diff --git a/dfm_tools/__init__.py b/dfm_tools/__init__.py
index 537d104e..ab30950f 100644
--- a/dfm_tools/__init__.py
+++ b/dfm_tools/__init__.py
@@ -6,7 +6,7 @@
 __email__ = "jelmer.veenstra@deltares.nl"
 __version__ = "0.33.1"
 
-from dfm_tools.deprecated import *
+from dfm_tools.deprecated_functions import *
 from dfm_tools.download import *
 from dfm_tools.get_nc import *
 from dfm_tools.get_nc_helpers import *
diff --git a/dfm_tools/deprecated.py b/dfm_tools/deprecated_functions.py
similarity index 100%
rename from dfm_tools/deprecated.py
rename to dfm_tools/deprecated_functions.py
diff --git a/dfm_tools/download.py b/dfm_tools/download.py
index 2e39b9e6..81d3a925 100644
--- a/dfm_tools/download.py
+++ b/dfm_tools/download.py
@@ -3,12 +3,14 @@
 from pathlib import Path
 import xarray as xr
 from dfm_tools.errors import OutOfRangeError
+from dfm_tools.interpolate_grid2bnd import _ds_sel_time_outside
 import cdsapi
 import copernicusmarine
 from copernicusmarine.core_functions.credentials_utils import InvalidUsernameOrPassword
 import cftime
 import getpass
 from requests import HTTPError
+import logging
 
 __all__ = [
     "download_ERA5",
@@ -16,6 +18,8 @@
     "download_OPeNDAP",
 ]
 
+logger = logging.getLogger(__name__)
+
 COPERNICUSMARINE_OPTIMIZE_ARGS = dict(
     # speed up copernicusmarine.open_dataset() with the following arguments
     # this optimizes chunking for downloading with daily frequency
@@ -204,16 +208,34 @@ def download_CMEMS(varkey,
                    dataset_id=None,
                    dir_output='.', file_prefix='', overwrite=False):
     """
+    Download CMEMS data by providing varkey and optional dataset_id. If no
+    dataset_id is provided, it is automatically derived based on the varkey
+    and the requested period. For daily mean data, the dataset is automatically
+    shifted with 12 hours to convert start-of-interval to center-of-interval
+    timestamps.
+    
+    The requested spatial and time extents are automatically buffered to make
+    sure all requested values are included in the returned dataset. This
+    behaviour is more inclusive than the default xarray.Dataset.sel() method.
+    
+    The data is downloaded in files per day or month, depending on the freq
+    argument. When downloading monthly or yearly means make sure not to request
+    a higher freq, since that would result in many empty files.
+    
+    More info about copernicusmarine toolbox available at:
     https://help.marine.copernicus.eu/en/articles/8283072-copernicus-marine-toolbox-api-subset
+    
     """
     copernicusmarine_credentials()
     
     if dataset_id is None:
         dataset_id = copernicusmarine_get_dataset_id(varkey, date_min, date_max)
-    # date_range with same start as stoptime is a bit tricky so we limit freqs: https://github.com/Deltares/dfm_tools/issues/720
-    if freq not in ["D","M"]:
-        raise ValueError(f"freq should be 'D' or 'M', not {freq}")
-
+    # date_range with same start as stoptime is a bit tricky so we limit freqs
+    # https://github.com/Deltares/dfm_tools/issues/720
+    if freq not in ["D","M","Y"]:
+        raise ValueError(f"freq should be D/M/Y, not {freq}")
+    print(f"downloading {varkey} from {dataset_id} with freq={freq}")
+    
     dataset = copernicusmarine.open_dataset(
          dataset_id = dataset_id,
          variables = [varkey],
@@ -221,13 +243,15 @@ def download_CMEMS(varkey,
          maximum_longitude = longitude_max,
          minimum_latitude = latitude_min,
          maximum_latitude = latitude_max,
-         # temporarily convert back to strings because of https://github.com/mercator-ocean/copernicus-marine-toolbox/issues/261
-         # TODO: revert, see https://github.com/Deltares/dfm_tools/issues/1047
-         start_datetime = pd.Timestamp(date_min).isoformat(),
-         end_datetime = pd.Timestamp(date_max).isoformat(),
          **COPERNICUSMARINE_OPTIMIZE_ARGS,
     )
-
+    
+    # possibly shift times with 12 hours (to center-of-interval times)
+    dataset = copernicusmarine_dataset_timeshift(ds=dataset, dataset_id=dataset_id)
+    
+    # slice to outside times after opening dataset and correcting times
+    dataset = _ds_sel_time_outside(ds=dataset, tstart=date_min, tstop=date_max)
+    
     Path(dir_output).mkdir(parents=True, exist_ok=True)
     
     # get time extent from dataset, it can be different than requested
@@ -243,7 +267,7 @@ def download_CMEMS(varkey,
             print(f'"{name_output}" found and overwrite=False, continuing.')
             continue
         dataset_perperiod = dataset.sel(time=slice(date_str, date_str))
-        print(f'xarray writing netcdf file: {name_output}: ',end='')
+        print(f'writing netcdf file: {name_output}: ',end='')
         dtstart = pd.Timestamp.now()
         dataset_perperiod.to_netcdf(output_filename)
         print(f'{(pd.Timestamp.now()-dtstart).total_seconds():.2f} sec')
@@ -375,11 +399,31 @@ def copernicusmarine_credentials():
         raise InvalidUsernameOrPassword("Invalid credentials, please try again")
 
 
+def copernicusmarine_dataset_timeshift(ds, dataset_id):
+    """
+    correct daily means from start-of-interval to center-of-interval times.
+    Only the daily data is currently corrected with an offset of 12 hours.
+    This does not shift yearly, monthly, hourly, 3hourly or 6hourly data.
+    Also daily averaged datasets called *-d are not corrected.
+    https://help.marine.copernicus.eu/en/articles/6820094-how-is-defined-the-nomenclature-of-copernicus-marine-data
+    """
+    if "P1D-m" in dataset_id or dataset_id.endswith("rean-d"):
+        # TODO: remove rean-d https://github.com/Deltares/dfm_tools/issues/1090
+        # check if dataset times are indeed at midnight (start-of-interval)
+        assert (ds["time"].to_pandas().dt.hour == 0).all()
+        # add offset to correct from midnight to noon (center-of-interval)
+        print("corrected daily averaged dataset from midnight to noon by "
+              "adding a 12-hour offset.")
+        ds["time"] = ds["time"] + pd.Timedelta(hours=12)
+    return ds
+
+
 def copernicusmarine_dataset_timerange(dataset_id):
     ds = copernicusmarine.open_dataset(
         dataset_id=dataset_id,
         **COPERNICUSMARINE_OPTIMIZE_ARGS
         )
+    ds = copernicusmarine_dataset_timeshift(ds, dataset_id)
     ds_tstart = pd.Timestamp(ds.time.isel(time=0).values)
     ds_tstop = pd.Timestamp(ds.time.isel(time=-1).values)
     return ds_tstart, ds_tstop
diff --git a/dfm_tools/modelbuilder.py b/dfm_tools/modelbuilder.py
index d5d75391..686f3b14 100644
--- a/dfm_tools/modelbuilder.py
+++ b/dfm_tools/modelbuilder.py
@@ -52,10 +52,6 @@ def cmems_nc_to_bc(ext_new, list_quantities, tstart, tstop, file_pli, dir_patter
     for quantity in list_quantities: # loop over salinitybnd/uxuyadvectionvelocitybnd/etc
         print(f'processing quantity: {quantity}')
         
-        # times in cmems API are at midnight, so round to nearest outer midnight datetime
-        tstart = pd.Timestamp(tstart).floor('1d')
-        tstop = pd.Timestamp(tstop).ceil('1d')
-        
         quantity_list = get_quantity_list(quantity=quantity)
         
         for quantity_key in quantity_list: # loop over ux/uy
diff --git a/docs/notebooks/modelbuilder_example.ipynb b/docs/notebooks/modelbuilder_example.ipynb
index e2b560b0..6a646dd4 100644
--- a/docs/notebooks/modelbuilder_example.ipynb
+++ b/docs/notebooks/modelbuilder_example.ipynb
@@ -128,8 +128,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">> reading coastlines: 2.10 sec\n",
-      ">> reading coastlines: 1.16 sec\n"
+      ">> reading coastlines: 2.06 sec\n",
+      ">> reading coastlines: 1.95 sec\n"
      ]
     },
     {
@@ -175,7 +175,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">> reading coastlines: 1.22 sec\n"
+      ">> reading coastlines: 1.95 sec\n"
      ]
     },
     {
@@ -224,8 +224,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">> reading coastlines: 1.21 sec\n",
-      ">> reading coastlines: 1.84 sec\n"
+      ">> reading coastlines: 1.95 sec\n",
+      ">> reading coastlines: 1.94 sec\n"
      ]
     },
     {
@@ -263,7 +263,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">> reading coastlines: 1.78 sec\n"
+      ">> reading coastlines: 2.09 sec\n"
      ]
     },
     {
@@ -339,7 +339,7 @@
       "> interp mfdataset to all PolyFile points (lat/lon coordinates)\n",
       "> actual extraction of data from netcdf with .load() (for 71 plipoints at once, this might take a while)\n",
       ">>time passed: 0.00 sec\n",
-      "Converting 71 plipoints to hcdfm.ForcingModel(): 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.13 sec\n"
+      "Converting 71 plipoints to hcdfm.ForcingModel(): 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.19 sec\n"
      ]
     }
    ],
@@ -359,8 +359,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:39:33Z - Checking if credentials are valid.\n",
-      "INFO - 2025-01-20T14:39:34Z - Valid credentials from configuration file.\n"
+      "INFO - 2025-01-29T19:49:05Z - Checking if credentials are valid.\n",
+      "INFO - 2025-01-29T19:49:06Z - Valid credentials from configuration file.\n"
      ]
     },
     {
@@ -374,18 +374,45 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:39:34Z - Selected dataset version: \"202311\"\n",
-      "INFO - 2025-01-20T14:39:34Z - Selected dataset part: \"default\"\n",
-      "INFO - 2025-01-20T14:39:38Z - Selected dataset version: \"202311\"\n",
-      "INFO - 2025-01-20T14:39:38Z - Selected dataset part: \"default\"\n",
-      "INFO - 2025-01-20T14:39:42Z - Selected dataset version: \"202406\"\n",
-      "INFO - 2025-01-20T14:39:42Z - Selected dataset part: \"default\"\n"
+      "INFO - 2025-01-29T19:49:07Z - Selected dataset version: \"202311\"\n",
+      "INFO - 2025-01-29T19:49:07Z - Selected dataset part: \"default\"\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO - 2025-01-29T19:49:11Z - Selected dataset version: \"202311\"\n",
+      "INFO - 2025-01-29T19:49:11Z - Selected dataset part: \"default\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO - 2025-01-29T19:49:15Z - Selected dataset version: \"202406\"\n",
+      "INFO - 2025-01-29T19:49:15Z - Selected dataset part: \"default\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n",
       "The CMEMS 'reanalysis-interim' product will be used.\n"
      ]
     },
@@ -393,15 +420,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:39:47Z - Selected dataset version: \"202311\"\n",
-      "INFO - 2025-01-20T14:39:47Z - Selected dataset part: \"default\"\n",
-      "INFO - 2025-01-20T14:39:50Z - Checking if credentials are valid.\n"
+      "INFO - 2025-01-29T19:49:20Z - Selected dataset version: \"202311\"\n",
+      "INFO - 2025-01-29T19:49:20Z - Selected dataset part: \"default\"\n",
+      "INFO - 2025-01-29T19:49:23Z - Checking if credentials are valid.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n",
+      "\"cmems_so_2022-10-31.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_so_2022-11-01.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_so_2022-11-02.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_so_2022-11-03.nc\" found and overwrite=False, continuing.\n"
