYZvDvPur\nb+rjEgwOTf5jxj6jN3xtYdSP7ybvv7OvyuQY3nWEXkBDW4R/lFhEuFuHe+Hi6MLr3hYW7BliNGib\nWfon1v++zuAyjZFdDR9XLVXsVVmuRNj6ECw8Nh/HryWY9ZrKukqDWcHGrD3/kWS6h3sw67i0EkYD\nGwCgUCiwdetWvPfeexgxYgQEQcC5c+eQlJSEiooKPPzww1i5ciV27twJPz/9VFBqfrYWsW8tNFku\nzV3MD5AerKcGP9mobfzj+GtYeGw++m0IaXRtgC8ufFb/ijpKq0oQER+OnzJ+bPBrASCtWDq8m6ao\nnzZNX/In738Gbo5uJre3+8p2vdojmqemAJBeYjh4MyHwEayL3oSfH/vF4MlHLpPjub7P49dpSZjS\n4wmD2/gmfTdGx+t30UkrStVbV/dpfkMoXBV4zcjNUK6QI94MtXNoJ1n2fOgLRj/bhKBJCOhwZzhH\nRztHi2dsCCoBBzP363V3+v/snXlcVFX/xz8zMCDDhREEJlFBFkWEEvfcIzTcNRW0R1N/ppVpZo/1\nlFmplUulbZotVk+ZPRqm5Za5ILmLyuaGC4iAiCwiywDKwMzvD5px7tx7ZwaYGWD4vp+Xr5577nIO\n986595zv+X4/3+khs8BIGPjJ/BHpO8Lk60UFTTFb2/jclQ9nH6pTX9JPRSgkXCckaivEyvjl2pUu\nvvfQocz9nLLDWQeNXref9wCT9GZejZuPiJiBvHXfKsvSGgOUqqp6G8NCPEJ5PZHuPSgy2zfL0KSI\nkTDo7z2QVcZn7LpVlgX1PwKO6gYYJ3Xr7ddO2MDw2t8LoFAqalfsdbJs6OunGNO7cG/VhvW+8ZP5\n45cx2wy+T18KW4B9E2OxY/xeLD/5til/js0Q4TOMU3anIpd3YqNQKrDsxNso0XuvHTBiRBdCY8TQ\nzyrDSBgs6Plv3lS/QhO+5pb1I8yrB+edxKc7YgiNZ9yOcXtgL7bXZk+zxliOqIWRMBjQfpDg/oPZ\nD8eLfO8gXSFoALij4z1pDrHXvem7WCHKpjIv9nmTxwQzQ55jbesL2xKNh0HDBgCIRCKMGTMGX3/9\nNY4ePYqLFy/iwoULiIuLwyeffIIRI0ZAJKqDLDRhVmzBYm9tdL1cemwKEfR2MWeIDyNhEOQejK9S\n1hk/2ADV6mqjsel8JOcnshTxRRBh1cA1Jp8/bV80vj//bZ0ytlwqvMj5ezXCoXwwEgaHJh/jHdhp\nSCtNQ99fwjjPTPNM9UNCgNpVnU8jvsSYgHFGP5Z+Mn+sH/YN4qJPcgTbgFrxKX03cf2V8SV9lzY4\nDaKfXlpAXTSToQmdo2D/TxiPvVjCm/5WAyNh8MGgD7Xb1epqg+EMdUWhVCAiZiBejZvP2dfG6eEE\n8s2+75h8zVl/TWtw39NkQXFx4A6u1FBjY8rXAEzr6/ox4IaMV+E+EYKu5UJklt7kTbE51DeSUxbQ\n2rg3BiNhcOyZM1ja7wOjx2aU3ODNVKKbfrGh4Ut9vbnaN26O7lb7Zukbt/iMXe6t2sBerPl76+eh\nok+YVw9BkeTc8lwk5yeCkTD44+l9+DR8Pa9+yqiAsQbfi/smxvIac2Y9+jzv8RoNjZ7y3jhfkIz8\nSstkSWqqjPAfBQeRI6dcV3NDoVTgeM5RhP/aH5suc7+5mlDD+iA0edOk+tXV09CI0Qqd05yyfjAS\nBn9NikOHf/pVfcesjISBk70TK9XzyO0R5LlsRd7ut9yk49RqNUfrK0fPG/P1IwvNmqlNVYeMnvpM\n2x1lNERSoVTgjaPs1OqvH11Iv7smglHDBtG0aYoWe3MZBCylHaLr5SLkmmyJEJ/k/EQowfVYAABn\nOwYLui/C95GbILPn5q3XZc25VTiXe6ZOdZ/VO14NNXxkvvB17WjyNRYffw0Tdo4WXFnW5+uULzll\nGuFQIfxk/jg/8xpWD1qL7yM3ccIadNF9ZkLCla/1ehNxk0/WuV+EeITii4iveffpa5V4O7OzQY0N\nfLrB/bCsSjh7TW55Lq4WpcJZ4oy2zrXpZNs6t4WzxNngNY1l7agvCqUC353/RnAwoJslQhOWMNJv\nDKZ2mY5ZXdkTLwke6q1klN4wyVVf6D2RV5GnzYLycuyLvEKqXyStxbncMxi0pQ+vcKMumsmnJlOH\nIeOVZlV2x7g9sNfJ2mOMXMVtTlmteORGVhmfkUCoHfO6L0Bc9ElMDpqKuOiTmB3Kv7L0Whx7YFab\nfnEUS3C1Icawft4DtBMaoNa4+tekw1b7ZunH4utva9NNqjR/b/09VHQxJpJ8736RVhz21bj5vOr6\ncqkcp6cm8eq3vNJ9kdY1X58+Ar8TmWNr7fsiKY9rTLPUu6KpwEgYrBrMNeyrUIPwmP54escohP3Y\nBRN2jmbpGGloJXLCCP/RFmlbiEcokmdcwafh65E4/bLNaZ3IpXIcmXK6wWNW/cxI2f/0VfJctg4h\nHqGYHcofNquLokaBjZE/aoW1O7XujDB5T9YxKqjqLOYt9N1XKBVYddo0owsfKXeT0feXMGy7+qvg\nWCA5P5GTwSe3/LbJoYWEZWmyho09e/YgKCiI9e+ll2rzeefk5GDWrFkICwvDiBEjcOTIEda5p0+f\nxpgxY9CtWzc8++yzyMzMbIw/oUViLoOAJbVDdD+Imvhx/ZUDS4T4nM9P4ZS1Y9pjx7g9uDDrGt7u\ntxRjAsbj+LRzcJUYNm6M/H2oycJ7GSU3sOrMe5xyJ3snxE0+qY1LN1V0ztTY8Be7sdXP2zMdeFNU\n6qPJhjAmYDwORh0RPE73mbV38eH1sOjfbmC9B04j/EfxpqtcfPR1lqdI9K5xrP3mUGk3lNFEoxNy\n6vYJ7WAuuyzL6DPp5BakFWqViNkZLuqLQqnAsJjBWBnPP5AIcuvCGZiHeITixxG/4NMn1+PtAcu0\n6To9W3libvcFrGMvG9F30dSvSaGqq1Xx+bk12km56p//8TH+95Fa3Yz04jTB+6iZ6L95bBGWnVhi\nsF3Aw9CIX8f8zip3tRPu2/oGSA2DOzyhNaD5ydipVU0hxCMU6yK+QohHKDq4+vIec6+qiOW1UZt+\nkZ2Z5t79e/qnmQwjYXBkymn8MmobVg9ai/MzrwlOyC1BP+8B2nAs/fS0AHewak4xuOF+IwX3PX/g\n/7Dvxl6D4qHAP0KsPPotfBmZNDzmGcb7HtFNIV3yoJi1T+YgM+k93dx5uvNEuDm48e47cecYSpVc\nwXwN+6K4HjLmRBOeaWtGDQ3m8DLRLOr9Mmob693u69oRldWVtHpuBV7pxQ0v1EcsskOftv1wemqS\n1pjVyp7rLfWg5gHP2fwYmh9cLUpFWbXwwpCpzIudI7jQUSmgecQXlmxNKJFELU3WsHH9+nUMGzYM\nx48f1/5bvXo11Go1XnrpJbRu3Rq//fYbnn76aSxYsADZ2bWuTbm5uZg7dy7Gjh2L7du3w8PDAy+9\n9JI237CtoUm7NGJ7BCJ+5Y+TtibmMghYUjtE18slcfol3pUDXdE8O5G9WXKl/36dna0jUNYZx545\nw4kJl0vlODH1HDydvAxeb+2ZDw3u1/BdCtfzQObQWitapolL14jOyQxMvDT8dP57o781X1lHdGBq\nV0W9nOTYV4/VWT+ZPzaPiOHdt3rQWu31atO+stN4tXX2btAAnZEwmK4XRwkA+ZV52snv1aJUFNxn\nx7+bQ6VdLpVjY+RPvPumBtfqtCTlsVNxG/uo1uol1HoMmUs8VF93Qp8ZIbMNns9IGBz71xnsmxiL\n+GdTwOhN0h7UVBk8Pzk/UVt/bsVtTN0bhWHbBuNc7hl8d/Ebk/6GKrDr0IT66FPfd5ImQ4Ym5W/y\nrFQM9B7Me+yuG9xUpBqDyu3yHHRgOmDX0/sbNCHQ9aDR53DmQ6NcexcfrZCmhoKK/HrXC9Q+72G+\nkZj16ByrT9oYCYPYyce16WkBsAaB+oPV9wasNNvktej+XcF9NeoavHmE7dYslMHKT+bP8d4J8QgV\nvPatsixeg55uGNXsx9gePH+M508lbGswEgZH/3WG5SUmxGu9FuO1Xosx59G5iJ+abPCeE9blP3+/\nitzyh55ut8puaXU3Gns8bOvIpXKsHWI4vFqlrsH1e1dZxiw+zSBT9KA0GPoWt3fxQRtHD5Ou84i0\nLd7qIyxqLpTCVcijzVCotaWhRBIPabKGjfT0dAQFBcHT01P7z9XVFadPn0ZGRgbee+89BAYG4vnn\nn0f37t3x22+1k8aYmBh06dIFc+bMQWBgIFauXInc3FycPn26kf8iy3Dq9glt2iVTXbctibk0Pyyt\nHaIrOJmSn4xTt0+wxKd0RfNq1NWYtGssFEqFNma/rvGACqUCOQp2XOGy/h8IDiDlUjnipyXjy4hv\n4STiTx+5J2OnSYJZMp5UgfO6v8Jbt5/MH0mzUvF95CbMDH4Obg78oSMHsv/C45u7G7wPV4tSka2o\nnTznV+bVeyItdeD/+6fvm6L9u4Pcg9FW6q3dZwc7/DH+zwYP0NsybXnL42+f1tbbQS8OP9AE/QNT\nCPeJwCNSbv0r4pdj0JY+WHuObdgy9lHVN87p53evD2qVcCyri70rpgT/y+g1dAc8Aa0DWPt+STWc\ncphv5SS9OA3vnjCe6lQITaiPPg15J+mm/GUkDNaGf8F7XNH9Iuy7sZdVpjuIy1ZkN9ggxRfaomHL\nlZ+1nmC6QppAbWrYUQFjG1R3Y6P73jc2CDRnZpAg92B4OQkbcvRXGG+V3RI4staTTOPpYsx7J8g9\nGJ56+h5zHp3LCqPylHpp32EdXHzgK+to8G+xJeRSOQ5EC3sFaujfbgD+02cxVgz60KpeRoRh+EIC\nav7x0qOQFOvwdOeJ2pBmZ4Hxln6IJd93pLDSsECyLkLfYoVSgZHbI1ip3R3Ftd4hnk5eWp0iO9jh\nl1HbcHJqAmZ3e0FwnAtw07oCEEzPXFBR0GgGBUok8ZAma9hIS0uDnx9XQC8lJQVdu3YFwzzsQD17\n9kRycrJ2f+/evbX7nJycEBISgqSkJMs3uhHQj4/li5e1JhpviB3j9uDDIZ806Dqfh29AT48+cLF3\nwR/Xtpv9haGJwX/z2CJM3RuFR3/spI2z15/0aVz9e2wKwatx89FjU0idjBunbp9A4f1CVlkbqWEv\nEE0q0kuz03hTkVZUV2D4b+FGLbRt9TQg7ER2RoUmxwSMx0fhn+I7Aa8BoNZYMXJ7hEVTRRqivLpc\na8jLLLmJ3IqHH88a1HAystSHCZ2jeEX7frr0nfbvLq8qZ+0zRygK8I9OQ/RRSO242hk5iluctMHG\n9Ev02xW9e3yD+pRCqcCk3eME9x+aXHcBVf2/QcjIYAjPVp68rvwaHMGfpk4XPoONOfWM/GT+iJ+a\njFEdx3D2LT66iPVcLGHkFTLYqaDCqB3DoFAq/um/tavZYohxKOqYzbjG8w0C9VfhzKkzUat18orJ\nxxsTWY6N/sfzRCetrdCxeyYeZAnALuj5b9Y5+iFthvqOLaLR/XEA1z0eMD2EkmhatHX2NvuYg+DC\nSBjETT6JfRNj8dFg/jF/cj57/sVnXPdwMs3LQlMn37c4OT9R+y7TMLHz5FqP0GnJOD/zGj4NX4/k\nmVcwzDcSjIT5x3MrHq72rnxVYeLusZywb42G1veRm1jlbx5b1GjeEpRI4iFN0rBRVVWF7OxsxMXF\nYdiwYRg6dCjWrFmDqqoqFBQUwMuL7aLfpk0b3LlTm19caH9enm2qfhdWsl2DC42khbMWbxz5d53d\nATXxYRklN/Du8SUY+ftQJBSeQWJhAv595GWE/dgFnyesbbB68qXCi3jx4GwsilugjcHXJb04jZN5\nxMPJE9ml7NSHfGkYhYi/fYq1XZdsAJpUpHyrrBptACELrUKpwIrTy1hlr/b8j8kTlEEdhuC7YZsE\n92eXZQlOPK/fu2qWVJFhXj1Y3his+ktrr7nm7GrBfQ1BLpXjrb7vcspLqkqQnJ+I5PxEFD1gu5mb\nIxRFt/69E42n9pRL5UYH3/rtKqjMN8loIBS3GZd1CBXV5bznfB+5qV4rm3zuqEJhYAqlAu8e56bF\nLbhfgGoDqd4e4D5cJfyDGA2fJ6w10tKG4yfzx7ph30Bmz/aoKlWWstJOWkIgOsyrB9o68/epwsqC\n2on/vava0CUVVLj3gD88ojnCNwg0lnK1oUzoHKU1MBjDmLdIXTQK/GT+SJqRyitGqVAq8Foc2+Ai\nFD9uy4R4hCJh5kXtvRFBhP6PDMSXERtx9Jn4FhGa0xzRXTnX15LJLb/NK8RLmB/N+2iE/2heQfrH\nvftxygZ3eIKli/Zy7IvajER1qVO3b57NjeccJwK0xwlp18ilcpyYliCQHlutNfbr119axdXhaSxv\niaaYSKKxEPzKjhwpLHYlhEgkwt69e40faITMzExUV1dDKpVi3bp1yMrKwooVK1BeXo4HDx5AImHH\nRDo4OECprB2AVVZWwsHBgbO/qspwrDYAuLlJYW/PFSBsqiiqFPg7h70Ke+R2LJxkIk6suqXw9OS+\nCG7cusxaDctXZcHPs6/B6yiqFOj/zWCkFQnH65cqS7EifjlWxC/H0sFL8WLvF/EI80id2nv+znmE\nx/Q3etyWyz+ztlWowZBO/YFjD8vGhA6Hpzvfi5BLfC47RKhf+8fh582/airEdNkUvHHkVSiquR/q\nQPdADOzch/PcL2ac40y8wzsN5H1uQjzn+Sx6+3dDt2+6cfa5Orjy1quoUuA/WxdqtyViCcI6doUn\nY3q9GjzhgreHLMG8ffM4+yaFjYOnuwvauHDDbYZ06l+nv1OI/v59AO73EjlVGWitF+bjJfXC2MeG\nN6j/6bf5Cc9+eLn3y1h3VjiWdc1Ta4z+nsbKhqPjiY64WXwTgPBvRhdFlQIDv30C1+5eQ+c2nZHw\nfIL2+JRz53jP8XbxRnSPp+t1D3Zlc695tug4+gRyf3s3bl02qO9hiLWRazFnzxzB/cdvH+W8RxVV\nCgze+CSuFF5BF48uODvnbIPfs55wQWTnpxBzma0j83Lsi4js+iQC3GtDc2oU5ci5k4Ew1/r1Ib56\nE19MQPdvuuOO4g5rnxhihHXsihNZ7HeWyuG+WfpTY6Dfbk+4IHFuAi7lX4KH1AN/pu2An5sfjs8+\nhsziTIR4hZj9G+oJF2T/Oxuv7HuF87z1adumjVnvtSdcEOrLdZ2+cesyy9PNEnU3FzzhgrRX0nAp\n/5JFnr+t0RR+I55wQfLcJFzKv4Rbpbcwadsk1v704jR8l7oeL/d9uc5jRaLueMIFF+ddwNmcs5i1\naxZuFt+Ev5s/73jgxq3LLF00FVQIj+mPtJfTUFhRWOc+qKhS4OOzqzjlw7sMM+m36gkXJM1NQuA6\n7nuysLKAdx4zxm44Xo1jH9u5TWej4yqD7WhAv/KES53nFbaIoGGDYRiIRMJ50y1Jp06dcPr0abi5\n1SpWd+nSBWq1GosWLUJUVBQUCvbErqqqCq1a1boXOzo6cowYVVVVaN2aO/HR5949bixVUyYh76x2\nkqIhozgDx6+d0cYRWxJPTxcUFHDVh73EPujUujOuF19Dp9ad4SX24T1Ol4OZ+w0aNfRZfnQ5Gd8k\n9wAAIABJREFU3j/6PlJmXq2Te/TKvz8y6bgHYCs0F1UWod8PbKtzTNLvHOE1Pg5k/IX4O+yZcVe3\nbkbvCR8j/ccg5toWTnlJZSkKCstQKWG70OfeZRs1vJzkCGa617nutnZ+2D5mNybuZrvOl1aV4tiV\nePRq24dVnpB3lvU8lSolTqUlYGA7ftFEYwyWPwU72KNGbyX++u1MuNZ4YUjbodh0nu1Z8nPCFgS0\nCqlXfboEM93h5SRHfiXbU+jNQ4sR1Xkyq2xkx7GoLFGjEvVT5ebrUwqlApuShb1mAOBGfrbRZ6pQ\nKiBSP1zVUlZX4+DlI1oRWYVSgatFqQhyD9Za+4/nHMW1u7VGymt3r+Hg5SPaZ+jrxNUS8XTywv6J\nR+p9D/q2GQIRxCxtByeVTPA9EyALrJdx425JKaRiKSpU/O/88upybDqzBVFBU7RlCXlncaXwCgDg\nSuEVs71n54Yu5Ex0VVCh//cDcHpqEsqV5eixKQRKVRUkYgckTr9klpAQOzhjQ8R3mLCTnbZSBRX+\nd24b7pTnssrjMxIw2POpBtdrbYS+UwDgXNMGQeu6aN8rbZ29cSCq/r9fY9jBGeP8ogwaNuxFEniK\nO9Tr+1BXKsvYwqI+Lr7o6NjFKnU3Vfwdu1rs+dsKhvpUY+Dv2BVerX3gJ/PnhA2sPL4SH534GEkz\nbC91blMllOmFw1EnteMJvv7kJfaBZysvFNxne533/rYP7j0oQjumPcYFTMCM0FkmeX8ezNzP64Ht\nrHYz+bfqCi/ERZ/kXfwsKlKgwJF9net53IybNTUq3My9g1tlWayxlCk0tX7V1BEyAgmGosTExODX\nX3+t8z9zoTFqaAgICIBSqYSXlxcKCtjhFoWFhfD0rBXIksvlBvfbEnwpLv1k/k0iturDIZ9gx7g9\nJrlEKZQK/Ha17r8dFVT4+DTXQmuIGV3/r871CPHW8dfxwanlrBSTfKzgSYU5I3RWveqMFEgbWFCZ\nb5Jw7KrBH9fbRU1IxHPU78M44UHtXXw4rqENcXGWS+VInpmK13othp2o9jevq9sR7jMUbVqxYzR7\nPtKr3vXpwkgYrBrM1TgpVypQVMl2zx/UoX6GG0NcLUpFibLE4DGmxKdeLUplDfoyS29iws7RGBYz\nGAcz92PYtsEcvRb9Z6a7ratEDwBPB0YhflpygwaPcqkca4Z8plfKL1DKSBi8N9B4/x/jP54lDiYR\nSzAqYCx+G7fL4HkHMvaxtoPcg1mijeZ6z4Z4hGJ9+Lec8vyKPPx86UfsTd9V7xA4Y4R59dCmQNVl\n0ZEFyCy9ySorbkCq16bKltTNLGNpbvltg7pB5qCf9wB4Gegj1WrzZCwyhkKpwKQ/2Ibq6KB/tWgX\nZqL5otGe2TFuD34ZtQ1zQl/U7qtWK7E33fD7njAvxsLlGAmDGaHcrHOakMccxS1sSPkCfX8Jw4GM\nv7Dy9Hsco5UufFnhfF071jmkUKO5o8/EnWNxPOeo9tugUCpQWV3JERFNL07DyO0RlJ2kETGrxkZ6\nerpZrnPgwAH079+f5Xlx+fJluLq6IiwsDFeuXEFFxcOVtoSEBISFhQEAunXrhsTEh+JXlZWVuHz5\nsna/LXEm9xQnxWWNqkbgaOugUCowLGYwJuwcjdf/XmjS8X1/DsPvab8ZPZaPTVd+wNJjS0x+edxX\n3a9XPUJ8kbQWU/dG4Ymt/QTbEN3pGdb2yv4f13vyF+4TAVc7fn2AY9lcdff7ZoyXDnIPhq9LR065\nGmrsuLaNVXb93lVOmsGGivHJpXJE+A5Fjbr2N66r28FIGPw95RTk0lp3U1/Xjgj3Gdqg+nQREubc\ndeN37f/v4OJj1jo1BLkHa2P/hSirMm7lD3IP5p3EppekYereKKQX13o+6MaICgkqKpQK/HCBnU41\nzKu7WSZF+p4C+zP2sQYUumSVcFdM9BnfaQISZlzEL6O2YfWgtVqdgV5t+yAu+iQmB03F9jG7Mcb/\nadZ5HlI5q85yZTmyS2szG2WXZqNcya8vUh90Vdx1WXryLXwYvwJirTFPgqG+kWar11CGFn170r+6\nTjdbvU2BvIo8rIp/n1NuSDfIHGgmYC4CYnUuDq5WWZy4WpSKu1Vsj76SB8UWr5cgLIUmfX0/7wFo\nr6cpZU7tK8I8ONg5GD8IwLR90fgscY3WyKFQKnA85yhrXKAvuLyg+78RN/lkvcYk92u44+ZKVYV2\nISivIg+R256o9XZUA2uHfKFdcLMT2WsFTK2ptyGkhdYSMdmwUV1djXXr1iE6OhqjR4/GyJEjtf8i\nIyMxcOBAjB492viFTKB3795Qq9V49913kZGRgb///hsfffQRnnvuOfTp0wfe3t548803cf36dXz7\n7bdISUlBVFQUAGDixIlISUnBV199hbS0NCxZsgTe3t7o148rXtPcOXqLO5HNKsts1JSvyfmJWtfw\n9JI0owrrv6b+j+OKVle+urAOYT8FG/WcAGpDdfh4pfsi9JcPrHcbssoyEZd1iFtfyQ0sj3+bVebk\nWP8JPiNhsHPiX7z7ku4kcMr084Xz5Q+vS91xU05i7mMvc/Z9GP8By5qun97L08nLLGJ8hpSf5VI5\nTk1NxL6JsfX+oAlhSGxRw+rBay2y2mnMM8FeZG9SGk5GwuCDQR8aPU73vuqu6Pu5+mufYa1o6kNv\nFTuRHSZ0jjJ6bVMo1ptcxVzbUjug2DaY07/3Z7K9KvSRSx9BuM9QMBIGw3wjMevROSyjYohHKNZF\nfIVBHYag1yPssJLvL36Nvpu7ab2RDmXuR7W6VsupWq00q+eEIe5VFUH1jzGv2gKG6zCvHpBJZJxy\nV0d2Gd9gzxJYa4B2KHM/K+RJg53I3uLZFORSOQ5NPsq778fIX6ziNRHkHgwPPS+3Ie3DLV4vQViS\nvIo8DNn6OJaefEs72TSWFploHEI8Qut8zrR90Qj7IRgTdo7GhJ2jEREzEAqlgiO43Ne7X73fo/pZ\nEXVJL0nD3vRdWh3B9JI0vH5koXbBrUZdrU2fba3sJAqlQutxO2hLnwYnWGjumGzYWLduHb788kvk\n5OSgpqYGGRkZcHZ2xv3795GZmQmFQoHXXnvNLI1yc3PD999/j5ycHEyYMAHvvPMOpkyZghdeeAF2\ndnbYsGEDioqKMGHCBOzcuRPr169H+/a11rr27dtj3bp12LlzJyZOnIjCwkJs2LABYnGTTADTIPgy\nCAD8K/dNEaGsBrqsD/+WE9LAR2lVCabujeKd/OjWt/zkEk65j4svXum1CJvHxnAGenVh4eH5nLq3\npG5mbYtF4gavuApNMNJKr3PqD/eJMLhdVxgJg4E84RYVNRV4/JfuWlVrfXXr8QETzDJYN6b8XJds\nAXWt90DUEbg5uAkek1ly06x16mLI22Ve2CsmewDdrzbusdTZLYj9t4jY/1UoFTh35yzrnC+e/Mps\n8ctCujXpxWmc1Y//9OK+PzSD2Q5MBxyKPmbybyHQjasZUlBZgL4/d8Pu9J0I82Qb5vp7198Qqo+p\nRiE1VBzvqIbCSBisHLyGU/79xYceOQGtA602QIvc9oRV3HiH+kZCDK5YeI26GtfvXbVYvRr8ZP5Y\n0H0Rp9zcXoVClCvLOSnId9/YaZW6CcISKJQKjPztSe2KeY26Bl5SOXY9vZ9CrJogj3mGQYS6azmW\n1jwMzc0ouYHk/ESzpuvembbD4H5Pqad2gc3doQ3LO7lNKw/8OTHWqtlJkvMTtR63OYpbLT4ExuTZ\n/p9//omePXvi77//xn//+1+o1WqsXr0ahw8fxrp166BUKiGTcVd96kvXrl3x888/IykpCceOHcP8\n+fO1Yqa+vr7YvHkzLly4gL1792LgQPYAc8iQIfjrr7+QkpKCTZs2wcfHNl3QngmextHYAIBLhRca\noTW16KbfCmhtOGXevht7oYSSd5+bozvipyYjOngKUmZexafh6xEXfRJTuxh2h+ab/Gg4dfsESpXs\n9EyrBq7B31NOafNZn3n2PL6P3ISnAybByY5fU0KIMr00jQDwlO9w1vam4VsbPAHU9VrQ5e79Qo63\nTloxO+5Qkx62IQh9MNRQIyJmIA5m7kdXd7YlfrjfqAbXq8FSxgtjyKVyPGdALPbtE29YzFIe5tUD\nnk78OkElDwzrb+hSUGHcO2pvxm6Ex/THpcKLSM5P1HriZJTcwKnbJzAsZjBW6unGPKh+wHepeuEn\n88f3kT/z7tNP/dpB5ovozv9ilW0auRX7JsbiyDPxdepr/bwHoI0j17BZUVOB5/Y/i2f2TGSVm6Mv\naZBL5XiNx0hjLUb4j4K3cztWmVonFuW9Aaus0t+uFqWyMmpZ0o1XLpVjz9P8XjfWSnna1/txThlf\nrLgl4DOQvdiNm3mKIJoLV4tSka3IZpXlV+RZRbOGqDu3yrJY35n6UlldiTCvHtpUs/XR1tDlmeBp\nBve3snfC/qi/sWPcHo4cQHVNNZwlzlYdo+p7SN8uzzHqLW/LmGzYuHPnDoYPHw6JRIJHHnkE7u7u\nWi2LYcOGYdy4cdi6davFGkpwqRVUvMJZ9VnY0zyeM/WBkTA4GHUU+ybG4mDUUYMde0sq/+Tly4iN\nSJh+USvUp8k9HeIRivcHreaI9egTm3GQ11qpP2B8rddiPPfY86w2MhIGYwLG45vIH3BpVhr2TYzF\nhZnXtfH5QhMuDfNjX2BNbvVXwFKLLhk83xR0vRZe15sM6f6NCqUCrx6ez9p/7z5b7LI+hHn10Lra\n6aOCClP3RmF+3POs8mM5zcOLyDjCqwsqtcpi4QmMhMGeCQd5vZfqIljat63pIXlreFKnZZdm8WYh\nic06aPJ1TSHcJwLujm045XFZD9Nb51XkofumYMRc+5+2rFPrzujnPaBegwpGwiDCd5jg/jsVbO0P\nc09+u8uND8TEEJst5EcXRsLgfQPhTtYSDm3v4gOJuDbuWlcc2FKcL0zhLW+oHpCp9PMewDFYtnfp\nYPF6FUoFvk5ezypbOfDjermGE0RTQXfRx15Um/TRWuEARN0RWqSrK072TihXliOnrHaxIafsVoM0\nsPxk/oifmoyI9vzjAY12XWbpTZRUsUNnS5TF+PnSj1b1mND3kAasZ5xviphs2HB0dISjo6N228fH\nB1evPnTX7N69O7Kzs/lOJSyIXCrHwl6L0M75YVjKf4692qhuSKasqF8qvIjjt9kxxj5MR8RFn0RU\n0GSDSsoHo45ix7g98HLiX41dk7gaQ7Y8zlIv/vnSj1h5kr3K/GQHw2EZmr9DLpVr4/PDfSIMpp7S\nFdLMKLmBr1LWsfbnlJpnlVfTti5t2B9sDycPbXx6cn4iJ0VpQzQ2dOs+MuU0x6hiiHGBExpcb1PA\nxUE4x7g5wowM4Sfzx8bIH1llcqm8ToKlyQWmW/EdxI7o5Bak9Qqzg13t759HgDS4TcPT6upSUJGP\nogd3OeWe0tpJYF5FHl6JfQnVqocZLaZ2mW4G18/GSXEO1E5yNStOQkzqNNliKQtvlQm/m/gGTpZp\nQxYrA4ylV1r5BAXbMx3MogdkCoyEwWdPbmCVubUSDnczF1eLUpFb8XCVz05kjzGB4y1eL0FYEt1F\nn6QZqVYNByDqju7zujDzulGPbD403hnfpXytTfdara5ucBYcP5k/No74CS723DHfrbLacI9X4+aD\nb8yw9ORbVg0HqW+WRVvFZMNGUFAQjh8/rt329/dHSsrD1Y6CggKo1Q13KSLqztWiVOSUPxyUGgrH\naCp8lsCN6Y7sONykFSON8vXpaUlYOZCbhhMAshW1yvYKpQKDtvTBoiML8ABsd/lfUjfVud26KcW+\nj9zEyaQAADeKale0v0j4hLNvkM+QOtdpCH3NhP8ceRUjtkcgImYgRyhVDLFJIpOmwEgYTK/Dy9Ra\nwoOWxtBq+fywVy026dSgn53lk/D1dRq01UUXYmf6DmxM/lrralmDGqQVX+cIkIogMvuH9aeLP/CW\nv39qKTJKbiDsxy44nM32EimoLGjwAJZPZ0MIc6/qMxIGcZNPYse4PVjQ/d+8x0T686d7NgeG/vYI\nH2FPFnNiSBzYEvTzHgA3R3afsvZKVz/vAdqsRwEyw+Gb5iLIPRgdmIeeITXqanLXJ2wC3QWpxghZ\nJeqG7vN68/F3eMPr3+q7FK/1epNTvqzfCsRNPonMkpv4PGkta585suAwEgaHJh/TKxUh0K2TNmRS\nKB29NTOi+Mn88WXERlaZtbwOmyImGzaeeeYZHDhwADNnzoRCocDw4cNx4cIFLF26FJs2bcJPP/2E\n0FByY2wM9NM4SsQSi7vwNhSpvTOnbHa3F3mOFIaRMJj92AuCsemt7JyQnJ8oGAt/70H93Ks1hpUx\nAeMxoB13ovjTlR9wqfAifr/KTmHrJJaaPR1ocn4Sa7u8utb9LqPkBv68wbZYT+o8xawTb1MF9lwd\nZDbjCiqXyjHn0bmcchFEmFPH32990NewqavSe9F9rheEECqo8EUye7CQlJfISSG8ZsjnZjfoCIWb\n3SzNwOcJn3DiWoFaIbKGIqRbpA8jcbHIBFTzblnY6zXOhNvN0b3B4r+G6Oc9AB6t+HVcrBVKZkwc\n2BL1zQ1jZ3m6e7/QqgsDjITBweh/wjejDYdvmrPOPycdtrp6P0EQhBCa8Pq3+i7V6mn5yfwx+7EX\n8FL3Bejo6gcAcBQ7YvuY3Xip+8tgJAy+TvmSdR1ne8ZsWXD8ZP7YPma3Tokabg5u2jGKkJelm6O7\nVedhgzs8wfKu7eQWZLW6mxomGzZGjx6Nt99+G7du3UKrVq0wePBgTJo0Cb/++itWrlwJR0dHvPHG\nG5ZsKyEAI2GwNvwL7bZSpWzSqy95FXnYcpWtVRHd6RmDIR6GEFotXnNmlUFNidd7N1ysT8gDYtmJ\nJahQV7DKxgSOM/ug9XFvYc2EB3reHD6uvmat21RWDVpjU6smfFk7vov8yeLeGkDdNGz4CHIPRlsp\nO23t9C7/ByeRaUK5a8+tRko+W5fAEu6Wdw0YYH6/yp8VxBxeI3KpHCenJsDLyLN8q+9Si/6mGQmD\nvyYdht0/ceJ2Ijv8Nemwxev8PGID7z5rhpJZWxz4meBprOwofjJ/q0/yG0MQWS6V48iU0+SuTxBE\nk0EulWNhz0U49+wF7JsYi9jo41px/8OTT2DfxFikPpeBQR0eej/P6Pp/rGtsGrHFrO+zmGts/cg1\n5z6ESl2bCUUsEvNmt7r3oAgT/hhltXCU8wXJLO/aM7mnrFJvU6ROOVCnTZuGQ4cOwd6+drD1wQcf\n4K+//sLWrVtx4MABBAW1XAtRY6Of+lU/e4ClyKvIwy+pm1iCmQqlQqvzwAefm/miPvU3ismlcsRF\nn+SU7725G6/HLeQ9Z+2QL8wilCaXyvHn04c45Udy4jhlkX4jGlyfPuE+QyEVyN5yPJftQhfcxryD\n9TCvHrx6C7o4SxiM8DdfRpSmgJ/MH3HRJ9HasTYWPqB1oNk9cQzRkEkQI2FwIPoI2jrXGjf8ZP5Y\nNmgFLs1Ow/eRmyCBg8Hz1VDjy6TPONc0N452joL7KtXcUIERHUebzbDkJ/PH6alJ2DFuDzwEMtGI\nRZbX4vCT+SN5Rio+DV+P5BlX6m34rQtCXi+2EkrGh1wqR8rMK1g9aC1+GbVNO5BuCTRWhimCIAhD\n8L2bhN5XuXrC3uZOma2fLepw9kFWtrhuXt20aeZ1uV58zWrZSTjJEeIWttiUryYbNubMmYP4+HhO\neceOHREWFob4+HhMmGAbAoHNEd1sAXzbluD8nfN47MfOeDVuPh79sRN2Xf8DCqUCw2IGY8T2CAyL\nGczbsVL1hOiGeIc3eNAe4hGKzSNiOOVFVVyPjYDWgXi686QG1adLr7Z9EOlr2GghEUksMvllJAzW\nDf3apGP19RnMUXfs5ONYPWit4DHjAyba5KA5xCMUidMv1dtzojGRS+U48a9znNWQMQHjcXzqGaPn\n64eBXLGA2/6EzlG8AwUh5AJCwvVFExLy+ZNcDwZ7kb3ZtGqMockIZQ1vIAC8nn7tmPY2H6Ygl8ox\n69E5GOYb2az6MkEQREtGoVTgtbgFrLLkPPMaE0I8QjGkXbjgfrdW7tg9nj8j3qK/F1jFwNDehb24\nfa+qCPtu7BE8nm9R2lYQNGxUVVXh7t272n/Hjh3DjRs3WGWafwUFBTh27BjS0rhpAAnroMkWILRt\nbvIq8tDtm26sHNSzD07HZ2fWaNNBppek8Vor/VuzRerMJYhXcD/f6DFTu0y3yET0lR5cVzRd3nrc\ncq7r4T5D4WrvavAYJzupxTQBors8oxW/02dBz1fNXmdToTmvdgq13U/mjwszr2NS4BSTr2UoHKq+\nyKVynPxXAtq08jDp+Lk9XjZ+UD3QFXaUOz2C5f1XImlGqtUMDdamvYsP7EUS7fYj0rb4a1Jcs/yN\nE9bHmLcmQRCEOblalIp7VWy9PEukJw8USEvrJ/NHmFcPnM3jXxTKKLlhFc0mvoXLJcf/w/suzii5\ngW4/BmkXpVecWm5TBg57oR0lJSUYPnw4KipqdQJEIhHee+89vPfee7zHq9Vq9O3b1zKtJIzSSk8B\nV3/bXFwqvIilJ5bgUuF53v1fpHAzgeifvy6ZfYxSpTRL20xJtdnZvYtFBukisbBreiuRk0XTMTES\nBquGrMW82DmCxwz3G2mxyYlG/O7U7RNYFLcAdypy4eogw87x+6ziPk+YF7lUjue6zcFvaVuNHutq\nL7NYGI6fzB9nnz2PN48sQsy1LYLHrR3yhcV+Z5rf9tWiVAS5B9v8BP9WWRaq1Q/fxxuGbbRZIw5h\nXhRKBSK3PYHrxdfQqXVn0u0gCMLi8IXd1zURgSmIxYYDHKpqHgjuu1F8w+LjhzCvHvBo5YHC+4Xa\nsuIHxbhalIqe8t7aMoVSgSe3DIAKKm3Z50lr8WXy5zazaCNo2PD09MSHH36IlJQUqNVqfPfdd3ji\niSfQqRM3JZxYLIa7uzvGjrWOey5hnKS8BPTzHmDWjnQu9wxG/m76JMZeZM9R5uVL81qXFIuGkEvl\nmN5lFjZd4U8VCRhO19kQgtyDIbWToqKmgrNv7ZOfW3yAN8J/FHzPdkRm6U3e/aMt7DrPSBgM843E\nyakJLWYSaMto0m5eL74GO9jxZiEBgEHtB1tc0HJcpwmCho1WYiezhpUJtUF3YGDL6D73Tq07WyX1\nKGEbXC1K1aZA1KQ6bCn9hiCIxmFX2u+s7bmPvWyRhY7Zj72AjRe+4pRrPDK6GtDsmxc7BwEJgRYN\nW2YkDJ7vNg8r45dry8QQcww/+27sQbmqnHN+tboaW1M345Wehr3PmwOChg0AGDp0KIYOrZ3I3r59\nG9OmTUOPHjTQaYro5yxec241tl+PMZsQmkKpwPg/6iYCWa2uxvV7V1kWQE+9dIIu9q5mS8sEAAHu\n/CERADCw7SCLWSMZCYPfxu7iGH7k0kcwwn+0RerUrz9u8knEZR3Cc/uns/Z5O7ezmrhlS5oE2jKa\ntJsaI1XSnQRM3D2Gc9xrfRqeWcgY/bwHwNeV32g3wHsgGdDMiP5zp3tLmIq+UczWdVkIgmh8bpbc\nZG2XVpVapB4/mT/e6rMUK88sZ5WLRXZo7+JjNLVrenGaRY29fCEnKqgwaddYHJlyWvst1zcE6ZJf\nbhvhKAYNG7p88snD8IErV64gJycHEokEbdu25fXiIKwLX85ijSXRHB1pQ+I6VKmFXa2EyFXcBlDb\n6a4WpaJUyX7pTOwUZdbB84TOUVh2cglL+0PD+4M+NFs9fPRq2wd/Pn0Ik/dOQFlVKTowHfCnhVM0\n6qIRgIyfmoyvEtdBqVbiSd9hCPeJoAkKUWd0jVSDOgxBXPRJrEv6DEFuXXCt6Arm91holsxCprQj\nbvJJ/H7tNyw6whYJe7v/coGziPpCxkmiPpBRjCAIa9OWYaev95V1tFhds7u9gE/OfoT7OpnZVOoa\nXtFtfRg7F6PGj/qiGwaoT3ZZFpLzEzGwXW0yh1O3TghexxIhPI2ByYYNADh+/DiWLVuGnJwcVnm7\ndu2wdOlSDBo0yKyNI0xHqGOZI+3rsewjWJOwSnC/I1rhAfjTK82LfR4/pGzEleJUlFdzLYrmFv2T\nS+U4PTUJI7cPxd37hbCDPQa1H4yl/T+wyiSsV9s+SJlxpVEHd34yf3wU/qnV6yVsmxCPUHw97LtG\nqZuRMEgvZotTTw2abpU+TRCEaZBRjCAIa6BQKnDq9gn898JGbZkYdngmeJrF6mQkDCYGReGXK5u0\nZTIHmdY7zc/VHxmlN/jbW1OGEb89iaPPxJt9XqAbBsjHvIPP48TUc4jLikVpDXtxuZ1ze/Rt2w9v\n9F1iM5p4Jhs2kpKS8OKLL0Imk2HevHnw9/eHWq3GjRs38Ouvv2Lu3Ln43//+h8cee8yS7SUECHIP\nhrujO4oesNObxmXFwu/R+v9Yz+We4XVB1+Wv6MOQSqSYvOtp3CzL4OxPKDzLe95rvRZbpCNpRAcb\ny7hAgzuCMC8KpQK70/9gldnK6gJBEARBEKahUCoQvrU/MstussrbOLnDWeJs0boX9Pw3y7Dxx/h9\n2jlG7OTj2HdjD+bHvsDrNX5LkY2tqb9g9mMvmLVNQe7BCGgdiPTiNNiLJCwBcADIrbiNram/4FZZ\nNufcdUO/xsB2g83ansbGsMyrDuvXr4dcLseePXswf/58jBw5EqNGjcLLL7+MvXv3om3bttiwYYMl\n20oYgJEwWPI41y27g2v9XZ+OZR8RFAv1cPTAgj4LED81GSEeofCT+ePXscKxW3wEt7FcDG5zTsVJ\nEASbq0WpyFawvdLu11QKHE0QzRtrpE2l1KyWge4rQViWU7dPcIwaAFBQWWDx1Kp+Mn/ET03Gwh6v\naec/GhgJg6igKTg9NQkeTp685791/HUcyz5itvacyz2DKTsn4k5ZLgDAWSIVrLerO9vDta2zt00K\nhJts2EhKSsLkyZPh5ubG2SeTyRAVFYXExESzNo6oG0pVFacssHX99E+OZR8R9NQY4zeQEp0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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'.g')\n", @@ -331,8 +288,7 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", "start_time": "2017-05-09T11:54:56.761091+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -354,8 +310,7 @@ "ExecuteTime": { "end_time": "2017-05-09T09:54:57.358210", "start_time": "2017-05-09T11:54:57.350077+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -500,161 +455,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)]\n", - "data_series = dataset.data['CODtot_line3'][arange[0]:arange[1]].copy()\n", - "data_series.replace(0,np.nan)\n", - "data_series.dropna(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "data_series.index[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sign(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, "metadata": { "code_folding": [], "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No