DP02_07b_Variable_Star_Lightcurves.ipynb

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   "source": [
    "<img align=\"left\" src = https://project.lsst.org/sites/default/files/Rubin-O-Logo_0.png width=250 style=\"padding: 10px\"> \n",
    "<br>\n",
    "<b>Variable Stars in DP0.2</b> <br>\n",
    "Contact author(s): Jeff Carlin and Ryan Lau <br>\n",
    "Last verified to run: 2024-01-31 <br>\n",
    "LSST Science Pipelines version: Weekly 2024_04 <br>\n",
    "Container Size: medium <br>\n",
    "Targeted learning level: intermediate <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba3304f-ee93-4469-978b-a98ff0f2bbb4",
   "metadata": {},
   "source": [
    "**Description:** Use catalog data to identify variable stars and plot their lightcurves. **Adapted to use the dp02_dc2_catalogs_frdf catalogs produced at CC-IN2P3.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e1fddb-395d-4aad-971f-f120dab415ca",
   "metadata": {},
   "source": [
    "**Skills:** Use various TAP tables, including joining multiple tables. Extract time-series photometry. Measure periods and plot phased lightcurves."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7980fe9c-9d9a-429d-ab4b-4812c6fef3fe",
   "metadata": {},
   "source": [
    "**LSST Data Products:** TAP tables dp02_dc2_catalogs_frdf.Object, ForcedSource, CcdVisit, DiaObject, DiaSource, ForcedSourceOnDiaObject<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e317548d-7f06-4b65-928a-bce2bd5a95e6",
   "metadata": {},
   "source": [
    "**Packages:** numpy, matplotlib, astropy.units, astropy.coordinates, astropy.io.fits, astropy.timeseries.LombScargle, lsst.rsp.get_tap_service"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9d68a6-93b9-42dc-8291-88564cd53563",
   "metadata": {},
   "source": [
    "**Credit:** Originally developed by Jeff Carlin and the Community Science Team for Rubin Data Preview 0 with improvements contributed by Bob Abel and updates contributed by Ryan Lau.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2912bf-a172-4f6e-b8ce-38e03a253d68",
   "metadata": {},
   "source": [
    "**Get Support:**\n",
    "Find DP0-related documentation and resources at <a href=\"https://dp0-2.lsst.io\">dp0-2.lsst.io</a>. Questions are welcome as new topics in the <a href=\"https://community.lsst.org/c/support/dp0\">Support - Data Preview 0 Category</a> of the Rubin Community Forum. Rubin staff will respond to all questions posted there."
   ]
  },
  {
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   "id": "c9413598-6c09-45b5-baf9-3b0e9a38da44",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "This notebook demonstrates use of the Table Access Protocol (TAP) service to query the time-domain data products at the location of a known RR Lyrae variable star.\n",
    "\n",
    "In Section 2 a known RR Lyrae star is identified in the `Object` table, and then photometry is retrieved from the `ForcedSource` table.\n",
    "Recall that the `Object` table contains astrometric and photometric measurements for objects detected in coadded images, and that\n",
    "the `ForcedSource` table contains forced photometry on the individual processed visit images (PVIs; direct images)\n",
    "at the locations of all detected `Objects`. \n",
    "A Lomb-Scargle periodogram is used to identify the period and phase-fold the time-series photometry, and then\n",
    "a phase-folded lightcurve is displayed for the single RR Lyrae.\n",
    "\n",
    "In Section 3 the data products of difference image analysis (DIA), in particular the lightcurve summary statistic parameters\n",
    "available in the `DiaObject` table, are used to identify a sample of likely RR Lyrae from the DP0.2 data set. \n",
    "\n",
    "A cautionary tale on the difference between using time-series photometry for `DiaObjects` in the `DiaSource` or `ForcedSourceOnDiaObject` tables is demonstrated in Section 3.4.\n",
    "Both contain forced photometry in the PVI (direct image), but the `DiaSource` table only contains photometry for visits in which the\n",
    "`DiaObject` was *detected in the corresponding difference image*,\n",
    "whereas the `ForcedSourceOnDiaObject` table contains forced photometry for *all* PVIs and difference images.\n",
    "\n",
    "> **Warning:** When variable stars are observed to have nearly the same brightness as they do in the template image,\n",
    "they will not be detected in the difference image,\n",
    "and photometry for that visit's PVI will not be included in the `DiaSource` table.\n",
    "\n",
    "This notebook demonstrates that, for variable stars, it is better to use either the `ForcedSource` or `ForcedSourceOnDiaObject` tables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694404da-f100-474f-826a-02b31706d878",
   "metadata": {},
   "source": [
    "### 1.1 Package Imports"
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import astropy.units as u\n",
    "from astropy.table import unique\n",
    "from astropy.timeseries import LombScargle\n",
    "from lsst.rsp import get_tap_service"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b16a970-4179-4ce7-9e99-a59e807401d2",
   "metadata": {},
   "source": [
    "### 1.2 Define Functions and Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0571eaa5-bcce-4ba2-8dac-ee9e25810f69",
   "metadata": {},
   "source": [
    "Set up some plotting defaults so plots will look nice:"
   ]
  },
  {
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    "%matplotlib inline\n",
    "plt.style.use('tableau-colorblind10')\n",
    "params = {'axes.labelsize': 24,\n",
    "          'font.size': 20,\n",
    "          'legend.fontsize': 14,\n",
    "          'xtick.major.width': 3,\n",
    "          'xtick.minor.width': 2,\n",
    "          'xtick.major.size': 12,\n",
    "          'xtick.minor.size': 6,\n",
    "          'xtick.direction': 'in',\n",
    "          'xtick.top': True,\n",
    "          'lines.linewidth': 3,\n",
    "          'axes.linewidth': 3,\n",
    "          'axes.labelweight': 3,\n",
    "          'axes.titleweight': 3,\n",
    "          'ytick.major.width': 3,\n",
    "          'ytick.minor.width': 2,\n",
    "          'ytick.major.size': 12,\n",
    "          'ytick.minor.size': 6,\n",
    "          'ytick.direction': 'in',\n",
    "          'ytick.right': True,\n",
    "          'figure.figsize': [10, 8],\n",
    "          'figure.facecolor': 'White'}\n",
    "plt.rcParams.update(params)"
   ]
  },
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   "cell_type": "markdown",
   "id": "e94db4b3-8feb-45c3-8f99-3140f85f7d4d",
   "metadata": {},
   "source": [
    "Set up colors and plot symbols corresponding to the _ugrizy_ bands. These colors are the same as those used for _ugrizy_ bands in Dark Energy Survey (DES) publications, and are defined in <a href=\"https://github.com/DarkEnergySurvey/descolors\">this github repository</a>."
   ]
  },
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   "source": [
    "plot_filter_labels = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "plot_filter_colors = {'u': '#56b4e9', 'g': '#008060', 'r': '#ff4000',\n",
    "                      'i': '#850000', 'z': '#6600cc', 'y': '#000000'}\n",
    "plot_filter_symbols = {'u': 'o', 'g': '^', 'r': 'v', 'i': 's', 'z': '*', 'y': 'p'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbb728a-3d0b-4930-826c-2c65b1b0780d",
   "metadata": {},
   "source": [
    "Start the TAP service, which we will use for all data retrieval in this notebook."
   ]
  },
  {
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   "source": [
    "service = get_tap_service(\"tap\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "056c4f66-ff8d-4a96-ac54-fc787560f0a2",
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   "source": [
    "## 2. Create a Lightcurve for a Known RR Lyrae Variable\n",
    "\n",
    "To start out, we will use a star that is known (from the DC2 truth tables) to be a simulated RR Lyrae star in DC2. This will help us get our feet wet at extracting time-series data and manipulating them.\n",
    "\n"
   ]
  },
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   "source": [
    "### 2.1 Extract Forced Photometry Measurements for a Pulsating Variable\n",
    "\n",
    "The known RR Lyrae star we will use is at position (RA, Dec) = (62.1479031, -35.799138) degrees. We will initially look for this star in the `Object` table to identify its `objectId`. Then we will use this `objectId` to identify measurements of the star in the `ForcedSource` table.\n",
    "\n",
    "#### 2.1.1 Define star coordinates\n",
    "\n",
    "Initialize two variables with the known position of the star of interest:"
   ]
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   "source": [
    "ra_known_rrl = 62.1479031\n",
    "dec_known_rrl = -35.799138"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39ed8a4-cb0a-4f3f-8bfc-a12717375458",
   "metadata": {},
   "source": [
    "#### 2.1.2 Find the Star in the Object Table\n",
    "\n",
    "Query the `Object` table for this star. We use a spatial query at the RA, Dec position of interest, and specify a very small area to search (0.001 degrees), so that we will retrieve only the star of interest.\n",
    "\n",
    "Notice that in addition to selecting on position, we specify \"detect_isPrimary = 1\" as well. The <a href=\"https://pipelines.lsst.io/getting-started/multiband-analysis.html#filtering-for-unique-deblended-sources-with-the-detect-isprimary-flag\">detect_isPrimary flag</a> is a composite flag that, if TRUE (or set to 1), ensures a unique, deblended source.\n",
    "\n",
    "Notice also the use of the `scisql_nanojanskyToAbMag` function to convert fluxes to magnitudes. (This could also be done after extracting the fluxes if desired, but this step streamlines things.)"
   ]
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   "source": [
    "results = service.search(\"SELECT TOP 100 \"\n",
    "                         \"coord_ra, coord_dec, objectId, \"\n",
    "                         \"g_psfFlux, r_psfFlux, i_psfFlux, detect_isPrimary, \"\n",
    "                         \"scisql_nanojanskyToAbMag(g_psfFlux) as gPSFMag, \"\n",
    "                         \"scisql_nanojanskyToAbMag(r_psfFlux) as rPSFMag, \"\n",
    "                         \"scisql_nanojanskyToAbMag(i_psfFlux) as iPSFMag, \"\n",
    "                         \"g_extendedness, r_extendedness, i_extendedness \"\n",
    "                         \"FROM dp02_dc2_catalogs_frdf.Object \"\n",
    "                         \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \"\n",
    "                         \"CIRCLE('ICRS',\" + str(ra_known_rrl) + \", \"\n",
    "                         + str(dec_known_rrl) + \", 0.001)) = 1 \"\n",
    "                         \"AND detect_isPrimary = 1\")\n",
    "\n",
    "objs = results.to_table()"
   ]
  },
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    {
     "data": {
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       "<div><i>Table length=1</i>\n",
       "<table id=\"table140485996643216\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>coord_ra</th><th>coord_dec</th><th>objectId</th><th>g_psfFlux</th><th>r_psfFlux</th><th>i_psfFlux</th><th>detect_isPrimary</th><th>gPSFMag</th><th>rPSFMag</th><th>iPSFMag</th><th>g_extendedness</th><th>r_extendedness</th><th>i_extendedness</th></tr></thead>\n",
       "<thead><tr><th>deg</th><th>deg</th><th></th><th>nJy</th><th>nJy</th><th>nJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>62.1479018</td><td>-35.7991382</td><td>1651589610221899038</td><td>148048.9266238</td><td>185964.388121</td><td>173109.3568595</td><td>True</td><td>18.473986842403193</td><td>18.226425536273844</td><td>18.304198642809727</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
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      "text/plain": [
       "<Table length=1>\n",
       " coord_ra   coord_dec        objectId      ... r_extendedness i_extendedness\n",
       "   deg         deg                         ...                              \n",
       " float64     float64          int64        ...    float64        float64    \n",
       "---------- ----------- ------------------- ... -------------- --------------\n",
       "62.1479018 -35.7991382 1651589610221899038 ...            0.0            0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objs"
   ]
  },
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   "source": [
    "Looks good - our limited search retrieved only a single object. Notice that the \"extendedness\" flag is zero for all three bands, meaning this object is likely a point source (i.e., a star). It is around 18th magnitude in all three bands, and has fairly blue colors as expected for an RR Lyrae star.\n",
    "\n",
    "#### 2.1.3 Extract the Measurements from the Forced Source Table\n",
    "\n",
    "Now that we have identified the candidate in the Object table, we can use its `objectId` to identify it in the `ForcedSource` table. First extract the `objectId`:"
   ]
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     "name": "stdout",
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     "text": [
      "1651589610221899038\n"
     ]
    }
   ],
   "source": [
    "sel_objid = objs[0]['objectId']\n",
    "print(sel_objid)"
   ]
  },
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   "source": [
    "Now we will use this ID to pick out all measurements of this object from the `ForcedSource` table (by use of a WHERE statement in the query).\n",
    "\n",
    "Note that we are also using a JOIN here to extract the visit info for each entry in `ForcedSource`. This is necessary so we can get the time each exposure was observed (\"expMidptMJD\"), to be used in constructing lightcurves.\n",
    "\n",
    "Define the query."
   ]
  },
  {
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   "outputs": [],
   "source": [
    "query = \"SELECT src.band, src.ccdVisitId, src.coord_ra, src.coord_dec, \"\\\n",
    "        \"src.objectId, src.psfFlux, src.psfFluxErr, \"\\\n",
    "        \"scisql_nanojanskyToAbMag(psfFlux) as psfMag, \"\\\n",
    "        \"visinfo.ccdVisitId, visinfo.band, \"\\\n",
    "        \"visinfo.expMidptMJD, visinfo.zeroPoint \"\\\n",
    "        \"FROM dp02_dc2_catalogs_frdf.ForcedSource as src \"\\\n",
    "        \"JOIN dp02_dc2_catalogs_frdf.CcdVisit as visinfo \"\\\n",
    "        \"ON visinfo.ccdVisitId = src.ccdVisitId \"\\\n",