@@ -411,7 +440,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:39:51Z - Valid credentials from configuration file.\n"
+      "INFO - 2025-01-29T19:49:24Z - Valid credentials from configuration file.\n"
      ]
     },
     {
@@ -425,15 +454,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:39:52Z - Selected dataset version: \"202311\"\n",
-      "INFO - 2025-01-20T14:39:52Z - Selected dataset part: \"default\"\n",
-      "INFO - 2025-01-20T14:39:55Z - Checking if credentials are valid.\n"
+      "INFO - 2025-01-29T19:49:25Z - Selected dataset version: \"202311\"\n",
+      "INFO - 2025-01-29T19:49:25Z - Selected dataset part: \"default\"\n",
+      "INFO - 2025-01-29T19:49:28Z - Checking if credentials are valid.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n",
+      "\"cmems_thetao_2022-10-31.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_thetao_2022-11-01.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_thetao_2022-11-02.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_thetao_2022-11-03.nc\" found and overwrite=False, continuing.\n"
@@ -443,7 +474,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:39:56Z - Valid credentials from configuration file.\n"
+      "INFO - 2025-01-29T19:49:29Z - Valid credentials from configuration file.\n"
      ]
     },
     {
@@ -457,15 +488,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:39:57Z - Selected dataset version: \"202311\"\n",
-      "INFO - 2025-01-20T14:39:57Z - Selected dataset part: \"default\"\n",
-      "INFO - 2025-01-20T14:40:01Z - Checking if credentials are valid.\n"
+      "INFO - 2025-01-29T19:49:29Z - Selected dataset version: \"202311\"\n",
+      "INFO - 2025-01-29T19:49:29Z - Selected dataset part: \"default\"\n",
+      "INFO - 2025-01-29T19:49:32Z - Checking if credentials are valid.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n",
+      "\"cmems_uo_2022-10-31.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_uo_2022-11-01.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_uo_2022-11-02.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_uo_2022-11-03.nc\" found and overwrite=False, continuing.\n"
@@ -475,7 +508,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:40:02Z - Valid credentials from configuration file.\n"
+      "INFO - 2025-01-29T19:49:33Z - Valid credentials from configuration file.\n"
      ]
     },
     {
@@ -489,15 +522,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:40:02Z - Selected dataset version: \"202311\"\n",
-      "INFO - 2025-01-20T14:40:02Z - Selected dataset part: \"default\"\n",
-      "INFO - 2025-01-20T14:40:06Z - Checking if credentials are valid.\n"
+      "INFO - 2025-01-29T19:49:33Z - Selected dataset version: \"202311\"\n",
+      "INFO - 2025-01-29T19:49:33Z - Selected dataset part: \"default\"\n",
+      "INFO - 2025-01-29T19:49:36Z - Checking if credentials are valid.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n",
+      "\"cmems_vo_2022-10-31.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_vo_2022-11-01.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_vo_2022-11-02.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_vo_2022-11-03.nc\" found and overwrite=False, continuing.\n"
@@ -507,7 +542,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:40:07Z - Valid credentials from configuration file.\n"
+      "INFO - 2025-01-29T19:49:36Z - Valid credentials from configuration file.\n"
      ]
     },
     {
@@ -521,55 +556,57 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2025-01-20T14:40:07Z - Selected dataset version: \"202311\"\n",
-      "INFO - 2025-01-20T14:40:07Z - Selected dataset part: \"default\"\n"
+      "INFO - 2025-01-29T19:49:37Z - Selected dataset version: \"202311\"\n",
+      "INFO - 2025-01-29T19:49:37Z - Selected dataset part: \"default\"\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "daily averaged copernicusmarine dataset was corrected from midnight to noon by adding a 12-hour offset.\n",
+      "\"cmems_zos_2022-10-31.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_zos_2022-11-01.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_zos_2022-11-02.nc\" found and overwrite=False, continuing.\n",
       "\"cmems_zos_2022-11-03.nc\" found and overwrite=False, continuing.\n",
       "processing quantity: waterlevelbnd\n",
-      "loading mfdataset of 3 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_zos_*.nc\n",
+      "loading mfdataset of 4 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_zos_*.nc\n",
       "variable zos renamed to waterlevelbnd\n",
       "> interp mfdataset to all PolyFile points (lat/lon coordinates)\n",
       "> actual extraction of data from netcdf with .load() (for 71 plipoints at once, this might take a while)\n",
-      ">>time passed: 0.02 sec\n",
+      ">>time passed: 0.03 sec\n",
       "Converting 71 plipoints to hcdfm.ForcingModel(): 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.04 sec\n",
       "processing quantity: salinitybnd\n",
-      "loading mfdataset of 3 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_so_*.nc\n",
+      "loading mfdataset of 4 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_so_*.nc\n",
       "dimension depth renamed to z\n",
       "varname depth renamed to z\n",
       "variable so renamed to salinitybnd\n",
       "> interp mfdataset to all PolyFile points (lat/lon coordinates)\n",
       "> actual extraction of data from netcdf with .load() (for 71 plipoints at once, this might take a while)\n",
       ">>time passed: 0.02 sec\n",
-      " 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.18 sec\n",
+      "Converting 71 plipoints to hcdfm.ForcingModel(): 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.13 sec\n",
       "processing quantity: temperaturebnd\n",
-      "loading mfdataset of 3 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_thetao_*.nc\n",
+      "loading mfdataset of 4 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_thetao_*.nc\n",
       "dimension depth renamed to z\n",
       "varname depth renamed to z\n",
       "variable thetao renamed to temperaturebnd\n",
       "> interp mfdataset to all PolyFile points (lat/lon coordinates)\n",
       "> actual extraction of data from netcdf with .load() (for 71 plipoints at once, this might take a while)\n",
       ">>time passed: 0.04 sec\n",
-      "Converting 71 plipoints to hcdfm.ForcingModel(): 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.20 sec\n",
+      "Converting 71 plipoints to hcdfm.ForcingModel(): 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.13 sec\n",
       "processing quantity: uxuyadvectionvelocitybnd\n",
-      "loading mfdataset of 3 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_uo_*.nc\n",
+      "loading mfdataset of 4 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_uo_*.nc\n",
       "dimension depth renamed to z\n",
       "varname depth renamed to z\n",
       "variable uo renamed to ux\n",
-      "loading mfdataset of 3 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_vo_*.nc\n",
+      "loading mfdataset of 4 files with pattern(s) c:\\DATA\\checkouts\\dfm_tools\\docs\\notebooks\\Vietnam_2D_model\\data\\cmems\\cmems_vo_*.nc\n",
       "dimension depth renamed to z\n",
       "varname depth renamed to z\n",
       "variable vo renamed to uy\n",
       "> interp mfdataset to all PolyFile points (lat/lon coordinates)\n",
       "> actual extraction of data from netcdf with .load() (for 71 plipoints at once, this might take a while)\n",
       ">>time passed: 0.06 sec\n",
-      " 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.41 sec\n"
+      "Converting 71 plipoints to hcdfm.ForcingModel(): 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71. >> done in 0.23 sec\n"
      ]
     }
    ],
@@ -614,13 +651,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">> reading coastlines: 1.19 sec\n",
-      "1.13 secng coastlines: \n"
+      ">> reading coastlines: 1.03 sec\n",
+      "1.20 secng coastlines: \n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x500 with 4 Axes>"
       ]
@@ -662,7 +699,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 6 Axes>"
       ]
@@ -716,21 +753,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-01-20 14:40:18,571 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:18,572 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:18,575 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:18,578 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:18,750 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:18,753 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:18,754 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:18,757 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:18,979 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:18,981 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:18,983 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:18,985 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
+      "2025-01-29 19:49:46,393 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
+      "2025-01-29 19:49:51,527 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
+      "2025-01-29 19:49:51,741 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
+      "2025-01-29 19:49:51,743 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
      ]
     },
     {
@@ -746,21 +772,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-01-20 14:40:19,155 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:19,157 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:19,158 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:19,158 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:19,304 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:19,307 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:19,309 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:19,311 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:19,523 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:19,525 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:19,525 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:19,526 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
+      "2025-01-29 19:49:51,900 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
+      "2025-01-29 19:49:52,080 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
+      "2025-01-29 19:49:52,280 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
+      "2025-01-29 19:49:52,282 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
      ]
     },
     {
@@ -776,28 +791,30 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-01-20 14:40:19,688 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:19,689 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:19,692 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:19,693 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:19,856 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:19,859 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:19,860 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:19,862 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:20,063 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:20,065 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:20,066 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:20,068 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