drift detected\n" - ] - }, - { - "data": { - "image/png": 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vjh1bmTp1EjY2BWnRwpU3b97g5+fLyZPHmDVrgVYeUvKp8peSVau8WbRoHqVK\nOeDm1olbt0JYv34NV69eYc6cReTIkUNnHZVKhc0ZG3I+z4nKXqVnq/olJCQwbtxoTpw4Ru3adXF2\nbszJk8eZMWMqoaGPGDx4uFb6v/6awo4dW6lcuSp169bn8uWLeHktJCQkmEWLFqS6vxcvnuPp2ZcX\nL57TtGkLLC0t8fPbx3ffDeb336fj5NRAK/3NmzcoVsxe702po2P5VPd3/vxZvvtuMFZW1rRs6crr\n16/x8/Pl/PkzeHmtUuoOgDt3bjNwYB9UqkSaNnXBwMCAfft8GDiwD/PmLU72O587d2569OjF9Ol/\nsGrVhmQfZqTEwaGMTp135Ig/ISHBuLi4Ymtrp7OOnV0hPDz6UaFCpXTvL6PNnz+bNWtW4u29OrOz\nki7h4eHMnv0nAwYMxtzcnJcvI3TS3L59i4EDexMdHU3duvUpUqQoQUHX2bZtMwEBJ1myZIVWoJrS\nb+j//m+q3nzEx8fz229jtR4ov2/y5Ans27cHR8fytG/fkUePHrBt2yZOnDiKl9eqNAfLnyp/afH8\n+TPGjh1NQkLaho9cu3aFjRvXpmsf6amDYmJiGD58ECEhwTRs2JiCBW05fPggv/76ExER4bi5dUp1\nfxlRByWVnuvHh17fatb8kooVK/PXX1OYPHmasrxly1ZUq/aFUndpfIw68kNJoCyEyDLi4+OZOnUS\nTZu2wN6+uNZnJiYm9OkzQPk76f9B3eJy5Ig/1ap9ofNZcut8riIiwklMzHrdY/fu3c2lSxdYvXqT\nsszU1BQAMzMzADZuXEdISDA9evRgwIBhSrrz588yfLgnf/75BytWrAMgOjqav/6aSqFChfH2Xo2F\nhSUANWtu548/JrJixVKdQC0hIYFFi+ayZs2qZPMZFfWamTOnY2en3q6lpXq7bm4d6devJ7Nn/4WX\n18pUj/dT5S8ljx+H4uW1kIoVKzN37mKlBcfLayHLl3uxY8cWnZuzN29iebHrOblvaG6E0x4oHziw\nnxMnjtGlSw8GDVKXV79+Axk5cgjr16/GxcWVUqXUPV4uX77Ijh1bcXZuzMSJf2BgYIBKpWLSpPHs\n3bubQ4cOUbFiyi06S5Ys5MmTx0yZMoO6desB0LVrT/r06c6ff06hVq3amJiYABAa+oioqChcXdt8\n0G87MTGRadMmY2pqhpfXSmxsCgLQrJkLI0YMYt68mVo3/rNmTScmJhovr5VKz4d27dzo378Xf/45\nRfnOvP9YhHhbAAAgAElEQVSdB+jQoRMbNqxh2bLFOt+JtHBwKKvT2+Lx41AlUNbXUmZnVyjL1Hnh\n4S8yOwsfZMGC2ZibW+Di4groL/s5c2bw+vVrJk2aSoMGjZTly5d7/e93u4Thw38AUv8NHTt2RPld\naERGvuTXX3/i9OmAZPN56tRJ9u3bg7NzIyZOnKL0BNm2bTPTp//O6tUrlN93StKSv7ZtW6Y7f2lx\n40YwP//8A48ePUxT+rdv3/L777+lOajWSE8dtHHjWoKDAxkxYhRubh0B6NWrLwMGeLBgwRwaNWqa\nYs+iT1UHJSc914/0XN/0fe/79/ekd+/uWt/Zli1bKflIGijDv68jP5SMURZCZBmHDx/gwYP7dOjQ\nObOzItJJpVKxevUKvvyyDkWKFFWW29uXIHfu3MrNwj//HMTAwIDhw7UvhNWqfUHVql9w82YIYWHq\nyev8/Hx59SqSTp26KhdpAFfXNhQrZs+ePTu1boKCggLp06cHa9asSrElPiTkBvnz58fNzV0JkgFK\nl3agRImSBAVd5+3bt6ke86fKX0q2b99CQkICPXp4aHVz7NHDAwsLC3bu3K6V/vTpALp370TM1Wii\nbKMAUKU9Tmbr1g0YGRnRo4eHsszY2Jh+/QaiUqnYtevd/rZs2QhA7979lBtxAwMDvv12MAYGBmzc\nuDHFfUVHR+Pru5uyZctpBQP58xegQ4fOhIU95eTJ48rymzdvAFCqlEPaDyiJs2dPce/eXVxd2yg3\nqAA1atSiZs0vOXLEX2khvH//HqdPB1CvXgOtgLVkydI0a+ZCYOA1btwIAqB48RIAlChRSklnamrG\n11+3Yfv2zURGRn5QfsXn5enTJ/j6+tCunbvyW8ybNx/W1rkoXrwkoB5mcvbsKcqWLacVJAN0794L\nExNTre90ar+hXbu2aW1j//69dOvmzunTAXq7BWvcuXOLvHnz0b17L63hEk2bNgfUXX/T4lPlLzXz\n58+if/9veP78WZp7+6xcuYwHD+5To0atNO8nvXXQ1q2byJs3H23buinLzM0t6NmzN7GxsezfvzfF\n/X2qOig56bl+pOf6VqpUKQwMDJTvPUCZMo5UqlSFVau8U8yTRmbVkRIoCyGyjPXrV2NvXxxHx3Kf\nZPtOTjXo1aur8vfSpYtwcqrB/fv3mD9/Fm3atKBx47oMHNibwMBrJCYmsnr1CtzdW9OkiRP9+vXk\n3LkzOtt9/vwZ06f/Qbt2LWnYsDbu7q2ZP3820dFRWuni4+NZtmwx33zTmSZNnHBxacR33w3mzJlT\nSppJk8YrYxFnz/4LJ6cahIY+UtbfsGEt/fv3onnzBjg7f4WbmyvTpk0mPDxc51j/+GMi58+fxdOz\nL40b16VNm+YsWjSPhIQEbt++xXffDaFp0/q0bevCjBlTiY2NVdY/d+4MTk412LlzG1u2bKRjxzY0\nblyXb77pgo/PTt538uQx7ty5TfPm2mNfS5YsRcmS78bYt2njRv/+nloBqoaJibrLV0xMNAAXL6rH\nMlWrpttaVq3aF7x8+ZJbt24qy44e9efhw/sMHDiEadNm6ayjUaVKNTZs2E7nzt21lr9584bHjx9j\nZWWtt/vy+z5V/lLe53ll+0mZmppSoUJlQkKCef36tbJ83749xMREkcslN09qPQG0J/ZKSVxcHNeu\nXcXBoSzW1tZan5UrVwEzMzMuXDirlbfcuXNrlTeobzKLFi3G6dOnlWWhoY9wcqpBhw6tlGXXrl0h\nLi5Ob+uo5hwn3V9ISNoDZR+fnTg51WDSpPHKsgsX1Ocyuf0lJCQo459TKmvN+ufPq9NoAuT3z0Oz\nZi7ExMSwffvmVPP7MWh+w7Nm/aksGzy4P506teXx41DGjRtDixbOtGjhzNixowkPD+fVq1dMmTKJ\nr79ujItLI0aPHqHUP0kFBQXy448jadmyMY0a1aVXr65s27ZJa3Z1gGfPnvH777/RqVNbGjWqQ5s2\nLZg4cRwPHtxX0nTo0Io9e3YB4OHRTes7ERERwbx5s+jWrQONG9elceO6dO/ekZUrlxEfH69zrL6+\nPuzYsZVu3TrQqFEdunZ1w9fXB1D//nr37k7jxnXp3Lk9mzdv0Mqr5npw82YIM2dOx9W1Cc2bN2DY\nME+94+A3bVpHYmIizZq10FpeokRJpZdFYqKKgQOH0KlTN531jYyMMDIyUuo7SP03pPmOaWzfvgVT\nU1OmTJmh9TDrfR07dmXHDl+d4Qh3794BIG/elOdT+NT5S82aNatwdCzP0qV/88UXNVNNHxJyg1Wr\nvOnevZfWA6vUpKcOevjwAWFhT6lcuSpGRkZaad+vEyBj6yBQ30s4OdXQul6n5/qRnuubg4MDtraF\ndLpMN2vmwpUrl9L8ICaj60iQrtdCiCzi4cMHXL9+DXf3Lhm+719+GUNkZCRNmjTjyZMnHD58gJEj\nh1C3bn2OHz+Ks3Nj4uLe4Ovrw+jRI1i7dgv58xcA4PHjx3h69iEs7Cl169bD3r4EN24Es2bNSs6c\nCWDePC9y5swJwMyZ09i2bTNVq1anffuOREW95sCBfYwcOYQZM+ZRvXoN6tVz5vXrVxw54k+tWrWp\nUKEilpbqcUDjx//E4cMHqVy5Kq1btycu7g2nTp1k+/YtBAUF6nS7unr1Mr6+PtSu7UTbth3w9z/I\nqlXehIe/4PDhgzg6lqNdOzdOnDjG5s3qlsOhQ7UnWdm6dRM3b96gYcMmWFtbc+SIP5MnTyA09JFW\nt04/P18MDQ11Wg369v1W629X1zZ6yyAiIoKLFy+QM2dObG3VY7IePlR3sStcuLBOek2a+/fvKZNv\n1a1bn3btOpA3b/rGocfFxXHz5g0WLZpHZORLBg1KW7evjMqf9j4fkDdvPr1juOzs7P63z7vKWDVX\n17YMH/493xzoBoGalGkLlB8/DiUhIUHv8RkZGWFjU5D79+8B6nP49OkTypevqHdbtraFuHfvLuHh\n4eTJk0eZZyDpGLeHDx8A+s/nu2O7pyy7eTMEAwMDLl26wJQpE7l37y5WVtY4OzemT58BWg9jNGN8\nk7bEvNuf7oQ57+/vXVnrpk1a1po0R4/qPlArXrwENjYF8fPz/VdBw78VFRXFwIF9KFDAhtat23Hx\n4gUOHz7Ay5cRREdHExf3hhYtXLl9+xbHjh3h2bNneHmtVFoQT5w4xs8//4CxcQ4aNGhInjx5CAg4\nwfTpfxAUFMTo0T8D6gdPQ4b0IygoiAYNGtGwYRMePnyAn98+AgJOsmbNJqytc9GxYxd8fHYREhJM\nmzbtlWE3r1+/pn//b3jy5DFOTvWpV8+ZiIhw/P0PsXjxfCIjI3W6aK5b9zcPHjygSZNmVK9ekz17\ndjJx4i/cuBHM5s3radiwCVWrVmPfvj3MmDEVGxsb6tVz1trGpEnjefToIc2atSA6OppDh/wYNmwg\nU6bM0OoJ4ue3j1KlHMiXL7/W+vPmLVH+b2lpqfNATuP06QBiYqKV30x6f0OgnnejYsXKmJqa6n2I\nm5yoqNecP3+OWbP+JEeOHMnmMam05u/FixdAjn+Vv/dNmzYzzZM8JSQk8McfEylSpBg9e/Zm/vzZ\nad5PeuqglOqPfPnyY2JiqlVfZWQdBFCvnjO2tnZak1Om5/qRnutbhw4daNCguU46ze/Fz8+XihUr\n63z+vsyoIyVQFkJkCefPqy+in6o1OSWvX79m+fK1yk37+PE/4+fni7//QVav3qQExba2dixbtpgj\nR/xp1049m+6ff/5OWNhTpkyZQZ067y7kGzeuY9as6Xh7L8bTcxhRUa/ZsWMrVatWZ+7cxUq6Vq3a\n0rdvT7Zs2Uj16jWoX/9doPzVV7Xp2FHdAn7lymUOHz5Is2Yu/PLLRGX9+Ph4+vTpTmDgNe7du0ux\nYvbKZ7dv32Lo0O+UbbRp046uXTuwa5e6NVVzk/nNN31o3/5r9u/31QmUg4MDmTjxDxo2bAKob3wG\nDPBg5cplNGvmQtGixQD1k2w7u0JYW+f6oDKYP38W0dFRtG3bQRn/9fJlBCYmJpiamumk1wRBUVHv\nWk8/5LsTHx9PkyZOyphwd/cudOmS+k1jRuXvfZGRL7Umd0lK0z0uaYtylSrqbopJW5HT2vU6MvIl\ngPKgRt/+YmPvEh8fr3SVSy5t0vORJ08erKysdMbPprQ/zfpJj+3mzRuoVCqWLl2Is3NjqlSpzoUL\nZ9m4cS1nz55iwYKlyjnRN8b33f50eze8fy413R9TylvSsk6Oo2N5jhw5zMuXEeTKlfaZhj+miIhw\n6tdvyKRJUzEwMCA+Pp5Ondpy/vxZKlWqzMKFy5QeFUOGDOD8+bPcvXuH4sVLEBsby6RJ47GwsGTx\n4uXKd/Hbb4fwyy8/snPnVurXb0Dt2k6cOXOKa9euKbN/a6xZs4r582exf78vbm4d6dixKzduBBMS\nEkzbtm5KOW3duolHjx4yevRYWrVqq6zv4dGPLl3as3//Xp1A+datmyxatFz5rZUu7cC0aZNZt+5v\npk6dqdTR9eo5M2TIAPbv99UJlB8+vM+yZauVgKRdO3c8PfswffrvrFu3FUNDQx4+fMDTp08+eAhF\nbGwsc+b8BUDr1u0A0v0bAtLUuvq+M2dOMXy4J6B+4DV+/CQqVaqS6nppzd+rV68wN8/7wfnTJz0z\nIa9du4rg4EDmz/dKU8+gpNJTB6VUJwBYWFho1QkZXQfVr++sMyt4eq4f6b2+6VO4cBGsrXNx/vzZ\nFNMlldF1pHS9FkJkCUFBmvF9JVNJ+fG5uLhqtWxpbhqaNGmuBMmA8iRd0xXx2bNnnDx5nNq162oF\nyaCeGMrGpiA+PuouhYmJKlQqFU+ePOH582dKOkfH8qxfv43x4yelmEcbGxt+/nm8TnBhbGxMpUrq\nYOj97tcmJia0a+eu/F2sWHFlZtOkwaCFhSX29iUID3/BmzexWtuoVKmKEiQD5MmTl549PUhISODg\nwf3Kfp8+faKMzUyv5cu98PHZia2tHf37eyrL4+MTkr3R0SyPi3vzQfvUiI6Ool27DnTo0IlChQqz\nceNapkyZpNOFVJ+MyJ/uPuPJkcNE72eaBwxxcXG6HyY5nrR2vdZ0bU3L/jRpNd3n35eW85HS/jTL\nNMeWmJiIpaUVDg5lWLVqA2PGjGPYsJEsXfo3bdq059atmyxbtlhnO2nd3/vnMqXjS09ZlyhREpVK\nRXBwYKppPyV3985KC7GxsbHSA8HNrZPWd/r9Ou/oUX8iIsLp0qWH1g23oaEh3347GIDdu9VdPVX/\ne193SMgN3rx5d27at3dn8+ZdtG//rm7S58svv+L7739UJsrSKFjQlkKFChMREa6zTuXKVbUeSGnq\n8mLF7LXq6PePKyk3t05arXYVKlSkSZNmPHr0UOlCGhSkLr8PqfPevn3LuHGjuX37FvXqNaBx46ZA\nyt8x+Hh1iomJCV279qBly1aYmZkxfvzPeofTvC+t+Uta1hnt3r27LFu2hHbtOqSpBfN96amD0nI+\nUiurjK6D0nP9+FjXt+LFS3Dr1s00zfsBGV9HSouyECJL0Mx6mhmtLEknnwKUrtLvP3nVXEg0FX5w\ncCAqlYqXL1+ydOkine3myJGDp0+fEBb2lAIFbGjUqCkHDuzDzc2VSpWq8NVXdahTpx4lSqT+cMDG\npiAuLq7Ex8cTFBTIvXt3ePjwATduBCljnBMTE3TWef9CZ2aWE1PTGJ3ugu8ukm+1niBXrVpdJy/l\nyqlvMjVjRP9N2Wlm28yVKxdTp87UGgtramrK27fxetfTlIGZWc507zMpa+tcjBgxClC3in3//VB2\n7txKzZpf0qhRkxTXzYj86dtnfLz+Gw7NDY7m+5uUdnCctkBZM5NpSvszMDDAzMxMuTn+N+cjpf29\nfat9bIaGhixevFwnnaGhIYMGDcfX1wc/P1+GDPnug/b3/rnU/Cb0HV96ylrzGwkP132NUEZKvs7T\n7mb5fp2nCRCDgq7rrfOMjIyU2Wxr1PiSokWLcuTIYVq3bkaNGrX46iv1Q8WCBW1TzWOZMo6UKeNI\ndHQ0V69e5sGD+9y/f4/r169x//49vbMZp/W4NGWv7+a9WjV9dV4FfH33EBJyg8qVq35wnRcTE8PP\nP4/i1KkTlCtXnnHjftOTp09bp1SuXFWZEKt37/707duDadMmU6NGLa0Jpd6X1vzpq38ygkql4o8/\nJpInTx4GDBj8QdtITx30rk7QXz++ffs21bLK6DooPdePj3V9y5Ur9//ukyK0Gh5SSg8ZV0dKoCyE\nyBI0XXiSvl4goyRX2WtuEpPz+vUrQD0W+OrVy8mmi4yMpEABG8aN+w1Hx/L4+Ozg/PmznD9/lgUL\n5uDoWJ7Ro3/W6Zb1vm3bNrN8uRfPnoUB6i5YFSpUwt6+BNeuXdFpBU3uuNLTHa1AARudZZoxtpoy\n+5Cy04wj27VrO3ny5OWvv+ZSsqT2pCtWVlbExb0hLi5Opyw03cP0dVn7UGZmZvTv74mnZ1+OHvWn\nUaMm+Pjs1Gl1cnAoS/36zp8sfxs2rOHVq1day6pV+4Lq1WtgZWWt1f04KU05JJ2hVJ+0dr22slI/\ntEhpfzlzmmNoaIilpSWGhobJdsVLy/lIaX+aZRYWFqnm29zcnKJFi3HjRjBv3rxRbkZT2t/748bf\nnUuL/6W10lquL29pKWvNTeirV5k783XydV7KdYOmzjtwYF+yaTRddM3MzNiwYQN//TWbgwf34+9/\nCH//QxgaGlK/fkNGjfopxaEab968YfHieWzfvkWZaLBAARuqVKlG7tx5tHrm/NvjSip/fn11nvrB\n4r+p88LDwxk1ahjXr1+jQoVKTJ+ufrWUxsf4DaWXra0d7u5dWLJkAQEBJ2jVqq3eByD16ztTvHjJ\nNOXPysqKNDYeAuqu2hs2rNFZ3rJlq2S7CeuzZcsGLl26wLRpMz/4PbzpqYPe1QlROmk1y1ObJC2j\n66D0XD8+1vVNU+dFRkamKVDO6DpSAmUhRJagaUmMinqtdA/+3Gkq9F69+upMWqWPsbExXbp0p0uX\n7jx+/JgzZ05y8KAfp06dZNSoEWzcuEPrlQ1JHTzox/Tpv1OqlAMjR46mTBlHpVVm+vTfuXbtysc7\nsCT0daPT3CxrnvxqbnbTMkYT1E+uBw36gUOHDmFnV4i//pqrjHVOqmjRYly+fJHHjx9RrFhxrc9C\nQx/+L429znqpefjwAUFBgVSr9oUy1k/D1lY9oUlEhPppto/PTi5c0J7J1cXFlfr1nT9Z/jZsWMvj\nx6E6y6tXr0HRosW4cOEcb97E6owdCw19hKGhIUWLFtVZV/UBXa9tbe3IkSOH3u6pCQkJ/+tur+4N\nkSNHDgoWtFOO+32hoQ/JmzdvioGR5jugb3+aZZrz/OrVK+7cuUWuXLm1xuVrvHnzBkNDw2R/T+/v\n7/1tvL8/TVp973BNT1lrHoAkF7x/7jR13qxZC9I0/jRv3rwMGzaSoUO/IyTkBqdOnWDv3t0cPnwA\nQ0NDfvvt92TXnTt3Jlu3bsTZuTHt27tTurSD8v3p1q2D3kD5Y0hbnffuepUWjx+HMnz4IB48uEet\nWl8xadI0nZbXtPyGcufO80HzQAQGqlvhmzZtofPZ+3Wet/cSnTR2doVwcCibpvzlzp2bsLBXetPo\n8/r1K737rFbti3QFyocOHQDghx/0T8Y4dKj6Gr1x445kt5ueOkjze9dXJzx79oy4uDep1gkZXQel\n5/rxsa5vmt9OWuu8jK4jJVAWQmQJmq7AERERemd1/BxpXksTGHhN7+dLly7CxMSUzp27ERb2lJ07\nt1GxYmXq1q2Hra0trq5tcXVty7BhAzl79jSPHj2kWDF7rfdcamjex/jrr/+n0/J6587tj3xk7wQG\nXtVZphmnpxnn967sXqa6PZVKxYQJP+Pvf4gSJUoyY8a8ZJ8yV65cFR+fnZw/f07nQn3+/FksLS0/\naIygn58vS5YsYPjw73Xe2a3pTq75DiadeC2j8rdpU/LjBStXrsq5c2e4ePGC1kRCb9684erVy5Qo\nUVKrlUrjQ7peGxsbU758Ra5fv0p0dJTWdq9fv0psbCwVK1ZKkrcq+Pr66Ewq9+xZGPfv36Nhw4Yp\n7q9s2XKYmprqPJgAlMlgNPsLDg5k2LCB1K1bjylTZmilffbsGY8ePcTBoazOa1uS0nQ/vXDhHF9+\nWVtnf4aGhsrY3aRpk74zVTtvqY+J1EzIU7Bg8l1cP2dJ67z3A+XIyJd4e3vh6FiO5s1bcuHCOQIC\njuDq6kbhwkVwcCiDg0MZ3Nw60apVM+VVNUCydV6ePHmZOPEPrc/fvIlVHiSpVCq96/4bgYFXdSbe\nS77