    "        \"WHERE src.objectId = \"+str(sel_objid)+\" \"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7017b75d-c8d0-45bb-bf25-6ca5b4f75c17",
   "metadata": {},
   "source": [
    "Execute the query, and extract the results to a table."
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "864\n"
     ]
    }
   ],
   "source": [
    "results = service.search(query)\n",
    "srcs = results.to_table()\n",
    "print(len(srcs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fe56c5-f4f4-43c9-8f13-8afd26ff6963",
   "metadata": {},
   "source": [
    "Uncomment and execute the following cell to see what this table looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e804145-7308-4e01-8ef6-925ef22a01ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:11.275044Z",
     "iopub.status.busy": "2024-04-29T13:59:11.274684Z",
     "iopub.status.idle": "2024-04-29T13:59:11.278938Z",
     "shell.execute_reply": "2024-04-29T13:59:11.278056Z",
     "shell.execute_reply.started": "2024-04-29T13:59:11.275018Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# srcs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3b0c5e-b0bb-4fcd-ada3-719fc40d36e9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.1.4 Select Forced Photometry Measurements from Each Band\n",
    "\n",
    "The `ForcedSource` table contains measurements from all bands (i.e., _ugrizy_ filters). It will be useful to extract measurements for each band by selecting on the \"band\" column.\n",
    "\n",
    "In the following cell, and throughout this notebook, we store arrays in a python dict (in this case, one called \"pick\") that is indexed on the \"band\" or \"filter\" name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec6a7816-d415-43c0-906b-7859925f10ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:11.284413Z",
     "iopub.status.busy": "2024-04-29T13:59:11.284049Z",
     "iopub.status.idle": "2024-04-29T13:59:11.720880Z",
     "shell.execute_reply": "2024-04-29T13:59:11.719512Z",
     "shell.execute_reply.started": "2024-04-29T13:59:11.284388Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pick = {}\n",
    "for filter in plot_filter_labels:\n",
    "    pick[filter] = (srcs['band'] == filter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83239aaa-574c-40da-81c0-7c0471c2b08d",
   "metadata": {},
   "source": [
    "Plot the observed \"lightcurve\" for the _r_ band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0db9bbb-09cb-4c3f-888b-cc5093e793c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:11.722887Z",
     "iopub.status.busy": "2024-04-29T13:59:11.722445Z",
     "iopub.status.idle": "2024-04-29T13:59:12.498996Z",
     "shell.execute_reply": "2024-04-29T13:59:12.498268Z",
     "shell.execute_reply.started": "2024-04-29T13:59:11.722835Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jVVVVJlPNV1ZW8qk6IoUpKCiwusqATqfjNAUSKigoQFZWFsrLyxETE4Ps7GwkJyfLXS1y0fbt2zF79mzjeZ07d65L141T91CnwzSFmjx5ssszZTc2NopnnnlGaDQaq/sGBgaKBQsWWKyLPS1OQghRWFgooqOjLR4nOjpaFBYWmt2XLU4kl7S0NGOrq5+fn0hLS5O7Soqm1Wqtfp5otVq5q6gabNkjezlzD+UYJzM0Gg3eeOMNFBUVYdq0aejduzfCwsLg7++PiIgIJCYmIjMzE7/++iuee+45l4935513Yt++fZg9ezZ69+6N0NBQhIaG4rbbbsPs2bPx66+/4s4775TgNyOSRnh4ON577z00NjYCABobG/Hee+8hIiJC5popl605axyZ04Ysc8ds1UTNqa6rztewq841bM53XHp6Ot577z2L6VOnTsU777zjwRp5h7CwMKuT3YaGhuL8+fMerJE6DRo0CIWFhRbTtVot9Hq9B2tESubMPZQtTmSWLzz5k5aWhpSUFBQWFqK0tBSFhYVISUkxGQdHLTU95WlJXl6eZyriZVq3bu1SOtlHSfP9kDoxcCITvhBQsDnfeU3dc86m+6ouXbq4lK5kSvqipaT5fkilPDL6itxG6sHh+fn5VgewOvsYqdJwoK7zmgaEW3ppNJoWjx7n5+fLXWVFUOu1pbSB2Gp9n8k9nLmHMnDyclIHTr4SUMTHx1v9PePj4+WuomKlpaU59OSqnDdRpXHHPDRyUmqQorb3mdyHT9WRy3xlfACb852Xl5eH8PBwh/dh9yeQm5sLnU4HrVaL+Ph4aLVa6HQ65Obmyl01pyhp4dXm1PY+k7LwqTovJ/VTdQkJCVYXLE5ISMCBAwecKltJOBmh6x577DHk5eWhsbERfn5+aN++Paqqqizm59NM6tO5c2eUlpZaTI+Pj0dJSYkHa0TkGD5VRwCAPn36IC4uzuSVk5Njc1+NRuOBGspPyjWUfNU777yDhoYGCCHQ0NCAkJAQq/nV0lpJ17DllrxJTk6OyX2xT58+DpcT4Ia6kcwqKirMbjcYDDb3vXjxokvp3iQ3Nxfjx4+XdPp+XxYTE2O19YE3UfXJzs622nLr0YVXiWwwGAwoKytzuRwGTioUHR0Nf39/k+32jEvxtZtfUlISAyWJ8Cbqe5pabs3N3cWWW1Ka8PBwxMbGttjW0NBgsbHBEo5x8nJSj3Hi2B9yRUZGhsWbKAfmqpfUC68SeQrHOJHLOPaHXMGnmXxTUlIS9Ho9SkpKoNfrFfE5oaRJOUldGDiRidzcXMybNw+hoaHw9/dHaGgo5s2bx5sf2UWJN1HyLb6w+oE3UVsQy646L+eORX7T0tLMLkfC7hYiUjoON1AWpd9P2FVHLvO1Ndyk/Caktm9VRN5IqZNy+iLV3k/cPZ05uReXXHGelGtsKW29LiJfxeWUlMMb7idccoVc5itLrkj5TUi136psYAubdPheSoeTciqHau8nHgrqyE3Y4uQcKX9PX3nPmmMLm3T4XkpLqQsP+yJv+GxkixO5LDs722q6WiYxlPKbkGq/VVngqy1s7sD3UnqcUkU51Ho/YeBELfjKh46Uzfm+1jXAwbeuad4tN3bsWKt5+V46h/OJKYNa7ycMnMiEL3zoSPlNSK3fqiw5duyYS+m+7Pr5hS5cuGA1v9paKz2J84kpQ2Njo9xVkBwDJzJL7R86Un4TUuu3KktqampcSvdV1rrlLFFbayX5FrV2RTNwIp8lZcuaL7TSNQkJCXEp3VfZ6uI0R22tleRb1NqtHyB3BYjklJSUJFmLkJRlKVnXrl1RVVVlNZ1MOdrtpsbWSvItan1whi1OROQQXxvTJRVb3W6hoaGqb60k36LWB2cYOBGRQ3xtTJdUbAWcGzZsUO2YQvJNav2SxcBJhfr06YO4uDiTV05OjtxVI5XwpTFdUmHASb5GaX/zOTk5JvfFPn36OFwOxzipUEVFhdntBoPBwzUhNfOVMV1Sys3Nxfjx4zF79myUl5cjJiYGc+fO5ftIqqWkv3mDwYCysjKXy2HgpELR0dHw9/c32R4eHi5DbYioOQac5GuU8jcfHh6O2NjYFtsaGhosNjZYohFCCCkrRp5VVVWFDh06tNhWWVmJqKgomWpE5JiCggJkZWUZv41mZ2cjOTlZ7moRkQ9w5h7KMU5EJJvrZ9IuLCxESkoKMjIy5K4aEZFZDJyISBZqnVWYiNSNgRMRyUKtswoTkboxcCIiWah1VmEiUjcGTkQki9atW1tNv+GGGzxUE+9TUFCAQYMGoXPnzhg0aBAKCgrkrhKRz2DgRESy0Gg0clfBK3FAPZG8GDgRkSwuXrzoUrov4oB6IvkxcCIiWah1AVB34oB6IvkxcCIiWah1AVB34oB6IvkxcCIiWShtAVBvwFY6IvkxcCIi2eTm5kKn00Gr1SI+Ph5arRY6nQ65ublyV02R2EpHJD8u8ktEslLKAqDeoKmVLi8vzySNrXREnsEWJyIiL8JWOiJ5MXAiIvIySUlJ0Ov1KCkpgV6vZ0sTKZ6aJm1l4ERERERuo7ZJWzVCCCF3Jch5VVVV6NChQ4tt0dHR8Pf3N8mbmZmJzMxMT1WNiIh8XEFBAVJSUiym63Q6j7WY5uTkICcnp8W2hoYGVFRUtNhWWVmJqKgoi+VwcLgKXf9H0MRgMHi4JkRE5Muefvppq+kzZszAgQMHPFIXg8GAsrIyl8th4KRCllqcwsPDZagNEVlTUFCArKwslJeXIyYmBtnZ2UhOTpa7WkSS+P33311Kl1J4eDhiY2NbbDPX4mQLu+q8nLmuOlvNjESkDGlpaWbXnktPT+dTch7GANY9WrVqhbq6OovpQUFBqK2t9WCNWnLmHsrB4UREMuCCvcqhtsHLStKlSxer6V27dvVQTaTDwImISAZcsFcZGMC616JFi6ymL1682EM1kQ4DJyIiGXDBXmVgAOtealyTkoETEZEMuGCvMjCAdT+1zXbPp+qIiGSQnZ1tdX4bLtjrGTExMSgtLbWaTq5T05qUbHEiIpKBHF0Yalr2QirZ2dlW0xnA0vUYOBERycSTXRh8csw8NY7BIfdSXeBUWVmJzZs3IysrCyNGjEBkZCQ0Gg00Gg2mTJniUFklJSWYNWsWEhMT0aZNGwQGBqJdu3YYPHgw5syZg6qqKpfre/jwYbzxxhv485//jJtvvhkhISFo3bo1br75Zjz88MPYsmULONUWkXo5s2Cvoy1HfHLMOrWNwSE3EyoDwOJr8uTJdpezevVq0bp1a6vltW/fXmzfvt3puk6aNMlq+U2v4cOHi7Nnz5oto7Ky0iR/ZWWl03UiU/n5+UKr1Yr4+Hih1WpFfn6+3FUiH5aammr2cyI9Pd3iPlqt1upnjFar9eBvQKQcztxDVdfi1FynTp2sDr60RK/XY9KkSaipqYGfnx9SU1OxYcMG7Nq1C5988glGjhwJADh9+jRGjRqF48ePO1W/pjVz2rVrh8ceewxr1qzB999/j127duHtt99G9+7dAQBffvklRo4cicbGRqeOQ85j9wYpibMtR3xyjEhCHgrqPCYrK0t8/vnnoqKiQgghRHFxscMtTg888IBxnyVLlpjNk5mZacwzY8YMp+o6efJk8fbbb4vLly+bTb948aK4++67jcdZuXKlSR62OLlPfn6+1W/pOp1O7iqSj3G25YgtTkTmOXMPVV3gdD1nAqe2bdsau+IsOXfunLHcxMREiWprat++fcbjjBo1yiSdgZP78GZDShMfH2/1bzI+Pt7sfvwSQGQeu+ok0rQg4c0332wxT0REBCIjIwHArQsU9u7d23gcT64iTezeIOVxdtJMPjlGJB0GTmZ069YNAFBcXGwxj8FgQHV1dYv87tIUyPn58XR5Emd2Jk+YN28ewsLCEBAQgLCwMMybN89iXlfmHOKTY0TS4MzhZjz++OOYNm0aTp8+jWXLlmHatGkmeebMmdMiv7v89NNPMBgMAIAePXrYtU9TQOeMqKgop/dVG87sTO7WrVs3HDlyxPj/Cxcu4MUXX8Ty5ctx+PBhyY+nptmbiZpzdnogp+6XHupGlI0zY5zq6+vFo48+KgAIPz8/kZGRITZt2iSKiorEp59+KsaMGWMs84UXXnBr/R988EHjsT7++GOTdHP9s668qKX09HSHH/0msse///1vq9fi/PnzTfbhuDvyNp6azkXK+yAHhzsRODVZu3at6Nu3r9k3dtiwYeLLL790T6X/zyeffNJiAHpjY6NJHgZO7qfT6Vpc+BxIS1IIDQ21ei2Ghoaa7OPs4HAiOTgz55izPBk4cdCMBYcOHcKaNWuwb98+s+l6vR4rV67EqVOn3Hb81NRUAEBISAhWrlwJjUbjlmORdc7M7Exky6VLlxxO57g78hZqnq2egZMZO3fuxKBBg7Bx40bExsZi1apVqKioQF1dHU6cOIElS5YgJCQEq1evxsCBA3Hw4EFJj19eXo4RI0bg/Pnz0Gg0yMvLQ8+ePSU9BhHJKyQkxOF0LkhL3uLpp5+2mj5jxgwP1UR6HBx+ndraWowfPx7nzp1DdHQ0CgsLER0dbUyPi4vD9OnTMWTIEPTv3x8nT57EpEmTUFRUJMnxz5w5g5SUFONs5AsXLsT48eMdKuPAgQPGKQyISJleeuklvPjiixbTs7KyTLY1TSuQl5dnksZpBUhJbE2fI/X0OpWVlU7tV11d7XjDhOQdjQrj6BinDRs2GPO/+uqrVvNmZGQY8/78888u19VgMIgBAwYYy5wzZ47NfTgBJpH36tatm9kxFt26dbO6H8fdkdIFBQVZHUcUFBQkdxWFEJwAUxLNu9369etnNW9iYqLx50OHDrl03EuXLmHkyJHGlquZM2di9uzZLpVJRMp2+PBhzJ8/H6GhofD390doaCjmz59vcyoCjrsjpevSpYvV9K5du3qoJtJj4HSdgIBrvZf19fVW8165csXsfo66cuUKxo0bhx07dgAApk2bhvnz5ztdHhF5j5kzZ+L8+fOor6/H+fPnMXPmTLmrROSyRYsWWU1fvHixh2oiPQZO12m+zMrOnTut5m0KdK7fzxENDQ145JFHsHXrVgDAxIkT8dZbbzlVFhH5hoKCAgwaNAidO3fGoEGDUFBQIHeVyI288XyrepkfD3UjysbRMU5nz54VrVu3FgBEWFiY2Lt3r9l8X3zxhfDz8xMARGxsrGhoaDDJM2TIEOOxi4uLTdIbGxtbzHMxbtw4UV9f79DvxzFO9vPURGxE7uTJuXFIft5+vpU+Hs+Ze6hGCCE8FaR5wnfffYejR48a/19dXW1s+r7rrruQkZHRIv+UKVNMypgzZ47xiZbQ0FDMmDEDycnJaNu2Lf744w9s3LgR7777rrErb9WqVZgwYYJJOUOHDjW2ShUXF+Omm25qkf7ss88iJycHwNXFfN9//30EBQVZ/f169+7d4v9VVVXo0KFDi22VlZVcOuU6aWlpZucUSU9P51pd5DUKCgqsLgOk0+m8+5s8tcDz7X5O3UM9EtJ50OTJk12eKbuxsVE888wzQqPRWN03MDBQLFiwwGJdbLU4de7c2eWZvdniZFt+fr7V91Rp34CILOGSK76F59v9+FSdRDQaDd544w0UFRVh2rRp6N27N8LCwuDv74+IiAgkJiYiMzMTv/76K5577jm5q0s2mJsPpzk+vUje4tixYy6lk3cpLy93KZ3cQ3UTYK5YsQIrVqyQpKzExMQWUw446ptvvrGa3jTJJbkXP3xILQwGg0vp5F1iYmJQWlpqNZ08jy1OpHpc34vUwtYUKbbSybsodYkdb3zKT0oMnEj1lPrhQwQ4dhPy87P+kW0rnbyLEh/pT0tLQ0pKCgoLC1FaWorCwkKkpKSYPHilZrzKSPWU+OFDBDh+E7I127I3z8ZM5uXm5kKn00Gr1SI+Ph5arRY6nU6Wp4ELCgrMPp0MAHl5edi+fbuHayQP1U1H4Gs4HYH9tm/fjtmzZ6O8vBwxMTGYO3cugyaSjTOPmvPxdJLToEGDUFhYaDFdq9VCr9d7sEauc+YeyhYn8hlc34uUxJmnPdl6SnLigzZXMXAiIkn4+oBRRzl7E1JS1w35Fj5ocxW76rycuWbG6Oho+Pv7m+TNzMxEZmamp6pGPoQzsztOjd0epG7e3lWck5NjXK2jSUNDAyoqKlpsY1edD6qoqEBZWZnJi3O8kDtwwKhz+LQneRtv7yo2GAwm98XrgyZ7SB44paWlYenSpVIXSw6Ijo5GbGysySs8PFzuqpEKcWZ253j7TYh8kzd3FYeHh5vcF6Ojox0uR/KZw1esWIFevXpJXSw5YO/evXyqjjyGA0ad19jYKHcViByWlJTklYG9ueEq5oa72CJ5i5O/v7/DlSAi78UBo85hFyeRd5I8cIqKisLZs2elLpbIZXzqyz04Vsc57OIk8k6SB0533nkn9uzZI3WxRC7hMgHuw7E6zmEXJ5F3csvg8A0bNqCqqkrqoomcwi4R9/PmAaNyYRcnkXdyyzxOo0aNgkajwfr167nopJtxyRXbOF8OKZG3z4lDpAaKWXJl7dq1uHjxIsaMGePUHAlEUmKXCCkRuziJvJPk0xHk5+cDAJ599lksXLgQnTt3RlJSEgYOHIhbb70VYWFhCA4Otqssa9/GiOwVExOD0tJSq+lEcsjNzcX48eMdXny6oKAAWVlZxn2ys7ORnJzsoVoT+TbJu+r8/Pyg0WgAAM4UrdFoIISARqNBQ0ODlFVTJXbV2cYuEVITR5a3YYBFZJ0z91DJA6cdO3ZIVtaQIUMkK0utGDjZJyMjA3l5eSbbuZYaeRNHvgRw/UDpMRBVH0UETuRZDJzst337doe7RIiUxN4HHdjKKj0GouqkmMHhREqUlJQEvV6PkpIS6PV63jhIERyZmNXeBx04uaa0OKUJNcfAiYhIJo5OzGrv3E98klRaDESpOQZOREQycKYVw97lbTi5prQYiFJzDJyIiGTgTCuGvXM/cf1AaTEQbcnX1/1k4EREJANnWzHsWd6Gk2tKi4HoNVz3k0/VeT1zTwRER0fD39/fJG9mZiYyMzM9VTUissITSwHxSVLpcEoT758TLycnBzk5OS22NTQ0mKxwYuupOslnDif5WVrmxmAweLgmRGRJdna21ZuQFK0YSUlJir6ReRNnZ3lXE3u6l5X8fhgMBpSVlblcDgMnFbLU4hQeHi5DbYjInKbuNEutGEq+AfkqXw9EvX2QfHh4OGJjY1tsM9fiZAvHOKnQ3r17cfLkSZMXu+nIUb4+CNTd7BmvRMrh69eDtw+Sz8zMNLkv7t271+FyOMbJy3HmcHIXzpRMdA2vB+8f42QOZw4nIklwpmSia3g9XMWnNa9i4EREJjhTsvfz9W4lKfF6uIbdyxwcTkRmePsgUF93fbdSaWkpUlJSfKpbSUq8Hlry9UHybHEiIhPePgjUl7FbSXq8Hqg5Bk5EZIIzJXsvditJj9cDNcfAiYhMcBCo92K3kvR4PVBzDJyIyCwOAvVO7FZyD14P1ITzOHk5zuNERM2pca4dInfhPE5ERD6O3UpE7sXAiYhIZditROQ+nMeJiEiFfH2uHSJ3YYsTERERuY3aZrFn4ERERERukZaWhpSUFBQWFqK0tBSFhYVISUlBRkaG3FVzGgMnIiIikpxaZ7Fn4EREpEJq6x4h76PWWew5OFyF+vTpA39/f5PtmZmZyMzMlKFGRORJXOSXlEBps9jn5OQgJyenxbaGhgaHy2HgpEIVFRVmtxsMBg/XhIg8zVb3yPjx4/m0HXlETEwMSktLraZ7ksFgQFlZmcvlsKtOhaKjoxEbG2vyCg8Pl7tqRORmau0eIe/j7OLI8+bNQ1hYGAICAhAWFoZ58+ZJUp/w8HCT+2J0dLTD5XDJFS/HJVeIqLnOnTtb/ZYfHx+PkpISD9aIfFlGRgby8vJMtlvqNu7WrRuOHDlidvvhw4clrx+XXCEi8nFc5JeUxJFZ7OfNm2c2aAKA3377DQsWLHB3de3CFicvxxYnImqOi/yStwoLC8OFCxcspoeGhuL8+fOSHpMtTkREPo6L/JK3unTpkkvpnsLAiYhIZZq6RxISEhAUFISgoCAkJCTg4YcflrtqRBaFhIS4lO4pDJyIiFRo9erVOHjwIOrq6lBXV4eDBw96/VIXpG4vvfSS1XRbT4x6Csc4eTmOcSKi63GcE3mr7t2747fffjPZzqfq3KiyshKbN29GVlYWRowYgcjISGg0Gmg0GkyZMsWhskpKSjBr1iwkJiaiTZs2CAwMRLt27TB48GDMmTMHVVVV7vklALz11lvGems0GqxYscJtxyIideFcTuStDh8+jPnz5yM0NBT+/v4IDQ3F/Pnz3RI0OUt1M4d37NhRknLWrFmDqVOnoqampsX2s2fPQq/XQ6/XY+HChVi3bh3uvfdeSY7ZpLy8HC+++KKkZRKR71DaUhferqCgAFlZWSgvL0dMTAyys7ORnJwsd7VUa+bMmZg5c6bc1bBIdS1OzXXq1Mlqc7Uler0ekyZNQk1NDfz8/JCamooNGzZg165d+OSTTzBy5EgAwOnTpzFq1CgcP35c0no/+eSTMBgMJs2HRET24FxO0klLS0NKSgoKCwtRWlqKwsJCjhXzcaoLnLKysvD555+joqICpaWlePvttx0u41//+pdx4b/Fixfjvffew+jRozFgwACMGzcOmzZtMi6We/HiRZNFA12xceNGrF+/HlFRUXjhhRckK5eUhSvXkzs5u9QFtWRr3b/t27d7uEakCELliouLBQABQEyePNmufdq2bSsAiPbt21vMc+7cOWO5iYmJktTVYDCIuLg4AUC8//77Yvny5cZjLF++3Ow+lZWVxjxNr8rKSknqQ+6Rmppqcs4AiPT0dLmrRiqSnp7OvzMXabVas+9h00ur1cpdRXKRM/dQ1bU4SaGurg4AcPPNN1vMExERgcjISABAbW2tJMd98cUXcfLkSQwdOhSTJk2SpExSFn6DJU9xZKkLMo9jxcgcBk5mdOvWDQBQXFxsMY/BYEB1dXWL/K744YcfsHTpUgQFBWHp0qUul0fKxKedyJOSkpKg1+tRUlICvV7PKQgcxLFiZI7qnqqTwuOPP45p06bh9OnTWLZsGaZNm2aSZ86cOS3yu+LKlSuYOnUqGhsbMXPmTPTo0cOl8poCOmdw/if34jdYIu+RnZ1t9QEjjhVTDmenB3LmfsnAyYyMjAzs3LkTq1evxt/+9jfs2bMHo0aNwo033ojS0lJ88MEHWL9+PQDghRdecOrJveYWLFiAffv2oUuXLjZnTrVHz549nd5XcD5Ut4qJiUFpaanVdCJShqZ1//Ly8kzSuO6fsnj0KXTPDL+SjzODw5usXbtW9O3b1+ygwGHDhokvv/zS5fodOXJEBAcHCwBi69atLdKcHRzuyovcKz8/3+r7r9Pp5K4iEV1Hp9MJrVYr4uPjhVar5XWqQFLeBzk43EmHDh3CmjVrsG/fPrPper0eK1euxKlTp1w6zrRp03D58mU89NBDuO+++1wqi5SPK9cTeR85x4px6hLlYeBkxs6dOzFo0CBs3LgRsbGxWLVqFSoqKlBXV4cTJ05gyZIlCAkJwerVqzFw4EAcPHjQqeOsWLEC27dvR3h4ON58801pfwlSLD7tRET24OSbysQxTtepra3F+PHjce7cOURHR6OwsBDR0dHG9Li4OEyfPh1DhgxB//79cfLkSUyaNAlFRUUOHaeqqgrPPfccgKsDzaUc23LgwAHjVAmkTElJSWxdIiKLbE1dMn78eH6GNFNZWenUftXV1Q6PC2bgdJ1t27ahrKwMADBjxowWQVNzvXr1woQJE5Cbm4vdu3fjl19+we233273cXJzc3H69Gm0adMG7du3x0cffWSS54cffmjxc3BwMADg3nvvtToQLjIykk/HERF5mJRr2tkzdQkDp2s8ec9j4HSd5t1u/fr1s5o3MTHR2L1y6NAhhwKnpkkzz507hwkTJtjMv2zZMixbtgwA8PXXX3MdOyIiBUlLS2vRQlRaWoqUlBSkp6c71Q3PqUuUi2OcrhMQcC2WrK+vt5r3ypUrZvcjIiLf4Y4VATj5pnIxcLpO82VWdu7caTXvjh07zO5nj1deeQVCCKuv5hfi8uXLjduHDh3q0LGIiMh93LEiABdqVi4GTtdJSkpC69atAQBLly61OB3B1q1bjZNgxsbG4o477jDJM3ToUGg0Gmg0Ghw/ftxdVSYiIhm5o1uNU5col+r6l7777jscPXrU+P/m06kfPXoUK1asaJF/ypQpLf7fpk0bzJo1C1lZWTh//jwGDx6MGTNmIDk5GW3btsUff/yBjRs34t1330VjYyMAYN68efDzYwxKROSL3LUiQG5uLsaPH4/Zs2cbB5zPnTuXQZPc3DqVpwwmT57s8kzZjY2N4plnnhEajcbqvoGBgWLBggUW6zJkyBBj3uLiYod/F2dnDrc16ykREUmHKwJIKz8/v8VM7fn5+W47ljP3UDaTmKHRaPDGG2+gqKgI06ZNQ+/evREWFgZ/f39EREQgMTERmZmZ+PXXX41zMRERkW9it5p0vGHST40QXNXVm1VVVZlMTVBZWcl5nIiIPGz79u3sVnNBQUEBUlJSLKbrdDrJ309n7qEMnLwcAyciIlKDhIQEHDp0yGr6gQMHJD2mM/dQdtURERGR5BxdoPjYsWNW03///Xcpq+c0Bk5EREQkKW8Yq+QsBk5EREQkGWdnUu/atavVcm2lewoDJyIiIpKMszOpL1y40Op+ixcvdrpOUmLgRERERJJxdiZ1b5nWgYGTCvXp0wdxcXEmr5ycHLmrRkREKufKAsW5ubnQ6XTQarWIj4+HVquFTqdDbm6uy/XKyckxuS/26dPH4XJUt+QKARUVFWa3GwwGD9eEiIh8TXZ2ttX5mGwtUJyUlOSW1iWDwYCysjKXy2HgpELR0dHw9/c32R4eHi5DbYiIyJc0dbnl5eWZpMnZ5RYeHo7Y2NgW2xoaGiw2NljCCTC9HCfAJCIiJfKGmdSduYeyxYmIiIgk564uN7lxcDgRERGRnRg4EREREdmJgRMREfk8R9dVI9/FwImIiHyamtdVI+kxcCLycfymTb7M2XXVyHcxcCLyYfymTb7O2XXVyHcxcCLyUfymTeT8umrkuxg4kU9i9xS/aRMBrq2rRr6JgRP5HHZPXcVv2kTA6NGjraaPHTvWQzUhb8HAiXwKu6eu4TdtImDjxo1W0z/77DMP1YS8BQMn8insnromOzvbarqtFcyJ1IAtr+QoBk7kU/gheU3TCubmyLmCOZEnebLllWMr1YGBE/kUdk+1lJubC51OB61Wi/j4eGi1Wuh0OuTm5spdNSKP8FTLK8dWqodGCCHkrgQ5r6qqCh06dGixrbKyElFRUTLVSNkKCgqQkpJiMV2n07GlhcjHZGRkIC8vz2R7enq6JF8i+LmjXM7cQ9nipEJ9+vRBXFycySsnJ0fuqsmO3VNEdD13t7xybKUy5OTkmNwX+/Tp43A5AW6oG8msoqLC7HaDweDhmihTbm4uxo8fj9mzZ6O8vBwxMTGYO3cugyYiH5aUlOS2zwCOrVQGg8GAsrIyl8th4KRC0dHR8Pf3N9keHh4uQ22UyZ0fkt6moKAAWVlZxiAyOzsbycnJcleLyOtYupZiYmJQWlpqcT9fG1spl/DwcMTGxrbY1tDQYLGxwRKOcfJyHONErkhLSzM7r5VUYzuIfIW1a+nhhx/mGCcXuPPLHcc4EZHdOBkokTRsXUt+fn4cW+kkJT6NyMCJyEe5e8Aq56zxLjxfzrPnWuLUH45T7Jc7QV6tsrJSAGjxqqyslLta5AXi4+NN/naav+Lj450uOzU11WyZ6enpIj8/X2i1WhEfHy+0Wq3Iz8+X8LciZ1g7X2SbO68lX6bVaq2+r1qt1uVjOHMPZYsTkY9y12Sgtr4lKq3Z3dcp9lu9F7H3WmKrnmOU+jQiAyciH+WuGZNtdVuYwxu0fDjHkOvsuZaUOFZH6ZS60gMDJyIf5a7JQI8dO+bUfrxBy0Op3+q9ia1rqbGxka16Thg9erTV9LFjx3qoJi0xcCLyYe4YsFpTU+PUfrxBy0Op3+q9jbVria16ztm4caPV9M8++8xDNWmJE2AS+TipJwMNCQnBhQsXHN6PN2h5ZGdnW51jSKpFbn2BpWuJrXrOUer7xhYnIpJUZGSkU/vxBi0Prt/ofmzVc45S3zcGTkQkKeHEYgS8QcuLcwy5l7sexFA7pb5vDJyISFK2xjhFRUXxBq1ASUlJ0Ov1KCkpgV6vZyArIbbqOUep7xsDJyKSlK3m865du/IGTT7HV1v1XJ27SonvGxf59XJc5Nc5jiwa6c4FJtWooKCAC5oSkVcsIu7UPdTl+cpJVlxyxXGOLC/BpSick56ezveNyIfl5+dbXS5Fp9PJXUUhhHP3ULY4eTm2ODnGkdYQtpy4Zvv27Zg9e7axpW7u3Ll8v4h8xKBBg1BYWGgxXavVQq/Xe7BG5jlzD2Xg5OUYODnGkYvZWy58IiKl6dy5M0pLSy2mx8fHo6SkxIM1Ms+ZeygnwFShPn36wN/f32R7ZmYmMjMzZaiRcjgyoZpSJ18jIlK6mJgYq4GTHHMw5eTkICcnp8W2hoYGh8th4KRCFRUVZrcbDAYP10R5HLmYlXjhExF5AyXOSG8wGFBWVuZyOQycVCg6Otpsi1N4eLgMtVEWRy5mJV74RETknPDwcMTGxrbY1tDQYLGxwRKOcfJyHOPkuIyMDOTl5ZlsN/eIrCN5iYjoKm8ZI+rMPZQTYJLPcWRCNSVOvkZEpHRqHiPKrjrySZZWMXc1LxERqXuMKFuciIiISFJKXaBXCgycSNVcXSeJiIgcp9QFeqXAwIlUKy0tDSkpKSgsLERpaSkKCwuRkpKCjIwMuatGRKR6ah0jyqfqvByfqjOPy6UQEZEtfKqO6P9kZWVZTZ89e7aHakJERGqiusCpsrISmzdvRlZWFkaMGIHIyEhoNBpoNBpMmTLFobJKSkowa9YsJCYmok2bNggMDES7du0wePBgzJkzB1VVVZLW/eLFi1iyZAmSkpIQGxuLVq1aoWPHjujXrx9mzJiB/Px8SY+nZmp+FJaIiOSjuukIOnbsKEk5a9aswdSpU1FTU9Ni+9mzZ6HX66HX67Fw4UKsW7cO9957r8vH+/rrr5Gammqy6GFlZSUqKyvx008/YefOnVa7n+gaNT8KS0QtFRQUICsrC+Xl5YiJiUF2djaSk5PlrhaplOpanJrr1KmTU4GGXq/HpEmTUFNTAz8/P6SmpmLDhg3YtWsXPvnkE4wcORIAcPr0aYwaNQrHjx93qZ46nQ73338/SkpKEBYWhmeffRZffPEF9uzZg23btmHZsmUYPXo0QkJCXDqOL/HUo7B8ao9IXnwIhDxOqExWVpb4/PPPRUVFhRBCiOLiYgFAABCTJ0+2q4wHHnjAuM+SJUvM5snMzDTmmTFjhtP1raysFO3btxcAREJCgjhx4oTFvLW1tWb3b6pH06uystLp+qhJenq6yXsDQKSnp0tSfmpqqlvLJyLr8vPzzV6DTS+dTid3FUnhnLmHqi5wup4zgVPbtm0FANG+fXuLec6dO2csNzEx0en6Nd3cW7VqJQ4dOuTw/gycrNPpdEKr1Yr4+Hih1Wol+yDlBzaR/LRardXrUKvVyl1FUjhn7qGq7qpzVl1dHQDg5ptvtpgnIiICkZGRAIDa2lqnjnPu3DmsWbMGADB+/Hh0797dqXLIsqSkJOj1epSUlECv10s2BYGan9pj9yN5Cz4EQnJQ3eBwKXTr1g0//fQTiouLLeYxGAyorq425nfG559/jkuXLgEAHnroIeP28+fPo6KiAhERESbzS9ijqV7O8PX5n+yl1g/stLQ0LF++3Pj/0tJSpKSkID093esnrSP14UMg1MTZp9ydul96qDVMNs501S1btsy4z9KlS83mee6554x5vvzyS6fqNn36dGMZZ86cEVu3bhWDBw9u0WR44403ir///e+iqqrKbBnmmhldeZF91NhFwO5H8jb8m6UmUt4HOcbJicCpvr5ePProowKA8PPzExkZGWLTpk2iqKhIfPrpp2LMmDHGMl944QWn6zZ06FABQERERIj58+dbPZFxcXFi//79JmUwcJKHGj+w1RgMknvk5+e3GDuYn58vW11uvfVWs3+v3bp1k61O5HkMnCTkTODUZO3ataJv375m39hhw4Y53dLUpE+fPgKACAoKEhqNRrRq1UrMmzdPnDx5UtTW1opff/1VTJo0yXjMW265RZw/f75FGQyc5OPup/Y8LT4+3urfRnx8vNxVJAVQ0tOkavwCQ85h4CQhZwOngwcPitGjR4uAgACzb2xwcLB49NFHRXl5udN169q1a4sy161bZzbfY489ZsyzYMGCFmkMnOTlrqf25MAWJ7JFaYEK/2apiScDJ9Uv8nv8+HHj03GTJ0/GihUrbO6zc+dOjBo1CufOnUPnzp0xd+5cJCcno127dvjjjz+wadMmzJ49G2fPnkVcXBzy8/ORkJDgcN169+6N/fv3AwC0Wi30er3ZfKdPn0ZsbCxqa2vRr18/7Nmzx5hmboHCAwcOGJ/4c5SvDA7nTMOmuDAy2dKzZ08cPHjQYnpCQgIOHDjgsfp07tzZ6uDw+Ph4k9UYSJ1cGRzes2fPFttsLfKr+iYGR1ucLl++LGJjYwUAER0dLU6dOmU236+//iqCg4MFANG/f3+n6tb829I///lPq3nvuusuAUD4+/uLuro643bO4+Q4JXU1KI3auh9JWkFBQVa/qQcFBXm0PgkJCVbrk5CQ4NH6kPfhPE4S2LZtG8rKygAAM2bMQHR0tNl8vXr1woQJEwAAu3fvxi+//OLwsTp16mT8OS4uzq68DQ0NOH36tMPHoqsKCgpaPG7fXF5eHrZv3+7hGilLY2Oj3FUgsptQd4cJKRQDp+s0b4bu16+f1byJiYnGnw8dOuTwsXr16mX8uaGhwWre5ukBAZx+y1lqnrjSVQwqyZYuXbpYTe/atauHanLV9YuwX+/ixYseqgn5EgZO12kelNTX11vNe+XKFbP72euee+4x/vz7779bzduUHhISgnbt2jl8LLpKCRNXVlVVQaPRtHg52z8vJQaVrlPquZXKokWLrKYvXrzYQzW5ytYEl1JPgKn280v2YeB0nebLrOzcudNq3h07dpjdz1733HOPcQDahg0bLDY7FxcX4+effwYADB48GH5+PG3O8vQHrTdRQlBJypacnIz09HSzaenp6R5/eGD06NFW08eOHeuhmpAv4R34OklJSWjdujUAYOnSpdi3b5/ZfFu3bsX69esBALGxsbjjjjtM8gwdOtT4reT48eMm6f7+/njuuecAAIcPH8Zrr71mkufKlSuYPn26cezJtGnTnPm16P9kZ2dbTZ87d66HaqI8DCrJHrm5udDpdNBqtYiPj4dWq4VOp5NlSZ6NGzdaTf/ss888VBPyJaobLPPdd9/h6NGjxv83X4fm6NGjJtMRTJkypcX/27Rpg1mzZiErKwvnz5/H4MGDMWPGDCQnJ6Nt27b4448/sHHjRrz77rvGYGbevHlOtwI99dRTWLt2LX788Ue8+OKL2L9/PyZMmICoqCgcPXoUOTk5+OGHHwAA999/P8aNG+fUceiqpm/MeXl5JmlyfGNWkuzsbKvTEfhyUEktJSUlKeJaYSspycIjz/t50OTJk12e8LGxsVE888wzQqPRWN03MDDQZELK5oYMGWLMW1xcbDFfeXm5SExMtHqs+++/XxgMBpN9OR2Bc+ScuFLJ54zTEbhGyedWjTw9ASbPr/pwOgKJaDQavPHGGygqKsK0adPQu3dvhIWFwd/fHxEREUhMTERmZiZ+/fVXY1ebK2688UYUFhZi2bJlGDJkCKKiohAYGIjo6GiMGjUKn332GbZs2YKwsDAJfjsCrn5j1uv1KCkpgV6vV8S3ZyVQUjcMkS3seic5qH7mcLUzN3O4zVlPSVY8Z+rFc+t5GRkZFrvepQ74eX7Vx5lzyhYnIiLyWmwlJU9T3eBwIiLyLUoZrE6+gYGTD8vJyYHBYEB4eDgyMzNZvofLdzd31t/b33ueW5avVN7+3nh7+XbxyLB1chtXnvJoWsw4NjbWLXVj+eZ56skcd74/3vreu7t8NZxblm+ZJ86vt7433lo+n6ojIiIiciN21alQnz594O/vb7I9MzPT65qtiYiIpJCTk4OcnJwW2xoaGhwuh4GTClVUVJjdbjAYPFwTIiIiZTAYDCgrK3O5HAZOXq5p2ZfmOnToYHYJGD8/vxYreTdF2g0NDW5Z4Zvlm9d8GSBr21zlzvfHW997d5evhnPL8i3zxPn11vfGG8r38/NDdHR0i22NjY2orKw02WYNJ8D0cgcPHkTPnj3lrgYREZEqHDhwAAkJCRbTOTiciIiIyE4MnIiIiIjsxMCJiIiIyE4c4+Tl6uvrceTIkRbb2rVrZ3ZwOBEREV3T2NiIM2fOtNh26623IiDA8rNzDJyIiIiI7MRmCSIiIiI7MXAiIiIishMDJ/IJ1dXVmD9/Pu666y5ER0ejVatWiImJwZ133omZM2dCr9fbLGPbtm0YO3Ys4uLi0KpVK8TFxWHs2LHYtm2b3fWoqanBggULMHDgQLRr1w6hoaFISEjAc889h9LSUrvL2b9/P6ZNm4ZbbrkFISEhiIqKwj333IO3334b9fX1dpfz0UcfYfjw4bjxxhsRHByMm266CRMnTkRhYaHdZchN7nM7dOhQaDQau1724Lm9xtlz29jYiAMHDmDFihWYPn06BgwYgFatWhnPwzfffONQPXjdUguSLC9MpGDr1q0T7du3N1kBu/lr9OjRFvdvbGwUjz32mNX9H3vsMdHY2Gi1HkePHhXdu3e3WEZERITYsmWLzd8nNzdXtGrVymI5Wq1WVFdXWy3j0qVL4oEHHrBYhp+fn8jOzrZZF7kp4dwOGTLE6v7NX7bw3F7jyrldsWKF1f2+/vpru+vB65aux8CJVO39998Xfn5+AoDo0KGDePnll0VBQYHYs2eP2LJli1i0aJFITk4WDz74oMUy/vGPfxg/mPr27Ss+/PBDsWvXLvHhhx+Kvn37GtNeeukli2WcP39e9OjRw5h36tSpYvv27eL7778Xr776qggNDRUAROvWrcUvv/xisZxt27YZf5+OHTuKRYsWiR9++EFs3bpVjB071lj+PffcIxoaGiyW88gjjxjzDhs2TGzYsEHs2rVL5OXlia5duxrT3n33XfveaBko5dw2BU79+/cX+/bts/qyhuf2GlfP7fLly42/Z2BgoOjbt6+47bbbHA6ceN2SOQycSLUOHDhg/Ib3pz/9SZw7d85i3traWrPbjxw5IgICAow3xpqamhbpFy9eFP379xcAREBAgDh69KjZcl5++WXjh9r8+fNN0r///nvjcYYNG2a2jCtXrohbbrlFABDh4eFmjzV9+nTjcd5//32z5XzzzTfGPCNHjhT19fUt0quqqkR8fLwAINq2bSvOnj1rthw5KencNgVOQ4YMcfr34bm9Ropz+8MPP4iFCxcKvV4vLl26JIRoeQ3aGzjxuiVzGDiRaiUlJQkAIjIyUlRVVTlVRvMPNL1ebzaPXq835nnyySdN0uvq6kSbNm0EAJGQkGDxG+Xjjz9uLGf37t0m6evWrTOm//vf/zZbxsWLF0Xbtm0FANG7d2+zee6//34BQPj7+4sTJ06YzfPhhx8aj/X666+bzSMnpZxbIaQJnHhur5Hi3JrjaODE65YsYeBEqnTw4EHjB8grr7ziVBmNjY0iNjZWABA9evSwmrdpDERcXJzJeJj8/HxjXebNm2exjOY36X/84x8m6c2b6U+dOmWxnOYf5L/99luLtPPnzxu/zd93330Wy6itrRXh4eECgBg8eLDFfHJQ0rkVQprAief2KinOrSWOBk68bskSPlVHqvTxxx8bf37ooYeMP589exZHjhzB6dOnbZZRXFyMsrIyAMCQIUOs5m1KP3nyJI4fP94ibefOnSb5zOnfvz9uuOEGAMB3331nkt5UTvfu3REdHW2zLubK2bVrF2pra23WJSgoCFqt1rjPlStXLOb1NCWdW6nw3F4lxbmVCq9bsoSBE6lS02O5ERERSEhIwOrVq3H77bejXbt26NatGyIjI9GlSxf885//xIULF8yWcfDgQePPPXr0sHq85unN93OknICAAHTt2tVsGRcuXMDJkyc9Vpfm6eaW9ZGTks5tc4cOHcKAAQMQFhaG4OBgxMXFYfTo0Vi5cqXVGxjP7TVSnFup8LolSxg4kSodOHAAAHDTTTdhxowZmDBhAvbu3dsiT3FxMV555RUMGjQI5eXlJmWcOHHC+HNcXJzV43Xq1Mnsfs3/f8MNN6BNmzZ2lVNVVWX8hglcbe0Q/7c6khR1cbUcOSnp3Db3xx9/YPfu3bhw4QJqa2tRVlaGTZs2YfLkybjjjjssBl08t9dIcW6lwuuWLGHgRKrUtGjjoUOHsGTJErRp0wbLli1DZWUlLl++jKKiIowYMQIA8Ouvv+Khhx5CY2NjizLOnz9v/Dk0NNTq8Zqa6gGYfBNuKsdWGdbKkbourpYjJyWdWwDw8/NDUlIS/vOf/0Cn0+Gnn37Ct99+izfffBMJCQkArgYEw4YNMztZIs/tNVKcW6nwuiVLGDiRKl28eBEAUFtbC39/f2zduhWPP/44oqKi0KpVK/Tv3x+bN282fgh///33+Oyzz1qUcfnyZePPQUFBVo/XqlUr48+XLl0yW46tMqyVI3VdXC1HTko6twDw2WefQafTITMzE0lJSbjjjjvwpz/9CU8//TR++eUXTJ48GcDVFqlnnnnGZH+e22ukOLdS4XVLljBwIlUKDg42/vzQQw8ZB0w25+fnhwULFhj//+GHH1oso66uzurxmjfPh4SEmC3HVhnWypG6Lq6WIyclnVsAVrtxAgMDkZubaxx3sn79euOgdKnrwnPrnrrwuqXrMXAiVQoLCzP+3PTt1JxevXohNjYWAFBUVGSxDFtN3k3flAHTpvSmcuxpNrdUjtR1cbUcOSnp3NojICAA6enpxv/v2LHDLXXhuXVPXXjd0vUYOJEqNR8gae9gysrKyhbbm+/X9GSMJc0HYTY/dvNyLl68iHPnztlVTlPXhLvq4mo5clLSubVXz549jT9f3+LEc3uNFOdWKrxuyRIGTqRKvXr1Mv7c0NBgNW9TekBAQIvtzW92hw4dslpG8/SmAcGOllNfX4/ff//dbBmhoaHGD0FP1KV5ekBAAG655RareT1JSefWXk1PVpnDc3uNFOdWKrxuyRIGTqRK99xzj/Hnpg81S44dOwYAxqb/JjfffDNiYmIAmHavXO/bb781lnHTTTe1SLv77ruNP1srZ/fu3cZm9rvuusskvamcw4cPo6KiwmI5zY9xfTkDBgwwDi61Vpe6ujrjnDrN91ECJZ1bezU9Zg/AeNzmeG6vkuLcSoXXLVkk78TlRO5RXV0tAgMDBQCRnJxsMV/zhTPT09NN0p944gmH1jObPn26SXptba2IiIgwrnllbtkOIVouubBr1y6T9LVr1zq05lXPnj3N5hkxYoRx4Vp71rwyt7ipnJR0bu1x5coV0aNHD2M5paWlJnl4bq+S6tya4+iSK7xuyRIGTqRazW+MH374oUm6wWAQd9xxh9UPvcOHDxtXP+/fv7+oqalpkV5TUyP69+9v/EC7fo2pJv/zP/9j9QOt+SrrltY8q6urE127dhWAfausL1++3Gw527dvN+YZNWqU1VXW27RpI86cOWO2HDkp5dx+9dVXVlehr6urE5MnTzbWY+TIkRbz8dxeJcW5NcfRwEkIXrdkHgMnUq3KykrjB0lAQIB48sknxVdffSV2794tli9f3qIV4IknnrBYzqxZs4z5+vbtKz766CNRVFQkPvroI9G3b19j2osvvmixDIPBILp162bM+9hjj4mvvvpK6PV68a9//UuEhoYKACIkJET89NNPFsvZsmWL8PPzEwBEx44dxeLFi8UPP/wgtm3bJsaNG2cs/+677zb5YG3ur3/9qzHvsGHDxMaNG0VRUZF47733jB/yAMSyZcvseq89TSnndvLkySI0NFQ88sgj4p133hE7duwQP/30k9i5c6d48803RUJCgrGMDh06iGPHjlmsC8/tVVKd2+XLl7d4jR492rjfCy+80CJt586dZsvgdUvmMHAiVTtw4IC45ZZbjB8o5l5paWmirq7OYhkNDQ0iLS3Nahnp6emioaHBal2OHDkibr31VotlhIeHi88//9zm7/TOO++IoKAgi+UMHDhQVFVVWS2jpqZG3H///RbL8PPzEy+//LLNushJCee2eWuStddtt90m9u/fb/N34rm9Sopza895aXpNnjzZYjm8bul6DJxI9S5cuCAWLFgg7rzzTtGuXTsRFBQk4uLixMMPPyy++uoru8vZsmWLGD16tIiJiRFBQUEiJiZGjB49WnzxxRcO1eW1114T/fv3F23atBGtW7cW3bt3F3//+9/F8ePH7S5n3759YurUqaJLly4iODhYtG/fXtx9991i6dKl4sqVK3aXs3r1apGcnCw6dOgggoKCRKdOncQjjzwivv/+e7vLkJPc5/bAgQPijTfeEH/5y19E7969RceOHUVgYKAIDQ0VXbt2FQ8//LD4+OOPrbYiXI/n9ipXz61UgVNTXXjdUhONEFaekyUiIiIiI05HQERERGQnBk5EREREdmLgRERERGQnBk5EREREdmLgRERERGQnBk5EREREdmLgRERERGQnBk5EREREdmLgRERERGQnBk5EREREdmLgREQkkUWLFiEiIgLV1dWSlrtt2zZoNBp88803kpbrbcaPH48BAwaAK4WRnBg4EZFZU6ZMgUajwcCBAx3e9/z587jhhhug0WiwYsUKm/m/+eYbu/K+8sor0Gg0Ji9/f3+0bdsWN910E/r374/09HS8/fbb+P333x2uu7POnz+PuXPn4vnnn0dkZKTHjutLXn31Vfzyyy9Yu3at3FUhHxYgdwWISNmKiopw+PBhdO/e3e59PvnkE9TU1LitTm+88YZJcHLp0iWcPXsWVVVV2L17N9auXYuamhqMGDECzz//PIYMGeK2+gDA4sWLceXKFTz11FNuPY4v69KlCyZMmIB//vOf+Mtf/gI/P373J89j4ERENq1cuRKvvvqqQ/nd6c9//jNuuukmq3muXLmCDz/8EAsWLMCwYcMwc+ZMvPrqqwgIkP5j79KlS1i4cCGmT5+OsLAwycuna1544QUkJCTgs88+w4MPPih3dcgHMVwnIqsSEhLwwQcf2D2upLS0FDt27EDPnj3dXDPrAgMDMWnSJPz444+YNm0a5s+fj7S0NLcc68MPP0R1dTWmTZvmlvLpmu7duyMpKQmLFi2Suyrkoxg4EZFVEydORGlpKb799lu78q9atQpCCEycONHNNbNPYGAg3nrrLTz22GNYtWoV3nrrLcmP8fbbb2P48OHo1KmT5GWTqfT0dOzcuRMHDx6Uuyrkgxg4EZFVEyZMgEajsbv7bdWqVbj11luh1WrdXDPHvPnmm+jSpQuysrIkHX919OhR7Nq1Cw899JBkZZJ1I0eORHBwMNasWSN3VcgHMXAiIqs6deqEoUOH4pNPPsGlS5es5t21axcOHz6smNam5kJCQvDUU0/h9OnTWL58uWTlfvLJJ/Dz88PIkSOd2v/MmTPIzs5G3759ERERgfDwcNx///3Yvn27Q+U0NjZi69at+Nvf/oZ+/fohMjISQUFB6NixI1JSUrB06VLU1tbaLEer1aJv374O/x6PP/44IiIiLP6NCCGwYcMGPPjgg4iPj0erVq3g7++PsLAw9OjRA08++SR++OEHu451ww03IDk5GZ988onD9SRyFQMnIrJp4sSJMBgM2Lhxo9V8K1euhEajwYQJEzxUM8dMmTIFAQEBWL9+vWRlfvnll+jTp49TUxBs374dCQkJeP3119G/f3+89tprmDt3Llq1aoXhw4dj6tSpqK+vt1nOli1b0KNHD4wcORI///wzhg8fjuzsbCxbtgyZmZmIiIhAZmYmevTogT179lgtKzU1FT///DN+/vlnu3+Py5cvY+3atfjrX/+KkJAQk/SzZ8/i3nvvxZgxY3DixAk8+uijWLRoEd577z289tprGDlyJL7//ntotVqcO3fOrmMOGzYMhw4dQmlpqd31JJKEICIyY/LkyaLpI8JgMIiQkBBx//33W8xfV1cnIiMjxd133y2EEOLrr78WAMTy5cttHsvevC+//LIAIIqLi+39NUzccccdIiwsTNTX1ztdRpPLly+LVq1aiSeffNLhfb/55hsRHBwsUlJSRHl5uUn6nj17RNeuXcWzzz4rtm7dKgCIr7/+2mxZ99xzj5g6darV9+XUqVNi2LBhIiwsTBw+fNhivnPnzong4GDx9NNP2/27rFmzRgAQer3ebPqYMWNEeHi40Ol0VsvZuHGjqKmpseuYRUVFAoB4//337a4nkRTY4kRENoWFheHPf/4z8vPzUVlZaTbPli1bUF1djUmTJnm4do4ZMGAAzp8/j99++83lsvbv34/a2lrcfvvtDu1nMBgwceJEDB06FFu2bMGNN95okqdfv37Q6/X49NNPsXfvXqvlrVmzBu+8847VKRqio6OxadMmREVFYebMmRbzRUREYMyYMVi9ejWuXLli1++zfPly9OjRw+y4tpMnT2L9+vWYNWsWkpKSrJYzatQosy1W5vTu3Rv+/v42W9CIpMbAiYjsMnHiRNTX11sckLtq1Sq0atVK8YOkY2NjAQBVVVUul7V//34AcGhyUABYuHAhTp8+jZUrV1qdVyoqKgpLly7Ff/7zH6vlNf1OtoSGhuL555/Hli1bcPr0aYv5pkyZgurqamzevNlmmSdPnsT27duRmppqNr0psLn33nvtqqO9goOD0blzZ+M5IPIUBk5EZJeUlBR07NgRq1atMkk7e/YsNm/ejJEjR6JNmzaer5wDmup39uxZl8sqKSkBAMTFxdm9jxAC77zzDsaPH4+oqCib+YcPH46IiAin63i9lJQUNDQ0WG2p+a//+i906tTJrkH077//PjQajcUHApoGi7dq1cq5ClsRGxvLMU7kcQyciMgu/v7+GD9+PH788UeTb/kfffQR6urqFPk03fVuuOEGAMCFCxdcLqup29KRgeG//fYbTp48if/6r/+yK79Go8GwYcOcqh9wNXA5ffo0qqurUV1djaCgIGM9LPHz88PEiROxdetW/PHHH1bLf//993HfffeZ7W4EgMTERADAF1984eRvYFlUVJTFrmMid2HgRER2axq/dH2r08qVKxEZGYkRI0bIUS2HNAVMUiyN0tSaYu+4HOBa15Uj46LuuOMOu/JdvnwZa9aswUMPPYRu3bohMDAQrVu3RmRkJKKiohAVFWVsHfvf//1fq2VNmTIF9fX1+OCDDyzm+e6773DkyBGL3XQAcOutt+Lpp5/GK6+8gvnz59s1JYK9QkJCcPHiRcnKI7IH16ojIrv17dsXvXr1wurVq/Gvf/0Lfn5+OHLkCAoLC/Hkk08iMDBQ7ira1PS4e9u2bV0uS9i5DE1zTWOr2rdvb/c+7dq1s5lHr9dj4sSJOHHiBFJSUpCamoouXbogPDy8xXlpbGzE8OHDbQYct956K+666y6sWLECzz77rNk8K1asQPv27W3OYfXGG2+gQ4cO+Oc//4k333wTo0aNwvDhwzF48GB07NjR5u9miRACGo3G6f2JnMHAiYgcMnHiRMyaNQtff/01kpKSjDOKe0M3HQCUlZUBADp06OByWU3dfpcuXbK7Bev8+fMt9nXkOJb88ssvSElJQb9+/bB9+3Z07tzZYt7Lly8DuBpA2ZKamoqMjAzs3r0b/fv3b5FWU1ODdevWITU11dj9Z4lGo8E//vEPPP7441i7di22bduGjIwMnDlzBr169cJDDz2EJ554wuFzcvnyZYda+4ikwK46InLIhAkT4OfnZ1yT7oMPPkD37t0xcOBAuatml6KiIkRERKBbt24ul9V0o6+urrZ7n6YAy5EuJlt5p0+fjqioKHzxxRdWgybAsbr+5S9/QevWrbFixQqTtE8//RTnz5+32k13vfbt22P69OnYtGkTqqursWfPHvz5z3/GsmXL0LVrV7MPHlhTXV3tUosVkTMYOBGRQ2JjYzFs2DB8+umn+PLLL3H8+HGvaW06d+4c9u/fD61WK0kXT3x8PICrj+Tbq+lJOmvTAVzvzJkzFtN+//13fP/993j66aftasWyNSdUc2FhYRg3bhzWrFljMjZp+fLluP322+0ef3U9jUaDfv36Ye7cuSguLsajjz6KyZMnY9u2bXaXcfLkSeM5IPIUBk5E5LCJEyfiwoULmDp1KjQaDR599FG5q2SX5cuXo76+HuPGjZOkvF69egEADh48aPc+/fr1A3C1e81e1pY/2bdvHwDgzjvvtKusrVu32n1c4Oog8bNnz2LTpk3GbcePH8c333yDtLQ0h8qyJDg4GEuWLEGXLl3w+uuv27XP5cuXUVJSYjwHRJ7CwImIHDZu3Di0bt0aJ0+exJ/+9CerM1YrRU1NDRYvXoyOHTtK1kLWu3dvtGrVyqEgqHv37oiLi4NOp7N7n6+//tpiWk1NDQD7xkzV1tbi888/d6i1bdiwYbjppptazOn0/vvvIzAwEI888ojd5dji7++Pvn374siRI3bl379/PxoaGozTHRB5CgMnInJYaGgo9uzZg6KiIosziSvNU089heLiYrz66qsIDg6WpMygoCAMGjQI3333nd37aDQaTJ06FR9++KFd4422bdtmdeqApjE+TYPerXnvvfcwdOhQ+PnZ/9Gv0WgwadIk5Ofno7y8HEIIrFy5EiNHjnRqYWNrDAaD3ZN97ty5EwAwdOhQSetAZAsDJyJySo8ePdC/f3+7l/uQS11dHaZNm4a8vDxkZGQgPT1d0vLvu+8+7Nu3z6GJGJ9++mm0bdsWkyZNQn19vcV8VVVVeOKJJ5CZmWkxz8CBAxEUFIT169dbPWZ5eTlefvlli1MLWDNlyhQ0NjZi1apV2LFjB44dO+bQoHB7lJWV4dtvv7X7IYOvv/4aPXr04Bgn8jgGTkSkGFLOyXPlyhWsXLkS/fr1w7vvvouXXnoJy5Ytk6z8JuPGjYMQosUYIFsiIiKwatUqfPXVV/jv//5vVFRUmOT58ccfMWjQIIwZM8bqZJlhYWFIT09HXl4eli9fbnZuqSNHjiA5ORmTJk3CbbfdZnc9m9x888245557sGLFCqxYsQLR0dG477777Np3wYIFyMrKstpqdurUKYwdOxZXrlzBjBkzbJZ58eJFFBQU4MEHH7T7dyCSCudxIiLZ1dXVAYDdXWgbNmww6Sa6fPkyzp49i+rqahQVFWHXrl24dOkSRowYgWXLluHuu++WvN4AcMstt2DAgAFYt24dMjIy7N5v2LBh2LRpEyZMmIBu3brh4YcfRv/+/VFXV4evvvoKn3/+OaZMmYL58+fbHA/1+uuv4+DBg0hLS8OiRYvw3//937jppptw5swZ/PDDD9i0aRPGjx+P1157zenfMzU1FVOmTMGRI0fw7LPPwt/f3679AgICMGfOHLz55psYNmwY+vbti+joaAQHB+PMmTPYs2cPNm7ciCtXrmDZsmV2zai+efNmXLp0CePHj3f69yFyFgMnIpJd0zIooaGhduX/+9//3uL/Go0GYWFhaNOmDSIjI3HHHXfgr3/9K5KTk3HzzTdLXt/rPf7443jsscdw/PhxhwbKp6Sk4ODBg/h//+//Yf369Vi7di2EELj77ruxbds2u9eza926NXQ6HZYvX47Vq1fjrbfewsWLFxETE4O77roL27ZtQ1JSkpO/3VUPPvggnnzySVy4cAFTpkyxe7+///3veOCBB/DRRx/hyy+/xJIlS2AwGNDY2Ijw8HB07doVTzzxBKZOnWr33Frvvvsu/vSnP6Fnz55O/jZEztMIZ9YMICKS0JIlS/Dkk0/il19+QZ8+feSujsMuXbqEzp07Iy0tDfPmzZO7Om4zYMAABAQEQK/Xy1aHw4cPIyEhAR9//LFk00oQOYJjnIhIdj///DOCg4Mlmc1bDiEhIXj66aexbNkyGAwGuavjFkeOHMHu3bslHxTuqNdeew3dunXDmDFjZK0H+S4GTkQkq4sXL2LTpk249957JZsmQA4zZsxAYGAgFi5cKHdV3OI///kPQkND8de//lW2Ohw7dgwffPABXn75ZYemVCCSEv/yiMjjGhsbUVVVhd27d2PMmDGorq7Giy++KHe1XBIeHo7/+Z//wYIFCxxaD84bHDt2DCtWrMDUqVMRHh4uWz1eeukl9OnTR9bgjYhjnIjI444fP24ctN22bVssWrQIEyZMkLlWZM758+dx9913o7y8HAcOHDCutUfkq/hUHRF5XHR0NHQ6Hdq0aYPbbrsNQUFBcleJ/s/Fixexfv16CCFw/PhxvPvuuzhz5gw2bdrEoIkIbHEiIqJmmrcGhoaGYvjw4fjXv/7ltQP3iaTGwImIiIjIThwcTkRERGQnBk5EREREdmLgRERERGQnBk5EREREdmLgRERERGQnBk5EREREdmLgRERERGQnBk5EREREdmLgRERERGSn/w/cZhDCoctNGwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 4))\n",