+      "2025-01-29 19:49:52,446 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
+      "2025-01-29 19:49:52,588 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "found ECMWF API-key and authorization successful\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-29 19:49:57,762 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
+      "2025-01-29 19:49:57,764 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
+      "2025-01-29 19:49:57,891 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found ECMWF API-key and authorization successful\n",
       "retrieving data from 2022-11 to 2022-11 (freq=<MonthEnd>)\n",
       "\"era5_v10n_2022-11.nc\" found and overwrite=False, continuing.\n"
      ]
@@ -806,32 +823,32 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-01-20 14:40:20,210 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:20,212 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:20,215 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:20,217 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:20,390 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:20,392 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:20,395 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:20,397 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n",
-      "2025-01-20 14:40:20,595 INFO [2024-09-28T00:00:00] **Welcome to the New Climate Data Store (CDS)!** This new system is in its early days of full operations and still undergoing enhancements and fine tuning. Some disruptions are to be expected. Your \n",
-      "[feedback](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202) is key to improve the user experience on the new CDS for the benefit of everyone. Thank you.\n",
-      "2025-01-20 14:40:20,597 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
-      "2025-01-20 14:40:20,599 INFO [2024-09-16T00:00:00] Remember that you need to have an ECMWF account to use the new CDS. **Your old CDS credentials will not work in new CDS!**\n",
-      "2025-01-20 14:40:20,601 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
+      "2025-01-29 19:49:58,027 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "found ECMWF API-key and authorization successful\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-29 19:50:03,215 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n",
+      "2025-01-29 19:50:03,217 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found ECMWF API-key and authorization successful\n",
       "retrieving data from 2022-11 to 2022-11 (freq=<MonthEnd>)\n",
       "\"era5_chnk_2022-11.nc\" found and overwrite=False, continuing.\n",
-      "0.15 secng multifile dataset of 4 files (can take a while with lots of files): \n",
-      ">> writing file (can take a while): 0.06 sec\n"
+      ">> opening multifile dataset of 4 files (can take a while with lots of files): 0.15 sec\n",
+      "0.07 secng file (can take a while): \n"
      ]
     }
    ],
@@ -886,8 +903,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">> reading coastlines: 2.03 sec\n",
-      "1.94 secng coastlines: \n"
+      ">> reading coastlines: 1.97 sec\n",
+      "1.93 secng coastlines: \n"
      ]
     },
     {
@@ -939,22 +956,22 @@
      "output_type": "stream",
      "text": [
       "               x          y            name\n",
-      "7285  105.970313  18.442662  x105p97_y18p44\n",
-      "6241  105.960938  18.454094  x105p96_y18p45\n",
-      "7484  106.029689  18.374031  x106p03_y18p37\n",
-      "3779  106.531251  18.421219  x106p53_y18p42\n",
-      "4627  105.996875  18.469813  x106p00_y18p47\n",
-      "6842  106.242188  18.213720  x106p24_y18p21\n",
-      "4151  106.473438  17.877977  x106p47_y17p88\n",
-      "3958  106.298438  18.173602  x106p30_y18p17\n",
-      "1563  106.098438  18.299634  x106p10_y18p30\n",
-      "7077  106.142188  18.282455  x106p14_y18p28\n",
-      "2.02 secng coastlines: \n"
+      "4375  106.176564  18.288181  x106p18_y18p29\n",
+      "5492  105.898438  18.514099  x105p90_y18p51\n",
+      "3034  106.081250  18.386904  x106p08_y18p39\n",
+      "1015  106.005209  18.400250  x106p01_y18p40\n",
+      "5236  106.257813  18.210856  x106p26_y18p21\n",
+      "6939  106.457813  17.869354  x106p46_y17p87\n",
+      "4904  106.385938  18.127735  x106p39_y18p13\n",
+      "6895  105.904688  18.396915  x105p90_y18p40\n",
+      "1301  106.843750  17.939755  x106p84_y17p94\n",
+      "4355  106.139063  18.279592  x106p14_y18p28\n",
+      ">> reading coastlines: 1.99 sec\n"
      ]
     },
     {
      "data": {
-      "image/png": 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ieSAk2ZWATBn9MTFEis4CEghqDYgYkySIGd02UrN1prESNsfTUfkw1aRtReZADkergr4PUTNTRYBR2FzHLCJsSKxo8QdCHDt2bOE57rvvvnbbZ555Zvx0N5Cy0bEPGTIk6sjzDO4VE5sprCLdIXFjz73ZZpvFWcMPf/jDqNtE6kavybQRkqcc55UeHDUBswYGKRtfIdXB2mBOTEnRb9rFL6vjRoJXcKqscylIUFZZ6sp0WsGLUh12GmDIlqVtDE5akEp12OxjpmEXdm15zq3zynHG6rBprxxS0rK8/FYnm9Vm2y9tWdpnB9FUh531LARUFpTXInEa/In1Cp61VU2kbc67f6hE6GPWXNSWRY/M/claq+AZoku2qkZblv5I+xSPPdVhF7VZ/U8LkakOOyt4li1v2/vMM8/Eflq0iJlV1t6/Ir75U0FZ+kyWirZM2fT+VSmf3r8U7OsoWjKWSD0pGztvIvqxEJkHdJS8DDw0OjvfSNoifjo7OmoCR0nChnQ4jg6mhSVeSnS8qEj4Ly94UarDhuTTjBVVdNhpUKCO6rBt2bI6bJvAwJZPddjUP9Vhp85TVXTYefUuo8POK0v7WCisp8NOrUPydNhF9y89vowOO83GUkWHndfm1PGjng47LU//ZyDjeTJLpk/kZXbJul8WWc+1bFmeeZHjTFFZ7l89x5m88t3pONPrCBugrsHUkJcF0q0HIvMx0vPSIvEQiwRChYQhc4gaMpAkwPecJ54YPvz447DI+edHKZYXXK6y4fjjw1ffeSeEc87p7qY6HD0KyIj+zrsBWaJG9BjZXYdeSdiMuOjMhw0bFhc+iwLUC3Q64iJA2kwJAaoRwCiMxGPjDX/944/DwpdcEj77+tfDZ0OG/FdNgF35iBGhLQn96nA0MxB+MPlE6pbjDO8Js4c81YCjOnolYWuKRWyTK664opQ1ClMcOp7VN6ICYGrIVBPJG8kaYo6S9kknhU+/9rUw96hRYaHp08NnxxwT5jjllBAuvTT865BDwuvbbx/+6yrUHnR2O/XXFFOkn2acSZFKNNRd0j0SUFHZdPBC36ayTOmLzDOZglobXAYwW++ioDdpmxWPQmXr6f3SejONV1nanJcQNet+2TZjzlkUZsDawwP6iK231d1mZdmx56YP2bL1Ms6kbWa9xfaRovgdaZu5R2pzGhgrBde1daHOSNWs60iXTzu5j0jZTthdh15J2CLtgQMHhv333z+a70k/nQUsVs4666x2polA+uonn3wyqkjQWUnnxfdb/fuHRT/6KMx/0klh+umnh0VYoIOsd9wxzD377HUzzqS6Lulgle3GLrjZBSTF9hYgWpW1ZldlMs7w8trFUutIk5VxJs2+YutNe9KMM2nMFjmkqC0qi0rJLkDVazPnTduMLrpMxpm0zdapJCvjDIuDNnNLXpvzMs4woOa1uV7GGTsIck9VFn0z95fnm7XoWKXNWRln2K+yynxjF155jjZYlKNr0GsJW6SNSgSTREwPs9zWebmJ7Y3rPC9A6nSj1FRkZWeBSBIXpEqHn/+ss0LbBReEvlOmhOlzzBE+Peyw8M3PP4/7OXeVbCK2flWzr+SVLZNxxpa1GVTKZJzhRVd5OSJlOc5kleWedEeby2ScsWU7knEmq81lM87k1bszfaRMxpm8NpfJOPPoo49GSVsZlxQLvygMgqM6er2BJJI1hH3++efPtA+JDakaW3MWJ1NzOQHJBIkOBxuy3BC1Tw427x50UOgzg6wh7a+NGRMJPrWkcDiaGQza9Gska6ycGAw9YmXXo9cTNp0Kpx4kJgI2WVx00UUxSQP7CBO759tvh0XHj888xzcuuSQsMGZMdPpBkkH6WGHy5DD/2WeHdw88MPQlJsUee4S+xxwTFrr88h5socPRM8DTGVUbgopHq+we9GqViJ0KkpWGNGVECURFgF6RD6qQG264IYZmne13vwuLjxsXy7w1cGCtPCTO/68MGBDVHCwW9T3xxLD42LGRrJ/r1y8s/LvfhT8OGBBYqlnk5JPDXz74ILywySZRMkcfiK4xdfywCzt2m+OstJ8a8BeVBbasHD+sHbY9vqhsng5bx2edS6DdtNnqsKWXVhuLrp222ZbNOpctm+qw03qik867bup4VO9+FzkeZdW7XpttHykqm7ZZYRRslpiyzzl1PEqPt+fieaJ3l66cWSoknkYeBNhpE0KZxfusgFHcZyyxaEvW/g8//HCmDD4WSt+XJ+mnbe6I40xWec008lIcZt2LsnDCngGS6eJtSVwTzP3uvvvusOmmm8ZOyoMfMGBAeGHZZaMjgiVtkfWbQ4aEvw0cGKZ+/nl8KTdZYIFI1uiwFzb6vkUuvTS8O++8YaH55our+DxwFiuxW031kVZfSactco6pUraeDtseX1Q2z/HDHs9LZ89lddjWcSarbFY7Otpme3yWDrtsm7N02EXXtmVTx5ky9S7SYVdpc5YOu2ybs3TYRc8ZgswLniXCQkhBlch7x8Cd5RwD0XOveDeysrC/8cYb8Tp5jjXUuchxJut+Cbzn9Rxn8sq740w348EHH4weWauvvnokH6w3WEE/6KCDoqQwfPjwcN5558WgVdOHDQvvfvnLYfGzzw6LXXVV1E9DzNP33z8sPON8kMEzSy8dO9mXPv88Sit2Qef9/fcPH842W/jsr3+NHY7ruU7b0eqwHpCQGcSlHKsQe2pKyOAoNUtK2B999FEUMjqTqLsZ0esJ+4QTToiELdtTpnNYgiARyINx1VVXjf8TSyRO//r1C+sbyw9UHmHG1Fdxsok+qOA3nAPphkFAi5FIDXREOiWSS1b6MYejVUD/hqwRiOQHwMyVxXxmeMxKrQUWpI4Qg7TKrID3c25jNsjxeCArM3tvwey9seNYO+Gbb745JuqlUzz//PNxKoPFCJluWEAUyBJP/BEkcdQgkHUbkcimTInb0mlD0D967LHwxWefDR8NHBj1c3QuzmtjHWMCqDySRA60hC3nC8qCVE/G1NrqMtP9yhojWJ0pU9ci5xd0u9RX106zr1gzrdQRhKku0pLKphJTkYkXdbJl02lsvTYra0zWteq1GUnPttk6eqRtTqFYIyqbxhQpul9pm1Ndaz2TOFQVKgvsegC/i9pMf7NttpIqdSzKVsP9KdtmgAck/d6mDuP6qB1kxsg5qa/2UzekcAgZkud6Cy+8cLtsOr0xu02vI2zSl9EpGOUVzhXpGcJG/cGojWQNoVoQFxvnmQUvuywsfvXV4b2VVgpffeaZ+J3qtL9z5ZVxAZIXEP0dHZtOqUwkSNjKOIMkQYfmPzlW8J/KZjmsZGWcASIDJHcbmpZOn2ac0YuRRuujLtwDXdtmjIH4Ud9oQUmLjtLJMciwT2VpA9dTeVz7VTYr44z0nVn1zso4Y9sMYZPhJ6usMs6ozemiI8dBDlltVsYZ1Tt1nOFc1EtlOc62mVmZymZlnOH+qawW74oyzqjNWvgkrk1WH1HGGbU5dZxJ22yzISnjjAaQ1HGG61CuTJu5f/RnhBU7CEs/LWihn5mnBAw9HwYEzjNt2rRYD8r21njzvY6wWeSg4yA5EvqRbSKe0cHo0HQWpmZ0IAtG+fO/+c3w/auvjguMdsFRpD3fc89FEn95p53CZ4cdFpb5zndiZxOpZGUikQMCJMoLS33olGxbvXbVjDO2bGcyztjF0I5E66uScYY25ZUtE60vr81VM87YNpfNOGOlzKoZZ/LaXCZany3bmYwzNrNQ1YwzRW2mfdwjBmfaTRn+43nYZ8m5EQa490jXKSFTv3fffbeWyq8ovEIro9cRtlycIWk6CB0FqZKOTEelQyE1MdqnnWLRb387XLDAAmGxHXYIdFWpQSDr6X37RrL+/ZAh4ZWttgofv/hiTYpCMmCAkBQi8yxIRlITUgsxuiVNOBytAsiZgYeZCgMOhAzhWzAgQOy8L8yG0tgvDEZvvPFGqZCnrYxeR9iM6nwgawiZ0dpK2EgkTHFTCRt8esQR4e2vfS28etttYdttt43/RUn7qqtqC5Bv7rxz+HhGIBxJA5JgGQRS12qZTykZK2oTOqWVPhyOZgd9Gokd4Yg+n5VXlfcFu+ysfbwfX/rSlyLpF8X9aXX0SlZg6k3HQf1wyCGHhA033DBKAeju6BRIwxA6ZAuZavoLweLGvtNOO0WdNlK5FiDlev6dq68On/bvHz0jV1555Xhe9IcK2MS0DjKWCkQStqRu1A54XHK8XfxKM84UZUypl3HG6mC7MuOMFqLyMs5Yxw+m9GnGGdqbl3GmXsaUoowzDJQ8h67KOGMdZ9Db80ytSiRtc0czzih3aE9nnFHmn45mnLG/mS1y/3X/KMu5rJonzSxEH9I9sX3ms88+i+XtQmte1hfuLekMyZDV0xln4BGu7xlnugiQE4tydAqmanQgpGxJ2BAxhIl6JJW0l1xyybD99tuH5557Luzx17+G+ceNi3bY2FZ/5bzzon0251n+lFNiWS0apXpc/rMSNuBYHjQLSUgbqYNFq2Wcycqa7hlnmj/jTHr/7BoAumkGodTxKM8hyj7XqVOnxr6VGgTY47kepM+z/v73v9/OMqUVMs70+lgipAubPHlyu/9wmCHzO8jKJ4dEPu+ZZ8Y4ISxAYofNaMs328tcd134fPjwdqZRSC1INRp11Yn0jcTPC8jLTKcsk1TB4XD8HzTj4INKhfenFS1JeqWEbYFUiN6YD2SKNMDoz+h61VVXhX333XcmaQDpdpFvfSvctcQSYTYTUwT8fuedwz/ffTfQVSBoBcMBjPpIz6hb0MUxGPBNZ0MCRDpgOmVNmxyOTmPkyPBlpuEnnzzzPjIgIVjsumtoVrz//vtR1cR7iVSLoMS7lM56WgG9mrCZbkGifJiaS4etWL5ZKhHAFO0f55wTyfy4jz5qpwJAYn5/v/3Cx3PNFVZZfPGaDgyjf8wImV5D4nSudNGRjsY+pnJWr4nOizppess+xR3OQuqAgb5cwfspWyR5pGVRU6isHBbyQNutQwsvja13UcYZytmpK4OYLcs0Mm+KmVVvXmLVm99FZmBFbUYHW+SAkraZPmTrXWTxQzlblntry1p9vo4vqjfTcNvmmqUF6qDRo8M0+uLIkf8tOyNdXTjuuNjHVNbab5fJLISgY+tdJGykbaZP2bJplp2sawu8Owg/CF14TOq83Afe21b0Hu7VhE0ngQggNCthP/7449Fd/dxzz42qiqwAL+jwdt5555hiDOK2nYiOT8dCVw45yilGoz7/08HobHbRUVYrTOvobLx0LGqx0ML1JDGgt8SxoSj7iiVWXkCV5Zv6aQGpXsYZVDlWUoFEijLOpNlXUkmnXsYZ6fdoiy3LM6iXccZ62NmyajNSWJmMM7bNfHN8UcYZm32F+2GvTR3TjDOW0GyWHe6JLUsfqZdxxp7LlkVXTt/hE3DieuutsNixx4Z3P/igtt4SZoT+fX/AgPDJP/7Rrs30i6KMM2Rpymuzfc78bxcd0yw7aZtJBGIzMaWLyf8yWXYoKyssOwhQX+6dE3YLgs7AYgoStVbRL7/88vCrX/0qSijqeCkgEGJl33777VGtoYUQzod0QiwRG8FM2TcgRiRyvjkWnbUkbKRw9vEfgwZ1wTabbYjNrlhXzb5iy/ISV8k4k1e2TMYZ6/kIQVbNONMdbS6TcSavbJmMM7RL5fldJeMMfSevzWUyztiykK72/3HEiDDt298O8x97bJj/wgsR/WvRJOev0+YyGWfy2pwu2lZtM0iz+XzXbHNPZd/NoMJAnBeutRXQ6xcdZb70+uuvR2lmv/32C3vssUeU/ugcRQ+eToJZ4MUXX1yTAug0dGw6PBlorrvuutihXnvttbh6zZTPOtAg9fGbY/ggzeEgQF2QpDm+FRdPHD0PIk0GZhWoaeacM0razY4555wzClZaE+J9auXkCb1ewkaiRVpCykQNgloCws6SXrKAmd/yyy8f7rnnnrDRRhvF/1B5yHYXBxqkAM5F54LM89x4JWnzzSACaSPN0SHj1Nbh6AT6jhpVI2u+o1rkrLNCs2PeeeeNawWoL5HWW9nCqtcTNhIwkvITTzwRo/SRFqyeZG1B2YMPPjjqs1GDaAES0kYdgH4T8iYgjvbJaiTL2UDfLD4yTWSKR13IEwmQuFFF2MWsrIwzRdv1Ms7Uy+wiZOmw7X5enrxzZemwU4cfu53WKw2rWVQ23WYWwz2USqTKubMyzhTVs0zGmSptTjPOWBQ9q9lPOinMdvnlNZ+Br55/flSHoJVmu6hsVsaZsm2mn6JSsTrszjznPxXUU1mb1KeY0XKv0wVUOW3R//LAO4GAZZ10LNJ6WrBmpEzyWeiMBVivJmyImQ/pwSDd008/PaYIq5pMgDLYbpNd/cgjj6z9j04aUqPDc0wqTQvpf/Zbkj5SOgMAEjpR6ZgCFmVfKbudl3GmzLnrOX7o5cg6V5YOu169y9ar3nZexpky587LOFOmnnkZZ8q2ucg5qHAba5DLLw/TjjkmzD9y5P/prM88M7zb1hZ9CWJbBgzIPVdexpkybc7KOFOlzaXbGNq/L1JPphlnZDHF87fBslIwsJbJONPTjjO