OS717aEREhBIkN27clHHjJib7ACe131DSd/umx8KFczlz5hQlS5ZWXl2l8X6dp2/G9k+ZPzu7\nQinuM61atmyl89ojgICAE1y7dgUXF1dsbe2SnXwL0lcH2draUrCgLZcvXyQxMRFDQ8Mkac9opU1O\nRtdB6bl+fKzrW0REBIaGhnp7p+mT0XWkTOYlhMgSNMHf7ds3U0n5+ShUqDBVq1bn5MnjHDrkp/XZ\n3r278fZeQkDAcXLkyIGpqSmrV6/Ay2uB1mRLb9++5fnzZ5iYmJAvn7rrlZGRsfKZhqbrk2ZsnMae\nPbuUi3rS94p+LP7+h7h48d07RJ8/f8aKFcvImTOnMobX0tISG5uCaSq7TZvW4+9/CHt7e+bMWZxi\nV6z69Z0xN7dgzZqVyuygALt2bef+/Xu4urbVujlJK2fnxhgaGrJmzSqtG92IiAgWLJiNgYEBLVu6\nprCFT5u/lDRt2gIjIyOWLVus9T1atcqbqKgoZQbd92nNep2O/bVo8TVxcXFa3THj4+NZsmQhAK1a\ntdNKC7B48TxlFnGVSsXChXMB6NSpU4r7ypkzJw0aNOLKlUscPeqvLH/2LIxNm9aRP38B6tRR34hX\nrlyVfPnycfLkca2b2rdv3zJjxhTi4+NTnSyqatXqFCxoy/btW7RakM6cOcXp0wHUr++s9DgoXLgI\nlSpV4fDhA1oPxm7dCmHfvj04OpanbFnHFPcH7+q30qXLpJLy81S/fkMsLCxYvXol9+7d1fps/vzZ\nbNy4VnlH8vPnz1m1ahVr1/6tle7Fi+fExb1RWjJBf51nampCXNwbrWEICQkJzJz5p9Lq+ynqvDVr\nVvHs2bvW6suXL7J//17Kli1H6dLqBwWa9wjfvn0r1e1NnTqJBw/u0aBBQ379dVKKvRxS+w21bt3+\ng46pUSP1hGELF87RGtsdGHidLVs2kDdvPmrXrpvqdj5V/j6Gli1b0afPAJ1/FSqog1UXF1f69Bmg\nNXHn+9JTBwE0b96Sp0+faL2TOzo6ipUrl2Fqakrz5l+nmOeMroPSc/34GNe3xMRE7t69TbFi9qkO\nZdPI6DpSWpSFEFnCV185Ke9ETe5du5+jUaN+wtOzH+PGjeGrr+pQsmQp7t27y/HjR7G2zsXIkWMA\ndQuEu3sX1q9fTc+enahd2wlDQwMCAk5w585tevXqq4wNKlBAHTxu27aZyMhI3N0707x5Sw4c2MdP\nP31PkybNsbCw4Nq1q1y4cI48efISHv5CeRL7MZmZmTF8+EAaNmyCubkFR44c4sWLF4wa9bPWhGC1\na9dl+/YtPH78GFtb/RP1xMXFsWKFFwBly5Zl8+b1etO1betGvnz5sbbOhafnEKZP/4NevbrSqFFT\nwsKecuiQH0WLFqNnzw97z6K9fXE8PPqxdOkievToSMOGjXn7Np6jR/0JD3/BgAGDlaf4KflU+Ust\n7507d2f16hX07t2NOnXqcefOLY4fP0qlSlW0AtektLpep3WQMuqbTx+fHaxfv4abN0MoW7YcAQEn\nCAkJpkuXHlqtUzVrfknjxk05cGA/AwZ4UL16Da5cucTFi+dxdm6Ms7Mzz56pu6pqxiVaWVkpry8D\n6N9/EKdOneTnn0fRpElzcufOjZ+fL+Hh4UyePE0ZX58jRw5GjRrLTz99z/DhnjRq1BRr61ycORPA\nnTu3ady4GS1btlK2e+NGEP/8c1gZXw7qiadGjhzDjz+OpG/fHjRt6kJMTDT79+8lV67ceHoO0zoX\nw4Z9z+DB/RgyZADNmrlgaGjEvn0+qFQqRo4cneq5VKlUXL58iVKlHMiTJ2+K5+FzZWVlxejR45gw\n4Wd69+5G/foNyZ8/P+fPn+P69auUK1eeLl16AOob7WrVqrFt2yZu3QqhYsVKREVFcfiwuots377v\nZvDX1Hlz586kRo1a9O7dn2bNWrJ27Sr69u1BvXrOJCQkcOrUCe7du0vu3HmIiAjn5cuX5M+fXzej\n/0Jk5Evl2KKj1fk1NTVl1KiflTSFCxehWDF7Ll26mOK2goIC+eefQxgYGGBra6e3i7GJiSk9evQC\nUv8Nvf92hbT6+uvWHDrkx4kTx+jduxs1a35FWNhT/vnnEEZGRvz66/+laRKuT5W/z0la6yCAbt16\ncvCgH7NmTefChbMULlyEw4cP8ujRQ0aM+EFraE9G10H//HOYGzeCqF/fWZn/JD3Xj49xfbt5M4So\nqChcXL5M07nXV0d+ahIoCyGyhPz58+PoWJ4zZ07pdGP6nBUrVpylS1exfPlSTp48xtmzp8mXLz/N\nm7ekV6++Wt3IPT2HUrRoUXbs2MaePTtJSEigePGS/PzzeK1XoFStWp327d3x9fVhy5YN1KhRizp1\nnJgwYTKrV69g3749mJqaUahQYb77bjQVK1aid+/unDx5TO8YtH+jRQtXChQowObNG4iMfImDQ1nG\njPlFp/XByakB27dv4fTpk1rvPE3q7t3bSgvuvn3JTwZUv76zEoS3bdsBKytrVq9eyZYtG7G2tqZF\ni6/p33/QB7+zGdTvYi1WzJ7169ewa9cOjIwMKVu2HGPGjEtX98FPlb+UfPvtYGxsCrJ16yY2bVpH\n3rz56NSpKx4e/dP41D7tgbKRkRF//jmHpUsXcfCgH5cuXaRw4cKMGDFKeZd4UuPGTaREiVL4+Oxk\n44wr7kYAACAASURBVMa12NjY0rfvt3Tt2lOri6xmXKKtrZ1WgGhra8uiRd4sWDCHY8eOkJiYSOnS\nDowdO4GaNbXfWVu3bj3mzfNixQovjh8/QlxcHEWL2jNixA+0a+eutb8bN4Lx9l6ijC/XqFPHienT\nZ+PtvYRdu7aRM6c5derUY8CAQRQqpD1TsqNjOebN82LRonns27cXY2NjKlSoTP/+A3F0LK//TCfp\nGhwYeI1XryLp1q1nqufhc9aoURNsbGxYtcqbkyePExsbi52dHb169aVLl+7KREo5cuRg0aJFzJo1\njyNHDrN58wZMTEypWLESPXp4KF1JAdq378jlyxe5ePECd+7cpnPn7vTv74m5uTm+vj5s3bqJ3Llz\nU7x4SYYP/4E7d24ze/afnDx5FFdX/fXNhxo27HsuXbqIn58vhoaG1KnjRN++A3W6mTo5NWDNmpU8\neHBfZ7ZtjYsX1b0dVCoV69frTlgF6h45mkAZ0v4bSg8jIyOmTp3J6tUr8PX1YdOmdVhYWODk1AAP\nj346w3lS8iny9zlJTx1kYWHJ/PlLWLRoHseOHSEg4ATFihVn/PhJNGnSXCttRtdBR44cZs+eXcr4\nco30XD/+7fXt9OmTAGm+L9FXR35qBqr0PDrOZtIz2YDIWAUKWEn5ZCOa8vbz82X8+J+ZMWOu1gWp\nQ4dWvH79ir17D2deJrOZc+fOMHTot7i7d2HYsJGpplepVPTo0RErKysWLFiWanr5jWesVlubExB6\nAoDf6k7m2yof9vqUf+O/XOanQgMICr9Oj/K9lGXXnl/FeX1tljRbTpvS7Zk+/Q/27dvDpk07tV6D\nduNGML/8Moa1a7dkQs4/raxU5kuXLsLbewmTJ0/XCmSS8+TJYzp1akuXLj0YMGDQp89gFpGVylx8\nHMmVeffu7lhb52L+fC+t5ZMmjWfPnl14e6/WCuKTqyM/Vh71yRpNMkIIgXocVdGixdixY1tmZ0Wk\nk4GBAT16eHD58qU0jdsTGSvpM/OE/40tzA6uP7/G94eHExsf+0n347q1KSMPDyU89t0cAs7r1ZPz\n/OA/nJiYGPz8fGnXroPODaCfn2+WHbOcnRUsaEuLFl+zd+/uTzJWWois7NIlde+Qnj17pyl9SnXk\npySBshAiyzA0NGTo0JH4+x9UZuLU0EwopO8dj+Lz0LRpCypVqszSpQszOyviPUkn80ok+wTK7bd/\nzcpry1h1zTtD9vc85jkAYdFhyrKINxGsW/c3ZmZmdO/eSyv969evCQ4OZPBg/a+0EZ+3vn0HEhsb\ny/btmzM7K0J8VpYuXUTt2nX56qs6yjIfn50sXbqIGzeCddInV0d+ahIoCyGylNq16+Li4srChXO0\nlsfFxeHtvUTvZCji82BoaMiPP/7KiRPHuHr107zXWXwY7cm8sk+g/DxWHbi+iH2RSsqPIyzmKfMv\nzGHs0VHKMqNYI9au/ZsffvhJZ8ZdS0tLZsyYp7wTXWQt+fPnZ9iwkSxfvpTo6OjMzo4Qn4WAgBME\nBV3XmgAP1IGyt/cSQkK0A+Xw8PBk68hPTcYop0DGUHy+ZIxL9iLlnf1ImWcsl82NOfvkNAA//j97\n9x3fVPX+AfyT1d1Cgba0pWxZssHKHiqCA1GWIIqiDNniQPTrTxwoIOJgIyjIngoiS7aAQGnZe+8u\numfm/f0RmibNaNJmtfm8Xy9fJveee/MkKe197jnnOdH/hwmtP3J6DK74zqMWhkCulmNMi/fweduv\n7HLOxWcWIMi7AvrXH6jbFjpPO1TQS+wFhUZhdMzt4YnwlRZfVbi84b9zz8Pv3POUhe+cc5SJiIio\nWJ409Npb4gMAUKjldjvnp4cmYsyeESb3mUqSARjMXSYiIvfARJmIiMjj6c1R9qCh194SbwBAngOL\ned3KuFlsm3y1Y4uJERGR7ZgoExEReTiDqteC2oWROJePVNujnK/Kc8j5BUFA9MpmxbaTq+zXo01E\nRPbBRJmIiMjD6Ve99qRiXlKxFACgFrTL92gEDabHfIPLqZdKfW5BELDozHyr2srZo0xE5HaYKBMR\nEXk4/R5ljQfV+JSKtImySqPtRf/n1g7MjJ2Op9d1KNH59D/Hv2/8hc8OT7LquHw7zpEmIiL7YKJM\nREREOp409LqwR1n7njPk6QDMF90qjlKj1D1+Z+cbVh8nd+AcaSIiKhm3SpQ///xz/O9/hmtq9e3b\nF/Xr1zf4T79NSkoKxo8fj9atW6Nt27aYMWMGVCqVwTmWLl2Krl27olmzZhgyZAhu3brljLdDRERU\nJuj3IXtSMS+xSAIA+PfefoTOC8L+u3ttOl6tURt8Xjcyrpttu+Glv/Br92WYFP0Zbgy9j6RRmXi/\nlXYZLg69JiJyP1JXBwBohyrNmjULa9euRd++fQ22X7t2Dd9//z3atGmj2+7rW7jW4NixYyESibBi\nxQokJiZi0qRJkEqlmDBhAgBg/fr1mDVrFr799lvUqlULP/74I4YOHYpt27bBy8vLeW+SiIjITQke\nWvW6oEc5S5EJANh4dZ1Nx7de0QSZikxU9qmMbjW6Y9HZBSbbvfX4O+hUrYvR9sq+VQAAcnXJerCJ\niMhxXN6jfPfuXQwePBirV69GRESE0b68vDw0b94cISEhuv8CAgIAACdPnkRcXBymTZuGBg0aoHPn\nzpg4cSKWL18OhUL7R2fx4sUYMmQIevTogfr162PmzJlISUnBzp07nf5eiYiI3JH+3FrPKuYlKdXx\n97PvIUuRiVuZN80myQDgI/U1uV0EUalen4iIHMflifKJEycQHh6OLVu2oFq1agb7rly5Ah8fH0RG\nRpo8NjY2FpGRkYiKitJti46ORk5ODi5evIiUlBTcunUL0dHRuv3+/v5o3LgxYmNjHfOGiIiIyhj9\nHmVPmqMsEZV8YJ1Koyq+0SMysayYFp5TQI2IqKxweaLcq1cvfPfddwgJCTHad/XqVQQGBuLDDz9E\nhw4d0LNnTyxZsgQajfZud2JiIkJDQw2OKXgeHx+PhIQEAEBYWJhRm4J9REREns6w6rUn9SibT5RX\nXPjd4rEFw7WtoTBT1VokYo8yEZG7cos5yuZcu3YNubm56NChA0aMGIETJ07gu+++Q1ZWFsaNG4e8\nvDx4e3sbHCOTySASiSCXy5GXlwcARm28vLwglxe/FENwsB+k0tINyyLHCQkJdHUI5ET8vj0Pv3Pn\nkUoL75t7+0hd9tk7+3V9vM3XKpn836eY0HmM2f3ZaQ+tfh1zn2lAgA8AICjI12N/3j31fXsyfuee\np6x+526dKE+fPh25ubkICgoCANSvXx9ZWVlYsGABxo4dCx8fH91c5AJKpRKCIMDPzw8+Pto/QEXb\nKBQKg4Jg5qSl5drpnZC9hYQEIjk5y9VhkJPw+/Y8/M6dS6kqHG6dnZvvks/eFd+5Wml+yHOWIksX\nj0KtQO/NL6JHrRcwpsV4AMCD1BSrXycnz/Rnmp2trXadkZHrkT/v/Hfuefide56y8J2bS+RdPvTa\nEqlUqkuSC9SvXx85OTnIyspC1apVkZycbLA/KSkJgHa4dXh4OACYbFN0ODYREZGn8tRiXlV8jad9\nmZKYm4CYhKP46sj/6bblqnKsfh3zw9k59JqIyF25daLcv39/TJkyxWDb2bNnERoaiqCgILRq1Qp3\n795FfHy8bv+xY8fg7++PBg0aoHLlyqhZsyZiYmJ0+3NycnDu3Dk88cQTTnsfRERE7s0z5ygLVhbR\nUmsMC5ztu7MHz27oYvXraATLr2NtHERE5DxunSh369YNa9euxaZNm3Dnzh2sX78eixcvxrhx4wAA\nLVq0QPPmzTFhwgScP38eBw4cwIwZMzBkyBDdGslvvfUWFi1ahK1bt+LKlSv44IMPEBoaim7durny\nrREREbkNT616bW3vuabIZzL31CybXsfczQcW8yIicl+lmqOcn5+PkydPIi0tDdWrV0fjxo3tFRcA\nYOjQoZBKpZg/fz4ePHiAiIgIfPLJJ+jXrx8A7R+YOXPm4IsvvsCgQYPg7++Pfv36YfTo0bpzDBw4\nEJmZmZg6dSpycnLQsmVLLF68WJdIExERUSH2KBtTF/lMxMUkuJ2qdcW/9/bpnr/ecLDtwRERkUsV\nmygrFAps2LABp06dQpUqVTBw4EBERUXh8OHDmDhxIlJTU3Vt69evj5kzZ6JOnTolCmb58uUGz0Ui\nEYYMGYIhQ4aYPSYkJARz5861eN4RI0ZgxIgRJYqJiIiovDO1PFR6fhriEo+jY7UukIll5bL3s7gh\n0QWK9rLnqfLMtu1crSumd/4BbVa2AADcGZ4EH6mPxfMLVsZBRETOYzFRzsvLwxtvvIHz58/rfolv\n3LgRCxYswJgxY6BWq9G3b19ERETg4sWL2LVrFwYPHoyNGzeiatWqTnkDREREVDqCwRxlbVL4+rZX\nEZNwFADwUp1XsLi75XWFy6KCmwIjmo3GwtPGN90FQcDn/32KQJlhRVSVRqV7/HLd3ohLjMWbj7+D\ncS0n6LY/XrkJzqechbfEcIlKfSIW8yIiclsWE+UFCxbg3LlzGD58OF544QVcv34dX331Fd555x1o\nNBqsXbsWDRs21LXfv38/Ro4ciblz5+Lrr792ePBERERUeoY9ytrHBUkyAPx1/U8A5TdRfr/VRyYT\n5W+PfWW0PSk3CWJRYYmXp6p3wy/PLjU6dmfffVALaqt64lnMi4jI/VhMlLdt24b27dvj/fffB6Ad\nWq1Wq/HRRx+hZ8+eBkkyAHTp0gVdu3bF/v37HRYwERER2ZcAASKIIECABp4zR7ngveonvvp+PjHT\naFvjpXVRPaim7rmPxPSwai9J8bVQ2KNMROS+LFa9TkpKMkqGO3XqBAC6NYqLqlmzJtLT0+0UHhER\nETmaAAESsQSA8VJI5VlB1WtbE9Y7mbd0j32kvvYMiYiI3ITFHuWIiAicO3fOYFuFChUwZcoUVKpU\nyeQxJ06cQGhoqP0iJCIiIoeTiCRQQWW2R1kjaMz2vJZVBUPOS/O+LM1BtjoODr0mInI7Fv8yPPfc\nczh27BimT59uUN26b9++eOqppwzaZmVl4YsvvsDp06fRvXt3x0RLREREdicIAiQibY+yQi3HtGPG\ndUaqzq+ID/aPc3ZoDlUwR1kkEmP/q0dKdI7iKlpbUh4riRMRlRcWE+Vhw4ahdevWWLJkCXr27Gm2\n3Z49e9C2bVusWbMG9erVw5gxY+weKBERETmGdui1dpDZ3ju78UPcDJPtll9Y6sSoHE9/jnKjyo/r\ntpubd2yKXXqUuTwUEZHbsTj02tfXF0uXLsWGDRtw+/Zts+0qVKiAyMhI9OjRA8OHD4efn5/dAyUi\nIiLH0PYoO29Y9fabWxHhH4FmoS2c9pqmFPQoFx167SXxRr4636pzVPKpXOLXZzEvIiL3ZTFRBgCJ\nRIJXX33VYpvWrVtj586ddguKiIiInEdA4dBrR9MIGry5fSAAIGlUplNe03wsj+YoFxlgZ8uQ6GqB\nUXaNiYiI3EOJbx/n5OTg5MmTuqWgMjIy7BUTEREROZnYSYmyQq3QPS7o0XUVjaCt8F3Qo9witCXC\n/SMgtrKn98CrRyEVF9vnUCwW8yIicj82J8oPHz7EhAkT8OSTT+K1117DqFGjAACrVq1Ct27dEBsb\na/cgiYiIyHEEwC4JnzUUarnucYbctctJ5qvyIRVLdUtj7eizDycHX7C6CnbDyo1K9fos5kVE5L5s\nSpRTU1Px6quvYvv27WjatCkaNWqkK0Dh6+uLBw8eYNiwYbh8+bJDgiUiIiIHEASnLf0k1+tRfpj3\n0CmvaY5cLYe3XuEukUgEsUgMkZOXwWIxLyIi92PTX4JZs2YhPj4e8+fPx6pVq9C1a1fdvrfeegu/\n/fYbVCoV5s+fb/dAiYiIyDEECE4rLKXfo5ylcO0cZbk6H74mlndy1mfBYl5ERO7LpkR579696Nat\nm0GCrO/JJ5/Es88+i1OnTtklOCIiInI8QRCcNgz4+9hpusf5KusqSztKvirfoEe5QPWgGgbPO0R2\nMmqz6NmljgqLiIjcgE2JclpaGqKiLFd3DAsLQ2pqaqmCIiIiIuextUf5btYdvLvrbcRnP7D6mDxV\nHt7c/hpWXlymty3XpjjtLV+db3Id5KJJcLcaPTC0yQjd80tv30Svur3tFgeLeRERuR+bEuWqVavi\nwoULFtucOXMGVatWLVVQRERE5L4m7BuLP65uwP8d/sRiu+vpVyEIAn49+wtq/BKG7Tf/Ntg/NWYK\nntv4NOr/WgM3026aPMeW65usSshLUhhMrpbDR+prtL1aYBSSRmUiMqAaAMBX6gt/WQAAbdGz0qyd\nrI9Dr4mI3JdNiXL37t1x5MgRrFmzxuT+JUuWIC4uDs8884xdgiMiIiLHEwQBsGHodZZCuyRktjLL\nbJvYhBi0XdUKH//7Pj45+KHJNmeSTyEu8TjS5Gnoubqn0f6TiXF4Z+dgdNvQ2WI8qy4ux2O/Vsem\nqxt12wRBwEcHJmD83lFQaVQmj8tX5cHHRI9ygU0vb8OEVh9iQINBGNjwdQDAD11mW4ylJFjMi4jI\n/di0FsS7776LAwcO4Msvv8TKlSuh0WjXP5w0aRLOnz+Pa9euoXr16nj33XcdEiwRERHZn3botQ3t\nHyV2lnpETyWdAAAsPf+rVee8knLFaFt8TjwAICk30eKxvz96jdWXVuDlx/oAAOISj+u2D2zwOtpE\ntDM4RhAEbdVrE8W8CtQIqolPnvwcAFC7Qh0kjbJv8TEuD0VE5L5s6lEOCAjA6tWrMWDAANy/fx/X\nr1+HIAjYtGkTbt++jV69emH16tUICgpyVLxERERkZ7bOUS7o/7R0jK3zbpUaZYnPIRZp10Hed3cP\nfoj9DgBw7uFZ3f4vj/yf0THyR9W3fUwU8yIiIrKpRxnQJsuTJ0/GZ599hps3byIzMxN+fn6oXbs2\nvLy8HBEjEREROZCtVa+tSWDzLFS0ruhdEZV8KuNGxnWD7Ucf/Ieq/uEYvH0Axrf8wGRFalMkjxJl\nAJgWMwUVvCsiW1E4LNzU/OV8VR4AWOxRdhYW8yIicj829Sjrk0gkqFu3Llq2bIkGDRowSSYiIirD\nbOpRfjT0WiwyfxmRq8w2u+/EG+fxfG3jOckvbeqB6JXNcCn1IkbuHmry2BxlDq6kXgYAzDrxI8bt\nHYmYhKMGbT45+KFBdW2Z2AvX0q4idF4Qtt/cCkC/R9n8HGVHYzEvIiL3ZXOP8vXr17F582bcv38f\nCoXCZAEKkUiE2bPtX+yCiIiI7M/WHs2C9pZ6obMU5gt9+Ur9ULfiY7rnr9Ttgz+vbTRqN/Hf94y2\nDd42AAfvH0C3Gt2x6/ZOs69xK7OwivbF1PP47vg3AIA3tw80aGeq6jUREZFNiXJMTAyGDh0KpVJp\nsUIji1MQERGVHbYOvS6w89Z2s/syFeYLX0nEEoMllir5ml5u6WHeQ6NtB+8fAACLSbK+LlFPYf/d\nvTgWf9Tkfi+xC3uUeb1EROS2bEqUZ82aBZVKhffeew+dO3dGQEAAf8kTERGVcTYX87JiOaOiibK/\nLAA5esOxG1ZupHssV8mtfm1LGlZqhIupFxDiG4rkvCQAQO/H+mH/3b2IzzG9FnOAV4BdXpuIiMoX\nmxLlc+fO4fnnn8eIESMcFQ8RERE5nTZR7hr1NPbd3VNsa42gLrZNdpGh1zWCauLX7r/DR+Krex7q\nFwYfiQ+q+IYUe765J2dhRLNRZveH+0dga5/dgCAgJuEoBvzdB94Sb4T5VbV4XlfOUS7AdZSJiNyP\nTcW8vL29ERJS/B8zIiIiKjsKhl6vfnEjWoa2KrZ93qOK0eZcTr2kGyJdoF5wPdSp+BgiA6vptsW+\nfhaHBh7HoEaD4SXRFgWtXaEORjQ1Toi/PPIZ2q0yjC3cPwIAEORVAaffvIQAWQACvALRMbILvu/8\nM44NOoVmoc2NztXnsf66x3K1oph3S0REnsimRLlDhw44dOgQ1Ori7yQTERFR2SIWiVEtsLrBtr39\nDxu1Ky5R/vrI5wbPa1eogx+6GBf59JH6wEfqgxpBNfHwo4f4qv23+OuVnejf4DWT59Uv0AUA/eoN\nwPLn1+LggGMG22USGQY/PgQRAZEGc6ELNA9tgU7VugIAgryCLL4XZ+DyUERE7semodcTJ07Ea6+9\nhvfeew9vvfUWatWqZXZZqIAAzvkhIiIqC/TnKPvJ/Az2hZgYFq2fKGcrsxEgM/ybH+xTyeD5qObj\nEOAVaDGGQO9AvNtsDABt8toqrDXiEmMtHvN4lcboXvM5i20AYGrH73En8zZ6P9YXu+/8gyGNh2Fg\ng9ex+OxCDGs2stjjHYXLQxERuS+bEuXXXnsNubm52LVrF3bv3m22nUgkwoULF0odHBERETmeftXr\niIBIg33+Jopd5eslyt3Wd8KR104AAI7FH0Vln8rwl/kbnt/GHlMfqQ+299mLNZdWYtxe84nsY8H1\nrTrfO02G6x43C20BAPCSeOH91hNtistROEeZiMj92JQoR0REOCoOIiIichH9HuUxLd5DYk4CDt8/\niBmdf4K/1N+offPQlohJ0C63dD39mm57zz+fNWg366n5+OXMfHSM7FSiuAY0GKRLlL/pMB3zT81B\noFcQknITkJKfgjoV65bovO6CK4cQEbkvmxLl5cuXOyoOIiIichFtf6Y2aQuQBeDHrnMstg/1CzPa\nplQrjbb1fqwfBjQYVKrYNvXaBpWgQsfIzhj8+NsQQYQMeQbS5WnwlfqW6txERETm2JQoExERUflk\nbe9mQk48VBrDpDhXmWu0DYCuknVptIvsoHvs/WgppxC/EIT4lZ9VOFjMi4jI/VhMlKdOnYqOHTui\nQ4cOuufWEIlEmDRpUumjIyIiIoezZY5s09+N5wXvv7sXrcJa2zMkj8BiXkRE7stiovz7778jMDBQ\nlyj//vvvVp2UiTIREVHZoT9HuSQyFRnIUeXYMSLPUh57lHOUOVhzaSX61XsVQd4VXB0OEZHNLCbK\ny5YtQ2RkpMFzIiIiKl/0q16bUj2wBu5k3Ta7f9zekfiy3bcG2z6J/j+7xVdelediXtNjvsGC03MQ\nl3gc855Z5OpwiIhsZjFRjo6OtviciIiIyr7iepT39j+EkbuHYtftnWbbTP7vU93jGkE1MaH1R3aN\nkcqWG4+qoV9Ju+ziSIiISkbs6gCIiIjItYrrUQ7yroDYhBirz5elyLRHWB6D6ygTEbkfm3qUrSUS\niXDs2LESHUtERETu55MnP8fEfydY1fb1hm85NphygsW8iIjcl8VEOSAgwFlxEBERkcsUX8zrqerP\nWHWml+q8gk+e5PxkW7hDMa+tN7agddVohJlYI5uIyBNZTJT37t1b6hfIzs5GZmYmIiIiSn0uIiIi\nsj/tHGXL/GT+Vp2raUgzSMSS0gflAdylmNfxhGMYsmMQqgVE4cTg864Oh4jILTh8jvLSpUvx9NNP\nO/pliIiIqISKm6MMAL5SX7P7lvRYic/afInHKzfBi7Vfsnd45GDx2Q8AAPey77o4EiIi92GxR5mI\niIjKP2vWUTaXKDeq3Bgv1O4JABjX0ro5zGSIxbyIiNwPq14TERFRsT3KYpEYC7r9irYR7Q22C4LG\nkWGVayzmRUTkvpgoExEReTi1oIbIikuC3o/1Q5dqT+med67WFXOfWeTI0DyCOxTzIiIiQxx6TURE\n5ME0ggYqjQreEm+r2qsFte7x+pc2Oyosj+AuPcpM1ImIjLFHmYiIyIMpNUoAgEwis6p9wRBtP6mf\nw2IiIiJyNSbKREREHkypVgAAvMReVrV/p/FwdIl6Cn/0+tuRYXkUV/foOqJn29XviYiotDj0moiI\nyIMpNNpEWSaxLlGu6BOMdT03OTIkj+Eu6ygzqSUiMsYeZSIiIg+m0PUoWzf0muyvPC4P5S7zr4mI\nSoqJMhERkQe5n3UPJxPjdM8LEmVre5TJfphMEhG5LybKREREHqTF8kbovrErlGptES+lxrY5ykSu\nIlfLsericmTI010dChF5AJsS5U2bNuHSpUsW28TFxWHu3Lm659HR0Rg9enTJoiMiIiKHSJWnAgAU\natuqXpMjlL+h144w/9RsvLdvNCbsG+vqUIjIA9iUKE+aNAl79uyx2GbXrl345ZdfdM+jo6MxZsyY\nkkVHREREDpGS9xCAXo+ylesok/24SzGvsuJa+lUAwJnkUy6OhIg8gcWq13/88Qf27t1rsG3r1q24\nePGiyfZKpRLHjh1DxYoV7RchERER2d3DvGRMi5mC3bf/AcCh165UHot5ERGVdRYT5Y4dO2LKlCnI\nzc0FoL3zeePGDdy4ccPsMV5eXhg3bpx9oyQiIiK72nztTyy/sET3nEOvXYE9ykRE7spiohwSEoLd\nu3cjLy8PgiDgmWeewZtvvonBgwcbtRWJRJBKpQgODoZMxj+2RERE7kw/SQbYo0xERKTPYqIMAJUq\nVdI9njp1Kho2bIjIyEiHBkVERET2IwgCTiefxI5b28y24fJQriOwmBcRkdspNlHW98orrwDQ/sGN\njY3FpUuXkJeXh+DgYNStWxctWrRwSJBERERUcvvv7sWrf79isY2XmKPBnK08r6PM5J+IyjqbEmUA\nOHPmDCZOnIjbt28DKCxAIRKJUKNGDcyYMQNNmjSxb5RERERkk7jE49h87U/8r81kxCQcNdkm0CsI\nWYpMAICUibLLsJgXEZH7sSlRvnXrFt5++23k5OTg2WefRatWrRAaGorMzEzExMRgx44dGDp0KDZs\n2ICoqCibg/n888+hVqvxzTff6LYdOnQIM2bMwM2bN1GjRg18+OGH6Ny5s25/SkoKvvrqKxw+fBgy\nmQy9e/fGhAkTIJUWvrWlS5fi999/R2pqKlq2bInJkyejZs2aNsdHRERUVozfOwpX0i6jondFyNVy\nk20CZAG6RJlr+TqfuywP5YhE3RG95byhQETOZNM6ynPmzEFeXh4WLlyIn3/+GYMHD0aPHj3QdwaX\nowAAIABJREFUv39/fP/995g3bx6ysrKwcOFCm4IQBAE///wz1q5da7D92rVrGDlyJHr06IE///wT\nTz/9NEaPHo2rV6/q2owdOxYPHz7EihUrMG3aNPzxxx+YPXu2bv/69esxa9YsfPzxx1i3bh28vb0x\ndOhQKBQKm2IkIiIqSzLkGQCAo/H/YfvNvwEAs56aj4SR6ehXbwAAQCwqvAzIV5lOpomIiDyRTYny\nkSNH0LVrV3Tq1Mnk/k6dOuGpp57CoUOHrD7n3bt3MXjwYKxevRoREREG+5YtW4bmzZtj5MiRqFOn\nDt577z20aNECy5YtAwCcPHkScXFxmDZtGho0aIDOnTtj4sSJWL58uS4RXrx4MYYMGYIePXqgfv36\nmDlzJlJSUrBz505b3joReaDVF1dg3eXVrg6DyCaCIGDZ+SVIzE0AoJ2ffD39GjpV64oBDQYZJMeR\nAdUwpPFQAEDDyg1dEi+5fj6vu/RsExG5E5sS5YyMjGKHVEdFRSE1NdXqc544cQLh4eHYsmULqlWr\nZrAvNjYW0dHRBtuefPJJxMbG6vZHRkYaxBQdHY2cnBxcvHgRKSkpuHXrlsE5/P390bhxY905iIjM\nGb9vFMbsGeHqMIgsEgQB97LuAgC239yKsPkV8OGB8UbtwvzCdI/Ht/wAdSrWxbvNxuDbDjNwbNAp\nPFW9m9NiJi13KebFIc1ERMZsmqMcHh6OkydPWmxz8uRJhIaGWn3OXr16oVevXib3JSQkICwszGBb\naGgoEhK0d8kTExONXqvgeXx8vG6esqVzEBERlWULz8zF54c/xZoX/8CsEzMN9tUMqoVbmTcBAFV8\nQ3Tb61WqjyOvndA9r1WhtnOCJZNc3aNcVrDnm4icyaZEuVu3bliyZAlmz56NsWPHGuxTKpWYPXs2\nTp8+jSFDhtgluPz8fHh5Ga7r6OXlBblcO48qLy8P3t7eBvtlMhlEIhHkcjny8vIAwKiN/jksCQ72\ng1QqKc1bIAcKCQl0dQjkRK78vvmz5hr83M1TqBWI/CESrzV+DUtOLQEADPi7N8IDwg3aNQ1vokuU\na4VEuf1n6u7x2VvFFD8AQIC/j0vfe1Cir+6xveLw8tZeYkqlYovntOX1vB+dUywRedzPSnnC787z\nlNXv3KZEedSoUdi7dy/mzZuHTZs2oVWrVggMDERiYiLOnj2LxMRE1KpVCyNHjrRLcN7e3lAqlQbb\nFAoFfH21v9B9fHyMinIplUoIggA/Pz/4+PjojjF3DkvS0nJLEz45UEhIIJKTs1wdBjmJq79v/qw5\nn6u/c1c6k3wKm679gc/afIH47Af47dwivN7oTfhKfVHJpzK8JF64m3UHD3MfYlbMLINj47PjDZ5X\nlhWOuvJS+7v1Z+qJ33lGpvaGfnZOvkvfe+ajOAD7/b5TyFUAAJVKY/actn7n+fnaa0KNWvC4n5Xy\nwhP/nXu6svCdm0vkbZqjHBAQgDVr1uCVV15BSkoK/vrrL6xcuRK7d+9Geno6evfujVWrViEw0D53\nDcLDw5GUlGSwLSkpSTeUumrVqkhOTjbaD2iHW4eHa++sm2pTdDg2EZE+ztmj5ReWYuyed53+s/DM\n+k6Yc/InHL5/EK9t7YfZJ3/Ekyubo+nv9VFncST+vv4XZp/40ezx77eeiEo+lXTP+9Z7FQDQpEpT\nh8dOJVMef91wODkRlXU2JcoAULFiRXz77bc4fvw4/vrrL6xatQqbN2/G8ePH8e233yI4ONhuwbVq\n1QrHjx832Hbs2DG0bt1at//u3buIj4832O/v748GDRqgcuXKqFmzJmJiYnT7c3JycO7cOTzxxBN2\ni5OIyh+lRll8IyrXPtg/Dmsvr0KeKq/4xqVw4O4+tF7eBIfvH8TR+CO67fmqPFxMPW/QVq6W4+2d\nr2Pp+V8Nto9oNrow7lYf6x6LIML0TjNxeGAsmoQ0c9A7oJJyl2Je5N4ScxLQ56+XcCb5lKtDIfIo\nNiXKgwcPxqZNmwBo5wLXq1cPLVu2RP369XVziZcvX44ePXrYJbjXX38dsbGxmDVrFq5fv46ff/4Z\np0+fxptvvgkAaNGiBZo3b44JEybg/PnzOHDgAGbMmIEhQ4bo4nnrrbewaNEibN26FVeuXMEHH3yA\n0NBQdOvG6p5EZN7u2//oHrN32bPZu4DQvay7GLLjdSTlakdA9dvSC3eybuOVzS/gpT+769oN2tbf\nqvNVC4jCV+2+xTcdpmP+M4shk8gM9gd6BeGx4Hr2ewNkd+Wx95U3AexnZux0HLy3H4O3DXR1KEQe\nxeIc5fz8fKhU2jkmgiAgJiYGLVq0QHZ2tsn2CoUChw8fxoMHD+wSXP369TFnzhzMmDEDixYtQu3a\ntbFgwQLUqVMHgPbiZc6cOfjiiy8waNAg+Pv7o1+/fhg9uvDO+sCBA5GZmYmpU6ciJycHLVu2xOLF\ni42KhBER6Xtrx2u6x0qNEl4S/s7wVPa+UTJu70gcuv8vpCIpvu4wtdTnG9PyPYhEIgxrap/6IOQ8\nrOJcQh72uakFDQBAJahcHAmRZ7GYKG/cuBFTpkwx2PbLL7/gl19+sXjSZs1KNrxr+fLlRtu6dOmC\nLl26mD0mJCQEc+fOtXjeESNGYMQIroVKRNZRqIsUCWSi7NHs3duXLk8HAFxOu4gxe9612PaPXn/D\nX+qPaTFTMLzpSMQlxuL72Gl4veGb+LrDNGTI0xHuH2HX+IhKS61R45/bO1wdBhFRqVhMlAcOHIjj\nx48jJSUFABAbG4vw8HBERkYatRWJRJDJZAgNDbVb1WsiIlfIVeYYPFeqFdBIfSEW2VzWgcoB4VFv\njj2k56chS5EJALiUehGXUi9abN8hshMAYG3PPwEAT9d4FhOjP9Xt95f52y02ch1XT++w982gLdc3\n2fV8VP6G5hOVBRYTZbFYjJ9++kn3vEGDBujduzfGjBnj8MCIiFwlV2W4NNxzfzyN6+nXcHhgLOd6\neiCNnRLlf25tx+vbXjW577UGb+Bu9l0cvLffLq9FZUN5HUCclJvo2Bfw0LoRnPdN5Fw2dY9cunSJ\nSTIRlXu5SsNE+Xr6NQDsJfFU9uptW3Jusdl9Pz01Fy1DW9nldajscXUxL3snYJx77Riu/jkh8jQW\ne5SLevjwIU6cOIHk5GRkZ2fDz88PUVFRaNq0KSpVqlT8CYiIyoC8Ij3KBWScp+yR7NWjbCoZaRbS\nAt1qaCtdT2j1EUQQ4Z0mw/HVkc8xsOHrdnldcl/u0kNo7wTM4UPJPS4R97T3S+QerEqUT5w4gR9/\n/BGxsbEm94vFYrRr1w7jx49H48aN7RogEZGzmUuMZGKb7i1SOaGx00W/fi/bxpe2IEuRhedrv6jb\n5ifzw6dtPgcAzH3GctFMKl/Kak9hnioPvlJfV4fhAcrmzwdRWVfsVd/69evx5ZdfQqVSISIiAi1b\ntkRYWBi8vLyQk5OD+/fv49SpUzh48CCOHDmCL7/8En369HFG7EREDqEW1Ca3y8Qyk9upfLNXj3LB\nusnvNBmOjtU62+WcRK6SkBOPpr/XxxuN3sLMLrNcHY5HcJcRCESewmKifObMGXzxxRcICAjAF198\ngeeee85kO7VajR07dmDKlCmYPHkyHn/8cTRo0MAhARMROZr5HmUvpOWnYsbxqXi78XDUDX7MyZGR\nKyw+Ox+fPPl5qc8Tn/MANYNqYWrH7+0QFZUHZXku76mkkwCA5ReW2pQoK9VKtF7RBP3qDcDPL810\nVHhERKVmsZjX8uXLIRKJ8Ouvv5pNkgFAIpHghRdewJIlSyAIAlasWGH3QImInMXcUFuZWIavjnyO\nxWcXYs7Jn0y2ofLnx7jSJ7YKtQLJuUkID+Cax2TM1ctDlYSlJN/SvvicB4jPeYBZJ39wRFjlWlkd\nok9UVllMlE+cOIH27dtbPe+4QYMGaNOmDY4fP26X4IiIXEED0z3KErEEiTkJAIDzKeecGRKVcX9c\nXQ8BAsL8wlwdCrmRsjyUVuyC2JkoEpEzWUyUU1JSULt2bZtOWK9ePSQmOnj9PCIiB9JoTM9RBgCx\nSPtrkxdsZIsdN7cBAKKrtnFxJOSeyt7vk5IOGy/Lw81drSzfWCEqiywmynK5HP7+/jad0M/PD3K5\nvFRBERG5krkeZUEQdImyvQo8UdmgUCtKdfyFlHOo5FMJ7zQZYaeIqDwoy0mjK5I2JopE5EwWE+WS\nzJkpy7/0iYgA80mwAEG3ficTZc9ibm1ta2y/uRW3Mm+iUeXG/BtJ5YZIZP4SkgktEZUHXBSUiKgI\ntaWh14/uLwpMlD1KrjIXFbwr2nRMUm4SXtvaF2eSTwEAvCXejgiNyoEyWcyLybDTlMWfD6LyoNhE\nOSYmBnPmzLH6hMeOHStVQEREriaYGXqtETQceu2hStKjvOf2P7okGQAmRX9mz5CoXHD/ZFOtUeN6\n+jU8FlzPYESEK0ZHsDYEETmTVYlyTEyMTSfl0DIiKsvMJcFqQc1iXh4qV5Vn8zEX9Cqjv/X4O2gW\n2sKeIVE54s6/T7459iXmnPwJC7r9it6P9dNtZ4+y8/C6msg1LCbKU6dOdVYcRERuw2yirFHrLg7Z\no+xZcpW29yjfyLiuexzuz/WTyZi7JJuWEvU/rqwHAPx7d79Boiy2MEeZ7ItDr4lcw2Ki/Morrzgr\nDiIit6G22KOsvbB15x4gsj9bhl4LgoD72few6/ZO3bbqQTUcERaRyzBRdj72LBM5l83FvBQKBRIS\nEpCWloZKlSohLCwMXl5ejoiNiMglzPUWawQ17mXfs9iGyqdcGxLlzmvb4FLqRd3zWU/Nx8t1+zgi\nLLPYA1W2uPr7KknPNpeHIqLyzupE+d9//8Xq1atx6NAhqFQq3XaJRIIOHTpgwIAB6NKliyNiJCJy\nKnNJ8PfHpyFNnmaxDZVPucocq9odvHfAIEkGgAENBjkiJCoH3KWHsCQjZEoae2luCnj6SB5X31Ah\n8jTFjptRKpX4+OOPMWLECOzbtw8SiQS1atVC8+bNUb9+fchkMuzfvx8jR47ERx99BIVC4Yy4iYgc\nRiOYXh6qIEkGeMFSFq28sAx1FlfD9fSrNh+brcy2qt2E/WMNni96dqnNr2VP7pKIkWVlMQG01LvL\nnzsiKg+KTZS//vprbN68GbVr18bs2bNx7NgxbNu2DatXr8amTZsQGxuLX375BQ0bNsTff/+Nr776\nyhlxExE5jDW9xexRdj/ZiiwM2fG6bkkm/ZsZao0aE/aPQZYiE18f+aIE587G+strkKXIBKDtOW72\newP8dm6RQbs7mbd0j1uFtUavur1tfyPkMdx9KLGlG4IizlF2Ot6AIHIui7/lTpw4gXXr1qFdu3bY\ntGkTunXrBm9vb4M2EokEnTp1wrp169C5c2ds3LgRsbGxDg2aiMiRrEmC72XfRWJOghOiIWs8yL6P\ncXtHYeuNv/Dqllew+uIKPPZrdZx7eBaA9vsqUJKLzTWXVmD0nuGos7gaAGDqsa8Rn/MAk/79AFuu\nbwZg/HNTlZWuqQzbf3cvwuZXwIOc+8W2PXjvAFZfXGHVeZnsEVFZYTFRXrlyJXx9fTFz5kzIZDKL\nJ5JKpZg6dSoCAgKwbt06uwZJRORM1vYWf3TgPQdHQtZqvqwh/r6hTVhT8lMwft8oZCoysOPmVnzx\n32dYceF3XduHeck2n/9y2iXd4+03tyI2MUb3/J2db2Dasa9xL0ubjFfwroh+9QbgvZYflPTtkIdx\np6HXCrV2Ct1XRz432L7q0nKD5/ox9/mrJ8bvG+X44IiInMhiMa9z586hS5cuCA4OtupkwcHB6NSp