    "plt.plot(srcs[pick['r']]['expMidptMJD'], srcs[pick['r']]['psfMag'],\n",
    "         'k.', ms=10)\n",
    "plt.minorticks_on()\n",
    "plt.xlabel('MJD (days)')\n",
    "plt.ylabel('r')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09713c4-093b-4013-8c96-5019f93c9422",
   "metadata": {
    "tags": []
   },
   "source": [
    "There is a spread of almost 1 magnitude, so clearly we have identified a variable star. Hooray!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd9de32-b11b-4de3-b0f9-ea566ffcfacb",
   "metadata": {},
   "source": [
    "### 2.2 Create a Phased Lightcurve\n",
    "\n",
    "In the plot above, it is clear that this is a variable star, but we want to know more about it. Assuming it is a periodic variable, let's try to estimate its period so we can create a phased lightcurve.\n",
    "\n",
    "> **Caveat:** The author of this notebook is not an expert in periodic variables or analysis of time-series data. The following is almost certainly not the **best** way of doing these analyses, but is simply one way to do things. It is shown here as a demonstration only.\n",
    "\n",
    "A common way of searching for periodicity in unevenly-sampled time-series data is the Lomb-Scargle Periodogram. In this section we will create such a periodogram using the <a href=\"https://docs.astropy.org/en/stable/timeseries/lombscargle.html\">Lomb-Scargle package from Astropy</a>. We will then estimate the best period, and reference all observations to that period to create a phased lightcurve.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c43b66-d544-440c-979b-df1faacea52e",
   "metadata": {},
   "source": [
    "#### 2.2.1 Extract and Plot a Periodogram\n",
    "\n",
    "For convenience, we first extract all of the times of observation and measured magnitudes to separate arrays for each bandpass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "58cba6ea-6d28-491b-9fe4-349f909866a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:12.500133Z",
     "iopub.status.busy": "2024-04-29T13:59:12.499819Z",
     "iopub.status.idle": "2024-04-29T13:59:12.688718Z",
     "shell.execute_reply": "2024-04-29T13:59:12.687719Z",
     "shell.execute_reply.started": "2024-04-29T13:59:12.500115Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mjd_days = {}\n",
    "mags = {}\n",
    "for filter in plot_filter_labels:\n",
    "    mjd_days[filter] = np.array(srcs[pick[filter]]['expMidptMJD']) * u.day\n",
    "    mags[filter] = np.array(srcs[pick[filter]]['psfMag'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd1f449-aa9b-4d7c-8b45-a24960220369",
   "metadata": {},
   "source": [
    "The Lomb-Scargle periodogram returns the power at different frequencies. Because we know this is an RR Lyrae star, its period must be between ~0.2-0.9 days. We will use the min/max frequency settings to limit our period search to 0.05-1.05 days. (Recall that frequency is 1/period.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b32afc3b-4c21-4fd1-9cee-8a3e791ee95f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:12.690189Z",
     "iopub.status.busy": "2024-04-29T13:59:12.689866Z",
     "iopub.status.idle": "2024-04-29T13:59:13.024319Z",
     "shell.execute_reply": "2024-04-29T13:59:13.022913Z",
     "shell.execute_reply.started": "2024-04-29T13:59:12.690160Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "min_period = 0.05 * u.day\n",
    "max_period = 1.05 * u.day\n",
    "\n",
    "min_freq_search = 1.0 / max_period\n",
    "max_freq_search = 1.0 / min_period"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4e1999-10ca-44ba-85fe-2965bed31e2a",
   "metadata": {},
   "source": [
    "Now we run the LombScargle algorithm on each of these datasets. We will set only the min/max frequencies, and otherwise use the \"autopower\" method from LombScargle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36330abc-f5ad-4851-9ab2-e3ab8fa29bcf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:13.026511Z",
     "iopub.status.busy": "2024-04-29T13:59:13.025919Z",
     "iopub.status.idle": "2024-04-29T13:59:15.324199Z",
     "shell.execute_reply": "2024-04-29T13:59:15.322875Z",
     "shell.execute_reply.started": "2024-04-29T13:59:13.026466Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "frequency = {}\n",
    "power = {}\n",
    "for filter in plot_filter_labels:\n",
    "    frequency[filter], power[filter] =\\\n",
    "        LombScargle(mjd_days[filter], mags[filter]).autopower(minimum_frequency=min_freq_search,\n",
    "                                                              maximum_frequency=max_freq_search)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1dd6f8b-6036-4758-9000-ed466723b6d5",
   "metadata": {},
   "source": [
    "In a well-behaved and well-sampled scenario, the Lomb-Scargle power will peak at a specific frequency that corresponds to the period of the star's variability.\n",
    "\n",
    "We will assume that the frequency returning the highest power corresponds to the real period of the star (which is not necessarily true in the case of real data with uneven sampling, period aliasing, and other effects). Let's find this frequency for each band.\n",
    "\n",
    "In the following cell, we find the index with the maximum power and assign it as the \"peakbin\" and then store the frequency according to the peak power. Then we calculate the mean frequency and period from the peak frequencies of the results from all the filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b546ca6-3b29-4a36-8ac6-c3bdbbd105a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:15.326200Z",
     "iopub.status.busy": "2024-04-29T13:59:15.325748Z",
     "iopub.status.idle": "2024-04-29T13:59:15.335786Z",
     "shell.execute_reply": "2024-04-29T13:59:15.334939Z",
     "shell.execute_reply.started": "2024-04-29T13:59:15.326176Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean frequency: 1.962589383860051\n",
      "Mean period: 0.5095309330743371  days\n",
      "\n",
      "ugrizy frequency results:\n",
      " [1.96261051 1.96261062 1.96261288 1.96252393 1.96255106 1.9626273 ]\n"
     ]
    }
   ],
   "source": [
    "all_peak_freqs = []\n",
    "\n",
    "for filter in plot_filter_labels:\n",
    "    peakbin = np.argmax(power[filter])\n",
    "\n",
    "    all_peak_freqs.append(frequency[filter][peakbin].value)\n",
    "    \n",
    "all_peak_freqs = np.array(all_peak_freqs)\n",
    "\n",
    "mean_peak_freq = np.mean(all_peak_freqs)\n",
    "\n",
    "print('Mean frequency:', mean_peak_freq)\n",
    "print('Mean period:', 1.0/mean_peak_freq, ' days')\n",
    "print('\\nugrizy frequency results:\\n', all_peak_freqs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b62127-d994-43d0-9a06-b35eb62abd16",
   "metadata": {
    "tags": []
   },
   "source": [
    "Plot a \"periodogram\" -- a figure showing the power vs. frequency from the Lomb-Scargle analysis. We'll also add a panel for the period. (This figure shows only the r-band results. You could try it with others!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc11d6dc-c44a-4d85-8b07-680a7d9410ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:15.337133Z",
     "iopub.status.busy": "2024-04-29T13:59:15.336768Z",
     "iopub.status.idle": "2024-04-29T13:59:16.009588Z",
     "shell.execute_reply": "2024-04-29T13:59:16.008910Z",
     "shell.execute_reply.started": "2024-04-29T13:59:15.337110Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "plt.sca(ax[0])\n",
    "plt.plot(frequency['r'], power['r'])\n",
    "plt.vlines(mean_peak_freq, 0, 1, linestyle='--', color='red')\n",
    "plt.minorticks_on()\n",
    "plt.xlabel('frequency (1/d)')\n",
    "plt.ylabel('power')\n",
    "\n",
    "plt.sca(ax[1])\n",
    "plt.plot(1 / frequency['r'], power['r'])\n",
    "plt.vlines(1/mean_peak_freq, 0, 1, linestyle='--', color='red')\n",
    "plt.minorticks_on()\n",
    "plt.xlabel('period (d)')\n",
    "plt.ylabel('power')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d752a584-bcba-4906-8615-d33b17832b76",
   "metadata": {},
   "source": [
    "#### 2.2.2 Create a Phased Lightcurve\n",
    "\n",
    "The red line we overlaid on the previous plot corresponds to a clear peak in the periodogram, so it looks like we have found a good estimate of the period. Now we will use that period to extract and plot phased light-curves of the variable star.\n",
    "\n",
    "Recall that period = 1/frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d5cef36d-06c5-4800-a9ed-b9eee942af03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:16.010615Z",
     "iopub.status.busy": "2024-04-29T13:59:16.010413Z",
     "iopub.status.idle": "2024-04-29T13:59:16.013772Z",
     "shell.execute_reply": "2024-04-29T13:59:16.013136Z",
     "shell.execute_reply.started": "2024-04-29T13:59:16.010597Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_period = 1/mean_peak_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0236bf09-d4eb-4b40-b41e-4968356da2c1",
   "metadata": {},
   "source": [
    "To \"phase\" the lightcurves, we calculate how many periods have passed since some fiducial time \"t0\". Here, we select t0 to be the time of the first g-band observation, but that is completely arbitrary.\n",
    "\n",
    "The second part takes only the non-integer part of the number of elapsed periods. For example, a point that happens 2.75 periods after t0 will have phase 0.75."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e57f91e1-f1a5-4ded-84c4-3e1146dc9ddf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:16.015164Z",
     "iopub.status.busy": "2024-04-29T13:59:16.014596Z",
     "iopub.status.idle": "2024-04-29T13:59:16.216313Z",
     "shell.execute_reply": "2024-04-29T13:59:16.215119Z",
     "shell.execute_reply.started": "2024-04-29T13:59:16.015136Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mjd_norm = {}\n",
    "phase = {}\n",
    "\n",
    "t0 = np.min(mjd_days['g'].value)\n",
    "\n",
    "for filter in plot_filter_labels:\n",
    "    mjd_norm[filter] = (mjd_days[filter].value - t0) / best_period\n",
    "    phase[filter] = np.mod(mjd_norm[filter], 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f0cb659-6ae6-4520-875c-aba956005bd4",
   "metadata": {},
   "source": [
    "Now plot them separately for each band (using an integer offset to separate them):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce708016-79f3-4371-acec-5032fc655df7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:16.218477Z",
     "iopub.status.busy": "2024-04-29T13:59:16.217922Z",
     "iopub.status.idle": "2024-04-29T13:59:16.918114Z",
     "shell.execute_reply": "2024-04-29T13:59:16.917314Z",
     "shell.execute_reply.started": "2024-04-29T13:59:16.218435Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 6))\n",
    "\n",
    "i = 0\n",
    "\n",
    "for filter in plot_filter_labels:\n",
    "    plt.plot(phase[filter], mags[filter]-np.mean(mags[filter]) + i,\n",
    "             plot_filter_symbols[filter],\n",
    "             color=plot_filter_colors[filter], label=filter)\n",
    "    plt.hlines(i, 0, 1, linestyle=':', color='Gray')\n",
    "    i += 1\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.legend()\n",
    "plt.xlabel('phase')\n",
    "plt.ylabel('mag - mean (+offset)')\n",
    "plt.minorticks_on()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b3a216-8f69-440e-9f98-d1b833b287f3",
   "metadata": {
    "tags": []
   },
   "source": [
    "...and all together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a1421c45-60f7-4a35-a87f-2bd23f7bc851",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:16.919462Z",
     "iopub.status.busy": "2024-04-29T13:59:16.919026Z",
     "iopub.status.idle": "2024-04-29T13:59:17.273243Z",
     "shell.execute_reply": "2024-04-29T13:59:17.272532Z",
     "shell.execute_reply.started": "2024-04-29T13:59:16.919443Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "for filter in plot_filter_labels:\n",
    "    plt.plot(phase[filter], mags[filter]-np.mean(mags[filter]),\n",
    "             plot_filter_symbols[filter],\n",
    "             color=plot_filter_colors[filter], label=filter)\n",
    "plt.hlines(0, 0, 1, linestyle=':', color='Gray')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.legend()\n",
    "plt.xlabel('phase')\n",
    "plt.ylabel('mag - mean')\n",
    "plt.minorticks_on()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1992314a-42fc-4cfc-a1f6-bd4c224e969f",
   "metadata": {
    "tags": []
   },
   "source": [
    "Success! That looks like an RR Lyrae star. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7061ac0e-996e-48e7-8d6b-3a302c2e1a42",
   "metadata": {},
   "source": [
    "## 3. Identify Candidate RR Lyrae From Catalogs\n",
    "\n",
    "Now that we know we can extract a lightcurve, find the star's period, and plot phased lightcurves for a _known_ RR Lyrae star, we move on to exploring how to _find_ candidate variables based on data in the catalogs.\n",
    "\n",
    "Data products from difference image analysis (DIA), where sources are detected and measured on the _difference_ image resulting from subtracting an approriately scaled deep coadd template of the same sky area from a processed visit image (PVI), are a good place to check for transient and variable objects.\n",
    "\n",
    "The three DIA products we will use are:\n",
    "\n",
    "- `DiaObject`: table of all spatially unique objects detected in all difference images (all `DiaSources` associated by coordinate)\n",
    "- `DiaSource`: table of sources detected in the difference images\n",
    "- `ForcedSourceOnDiaObject`: table of forced photometry in all PVI and all difference images at the location of all `DiaObjects`\n",
    "\n",
    "The DP0.2-era `DiaObject` table contains summary statistics for each object, which we can use to identify candidate variables.\n",
    "In the future, this table will contain additional variability measurements as described in the Data Management Tech Note (DMTN) \"Review of Timeseries Features\", <a href=\"https://dmtn-118.lsst.io\">DMTN-118</a>.\n",
    "\n",