9mrAlGU+aNCmss846YYUVVuhwpue11147WoygVrEvJC83VgeELEU6sJKZjTfNy8ix6aiMlMD/SOikLPPMNI7KmDYtfDB0aPjisGHB9m4k60jWBaaarYJPP/00znYRTBj0FPCt5RcdH3jggbD11ltHvSokg1SZLuLtv//+0duIm4MLOGqGsrj22mvjeRVcqbtwzTXXxAeGlEWiTwgbScIG7K8COgGS+pgxY9pNWxndWTRE5fLII4/ETqOYx3xjwsRvplcsTCousY6BoDk395MpXD07VYdjJowcGf51wAHZ+4YPr9lmt7IH5F//+tf4DsFbqCvS2UKzoLKEDWEgiZKBBbO2FEOHDo0LcBMmTIimbnfeeWe0U+ZG9SOVVgGY5kN6kGd3Ah0XyQi41oknnhiT7hZNj8qCBUgiiRH4Cfd1C6aGEDcEjKrExoDQdZHMmTKzH0mc6RhTK36jtuB+0vEg8yLpQBlUymQ9ScGAxcClqWCaM886kaTn4Tro3lU2jTWROqDY8rJtVr2z9I5FSNtsdeNZbbZrFGmbUwefomsrK7jK2jRWZdrM81W909ldUVmbNaYjbeY9zmuzgksVtdmWTT0KbVlrK69z27JpVMp6z/lfSZvT/mivhfCIxRWqCaue4PoIQbJ/b2nC3nzzzeMnD0iRRLQjShbYa6+9otSJhFlE2HS2XXbZJRx77LFRrdAdN1MP99RTT40Jd3E932GHHSIZZmVlrgo6AQOWFiBt1DoREhHEFJOba6qdkLAWpeTMgw4NYuZDR0UCZ2Hl5Zdfbpe+LHUiSbNQc74044xe3jRaH8dhKSOnEltWGWdEpumiIwuY7FPZNBMJ9VZZLTrqmWgxVWWpny2rjDNl22zLKuOM2pw6zvBSo7LKarMyztjUVtZxhjZQTmXTLDuEC1XZrIwzkIrKcpwtm5Vxxkbr41xWyLD1VsYZtTmN1pe2mbqkGWdU76yMM/TtMm1WtD4tkiuKXV6b9V7YeyBMnTo1vh95bWbgpD+qzcrPKi9K6xRV5MnZyOhyHfZaa60VgykhgSNV33fffdHG+cwzzywsd9xxx8WFAEzqIOx6QCKwUkE9RT4PGkuLq6++On5vscUWsVNC3Ei2eenuq4LFxT333DNceeWVYZ999mlXX14WTP/owLx0eGNpkUSOM7wILOJB6oq4RudWFhBmN/yPKWFRtD67WMKA1NFofTyTnsw4Y+tdNeNMXpurZpyhPT2ZcSavzWWi9dmyNstO1YwzNstOV2ecsfcPYQHhLK/N9aL1zT333O3K2pmx+p7NkcnxDOYKTcAgx0DVrOhywsadG6makY0OxYO+5JJLwrrrrptb5qGHHgqXXXZZdDQpi5NOOilK4/WACy0PiaS6eA8So5qOSEJd1CFdSdbCZpttFm644YYoLejlVMJRBi8WPHhZuTfWnA8pRBK2kuEiwfGS8x8Lm5TNim/icDhmBkIAghkDBUTe7Ml7u4WwSXSLlA1ZsUiJ9yCj+8YbbzzT8RDTwIEDI6lX8ejDfA71gwChpaZDALKD9FAh/OhHP4pkhwRFfRSAqavBOZkxoCMfNWpUbYqGBMA0jZGf+qTBn6yErZyO1JNtyJupOcd5NhqHozx4r1DlKclvM6NL33ymUkcddVQ0kyMjC2DqjuSMjXMWYSvUKJYngvSaEBN62yxSZZQsM1Iixa655ppxQZDQplav3J1AGmbxNF2AhHzluHHXXXfF3wApIJWwmfoxBUfHh8qAqSmDjIL9M73lOKIA5mUPSbdTx49Uh61jO5Jxhn1IMXnZV6zjR5pxRiECijLOoFvVwlyqw06dSmzZ1PGjsxlnrA5bjkdWv56XfSUN/sRzTh2P0vtVlHHGrgGkZdHn8n50JOMMx3F/uiPjTJbjkT2+6LlOTdYA0rL0P95vqeTQtXMs5+ObNRRUilq0zMsYQ11RVSIAWv13irzy6f1r2IwzdL4szyIedt7qL6FEUVtYHH300bFRZ599dqbUXAVpRo6eBNL1euutF3784x+385hCL6lFRmXFyJKwFXxKOjrpiHkB0N0pJRf7PeNMc2ScydJh96aMM9JhFzke5T3XqTN02HkZZ7J02NZxhvtV9J5wTxmAuSaqU+5do2WcqUzYNrwmYIqPBM1LD+FAUGRrUcfExpkFuNGjR9fKDBo0KDYGPTQPAMnXQi9W+n+zAQkEyxd05XYBEiAt0nmZgWDBwAPm/mUBCQNpgU7OvVF4SsgeKavZp3kOx6zGB8YDkkVJazrYSKhM2JilbbDBBrVt6ZEx5Rs3blx0fEG/DFFBKpA2kqYlLKTLZjWrqQpMIFngtAuQgIEK6YzckIzsEDiSgVWJYIKkaZeckFAf0bEUnpWPnZo6HI7qHpC8i0jLjc5LlQkb++oixwumtGPHji08B6Z+RYD4WwV5C5AAUl7zjjvC3PPOGz455JBoWQP5MsIjRS9w6aXhq++8E75y9tlxKo4UwCCIHpvpLWQPidtIfpB/GlxGGWsA56ji5SWnEsB5rf4QFPUF9JfUW+VTJ5wixw7pOVW2qt6PQU9lOU/RLCQN9GXbzJTeOqTUCwomnbctX/Z+pW2u59Wa1oU+Y9tclGUnLStHGk3pi+qZluW52nqnWXaKzsW9tWVTT+OiPpK2WQv1ZcsyO4Wgee+axXLEzQ16aAHyBz/4QaYHZN855gjfGjMm/P6zz8LkzTaLejc62jxnnBHCRReFD4YMCe/+7ndRFUXnYj+qE6RsSAmy1kIN2yzSQKpWVwnZSXJAdQXpK+NHvYwzui7gGOvkoEVHq5NLs6/Y8lkZZ+zaBucWMXMPbFn0lvUyzlhVnW0zZM1LzUJcmYwztiwzG+tUkpVxhvOpLGSV1+asRTPOp0VH7oUti34/zThTlFnIlmVqz2xNab/SRceiNkN6itOel3EGXwbrJZnXZmWcsSSubEqA+tuyCDhpxhmLfyVZdmy9GaB4H+jTWYu2aZsZpOp5/jYanLB7CJg2ZnlAvjUjie93xo0L88w9d/js8MPDYhMmRLIm1970AQPCEjOi9aEyYSGExRU6Jp2bl1pSnBxPeDHTxRArVfPiFUXrS2OB55Utk3GGgUPlbdmyGWfstW0GlXjP6mRfsWUh66JofWXbXCbjjOKep2XLZpzJa3OZjDN5bc5adCzb5jIZZ7iXWW0uk3FGST86knEG2LLWEzIrWl9annqxPkT9IfyOBn/rKTS2wqaFgAfkAQccEBdgU0Davxs0KCx40UVhkSWXjIlRX95pp/C7AQOiDhs3Yhxm6Iw2Ww3E/PTTT0fVCGQACdHZm2V653DMasw555zR0gYhSLbajQwn7B7EbrvtFkk2K/D5nwYPDtPnmCP0nTIlfs82cmSUBFB/4MKOHTkSktx4+Z+42GyjbkGKQFJwixGHozqYufKeobpBIMqzoZ7VcJVID4OsNhA3IWeRhmXKR7omkTXf819wQXj7sMPa6SjzIL02FiQQ+Kuvvhr/Z+rOtDgNpGUHDKar1nEmzaRRlB0E/SEdvGzGGftbYS9nRcYZ1AxWh10lyw5qIGYzZTPO2N+UQ5VldbA8s7Jtpl5FGWeKtlMddr3nbOtF/+FZWh12lYwz9EG7+Fq1zWXbCNI+Yv1C6rVZYOETD+0VV1wx13GmKOMMbeWeecaZFgDEiv00qpHvfe978cEvOn58mH/cuPDmkCFRPbLk9deHhc45J/z188/DX37yk0jIaQox+62XWJHLeCnRK0MOWJEonKRgdaFZOmyg/ane1G5n6bDT46lb1rmydNhF10rrVRQkqN52nuNHmTZn6bCL6pk6fqQ67CptrtLGdDtLh1323Fk67LL1zMo401PPebYMHXZRm+mrWqTFKxvhp57jTJYXdnc6zrhKZBYtQN58883xwULWixuyBq/95CfhrT32iDrtha+4IhILRC9ViL4xR+JbAaw4jn10FqRefuNok66C11sVT+MXd7RsvXNVKVuvfGfK1qunt7n12/zhhx/GtSIGNN6bVIApU5eegEvYswBIvTgXnXfeeeFHiy3WjqyF+9dZJ3z0n/+EL/bpE60/cN/XYiPgmw9TNsgZaYH9qDb4yJwqdQF2OByhncpGViIIPY1uJeKEPYtA4gS8Qq9cfPGZMuwwcmP612fEiPDep5+GL3zwQbuYwTbWBCoFVrc1NeMbyR2iZioLiafJZosgnbR0tKnjRxFQFdiyVXPmKWExKJu13uodrV65jO5foI223qnjRxG4ji1b1esUyU5lq3rZYUds21wvW0tWZu+OtBnVji1b1SrJtrkqQX6UtLmKlJu2GaKmn6G6SDMFNSqcsGch9t577yhpY+1hY1zTiVBl4FDAAg2OGdqPVM02empIipdUDhUcSwdmsQV9JUDKZltR24h+aF+SrIwzWJro5bcOKSyWYF6Yt+iIzpxyKktdbHmcLfIWHXkRWZzSImyaiSTNOKNQszaqm9UZZmWcEXDYSTPOoIdXvVkwSjPOWLKx0ebkLVemzemiI3VQ+IasetMmbO+1rXooWh/3BLVYVlllnBGyMs7ktZlykJmQlXGGe1emzUonpoFfZK02p5mF0owz6aLtX5I223or44zAu6JofVltllNNVeFgVsIJexYCSZmExeecc0445phj2nUcXhA6Nx1+pZVWivus4wdqEl4cLdqlEjZEIKkczzXc3SE8Xg68Bhs144xV4VTNOGPLQgodzThj29zdGWcgn7w2l4nWl9fmqhlnbJt7IuNMXpvTemVF65s/p831ovWl52Yf90Qpz+h/LRdLxNF1UEckE86tt95aiyEu0Hl44RRzmbjXkA4vCtIZ0nGWhI1ZHynQRLJIzDjd0JmbNVu0w9EdYABQQl4kdQairsjv2l1wwp7FoLMccsghMesOkrRUGQLSAuTMZ7XVVovELAmbzpYlYSOVM3VEwkCyQWJCqoG0cb9tptgJDkdPYL755ovCEUIQMwqbWLmR0Njyfy8B9tinnHJKuPTSS+M0FD2u/eCBxdSZ0Z8pJro4kS7fUqXoG0LnPGyr09EZmQ5btQvEjr7RRkizemXpJvNQlfhTXaHd7spzZaFK9LmuRNG56wUeqtfGrjx31f3N0OY+FfsIM1oEJoQaMr9XWcTtKbiE3SBAEl511VVjKrUBAwZkHoNp3yuvvBIJmcU/Fh+lEgF8a9suBLH4gt4PSRu9KYTM4qPibdORiafAYIA6BckdvTblOZeNvteZFGH81kugaH2KXMeiIPWpkiIM2BRXeemy0HOi781LcZV1rq5KEWYjDKYpwrKiHab3yy6a2XpmeYrasrSVfiJ9blaKsLw2y5pCA3nVFGG2zWmKMC3aWu/btGzec52a4SmapkVDF63gavJ0zLp/aVn9T11pG8+T4613Z9kUYbp/DZ8izNFxoMrYcccdo3rEmulZ0AHQQ5PAl8UoSM6qQuw3iyuKTEanoxOz8IgUAekvt9xy8WVHmojelosuGl9E8l/KAYfyWLB4ijBPEdYKKcKK2kzfQ4Bgv9Z8GJCaPkWYo/vAC/Hzn/88JjogfVqqQ6MDQmh0JtKy0fkhIS06SrKRJCEJgJdbYSQhHCQAiIP9dEhefEiJl0sSCpIML2QVO2yHo9kwZcqU2nqPO844KkGjf//+/cPll18+Ux5IQdMtFiEpg+SRJWHrG0kc4mVkRzJCH46UjZSjbOLPP/981KUrOwuWK0QBRNqXyoNzVCFwXgamf7Z8WTDw2LJpJpJ6oM0qy3mqrPxrSqs6V3HCSdtcxSGFgdWWrRoxDinRtrmKQ4ttsxapy4I2zqo2/9uUpf3p7K4IihuCJFzGFb0R4ITdYIBwSXSApA2JkqQ3BZ0L9QYkdtNNN0UJgamuJGxI17qxMz1jOqwpOcRN5+ZYiA1JnW86MATOddFhs/DCNSB5Xgqyn6A20RQTPbpsu/XyIcWLHCE79IpydIDM0owzdoGTekiHrUiDKsvMoijjDG22C05qM+Al5lx5GWe4ls1EojRsAiSSl3FG7SjT5lSHrXUG3T/uhS0LkRRlnJF6xrZZ03BUIVzLZpyxawBFbeb50h/yMs5QlvWUvDbTzjTjjAZrtdlmnLFlmQnajDNphML333+/XcYezq2ysvIQlH1JOuy0zbSHPmLVbI2O5qlpLyNtMq0PGTIkOtRk6dEgaIiHDr/66qu3k7BtVg2+6fBIzbwokC+EjTQN2UJckDOkRsen86q8YpNwPGoSdIeQHOTJ8ZSlTFHGGaQ87VdAqrIZZyAOe26byURtzSsLbFl77TIZZ2xZ2l8l40xem8tknIEY7bVt1pgyGWdsWQbAKhln8tpcNuNMVpvLZpzJa3OZjDNfy3nOZTLOMEgxEKL/pk80utejE3aDgli8xx9/fNRnk8Q3SwqAaOncSKW33XZb/A05pyFY6aRWQuJ/SBfCVWxnyqEXR2qy5dn3ox/9KBI1kiEvhyxF6i3IOByNjnnmmacW4RJzWd6LvBjYjQAn7AbG1ltvHdUOF198cdh3330zj0FylkqEONtpgCh9Y02AZGj/h8ita3oqmQM6MNNMCBtphGtB7vzHvjSLusPRjOAdQABh1orgw4yhER1nnLAbHAceeGDUJeOWvs0222Qegz5b9tJIxHS6LHOjjoBOy/khai3MIGXLVE6LkhC3bLp7IuNMZzKRcH+Y8kslUuXctNUGf6pXT/s7tWPvyYwz6NlZj5BKpErGGdqqxemy9cwL/tSRNpdtY7rNeooyztDPEDKsgFGUMYZ1HNQ4ebPIoowzrBXQvzzjTC/FoYceGtOKoUcmt2Mesd57771Rp0yHSWNn531TTtH68o4DqE/sObVwhO5PAwU6QLY940x+xhlrx161zZ5xJnQo4wz/MVPl3eA/2XFnXVegf5Sxw+7pjDNO2E0AFghPOOGEcPDBB4ejjz66XShWCxwGUFugg6NzIknKYSZVddBpIR4WDzkfOnLI1apCsMXmnNYJR+qV119/PZ4XSQTyktt86sXV7JlIqpatd64qZeuV9zaHUmWRtBlsGDhsLtDOxtTxjDOOXBBTBPXIqaeeGhcjs5IDQMBIDc8991x4+eWXo1TEIiKqh1TiVrxkJA6kRvR20mOzX9O2LGkdCYJzyp2cF4GXgkGgM263DkdX45133ol9XEIO7wRqmkbUT5eBE3aTQFMv9GYXXnhhOOCAAzKPQ1XBVJ8pKB2VaTiEmkrYSOHWrA9JGqlcttn8pmMrx50ty9QaQmcQQcpG2kZSZ7CgrGypO5I1xsYSqQKke1u2SuAe2mLrXUXHyHVs2ao2vZ25XwymHW0zz7ajbUYf3ChtbiuQchU3JzVdhMTz1BWNDifsJiNtwrCy0HfLLbfMFD9bgDyVMksBlnipkI4haOxzUZVA7HR+tunEvAzKE0l5JGmru+Y86AL5vdFGG4U777wz6vggCgiARR1loEEdo5yScqLAuzJv0ZHrUd46b9hMJJzPSkWpEwnbWMwIch4CnMe+5GnGGe4n7VBWGuptM85YXX7qOKMAXCorR6KsNkOK1nGGOinkLWCAtWWpp2Y9aqMN/kS9Urtr6+xiF72YAdngTxzH7Ev1tveLwa8o4wzPTc41gDrmtTl1nOG+M5tTm6mPLct1ETDyMs78+c9/zm0z9bMDD2VR1XEtxRfh3eC+NmIkvm4h7AceeCCcdtpp0WOIzjpp0qSw7bbb1vbTCY844ohw4403xhvIohLSYJ6bNZg4cWJ0FOHGc8MJJ6oY0Y6ZSXvEiBFhjz32iCoIgjOlWPzKK0Nb377hL5tvHvfzUkjKWOjyy0Pb1KnhP4cemuk4g0QtaV7qDZVlAYeXUy/MY489FnWDRBfkhYYQeDEhX6Rwzkd/KBv8iZfLehhaJ4gywZ9sWere0YwztmxXZ5xJg2fRpjxnlzLBn/LazEBQJeOMLctz5F51VcYZ6zhD+5iZ5WWNSeuVFfzpGzltzgr+RB9EEGCQoD20n/5urXxamrAZVVdYYYWw++67x5gXKYYOHRruueeeMGHChNjZkMKwIaaz9OvXL/Oc3MBhw4ZFO2KknptvvjlaRdApNt100461rIXBgEZaMe7RUUcdNVPkM8h68XHjovT6h/nmixIZ4VQXvuKKMM/EieH1XXcNrzz6aHyZebF5gVBr8FyffPLJKFUyMEMsSCNsP/roo2HllVeOJoa8QKQ2Yx+DB56WksD43mCDDaIkTvmOqjgcjq7AbCaJhzwamYnRPxvdq7FLCHvzzTePnzw88sgjYfDgwWH99deP23vttVcYM2ZMeOKJJ3IJW8cKLK5dccUV4aGHHnLCLvCEJOkBAx2LkFZd8NaMmckS48bFDvvYj34UNn700fD1iRPDn/faK7w7ZEj4n+nTYxkkJ6QWpA+OhbzpyEi++s0xMolCZ400edhhh0UpbOTIkVHSYx+DMpKLFnqaKUaDo/d4NP7+97+vqfGaDV2ecWattdYKkydPrnnEYRuM9LbJJpuUKk+Z3/zmN1EiXHfddXOPU/Q5++lt4J4yII4dOzZKERCmstRMHzYsvHvggVHS3mnIkPD1886LZP3x0KFxRsO0kqkjUjDErWnl0ksvHaeeyy67bPzmXHyLsDmO45FUcIDgBWDWJYcE6oGkzezIAjUJrr8C0r+NwFfGFMwekyZL7Yy0VCXxanqdehlU7LnrHVvvWrOqzT157qI2tlXsI1ll6PvMEFmvQaXbHehOyb3LRaBzzz03kogCCfEALrnkkkLylb4SKQ8ihhguuOCCGMMiD8SLPvbYY0Nvx4YbbhjVFdx3MtbYzvKnfv3C+hdcEPpOmRKmzzFHeGPHHcOXExWFSFMLVNheAzqzosihn+Q333R0cNddd0UJhWeMXhD1F9vKOMPCE8cTbQ7VCaoRfmNyiJ4RvSZkLz1omYwz6htAkfuqZJxhkMnLOJOeq0rGGTkeZV1bWYBstD4NbmnGnjIZZ1TPrsg4k9fmNGNKRzLOqM15GWesDjkrW01RxpmpxluxXsYZe//0PBXrnTZpwTJFev9aNuMMxMFiFFI20hu60P322y9KfxtvvHFuOV4mgg/xwiJhowvHQSNVlwhHHnlkPEagI1hvqt4CpAX02AyS3ENrrrTo+PE1suabbalLyoCXnPOJUCyxQNLo0vVy8ZvFHY6HkHkRpCskQw5ExSBOHVG38CKxeq+MKZ5xZuaynnGm6zLOTJ8+PfZPfv/whz+Mdc+K/Cdw/1o+4wwVhTywHJHJGfGcIWJyFRYRNjdSjUc/i4SGFJ1H2NzwKgHaWxnct7POOiumGCP1Fy8T5Iw65M0hQyJJaxtUIe08QASSfHgBGWQhNhYgeWGQPuiYSBsiCV4myB1JiPKezcbRE3h/xroKakDFSG9WdClhM71TsBULRrqqdo8c7y90eTAwsgDJQuTVSy0VFr/mmhpZA33nkfZiV1wRrUuyyByy7zN9evjD4MHtJB9J3Eg+qDSwIkHS1pQUSZrp31NPPVVb8EHqIPg92WyQ4IUs3SMCgFQ2UsWUBcerrLwxy4JpvMrSB6v0Xe6FrXcVh5S0zVX7f9rmKgKNbTN16Eybq0Rw5Dq2bNWFwE9MWeU8tfeDBUY5jnWn3r5hCZtpq9X7oJdEgkaZzwLVeuutFy0INPW7//77w5VXXhlGjx5dKzNo0KD44iJBA755gZn60ElvvfXWMH78+OjR5ygPEujuvffe4cHjjguz7bpreDshX5Ex5JtCpoD2OGAl9TwwNef584LwvJGsmebzLHmJUHMgVfMb/SEkzoKldLBssyDJlNtmnEH3LWcYpqY2ylvqOKNY4AJ6Vk27OQ7pXxlnUscZSNVmIkEa0/RfqcryMs5ATjbjDPfA1hsyUdnUcUbZw23GGVuWe1qUcYb2WGK1beaZcK9txhmrw07bzHG2zTzDvIwztNlmnOG8eW1OHWdoo804o/unsnznZZxpa2uLdbADgi2rpB7SWdOnuB9wTSuQdYcIG2kJO1tBemRM+caNGxeuvfbaqF/eZZdd4oOCtAnCbx1neAj2BiKdYauNlxNEjz02dtxM8R3lIV3fpP/8J+w5eXI4eoaHo0WeOiRLAl/uxhvD4hMntpPU86DMNBDQOuusEyUlpBqbgd1aoyg+txxekMqtWgywjRmhkOoc83SwQl7ZMhlnbFkIqErGGe6FytO38xxnssrKdDLLMaRIT1+vzWUyzuS1uUzGGaWtS9tcJuMMfdRe22a7SdvYp0+f0m2WqSm6a8o1WzqwLFSuPTrlIvMapCnMzIpw3333tdsmEh0fR+fBC73ddtvFTo9eG4/RsmZGlrQXveqquFBZhqztSwvBMPAi/RHDmxdEViaSJJH2ID0sRrDNl8RUNdGuw1EPDFAMkAwizFaYFSCRN6PTDGju4caRS9qonSDL6667Lhx++OGZxxEOYKuttopEC4ESaxv77bYZZI11CdsLV7g2ajFNoVFzMQVGPSKzQCthI30h/SAAIIHlBYR3ODqLL37xi7HvMVtACm/WxUcn7BYFEsTaa68d1VRnnHFG+MlPfjLTMUi222+/fa1Dn3/++WGd++4LfYwpYN9RoypblUDSMtFDqsHih8FDwYIAJC0TLMgcSQjJm3ozeKCLRDdqJSHpcEGa3Sbdtsem27IjTjPO5J0be15tK+MMqp+scxedSzrsNOtJXj2zzmVRpc3SYacZZ/LabPcxAPNbOuu0bNG5UI0x27KLiVXbnPcs6p1LGWdkuy/w/B588MHYR/MkbYide5WX31HZlLLKyw47b6G8Mx6WTtgtCuk6WVvANp6YLqm3KVnZ+dBxb7rpprD2ffe1MwX86vnnhxU7YAqoLNgPP/xwXCi0Fhoy+dOiIrbZitYGgaMbZrEMqd/aaYvktc2gkLev3jYvotVhyxFF++3vtGyWDju9Vt65ZEdsddj16l22XvW2pc8VYadtLiqbpcNO65JXT2ZRPFerw67S5s485z59+syUNR2yZPDCo5cBLLVjF+izRXbYXCetW0/YYbfG0qkjl7RxMsA2nswwSNC8fHQ0mWBCInTMI6dODd8ZNy78fsiQ8PqMxd5nttoqkjckvsj48ZVz0ckGe4sttogdHBtxOjhkx/SUl4k6yuwKqRU9NotIkAsvGi+dPkC/i/aV2bYfvdB5+4v2Vb1W1bI91eaqbWi2NgMEASR0+heqOwaxsnWu2mfK3IOOwCXsFgeEyAcpG/UIi7uQI0G16HRImRDrAh9+GJ7q1y/8a9ddw2svvhjtulEdPL/NNuF94mfPiFetUKxlgEUEkjKxZJBon3766ShJ89LwvxLDamoJYUPm/I90kuf663BUARItfZdZX+pJ22xwwu4lwIkFG/ldd901mkxi445eG2cXCPylr389upB//qc/hZVWWilOS9GBQ9rfHjMm3H777eEHq6wSp3pVJG2IWNYh6LCRsrkeH/TaEDPTTlQj7Cd8KxJ/1RyD1MnqDKsEWWLGwdRd5W18jnrgOrZsqi+tB6boKltV8upMm5lZdbTNcmrraJtt2apt/tzcr3pt5jqERlCkvlawxXbC7iXgxYAcZScNeZ955plRp4yjAp/HH3+85pzCCww5I50QHhfdLcTKNi8JVijEz64CXhzOCbgeC3+cg/84P4MDswH+R3UD0eP8wKIVKh0GEpuJhDbpheXFtLpyyosMqLMNKsV12JY+l7LMBuRYo/CwIgibuDUr+BMEIl0n57EZVJhdCKnjDLMLZhNa2EozziAVKuaF2mz1zyzo2janWXasmWTqOMNxPPu8NqcZZyA7tZE60z+0zYwrr82ci2chxxnuO+1SWZ5BmlmIe6htO8uaNm1a7J92ITDNsmO9Q1nsxoSPoGitQNbACbuXAPJaaqmlaotmdGacldBv8wLsvPPOUQpHymZBRskNeHnI2s5LIc9VRVO84YYbooMU5oBl6yBvNZyjlHGG62CBoCmrzK4gQ45H3wiYDRQFf0JKFyDRjmacsWU7knGGAahsxhn0qTb4k3U6KRP8Ka/NVTPO2LIQO4TaVRln0uBP/LaLttY5KK1XGvxprrnmym0z/SUN/sT9ZRCg3fyuOhtoNDhhtyggOkgZ9YKyREs6xO0f12JeyFdffTU6Q0HSkCWEwbFIQbxcHA+BUhaJjhcIF3hIlJjlqFaw9f7pT3/azuOsCLzAkg6RmJA4eYl5+ZD4kKpko42uHQk3je7mcJQBfVkxbZjVIXErg3ozwgm7RQHRIvUh+ZDOTZmukTSIoY1EzTRZbuRIfYphXgSkbbuYyTZT9aOPPjoGnipySLDSMoDwIXmkQAYLpstIUPwHQaM+YNCQZOVwdBQIB0je9KlmdpxpDcWOIxOQ9E477RQT9l511VVxeo0TDRLsGmusEZ1m+B+VBuTeEXMjyqBq+NnPfhZ+/vOfR3dzzp/1Qfdpt7FJlq2rsrYzoGjazgwASZypuF1cSjOZK8pdEYoWpzpjZlUvi0x67ioLg+nxnalnWr7oXFXrWHSdrHN357Ood/+IW4NwwsxO+vtmgkvYLQp0juhS0etB0sQUQZIlXyYLfZA0agjp9pgyKqN0FUi/iATDwECgLzIBlTH94wVC9YHXGWSNlI00LTd2Fr4gdIgePSrHoEPmg3SOFM51kMJRm+RlfVHGmbyMKXYxrzsyzui8Hck4w6ArEksXHRWTpaMZZ/La3BUZZ3TevIwz1lM0LZvWy96/v3cy44zOobr1+owzjsYBsanxbiTVGh2QiIkkkcBE74477oh20eiuUVOkGVOqAtUIHzo4tt4k5y2jxhBZyZEGaZoXAQcbiILzaeEN8mKxkg4vVQr6SSxJ0kUvnRN4xhnPONM2I3EG1yfGjbIiNVvGGVeJtDAgKBYDeWnJdH/OOefE1G1bb711lGbRZXd1hDwyznAtYpyXtdemfrxUhO596KGH4nSVBSJ02nR6JGxUI9SVY5GMZM7GC8hL3CpmW46ux39mxPCH/BFiyjp+NSJcwm5h0EEhNixCMIljoRGJEElLKdbozJ3VWVoss8wy8aVAgiJpBcks6pEp+5E6WBTFrFBmXpJIIWgka4gZyxdIHKkPyfTll1+OKh5rNwwoqwEjKwBQEeS6r7JV7g+Sr9z+tV0WXMeWrZK5RXW19a6yUNsoba7iwJPVZgu2GfyZVTATaXaTPuCE3eLAjAnrD0zkALbY1jEBaQOilN6vK8BUkgVIiJcQrsOHDy9FANQBVQ115ngcWqgfU1mmtkw1eQEhbjnOsA9dpWy2OYZy6BHVJunFtciU6mA51mZfQd8rFQfkQ1lFWEt12AomJHUBswDqIymOqbHutfSnlpBtxhm21WZAHdgWrLMQHxZqLTlyb1RWmXzyMs6kbVbyELVZ+TizHGdoM/1Jbab/UF7Xts4sacYZtVn3j3th28wzsxlnrD68bUab7UDGzMu2mdmYBAQGdwQHHGdagayBE3aLA+kCNcWswP777x/1yxMnToxRAW0OxxS8jLzIq622WqyzXInRr0MQfEMCDDjSafJNOUnjEDCEhC5dZTuacSavbJmMM5COyivzTpYOO6ss9c+7ttXnajvVoeaVLZNxJq9smYwzkGZWm8tknKFOedcuk3Fm4ZyykDRWIQwmPBN+N7vqzAnb0W1ACttss82iXhrd+brrrtvu5Uqh8KoQBNIiH2uJAEQaWvmXVQtSGrFPePmRwjoTc9jROph77rnjICepn4Fai8/NCCdsR7cBSRf1xg477BBtvtE39+/fP0o6eWCKy+IjDj2QMwTP1J2XTWZUHCOzML6ZckP2fCNBodsukuYdvQ/zzTdfXLBGJYVAkGfB0ehwwnZ0KyQBn3zyyeG8886LbvFMkY877rh2tssC+lBMDdGh8pKhR+VF0zRXCX3Zh+Rkp8BMe5GuIXmuIemc/ziftpH8Oa9ViaD71H77O91G3yyv0az9RedCJaKEsAIDS17ZrHNZlUbRsVllub7NOFO2zco4I/vhtGxRm1GJQJB2DaNKvVlvKDr2b2Z/Wi8N2lkZZ0iuwYJ7OoMTqLPCOeQhrYtA35L5YhZsgKqqcMJ29Ji0TfYbwMIisbmxC0/ByyQnGFQivJAKYi8J2zpT2P8gal4WSFyLZLy0LASialEsZBa60owzELD2K72ZYPdRJtVh2/3ptv0NYfKxxxZdq+hc9bbTNskxyRK+vXbRuSiT6rCL6m33QU6ULVvvonNlHZvut9sssmKvb/XWkCh9ioV46pYXHxuSr2eHndZFUCq2vHO744yjKYDJHkA6vueeezIdFyAaXhQCTBFSVamctFCXOlho0VH7cI1HDQPpIxlC4kjeWpQEfOsj8IKl+/P2Qdh5+9Nt+1sJI+yxRdeqUq9627quJey8emZdl7p3pJ56nh2td72yc5Z4jiJspGY+kDjmrvS/PAk7q4+kSOtiB4WisvYZVIUTtqPHAdFinz1s2LCZVCNINki/qDGwqEByRmeNHhvdNNtIPZqmv/jii+0kbaai6CeRfrCKQDXC+Zi+OnonPjZxx/ER6GxMllkJJ2zHLAHS8I9//OPoxr7n22+HpZZdNnxy6KE10mbayPdXzjsvfPGTT8LXzj8/krBM25COrISN1IubOtINU3d+Q/TAxk8uSqgqnWMVp5G0bFUy6ExZ2lzV0aQ3tXnq1Km1gZwZWmck20aBE7ZjlgCyJUUZcU6+esEFYbHLLw/P4yTzs5/Vjll0/Piw+NVXh5d32ik6UKCPRmImPoqcSBQsCALnhdQiFISP5K7MK8o4gwSOCeCyyy5bm7JC7HZ6yzlEJOi7rcWJrFPk+EF97OIVcSxsBhUbXCh1nEHVg+pGZbHBtmWZZfBfVsYZPti4WxJScCZA++3CKG1Aj60FOO6BnbbbxVBMIrkHQuo4Qxuou8qiXshrc+o4w7NQUK+sjDPcz6wsO2DatGm15yxYxyTOY9OHMftCPUZkylYga+CE7ZhlqDmBXHZZ+ENbW1h+7NjwxjzzhD8PGfJ/ZD1uXMza/r8DB4b33n47vrC4oUuythI2ZCMJW9+yKEHihoQgMBw4UJFgWpiXcUZlAUQDCVcJ/gRxlA3+xMKUDf7E9coGf4J8aWtWvatmnLFlO5JxhoEsL/iTdZyRysou2tqsMWUyzixqgj/ZemdlnOHeQvoMKtS5mdUhwAnb0RBAwv7te++FVcaPD4tMmBDmbGsLd62zTnhzjTXCGvPPH9UaWI1AUEi0THVF1AIkhlszsI4zkAY24DjWIHXbHI2O1sYXvvCF2E+QxJmRaMGxWeGE7WgYzHfGGWH6rbeGOadMCdNmnz08s9VW4a3nngvXXnttzGiDegArEKQ0kTMkjpQNgUPSypBtnW0kXfGbKbKj9wF/AGYNqHqIKdOsjjPN7VjvaCl85+qrQ98pU8L0OeYIs02dGnb5wx9iUgQI+Oabb47HQNQQM/FRFD9bsbSxALDxoJHGUQPwzZSc/UjbsiwRqk6T7QJdlQwq6bH1yhadq0wd02t19FxVUe/c3ZVxpt61UJEwU0PKRtrWOkRLS9gPPPBAOO2006IEw2g1adKksO2229b2M1094ogjwo033hh1X7wsBxxwQKaThHDJJZeEK6+8svYiYa9LlDcCATl6CY4/PoQRI0I47rjQd/jwuL3QiBFhoQUXDIcffngYNGhQWH755Wu5J1kofOKJJyJ5o6eVM40WGdlm4Q1pim+kc/Sw6FPRaSKddybjjBYfuyLjjA1o350ZZ9AV9/aMM9OnT6+lo+sVGWdYuGHhh8SuxIVIMXTo0OgUMWHChLg4cOedd4Z99903Sjr9+vXLPOd9990XBgwYENZaa63Y8UnmiiXASy+91LRTF0fHyDpA1kDfI0aEJUKIac7222+/mBgBooAEIDuyh9jFRznjyLuR/iOXZaRxLXhRxjPO9J6MM7o2zxILIe5PM2acqUzYZBPhk4dHHnkkuiCvv/76cXuvvfYKY8aMidJQHmGTCNbi0ksvDTfccEP4zW9+EyUrR4sDu1pL1oK2p02LgzkDP32JWNvoopGUkKpuueWW+IJY93X+xxIEgoVweEmQ0DFnsy7WjtbHp59+GmcYWoBs5hCrXb7oyItFKE0kcEZ0pOfXX389nHnmmaXPgXSE9FSUEJaR2wZR6cyo5ZjFGDkyf58hcdKdEaoVoYB+xpQWMiZOCZKmdVEHEDbTTxIOo6qjH3o6sd6D6TPURnAJEn6Rm3mvJexzzz03StXoGaU3REdNLOSy+MUvfhHJnoSxeWBqTHZuR+8CEjahVyFhTYMVLAqHDHTbkDOLSkDWI0jf7GcqjlqPY+ifSOboTZWpHchD0mZfkXDAtNpmnEF647wSLjivLa8M3QDiYFZgU1pxHptxhiwpFjbjDG2xGWdwdrE67FdffbX2W3revIwz3AObccZeB5UFQpPVYVvHGf5Hj602MyhirSPomkCDqu4f29xDVCrafu2112rHcx2bcYZnazPO/D3RYWOuaevNueUcRCwaroPjjNrd7OgWwn7ssceilI3+iEVKdI/1CFggDCdmXEjmWeE3hSOPPDLqywVeKpvVwtGaoE/gzn7MMceEn/3sZ/Hl5AMRkk8SoFtdbrnlZgrJyn/oNyEgpseQNS80/QaCynPeqJpxhmvk6bC5br2MM3nXLpNxJq9smYwzeWXLZJxh4Cyjw84qi+Sbd+20Xn0qtJl+wXNiAKXurWDS2aWEzch71FFHRcuRLbfcMv6H3vDZZ58Np59+el3C5hgI++67747liqAkso7eh0033TQmOXjwwQejRRLghVRIS76RKJHGbHYRFvzYhxTGh3RjSK3yhnO0Huadd944YLCmwayLQcuzpicZjFMdISNdvUzKp556ahg1alSMlczKv8NRhB133DHssssu4fbbb48hWG3CXKbuqAuwu1Z8bC1Gsp/ZGMStWM2tEmfCkQ2kcgZlBnUG8mZ2nKlM2OjorO0i0w0kaPRZxB5eb731YuhMmS/df//90cZ69OjRtTJYfnDD0EMDzPhGjBgRrr766miyowzfjI5FdpCO3gumvczmUI0cf/zxUWqSLphvptn8p3Ct1uyPbczdMBtFjYI6BJUKfQ21BfpY2++0z5qlSQ+d7ldmb23b39Jh25kh+t68a2Wdy0qHVcumg5PdX3QuVCLcR7vIX7bNlIEg7XWr1Juy9th/VGwz99oKkGzzvMk4g+49j1+4Ln0oz5Y6vZYFZawuPYUNUNXthM1UdIMNNqhtS4+MKR9ZRNA/o19G+kFHCGkjOVvHGaQdexMvvPDCuPhAuE0LXsaRRRYEjl4N7Gl32223uG4i1YgAIfNCsijFS4pqRGZ/kAe6TSQuFuqU4IAXicU0XjgbbwKS1DbHIclrmxdX027BbqdlqVPesel2em6EoLx61StLG/ltidPu5zvv3LybvK955063bVmRnj23vVa9+5e2ed7k2KJ7oDZbruF6EDZkyr3IiytCnek3RXFH0rrYdQgG57yynfHmrEzY2FcXuZaiQxw7dmzhOVhQtLArzA5HWbAAt9VWW0Vrh+kjRoTFl1givDVwYO2lkJSz8aOPhj7Tp4f5zjprJscPJG70mkyVefkxBUSlYiVZXnxtQ14Qtt0PqeRt27IQNr/zzl3vXBCE3Zeeq6isjrWEbfenZW29GMggvY60GfKqUs+8eufdry8U3D+VFWGjCmP2zuKj4mrn6bO515ql5SGtt8D1mFnkle1oDHPQGrYujl5N2mSuuXfDDcPGDz8c/xNp8+KsdscdYf7x48NLO+4YrhszpmZJgqTLVB+rE6RqhBCk8KLErI7mxOeffx4HZAYr+gvbPGMb0bFZ4B4EjqYHNtnfvvjicOGCC8YY2sTSBnwvMX58eGPgwPDs1luHlVdeOX6Ib8OLyzoKcWsIuwq5YynSCqZfjv+DbNWx5SbgE74hMgFNwwE0C1zCdrQEvv/974fH99gjXDdxYtgR0r7qqhj5jwQIfx44MMz13ntxSvz4449HFRzOFJj9MT3lheZbjifKMIMKA4cS6/zCIrsNmo9zhgXqA6bDWpi3TjToNVk004Ilx9oAQmnGGdaAGEi0rWNtxhkLm3GGmYLNOINzj3WcSc0Ybc5L6mfbnDrOKMuOXS+w9bZtToNnUQfbZiRdW5Y22Cw79tjpJt55VsYZZk12kfCZZ56JlkIrrrhi7T+encLwNiOcsB0tASRmrI+Ofv318OPXXguzTZkS2uaYI0wfNiwsHEK0YIJkIC7Ilg8vLVZJcsawTheoRyAa1mQUcAgi4T8RNt+QO/pwBZyCQPIyqKSOMwwg6MyttMcxOp7Bol7GmbzgT1UzzthtSI9yVYI/0ZayGWeoh3U8gkDLZpyZc8452wV/ss8szThDWS0mywmPtqSBp5oJrhJxtAx4QY+bEUubBAh98AkYNSpKaXx4WSEjCBOylXu5Vu2RMpHgXnjhhUiIEAMvOqSKmReEDyFAZhAU5xPZQjrNPNVuRfSdEe2QZy93dgYM60zVbHAJ29FSCRDChAnhqX79wsHvvRfGffe74TvjxtUWIiFmpCvUAYRPgHghaE27UQUgsfJSKwYGOm2kRAhaIVsBBA4JKAwnulLlHHQ0DuaYY45aejkGU0ztmjmvo0vYjpaLqf2V0aNjaIPRX/hC1GHbhUgAGWvqzHQbiZtvyJqXGRJGOkPdIZUCxytoP6oRJHVNs9mHpUm6YImUbnW96J2tww377bZsvO3+jiLL9LYriapKppyuvE6fgjbk1Qk1FM8Gm/yyi8r2uTUSXMJ2tFxMbTSeBx98cHSq+c2MeOrYYf/30Gk1XbMkbNlts49tpDIWw9AjcyySGRI22xCDvB1Z2EO1guSNPhWypzwSOSoW1CtI3Uh6SOV4BVOe67HICTEoFAOR+jgfgwXHPProo7VjATMDRevrSMaZdNFR5+qOjDNCXsYZm54rLZtXr6kzMs7YQa5Kxhnurc1gk5UxRgvDPJuVVlqpMMRzU2SccTgaEolHLAtRZKnZe++9w0KjRrWL/AjZKpg9krUWtrSAxUsLYfM/i5WQNQtukCskKKcLzsl/yjhDeRbXIB3MyHjZIXJ0pmxzXswKWbDjnLy4nIdrcE5InZjd1AVCIzgVvz3jTNdknClqs1zNIVoyanEeLEyaPuOMw9EsICfonnvuGS644IJ2oXghGsiSjEa83JKwJTGhjxahQsK8fHyQsJEqJWFzHqQ29kEklJcuG0LDvA29KeeBOCF1yiMJQt4QoIiC81GG8pAa5/TMOD2Df//733HWwCACSTMbaVRLEidsR0tjs802izHZCYegtHXST/7whz9sJ2ErQBRAwmZqi2TIFDmVsPnw20rYgPOQ0xTy5qVHOkXClvQOWQOImWtA3JJgOQfHYnrIt536O7oe3F8GVWYqPDdFFWVgblRLEidsR0uDF5EAYgMHDoxhWBWpD8mYD2SOHhuJFh0y0jEpyHiZCXQGIaPiEPkqop/igkDqEDcqFmWyQZ0BEUjvjfTMOThO0eYgbkia64r4IQvqgFTOtJlrKhMM52TRzGacSXXYNuOM3K6lZ0512Ez98zLO0Dbqk5dxhn1FGWesEw4qEavD5psZBKaSWRlnuFd5GWemT58er5uXcUb6cql9GPisDjtt83PPPRe23nrrdjE/aKtmPo0IJ2xHywNdMCF8CcN60UUXRdJCbwx5Qh6rrrpq/A1Rsk9JECBMiBjSg3whDFQfkIp+MwBAUJTleKRoZZxBqubl5zfnRZ+KFE0ZiAGdsiRvfSvjjLZffPHFeB1IBm/OZsw4Yx1nOptxpm/fvpUyzqQ6bLufgSYNWQvhc4+t12gjwc36HL0CLFThjn7iiSfWHGl4YSFsJenlGCRbXnK2UVlAOBAs5IhUCIFDnkiXkB//IY1jLoY0x6Ihkp21ggBSmSC5QSBZ9tqUpyxSJIDosIoQ6bhOu2vBIMPz1P1mhsRzauQkzS5hO3oFkJrIun7AAQdEHfOGG25YSymGFIuaBLJFEoQkIWWkLb6VxSZLp4wqAPIlbgWxPTgf0jbHo2awSRUYDCBhiJltSF0LnTI3kwSO+R/qFspwXepqVQGOrgH3GrWLkiFjzdPIcMJ29CrSJvMRyXshQLKvA5EqErcl2KzvPFCWc6677rq1AE+QNh+pOJDglYnJTs3TZMFM5VWO8zCgkEBYunaASobpvLUTlnVJ1rYsWaxKxO6HsPLKSodts5cXXcueS8GfUj2x9qfnSrfTer03w2In61xZbU4TGGSdGymbwZYBUesb7ENVkxe7Wrp4e20B3T/l8wJM2aBcVeGE7ehVQP2BmR/22ZAXapKuAC89kjbR+6S7thlHFM9CZG4l66JvXnzseVlcoyzXQT2DZM8ggV5bgJisJ6Dd5lsfq5rRNoNE2bL1rpWWtd9l6pl3rqztonrrd16b7X4Gcwi4qM2ARWTUVnwzqGZ5XuaVtfs7CidsR68DJEdaur322isu8nVV0mfIgwVIiBsTQiRgK0FbCdv+L0laDiF5kjcSMpI1i5qobhgArF6bhbK8bVQryiwvWFvvorJcM9Whp3bi9niITL+RRGVzLtj9Vc4FMLcr22YImN9WwrbXSs8tCxEbGVAWJWwr7C5rGzwDnm/WugLPicEhb80hL9djGThhO3olMPHDYgTHGl5MYo90BVh85MXHxA6SlTmbptzox2UWZ3XXqDtsXGkraWOzDdEjZWM7DhF0JpGrozx4NphgKiys1Cf81oDck3DCdvTqJL6XXHJJ2H333aPU01XOEhAqLzWSNiaFSMhMg61ZH8fw0gPtR/WB/jOVsPUbFQukjxTPOUT6ju4BA6ucpvSMtAA9qyL+OWE7ejVYzLv44ouj9cguu+wS7bO7AlgbYH0AyfLSQ65ShcjxBelbWWNwrJFTi4B0jTMIEjZpzSAQclKyCIn6RNlXUFlwLcUNAehY05yFlmTQgdsgTTY6nRxn8kB7rKONvW69BVpmBljjCGn+zNQc0tZ5ekbGGbsoyGKebXNKqtTZttku/imQl60numps9O1zQdrWc5wVcMJ29HpAOMcdd1zYb7/9oo4XcuwKQJpE3ENNAiGg6oDsWCyUiZ+cbSAXSBhPPKlEIBDIk6k3+yELpDum5xA9ZEd59nMNSMam2rLbWdH6uJ4ccXRdQDkGGAVRyorWhx7XzgAYMGxaL+l+s6L1KUOPYK9NveTKr3PJOmXqjOBPeWUVo0Xb1IltG+0wr808KwZGWbPQVspyL3QfaAP3Lx1YexJO2I5eD15gPieccEL0hkTa7gopCmLmPAwIENe1114bpeMzzzyzZkUCIchqBHUJRIeqRBYgEC1SNVIrAwn7IGxIC0KCRJgVQKKQTUej9cmMEEDs1K9etD4LW75MtL4yZfOi9eWVrRqtz5ZlcKRN2NFrH8TMfeX+KqzArM4o1LguPQ5HD4OwmsOGDQvnnHNOzSKgs4CcIIN99tknkgkvPxYqxCm59957wx133BG/ISKkZIj5pZdeihlxkKIhG8ibb1QJv/rVr2K8ESRDBgEIGAl2VhNJK6Jv376R8JHMJW0r1vYsq9MsvbrD0SBAP4r0RdD6I488Mpx++unRokP65rxPmawwuMOTTIGkCnvssUccDK6//vpw1113hbPOOisGP0KaRsUBqWNmiHSL2gFA4vKMZABgv2YFSKyoAiRVCh7pr2vAM+FZECiqbMhVZgLdFTzKVSKOXg8kWeymR4wYER1qwL777hv12pj9sYDI1F7hN61TBASLzjXPaoCwrrzoW265ZZxiE3vk0EMPjVIzLzaS9MYbb1xbTEPnjZ6XKTiSs81OghQNOTNNR8pG78qiGIPM5MmTo0pFHpA48JAsQSTDAhoSomyAizK7oBLhut2RcYaBpyjjDIuhtl6cy+qw//73v7dz0U8zznAPpIeWs1JWxp60LLMg7qcWW9OMPdSba1vPxqyMM7SL/kS5PNNLzzjjcHQQkMcvfvGLcMQRR4SxY8fGhSd+9+vXL77AxAhBh4y0iw4TnTDSKx+kXXSeHJdnGQFhyhIEoPaAUHixTz311HgtTP9QcSAxQ7ZWPZJC4UoJtA+hfO973wu//OUv44Dw9NNP17w5IVO+PePMIqUyzkDYEKnVYVvHmaz7Z8vTBzC3lL6be2UjFHZVxhlXiThaHryILNBhFXD77bdH6RSgP1577bWjBA0RIPlCVP3794/SGmoKHGBk8gVh8GJCQkhPfPgfss1SjXAuYmuje4aoBIgAHfZDDz0UCQkp/LbbbosLkkikvNBFpnGQMFIuBIH+W84c1BVCor6NmjGl1TBtRg5QBnMGBwYMnkd3RfyrLGET8P20006LnZ9OM2nSpLDtttvW9jOlQGq48cYb48jMyjar7iy65IFFFqajnJNpC6voBx10UMdb5XAYQIJMgyFF+iRSkrwRMeVjHySKRHT22WeHyy67LJL38OHDwyabbBIJECk7K+OMpsiU5zhLtOiqIWECTlmnHJIboNdGsmewQBpDqmVAQKqtJ4EhOfJhNoA6hWsi6fHNNRlIPBRr94NnTz9A8pZUj+oJ1VV3ZV2vTNhIFKym4x2GJJKC3HmEr5wwYUKcutx5551RH0ijmGZmAamFZKI/+clP4sKMw9EV4KXBnRtpB/JluovkiXu3gikhHDDtxZxPelU8ICFSovpBxBA8ZI8kzW