\nE06dOmWX4IiIXEFtZo5yUXey7jg4Eirq33v7kavMRY9az1vV/rvj3xpty5Rn4P19Y3E76zbWvfgn\nJGKJTTG8uX2g0bYf4mbgh7gZAIBRzcZiQuuPbDoneSZ37F1dc2klBj8+BCqN0mI7wYXTT9zvU3Ms\nd7qRQq6h1qht/ltFpWexRzkhIQFRUVE2nbBatWpISkoqVVBERK5kbaGuTHmGgyMhfYvPLEDfv17C\n4O0DSnWe1PxUrLj4Ow7e24/+W17G8xufwbCdb0GhVmDCvjHYe2d3qc7/ZuO3S3U8eR53Kg6Ymp8C\noLBn2RzLMZtPZfWPu5V+y5bQiDzSw7yHCF8QjMmH/+fqUDyOxUTZz88P6enpNp0wPT3d6h5oIiJ3\nZO3Qa7la7uBISN+nhyZa1e6Vun0wpf00s/sTcwvnlh+8fwCxiTHYfP0PNFpSBysvLsOAv3ubTAI6\nVeuqe/xh60l4uno3ozYv1+2NSj6VrYqTyJXFvFLyUqBUm+81VhbXo1zCXk7942r9XAsnE+NKdB5P\n4u5F38ixTiZqaz/NPz3bxZF4HotDr+vVq4dDhw5Bo9FALC6+uqFarcbBgwdRu3ZtuwVIRORs1g69\ndsdhk55CEASzn//CZ5foHn92eJLBvvrBDQzmG+vLVBSOEJh/eo7u8Z5+B3El7TLaR3ZE+9VPoG+9\n/pgY/Sky5OnovqErbmRc17VtH9mpRO+HyJmyFVlouKQWmoY0x+5+/xrsK7hJVNyNQMuJsvl9RW9C\nnUiKQ4uwVpYDtvrsRET2YzH7ff755/HgwQMsWrTIUjOduXPnIj4+Hn379rVLcERErmBtjzLv8ruO\nQmN6WOgv3QqT5DoV6+oeD270NmY9NR/96hsP297V94DRti/+0w5x+6zNl2gS0gx96vVHVf9wXHvn\nLqZ3+gGAtmjX5HZTDI6rGVTL9jdDHs/Zc1BTHg2vNrWUWkEsKXkPLZ6jpMPFOd/WdvzMPBu/f9ex\n2KPct29frFixAj///DPy8vIwbNgw+Pv7G7XLzs7G7NmzsWzZMjRr1gzdu3d3WMBERI6mgZWJMnuU\nXSZPmQtvSeFyhRKRBGpBjZcf66PbFuRdQfd4RucfIRKJsOvWDt22j6P/h3rB9REZWFiLw0vsBZFI\npOtNe7F2T4PXLfqdP1frBezpdxBHHhzG3ru70SainX3eIHkEV/0OkYrMX/4VXJQXN7LG1A3FwpEe\nFuYo2+Gi31N/8/LmLJFzWUyUJRIJFi5ciDfffBMLFy7EsmXL0LJlS9SqVQsBAQHIz8/HrVu3EBMT\ng5ycHNSuXRvz5s2zapg2kSdYd3k1qgfVRJvwtq4OhWzgymquZJ6X2EvXk5ycl4yKPtp6GEcf/Ae1\noEb1oJoG7WtVqAMA6FvvVV1C0qrqEwjyqoBBDQfjg9YfAzC84FdoFGhcpSnOPTyDYO9g1NbrlTan\nSUgzNAlphuHNuDwOlYyzi3kVTdBNJeyhfmFIyk00ew5TCa8Aofhkzo0KlxGVBbxB4joWE2UAiIiI\nwJ9//omffvoJGzduxKFDh3Do0CGDNkFBQRg2bBjGjBkDb29vM2ci8iyCIGDMnhEAgKRRmS6Ohmyh\n1lg5R5l/vOwqMScB3hJvVPQJRqY8EzfSrxskqvoX8+1Xt8ZHT3yCY/FH8e+9fQCAO5m3DM5XxbcK\nbgy9Dx+pr25bJZ/KuDDkOmRimW6bWCRGo8qNcSHlHP735GQsPrsQAJAmT3PE2yTScdXvEP3e4pS8\nFMOh148eV/CqYDlRNpHwCoJQbHdv0QSbI3OsxyG4RM5VbKIMAAEBAfjss8/wwQcf4NSpU7hx4way\ns7MRFBSE6tWrIzo6GjKZrPgTEXmQ4iqGkvtKtHBxqC9XlYvXt/bH6Bbj0TaivYOjKt/UGjW6beiM\nhJx4PF+rJ44nHkVybjLmP7MYLcJaISqgOuRqOWRime7f1ozjUw3OERlQzei8AV6BRtu8JF5G2/a/\n+p9u2OiPcd/b6V0RuSf9m4F/XF1nVKk9JS8FV9OvWDyHqaRNI2gggeW1XtmhTGQb3iBxHasS5QK+\nvr5o27Yt2rblMFKi4jBRLpvUGjVmn/zRqrYZ8nT8c3sH/rm9g6MGSmn7za1IyIkHAGy7uUW3feTu\noQCAq+/cAQA8Vf0Z1AyqhYVn5hkcLxVLseqFDaWKoaBna8ULa/Hh/vFY/9LmUp2PyFrOvhDW6PUo\nF+0ZFiBg2D9vWnEW00OvAcu9xLzoLzmOYiJyLqsnE9+4cQNpaaaHoc2aNQuxsbF2C4qoPFCqTVfl\nJfeWrcxydQgeaeetbRb33340rNpfFoCvO0zD+60N11S+/PYtNKzcyC6xdIjshKODTiIqsLpdzkdk\njqsSH5Vej7JUIjNIXgVBQFzi8WLPYbKYl4OTYGfP5SZyB7xB4jrFJsoKhQITJkzAiy++iAMHjJfQ\nSE5Oxrx58/DGG29g9OjRyM7OdkigRGWNUqNydQhUArwQcx6lWokshbYnPjkvCQBwc1i8ybbPrNeu\nTxwg0w6lnhT9Ge6OSMZfL+/ApbdvItAryAkREzmGs3tZ9ecoK9Ryg997P8R9hzxVXrHnMDtHuQTH\nkWX8zIhcw2KirFarMXToUGzfvh1Vq1ZFcHCwURtfX198+OGHqF69Ovbs2YN3332X/6CJACjNrPNK\n7o3DAh3v9/O/YeqxrzB6zzDUWVwND/Me4mHeQ/hJ/eAv80ejyo3NHusvK1yi0FvijTYR7YzmV1Ih\n/j12b64qZKUSCm/k5qvyDfZZWkd+Zux0HLyn7TQxV/W6OKX5Hetuhb9ylDlIyk1ydRhUzvG6xHUs\nJspr1qxBTEwMXnrpJfzzzz/o3LmzUZuAgAAMHToUmzdvxtNPP424uDhs2FC6eWJE5YGCQ6/LJI2J\nxOLNx99xQSTl10cH3sOPcd9j07U/AABnkk/hTPIpVPEL1e5/4hOzx+onymSeuyUU5F40ekOv89X5\nVl+IT4/5Bn3+0q4tbuoIq3qUy9FFf4tlDdF4afFLyJUW/z0TuYbFRHnLli2IiIjAN998A6nUct0v\nHx8fTJ8+HcHBwdi0aZNdgyQqi1Qcel0iKo0K7+56G/vv7kVibiKWX1jq1F4x/Yu4jS9twYN3UzG6\n+Tinvb4nGvB3bwCF61e/ULsnkj5Mwvqem9EspIVBW39ZgNPjI3I4J/f86w+9zlfll+h3bEnnKBd9\nLWvnX15OvYQNV9ZaF5yTpMvTAXDkBjkW5yi7jsVE+erVq+jQoYPVSz8FBASgffv2uHz5sl2CIyrL\nFBx6bbULKefx3MancSP9Go7G/4c/rm5A/y0vI3pFU3ywfxyOPyi+sIy96F/8SUQSSMXSctUD4s6e\nr/Wi7nGIfwg6R3XFMzWeNWjDHmUqT9yhmNe8U7OQmp9q0/HrL6/BW9tfM9ruyKHXHddEl+g4Z7A0\nXN0emIgTuUaxc5QDA43XoLQkLCwMKhV70ohUXB7KauP3jkJc4nFM/u9/8BJ767YXFJRx5jB2/Ys4\nsUj7K7Kid0WnvX55p79+a1FftZ9qtK1okS4mylQeuXJ5KACY/N+nNh0/es9w0zG7sJiXUq20qgiZ\nIzg6US7AnkXPxJv1rmNxPHV4eDju3Llj0wnv3LmDsLCwUgVFVB5w6LX1Ci4yBEHA3jv/GO1Xqp14\n00H/Iu7RvLBgn0r4tsN3+PTQRDMHAS9veh5do57G+FYfODrCMmvjlXW6dZELvN96In6I/Q6/dV9h\nch5eoJfhzVoOvaZyxUVzT9WC+RtWpVFwQa+xcEPMURf9rVY0RkJOvEvWtNfAOYkykSNoBAFqtQC1\nRgOVWoBaI0Ct1kD16P/a5wJUGo223aNt+vtV6kf7Ch5rCs/TqVV1VPCRuPptlojFRPmJJ57A5s2b\nkZycjJCQkGJPlpycjP3796NLly72io+ozHLWHebyoODC6Z/bO/DP7R1G+53Zo6z/venfvW9aZK5s\nUf89OIT/HhxiomxB0SQZ0K5ZPCn6M7PHBMqKJsrsUbYGh2qSJY66kVvwc2fppqKjEuWEHNNLyzmD\ns/69sWfRPQmCoEsm9ZNNXcKo1ktAdfu1iaXusUF7wwT0aqoU9fNfhxhSrNp1xSgZLUhSdYmsQaJb\n+FoGiXDBY7VgsoipPT1IzcOIno0c+hqOYjFRHjBgANavX49x48Zh0aJFCAgwfyc/OzsbY8eOhVKp\nxIABA+weKFFZwz9oWicT4/DziR8w++n5Zte6Le4io8fKHjj++hnUCKrpgAiLxKL3vRkOw+aQN0do\nHtrS4n7jHmUmyrZgtdyywdn3NaztUe5XbwDWX1lj9Xmt+rtXDm/i8MZ46egSzaI9lwXJZNGeTV2S\nWKQH1CDp1E8Gi/SKFpzPRA+oqaRTZapXVT8mjaN/piV4DH0BALvj7ll3hFgEiUQEiVgMqUQEiVgE\nqUQMHy8xJBIxpCb2SyRi3f/190skIkgL/l+w7dH5Cl5H97jIdolYjNZNIpCXnV980G7IYqLcqFEj\nvPvuu5g/fz569OiBQYMGoX379qhVqxb8/f2RkZGBO3fu4NChQ1i5ciVSU1PRp08ftGvXzlnxE7kt\nR9+hKyte2tQDcrUcT15oi5HNx5hsY83F1YLTczC14/f2Ds+IwQWPYDxfmezj2KBTCPENQUAxQ6kD\njOYoc+g1lR+umnOqtqJHuVaF2vix6xxcTbuMU8knrTqvpZueOcoc+En9SnQTuaSVsp3FWUOvzb1v\nQRD0hs+aT+wMejiL9lwW7Y000QNa0ANp0CtakJTqndtgWG6RRFcjAEqV2ig2d1OQ9BkmiyJ4yySQ\neBdJEh/tL5p0mks2dfsNkk69dkWSzhNJMZhy7HNoRCrs6X+g8NwFye2j2CR6x7nTTdIAX1n5TJQB\nYNy4cZDJZJg3bx5mzZqFWbNmGbURBAEymQzDhg3DhAkTHBIoUVkj8A4zAECulpvdt+X6ZvwYNwNZ\niuLnlElFxf66sgtzF3ESkXXza9ZcWokBDQbZMySXUaqVkEmsW/XAVjWDaln1h5w9yuQJnD0CSW3F\n36fHKtaDl8QL2/vsRfiCYKvOa+59ZMjT8div1dGrTm+MaTHeYJ81SW9SXpJVr1MaGhM9lMUlnSGq\nFhALUpy8nAIvcY7JnkmDXlHdHE/DXlNz80ILktHszGfQRd4GshxvfDD3sNF+tdr9xrCDIew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m78cmUNz+Goa9ocpXZES3JekkPOXx4wUS6jHqbn4dDZeChUGoNlTAqSy5a5H0EMKY4e8sfV2JMO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\nruPHj6u6utq6vri4WDdv3rRrO3nyZA0dOlTZ2dkqKSmx23by5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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=100,\n", - " period=dt.timedelta(5),time_unit='d',plot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'OnlineSensorBased' object has no attribute 'slopes'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslopes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'OnlineSensorBased' object has no attribute 'slopes'" - ] - } - ], - "source": [ - "dataset.slopes" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[Timestamp('2013-01-03 00:05:00'), Timestamp('2013-01-09 00:05:00')],\n", - " [Timestamp('2013-01-07 00:05:00'), Timestamp('2013-01-13 00:05:00')]]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.drift_periods" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sign(-1) == np.sign(-10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "test.append([3,5])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ - "len(test)" + "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", + " period=dt.timedelta(5),time_unit='d',plot=True)" ] }, { @@ -685,9 +493,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "len(dataset.data['2013/1/1':'2013/1/17'])" @@ -759,7 +565,6 @@ "end_time": "2017-05-09T09:55:01.060520", "start_time": "2017-05-09T11:54:59.898063+02:00" }, - "collapsed": true, "scrolled": false }, "outputs": [], @@ -783,8 +588,7 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", "start_time": "2017-05-09T11:55:01.063627+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -1029,7 +833,19 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "Remove the drift from the data, using the scipy.signal.detrend() function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.remove_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", + " period=dt.timedelta(5),time_unit='d',plot=True,clear=True)" + ] }, { "cell_type": "markdown", @@ -1053,7 +869,6 @@ "end_time": "2017-05-09T09:55:07.830400", "start_time": "2017-05-09T11:55:07.433945+02:00" }, - "collapsed": true, "scrolled": false }, "outputs": [], @@ -1075,8 +890,7 @@ "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", "start_time": "2017-05-09T11:55:07.833046+02:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -1095,8 +909,12 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [] }, { @@ -1106,6 +924,22 @@ "collapsed": true }, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from scipy import signal\n", "data = dataset.data['CODtot_line3'][:].copy()\n", diff --git a/Showcase_OnlineSensorBased.ipynb b/Showcase_OnlineSensorBased.ipynb index 37d3ab318..955d223e8 100644 --- a/Showcase_OnlineSensorBased.ipynb +++ b/Showcase_OnlineSensorBased.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.404080", @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": true }, @@ -70,20 +70,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.2.0'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ww.