    "For simplicity, the RR Lyrae candidate search we will conduct in this section will be a cone search centered at the coordinates of the known RR Lyrae from the previous section (RA, Dec = 62.1479031, -35.799138) with a 5 degree search radius."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fc9bc8-a06d-4057-8f7d-4c232af5be4e",
   "metadata": {},
   "source": [
    "### 3.1 Query DiaObject and ForcedSourceOnDiaObject to Retrieve Candidates and Photometry\n",
    "\n",
    "The `DiaObject` table has several parameters that characterize the variability of `DiaSource` flux measurements (see the <a href=\"https://dm.lsst.org/sdm_schemas/browser/dp02.html#DiaObject\">DP0.2 Schema Browser</a> for a full list).\n",
    "These include the min/max fluxes, and various statistical measures of the scatter in flux measurements.\n",
    "A particularly useful statistic for variable stars is the \"StetsonJ\" statistic, based on <a href=\"https://ui.adsabs.harvard.edu/abs/1996PASP..108..851S/abstract\">this 1996 paper</a>.\n",
    "Variable stars have flux that varies more than expected based on the measurement uncertainties.\n",
    "High values of the StetsonJ index should identify objects whose fluxes varied much more than expected -- i.e., variable stars.\n",
    "Here we use StetsonJ>20, but users are encouraged to explore this and other statistics.\n",
    "\n",
    "Here are all of the criteria we apply to our selection from `DiaObject`:\n",
    "\n",
    " 1. g-band measurements only\n",
    " 2. coordinates within 5 degrees of our chosen RA, Dec position\n",
    " 3. `gTOTFluxSigma`/`gTOTFluxMean` > 0.25 -- the scatter in measured fluxes is larger than 25% relative to the mean\n",
    " 4. `gTOTFluxSigma`/`gTOTFluxMean` < 1.25 -- the scatter in measured fluxes is no larger than 125% relative to the mean\n",
    " 5. 18 < `diao_gmag` < 23 -- mean g magnitude between 18-23\n",
    " 6. `gPSFluxNdata` > 30 -- at least 30 observations in g band\n",
    " 7. `gPSFluxStetsonJ` > 20 -- StetsonJ index greater than 20 \n",
    "\n",
    "In the ADQL query below, we implement all of these criteria and perform a table JOIN between `DiaObject` and `ForcedSourceOnDiaObject`,\n",
    "the latter of which contains point-source forced-photometry measurements on processed visit images and difference images.\n",
    "Since we are looking for **variable** candidates, which do appear in the template image,\n",
    "we use the forced-photometry measurements from the **non-substracted** processed visit images (PVIs), also called the \"direct\" images.\n",
    "\n",
    "We must perform one more table JOIN using the `ccdVisitId` from `FourcedSourceOnDiaObject` in order to get the MJD of each visit\n",
    "from the `CcdVisit` table.\n",
    "Note that the order of the table JOIN functions is important, and a different order may change the result of the search. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c741ca88-ffd0-4758-ae59-55b1e03457d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:17.274544Z",
     "iopub.status.busy": "2024-04-29T13:59:17.274225Z",
     "iopub.status.idle": "2024-04-29T13:59:23.871637Z",
     "shell.execute_reply": "2024-04-29T13:59:23.870985Z",
     "shell.execute_reply.started": "2024-04-29T13:59:17.274526Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = \"SELECT diao.diaObjectId, diao.ra, diao.decl, diao.nDiaSources, \"\\\n",
    "        \"diao.gPSFluxMin, diao.gPSFluxMax, diao.gPSFluxMean, diao.gPSFluxSigma, \"\\\n",
    "        \"diao.gPSFluxMAD, diao.gPSFluxChi2, diao.gPSFluxNdata, diao.gPSFluxSkew, \"\\\n",
    "        \"diao.gPSFluxStetsonJ, diao.gPSFluxPercentile05, diao.gPSFluxPercentile25, \"\\\n",
    "        \"diao.gPSFluxPercentile50, diao.gPSFluxPercentile75, diao.gPSFluxPercentile95, \"\\\n",
    "        \"diao.gTOTFluxMean, diao.gTOTFluxSigma, \"\\\n",
    "        \"scisql_nanojanskyToAbMag(diao.gTOTFluxMean) as diao_gmag, \"\\\n",
    "        \"fsodo.diaObjectId, \"\\\n",
    "        \"fsodo.ccdVisitId, fsodo.band, fsodo.psfFlux, fsodo.psfFluxErr, \"\\\n",
    "        \"fsodo.psfDiffFlux, fsodo.psfDiffFluxErr, \"\\\n",
    "        \"cv.expMidptMJD, \"\\\n",
    "        \"scisql_nanojanskyToAbMag(fsodo.psfFlux) as fsodo_gmag \"\\\n",
    "        \"FROM dp02_dc2_catalogs_frdf.DiaObject as diao \"\\\n",
    "        \"JOIN dp02_dc2_catalogs_frdf.ForcedSourceOnDiaObject as fsodo \"\\\n",
    "        \"ON fsodo.diaObjectId = diao.diaObjectId \"\\\n",
    "        \"JOIN dp02_dc2_catalogs_frdf.CcdVisit as cv ON cv.ccdVisitId = fsodo.ccdVisitId \"\\\n",
    "        \"WHERE diao.gTOTFluxSigma/diao.gTOTFluxMean > 0.25 \"\\\n",
    "        \"AND diao.gTOTFluxSigma/diao.gTOTFluxMean < 1.25 \"\\\n",
    "        \"AND scisql_nanojanskyToAbMag(diao.gTOTFluxMean) > 18 \"\\\n",
    "        \"AND scisql_nanojanskyToAbMag(diao.gTOTFluxMean) < 23 \"\\\n",
    "        \"AND diao.gPSFluxNdata > 30 \"\\\n",
    "        \"AND diao.gPSFluxStetsonJ > 20 \"\\\n",
    "        \"AND fsodo.band = 'g' \"\\\n",
    "        \"AND CONTAINS(POINT('ICRS', diao.ra, diao.decl), \"\\\n",
    "        \"CIRCLE('ICRS',\"+str(ra_known_rrl)+\", \"+str(dec_known_rrl)+\", 5)) = 1 \"\n",
    "\n",
    "results = service.search(query)\n",
    "fsodo_sources = results.to_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d87585-b680-409f-8aa4-fa12fc9a9357",
   "metadata": {},
   "source": [
    "Uncomment the following cell to view the results table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "38012841-131c-44bf-bd01-e9e0ec937dd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:23.872629Z",
     "iopub.status.busy": "2024-04-29T13:59:23.872423Z",
     "iopub.status.idle": "2024-04-29T13:59:23.875727Z",
     "shell.execute_reply": "2024-04-29T13:59:23.875109Z",
     "shell.execute_reply.started": "2024-04-29T13:59:23.872611Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#fsodo_sources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debe28ea-a0cd-42cb-a256-6450ab7afb72",
   "metadata": {},
   "source": [
    "We can also check out the number of unique `DiaObjects` and the total number of measurements from the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7bf2124b-e87e-4532-a862-d9565994ea51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:23.876736Z",
     "iopub.status.busy": "2024-04-29T13:59:23.876424Z",
     "iopub.status.idle": "2024-04-29T13:59:24.389081Z",
     "shell.execute_reply": "2024-04-29T13:59:24.387885Z",
     "shell.execute_reply.started": "2024-04-29T13:59:23.876719Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1404 14\n"
     ]
    }
   ],
   "source": [
    "print(len(fsodo_sources), len(np.unique(fsodo_sources['diaObjectId'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765b8fe9-6a2a-40fe-b6cd-6374b952cb07",
   "metadata": {},
   "source": [
    "The statement and its output above tells us that the query returned 14 unique `DiaObjects`, with a total of 702 measurements in `ForcedSourceOnDiaObject`.\n",
    "\n",
    "In the following cell, we show the unique `DiaObjects` and their coordinates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc46254b-99e0-40a4-bb2b-d5b5106031c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:24.391243Z",
     "iopub.status.busy": "2024-04-29T13:59:24.390641Z",
     "iopub.status.idle": "2024-04-29T13:59:24.589987Z",
     "shell.execute_reply": "2024-04-29T13:59:24.589046Z",
     "shell.execute_reply.started": "2024-04-29T13:59:24.391200Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=14</i>\n",
       "<table id=\"table140485851799120\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>diaObjectId</th><th>ra</th><th>decl</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>1567428592185376787</td><td>59.4814837</td><td>-37.7323315</td></tr>\n",
       "<tr><td>1567762843720221100</td><td>62.5202159</td><td>-37.8646632</td></tr>\n",
       "<tr><td>1568246628836442456</td><td>62.5202159</td><td>-37.8646632</td></tr>\n",
       "<tr><td>1568950316278219135</td><td>67.2951662</td><td>-38.4159654</td></tr>\n",
       "<tr><td>1569082257673552562</td><td>67.0155078</td><td>-37.8278368</td></tr>\n",
       "<tr><td>1569135034231685181</td><td>67.4679316</td><td>-37.6786425</td></tr>\n",
       "<tr><td>1651510445384663056</td><td>62.6536501</td><td>-35.9700212</td></tr>\n",
       "<tr><td>1651589610221862935</td><td>62.1479038</td><td>-35.7991348</td></tr>\n",
       "<tr><td>1651739143803240517</td><td>63.2476384</td><td>-36.91414</td></tr>\n",
       "<tr><td>1736208025095503982</td><td>60.2837917</td><td>-35.4042502</td></tr>\n",
       "<tr><td>1736726994583814254</td><td>63.2038071</td><td>-35.0137971</td></tr>\n",
       "<tr><td>1736858935979147676</td><td>62.9779365</td><td>-34.5801823</td></tr>\n",
       "<tr><td>1824520799038472266</td><td>67.7539568</td><td>-33.9679688</td></tr>\n",
       "<tr><td>1910977597353754906</td><td>59.6754867</td><td>-31.5396859</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=14>\n",
       "    diaObjectId         ra         decl   \n",
       "                       deg         deg    \n",
       "       int64         float64     float64  \n",
       "------------------- ---------- -----------\n",
       "1567428592185376787 59.4814837 -37.7323315\n",
       "1567762843720221100 62.5202159 -37.8646632\n",
       "1568246628836442456 62.5202159 -37.8646632\n",
       "1568950316278219135 67.2951662 -38.4159654\n",
       "1569082257673552562 67.0155078 -37.8278368\n",
       "1569135034231685181 67.4679316 -37.6786425\n",
       "1651510445384663056 62.6536501 -35.9700212\n",
       "1651589610221862935 62.1479038 -35.7991348\n",
       "1651739143803240517 63.2476384   -36.91414\n",
       "1736208025095503982 60.2837917 -35.4042502\n",
       "1736726994583814254 63.2038071 -35.0137971\n",
       "1736858935979147676 62.9779365 -34.5801823\n",
       "1824520799038472266 67.7539568 -33.9679688\n",
       "1910977597353754906 59.6754867 -31.5396859"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique(fsodo_sources,keys = 'diaObjectId')['diaObjectId','ra','decl']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a64c0b9-5a7d-449b-bb6e-5efb0f026e93",
   "metadata": {},
   "source": [
    "**Important Note:** Due to a likely issue with source association, the same variable can be identified with multiple `DiaObject` IDs. As can be seen above, this has occurred in this search, where the `DiaObjects` with IDs 1567762843720221100 and 1568246628836442456 are associated with the same candidate. Scientists on the Rubin Community Science team (CST) are currently investigating this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecc4941-6812-4251-a15a-0ba41a9e3850",
   "metadata": {},
   "source": [
    "### 3.2 Run the Lomb-Scargle Frequency Algorithm on All Candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20949b11-873f-4be9-bd77-ce7d0af03ae3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:24.591717Z",
     "iopub.status.busy": "2024-04-29T13:59:24.591276Z",
     "iopub.status.idle": "2024-04-29T13:59:28.976621Z",
     "shell.execute_reply": "2024-04-29T13:59:28.975926Z",
     "shell.execute_reply.started": "2024-04-29T13:59:24.591686Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "objids = np.unique(fsodo_sources['diaObjectId'])\n",
    "LSpower = []\n",
    "LSfreq = []\n",
    "\n",
    "minfreq = 1 / (1.25*u.d)\n",
    "maxfreq = 1 / (0.05*u.d)\n",
    "for objid in objids:\n",
    "    findobj = (fsodo_sources['diaObjectId'] == objid)\n",
    "    obj_mjd_days = np.array(fsodo_sources[findobj]['expMidptMJD']) * u.day\n",
    "    obj_mags = np.array(fsodo_sources[findobj]['fsodo_gmag'])\n",
    "    obj_frequency, obj_power =\\\n",
    "        LombScargle(obj_mjd_days, obj_mags).autopower(minimum_frequency=minfreq,\n",
    "                                                      maximum_frequency=maxfreq)\n",
    "    max_power = np.argmax(obj_power)\n",
    "    LSpower.append(obj_power[max_power])\n",
    "    LSfreq.append(obj_frequency[max_power].value)\n",
    "\n",
    "LSpower = np.array(LSpower)\n",
    "LSfreq = np.array(LSfreq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cfbde2e-96a1-4bd6-ae60-25a2d91db530",
   "metadata": {},
   "source": [
    "### 3.3 Plot Lightcurves of Several Candidates\n",
    "\n",
    "Let's examine several candidates. To see if they look like RR Lyrae (and whether we identified the correct period), plot phased lightcurves based on the frequencies from the Lomb-Scargle analysis.\n",
    "\n",
    "Specifically, we'll be looking at the candidates with `DIAObject` IDs 1567428592185376787, 1567762843720221100, and 1651589610221862935."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fe8e3b0b-6090-48cd-9d52-f2d34bd2e963",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:28.978385Z",
     "iopub.status.busy": "2024-04-29T13:59:28.977601Z",
     "iopub.status.idle": "2024-04-29T13:59:29.695856Z",
     "shell.execute_reply": "2024-04-29T13:59:29.695142Z",
     "shell.execute_reply.started": "2024-04-29T13:59:28.978367Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiaObjectID:  1567428592185376787 \n",
      " RA:  59.4814837 \n",
      " Dec:  -37.7323315 \n",
      " period:  1.25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15079/1903130074.py:18: RuntimeWarning: Mean of empty slice\n",
      "  plt.plot(obj_phase, obj_mags-np.nanmean(obj_mags), plot_filter_symbols['g'],\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiaObjectID:  1567762843720221100 \n",
      " RA:  62.5202159 \n",
      " Dec:  -37.8646632 \n",
      " period:  1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiaObjectID:  1651589610221862935 \n",
      " RA:  62.1479038 \n",
      " Dec:  -35.7991348 \n",
      " period:  1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "objid_index = [0,1,7]\n",
    "\n",
    "for i in objid_index:\n",
    "    fig = plt.figure(figsize=(6, 4))\n",
    "    findobj = (fsodo_sources['diaObjectId'] == objids[i])\n",
    "    obj_mjd_days = np.array(fsodo_sources[findobj]['expMidptMJD']) * u.day\n",
    "    obj_mags = np.array(fsodo_sources[findobj]['fsodo_gmag'])\n",
    "    obj_period = 1 / LSfreq[i]\n",
    "    print('DiaObjectID: ', objids[i],\n",
    "          '\\n RA: ', fsodo_sources[findobj]['ra'][0],\n",
    "          '\\n Dec: ', fsodo_sources[findobj]['decl'][0],\n",
    "          '\\n period: ', obj_period)\n",
    "\n",
    "    t0 = 0.0\n",
    "    obj_mjd_norm = (obj_mjd_days.value - t0) / obj_period\n",
    "    obj_phase = np.mod(obj_mjd_norm, 1.0)\n",
    "\n",
    "    plt.plot(obj_phase, obj_mags-np.nanmean(obj_mags), plot_filter_symbols['g'],\n",