9IfIcddoghUCFOXlDUGuif0VFLVbDppptG3TQSl52KP/7441EaQ8eMhM1+rk39eL9Sj0v0ulIDMLjIuYd3CoKWTpeyCDzUkUHKqiVQtcizEqTJZZW6DCi+dx4gJxsPgwHG6q2LvAA14AnU05aVrjsLffv2nclJh5mTwKCHiiivHmmbrWs515U6JAtp1D8GXe4Tqildh20N/t2FyoS9+eabx08emAIOHjy4lj+PADtjxowJTzzxRC5hs3DCByCdOxxdATmmYPWBNIoqhN+82MwOERSYyUHeSMEIFnKkgOyQtFnQI8UYC4aHH354JDJmmNddd138D7XJ/vvvH1USWmwS2SFtKWiTnUJjNnjUUUfV1CSQNVIxKg4robMfE0D06OipAceIICANri8HGa69yiqrRMmPsKvUB1KivUq9ZZ0+rIMLJG/1yByruqSLjvIatATIIKPylNV9TFOEAbl3Z5VV+axFR5W1C38QeF7Z1HEmbXOa6YayGrTTRUfdb5XlnqexRBj0utu8sssXHddaa624Yo0EzoiOfg4dHpJMV4IOYDtBZxT5jtYDfUN2tFhiQNSo6SAeSBDJCDJGiECPffLJJ0ehgsQGvJioQJCykawhzHPPPbe28k+6MV5sAkhR/rzzzotqPwYHVBSo9iCKW265JQouVjLj2lwXiS3VU2sbwkE3TXmSLiA5Q7hplDekSQhEKcloJ4TO8ZyfAYr43BAixFeUIkwEK9iExGVShNnytmyZFGFF1wZF6cN+V1C2XoqworLpomPWtXnG8p6kLGsl9BlZ1zQFYdOxkapZVWc052YRYIeO05U46aSTwrHHHtul53S0DpB+kL4g39VWWy1KyRAcsz3ImReLFxJ1Ai8c9tGHHHJIVM2h1pAki66ZfobkhOoCczm92EjQ48ePDzfffHPUeUOUkDcvrJL2YiZoVRyKyVyEJ598Mi6IMpNFvQjhZRE215fFCotdHMO1mD0gAXMdpHSbPMDRdWBQxIyQ2RvSeE9kWu8WwmakR8pmdGORkoUdRnekj64CEhP6cith55nROHoX0JHijALJ0u/GjRsXHVUgV/oMxJ2apSE9suAI8aKuoOyPf/zjKOEyBZfulHNDlBAy/RuJdb311ouEjwSmrOvorJXz0eqvCeN6ww03FNZfbvHodzHXY8E+i+SR/tBnI8VK8lPOSa4pzzwW9Xk/aIOVsJXlPf2dbvPN9VWWQafoePubuhUdW+baHS370Yzf9t4Vtdlei28GdDtIZl0LvTdqFgZnnq2yzafHWnRmQbJLCZuK0tmxHKGjARrB4gyeY11J2LxsWYlMHQ5eUKyTJHXy8jDDIyUYagc5SNj+wwsHse26666R4JGasX7aaKON2ulMie4HGVr1AJI4ZM25ITUWNbGBzgIzz3opoiB51C71QNsYmABtsTG0GVhw9GDgkTMN17WkK2IB3BNbL7uPb4hLi2lp2fR4ey4Rtj3W7s86l8wl0/Om+7LOZffruiLsem1OyzL4Sudf1GbWC2Tmqf/TcwM9k4YhbE3PUmlAHmIOR08B1UZKgkWgfyJQIGVTFt3kz3/+87jPetZlCQqpM0YReJG7ym0ZgkD/DgFkZalhAGEf+npmBNwDK2GzT/cF/au9R3Zbqk07SNmy6fH2N4StjPNZx1Y5V5ltey7uNb8tH5VtM2VSHXZRPe0+CFuzK4E1DwZNBIkeTRFGxayyHh0hEjSjDNMzpoeHHXZYLRXS/fffH6688sq4Ci8MGjQo2puih9bUj5V6/cZUiXNqMcXh6E5AoPRrpF9M81AdECO7O3DTTTdFCbwrgM4UBxyICYKRFQcSNkTJoiPmh1ipZCWLdXQ/FFcbLpMJYGcMJCoTNh1kgw02qG1Lj4wpH1NJQkiiXyYYPJ0E0h41alQ7xxk6lB31WOBh5VxAfcIH8sfKxOHoTlx66aW1xTn6JYtJXZXIIAUqFt4PTAE7ImlZO2wImSTCSNJI+dbKQd8IP4ox4ug5oK7SojDPq6tssysTNmZKRVM6lPDEZChCSsIyi3E4egJIpEidmJriiUh/3GKLLcKtt94aCQ4y7S6w2Mm0GMGmI5I2wg1koAzfkDZTc35b1Q2SHYOP4pMwY1X0P9koKxgSsHpVzl9kmoYghhWKyqbOLPZcNtiSZtDM0FU2jc2RBkZKM868NSM4lK2rIG9RC3tt2mvbTD8QKGeDNVHOXgcba7QJ+i91wkn10qyHrLnmml1uOeLBnxy9DsrGgkUI0g/6XVR6EDhmf0xdrdqvK/P3QaBYlKAfx9wwJayypM2AQ51ZTMXhg/PabDW0EQJj+g0Jso5kHWfqZZxBOkQyL8o4Y3X3XFPlITY5nChan1J3UR8ci2zZKhln5phjjtyyyjijNjIopdH6ijLO0C45OaWOMwzkWKHZa3O+rDZz/+hTmGNy/SKv0apwwnb0SvBCspYCcfJiscYCobGuwkvfnWsn2ICzwIljjI3DUwYy4UNtAxErqzfbds0Hcz4IhW3II8txxjPOfLkw40xWtL4y9Va0Q2VWx+OUe9kVyQ88Wp+jV4I1kyuuuCImA8Aum238B3oiwSqkgX33gw8+WKkcLz/6ar4hJYiPxXqkVqRhviVhozpAqkVyTAnS0TNgsGEQQKrnmSk+d2fgErbDEUK0vz7jjDN6LFMLgwTEzVRbkmw9SMpDFYLahm3KokNGukQCTnM6QhrMHJAgUwnR0TPgOSEI8AxQnXQm9KoTtqPXgWkv0qgivaGvRRKCQDHr6wlXblQZOOFgdVWWsJlSK9s6kjRkj/SMygLVAxIcahKkcOlgKYN0x/FI4VaHXZRxhuvkZZxBh16Uccb+zso4wzXzMs5k1atsxpl33nmnFo/aBn8SQRa1WY4ueRlnaLPNOl/UZnTYtDNduOW6WgDuKJywHb1S4kGvqyD/EDgvGFnTkVzreSJ2BSAO0o8NHDgwbLPNNqXKoKtGuiZCH5I05IGTD+eSZM0CFySORQWEAXkoY45nnAndlnGm6P4BBkBijlD31KqmCpywHb0evOBYCBCPnZc7jeLWXeCFhlCtC3SRTS/HQyIkXpDUBzFLuuN/TAaR5CBZyHGzzTaLxFqUwcbRfWCGwbPjGTBY8Mw6YzXihO1wzAj+xDS9p1NrEb8aMrWOYyl44ZH8qCNx4xlQkKwlYQPrMMN+Qr8qfRVSovs59CwYSBVznYGWmQH/MUCnNtxV4ITtcIQQVQ0szvU0YUPUBJoqui4vOqoFFqwgbGVtT83ERBAQvGyGIXqkbSQ76ziT6uk5RpHpkAqLpu3owq2aQTkzVT71qrSDheKSCOiYbdm8TOOgT0abaZfK2pgquq69dlGbub/pM7D297TZbjPI2nqnIAcAgzEmnAJqks72Lydsh2NGsB9eyp7QX6cJPy666KLCY1BtKLgQgwomexCRgjvZBS8IBFJAwtZxIkYkb6t6wXFE0h7tt+74DA5SDWVlnGEBT2W5BmSo8rasMs7IcSbNOJOWlVVLUcaZD00sDiRXW2/bZgiVWYYl2qI2U1aqo6yMM5yvTJsZdLJMKbmPDCipN2cVOGE7HDNAKFKk1J4EC4iQAgNFnnWKsnCjEtGiGd82AwrbSHXEFkHqVH5B7c9aNEuzr6TSYtWMM7Z81YwztmzVjDOz5ZQtk3Emr2yZjDN5bWbREW9NFok5B8+PgZXn21mvWXeccThmAIkKyaizzg1VgZrjueeey92PigGpDCJG+kTiloOMvvmfunMM6p0036CjZwExYxGiAFA8j65wUXcJ2+Ew4KVSqN+eAm7q559/flSPZAEpFSmcpAipZG2/IQYkQqbuqCH4LUcgxaq30qT9D5WF3W+3Zcts96fns9tZv/OOTetRpV7pdl5Zm8CgSpsh3bJtTsuyzfXQYdOfkLR1vs4IBE7YDkdG2q2eJuzhw4fP9D8vN3pVsjYhYaMnRWdrM8vYb45Vlh2m4xCDVDw4nEAYNuMMx0gNg9rD6mvZp23IH9KzC4K2bLptfzPYcG6rOy8qm9bL1iNrO6vetmya5qvsuTkv90oR/dJ6peey9eA+Se8POA8qET0Lmzy8KpywHY4EZT0PuwoQKdYckKrNUsLiGuoNdM+S+PLiXvMNEXAcswScZQiBqsGHMqkOW7GaARKhHajstuKEWx120fH2N3VikLE67KKyVepVb5uBK9Vh59Uz3WYBOtVh23oVnQsdNvfM9iNmRdrfowkMHA5H9yQ2kNmewAsOiROUChMxAR22hSQ3YjAT19tKjY7WghO2w9EAINg9qfWw8hCYZmNmhpUB3yJqdNRYIUgVwn4kbKQ8CD7NVehoHThhOxwNAMK8yn5Xuk9iY6BSWH311aO0TdAgqUT4RrLGsgU9KySuDDPoTx9++OFCJw2sFlQ+zb6CVYq1m07BgEDgKZVNzRFTHa21o0avTl1VNnXJL3KcaWtra1dn1dWqLKz9c+o4w2Bmy9vgTJRL22xN8FBjYIGjsukspkgvnba5qI314ITtcDQIllpqqRjsHttsgA4VvTOLiU888UQkBXTaOJPI+xFJWk41zz//fHjhhRciMSm7iiLqQbA22hz6bfTKksQJTGQdPyAYlc2K1oeEbzOMW8cR7MFFTlkZZ9BpqyykyPmKMs5Y4vv888/bzR5oc5pxRgNe6jhT1GZlnFGbU8cZzqXM82oz58tqcxqtj5kROnGbrb2jcMJ2OBoEWIvcfffdNcIWePkhIvTbxA5RRhzIF4JRogLIEAmbhTbIhd9yMc9ynLFOIDaWdtmMMza8atWMM2XK5kXrm9OUhdyrROvLa3PZjDP2/tXLOKP7x2/aoXp3JC2c4ITtcDQIiBZIXkmrFhEpQUQQJ8l7IQRISFIzkiFkwm9IorMR4RyNC/d0dDgaBEhea6+9dnjooYcy9ykAP8l7keaQHvnGhA+3eraZojMlT3MfOloDLmE7HA2E3XbbLey+++4xG3oadwKvOXSp6GrvuuuuONXG3I8FRMXsUFow9M7SwfIbfesKK6xQOuMMeta8jDNI9EUZZzh3UcYZ9O/dkXHmn//8Z7SY6UjGGVQiqJXyMs6kawBFbU512On9cx22w9EiwKsRq5BnnnkmrLzyyu32QTwQBgRNND7CpZIbkv+zMs4oBgkWJkjgqEs848x3ejzjjHTYun/uOONwtBBGjhwZdt5557DtttvOtA/pELKEuLEYQfKTtCopkKwz/EdAKOy7IVYrATqaF07YDkeDAWkYaQyLESRoCxYUkdaQ/DBpQ+JUWjMkSxL7QtTK/whRuxNN68AJ2+FoQOy7777h+OOPD8OGDcvcj44Ukz302uhsma6jw4bQsS+GrFFDsA+dMxHjOEaw+medj7LSwab7LVALWP06ul90y9Idp9HorPOKVU/oWFs2K9O40McErxJQD6ks6hbUInlI28TxarPif1jYNqZtRoVi620ddtK1B9rc2TjYghO2w9GAWH/99cOpp54a1Rx5waggERbVHn/88ajPhhQwAUT6Rs8LGbGIBpHj2ILknZdxhsVBygpctyjjDPtVVgSt8uiCy2ackaeiyqKDTzPOWPzLLOYBjlNZZiW02WacoW55GWdsm/m2DjxZGWeIWV6mzemiY9pmX3R0OFpUyr744ovDQQcdlHuMJG0IWxYYci7BioFFNiRrJG5+52WcATZfIiRXNeOMykPuVTLOcG577SoZZ0Be2XoZZ9KyoGrGmaw2p4uOadnOLDpWtsN+4IEHwtZbbx07AQ/1xhtvbLefEX3//fePHQh3TFaz6+WsA9dff3308KKzoYe79dZbq1bN4WgpbLXVVlEa4+UvAiSNlI1kylSdqf6rr74a1QUQJURuydbRvKhM2HQI7DnJkJGFoUOHhttvvz1MmDAhvPLKK1E6gMAnT56ce85HHnkkDBgwIOyxxx7RnInVcT4vvvhi1eo5HC2Fn/70p2HixImFx6BD5p1cccUV4wIj03ScaXBjR7JLg/g7mheVVSKbb755/BSR7+DBg6MODuy1115hzJgxMXhNv379MsucffbZYbPNNovhJQGLLTgGnHfeeaWkc4ejVYEgc+655xYm6UXPysIielVyCGoBjKm3dMfMfOWyjsSOI43c19GDI5FbWN0xOmzUClY9YPcz07bb1qGE62OxYqPZpXrpsufK2i46Fzpsrq/Bqt657T7uEeoju4hZtp60lVmO1WHbY22asVmuwyYvHdI03lqoTe67774YyYoYCXl49NFHo2Rusemmm86kbrHgpthO0Bm9kMPRqIAY8H781a9+FY4++uioo81LYcai2A9/+MNovWAz0SBlo0+FcJUMFr036hb2Q/YsUqa64SIdtt1fpHdOHWeyypY9V9XtLB122XNzn1Iddnp83rmydNh2f4/qsOsBaUBeWDQYyRn1Ca62ecAMKbUVZZv/83DSSSfFm6lP2iEcjlbKRoPFwnHHHRelZKS5rA9eesTMtpIdv5G4IRCIGacakQfEAelA3EXmcI7GQbcQNimNkLIxoTnjjDPCfvvtF50AuhJHHnlkXEzRh+mPw9GKwLrjF7/4RZQUb7rppsxjIF7UGqgfFOcZEn/66aejhAxho9pA4sW0j+k+RgGoUKw5n6Ox0aUqEUbzo446KkyaNClsueWWtdgIjPqnn356jPebBSUgtWBbfv1ZQOJIYxk4HK1M2mRWR3X4y1/+Muywww4zHYPKYtlll41qFBxqIHH03hAyU3z2k+BAWdjZj7ckMUusepFFzKKMM9gZMzAo1jbEb2GdSFLnF35TF5VNF0PTsinef//9aCaYlXGG3/ZaKZhRMHjp2raNlLPnAtZ+G26r0mYLzmvbbLP7zFLCRpluo2MJPJT0ZlgQ7+A3v/lNO3tTFh353+FwhJoa48ADD4wL8QhF2223XW2f3NWZbWKdxW8IAkma3xAVpMPiI6oR1pVYY0KVyKIli2L8B7GQ4YZ3Ni/jDFI5A4FIi2twXTmO4FUpIs7KOMNvlYUrbNl6GWfefffddgke2J9mnBHRpo4zZPOhzbq2dXZRxhlxV+o4w3W4n2XanJVxhnqobL1BqUsJmweuSgI8qJCgMcxnMWS99daL1h5UDoX//fffH6688sowevToWplBgwZFhTx6aEAnpBzqEyRzgrQ/9dRT0WnA4XD8FywOoh4ZMWJEVHfYiH4QE0SBLwO6at5HrEuQrNFRQ1aoQ5QXkgEAcme9SB6QrD9xLO8z61Blo/VVzThjdeZVM858yZTtTMYZIgb2ZMYZ1btoFtDlhA2RbrDBBrVtWXdgyjdu3LhItuiXd9lll1hRbtioUaPCPvvsUyvDaGVvIpYlV199dVwFR6WyxBJLRAsRpncOh+O/EHkdcMAB4ZRTTpkpBCvEAPniC4HqAwGL9wkpGSLBpVwmafpGckRq3mSTTaKECrF0VewLR9eiMmFjX100QjBCjR07tvAcmPplpUfi43A46gMSRs2IUIQ0LCCFIi2us846NTUJx6L3TSVsfSMJoqqAtNnmODeTbUx4LBGHowkB2fbv3z/ceeedYaeddqr9DyGjqkCihtDZxm39pZdeiiSOJzEqCRzc+JbDDPpbVASYDSJlSweLvhn7blQxhH0tk3EmdZzpqYwz77zzTlSJKL4HA5DVYdfLOMOnoxln7O8sHbYGT+DBnxyOXgh8HK677rqoH7YqDOtYAzlANhA8ZAPpoo5Eb8031iTSvULK6ITR5UJY6IA5LxL4kksuWZPkPePMf+EZZxwORylIvXHzzTfHWCJZQJqFjEjci9QH8UJMSy21VJRyIWj2E7dHZnF8v/baa1E/DvkhMbpOuzHgWdMdjiYFZL3jjjsWRrZEukSqRspDspWEyzbk/OCDD0aTWqkAIG8sv5BmKYuaIZWGHbMOLmE7HE0MJGWm4Eyz5ZiRBaTrLEgtwvQd3TRqAKRqJHG2s7K8WCcbbTMQKAxskdNNCg0iKps6laQGDn0SSZ92qSy/8wJkZZVFX62yDFRFEQ3Tssw6OtrmzsAJ2+FocimbKJjEk0e3i48Eji94FRODpIxaBVKE7NAB42yCAwq/IW8WIBkMIDNc3vmw8AdZyaUdwkbNYkktzThjSZ5radERwrZlaU+9jDNTTLQ7Wxa9PHVTDKJ6GWdsWQYp2l2UcQavUJVN28y3Z5xxOBx1AWFjBQL5bb/99tHh5YQTTojSKslG6kGWERAJRARpQlYQIOSDFA6ZQe74RpAYGDKyC24cr0U1yK5KxhkGAxsJsGrGma+YsqhwqmScsWVtvctknMlrc3dmnHHCdjiaHLhq4/lorSCIMQ954+RmpcR6QK0COaMiwAQQyxDM5JAQiYOP9I61RBpL2tEzcMJ2OFoAqbka2WYg2ipkDZAqkdQhaDLYoL+GtJEgIWksSIoyqju6F07YDkcLAusPQj7kJTsQ8hIioMNmug9x40yDVI3KBPtr1CTSLSOJS5+b5zgD2VsdNlJ6RzPO/LEgw0y6jeOMDUaXnjsta+tVNeOM/c39Qe3RFBlnHA7HrMfll18e1SH1VBeQcZ6NNYRHaj/yQ0IyqEuwz0avi+SOThtSh8whJEn51tklK+OM3Z+1Dex2XjCoettZOmyQVc90OyvjTFE97b4sHbbd31AZZxwOx6wFagsIVC7aHQVSM2QN6bKQCflB1EjZ6Ln5RjqXpYlQLxpdur/edleVBUX17Ey9q5btKJywHY4WA+RA9EyyP3WGKCiLmgBJG2caInVi8YFUzj5I28b1cHQ/XCXicLQYUAWQuPfJJ58M9957b9hwww3j/0rAC8q4mkPKmK6hkuCDmSAqAmLdKxAStsk6ZxXJXY48Rc4qWfj444/bOQFVGZCka1f5KrpkBiZbtmq2K1sWvX9H4YTtcLQgIGSCQxGbHtd0ovqRa5U42liQEOSoHlmi+4WMISui+7GYRqhW9LOQNaoXhXOFSG32FSF1nIG4WIRUcgTqYDO34OyCrjwv48xf/vKX6Jkp4MBjnV0sCWdlnMHNXl6JOO+kGWcEzoUOW4uO1IPAUyqb1tu2WV6QWnTkOLbVZl90dDgc7QCxLLfccjGAE7ps1BjYaxPkadttt41eiqg0IMAiQEw41kBuq666alxIQ8qGrCFhkiRAZKlTSZ7jDMSF3tvGJ4HQq2Sc+dqMMK8iw45mnLFleyLjTFdE63MdtsPRgkCShSgg6JVWWikcc8wxMesT6gz00ZBKXnyRFBAfUu9DDz0U3nzzzehVifs4JAyBOnoOLmE7HC0IJODx48eHYcOGRUsPpFIkRBYiL7jggij5IgHivVgPSLZI2MQmQUWiHJGoCZBmcWF39AycsB2OFgJEzEIc+luk4cMPPzxua3HuRz/6UVQbkMiXHI6QeD3nGoC6gIS9ZK0BSOqoAFCp8L/1fkztni1sXbKQlq0Sh7stI7Kf/a9KvepFCaxX7/TcXRVP3Anb4WghoOpAN40KZNCgQdEsD+mYRTQWw9Zcc81w0EEHhYkTJ0ZPyCLHGSthowIh6BODgBbR+LAPCTtN84WEn5UiDP0t+ygrpGUtecodvkyKsH/+859RXy/7cwYtFvhsirD03AKLjkoTlpcijGPyUoTZNmelCIPQOSfwaH0OhyOCOCDYTUM2mPNBFhAqRLPYYotF076bbropWopg2WCtG7IA4UJElEUy55vzQ0bEGpEVBRK2pwj7bvz2FGEOh6MUkKaPPvrosMMOO4RHH300EjQqj9dffz1KoCTkxTQPy4969sDymET9AeFCtCxYch7+g3ggyCJ1gKNr4YTtcDQpIFRLlkjDpAtDoiaHIwTLb6TcVVZZJe5HB20j8BWZ9SEVUh5Jk8w2AOkVSVFBoDDnY1sqBVDF+5E22LJVM7d88skntbKcpyjjTAqupbJ8V3HCQdXS0TZ3Bk7YDkcTAqK45pprorRMQCbyMqKvxvZ61113jWQtUoJ4tS2C5Lve1FyLdkjU6IM5B/phCPyFF16I5I2zCfpt5Y4E1okmdZyR/loZZyA6zqGyfKcZZ/S7ra0tDjKWHCkrT0vUJUprJh2zdZyBZG3GGfThUlNQL/TOVodtHWc4z3PPPVfLsiNHG9WbgcO22TrOKFytss67Dtvh6GUgDdZdd90VLr300rgYiFcj+tq8vIspykiyECQEtvbaa0di45yQNFIsUfzQESPJsw1ZyQORMlUyzqBft96L1qEly5Hmu0nGGVvWnrtMxhlb1mbRKZNxhsErq82eccbhcLTD2LFjI4kOHDiwZhWBNIoKoyucWSAczssiHBI8UiznRYpHpYK1yA9+8IPKsUCqArUL10wXGnsrnLAdjiYDZHrfffeFyy67LBImkvC1114bLrzwwnDiiSdGibgsshIY8B/TdqRTyBmLDqRUBghJikjFqC6Y5lcN/lQW559/fth///1jXsrJkyd3yzWaDU7YDkeTgQBOWIFowfD++++PLuh777131C2XcYQRUjtsVBhItVqsRHUiEzx02fzHBx0vhI6kDYmj05XqAd2xfnMs5WzAI7ufwcFuUxdtYzPODALS5joAXbpVcTCryDuXgj9pFmDPDezv9Fy0j8EKl/yscxedS8GfpJZKy1ZdWO0UYT/wwAPhtNNOi8p7lO6TJk2KwWSEPCP8U089NRx22GGZ+xjNhw8fHs+FvovYByQRJdiMw+H4L7ChhgDJXi4g7UKw6FMx3+sMGATQ26KHxRJEC3dsQ0SQDTbeEBm23HwgJAgVG20tsuk3pIWO2+qwZdctUF7b/LblMVFkYTWv7PQ650KVYgndnrvoXOjqUx12UT3t7ywdti3bo8GfUK6vsMIKcbqSBUjcfkhVBImTwTkPe+65Z1xAIfYBEgIusxtvvHHdSGIOR28ChHLCCSfE98UCwYbwp+i1sRLpCCAUyBgChlwhWZxM+K2sMryTmPdBPFhL8F7zsVJnmU96rN1Oz8V22bJ9Ms5l96Xnrneuonp3RZt7RMImWExRwBh5ClmJgNxydIQsMBrdcMMN8bh11103/jdy5Mjw61//Ourk6KAOhyNEgQZClnmYgCSIigQd85JLLtlhqxO9v5AxwtKzzz4bpXmkaSRl1CcKy+poQR02do633HJLuOKKK3KPYbrFdCJd2caTinCOeZAuTejMNMPhaHRg6jZmzJjw05/+NHP/Gmus0anzQ/aoVdA1EzcbiRqVCNN6dMBIlPwHaaOfxT5Z719VSxEkeZWtKm1OmTKl3XtfxdkFnrHXVuyPMuBYW7YzbW7YBAYQNQ+2f//+ucewn8WF448/Pk636DQ4BOBWm9pbWpx00knh2GOP7aaaOxyNBVQeWH/Y4ENdARYLka5571