__version__" ] @@ -97,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.587365", @@ -105,29 +94,7 @@ }, "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Time', 'TSS_line3', 'NO3_line3', 'CODtot_line3', 'CODsol_line3',\n", - " 'TSS_line2', 'NO3_line2', 'CODtot_line2', 'CODsol_line2', 'TSS_line1',\n", - " 'NO3_line1', 'CODtot_line1', 'CODsol_line1', 'Cond_ns', 'Turb_ns',\n", - " 'Temp_ns', 'Ammonium_ns', 'Cond_es', 'Turb_es', 'Temp_es', 'NH4_infl',\n", - " 'NH3_line3', 'Turb_rz', 'Cond_rz', 'Temp_rz', 'PO4_mixinggutter',\n", - " 'TSS_efflPST', 'NO3_efflPST', 'CODtot_efflPST', 'CODsol_efflPST',\n", - " 'TSS_efflRBT', 'NO3_efflRBT', 'CODtot_efflRBT', 'CODsol_efflRBT',\n", - " 'Cond_line1', 'Turb_line1', 'Cond_line2', 'Turb_line2', 'Cond_line3',\n", - " 'Turb_line3', 'NH4_efflPST', 'PO4_efflPST', 'PO4_sandtrap',\n", - " 'NH4_splittingworks', 'PO4_splittingworks', 'Flow_line1', 'Flow_line2',\n", - " 'Flow_line3', 'Flow_total'],\n", - " dtype='object')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "measurements = pd.read_csv('./data/data_example.txt',sep='\\t',skiprows=0)\n", "measurements.columns" @@ -142,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.669059", @@ -167,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.780731", @@ -190,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.788079", @@ -212,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.793662", @@ -235,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:55.812335", @@ -257,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.047638", @@ -272,25 +239,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:56.758532", "start_time": "2017-05-09T11:54:56.050129+02:00" - } + }, + "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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MBjZUKhWcnZ3N3pCTkxOqq6st0iiihuCF/t1HWa7EyK+HMJilRVNd35Ae7j2x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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(18,4))\n", "ax.plot(dataset.data['CODtot_line2'],'.g')\n", @@ -326,26 +283,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:54:57.347519", "start_time": "2017-05-09T11:54:56.761091+02:00" } }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_highs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Flow_total'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2013/1/1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'2013/1/31'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'percentile'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" - ] - } - ], + "outputs": [], "source": [ "dataset.get_highs('Flow_total',0.95,arange=['2013/1/1','2013/1/31'],method='percentile',plot=True)" ] @@ -509,55 +454,17 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "code_folding": [], "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Drift detected in period 2013-01-04 00:05:00 to 2013-01-09 00:05:00 /n\n" - ] - }, - { - "data": { - "image/png": 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E73//e/bs2UNWVlaLdQ0NDYwZM4b09PQW+27atIkNGzbw/vvvM3jwYEaOHMmdd97JggUL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\nrFR1VJS+fcJMEkmrc5KikbGLEnOTBhB3xrAQ2zmLj7PZz2aSuerQYhE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sNixYsACNjY24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\nMVEmIpvCr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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dataset.detect_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", " period=dt.timedelta(5),time_unit='d',plot=True)" ] }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[Timestamp('2013-01-04 00:05:00'), Timestamp('2013-01-09 00:05:00')]]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.drift_periods" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -585,21 +492,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2013/1/1'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'2013/1/17'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" - ] - } - ], + "outputs": [], "source": [ "len(dataset.data['2013/1/1':'2013/1/17'])" ] @@ -664,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.060520", @@ -672,19 +567,7 @@ }, "scrolled": false }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n\u001b[0m\u001b[1;32m 2\u001b[0m order=3, plot=True)\n", - "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" - ] - } - ], + "outputs": [], "source": [ "dataset.fill_missing_interpolation('CODtot_line2',12,[dt.datetime(2013,1,1),dt.datetime(2013,1,31)], method='polynomial',\n", " order=3, plot=True)" @@ -700,26 +583,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:01.103135", "start_time": "2017-05-09T11:55:01.063627+02:00" } }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dataset.calc_daily_profile('CODtot_line2',[dt.datetime(2013,1,1),dt.datetime(2013,1,8)],\n\u001b[0m\u001b[1;32m 2\u001b[0m quantile=0.9,clear=True)\n", - "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" - ] - } - ], + "outputs": [], "source": [ "dataset.calc_daily_profile('CODtot_line2',[dt.datetime(2013,1,1),dt.datetime(2013,1,8)],\n", " quantile=0.9,clear=True)" @@ -962,7 +833,19 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "Remove the drift from the data, using the scipy.signal.detrend() function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.remove_drift(data_name='CODtot_line3', arange=[dt.datetime(2013,1,1),dt.datetime(2013,1,14)], max_slope=90,\n", + " period=dt.timedelta(5),time_unit='d',plot=True,clear=True)" + ] }, { "cell_type": "markdown", @@ -980,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.830400", @@ -988,19 +871,7 @@ }, "scrolled": false }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_daily_average\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CODtot_line2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2013\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2013\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" - ] - } - ], + "outputs": [], "source": [ "dataset.calc_daily_average('CODtot_line2',arange=[dt.datetime(2013,1,1),dt.datetime(2013,2,1)],plot=True)" ] @@ -1014,26 +885,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-05-09T09:55:07.842239", "start_time": "2017-05-09T11:55:07.833046+02:00" } }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dataset.calc_total_proportional('Flow_total',\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Flow_line1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Flow_line2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Flow_line3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'TSS_line1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'TSS_line2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'TSS_line3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 'TSS_prop')\n", - "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" - ] - } - ], + "outputs": [], "source": [ "dataset.calc_total_proportional('Flow_total',\n", " ['Flow_line1','Flow_line2','Flow_line3'],\n", @@ -1050,27 +909,37 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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a/wwdata/Class_HydroData.py +++ b/wwdata/Class_HydroData.py @@ -1615,7 +1615,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, time_unit : None or str if None, it is assumed that the index value can be used as is for slope calculation. In the case of time indexes, - the time unit is needed for this. Allowed: 'd','hr','sec' + the time unit is needed for this. Allowed: 'd','hr','min','sec' clear : bool if True, the tags added before will be removed and put back to 'original'. @@ -1719,7 +1719,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, if drift: for driftperiod in drift_periods: - print('Drift detected in period {} to {} /n'.format(driftperiod[0],driftperiod[1])) + print('Drift detected in period {} to {}\n'.format(driftperiod[0],driftperiod[1])) self.meta_valid[data_name][driftperiod[0]:driftperiod[1]] = 'filtered' if plot: detrended_values = signal.detrend(data_series[driftperiod[0]:driftperiod[1]]) @@ -1733,7 +1733,7 @@ def detect_drift(self, data_name, arange, max_slope, period=None, def drift_analysis(self, data_name, arange1, arange2=None, plot=False): """ - This function analyse the data before and after a given slope. It + This function analyses the data before and after a given slope. It gives out useful information about the data that can be used to fix the drift. Parameters @@ -1753,140 +1753,66 @@ def drift_analysis(self, data_name, arange1, arange2=None, plot=False): """ pass - def remove_drift(self, data_name, arange, max_slope, period=None, plot=False, drift_type='A'):#need to tag replaced values. + def remove_drift(self, data_name, arange, max_slope, period=None, + time_unit=None,clear=False,plot=False): """ - This function calculates the slope of the data in a certain given - period by fitting a line through it and removes the drift. + This function removes the parts where drift is detected (cfr. self.detect_drift) + and replaces the data with de-trended data. Parameters ---------- data_name : str - name of the column containing the data to remove drift + name of the column containing the data to detect drift arange : 2-element array of ints the range in which to apply the function max_slope : int the maximum slope a signal is expected to have over a certain period period : int - the period, in days, which a certain slope is allowed + the minimum period in which trends are expected to be drift and not + part of the signal + time_unit : None or str + if None, it is assumed that the index value can be used + as is for slope calculation. In the case of time indexes, + the time unit is needed for this. Allowed: 'd','hr','min','sec' + clear : bool + if True, the tags added fo self.meta_filled before will be removed + and put back to 'original'. plot : bool - if true, a plot is made... + if true, a plot is made of the orginial data, detrended data and + slope drift_type : str separates the different type of drifts when the slope is negative. 'A' is drift with no continuity in the data. 