hkQ41JfBEkbMzpAHpcCFvfHI9qVE4lqE9s9hWrR08dZ3CJp7zKck1blutjTqhtFgKFaTNc4xUrJM04w2/07IKCP0l9wXGU1bVx27cLhQiYQuo4w/1gcNA233ltToM/sUbH2pzK1rORn2WEjf56l112qWsXylRv9913r43mZMkYMGBAXNjMA6mPhg4d2k7CtgsbDkcrgRc+VYV0BSBBssYoewxkyXuIRMgiJmSDSgRilVcg7zPSt81rmJc1JivjDL9t8CdIvGzGmbnmmqvde875dTyEWyXjjC3LAIR5YndlnGHxs6GDPz344IPR4P66666reywPBNMkbhqNoaPsuOOOuXpvwIOrmgjT4WhWIPVar7ws4O4NuUKykKpM2iBfiBgi4Zv3DFJTdDoIE2kUAsIqBKmb/IeoRiAoJFEI2jHr0W2EjVE/AWewKCkLRkc+dLw77rgjmgI6HL0dTMVRiWDqyruRZWctZxcSvUK6EDeSJSoByBuSRu3BVB9JEBLGpR0yZ0aLdCkJEckQQkdfjZSJVOyE3RiobNbHaMzqMR+ATonfNpg3UvL1118/k/mRQKqh8847r7YNOd9+++3xXJj3YVXC6L7bbrt1rFUORwvhtttuC0svvXQkTkiUd42P8ivy/eqrr8YpN2oB1AjoutlmsYypPNI103KIG7tmxeuwi3aQu/I8MntlhgvxM6W3uuHOospCYWePbStZvl4mnM6gK89bWcJ+6qmnIqEK0iMPHjw4jBs3Lv7GTZZKoofOAi6t1jSIToJOmsDo6Omw2R41atRMwcIdjl6FkSNDW9++4cQ774wpvSwWHT8+/Ou998JrAwbUou8xO8XsDilcUflQiUC4WF2x2McxLKBBxPJzYOGMBUY+8vBDWteiGdI3i4GKZMdv9tu0YGn2FaEjGWdsai+7b2oHMs7YaH1F5y6TcYb7UZRxRmiojDPrr79+3RFjr732ip88aPVZwIaUj8PhMJhtttBnxIjwi1VXbRfnGbJefNy48PvBg6PUDFnwXkJmbLOQCClCOMyIlTQXSRvCYc2Id5hvxQbhHDjgQLAQDouCWjSDaBRLBHDedNEReMaZ78ZvzzjjcPRCTB82LFx2ySXhp08+Gd4cPz68NXBgWO7GG8PiEyeGZ/r3D3/abrvwvS9/OUrVkA3EIBM7SFGEzX5IFyJhFgt5SNqUlIg6BAmVhU0k6O6wSHF0Hk7YDkcDAImX7OOQrKRAPH2f3XTTsNHUqVGiXvSqq0LfKVPCqzvvHKYPHRoWmDYt+i4g9Um6k9SMZIgapJ6EzTdlkcghawgdKbiKOpJzUF6OKFV1trZsFWeWWVm2s23uKJywHY4GAIuGrA2hQmCBkak/C4xnnHFGeGueeWpkPXW22cIfdt01fKNPnygRQ86SkvkN0SoCH8SLNQjkrYVDSdjobPEkZhuyQeKmLISOKgCJXE4h6MTlOAM4D/pwTfEZFKiDSJ56SW+cOs4gyVMvZWphGxNCleXaNuMM57UZZ/7xj3+002HjsKKyzCpQN8gLkbZZxxnabDPOKLa3yjIDoS1ZOmzajHGF2syMBh25ynO82sx5qId02KiZ2FYYWs8443A0OZCU77nnnugdjKVUv379wjLLLBOlZHTWkPW02WcPs0+dGpafPDncv846kXAvuOCCWF4SNoGZIB+2IVwsPSAZiM5K2JAIvg/oviEmyEbR5Pi2GVJSHXZW9hWIzUauq5JxBtKzUfPS/fUyziyWU7ZMxpm8smUyzjBryWpzd2ac8XTHDkeDAILG9+DQQw+NJAeJrzB5clSHvLnbbuHZxx4Lf9pzz7DQRReFjR55JNx7773tpuJYMkAM9j902tayQQSE9ImlCJKurBccjQ+XsB2OBsOTTz4ZpeC+o0aF+ceNC8//+MfhqbXXDnO9+moI++wTZpt99rDQ+eeHW4YMieoDqUSQcpHimPZbVQmLkEz1ZZaG+oNjIWqZo3WlnbWj++CE7XA0GJ5//vkYwrjPU0+FN4cMCf87YED48r//HSVnvId/h5S84IJhvnfeCV+dMc2WCiSNcaH/0Z/KLI1tzNfkC4HjjXwgBHTUsmdmeo9aQTpZ6oEJofYDu21/KzuNjW5XVDbdZqZR9tisetBeqxLJq2e6reBPsstO25xu298Mfuj9bSTCdH9H4YTtcDQQeJlZVINo/jB4cPzv4w8/rC1a1STqAQPi98JGki76hjAlYSuLDBYOkArSNvpWzP8Efmsb/SykZ/dD4nYb9Yq27W+lGbPHMqDklaU+9tzULe/YrG17bkiT35aw8+qZtpnFSqvDTuuVbttzcZ+5Z3n11MJmR+CE7XA0EJ544ol26b8AhMMLT5wQa4oH+IYgkJghYPu//U4dP4grooBPlEO6ttImVira5vzUwe5nMTRv2/5moLDeh+m5s85l96dl5ym4bl5ZS9h59QQsvtqyEHZH2gyZM/DmHWutXKrCCdvhaCCQfYnFRguIAz00JmwQN/baVoJWND3FhraSNWoPBd+HeBWLmgVKiIVFSczMesqO2NE5OGE7HA2Ep59+OmyxxRbt/oOsIVbss7MkbEnMEG/6P1KdNetDwtY5MCNk0RHJHdttR+PDCdvhaBAwjWb6nnoZortm0Qp1CfvkTKIsJ3Izl3ke+9GT2kBG6FuRsEXMRMZE8sZpBbWF/kd3ywKk9fxLHWcgf86tRTWkfpt9hUVMAR025eU4Qx1QxahsmnGGuqJn17YNsDRt2rSow7eLebRLx9qAVSprHWeoF+dWeeWxBMxAmKnkZZzhWM6tstyLvDanwZ+4V3JY0rU6Cidsh6NB8Pjjj8cATGm8a9QX2EvjpCHpGIcYiFZJPlLJG0KxwZ9SxxkdC+GwDUGLeImPDelUCf6kcK1lMs5QDxv8CQKrknFmURP8yba5asYZWxZSRY9fJfgTbSkb/Iln6sGfHI4W018Tq9pKlUh9LFixmIakCkRAkBlEa6VKIU8nrbL6lhQOca+44orx+pzP7bIbE07YDkcD6a+33HLL2jaSGdN8pFS8IJHoUAkQAwNpE8mb+BgQPKRuFxuRNJFGIWSInsVHiFjJZDkH36hGOBZJkmOZ+iNpOxoTTtgOR4Pqr9GfMq2HsKPDzAz1hVQiqDdYMMS+V9NvqTf4D6lc4VUhZY5No/VxTcXDYEoPuWOFYm2IU9gkAI2ccaatYh2Ljrd686rgvJ0pb+GE7XA0ANABpySJFI2+E50skrUWF/mGlJGW+dYCF5I1x0O6fCRhIz2jh2ZQkDmfzqWEApTlXEjilLWmfmwzcGhRk3jZXKcZMs5MmzatyzLOcEzTZZxxOBxdDxbxpKMWIBskY6TpNIa1JGy+bSQ4LEo6knFGC2GYDlLOLpp5xpngGWccDsd/kTVlRi8Nibz44otRr2yzxFgJ2+quIUNJrxAOUqE160slbJVl+wc/+EG8jqRhR+PBCdvhaCBAwJIImVZDusstt1yhhA3SYE9lzPqyTAEpKwnV0XhwwnY4GgSoCVCLSF2AXTB62dtuuy1Oxa1UDNGmEjZZawDHke4LcD6ka+V0lITNYPDYY4/VHGbk+ME+9OUKFpXlOMMgYPM+MguwibWt12SacYZvZgB4ZkrH/dprr83kHJOVcWb69OnxulaH/fLLL8+kL5faIs2aTpttxhmuI+Bgk2acsY4ztJFBz6o1rN6aegmcx+qw0WlzPGoknaujcMJ2OBoEkpblOANBoJPFkUW6VysV2+wrWd9Iy1iAWB22XNMhRhIgYH0iElJZbLGrZpzh/GUcZ7LKQox2uyjjTN++fWfKOJNXtkzGmbyyZTLOoD4qo8NOy7oO2+FoAfCCP/fcczUJW/kPWQhEepT0Z6PAWSDlQcjWPA21iHW0QS8uyRUygsSRRB3NASdsh6NBsPrqq4frrruutg35otpAOsOpBqJGvwyJy2EG6BuJUKRvo/ahrrAZZ1ALKMkvx0PwnYlv4eg5OGE7HA0Cpu9ZUfNQKyAdI3lbUzyQfkPIRRlnOA8EjhMNulWImsBE6Kml9wboYGVmKDtiAYK3+9Pj7W8Ff7ILmZxP+6ucC8jGXCgqy3VQW6ju6bVsPbLKMgDaIFh2f9G5GBjZtjMhe6xsvTsCJ2yHo0EAodqIcQIky0IVqgtIu0yGmfQbfS4LmJA1i18QKSSCjhu3dyL/5XkK8q1P1v50G5KzZYuOLbNtf/dJvCyL6qV65O3P8tjMa3O9dqTHFl23M16iTtgOR4MAUs16mZHYkFBJbJAVD5sFRIg3K062viEvLCaIVUK8EJxP0G/L0gOJ0OZ0ZNFM25w3XXS0+9Nt+5uBgevmHVvlXFW3GZD4bWcHZc/NPU0XHZGQy9RT1iF5x1pvyapwwnY4Ggxp7Alc1llQvPvuuyPJprGikZrlnZhmnIFkWLxEukYtAnlB3vznaD44YTscDQQFxrdxRYifwSLhWmutlZtxBokZSbYo4wwSnuJrZKleHC1I2A888EA47bTT4kozo/6kSZPCtttuW9ufF5Xq1FNPDYcddljmPjrQyJEjw4QJE+J0DV3ekCFDwtFHH91lUa4cjmaxxUYKtkkMeB9w0uA/VBdYfCg0qhYR+bYOJViRsKDIRwt+kD6Ej9TN+0sZvV82kzfnKVoYg+xxtLEZZyyK7IxRkZDlxmacsbBBoYB9/6dPn97uuuIOgToXzRy4X3I+0j0SKGe3UzBo6n4Dqyqp12ZUJLbNPZpxhpuywgorhN133z30799/pv3W4wfgpbXHHnuE7bffPvecp5xySrjwwgvDFVdcERdAnnrqqbDbbrvFm3LAAQdUraLD0bSAnJ999tmZSBBie/TRR2u21BCA3MwB35CwVCI4uyi2NeoQCF5mfYDzIJWrvMoCrEewVsFpJy9anzK4C1xT5SFF1T8rWh/Zc2xZe23KMruwah/p9afOyDiTV5Z2QrzaTj0dOXdemyFr2iXLjqxofTj/2Gtzvqw2Z0Xrw7NTZXvUcWbzzTePnzwo2pVw0003hQ022KDmxZWFRx55JGyzzTa14O3c1GuuuSbmsHM4ehNWW2218Otf/zpzHwQCscirEPK1NtTWnI9j2bfGGmvUXLCR0K1Ea8nHloVoUKPUi9ZnYcuXidZXpmxetL68slWj9dmyCKK0qV60vrxrl4nW1/A6bKYRt9xyS5Sci4Bu7uKLL46mRd/73vei4f9DDz0URo8enVuGjmCjinVm1HI4GgUIK+ksVUBtoeh7SHZIfZADKhEl5wXKQKPMMor0l+aKdDQfupWwIWpGvSzVicURRxwRCff73/9+HBUZgUeNGhV22WWX3DInnXRSOPbYY7uh1g7HrAOqBgSdLCBRIz0iuUnC5jeELAk2TcILkeMYA3GjynQ0N7qVsC+//PJIuuk0JsUvf/nLcNVVV4Wrr7466rDR4R100EFxCjJ48ODMMkceeWQYOnRobRvCtwFmHI5mBNPwvIUzCBgyR7pG14qEjXCDjpnZJpI1DjaoHaSDRdLGQgTJHNWIdNhF2VdSHWyqw0a/zXU6knGG87J4aD0MizLOsG112H8vyDjDYigqEZtxJtVh18s4Y6P1pTpsuwZQ1Oas+2ejHTZktL4HH3wwhk20sRHygPUIUvZOO+0Ut4n/yw1Ais4jbG5mmvHC4WgFKBdjGpeaFx89MjNRSdhFGWcgIAhzpZVWiuqQNGNKXvaVVAfrGWdC62ecueyyy2LoxjLTMEY3e1N1s60fv8PRW8AiPZZS66+/frv/IUn02zLJy8o4A0Ej3SGlQxSbbbZZJDlJx47mRmXCZuSXKQvANhEVBkb5WtRgBLn++uvDGWeckXmOjTbaKGy33XZh//33j9tbb7111FlTHpXIM888ExccMR10OHobBg0aFNV9fFvwrvGOILmVyTgDIHXUIi789FLCZuRHAhCkR0Z1MW7cuPj72muvjXqiAQMGZJ4DXRo2k8K5554bhg8fHvbdd99aKqO99947jBgxoiNtcjiaGqgP0L9aJw/Ab6RmZqNM7VOVCeQMKIetM4kI+Ea3i+ksC5qCMqkI9n1ESk9VFhaoHeyMOM2+UhSsCTWDNevjWqn+Nw99+vSZqc02ljczdazMLOy10zZbb09UN9ZUMQ0PkLYZ1Y6td5HZHm3uKrO+Pm2dCR3VQKDTyK03dTpwOJoNAwcODOutt147/wUtuOGEwf/0dfo8qhAkb7ZRQyIQKQwrqhDIBTLHfNYumqm8flNWsGm6tOho3ytbNi1vyypFWBq6Na9sWh5SRA/dkbJ//vOf4yBlybJsm9NFxypt1qKj0qClZdGVk0WoI1zlsUQcjgbEJptsEsNAWMLmhUeKVGqqNC42jmbMUBXcif2rrrpq3IYwIXG7aGbLWzUnsPFKyqQIs+Vt2TIpwoqurbZ1pOxsdVKEFZUtkyIsr82eIszh6GXYYYcdojOZhRYSkRjTqHwiR/TVSJbsR5LDzZv4Gz7rbA04YTscDQg5vWBuppgeSNdM0WXWlxW1Dz2tXKbRD7/44ovR1I/fTNWlP0bqhuClM7a/Ab+1zTdSqra5bnq83bZl+R+JM+/Yettp2U/NubO2s8paCTuvnllluUfWdNHuT+9Ber+K2mw9tKvCCdvhaFCwuE8ux3XWWSdusyhGQCctSKJbRtrOyzSDKgPrLfTXTMPRmaLWQBcOEWHxJXM/zmVN/+R8A7TQKUsTyMruT7e5jn4zULBPg069axWdq952VlkWU1GNZO23v9N68RuViJx00rJpeVsPqUSU8Dg9tmhhtR6csB2OBl54PPjgg2uEDTlDmhAt2WfQz+ZlmEnN/CB7CAiJHdNZCJT/pWeFZKzO1W5jx53qsCEde7zdtmWRJuVCn3XurO28c1Uty2DBoqOVsMuem3uV6rDTNtvj09+Qdd6xrsN2OFo4EFRqYoZaBL00pIDEzQdIgrOSNnpsSBOpGvKB/CVxOpoPTtgORwNj6aWXjpIiNtUA8paZG9Etsb7AnTpPwrYZZ/iN+RnmZmkAfkdzwAnb4WhgIBETUx6rEQVekrSNHhp1A/rWF154od3UH5K3+lcAceMQgw785ZdfjsfIzM9mW0HtUpQVBX0t51DZ1JlFQY+yQJ3Qv6tsGpsjzfpiZxZtbW1RH2/LpE44aaxt62bCvbPlbT0pZ9ucuqeggybqocqmTjj2XKmTDNY9ts2+6OhwtCjwFh47dmzmPogBgoM811xzzagasRln0LnyjTTNN8dCtuixIRikdulVkcBtxhnOqWh8WdH6sDxRWaUpU3m+teCWlXEG/bktS2iLshlnpk2b1k43TOz8NOOMVD5ptD7r2i9PxzTjjNqcOs5A1rbNDI6cL6vNWRlnIHiVbchofQ6Ho/NgwQ8pWgtZKZSwFxKGXNJ42CxQIllDMJAZrttYjqROIBxTNeOM9ZqsmnGmTNm8aH2z55QtE63Pnpv7VjXjTJl650XrU9nOrCE4YTscTaAWISAaruVZUCwPSAGSQSqFuCBWiAgJGWmT/2w8EUfzoWUIW9MmTxXmaDWQBGSvvfaKoYqR3tKM5vy3/PLL16REptySEJHKkSR5P5AekZYh9zTYEWX07nA+u81vBgNJhmwj6VqnkvR4/eY6dptvdORWD51XNt2eOnVq4X5+Kxa43ScJu6iskhco+QH7qKPUKTq2TL3T+8e3vX/6vyNhnFom+BM6Js8443A4mgUKTtUrCZtRlcUQ9FhdFcqwK6EUZjyk3hLXwdvsbW5VfNiJNkO5SODoxNPELb1GJULDm0E/x8PtLZ1a8Db3Dniby6OjdvDV6N3hcDgcswxO2A6Hw9EkcMLuIbCifswxx/SqTO/e5t4Bb3PPoWUWHR0Oh6PV4RK2w+FwNAmcsB0Oh6NJ4ITtcDgcTQInbIfD4WgSOGE7HA5Hk8AJuw4eeOCBsPXWW0c3Ulzeb7zxxnb7MbIZMWJEWGCBBWKgnY033ji88cYbM53nlltuCauvvno8hjCV2267beF1y563VdpM6M9f/OIXYbnllotR5bj2oEGDYriBVn7OFvvss0+89llnnRVavc2vvPJK6NevX/T443mvuuqqtdRmrdjm//znP2H//feP3tiUIZPQRRddVLn+Tth1QBQvoqSdf/75mftPPfXUcM4558Sb//jjj8fOt+mmm7bLjHzDDTfEhKq77bZbDLj+8MMPh5133rnwumXO20ptJoA8GcKHDx8evydOnBhee+21+FK38nMWJk2aFB577LFIJD2FWdVm0pStvfba4fvf/3647777wvPPPx+fOxEAW7XNQ4cODbfffnuYMGFCHKwOOuigSOCTJ0+u1gDssB3lwO2aNGlSbXv69Olt3/zmN9tOO+202n8ffPBB21xzzdV2zTXXxO0pU6a0LbTQQm2XXnpp6euUOW+rtTkLTzzxRLz+H//4x7ZWbvPbb78dy7744ottiyyySNuZZ57Z1tPoyTbvuOOObbvuumvbrEbowTYvs8wybccdd1y7/1ZeeeW2YcOGVTqPS9idACmCSMvEtElgisdU6dFHH43bSIvkkSM41UorrRSnWptvvnl48cUXO3XeVmtzFsimwrSVeM6t2maiTCKtHXbYYWGZZZYJjYLuajPtRZ1A5hskV2Jyc85UNdFqz3mttdaK0jRlGSvuvffemER5k002qVRHJ+xOgIcLyEJtwbb2vfnmm/F75MiR4eijjw4333xz1Hmtv/76MXVQR8/bam1OwRQUnTY5DWd1BLjubPMpp5wSU0cdcMABoZHQXW0mdyT63JNPPjlsttlm4c477wzbbbdd6N+/f7j//vtDqz7nc889N+qt0WGTWIK2o5ZZd911K9XRCbuboewXw4YNC9tvv31YZZVVYlJVJMfrr78+tCI622YWIMkSjiRy4YUXhlZt829/+9tw9tlnh3HjxjVkDPfuaLPKbLPNNuHggw8OK664YjjiiCPCVltt1aFFuGbp2xA2axRI2Tz3M844I+y3337h7rvvrnR9J+xOQAk7//73v7f7n23tY8oEGF0FAsYsvvjiuaviZc7bam1OyZo0Vnfdddcsl667s80PPvhglDjJ5I2UzYd2H3LIIWHRRRcNrdhmMo/TTlsGLLXUUj1iJTIr2kzKsKOOOiqMHj06WqiQzo0Fxx133DGcfvrpoQqcsDuBxRZbLD7I3/zmN+0yUbC6vOaaa8ZtRmAeKBYPlpTeeuutmNG5o+dttTZbssaMCslj/vnnD42A7mozumssJJ599tnaBysR9Nl33HFHaMU2ow7AhM+WAehzi/pGM7d5ypQp8ZNmlyHHoyT20qi0RNkL8e9//7vtmWeeiR9u1+jRo+NvWS6cfPLJbfPNN1/bTTfd1Pb888+3bbPNNm2LLbZY2yeffFI7x4EHHhhXlu+44462V199tW2PPfZo+/rXv9723nvv1Y5Zcskl2yZOnFjbLnPeVmrz559/3tavX7+2b33rW23PPvts29/+9rfa57PPPmvJNmehJ61EZlWb+T3HHHO0XXzxxW1vvPFG27nnnts222yztT344IMt2+b11lsvWorce++9bW+++Wbb2LFj2+aee+62Cy64oFL9nbDrgBvMg00/gwcPrpkCDR8+vO0b3/hGNP/ZaKON2l577bV254CMDjnkkPhQv/SlL7VtvPHG0YTLgnPyEIUy522lNv/hD3/IvCYf6tOKbZ7VhD0r23zZZZe1ffe7342ktcIKK7TdeOONLd3mv/3tb21DhgxpW3DBBWObIfQzzjgjXq8KPB62w+FwNAlch+1wOBxNAidsh8PhaBI4YTscDkeTwAnb4XA4mgRO2A6Hw9EkcMJ2OByOJoETtsPhcDQJnLAdDoejSeCE7XA4HE0CJ2yHw+FoEjhhOxwOR2gO/H9Y7oTm1jkDoQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -1204,7 +1221,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/docs/whats-new.md b/docs/whats-new.md
index ed59fa74..3289246e 100644
--- a/docs/whats-new.md
+++ b/docs/whats-new.md
@@ -4,6 +4,7 @@
 
 ### Feat
 - usage of outside time buffer in `dfmt.cmems_nc_to_ini()` so noon-centered or monthly timestamps are also supported in [#1087](https://github.com/Deltares/dfm_tools/pull/1087)
+- correct CMEMS daily mean data ("P1D-m") from midnight to noon by adding a 12-hour offset in `dfmt.download_CMEMS()` in [#1088](https://github.com/Deltares/dfm_tools/pull/1088)
 
 ### Fix
 - made p-drive paths for tide models and gesla3 work on linux also in [#1083](https://github.com/Deltares/dfm_tools/pull/1083) and [#1085](https://github.com/Deltares/dfm_tools/pull/1085)
diff --git a/tests/test_download.py b/tests/test_download.py
index c0775c92..f6a3cf48 100644
--- a/tests/test_download.py
+++ b/tests/test_download.py
@@ -250,70 +250,112 @@ def test_copernicusmarine_get_dataset_id():
     assert dataset_id == 'cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m'
 
 
+@pytest.mark.parametrize("varkey", [pytest.param(x, id=x) for x in ['bottomT','no3','so','tob']])
 @pytest.mark.requiressecrets
 @pytest.mark.unittest
-def test_download_cmems_my(tmp_path):
+def test_download_cmems(tmp_path, varkey):
+    """
+    Test whether downloading cmems data works properly.
+    Especially whether time/spatial extents are correct (buffered)
+    and whether daily means are corrected with a 12 hour offset.
+    
+    the variables retrieved are arbitrary, but are present in the respective datasets
+    avaliable variables differ per product, examples are ['bottomT','mlotst','siconc','sithick','so','thetao','uo','vo','usi','vsi','zos','no3'].
+    More info on https://data.marine.copernicus.eu/products
+    
+    In the variable/dataset selection it is ensured to cover both
+    reanalysis and analysisforecasts, both daily and monthly means,
+    both bio and phy, both 0.25deg and 0.083deg spatial resolutions
+    and both variables including and excluding depth
+    """
+    
     # deliberately take inconvenient time/spatial subset to test if
     # coordinates_selection_method='outside' works properly
-    date_min = '2010-01-01 01:00'
-    date_max = '2010-01-01 23:00'
     longitude_min, longitude_max, latitude_min, latitude_max =    2.001,   3.001,  51.001, 52.001 #test domain
-    varlist_cmems = ['bottomT','no3'] # avaliable variables differ per product, examples are ['bottomT','mlotst','siconc','sithick','so','thetao','uo','vo','usi','vsi','zos','no3']. More info on https://data.marine.copernicus.eu/products
-    dataset_id_dict = {'bottomT':'cmems_mod_glo_phy_my_0.083deg_P1D-m',
-                       'no3':'cmems_mod_glo_bgc_my_0.25deg_P1D-m'}
+    dataset_id_dict = {'bottomT':'cmems_mod_glo_phy_my_0.083deg_P1D-m', # phy my daily mean no_depth
+                       'no3':'cmems_mod_glo_bgc_my_0.25deg_P1D-m', # bio my daily mean wi_depth
+                       'so':'cmems_mod_glo_phy_my_0.083deg_P1M-m', # phy my monthly mean wi_depth
+                       'tob':'cmems_mod_glo_phy_anfc_0.083deg_P1D-m', # phy anfc daily mean no_depth
+                       }
     file_prefix = 'cmems_'
-    for varkey in varlist_cmems:
-        dataset_id = dataset_id_dict[varkey]
-        dfmt.download_CMEMS(varkey=varkey,
-                            longitude_min=longitude_min, longitude_max=longitude_max, latitude_min=latitude_min, latitude_max=latitude_max,
-                            date_min=date_min, date_max=date_max,
-                            # speed up tests by supplying datset_id and buffer
-                            dataset_id=dataset_id,
-                            dir_output=tmp_path, file_prefix=file_prefix, overwrite=True)
+    dataset_id = dataset_id_dict[varkey]
+
+    if "_my_" in dataset_id:
+        date_min = '2010-01-01 01:00'
+        date_max = '2010-01-01 23:00'
+    elif "_anfc_"in dataset_id:
+        date_today = pd.Timestamp.today().floor("1D")
+        date_min = date_today + pd.Timedelta(hours=1)
+        date_max = date_today + pd.Timedelta(hours=23)
     
-    # assert downloaded files
-    file_nc_pat = os.path.join(tmp_path, "*.nc")
-    ds = xr.open_mfdataset(file_nc_pat)
-    for varn in varlist_cmems:
-        assert varn in set(ds.variables)
-    assert ds.sizes["time"] == 2
-    assert ds.time.to_pandas().iloc[0] == pd.Timestamp('2010-01-01')
-    assert ds.time.to_pandas().iloc[-1] == pd.Timestamp('2010-01-02')
-    assert np.isclose(ds.longitude.to_numpy().min(), 2)
-    assert np.isclose(ds.longitude.to_numpy().max(), 3.25)
-    assert np.isclose(ds.latitude.to_numpy().min(), 51)
-    assert np.isclose(ds.latitude.to_numpy().max(), 52.25)
-
-
-@pytest.mark.requiressecrets
-@pytest.mark.unittest
-def test_download_cmems_forecast(tmp_path):
-    date_min = pd.Timestamp.today()
-    date_max = pd.Timestamp.today() + pd.Timedelta(days=1)
-    longitude_min, longitude_max, latitude_min, latitude_max =    2,   3,  51, 52 #test domain
-    varlist_cmems = ['tob','no3'] # avaliable variables differ per product, examples are ['bottomT','mlotst','siconc','sithick','so','thetao','uo','vo','usi','vsi','zos','no3']. More info on https://data.marine.copernicus.eu/products
-    dataset_id_dict = {'tob':'cmems_mod_glo_phy_anfc_0.083deg_P1D-m',
-                       'no3':'cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m'}
-    file_prefix = 'cmems_'
-    for varkey in varlist_cmems:
-        dataset_id = dataset_id_dict[varkey]
-        dfmt.download_CMEMS(varkey=varkey,
-                            longitude_min=longitude_min, longitude_max=longitude_max, latitude_min=latitude_min, latitude_max=latitude_max,
-                            date_min=date_min, date_max=date_max,
-                            # speed up tests by supplying datset_id and buffer
-                            dataset_id=dataset_id,
-                            dir_output=tmp_path, file_prefix=file_prefix, overwrite=True)
-
-    # assert downloaded files
-    file_nc_pat = os.path.join(tmp_path, "*.nc")
+    if varkey == 'so':
+        # monthly mean, times are at midnight (no offset applied)
+        times_expected = ['2010-01-01 00:00:00', '2010-02-01 00:00:00']
+        fnames_expected = ['cmems_so_2010-01.nc', 'cmems_so_2010-02.nc']
+    elif varkey == 'no3':
+        # daily mean, so offset of 12 hours is applied
+        times_expected = ['2009-12-31 12:00:00',
+                          '2010-01-01 12:00:00',
+                          '2010-01-02 12:00:00',
+                          ]
+        fnames_expected = ['cmems_no3_2009-12-31.nc',
+                           'cmems_no3_2010-01-01.nc',
+                           'cmems_no3_2010-01-02.nc',
+                           ]
+    elif varkey == 'bottomT':
+        # daily mean, so offset of 12 hours is applied
+        times_expected = ['2009-12-31 12:00:00',
+                          '2010-01-01 12:00:00',
+                          '2010-01-02 12:00:00']
+        fnames_expected = ['cmems_bottomT_2009-12-31.nc',
+                           'cmems_bottomT_2010-01-01.nc',
+                           'cmems_bottomT_2010-01-02.nc',
+                           ]
+    elif varkey == 'tob':
+        # daily mean, so offset of 12 hours is applied
+        datetime_first = date_today - pd.Timedelta(hours=12)
+        date_first = datetime_first.date()
+        times_expected = [str(datetime_first + x*pd.Timedelta("1D")) for x in [0,1,2]]
+        fnames_dates = [str(date_first + x*pd.Timedelta("1D")) for x in [0,1,2]]
+        fnames_expected = [f"cmems_tob_{x}.nc" for x in fnames_dates]
+    
+    if '0.25deg' in dataset_id:
+        lon_max_exp = 3.25
+        lat_max_exp = 52.25
+    elif '0.083deg' in dataset_id:
+        lon_max_exp = 3.08333
+        lat_max_exp = 52.083332
+    
+    if 'P1D-m' in dataset_id:
+        freq = "D"
+    elif 'P1M-m' in dataset_id:
+        # when downloading monthly means with daily freqs, we would get many
+        # empty files for dates other than the first day of the months.
+        freq = "M"
+    
+    dfmt.download_CMEMS(varkey=varkey,
+                        longitude_min=longitude_min, longitude_max=longitude_max, latitude_min=latitude_min, latitude_max=latitude_max,
+                        date_min=date_min, date_max=date_max, freq=freq,
+                        # speed up tests by supplying datset_id
+                        dataset_id=dataset_id,
+                        dir_output=tmp_path, file_prefix=file_prefix, overwrite=True)
+
+    # open downloaded files
+    file_nc_pat = os.path.join(tmp_path, f"{file_prefix}{varkey}*.nc")
+    fname_list = [os.path.basename(x) for x in glob.glob(file_nc_pat)]
+    fname_list.sort()
     ds = xr.open_mfdataset(file_nc_pat)
-    assert ds.sizes["time"] == 3
-    assert ds.time.to_pandas().iloc[0] == date_min.floor("D")
-    assert ds.time.to_pandas().iloc[-1] == date_max.ceil("D")
+    times_actual = ds.time.to_pandas().dt.strftime("%Y-%m-%d %H:%M:%S").tolist()
+    
+    # assertions
+    assert varkey in set(ds.variables)
+    assert ds.sizes["time"] == len(times_expected)
+    assert fname_list == fnames_expected
+    assert times_actual == times_expected
     assert np.isclose(ds.longitude.to_numpy().min(), 2)
-    assert np.isclose(ds.longitude.to_numpy().max(), 3)
+    assert np.isclose(ds.longitude.to_numpy().max(), lon_max_exp)
     assert np.isclose(ds.latitude.to_numpy().min(), 51)
-    assert np.isclose(ds.latitude.to_numpy().max(), 52)
+    assert np.isclose(ds.latitude.to_numpy().max(), lat_max_exp)
 
 
 @pytest.mark.unittest
diff --git a/tests/test_interpolate_grid2bnd.py b/tests/test_interpolate_grid2bnd.py
index caf8847a..02ae578e 100644
--- a/tests/test_interpolate_grid2bnd.py
+++ b/tests/test_interpolate_grid2bnd.py
@@ -13,7 +13,6 @@
 import datetime as dt
 import xarray as xr
 import shapely
-import pandas as pd
 import geopandas as gpd
 from dfm_tools.interpolate_grid2bnd import (tidemodel_componentlist,
                                             components_translate_upper,
@@ -65,6 +64,7 @@ def cmems_dataset_notime():
                        [np.nan, np.nan,    np.nan],
                        [np.nan, np.nan,    np.nan]]])
     ds['so'] = xr.DataArray(so_np,dims=('depth','latitude','longitude'))
+    ds['so'] = ds['so'].assign_attrs({"units":"dummyunit"})
     lons = [-9.6, -9.5, -9.4]
     lats = [42.9, 43.0, 43.1]
     depths = [-0.494025, -1.541375, -2.645669, -3.819495, -5.078224]
diff --git a/tests/test_modelbuilder.py b/tests/test_modelbuilder.py
index 355413ce..000af755 100644
--- a/tests/test_modelbuilder.py
+++ b/tests/test_modelbuilder.py
@@ -38,6 +38,78 @@ def test_get_ncvarname_list():
     assert "quantity 'nonexistingbnd' not in conversion_dict" in str(e.value)
 
 
+@pytest.mark.parametrize("timecase", [pytest.param(x, id=x) for x in ['midnight','noon','monthly']])
+@pytest.mark.systemtest
+def test_cmems_nc_to_bc(tmp_path, timecase):
+    """
+    tests for midnight-centered data, noon-centered data and monthly timestamped data
+    """
+    file_pli = os.path.join(tmp_path,'test_model.pli')
+    with open(file_pli,'w') as f:
+        f.write("""name
+                    2    2
+                    -9.6   42.9
+                    -9.5   43.0
+                    """)
+    
+    ds = cmems_dataset_4times()
+    if timecase == "midnight":
+        ds["time"] = ds["time"] + pd.Timedelta(hours=12)
+    elif timecase == "monthly":
+        ds["time"] = [pd.Timestamp("2019-11-01"),
+                      pd.Timestamp("2019-12-01"),
+                      pd.Timestamp("2020-01-01"),
+                      pd.Timestamp("2020-02-01")]
+    
+    dir_pattern = os.path.join(tmp_path, "temp_cmems_4day_so.nc")
+    file_nc = dir_pattern
+    ds.to_netcdf(file_nc)
+    
+    ext_new = hcdfm.ExtModel()
+    
+    ext_new = dfmt.cmems_nc_to_bc(ext_new=ext_new,
+                                  list_quantities=["salinitybnd"],
+                                  tstart="2020-01-01",
+                                  tstop="2020-01-03",
+                                  file_pli=file_pli,
+                                  dir_pattern=dir_pattern,
+                                  dir_output=tmp_path,
+                                  )
+    
+    file_expected = tmp_path / "salinitybnd_CMEMS_test_model.bc"
+        
+    if timecase == "midnight":
+        times_expected =  [
+            '2020-01-01 00:00:00',
+            '2020-01-02 00:00:00',
+            '2020-01-03 00:00:00',
+            ]
+    elif timecase == "noon":
+        times_expected =  [
+            '2019-12-31 12:00:00',
+            '2020-01-01 12:00:00',
+            '2020-01-02 12:00:00',
+            '2020-01-03 12:00:00',
+            ]
+    elif timecase == "monthly":
+        times_expected =  ['2020-01-01 00:00:00', '2020-02-01 00:00:00']
+    
+    assert os.path.exists(file_expected)
+    forcing_obj = hcdfm.ForcingModel(file_expected)
+    ds_out = dfmt.forcinglike_to_Dataset(forcing_obj.forcing[0])
+    actual_times = ds_out.time.to_pandas().dt.strftime("%Y-%m-%d %H:%M:%S").tolist()
+    assert actual_times == times_expected
+    
+    assert "salinitybnd" in ds_out.data_vars
+    assert ds_out.salinitybnd.isnull().sum().load() == 0
+    
+    # check whether the cmems depth definition comes trough
+    assert 'z' in ds_out.coords
+    depths_expected = np.array([-0.494025, -1.541375, -2.645669, -3.819495, -5.078224])
+    assert np.allclose(ds_out['z'].to_numpy(), depths_expected)
+    assert ds_out['z'].attrs['positive'] == 'up'
+
+
 @pytest.mark.parametrize("timecase", [pytest.param(x, id=x) for x in ['midnight','noon','monthly']])
 @pytest.mark.systemtest
 def test_cmems_nc_to_ini(tmp_path, timecase):