'B' is drift which looks like a mountain(with continuity) + Returns - ---------- - the fixed dataset without drift - """ - from scipy import signal - org_dat = self.data[data_name][arange[0]:arange[1]].copy()#for plotting - - self.detect_drift(data_name=data_name, arange=arange, max_slope=max_slope, period=period, plot=False) - data_series = self.data[data_name][arange[0]:arange[1]].copy() - - if period is None or period is arange[1].day - arange[0].day + 1: - new_data = data_series - self.line_segment + self.line_segment[0] - self.data[data_name].update(new_data) - if plot is True: - fig = plt.figure(figsize=(16, 6)) - ax = fig.add_subplot(111) - ax.plot(data_series, 'm--', label='original data') - ax.plot(self.data[data_name], 'r--', label='new data') - ax.legend(loc='upper right', shadow=True) - - if period == 0.5: # Need a solution - print('Not yet possible with period = 0.5') - pass - - else: - """ - for n in range(len(self.list_value)-1): - if self.list_value[n][1] > self.list_value[n+1][0]: - ind = len(data_series[:self.list_value[n][1]]) - self.list_value[n+1][0] = data_series.index[ind] - """ - - for value in self.list_value: - detrend = signal.detrend(data_series[value[0]:value[1]]) - #df1 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]]) - 1:len(data_series[:value[1]])]) - line_segment1 = data_series[value[0]:value[1]] - detrend[:] - - #method shown in Showcase_OnlineSensorBased - if value[2] == 'n': - detrend = signal.detrend(data_series[value[0]:value[1]], type='constant') - df2 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]]) - 1:len(data_series[:value[1]])]) - - b = df2.iloc[-2][0] - a = line_segment1[0] - slope = (b - a) / len(df2) - f = [a] - s = df2 - s[:] = a - for val in range(len(df2) - 1): - a += slope - f.append(a) - - ds = pd.DataFrame(f, index=data_series.index[len(data_series[:value[0]]) - 1:len(data_series[:value[1]])]) - ds = ds[:] + s[:] - ds = ds / 2 - ds = ds.squeeze() # from dataframe to data_series - - # ax.plot(data_series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') - data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-line_segment1+ds - - elif value[2] == 'm': - if drift_type == 'A': - data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) - #if value[1].day - value[0].day == 1: - elif drift_type == 'B': - detrend = signal.detrend(data_series[value[0]:value[1]], type='constant') - df2 = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) - - b = df2.iloc[1][0] - a = line_segment1[-1] - slope = (a-b) / len(df2) - f = [a] - s = df2 - s[:] = b - for val in range(len(df2) - 1): - a += slope - f.append(a) - - ds = pd.DataFrame(f, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) - ds = ds[:] + s[:] - ds = ds / 2 - ds = ds.squeeze() # from dataframe to data_series - #ax.plot(data_series[value[0]:value[1]]-(line_segment-ds), 'm-', label='without drift') - data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - line_segment1 + ds - - # ax.plot(data_series[value[0]:value[1]]-(line_segment-line_segment[-1]), 'm-', label='without drift') - #data_series[value[0]:value[1]] = data_series[value[0]:value[1]] - (line_segment1 - line_segment1[-1]) - - """ - detrend = signal.detrend(data_series[value[0]:value[1]]) - df = pd.DataFrame(detrend, index=data_series.index[len(data_series[:value[0]])-1:len(data_series[:value[1]])]) - detrended_values.append(df) - line_segment = data_series[value[0]:value[1]] - detrend[:] - if line_segment[0] < line_segment[-1]: - data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-(line_segment-line_segment[0]) - else: - data_series[value[0]:value[1]] = data_series[value[0]:value[1]]-(line_segment-line_segment[-1]) - """ - self.data[data_name].update(data_series) - - if plot is True: - plt.figure(1, figsize=(16, 6)) - plt.subplot(211) - plt.plot(org_dat, 'k--', label='original data') - plt.subplot(212) - plt.plot(self.data[data_name][arange[0]:arange[1]], 'g--', label='new data') - plt.show() - - #ax.plot(data_series, ) - #ab = fig.add_subplot(212) - #ab.plot(self.data[data_name], ) - #ax.legend(loc='upper right', shadow=True) - #ab.legend(loc='upper right', shadow=True) + ------- + None; + creates/updates self.filled, containing the adjusted dataset and updates + meta_filled with the correct labels. + """ + ### + # CHECKS + ### + if type(self) == wwdata.Class_OnlineSensorBased.OnlineSensorBased: + self._filling_function_check(data_name,arange,clear=clear) + + ### + # CALCULATIONS & FILLING + ### + # Always run the detect_drift function, otherwise adjustment to the + # drift_periods isn't possible anymore from this function. + self.detect_drift(data_name, arange, max_slope, period=period, + clear=clear, plot=False, time_unit=time_unit) + + from scipy import signal + for driftperiod in self.drift_periods: + detrended_values = signal.detrend(self.data[data_name][driftperiod[0]:driftperiod[1]]) + self.meta_filled[data_name][driftperiod[0]:driftperiod[1]] = 'filled_detrending' + self.filled[data_name][driftperiod[0]:driftperiod[1]] = detrended_values + if plot: + self.plot_analysed(data_name) + self.detrended = detrended_values #============================================================================== # DAILY PROFILE CALCULATION @@ -2135,6 +2061,10 @@ def plot_analysed(self,data_name,time_range='default',only_checked = False): ax.plot(df.time[df.meta_filled[data_name]=='filled_profile_day_before'], df.filled[data_name][df.meta_filled[data_name]=='filled_profile_day_before'], '.',label='filled (previous day)') + if (df.meta_filled[data_name]=='filled_detrending').any(): + ax.plot(df.time[df.meta_filled[data_name]=='filled_detrending'], + df.filled[data_name][df.meta_filled[data_name]=='filled_detrending'], + '.',label='filled (detrending)') #if (df.meta_filled[data_name]=='filled_savitzky_golay').any(): # ax.plot(df.time[df.meta_filled[data_name]=='filled_savitzky_golay'], # df.filled[data_name][df.meta_filled[data_name]=='filled_savitzky_golay'], diff --git a/wwdata/Class_OnlineSensorBased.py b/wwdata/Class_OnlineSensorBased.py index 697c26944..e3f0c65d4 100644 --- a/wwdata/Class_OnlineSensorBased.py +++ b/wwdata/Class_OnlineSensorBased.py @@ -1126,15 +1126,18 @@ def _filling_function_check(self,to_fill,arange,clear): rain = (self.data_type == 'WWTP') and \ (self.highs['highs'].loc[arange[0]:arange[1]].sum() > 1) except TypeError: - raise TypeError("Slicing not possible for index type " + \ - str(type(self.data.index[0])) + " and arange argument type " + \ - str(type(arange[0])) + ". Try changing the type of the arange " + \ - "values to one compatible with " + str(type(self.data.index[0])) + \ - " slicing.") - + raise TypeError("Slicing not possible for index type " + + str(type(self.data.index[0])) + " and arange argument type " + + str(type(arange[0])) + ". Try changing the type of the arange " + "values to one compatible with " + str(type(self.data.index[0])) + + " slicing.") + except AttributeError: + raise AttributeError(str(type(self))+" ojbect has no attribute 'highs'. You need to " + "run the get_highs function to tag extreme values " + "and create this attribute.") if rain : - wn.warn('Data points obtained during a rain event will be replaced.' + \ - ' Make sure you are confident in this replacement method for the' + \ + wn.warn('Data points obtained during a rain event will be replaced.' + ' Make sure you are confident in this replacement method for the' ' filling of gaps in the data during rain events.') #============================================================================== diff --git a/wwdata/__pycache__/Class_HydroData.cpython-36.pyc b/wwdata/__pycache__/Class_HydroData.cpython-36.pyc index a9438e28e1fa22cabd1707acca090a3f25c3d6b5..42df4013d7efc640d3daa0abfe9ff916e40781f9 100644 GIT binary patch delta 2487 zcmZuyeQ;FO6@T~b+s%F?B%80zCMLWkAMBD3AP~R~8Z3}v4WTU{?JBP8p0~+vHoM{O z3rX;$s|H$2n-=C!+o_|Dj#DTdTI-^QT8lFE53MuQI>Su2EeSz_5Jd|OrAcS(xtpN$ zDbyF$~`B1Qz`K{0iPADW~gCZ#2 zEBWov1*PC3jsqf425v&RP!1I&%Y!IXLKSfqKsTu1A(Rh`pqgX_Py@B}?}QjE1}{kp zp$F=rflv{|p$V1}D%MJ(rNa`Yl)RDzG{Xwwl|e78gcd?>*oDt3?i7bR;7;0CLK0eG z9ib}FVLfahv=I7WBeW4xVK;mUHWBhbKimypCbS3!;2vlvR1Jf$89E5nz#jN2e2q{o zJPcc58=*QdU_0DPXfX`I4){7DFMJce0sg&gncoK~xDW0p<`Q@WcESUM>fuoc!b5}_ z;4uKu2sMIvtK=I_N6g}s3MWOux5x*^z;5-09J?O`4$5)q65i_G`1BuRzJkZR4C={O%M8XX(O|hTivX>7r zPx{T5ZHz6#)30o1)mS-N!D{gKQH|B&Go$XjI=3{;bVqcd;ef^X=4fM{*DZl*tz;A% zh2XF2-#`+WAu3rEZoifQcZv%ChG07-2q(C(k zQ^%GBe~}(L(QU$pYmW4m*Y+?rgKz)#7`uT3Cu=F6r%!q;cCJyrc;@6R_27@Mf2YKs z8;iuXKvL`M(c(IHGODHud*7&F8T{TGFS2rMJ+)Rw8OKk!t-*LOHkj0s>8DPelT#rp zxe||qmW|eWTFKXm=#n9dWKlV4>Xx}`NKwULhHO}XjhHBDDQeE{O@=}|*)W4?B!@|$ zfH`LA$r-XTX`q+1tki_?kj=10ZM3r+%qx~6N^ZvxaQu`x%7ddIvxfTa< zBbZJ0n?`u_SOa$a{;ss;)eW+Ev6#R4_M&iNE>4f82Ls_qET#dzu)nPMrd2ke2-n-E z<0G3Z@$_rmIQ`DYiV)x~dm}#Rx2Hl}3+h^OX`O8J9G#UmpTbpf>cocs zK#*cVh$ZxZmE)oh1O9b zH0kHk(!l57F2PBe=FapuEcPP*9=d~4Fvx*8h53XR#(Wv0n3Mf1g{SIlF|N;TLm zI}a>_DhmQ)DzGa|pXqG<*_ ze-nFgnL9G9pYHuV!pcTt2*ia=@^*Y>rkQ+(@yg6H`Lq?)+0w%8q~v2AbZ$EYa`86Y sG&`(vFYUN2Jc|7I0|GrOhzQ8HxapHl*za8MJg+!%u2*UJ}1Jc5?kf zLU^75vvnIyn{o@2*dNsJJisMbzXSF{-lg|?LNGa=XlqLsRHUw(ub z-NZCY@7(X6d(OS*o^$WH$1h%$j^B{1^BfNAdlkcL-`w*n>$sHYge-9GAgyNTf^2Xh zvOo^FcL=Rk2tgirkYfV{yx>D*hkPi&F$Z)*Arv7e6T;w!VnkU`0;M?Sgff_gZ8r2k z1ytgQ3nEYj)rfK+3bil?kz3AH@{$5e7#z91P!IEv=Y>9)4+{|afU*}1zC;Y}gC*E6 z1QnLTGDJnt4-Y~kqM5J>mO~RFKMcS_@Gzoc7=%aQ%ZN%~GkgVBAS#6+SP9LD%3uqu zhDQ<20u3I6HHgX~4v)jv5LLi8pas5;s1g$J1hnoTb*%x|3Tt5S3F(I%c6*4~a97(eqdsdKiYWerZvdASiXMcoTW}ol(u$T9rw|y)K z0cBaL_db*nn?%_majW1s$ zqpa?g=SgYm=qnaN%GjTNy^_pgg$D~rIlJeeOe)xq5BeOHJ|Rg|n=(_gL4X}SSmT)O z6F_PZG`Hr0D)#xoQp=vfUudpIVf|iKb?B*DKNLXm4@9s)_OM8V5wUN)MTnTy8BmfW zM9^HC&{hg=^jURq)WBB1;Y(dVv|3{Ojx9+&{k!`K`Ha2u`xnVgrX8MjPgx@kb*O%j zRk>Z2!5@nFdu1Amf`3cguSPkY6QMHSlm$sZq%(*h+$1&Z*x^snZ%1GI&fK&;9E!+o zF}brxj;Pd)0<;84+7?rTRBa1IKpsq6|0%BO`A@K~{s-TD(@d=s;6IAjE-xPg69Zo9Vb_!HG>?zPO1m z>k;DCDMOSD&8o-12pL>fH7gb<2J0}1+ccYE1zVED?V4S&X%?`912TstG>Z2pw}L4n z;hbjMF>hpmi-#F)%jp(J2(_yYaPvh*q)6|GG#EzJ%tq|3e=_dSGL$Sd(4qUo!MDKu zfs1DTJ6FpD6PWn8SBPh!PA(q2ylycuBGOmyJP~-nJ1oXCRi~1z;gpI?&B2|RCKjAn z3M8*E&7C%YkAV+$otjf|LkafKckyg3OK)9Tw&v3HoXG?!5aKzStCPgtnlqk@2Re7c z(gtC$TFu4Dok_x%NfNvi-gq8LhE&9PCeR(Z{rr&Rj-#Pt;OE3%XI9^ihd7+TYA+l*^ zD4M$C`pC@bkuOMG_}n`oPfrS{;7bZb023zi^59P-o+sJ$Ly$q`p6Dhy#w>r>W}3ik z-J93uPNeN>p1~owIaci-=nA!W`Gfv;nW{nL#=_BF*&mAegW+&=KnAu=T`bks*0P_z zb%hOCJ?z;wdyCp7`#VB|_!97EE+(t~fsoo2?Z+pAKNME~quML!zoIBDMUpnRMoWxA*#ENYm$rP>FGgzG@U-V0vq=+<#naFVx6EUDv z9>-z>@nUP1>Tt|T=A+JkloPA7@=%7$It@t5Q+wb3qrHL_a`!Gob1?eZ*sMt#@f^$a zwkmLEI!?x!<;0?FzPafW$BztKeInO}RRhL`lYg?l6Q!h)?LM)ToMZ2ukg@)`_hhA= z*7H#ZYSWKc^yCWSV6UEBPab0V@3)dp*8l#eq@MMiswH)7->Dihm%VdpE~(#jdO^WF z{x)|T=yBDIk4=ULd)tX>!9rm^TXp&oGMd_ZdbubW=oNPM%tALl9}iSvR59=S3;#R!fChRHf%Gsw96>eNb6{ni#gk>Hc@Qpga-EX`9vFU4 z>1H=~(we#w`gj`9r{o^7r z(l7C`mE$q;1UoWbBnlRGW}FpYvgkpv4)OiqP$?>7Xhz^Xf;_01r!L7W;}BnIP%aIR(2yWBwuCUpT5XohPIKPB0QO|(od5s; diff --git a/wwdata/__pycache__/Class_OnlineSensorBased.cpython-36.pyc b/wwdata/__pycache__/Class_OnlineSensorBased.cpython-36.pyc index e34ddb9c13bb9b63057bb4931d6fe29630f1f2d5..2cc03241f8e88e241d9cce54f2d177ec1b64b7c1 100644 GIT binary patch delta 6450 zcmb7IdvH|OdB5krcK2#udOvt*EkJlBfeCg&VU&_$j7<_N#&(M>DDt9nS!pG9#n~&2 z;3}K2;DE;@n1it=wXqZ9x_DfJyBSX=iSy{gZkjrclcue=lWEh;#I2LG&2*YhGyS8# z@7$GEQgCMqv!BlWzVrC*`F`j3ednJ1v0na)9=kObbG|fmZeR94H0{T-(QJLS$23c? 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