    "             color=plot_filter_colors['g'], ms=8)\n",
    "    plt.hlines(0, 0, 1, linestyle=':', color='Gray')\n",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.xlabel('phase')\n",
    "    plt.ylabel('mag - mean')\n",
    "    plt.minorticks_on()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec04b81f-c70d-4887-b934-683d3f69543a",
   "metadata": {
    "tags": []
   },
   "source": [
    "Hooray! A few of these look like actual RR Lyrae variables! Some of the others either aren't RR Lyrae, or have mis-identified periods.\n",
    "\n",
    "Note that the lightcurve with `DiaObjectId` 1651589610221862935 is the same as the one we target in Section 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83256b00-9b67-477a-b642-cb8f065a7e07",
   "metadata": {},
   "source": [
    "### 3.4 Cautionary tale: comparing lightcurves from ForcedSourceOnDiaObject vs. DiaSource\n",
    "\n",
    "**What if we used `DiaSource` instead of `ForcedSourceOnDiaObject`?** \n",
    "\n",
    "In this section we demonstrate the results when we perform a table JOIN between `DiaObject` and `DiaSource` instead of `ForcedSourceOnDiaObject`.\n",
    "Using `DiaSource` will only provide photometry measurements for visits in which the variable star is detected in the \n",
    "difference image (the detection threshold is a signal-to-noise ratio greater than 5).\n",
    "Variable stars will not be detected in a difference image when they are nearly the same brightness as they are in the template image,\n",
    "leaving a very small flux residual in the difference image.\n",
    "\n",
    "As demonstrated below, using `DiaSource` instead of `ForcedSourceOnDiaObject` can leave some points out of the\n",
    "lightcurve and impact any derived parameters like the period.\n",
    "\n",
    "First, we perform a similar search query as Sec. 3.1 except we use a table JOIN with `DiaSource`.\n",
    "Note that `totFlux` from `DiaSource` provides the forced PSF-flux measurement on the direct image, not the difference image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c648a038-3a21-4839-8464-ac6324022800",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:29.697052Z",
     "iopub.status.busy": "2024-04-29T13:59:29.696710Z",
     "iopub.status.idle": "2024-04-29T13:59:35.715195Z",
     "shell.execute_reply": "2024-04-29T13:59:35.714270Z",
     "shell.execute_reply.started": "2024-04-29T13:59:29.697033Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = \"SELECT diao.diaObjectId, diao.ra, diao.decl, diao.nDiaSources, \"\\\n",
    "        \"diao.gPSFluxMin, diao.gPSFluxMax, diao.gPSFluxMean, diao.gPSFluxSigma, \"\\\n",
    "        \"diao.gPSFluxMAD, diao.gPSFluxChi2, diao.gPSFluxNdata, diao.gPSFluxSkew, \"\\\n",
    "        \"diao.gPSFluxStetsonJ, diao.gPSFluxPercentile05, diao.gPSFluxPercentile25, \"\\\n",
    "        \"diao.gPSFluxPercentile50, diao.gPSFluxPercentile75, diao.gPSFluxPercentile95, \"\\\n",
    "        \"diao.gTOTFluxMean, diao.gTOTFluxSigma, \"\\\n",
    "        \"scisql_nanojanskyToAbMag(diao.gTOTFluxMean) as diao_gmag, \"\\\n",
    "        \"dias.diaSourceId, dias.diaObjectId, dias.midPointTai, \"\\\n",
    "        \"dias.ccdVisitId, dias.filterName, dias.psFlux, dias.psFluxErr, \"\\\n",
    "        \"dias.totFlux, dias.totFluxErr, \"\\\n",
    "        \"scisql_nanojanskyToAbMag(dias.totFlux) as dias_gmag \"\\\n",
    "        \"FROM dp02_dc2_catalogs_frdf.DiaObject as diao \"\\\n",
    "        \"JOIN dp02_dc2_catalogs_frdf.DiaSource as dias \"\\\n",
    "        \"ON dias.diaObjectId = diao.diaObjectId \"\\\n",
    "        \"WHERE diao.gTOTFluxSigma/diao.gTOTFluxMean > 0.25 \"\\\n",
    "        \"AND diao.gTOTFluxSigma/diao.gTOTFluxMean < 1.25 \"\\\n",
    "        \"AND scisql_nanojanskyToAbMag(diao.gTOTFluxMean) > 18 \"\\\n",
    "        \"AND scisql_nanojanskyToAbMag(diao.gTOTFluxMean) < 23 \"\\\n",
    "        \"AND diao.gPSFluxNdata > 30 \"\\\n",
    "        \"AND diao.gPSFluxStetsonJ > 20 \"\\\n",
    "        \"AND dias.filterName = 'g' \"\\\n",
    "        \"AND CONTAINS(POINT('ICRS', diao.ra, diao.decl), \"\\\n",
    "        \"CIRCLE('ICRS',\"+str(ra_known_rrl)+\", \"+str(dec_known_rrl)+\", 5)) = 1 \"\n",
    "\n",
    "results = service.search(query)\n",
    "dia_sources = results.to_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "528dcf02-5baf-4443-8508-66fb2ecbfea7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:35.716586Z",
     "iopub.status.busy": "2024-04-29T13:59:35.716219Z",
     "iopub.status.idle": "2024-04-29T13:59:35.723544Z",
     "shell.execute_reply": "2024-04-29T13:59:35.722731Z",
     "shell.execute_reply.started": "2024-04-29T13:59:35.716563Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "549 14\n"
     ]
    }
   ],
   "source": [
    "print(len(dia_sources), len(np.unique(dia_sources['diaObjectId'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b6bbf3-e099-479a-a04a-5ebd7f101628",
   "metadata": {},
   "source": [
    "The query returned 14 unique `DiaObjects`, the same as the query from Section 3.1.\n",
    "However, the query with `DiaSource` resulted in a total of 549 measurements, whereas the query with `ForcedSourceOnDiaObject` resulted in a total of 702 measurements.\n",
    "\n",
    "We now repeat the same Lomb-Scargle analysis with the `DiaSource` results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a84134c8-0e50-465c-8c3f-c78dfe1a02af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:35.724863Z",
     "iopub.status.busy": "2024-04-29T13:59:35.724546Z",
     "iopub.status.idle": "2024-04-29T13:59:39.942662Z",
     "shell.execute_reply": "2024-04-29T13:59:39.941720Z",
     "shell.execute_reply.started": "2024-04-29T13:59:35.724831Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "objids_dia = np.unique(dia_sources['diaObjectId'])\n",
    "LSpower_dia = []\n",
    "LSfreq_dia = []\n",
    "\n",
    "minfreq = 1 / (1.25*u.d)\n",
    "maxfreq = 1 / (0.05*u.d)\n",
    "for objid in objids:\n",
    "    findobj = (dia_sources['diaObjectId'] == objid)\n",
    "    obj_mjd_days = np.array(dia_sources[findobj]['midPointTai']) * u.day\n",
    "    obj_mags = np.array(dia_sources[findobj]['dias_gmag'])\n",
    "    obj_frequency, obj_power =\\\n",
    "        LombScargle(obj_mjd_days, obj_mags).autopower(minimum_frequency=minfreq,\n",
    "                                                      maximum_frequency=maxfreq)\n",
    "    max_power = np.argmax(obj_power)\n",
    "    LSpower_dia.append(obj_power[max_power])\n",
    "    LSfreq_dia.append(obj_frequency[max_power].value)\n",
    "\n",
    "LSpower_dia = np.array(LSpower_dia)\n",
    "LSfreq_dia = np.array(LSfreq_dia)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285d4e4a-336c-4c99-81cc-39e2156d644a",
   "metadata": {},
   "source": [
    "We now plot the resulting phased lightcurves from the `DiaSource` catalog (open markers) and the `ForcedSourceOnDiaObject` catalog (filled markers) of the same 3 selected candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1dd74942-a250-4292-9adb-4a718c5312a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:39.944106Z",
     "iopub.status.busy": "2024-04-29T13:59:39.943738Z",
     "iopub.status.idle": "2024-04-29T13:59:40.600608Z",
     "shell.execute_reply": "2024-04-29T13:59:40.599791Z",
     "shell.execute_reply.started": "2024-04-29T13:59:39.944086Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiaObjectID:  1567428592185376787 \n",
      " RA:  59.4814837 \n",
      " Dec:  -37.7323315 \n",
      " period (DiaSource):  1.0418601712065714 \n",
      " period (ForcedSourceOnDiaObject) 1.25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15079/2660292665.py:26: RuntimeWarning: Mean of empty slice\n",
      "  plt.plot(obj_phase_fsodo, obj_mags_fsodo-np.nanmean(obj_mags_fsodo), plot_filter_symbols['g'],\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiaObjectID:  1567762843720221100 \n",
      " RA:  62.5202159 \n",
      " Dec:  -37.8646632 \n",
      " period (DiaSource):  0.6515237959596147 \n",
      " period (ForcedSourceOnDiaObject) 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiaObjectID:  1651589610221862935 \n",
      " RA:  62.1479038 \n",
      " Dec:  -35.7991348 \n",
      " period (DiaSource):  0.5095414310681814 \n",
      " period (ForcedSourceOnDiaObject) 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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27cLXX3+tcSk42QLNuibQfuaZZ9C5c2csW7ZMbfasfv368jvrtdE036PMCy+8gJ07d+LWrVvVmmNSNpt7cXExgy4tGHQREZHBnB0c8ceUBRjx01okpaegg38QNo2aKg+OhDIpHCSFaOzlD+d6yoFFcXkZ7uRkyLNc6iZHBYBv/jyJDv6BCAtqobYP5sx2XblyBampqUpr8Fa1c+dOlJSUYOPGjfI71xX9+uuveOGFF9Tu6+PjAwDIycnR2ZfFixdjzJgx+P3331W2bd68GVOmTNFZR2xsrMY75gEYPTYuOzsbTk5OKkvZkTIGXXVISEiI2m87kZGRiIyMrIEeEVFtdi3zIZLSUzA7LAKfJsQgszBfnnEqLi7GnYI7cHVyQj2n/wVdgiDgXu5jlSyXbHLURwUSSEqKkJCSjHMP7mDDiMl4mJ8Lz3quFst2FRcX44033oCzs7Pa6RxkoqOj0axZM2zatEll28SJE7F161aNQZezszOaNGmi16Sio0aNQseOHdVOvFqdy4vqbN++HV5eXmjZsqXOutS5c+eO1qygNZNIJOjXrx9SU1Plr+lzI0R1MOiqQzQNCJVIJBbuCRHVdlXnzopPSdYr4yQLsBSzXIqTo+aXliBVko2lcZV1j20XilvZ6RqXETI221VRUYGEhAQAQH5+PpKSkrBhwwbcvn0b33//PZo1a6Z2vwcPHuDkyZN4//331WaQJk6ciLVr1yI7Oxve3t5q6+jduzcuXLigs48ikQiLFy9WmjhbxsfHR54101doaCgmTZqE1q1bo6ioCNHR0di1axc+++wzpUuDV69exdWrV+X/T0pKws6dO+Hm5oahQ4cq1Xnu3LlaO0l3RUUFHj16pBR0mQuDrjokICBAbaZLLBbXQG+IqDarutaiPhknWYDlbO8IBzt7FJSWoKCs8tHE0weFZaXwcXXH9stnkJCajD3j3oCjvYN88L26RbONzXYVFRWhZ8+eEIlEcHd3R9OmTTFw4EDs3r1b6zq+P/30EyoqKvDKK6+o3T5p0iSsXr0aP//8M2bOnKm2zPPPP4+JEyciLy9P52W5MWPGoEOHDkhKStL/zWkQHByMqKgopKWlQSQSoUOHDvjxxx8xceJEpXI7duxQunPyhx9+wA8//KC0PjBQ+YX+4sWLWLVqldF9qwl2dnZo0KABysvL5a9JpVKdd65Wh0gw1yQnVKMyMjJUZhdOT0+vtRMwEpH1EAQBvb5bDgA4PXWhPGOl+FpJSQnu3LmD5s2bywdZVwgVuJyeilJpuda6p+37DtnFhVgx4Dl5kOVoZ4/g+v6wU5PJEgRg3M6v4efmIe9PbVBWVoYmTZpg5cqVGoO32uDzzz/H559/jps3b9aan71McXGxyu8pYL5zKDNdRESk0bHbVzA3ZjvWRIzDoBZPAlDNcgGqGad+QU+o1GUnskMbn4Yoq6gcL1NQVoJ7uY/RxNMHbo6V475KysvwuKgAD/Jy8Mqebw3qa6m0HKXScjg71I675xwdHfHuu+8iKiqq1gZdUqkUn3/+ORYvXlzrAq6awKCLiIjUEgQBi2J349Kj+1gUuxsDm7cDAI2z0CuOrzrx4hy1dTo5OMAJDkoD6v1c/7e8jpuTM+Knvo9zD24DAJp5+UHfc7m/m7jWBFwy//rXvyCRSJCenq6SWakNHjx4gKlTp6rM/UXqMegiIiK1ZBkt2d2Jstnfq2a5ZBSzXX/cu4lAaA6Aqg6oV9TEywee9VxwM+sRWtb3VzuA3lY4Oztj0aJFNd2NamvcuDEWLFhQ092oNbgMEBERqah6d2JYUDCWxO2R31Woaa1FWbbrv+c0L4OjbQkgGXVrNRLVdgy6iIhIRdV1FZf2G4UzqbeRkKp9rUVZ2b/S7qGorFRtGXULXaurR3GtRiJbwKCLiIiUKGa5ZBmtwS2ehLuTM9ydnDH4/wfUaxIR3B6dApog9/8Xtq5at64sl4y5s11Lly6FSCRSeShOFXH//n1MnTpVfndbw4YNMWjQIPz4448q9Z0/fx7PPvss/Pz84OzsjCeeeALvvfcecnNzlcrdvXtXqT0XFxc0adIEo0ePxs6dOzW+12PHjmHIkCHw9vaGi4sL2rdvj48//hglJSV6v+fnn39eaTJs2c9A3bqOS5cuhbu7u951a/Lhhx9i8ODB8PT0hEgkwvnz51XKZGRk4K233kKPHj3g7Oyssd3169djyJAhaNiwIcRiMbp164YdO3YolamoqEDr1q0RHR1tdN9NjUEXEREpqZrlAoCjt68gv7QE+aUlOHr7itb9RSIRXu82ECXlZcgvLVbapk+WS7Eec2e7XFxcEB8fr/TYvn07gMqlbXr06IGzZ89i6dKlOHz4MFavXo0mTZrg8OHDSvX88ssv6NWrFx49eoT//ve/iImJwaxZs7Bhwwb06tULmZmZKm2vWLEC8fHxOHLkCFasWAEHBweMHTsWo0ePVpozCgDWrl2LwYMHw9HREZs2bcLBgwfx7LPP4oMPPsDgwYPlazlqc+HCBezfvx9z585V2Xbq1CmcOHHCkB+d3tavX4/S0lIMHjxYY5nU1FRs27YN/v7+CA0N1VjuP//5D4KCgvDVV19h165d6NmzJ8aNG4cvv/xSXsbOzg7z58/H4sWLUVZWZtL3YjSBbFJ6eroAQOmRnp5e090iIitXUVEhhH37oRD27YdCRUWF0mvBa+cL7b96X+jw1fvC+dQ7woUHmh/n/7klHE34Q7ic+o9SPVfTU4XEtPtCfkmx3o/EtPvC1fRUIeZWktD2v+8JR5Mvm+S9LlmyRHBzc9O4fcOGDQIA4Z9//lHZJpVK5c8fPHggeHh4CH369BFKS0uVyl26dElwdHQUxo0bJ3/tzp07AgDh559/Vql3/fr1AgDhP//5j/y1v/76S7C3t1eqQ+bgwYMCAOGdd97R/mYFQXj55ZeFUaNGKb0m+xn06NFDeOqpp9RuM5bsZxUbGysAEM6dO6exjK521Z3HxowZIzRr1kzptfz8fMHV1VXYtWuX1r4VFRUJV69eFYqKilTaMcc5lHcvEhGRnLo5uEql5UiRZCFFki0vF/rNMk1VAACaunlhXe9nUV4hhQABIoggQEBZhRSl0nJcy3xgUL8qBHssOPELrmU+xILjv2Bg83ZmnxcqJycHdnZ2aqdysLP734Wib7/9Fnl5eVi+fLnSMjpA5Zq3EydOxA8//IDVq1ejcePGWtucOXMmvv32W3z55ZdYuHAhgMrJR4HKzFhVQ4cORXh4OL766issXbpUaYJPRQUFBfjll1+wfv16tdsXL16M4cOHIy4uTuvC2NWh+LMypgwAtZOTdu7cGQcOHFB6TbZU0ebNmzFmzBj9OmoBDLqIiAiA+rFcAODs4IjTUxciozAPggBM2bsRALBp1DSNc2gJZVI4SArR3NsPdqLKE2rVyVENEXv3Gs4/uAsAOPfgjkkXuK56Kc/e3h4ikQhdu3ZFRUUFJk6ciLlz56Jbt25wcFA9bcbFxcHb21vtuCgAGD16NL7//nv89ttvKkvtqBMREYHly5fjn3/+QdOmTREXF4eQkBC0aNFCY/1xcXG4cOECevfurbbM6dOnUVhYqHH7sGHD0K1bNyxduhRxcXEa+yaVSnWOrxOJRGqXnDOXU6dOoW3btiqv9+7dG0uWLIFUKrVof7ThmC4iIgKgfiyXTGNPH3Rp2AxdGzXD6sEvICk9BZmFeejSsJnax5P+gXCyd4CTvXKQ4uTgADcnZ4Mero5O+ODkPtj/f5/sRSIsit1tksH1BQUFcHR0VHrIBmAPGDAA8+bNw549e9CrVy+IxWJERETghx9+UGo7NTUVTZs21diGbFtKSopefZJlw2Rr/5mi/vPnz8Pd3R3NmzfXWGbx4sU4efIkTp48qbFMcHCwys+r6mPgwIFa358p7d27F0ePHlU7Tq1Tp07Iy8vDtWvXLNYfXZjpIiIijVkudRRnno8Ibm/2y3wxyZdx7sEd+f+lgmCybJeLiwt+++03pdcUM0qrVq3Cv//9b+zduxenTp3C8ePHcfToURw9ehRbtmwxqC19f06ygM7Qn6u28g8fPoSvr6/W/UeMGIEuXbpg2bJlGgfV//rrrzrvltS1eLepXL16FZMnT8bYsWPVzogve79paWlo317777SlMOiqQ0JCQtSmWCMjI5VuISaiukfdWC5Nqq6zaKrLfOoI/78Ukb1IBKlCdkmW7TI26LOzs9N6txwANG/eHG+//Tbefvtt5OfnY+zYsfjxxx8xb948hISEIDAwEBcvXtS4/z///AMACAwM1KtPsoxVQECAfD9ZHdWtv7i4GM7OzjrbXrx4MUaPHo1Tp06p3d6uXTu9Li+aW0pKCp5++ml06NABP/zwg9oysvFtRUXa73yVSCTo168fUlNT5a9JpYZfAtcHLy/WIWlpaUhNTVV5SCSSmu4aEdUgWZYr2Nsfvq4e+PPhXZ0PX1cPBHv7Y+nJvWadMV6W5ZJWaUMx22VJ7u7uePXVVwFAftkqPDwc2dnZ+P3339Xus2/fPtjZ2Wkc81XVkSNHEBgYiCZNmsjrT0pKwp07d9SW37dvHzw8PNC1a1eNddavXx85OTk62x41ahQ6deqEZcvU3yhhDZcXHz9+jIiICIjFYuzdu1fjzQPZ2ZU3fvj4+Gitr6KiAo8ePVI6L8ou7ZoaM111SEBAgNpMl1gsroHeEJG1ULw7Udddier2LZWWm2WhaVmWyw4iVEA1sLODabJdmmRkZMDX11el7r///hvA/zJR06dPx+rVq7Fw4UIcP35cabD95cuXsWXLFowdO1bnnYsAsGHDBpw/fx4fffSR/LU33ngDmzdvxvvvv68y4WdMTAxOnDiBd955R2PwAQCtW7dGRkYGCgoK4ObmprUPixcvxrPPPqt2W01fXszPz8fQoUORn5+P06dPw9vbW2NZWZDaqlUrrXXa2dmhQYMGSjdUSKVSswReDLrqkMTERLW32xJR3aZ4d2JVgiBgyt7vkJSegg7+Qdg0aqpSEOLvJjZLwAVUBnT3ch+rDbgAoAIC7kuyzBb0bd68GVu2bMHLL7+Mzp07QxAE/PHHH1i5ciW6du2KPn36AAAaNmyITZs2YcKECQgPD8ebb76JBg0a4MKFC1ixYgVatmyJ//73vyr137x5EwkJCSgrK8O9e/ewZ88e7Ny5E2PGjMG8efPk5Tp16oRPPvkEb7/9NiQSCWbMmAGxWIy4uDisXr0affv2xdKlS7W+l969e6OiogIXL16U91uT0aNHIyQkBMePH1cJ0Dp0MPxS8smTJ5GRkYErVyon1T1x4gTu3r2LZs2aKV3a3blzJ4DKsVpSqVT+/27duslvFnjuuedw8eJFfPPNN0hJSVG6eaBz585Kl1DPnTuHtm3b6hzLJhaLcfLkSaWgNSMjQ+1UIUYzeqYvskqcHJWITOHwzUQBSycLsw9vFbB0snD4ZqJe+2madNJQ93IytU7Cej/3cbXr1jX555UrV4TXX39daN++vSAWiwV3d3ehXbt2wqJFi4Ts7GyV8ufOnRPGjBkj+Pj4CE5OTkJwcLDwzjvvCDk5OUrlZJOjyh7Ozs5CUFCQMGrUKOHnn3+WTyZbVUxMjBARESF4eXkJzs7OQrt27YQVK1YIxcXFer3fkJAQYcGCBXr9DHbu3CkAMMnkqP369VM5HwEQJk2apFROXRkAwqZNm3SWASDcuXNHqT7ZsdLG0pOjiv7/TZCNURelp6enM9NFRHoTBAG9vlsOADg9daHSc12X84qLi3Hnzh35moVU87744gt89tlnuHXrlkUGu9ekxMREdOnSBTdv3tQ6TYam31NznUM5kJ6IiNSqOm/X0n6jkJCSbPHB62Qa06dPR3FxMfbs2VPTXTG7Tz/9FK+88orWgKsmMOgiIiIVgpp5uxTn5+JFktrHxcUF33//vfUtAm1iFRUVeOKJJ/DBBx/UdFdUcCA9ERGpUDdvlyXn5yLzGDx4cE13wezs7OywYMGCmu6GWsx0ERGREnVZLhlmu4iqj0EXEREp0bYGo6FjuxiYkTWz9O8ngy4iIpLTluWS0SfbJZsgVHHCSSJrI/v9VJzQ1pwYdBERkZy2LJeMPtkue3t72Nvbc5kxsmoSiUT+u2oJHEhPREQA9MtyyShmu9QtwyMSieDv74+HDx/C2dkZbm5uNj83FNUegiCgoKAAEokEDRs2tNjvJoMuIiICoP6ORU30uZPR09MTRUVFyMzMREZGhrm6TVQtIpEIXl5e8PT0tFibDLqIiEie5Qr29oevqwf+fHhX5z6+rh4I9vbXmu1q2LAh/P39bX5uKKp9HB0dLXZZUYZBVx0SEhKi9hcsMjISkZGRNdAjIrIWpdJypEiykCLJRug3ywzeV9ui05YcM0NkqKioKERFRSm9JpVKzdIWg646JC0tTe3rHOhKRM4Ojjg9dSEyCvMM3tffTawx4CKydhKJBKmpqRZpy6xBlyAIBqWUnZyczNgbCggIUPttUywW10BviMjaNPb0QWNPn5ruBpFFicViBAYGKr0mlUo1JiqMIRJMNDNYRUUFdu/ejV9++QUXL15ESkoKCgsL9e+ISMT5XEzIXCukExER2TpznUNNkum6ffs2xo4di4sXLyIwMBChoaEYMmQI3N3d4ejIlDMRERGR0UGXRCLBwIEDIRKJcPjwYURERJiiX0REREQ2xegZ6desWYOsrCycPHmSARcRkRU5dvsKOq1bjGO3r9R0V4gIJgi6du3ahZdffhmNGzc2RX9qhcLCQqxevRrdu3dH/fr14e7ujrZt22Lu3Lm4d++e2dqtqKhAz549IRKJ5A8iInUEQcCi2N249Og+FsXu5sLTChiMUk0xOui6c+cOOnbsaIq+1ArJycno0qUL5s+fj3PnziE7OxsFBQW4fv06PvnkE4SEhODgwYNmafurr75CQkKCWeomItsim11+dliE1jUS6xoGo1STjA666tKEd/n5+RgxYgRu3LgBAJgxYwaOHz+O06dPY/ny5XB3d0dubi7Gjh2LxMREk7admpqKhQsXQiQSwdfX16R1E5FtUVxD8ZOI8fI1EhlgMBilmmV00NWqVSskJyeboi9Wb82aNbh+/ToAYNWqVdiwYQMGDBiAnj17YsGCBYiJiYGDgwMKCwvx9ttvm7Tt119/HRKJBFOmTMGTTz5p0rqJyLbIAoul/UbJ10hkgMFglGqe0UHXiBEjcODAAVP0xaqVlZVh7dq1AIC2bdtizpw5KmV69uyJadOmAQBiY2Nx4cIFk7S9a9cu7NmzB76+vli1apVJ6iQi26QYWEQEtwcARAS3Z4ABBqNU84wOul577TWkpKRg586dpuiP1YqLi0NOTg4AYNKkSbCzU/+jmzx5svz5rl27jG5XIpHgzTffBACsXr0aPj6cLZqINKsaWABggAEGo2QdjJ6ny8/PD9999x3+9a9/oWXLlujUqZMJumV9Tp06JX/er18/jeVCQ0Ph5uaGgoIC/P7770a3++677yI1NRVPPfWUUkBXHZmZmdXelzPZE1k/dYGFjGKAERHcvs7d/SwLRg9PjFQJRp+OjkJM8mUMadmhhntJppaRkVGt/Yw5X2pjkhnpx4wZAwcHB4wbNw7PPvssJk2ahDZt2piiaqtx7do1+XNt783BwQHBwcFITExU2qc64uPjsX79ejg6OuLrr782qi4AaNeuXbX35bdAIuunLrCQqcsBBoPRuqvqUj41zeigq1evXvLnjo6OWLVqFVatWgVPT08EBARALBZrvBSnSCQS4Y8//jC2O2Zz//59AICbmxu8vLy0lm3cuDESExORkZGBkpISODs7G9xeWVkZZs6ciYqKCrzzzjtGBUxEZPu0BRYydTXAYDBK1sLooMvJyUn+S+zn52ezl6Hy8vIAAO7u7jrLurm5yZ/n5+dXK+hauXIlLl++jObNm2PRokUG709EdYu2wEKmLgYYDEbJmhgddMXFxZmgG9avuLgYQGWQqYtikFVUVGRwWzdv3sTy5csBAP/973/h4uJicB1EVHfoE1jI1LUAg8EoWROTjOmyJuXl5XB0dDS6nk2bNikNXK9Xrx4AoLS0VOe+JSUl8ufVCZhmzZqF4uJiPPfccxg2bJjB+2ty9epVTqxKZIP0CSxk6lKAwWCU0tPTq7VfZmamWYb12FzQZS4eHh4AKi8X6lJQUCB/rs/lSEXfffcdYmNj4eHhIZ8XzFR8fX1t9vIvUV0lCyyCvf3h6+qBPx/e1bmPr6sHgr39bT7AYDBK1nbOs7mgy8HBwei7BgGgYcOGSv8PCgrCmTNnUFBQgJycHK2D6WWD7v38/Awez7Vy5UoAldNSKE5ToUgxct+2bRuAynFkI0eONKgtIqr9SqXlSJFkIUWSjdBvlhm8b6m0HM4Oxl8dsDYMRska2VzQBWif0qG62rVrh19++QUAcP36dYSFhaktV15eLl8WqW3btga3I7s0uX//fuzfv19n+QkTJgAAmjZtyqCLqA5ydnDE6akLkVGYZ/C+/m5imwy4AAajZJ1sMugyhz59+sifnzx5UmPQdf78efnlxd69e1ukb0RUtzX29EFjT65WoYjBKFkjBl16Cg8Ph6enJ3Jzc7F582bMnz9fber5+++/lz8fM2aMwe3cvXtXr76cPHkSACctJSLShMEoWRuTBl3l5eU4ePAgzp49i7///hu5ubkoKChARUWFzn2tfXJUJycnvPnmm/jwww9x7do1rFmzBvPmzVMqEx8fj40bNwKoHJPVrVs3lXru3r2L5s2by8vUlSk3iIiI6jqTBV3bt2/HW2+9hfT0dDRp0gR+fn74888/8eSTT8LNzQ2ZmZm4c+cOKioq4ObmpvHynDWbN28etm/fjr///hvz58/HrVu3MH78eLi4uCA2NhYrVqxAeXk5XFxc8Nlnn9V0d4mIiMiKmCTo+vHHH/HKK69g7Nix+PDDD9GqVStkZmbC398fn332GQYMGACgcrqFmJgYbN68GVevXsXcuXMxc+bMWnOHiIeHBw4cOIBhw4bh5s2b2LBhAzZs2KBURiwWIzo62mYX/iYiIqLq0b0oog7p6en417/+hcjISGzfvh2tWrUCALWBlLu7O5599lns3bsXe/bswQ8//IA+ffqYbTVvc2jZsiUuXryIlStXIjQ0FF5eXnB1dUXr1q0xe/ZsJCYmYsSIETXdTSIiIrIyIsHIkdhLlizBN998g7t37yotkfP48WP4+fnh2LFj8kxXVRUVFXj++edx//59JCQkwN7e3piukIKMjAyV1dXT09OtbqI4IiIia2Ouc6jRma7Dhw9jxIgRKmsS2tnprtrOzg6bN29GcnKyfJJPIiIiIltkdNB169YtdOigulyCbPkbXQs+e3h44IUXXsCPP/5obFeIiIiIrJbRA+nz8/Ph6emp8rqjoyNcXFyQk5Ojs4527drh119/NbYrpENISIjaS7iRkZGIjIysgR4RERHVrKioKERFRSm9JpVKzdKW0UGXj48PcnNz1W5r3bo1bty4obMOJycnPH782NiukA5paWlqX5dIJBbuCRERkXWQSCRITU21SFtGB12tWrXCzZs31W7r1q0bjh49ig8++EBrHTdv3oSPD2cNNreAgAC1mS6xWFwDvSEiIqp5YrEYgYGBSq9JpVKNiQpjGH334sKFC7F3715cvnxZZduJEycwePBgnDp1Cr169VK7f3FxMVq0aIFevXph586dxnSFFPDuRSIiouqx2rsXx44di6tXr+Ls2bMq2wYMGID+/ftj/PjxuHLlitr9586di0ePHuHVV181titEREREVsvoTBcADBo0CABw7NgxlW1paWkYMGAAkpOTMWLECPTo0QNubm5ITU3Fvn37cPXqVbzzzjv46KOPjO0GKWCmi4iIqHrMdQ41yTJA//3vfxEdHY2HDx+iYcOGStsCAgJw7tw5rFixAlu2bMHu3bsBAPb29ujWrRu2b9+OsWPHmqIbRERERFbLJJkuQ+Tm5qKwsBA+Pj4qE6qS6TDTRURkecduX8HcmO14sUMYtiYlYE3EOAxq8WRNd4sMZNWZLkN4enqqndeLiIioNhMEAYtid+PSo/tIzk5HfmkJFsXuxsDm7dSuR0x1j9ED6YmIiGzJsdtX0GndYhy7rf4GME1iki8jISUZY9p0QX5pCca06YKElGTEJKve3U91E4MuIiKi/6eYrVoUuxv6jsARBAFLT+5Fj8AWeJiXC3cnZzzIy0FYYDCWntyrdz1k2xh0ERER/T9Ztmp2WIRBWSrZfiNbdUJCajLe7T0cZ1JvY0Srjsx2kZzJB9IXFRXht99+w59//on09HTk5eVh7ty5aNOmjSmbIR04kJ6IyDCCIKDXd8sBAKenLlR6rm1Mlmw/QRAggggQAX9MWYDem1YovaarHrIeVjs5qoxEIsHs2bPRsGFDDB06FO+//z42bdqETZs24cGDB2r3qaioQJcuXTBp0iRTdYOIiKhaZNmqpf1GQSQSYWm/UXplqapmuZb2GwU7Ozss7TeK2S5SYpKg69atW2jfvj02b96MN998ExcuXEBZWRlu3bql9Tq2nZ0dXn75ZWzbto0LXhMRUY2RjckKCwpGRHB7AEBEcHuEBWkfk6U4lmv/35fU7v/r339xbBcBMEHQVVpaiqFDh8LV1RV//vknPvjgA3Tu3Bl2dnZ6pVEnTpwIqVSKgwcPGtsVIiKiaqma5QKgV7ZLXZar6v7MdpGM0UHXt99+i3/++Qe7d+9Gs2bNDN7f398fYWFhapcQItMKCQlBUFCQyiMqKqqmu0ZEVGPUZblktGW7tGW5qu7PbJf1ioqKUjkvhoSEmKUto4Ou3bt3Y8SIEWjbtm216+jTpw8uXrxobFdIh7S0NKSmpqo8JBJJTXeNiKjGqMtyyWjLdmnLclXdn9ku6yWRSFTOi2lpaWZpy+gZ6S9fvox58+YZVUfLli1x9+5dY7tCOgQEBMDe3l7ldbFYXAO9ISKqedqyXDKK2a6I4PYQiUR6Zbmq7q+Y7ZLVQzVPLBYjMDBQ6TWpVGqWwMvoTFdWVhZ8fHyMqkMQBJSXlxvbFdIhMTERKSkpKo/IyMia7hoRUY3QluWSUZft0ifLVXV/Y7Jd1Z0ln3SLjIxUOS8mJiaapS2jM11isRh5eXlG1ZGeng5vb29ju0JERKQ3fbJcMorZrsEtnsTSk3vRwssP26+cRZDYG5mFeYhOitfSGBDk4Y1tV84i2NvfoGxX1VnyuZZj7WV00NW0aVNcvmzc9emzZ8+idevWxnaFiIhIb7Js1eGJkTqDGFm26unoKBy8mYgUSRZSJNny7S/t/kavNlPyKvcplZajVFoOZwdHvfs5OywCnybEICb5Moa07KBXe2RdjA66Bg0ahOjoaEilUrXjhXTJysrCiRMnMH/+fGO7QkREpBdZlivY2x++rh748+Fdnfv4unog2Nsfy3/fjz+mLEBmUT7S8nORXVyod7v167mhgbsY/m5ivQIuxWzcJxHjEZ+SzDFhtZjRQdeMGTPw6aefYt26dXjttdcM3v/DDz9EeXk5pk2bZmxXiIiI9FIqLZdnq0K/WWbwvg3cPdHEy9dMvfufqtk4WbaN2a7ayeigKzg4GO+++y7eeecdNG/eHMOGDdN73//+979Yu3Ytli5dikaNGhnbFSIiIr04Ozji9NSFyCg0fEyyvlkqY+maJZ/ZrtrH6KALAJYtW4aMjAyMGDECY8aMweTJkxEeHi7frvhLkZGRgRMnTmD9+vWIi4vDzJkzsXjxYlN0g4iISG+NPX3Q2NO4u+/NSd2YM2a7ajeTBF0A8NVXX2HgwIFYunQpRo2qvHXW17cy9frvf/8b9vb2yMzMRGZmJgRBQJs2bfDTTz9h3LhxpuoCERGRTdB3lnxmu2oXkwVdAPDcc8/hueeew4ULF/D777/j8uXLyMrKQl5eHlxcXNC9e3e0a9cOffr0Qc+ePU3ZNBERkc3Qdmcls121l0mDLpmuXbuia9eu5qiaiIjIplV3lnyyfkbPSE9ERESmU91Z8sn6MegiIiKyEtWdJV8QBAv1kIzBoIuIiMhK6JPlkmG2q/Yxy5gusk4hISFqVw2IjIzkotdERDXMmFnyObar+qKiohAVFaX0mlQqNUtbZgu6SktLUVBQgLKyMr338ff3N1d3CEBaWpra1yUSiYV7QkREVRk7S76+azmSMolEgtTUVIu0ZbKg6/bt29i4cSOOHTuG69evIz8/3+A6zBVZUqWAgAC1mS6xWFwDvSEiIkW1YZZ8WyQWixEYGKj0mlQq1ZioMIZIMMHou2+//RZvvvkmysvLERYWhieffBKNGjWCq6srHBz0j+veeustY7tC/y8jI0Mlc5ieng4/P78a6hEREVHtYK5zqNGZrpMnT2LWrFkYOXIk1q1bh4CAAGOrJCIiIrI5RgddK1euRIcOHbBr1y7Y2fFmSCIiIiJ1jI6Szpw5g5dffpkBFxERkZU6dvsKOq1bjGO3r9R0V+o0oyOl/Px81K9f3xR9qTUKCwuxevVqdO/eHfXr14e7uzvatm2LuXPn4t69e0bXX15ejqNHj2LevHno27cv/Pz84OjoCC8vL3Tp0gVz585FcnKyCd4JERFZK1MFSoIgYFHsblx6dB+LYndzItUaZPTlxQYNGljsVktrkJycjOHDh+PGjRtKr1+/fh3Xr1/Ht99+i61bt2LYsGHVqj8jIwNt27bF48ePVbbl5ubi4sWLuHjxIr744gusWrWKNx8QEdmgqoHSwObtqj0Hl2zC1dlhEfg0IYaLZNcgozNdYWFhiI2NNUVfrF5+fj5GjBghD7hmzJiB48eP4/Tp01i+fDnc3d2Rm5uLsWPHIjExsVptlJSUyAOuTp06YcmSJTh48CAuXLiAEydOYN68eahXrx5KS0vx9ttvY8OGDSZ7f0REZB0UAyVjZpxXXFbok4jxXDaopglG2r9/v2BnZydcunTJ2Kqs3pIlSwQAAgBh1apVKttPnz4tODg4CACE/v37V6uNlJQUYfDgwUJ8fLzGMgkJCYKLi4sAQPD09BQkEolKmfT0dHlfZY/09PRq9YmIiCynoqJCCPv2QyHs2w9Vnhvq8M1EAUsnC4dvJqr9P6lnrnOo0Zmu4cOHY8iQIZgyZQqKi4uNrc5qlZWVYe3atQCAtm3bYs6cOSplevbsiWnTpgEAYmNjceHCBYPbCQwMRExMDMLCwjSW6dGjB1599VUAlZccjx07ZnA7RERknaquv1jd9RUFNYtnc5HsmmWSWw63bt0KJycnDBw4ELdv3zZFlVYnLi4OOTk5AIBJkyZpvFtz8uTJ8ue7du0yW3/69+8vf85B9UREtsGUgZK6xbO5SHbNMskyQF5eXjh16hTWrFmDPn36oEePHggPD0fTpk3h5eWl93QSTz31lCm6YxanTp2SP+/Xr5/GcqGhoXBzc0NBQQF+//13s/WnpKRE/lzfn29mZma12+NM9kRE5icLlA5PjFQJlJ6OjtJ7ELy64E1GMYiz9UWyMzIyqrWfMedLbUy29uL58+dx7tw5ZGVlYe/evdi7dy8A6HUwBUGASCSy6rUXr127Jn/epk0bjeUcHBwQHByMxMREpX1M7eTJk3r1R1G7du2q3R7T0ERE5mXKQEld8CZTnSCutqq6lE9NM0nQtXjxYqxYsQK+vr6YOHEiOnTogEaNGsHNzc2gtRet2f379wEAbm5u8PLy0lq2cePGSExMREZGBkpKSuDs7GzSvjx8+BCbNm0CAPj6+ipdaiQiotrJVIGStuBNpi5lu6yJ0RHRoUOH8J///AdvvvkmPv74Y9SrV88U/bI6eXmVq767u7vrLOvm5iZ/np+fb9KgSxAE/Otf/5L3Z9GiRXBxcTFZ/UREZHmmDJS0BW8ydSnbZU2MHkj/ySefoF+/fvjss89sNuACIL8z08nJSWdZxSCrqKjIpP1YsWIF9u3bB6ByMP3rr79u0vqJiMjy1A16r0qfQfD6BG8yvJPR8ozOdJ05cwYfffSRKfpiEuXl5XB0dDS6nk2bNindiSgLKEtLS3XuqzjI3ZRZqOjoaCxatAgA0KxZM2zdutWgNS+vXr0KX19fk/WHiIiMV91ASV22S58sl0xdyHalp6dXa7/MzEyjxkFrYnTQVV5eXicub3l4eACovFyoS0FBgfy5Ppcj9XHgwAFMmTIFgiCgQYMGOHr0KAICAgyqw9fXl3chEhHVgGO3r2BuzHasiRiHQS2eVNpmqkBJFrwFe/vD19UDfz68q7Nfvq4eCPb2t9mxXdZ2zjM66AoMDMSdO3dM0ReTcHBwMMldgw0bNlT6f1BQEM6cOYOCggLk5ORoHUwvG3Tv5+dnkvFccXFxeP7551FWVgZvb2/ExMSgZcuWRtdLRETmJ2hZR9GUgVKptBwpkiykSLIR+s0yg/pYKi1HqbQczg7GXykizYwOunr27InDhw/jP//5jyn6YxL6TqFgiHbt2uGXX34BULm4taYZ48vLy+WTlbZt29bods+ePYuRI0eiuLgY7u7uOHToEEJCQoyul4iILEPbgtOmDJScHRxxeupCZBTmGdxHfzcxAy4LMDromjJlCgYNGoSjR49i8ODBpuiTVerTp4/8+cmTJzUGXefPn5dfXuzdu7dRbSYmJuLpp59Gfn4+6tWrh19//RU9evQwqk4iIrIcocqC0/EpyUoZKlMHSo09fdDY08dU3ScTMzroGjBgAKZMmYLp06cjISFB5bKcrQgPD4enpydyc3OxefNmzJ8/X+217++//17+fMyYMdVu7++//0ZERASys7Ph6OiIX375BeHh4dWuj4iILK/qeC1147EYKNUdJll7cf369ejWrRt69eqFI0eOmKJKq+Pk5IQ333wTQOXs9GvWrFEpEx8fj40bNwKoXCqoW7duKmXu3r0LkUgEkUikMYi6d+8eBg0ahEePHsHe3h5bt27FsGHDTPdmiIjI7LjgNFVldKZrw4YNAICIiAjk5eVh2LBheOKJJ9C/f3+D116cOXOmsd0xq3nz5mH79u34+++/MX/+fNy6dQvjx4+Hi4sLYmNjsWLFCvndnJ999lm12nj8+DEGDRokH4w/Z84ctGnTBpcva16Y1NvbG4GBgdVqj4iIzMNU6yiS7RAJRobahswTpbUjVr72osytW7cwbNgw3Lx5U+12sViM6OhojBgxQu32u3fvonnz5gAqs2FxcXFK2+Pi4gxe1mfSpElKlzWBykU+q645lZ6ebnW3zxIR2SJBENDru+UAgNNTFyoNR9G2jayDuc6hRme6rGm6CEto2bIlLl68iC+//BI///wzbt26hdLSUjRu3BjDhg3DW2+9haZNm9Z0N4mIqAZxwWlSx+hMF1knZrqIiGqGPpksZrusm7nOoaa5NkhEREQATLeOItkeBl1EREQmwgWnSRsGXXVISEgIgoKCVB5RUVE13TUiIpugT5ZLhtku6xAVFaVyXjTXyi9GD6Sn2iMtLU3t6xKJxMI9ISJtCyBT7cQFp2sniUSC1NRUi7TFoKsOCQgIgL29vcrrYrG4BnpDVHdpWwCZai8uOF07icVilbkupVKpxkSFMXj3oo3i3YtE1uvIrSQ8HR0lXwD58MRIlWkDmAmrne7nPq72OopB4vpm6BFVh9XO00VERPrTtQCyrAwzYbUT11EkbTiQnojIgqoOtFY3kFpWZnZYBAdZE9kQBl1ERBaizwLIVTNhnFKAyHYw6CIishB10wlUzXbpkwkjotqJY7qIiCxA26SZsmzXkrg9EEGkMRPGKQWIajdmuoiILEDbpJmyjNaZ1NtISNWeCSOi2otBFxGRmemzNMzgFk/C3ckZ7k7OGFxligguF0NkGxh0ERGZmT5Lwxy9fQX5pSXILy3B0dtXlLYx20VkG8wedGVmZqJFixaIj483d1NERFZHnyyXvExgMHoEtlCb0WK2i6j2M3vQJZVKcffuXRQVFZm7KSIiq6NPlkteJnwUloWPVpvRYraLqPbj3YtERGaizwLIgiBg3tEd6OAfBF9XdwAijQsg805GotqNQRcRkZkYugBy6DcfKO1bdQFkWbbr6egoxCRfVlmvkYisG4OuOiQkJAT29vYqr0dGRiIyMrIGekRk25wdHHF66kKNCyALgoApe79DdnEBPh74PKCQuapfzw1XMlJV9vF19dCYCSMiw0VFRSEqKkrpNalUapa2GHTVIWlpaWpfl0gkFu4JUd2hbQHkkvIyZBcXIEWSjZd2f2NQveoyYURkOIlEgtRU1S845sCgqw4JCAhQm+kSi8U10Bsi0pUJ08bfTcyAi8gExGIxAgMDlV6TSqUaExXGEAlmvvf40aNHaNiwIY4dO4YBAwaYsylSkJGRAX9/f6XX0tPT4efnV0M9IiIiqh3MdQ41+5QR9vb2aNq0KVxcXMzdFBEREZHVMvvlRV9fX9y5c8fczRARERFZNS4DRERERGQBDLqIiIiILIBBFxEREZEFMOgiIiIisgAGXUREREQWwKCLiIiIyAIYdBEREZHcsdtX0GndYhy7faWmu2JzGHQRERERgMpF2BfF7salR/exKHY3zLxoTZ3DoIuIiIgAADHJl5GQkozZYRFISElGTPLlmu6STeGC13VISEiI2gWvIyMjERkZWQM9IiIiayEIApae3IuwoGB8EjEe8SnJWHpyLyKC20MkEtV098wmKioKUVFRSq9JpVKztMWgqw7RtGK6RCKxcE+IiMjayLJchydGQiQSYWm/UXg6OgoxyZcxpGWHmu6e2UgkEqSmplqkLQZddUhAQIDaTJdYLK6B3hARkbVQzHJFBLcHAEQEt0dYULDNZ7vEYjECAwOVXpNKpRoTFcYQCRwlZ5MyMjLg7++v9Fp6ejr8/PxqqEdEpMux21cwN2Y71kSMw6AWT9Z0d6gOOXIrCU9HR+HwxEilrJam122duc6hRme6fvvtN6P2t7Ozg7u7Ozw8PNCgQQO4u7sb2yUiolqn6l1jA5u3s9nMAlkXdVkumbqS7bIUo4Ou8PBwiEQiCIJQrYNRdb/mzZujT58+mDFjBnr37m1s94iIagXFu8Y+TYix+XE0ZD2qjuVSVJfGdlmC0UHXoUOH5M//+OMPREVFISAgABEREWjfvj38/Pzg5uaGgoICZGZm4vLlyzh69Cjy8/MRGRmJDh06oLy8HBKJBFlZWUhMTERcXBy2bNmCl156Cd9++y0cHR2N7SYRkdWqq3eNUc3TluWSYbbLdIwOuoYMGQJBEPDaa6/hjz/+wN69ezFw4ECd+504cQJz5szBoEGDsGrVKpWDuG3bNkydOhWenp74/PPPje0mEZHVqqt3jVHN05blkuHvpOmYZHLUCRMm4NatW4iPj9cr4AKAAQMG4I8//kBiYiKmTp2qsn38+PFYu3YtvvrqK1y/ft0U3TSpe/fuYe7cuWjbti3c3NxQv359dO/eHWvWrEFhYaHJ2tm2bRuGDBmChg0bol69emjWrBlefvllJCQkmKwNIqo5uu4a471OZC76ZLlk+DtpIoKRtm7dKnh7ewsZGRnV2j89PV3w9vYWfv31V5Vt5eXlgp+fnzBnzhxju2lS+/fvFzw9PQUAah+tW7cWkpOTjWqjqKhIGDFihMY27OzshA8++EDj/unp6Sr7pKenG9UnIjK9wzcTBSydLBy+majX60SmYujvWF36nTTXOdToTNf69evx3HPPwdfXt1r7+/n54fnnn8cXX3yhss3e3h5Dhw5FXFyckb00nUuXLuGFF15Abm4u3N3dsXz5cpw+fRrHjx/HjBkzAAA3btzA8OHDkZ+fX+12pk2bhv379wMA+vfvjz179uDs2bPYuHEjgoODUVFRgcWLF+Pbb781yfsiIssT9LxrTGBmgUxM9rsX7O0PX1cP/Pnwrs6Hr6sHgr39+TtpBKPHdP31118YO3asUXV07NgRO3fuVLstJCQEv/76q1H1m9Lbb7+NwsJCODg4ICYmBj179pRvGzBgAJ544gnMnz8f169fR1RUFBYvXmxwGydPnsTWrVsBACNHjsTu3bvlk5p269YNzzzzDLp27Yp79+5h/vz5eP755+Hl5WWS90dElmOKu8Y4txdVR6m0HCmSLKRIshH6zTKD9y2VlsPZgTe5GcrooKusrAzOzs5G1eHs7IySkhK123x8fFBQUGBU/aZy7tw5edZt2rRpSgGXzJw5c7Bp0yZcu3YNn332Gd577z2D775ctWoVgMpM31dffaUyi7yvry9WrlyJCRMmIDs7Gxs3bsScOXOq96aIqEZoy3LJ6LprTODcXlRNzg6OOD11ITIK8wze199NzICrmoy+vNiwYUPcunXLqDpu3bqFhg0bqt1WXl5uNVmcPXv2yJ9PmTJFbRk7Ozu88sorAIDs7GyDL43m5+fj+PHjAIDBgwcjKChIbblnn31WvnzPrl27DGqDiGqeLMu1tN8onXeNJaQkIyb5ssY6ZodFaCxDpEljTx90adjM4EeQuH5Nd73WMsnkqDt37sTy5cvVruuni1Qqxc6dOxEeHq52e2pqarXHi5naqVOnAABubm7o2rWrxnL9+vWTP//9998xePBgvds4e/asPOunWE9VTk5OCAsLQ0xMDM6ePYuysjKdGbXMzEy9+1FV1aUPqmYfXVxcYGenPoavWrZevXoaf1cKCwuVxgoYUtbZ2RkODup/pYuKilBRUWHyssXFxUqr0Ts5OWk8DsaUdXR0hJOTk9qyJSUlKC8vr1ZZBwcHjZnq0tJSlJWVVausvb096tWrp7ZsWVkZSktLTV62vLxcKWNuZ2cHFxcXk5cViURwdXVVW1YqlaK4uFhnWUEQsCxuD/o2bIHeAc3lnxE3NzeVshHB7dEzsAWWn9iD3gHN5QGaq6ur2rm9BjVvp9QHWVl1gZ0gCCp3WxtS1pDPPf9GVOLfiErm+htR9TOUkZGhdj9djDlfamN00DVz5kxs2rQJ77//Pj766COD91+0aBHu3r2LWbNmqd1+7tw5jdkeS7t27RoAoGXLlho/YADQpk0blX0MbaNqPZraiYmJQXl5OW7evIl27dppLa9ruzZVB02uWbNG6f///ve/Vdapklm7dq3Sh2369Okqi4vKfPnll0p/2CdNmoRmzZqpLbthwwbk5ubK/z9hwgS0atVKbdlNmzYpffief/55PPmk+vEvP/74Ix48eCD//6hRo9CpUye1Zbdt24Z//vlH/v+hQ4eie/fuasvu2rULN2/elP9/0KBBGldd+PXXX3H16lX5//v166fxi8mhQ4dw6dIl+f979uyJiIgItWWPHTuG8+fPy/8fGhqK4cOHqy0bFxeH+Ph4+f87duyI0aNHqy17+vRpnDx5Uv7/du3aaRzrefbsWRw7dkz+/yeeeAIvvvii2rIXL15UmoC5adOmmDx5stqyly9fxt69e+X/b9Sokfzmlqpu3LihNI7Uz88Pr776qtqyt2/fxk8//ST/v6enJ95++221Ze/fv4/NmzfL/+/q6op58+aplItJvox/UlMwE43wySefAKg8ES5YsEClrEgkwrxOA5B44Ji8LACETXxW7dxe+5PO4a+9R5TqeO+999SeZAsLC1U+y3PnzlUb/JWVlamUfeuttzReieDfiEr8G1HJUn8jlixZorRd0+9cTTE66OrevTvmzJmDlStXIicnBx9//DE8PT117peXl4d3330X69atw+zZs9GtWzeVMhKJBMePH8d7771nbDeNVlxcLI98dQWB3t7e8ln479+/b1A7iuV1tdO4cWOl/YwJqojIMmRjuYI86gMKw2kqBAF/Pryrdh/veqrZMk1ze609cxSac+REVJOMDrqAyoHfjo6OWLVqFXbs2IGxY8di0KBB6NChA/z8/ODi4oKioiJkZGTIlwHauXMncnJyMH/+fI0Zsm+//RYlJSUYNWqUKbpplLy8//111GdRblnQZei0EYa0o/ht1JjpKYjIcmR3jVXkFQBoJH+9qLwUXTeov4vMD454DcqZn6p3PcqyXeOjP0c/WMfVASJSZpKgCwCWL1+O559/HmvWrMGWLVuwYcMGjeMCXFxcMHr0aMydOxedO3fWWOeLL76I0aNHo0WLFqbqZrUpjpHQdC1ckey6dlFRkdnaUbx2bmg7RFQzZHeN3bx7B6f2HJC/7uLghAtTl6jdJy8rG3E79ym9pmlur84BTYC0ChCR9REJZpjhrKSkBImJibh69SqysrJQWFgIV1dX1K9fH+3atUNISIjR00xYWkZGhvza8Lhx47Bt2zat5Rs0aID09HS0b98eSUlJerfz2muv4auvvgJQOb5L27iur7/+Wj4OZefOnXjuuefU9lfm6tWrRk1iq4iDZCtxkKz6shxIb3hZQP1AegCoqKiQf7GKvXMN4375Grsmvq127q7DfyfiuZ/WYvtz/0b/5m0BcCC9DP9GGF62tv2NMOVA+qpDdtLT01XOhYYyWaZLkbOzM7p166Z2nFZt5eHhIX+uz6U82R8RfS5FVrcdxT9U+rTj6+tr9C+MjKaTg7FlNZ2gjC2r6YRqbFlNfwgsWdbZ2VnvLzGGlHVyctIrq2toWUdHR73nrjOkrIODg9YbXCxR1t7eXu/fd0PK2tnZwc3NDYIgYPmZwwgJaq5xbq8hT3RASFBzLD9zGMOf7Kp13i6RSKR3HwwpC/BvhAz/Rhhe1pR/I0x1zjMVkyx4XRfUq1dPniVKSUnRWjY7O1seECkOdteH4uB5Xe0oDro3tB0iqn1MMbcXEdUcBl0GaNu2MlV/69YtpfRrVdevX1fZR1+K6UzFerS14+DggJYtWxrUDhHVLvrMYC/DdRuJrBODLgP06dMHQOVlvQsXLmgspzgXiaY5VjTp1q2bPAWrWE9VpaWlSEhIUNmHiGyTPlkuGWa7iKwTgy4DKE76tmnTJrVlKioq8MMPPwAAvLy80L9/f4Pa8PDwwMCBAwFUTlKn6RLjrl27IJFIAABjxowxqA0iql1kWa5gb3/4unrgz4d3dT58XT0Q7O3PbBeRFTHLQHqg8q6R3NxcFBQUKN3loY2mmYKtRffu3dG3b1+cOnUKGzduxKRJk1QWvf7kk0/ks8q/9dZbKgP8vv/+e/m6jUuWLMHSpUtV2pk7dy4OHTqE8vJyvPbaa9i1a5fS3TmZmZl45513AFQGdtOnTzfl2yQiKyOb2ytFko3Qb9TP5aVt31JpORcoJrICJg269u7di82bN+Ps2bN4+PCh3vsJggCRSKR0+6u1Wrt2LXr37o2ioiJERERgwYIF6N+/P4qKirBt2zZs2LABQGUAOWfOnGq1MWDAAIwfPx7btm3Dvn37MHjwYLz99tto1KgRkpKSsHz5cty7dw8A8PHHH8Pb29tk74+Iasax21cwN2Y71kSMw6AWysvPyOb2yijM07C3Zv5uYgZcRFbCJEFXYWEhxo4di0OHDiEsLAzTp0+Hu7s75s+fj8jISDzxxBPIzMzEpUuXcOLECRQXF+PFF1/UuqCztercuTO2b9+Ol156CRKJRO1aaa1atcKBAweUpn8w1HfffQeJRIKDBw8iNjYWsbGxStvt7OywaNEijWtWElHtIQgCFsXuxqVH97EodjcGNm+nMm6rsacPGnv61FAPicxP2xcPW2GSoGv69OlISEjAgQMHMHToUADA48ePMX/+fAwbNgwDBgyQly0vL8e+ffuwevVqXL58GV999ZXGhUKt1ciRI5GYmIi1a9fiwIEDSElJgZOTE1q2bImxY8fi9ddfN2h+GHVcXFxw4MABbN26Fd9//z0uXbqEnJwcNGjQAH379sXrr7+ucmmTiGon2SD52WER+DQhBjHJl9VOfEpkq/T54mELjJ6R/vfff0e/fv1w6NAhpVXLHz9+DD8/Pxw7dkwp6FL09ddfY8GCBfj++++tYn1FW6JuRnpTzKZLRKYlCAJ6fbccAHB66kKl57Z40iFS58itJDwdHSX/4nF4YqTGLx6WyIiZ6xxq9N2LGzduRLdu3ZQCLn39+9//RlRUFF555RWkpqYa2xUiolqn6lQQnOqB6hrFOeg+iRivdY65qhmx2nZnrtFB16lTp+RTHCiSzRul6wcyZcoUNG3aFN9++62xXSEisgrHbl9Bp3WLcez2Fa3l1E14yolNqa4x5IuH4qX42vjlxOig6+HDh2jSpInK67K1APVZp3D06NHYu3evsV0hHUJCQhAUFKTyiIqKqumuEdkMQ76Jq5vwlNkuqksM+eJhSEbMEFFRUSrnxZCQEKPq1MTooMvOzk7t4pgikQi+vr5IS0vTWUeTJk1w+/ZtY7tCOqSlpSE1NVXlIZtklYiMp+83cW3L+jDbRZaib1bWXAz54mGuS/ESiUTlvKhP7FIdRgddDRo0QHp6utptHTp0wKVLl3TWkZ2drXUtQzKNgIAABAYGqjzEYnFNd43IJhjyTVzbsj7MdpEl1PT4KEO+eJjzUrxYLFY5LwYEBBj13jQxOujq0qULzp8/r3Zb//79sXfvXp2Tnv75559o1qyZsV0hHRITE5GSkqLyiIyMrOmuEdkEfb+J67N4NbNdZG41PT7KkC8e5rwUHxkZqXJeTExMNOq9aWJ00BUeHo4TJ06gtLRUZdsrr7yCx48fY+XKlRr3v3nzJnbu3Cmf34uIqDYy5Ju4PotXM9tF5mSu8VHVaV/nF4+4vVgSt8cmLsUbHXRNmDABhYWF8kWeFTVp0gSLFi3C+++/jwULFiA7O1tp+5kzZzBq1Ch4enoy20JEtZq+38T1OdnI1LYTCtUeNT1ViUFfPFKTcSb1tk1cijc66PL29sZbb72FRYsWqR2QvXDhQrz//vtYvXo1GjRogHbt2qFbt25o1KgRevXqhaysLOzbtw8NGzY0titERDXCkLEp+pxsZGrbCYVqh5qeqsSQLx6DWzwJdydnuDs5Y7CGiVBr05cTkywDtGzZMkyfPh2OjuoXVf3ggw8wceJE/Pjjj7h8+TIKCwvRtm1b9OnTBxMnToSbm5spukFEVCNkgdThiZEav4k/HR2FI7eSsOy3fQj29oevqwf+fHhXZ92+rh4I9vbH0pN7ERHcnrPUk9HU/b4q/p6aexkqbZ+Xqo7evoL80hL5c3X9On7nKtILJLidnWH1S2gZvQwQWScuA0RkGVWX8VF3EpGVqRAEPMjLRookW6WMLo3F9XHzjY/h7KD+yy2RPrT9vurzu2yq9jMK8rD9+X9DWxOCAEzZuxECgKKyUvi5eWjsc0JKMtydnPGkXyDip71vdN/NdQ41SaaLiKiu0udbu2IWYcvoGWjn38jgdvzdxAy4yGj6ZmXNlTEqlZYjRZKFFEk2Qr9ZZvC+pdJypc9B1cXiz6TetupsF4MuMilLLERKZC2qMyj+y/MnuJg11QhDpyoxx+VsZwdHnJ66EBmFeTr6Wpnlyi4qwMeDxgIioH49N1zJSFUoI2De0R3o4B+EiR3CcOz2Vfz9OA1L4vZY7aV4Bl1kMlUn2hvYvJ1V/tITmYohY1MsOWaGSB1Ds7Lm+j1t7OmDxp4+WsuUlJchu7gAKXnZeGn3Bp11hn7zgfx5cna6SkbMWjDoIpOpmubliYVsmSxrwEHxVBtUd6qSmvo91ZUREwQBU/Z+BwDYNGoqRCKR/DVHe3s42VtneGOdvaJap+pEe/EpyTyxkE0z9dgUInOqjVlZbRmxI7eSkJSegsMTI9G1UXP566sHv1Dj/dbGZEFXbm4uduzYgWPHjuH69evIyMhAQUEBysrK9NpfJBKhoKDAVN0hC6v6gbaGDyyROek7NkUdDoonS7K1rKy+8+JZW78BEwVdBw8exOTJk5GZmYmgoCA8+eSTCA0NhaurKxwcmEyzdbom2rPGX3wiU9BnbApRTbO1rGxN34FpDKMjosTERDz77LPo1KkTDh48iNDQUFP0i8wgJCQE9vb2Kq9HRkYatQxTTU+0R0REmtlSVtYcd2BGRUUhKipK6TWpVGqyPisyOuj68MMPERQUhBMnTsDV1dUUfSIzSUtLU/u6uuWb9FWb07xElsTpVKgm2UpW1hx3YEokEqSmpmotYypGr7148uRJTJ06lQFXLRAQEIDAwECVh1gsrnad2taR47pxRJWqTqfChUCIDGeuxeLFYrHKeTEgIMCUXZczOujKzc1Fo0aGz65MlpeYmIiUlBSVR3UvLRqa5uWJhuoqxelU+CWEqHrMtVh8ZGSkynkxMTHRlF2XM/ryoo+PD9LT003RF6plrGWiPSJrxulUiIxnK3dgGh10de3aFadOncL8+fNN0R+qJWrbRHtENYXTqRAZz1buwDQ66JowYQImT56Mu3fvolmzZiboEtUGtXGiPSJL43QqRKZhK3dgmiTo+uyzzzBz5kwcOXKEf0DqAFtJ8xKZG6dTITIdW7gD0+igSyQS4ZdffsGAAQPw/PPP47vvvoOnp6cp+kZWylbSvETmxOlUiKgqk0wX37hxY5w/fx7vvPMO2rRpg/HjxyM8PBxNmzaFl5cX7Oz0u0mySZMmpugOmZmtpHmJzKk2z5pNROZhsjV6cnJy4OrqivLycqxduxaff/65wXWYawZYMr3qpHmP3b6CqXu/4+SQZPPMMWs2EdV+Jgm6Nm7ciDfeeAMVFRXo3bs3OnTogEaNGsHNzY1rLxIA1ckhBzZvx5MM2SxOp0JE6hgdESUkJGDmzJkYPXo0vvzyS7PN4kq1m+LkkJ8mxPAkQzaL06kQkSZGz0j/0UcfoVOnTvj5558ZcJFaVSeH5Az1ZMvMNWs2EdV+Rme6fvvtNyxevFjvwfJU93BySKorOJ0KEWljdNBVVFQELy8vE3SFzC0kJAT29vYqr0dGRlZ7/UVdODkk2bJjt69gbsx2+c0hnE6FqPaJiopCVFSU0mvmurHP6KCrQYMGSElJMUVfyMzS0tLUvi6RSMzWJieHJFul7uYQTqdCVPtIJBKkpqZapC2jg65u3brh2LFjWLRokSn6Q2YUEBCgNtMlFovN0h4nhyRbpunmEFuYNZuoLhGLxQgMDFR6TSqVakxUGEMkGDmaeffu3Xj++edx5swZhIaGmqpfZKSMjAz4+/srvZaeng4/Pz+L9eHIrSQ8HR2FwxMj1WazdG0nslaCIKDXd8sBAKenLlR6zi8QRLWfuc6hRo9+HzNmDJ5++mlMmTIF+fn5xlZHNsLQySF5JyPVJlXvUOQdiESkD5Pccrh9+3a4ubmhX79+uHbtmimqpFpOn9vmebKi2uLY7SsI/nw+gtfOx9Hky/IvFHYiETqtWww7kYhfIIhIJ6MvL8bExACovP752Wef4cSJExg4cCD69+9v8NqLERERxnSFFNTk5cWql160XW4xpCxRTRAEAb02LkdCajIAoLVPAG48TsOhFyOx7Le9SEhJRlhQMJY8NQpDt/JyOZEtMNc51OiB9E8//bT8RCmL32JiYuTBmC4ikQiCIEAkEnHtRRuhzxIoMryTkaxdTPJlecAFADcep6GNb0MAgtJAekDgzSFEpJXRQVdsbKwp+lHr3Lt3D59//jkOHDiAe/fuwdnZGS1btsQLL7yAV199Fa6urtWuWyKR4ODBgzh+/DguXLiA27dvo7CwEJ6ennjyyScxYsQITJ8+3SrnR+PkkGRLBEHA0ri9cHdyxpN+gcgtLsL1xw8hVCivshCfkoxlv+2TZ7v4BYKI1DH68mJddODAAUycOBG5ublqt7du3RoHDx5EixYtDK770KFDGDNmDEpKSrSWa9CgAX766Sf0799f7faaurxYUl6Gll+8gxRJtsH7NhbXx803PuZcRWQ1ZHfYAsChFyMxO+YnXM98KN8uu5QoK3foxdlY9ts+ANovl1edVLW6ZYjIPKz28mJdc+nSJbzwwgsoLCyEu7s73nvvPfTv3x9FRUXYtm0bvvnmG9y4cQPDhw/HuXPn4O7ublD9jx8/RklJCezs7DB48GA8/fTT6NixI7y8vJCSkoLo6Ghs374djx49wogRI/DHH3+gU6dO5nmz1cDJIclWVM1yAQKuZz5EG5+G+DsrDS4Ojhj8/8GQ7E5cfbJd6iZVrRqc6VOGiGofBl0Gevvtt1FYWAgHBwfExMSgZ8+e8m0DBgzAE088gfnz5+P69euIiorC4sWLDarf0dERs2bNwoIFC9CkSROlbZ07d8bIkSPRu3dvvPnmmygsLMScOXNw/Phxk7w3U+HkkGQLFMdyLe03Gst+q7ycOKJlCN6P242CslIcvX0FQ1p2UBqbqGtsl6ZJVQ0tQ0S1D1epNsC5c+cQFxcHAJg2bZpSwCUzZ84ctG3bFgDw2WefoayszKA2xo0bh3Xr1qkEXIreeOMN+US0cXFxePz4sUFtEJF2ilmuHoEtIBs0v+SpUfj15iW4OzrD3dEZS+L2yG8gqprtUjcViuL8dZ9EjFc7zYQ+ZYiodmLQZYA9e/bIn0+ZMkVtGTs7O7zyyisAgOzsbHmQZmrh4eEAgIqKCty5c8csbRDVVbIsV35pyf9nufYh2Nsft7Ie4UzqbbzSsRfyy0pwJvU2vjx3HH8+vIuLaf9gYocwJKQk41bWI/nNIYrBkj6TqnLiVSLbxcuLBjh16hQAwM3NDV27dtVYrl+/fvLnv//+OwYPHmzyvigOtNd3HrTMzMxqt2fJ5YOIalLVsVzhzVpjxv4spEiy8cbhaADAV+f/d9f2G4eiVeqQlSuVlqNUWg5nB0e1qzRUXYMUgM4yHNtFpL+MjIxq7WfM+VIbBl0GkM2237JlSzg4aP7RtWnTRmUfUzt58iQAwMHBAS1bttRrn3bt2lW7PV7aoLpCcSzXsvDRqOfohNNTF2LvjYt441A0vhg6Eb0at8Tp+7fkAZfsNQDy178YOhGj23SR3xyibv66qvPUAdBZhmO7iPRX9Q7EmsagS0/FxcXyyDcoKEhrWW9vb7i5uaGgoAD37983eV8OHDiAxMREAMCQIUMgFotN3gZRXVQ1yyXLNgWJ6yM6KQFhQcF4rdtAiEQidA5oiujEBFzOSMGPifHKryclIDopAa91G/i/ejWsRSrPZMXthfD/g/A1lmG2i6hW45guPeXl/W8KBH2mgXBzcwMAky8CnpWVhddeew0AYG9vjw8//NCk9RPVZYpjuZaFj5YHN+rWEhWJRFgaPgr5pZVju2SZKnXjsLStRSovn5qMM6m3tZfh2C6iWo2ZLj0VFxfLnzs5Oeks7+zsDAAoKioyWR+kUikmTpyIf/75BwDw/vvvo3Pnziarn6guk2W5nO0d4OfqgczCfEQnxQMCsDRuD/xdPXDuwV2ce3BXcS/4u4mRU1yAuTHb4evqDpFIpLTKwuAWT2rMcskMbvEk3J2c5c/VYbaLqPZj0KWnevXqyZ+XlpbqLC8b6O7i4mKyPrz66qs4fPgwAGD48OFYtGiRQftfvXoVvr6+JusPkS0plZbjviQLJdJypORl46XdG1TKLIrdpXH/yxmpCP3mA6XXJCVFOHgzUedapEdvX0F+aYn8ubpxWxzbRWS49PT0au2XmZlp1DhoTRh06cnDw0P+XJ9LhgUFBQD0uxSpj/feew8bNlSeBPr06YOff/4Z9vb2BtXh6+vLuxCJNHB2cET8tIVISk9BdnGh0rbHhfmQlBRr2BMQO9fDE/UboIG7GIIgYMre75CUnoIgsTeWn9qvNcslG+/VI7AFRBBpzWQx20VkGGs75zHo0lO9evXg6+uLzMxMpKSkaC2bnZ0tD7oaN25sdNsrV67Exx9/DADo0qUL9u/fb9IMGhFV0rWagj7rIR65lYSk9BT5bPIAtGa5FO9qBKA1k8VsF1HtxqDLAG3btsWpU6dw69YtlJeXa5w24vr160r7GOOrr77Cu+++K6/ryJEj8PT0NKpOIjKcvmsmysZvrRk8Dt/8eRJlUil8XNzx58O7auoE5h3dgSAPb2QW5gMQECT2xryjO+Tjw6pSHC/GbBdR7cKgywB9+vTBqVOnUFBQgAsXLqBHjx5qy8nm0AKA3r17V7u9LVu24PXXXwcAtGjRAseOHeOYLKIaYsiaiYcnRqKsQop6Do7ILy1Bt28/0FDr/yiOIUuRZKuMD6tKceJVIqodRAJnvdTb2bNn5YHWrFmzsG7dOpUyFRUVaN++Pa5duwYvLy+kp6fD0dHwP4q7du3CCy+8AKlUiqCgIJw6dQrNmjXTe/+MjAyVSeHS09Ot7vo2mYc+l8FIf4IgoNd3ywEAp6cuVHouyzTJyuQWF8LRzh6fDBmPJ7wboO1XC1BUXob2foH4fvR0VE1MpeXnqowhq1/PDQ3ctc+/5+8mRpC4voneIREpMtc5lJkuA3Tv3h19+/bFqVOnsHHjRkyaNEll0etPPvlEPgv9W2+9pRJwff/99/J1G5csWYKlS5eqtBMTE4MJEyZAKpXC398fx44dMyjgopphLYGOPpfByDBVZ5NXN65KVqaNb0MkpqdgUexuLHnqGRSVVy56fzkjFZmFeRyHRVSHcXJUA61duxYuLi4oLy9HREQEPvroIyQkJCA2NhazZs3C/PnzAQCtWrXCnDlzDK4/ISEBY8aMQWlpKRwdHfHpp5+irKwMly9f1vjIyckx8bskQ1UNdGoygax4GYyTaRpP15qJgiDIy7TxbYjrmQ/lP/vZMdvQI7AFwgKD4e7kjCVxe7ikFlEdxkyXgTp37ozt27fjpZdegkQiwYIFC1TKtGrVCgcOHFCaZkJfhw8fRmFh5aWGsrIyTJw4Uec+mzZtwuTJkw1ui0xHn/E+lqAYIHwSMR7xKckmGXBtLVm8mmDImoltfBvKf/aHbiXheuZDHHpxNkQiEZ6OjpLPXM9sF1HdxExXNYwcORKJiYmYPXs2WrVqBVdXV3h5eSE0NBQrV67ExYsX9V6Emmq/qoGOYgbE0qouN2OKpWOsKYtnafqsmbgkbg+Wxv0vy7W036gqtYgqyzLbRVTnMeiqpqZNmyIqKgo3btxAQUEBsrOzce7cOcyfPx+urq4a95s8efL/LkeoGc+1dOlS+XZ9H8xy1SxzBDrVoc9lsOqoy5cr9Vkz8UzqbSSkJgOA/Gcfk3wZ1zMfoo1vQyz7bS8AqF2nkYjqFgZdREYwV6BTHRoXZTYiCLSmLJ6lactyycjWTKzn4KiU5ZLt92nEBPnPntkuImLQRWQEcwQ61aHPZbDqBEvVyeIdu30FndYtxrHbV6r1XqyFtiyXjGzNxOLyMrTxbSjPcsn2G9Lyfz97gNkuorqOQVcdEhISgqCgIJVHVFRUTXetVjJXoFMd+lwGMzQIrE4Wr7rjv6wtUNMnyyUr09C9coWI4vIyXHhwF/OO7kAH/yD4unrgYto/mNghDAkpyfjy3HH5bPLMdhFZj6ioKJXzYkhIiFna4t2LdUhaWpra1yUSiYV7YhvU3dUmY8k18vQJEKqzULI+d+1pmpHdkLs4rXFeMW3HVqZUWo4USRYe5ucCAO7mZCrNPB/6zTKl8m8cipY/93P14J2MRFZCIpEgNTXVIm0x6KpDAgICYG9vr/K6WKx95mtSZa5Apzr0CRAMDQL1zeIpvq/qTldhaKBm7ukrZO8j2Nsfvq4eatdMlJnZpR8Wx+3B/J5DEZ0Uj6ziArT0boBNo6cpzTx/+v4tvHEoGl8MnYhejVvCz9UDL+z8musnElkBsViMwMBApdekUqnGRIUxuAyQjeIyQOZ15FYSno6OwuGJkVoDBH3LVVfV5Wm0nbwNKVu131UDHXXvq+pr+rx3fZbXUVc+ISUZYUHBOt+HNpqCt5LyMrT84h2kSLKrVe+v49/CiNad1PYb+N97M/fvBhFVH5cBIrIS+mS5ZMyd7dInyyWjb7ar6vtTd/mv6vsCoHX8l6b3rs/yOurKGzsJrbZLms4Ojjg9dSEyCvO01qGYveoZFIwpe7+DpKQIDT281GbHJnYIwxuHovHluePo1bilfHwXs11EdQeDLiIDmSPQqQ5DLoPJ6HOir/r+jtxKUhvoqJuRXdf4L8Xs0sDm7QwK1Ew5276u4K2xpw8ae/po3F8QBLx28EcEe/ujZ1BLlEnL8aggF+kFeSpjuapSHNsFVI4NK5WWw9nBUcMeRGQrGHQRGcBcgU51yAZyp0iydZ7o1e2r7kSvLsulKdCRB0hxeyFA0Dn+a3CLJ5WyS+XSCoMG6huaFdPEFMGbMT/7Bm5i7Bn/BpzsK//8+ruJGXAR1REMuogMYI5Ap7r0vQymjqYTvaYsl6ZAR/Z/ADrv4vzo9wNK2aXZMT/pPVBf1/QVhgRMpgjejP3ZB4nrG7wfEdV+DLqIDGCOQMcYui6DGaJqFk8251QzL1+8fvBHRPYcgu6BLdDBPwjzju6Ar6sHfFzc4WzvAEd7ewzWcCdhRHB79AhsgY//OICwwKqLQf8vUFO89Fg1EKrO9BXa3qMpgjdT/uyJqG5g0EVkIFs92erK4r16cIvS/xXLiJ1dUFYhhbOd6nzLIpEII1t1wpnU2xjRqmOVrZU3T1cd2P7HlAVKlyUNnb5CE1MFb0RE1cEZ6YkIwP+yeBdmLsH5GUvQwT8Izbx8AQAvdggDAHwxdKJ8Wwe/ILT3C0QH/yBcmLlEYxZPEATs//sS3J2c8evff+HILcXFoPdBEASVRbWP3r4in0VfdlnS2Nn2rWkFASKqmxh0EZFcY08fdGnYDJmFeUhKT0E9B0eEBQXjxzEzERYUjOikBHRp2BSrB7+ApIwUXM5IxerBL2jN/MUkX0ZCajLe7T0cZ1Jvy8dyfRoxHgkpyThy67LaRbUHt3hS6bKkPpPQaguYzLFUEhGRIRh0EZESWUaojW9DXM98qHax68EtnoS7kzOc7R3g4+KOPx/eVfu48OCOfC3CIcHt0dDdE9czH2LJU6MwpGUHhAUFY3bMT2oX1T56+wpGtuqE/NISjGjVUeds+9oCJkNXELB0tsva1p4kIvPgmC4iUiLLCLXxbahxwHm/pq1Rz8ER+aUlSusNaiMr5yCyg2c9l8rFoNuH4Y3D0Wjm5SufgsPX1QMd/IMwN2Y7RCLA2d4Bv/79Fxb0HaEx8NI1tsscSyWZijWuPUlE5sGgi4jkqma5NA04P/nPDVyYsQQjfloLANg0aqpKoCAIAqbs/U6+vaxCitHbvsCjAol8SRyZuzmZWqfgeJCXo3W6DW0BkzWtIKCOqWbZJyLrx6CLiOQ0ZblkFIOS01MXYvXgF/B0dBQyC/NVAoUjt5KQlJ6CwxMj0bVRcwDAuRmL5dNtCAIwZe9G5JUW425OpnwxaACoqBDw1PcfQypIsXHkVPi4uuNKRqrWvmuahNZaVhBQx5Sz7BOR9WPQVYeEhITA3t5e5fXIyEhERkbq3J+LaNcO1T1OsgBANu7qi6ETcTHtH5VyimsI9gxqqTbQ0ZRdUpxuQxaUHXpxNpb9tg/RSQl4rdtAiEQilJSXwc3JCZmF+Xh5zzcGvX/FSWitaQUBRfJjFNwIeDkC2BKDzGdmceoKK8O/ebWDsccpKioKUVFRSq9JpVKT9U8Rg646JC0tTe3rEonEwj0haySbp+thfi4A1TUCq1LcXnW2fV3ZJcWgbEjLDhCJRErBhrODIy7MWIoRP30GANg0ahr0jX0UJ6G1phUE1ArvBNxPB5IfAKiZy5tEdZ1EIkFqqvZMuqkw6KpDAgIC1Ga6xGJxDfSGrI2zgyM+GvA8Xt7zjdKlPnVO37+FNw5Fy8spBjr6ZJdO37+FhJRkeTZNXXapiZePwuXLvGplfqxtBQElwY2Axv7Alhj5S5yolcjyxGIxAgMDlV6TSqUaExXGYNBVhyQmJjItThoJgoAvz59AWFCw/DKfJp0DmiI6KUHpkqCMIdmlqtm0qtklU2R+rHEFAUEQVLJcMsx2EVmWuiE26i5ZmgKDLiICYLoB57qyS1WzZIqqZpdsNfMTe/9vlSyXjK2+ZyJi0EVE0O+SYFXaBpxryi4JgoDXDv6IYG9/9AxSvXyZXiBBeoHyGENLDGy3JEEQsPrcUbVZLhlmu4hsE4MuIrLYgHOrH9huATHJl3Hh0T0g7i+NZZjtIrJNDLqIyGIDzq16YLsFyDKKXRs0wQUNWS4ZZruIbA+DLiICYLkB59Y4sN1SZOPmto2YhvHQvnwSs11EtodBF1mFqKgoSCQSiMVivSZqZZ01o7a8f2usU3HcnE89N6BhfaXtiRkp8C4vUHqtOuPZzPX7ZI0/U0vVaQ615b3Xpt+n2kAkCIJQ050g0zPHTMrmnJ05KCgIqampCAwMREpKitH11eU6eZyss86S8jK0/OIdpEiyDd63sbg+br7xsV6XV83x3s1Vr7XXyc9S7fh9qk3nO2a6iIgsQHE8W3Z2NgYNGqS0/dixY/D29la7ry2MZyMiBl1ERBYjG8+W4eAGPMxS2hbiF8TJi4lsnF1Nd4CIiIioLmCmy0ZVVFSovJaZmWlUner2N7ZOGdmK7lKpFBkZGazTCDxO1l9nbTtG5qrX2uusbcepttRp6nrNcZzU7a/uvGooDqS3UdeuXUO7du1quhtEREQ24erVq2jbtq1RdfDyIhEREZEFMOgiIiIisgAGXUREREQWwDFdNqq8vBw3b95Ueq1+/fqws2OcTUREpE1FRQWyspSndXniiSfg4GDc/YcMuoiIiIgsgGkPIiIiIgtg0EVERERkAQy6bNC9e/cwd+5ctG3bFm5ubqhfvz66d++ONWvWoLCw0GTtbNu2DUOGDEHDhg1Rr149NGvWDC+//DISEhL0ruPx48dYsmQJOnbsCE9PT4jFYnTs2BFLlizB48ePTdZXa2TO4ySRSLBt2zbMmDEDXbp0gZeXF5ycnODn54fw8HCsWbMGOTk5Outp1qwZRCKRzkezZs2M6q+1MucxWrp0qV4/W5FIhLi4OJ318bNk+uMUFxen9zGSPcLDw9XWVVc/S+np6di/fz8WL16MoUOHwtfXV/5eJ0+ebJY2rfrcJJBN2b9/v+Dp6SkAUPto3bq1kJycbFQbRUVFwogRIzS2YWdnJ3zwwQc66zl79qzQsGFDjfU0atRIOHfunFF9tVbmPE4HDx4UnJ2dNdYtezRo0EA4ceKE1rqaNm2qsx4AQtOmTavVV2tm7s/SkiVL9PrZAhBiY2O11sXPknmOU2xsrN7HSPaYOXOm2rrq6mdJ23udNGmSSduqDecmBl025K+//hJcXV0FAIK7u7uwfPly4fTp08Lx48eFGTNmyH9h2rRpI+Tl5VW7nRdffFFeV//+/YU9e/YIZ8+eFTZu3CgEBwfLt33zzTca60hJSREaNGggABAcHByE+fPnC7/99pvw22+/CfPnzxccHBzkgUFKSkq1+2qNzH2ctmzZIv8DM2TIEOHTTz8VTpw4Ifz555/Cvn37hHHjxsnbcHV1FS5evKixLtmJYtSoUUJSUpLGx40bN4z4iVgfS3yWFIMubT/bpKQkIT8/X2M9/CyZ7zjl5+frPDZJSUlCv3795G398ccfauuqq58lxWClcePGQkREhNmCrtpwbmLQZUPCw8PlvyinT59W2b5q1Sr5L92yZcuq1UZcXJy8jpEjRwrl5eVK2zMyMoQmTZoIAARvb28hOztbbT2TJk2S17Njxw6V7Tt27JBvnzJlSrX6aq3MfZy2bdsmzJo1S/jnn380lvn888/lbQwYMEBjOdmJwtR/HK2dJT5LikGXMfhZMu9x0iU7O1ueWW7ZsqXGcnX1s7R48WLh119/FdLS0gRBEIQ7d+6YJeiqLecmBl024uzZs/JfhFmzZqktI5VKhbZt28p/6UpLSw1uZ9iwYQIAwd7eXrh//77aMj/99JO8L2vWrFHZnpaWJtjb2wsAhCFDhmhsa8iQIfK2ZB/Y2s5Sx0kfoaGh8oxYZmam2jJ18URhqWNkiqCLn6Wa/yytW7dOr8CuLn6W1DFX0FVbzk0cSG8j9uzZI38+ZcoUtWXs7OzwyiuvAACys7P1GpyrKD8/H8ePHwcADB48GEFBQWrLPfvssxCLxQCAXbt2qWzft2+ffIV5TX0FIB9kKZVKsW/fPoP6aq0scZz0JRvwW1FRgTt37piljdrImo6RLvwsVarJ4/TDDz8AAEQiEV5++WWT10+61aZzE4MuG3Hq1CkAgJubG7p27aqxXL9+/eTPf//9d4PaOHv2LEpKSlTqqcrJyQlhYWHyfcrKytT2VVc9xvTVWlniOOlLdiwBcKUCBdZ0jHThZ6lmj1NycjJOnz4NAOjbty+aN29u0vpJP7Xp3MS/tDbi2rVrAICWLVtqXaagTZs2KvsY2kbVerS1o245Ilk9np6eCAgI0FhHw4YN5d9KDO2rtbLEcdLXyZMnAQAODg5o2bKl1rK//fYbQkJC4ObmBldXVzRv3hzjxo3Dnj17INjYohY1cYwGDx4MHx8fODk5wd/fH+Hh4fj444+RnZ2tV1/5WaqZz5IsywUAkyZN0mufuvRZspTadG5i0GUDiouLkZmZCQAa06oy3t7ecHNzAwDcv3/foHYUy+tqp3Hjxmr3U/y/rjoU6zG0r9bIUsdJHwcOHEBiYiIAYMiQIfI/IJrcuXMHSUlJKCwsRFFREe7evYsdO3ZgzJgx6Nu3L1JTU03ex5pQU8fo2LFjyMrKQllZGTIyMnDy5Em89957aNGiBfbu3atxP36Wavaz9OOPPwIAXFxc8Pzzz+u1T135LFlSbTo3GbdyI1mFvLw8+XN3d3ed5d3c3FBQUID8/HyztSP7IwdApR1ZPfr2VV0dtZGljpMuWVlZeO211wAA9vb2+PDDDzWWdXJywjPPPIOIiAi0b98enp6eyMnJQXx8PL7++mvcv38ff/zxBwYPHoz4+Hh4enqatK+WZulj1KFDB4wePRrdu3dHo0aNUFZWhhs3biA6OhoxMTHIycnBc889h19//RVDhw7V2F9+lrQzx2fp1KlTuH37NgBgzJgxOr+41LXPkiXVpnMTgy4bUFxcLH/u5OSks7yzszMAoKioyGztyNpQ146sHnP21RpZ6jhpI5VKMXHiRPzzzz8AgPfffx+dO3fWWP7s2bPw8vJSeT08PByvv/46nn/+ecTExODatWtYtmwZoqKiTNbXmmDJY/T2229j6dKlKq/36NEDr7zyCtavX49//etfkEqlmD59Om7dugUXFxe1/eVnSTtzvPctW7bIn8sG62tT1z5LllSbzk28vGgD6tWrJ39eWlqqs7xswGHVP+CmbEdxkHbVdmT1mLOv1shSx0mbV199FYcPHwYADB8+HIsWLdJaXt1JQsbDwwM7duyAj48PAGDDhg16vS9rZsljpO1nCwCzZs3C9OnTAQAPHjxQe7cVP0s1895LSkrw888/AwAaNWqEQYMG6dynrn2WLKk2nZsYdNkADw8P+XN9Up0FBQUA9EuhVrcdWRvq2pHVY86+WiNLHSdN3nvvPWzYsAEA0KdPH/z888+wt7c3qk5PT0+MHz8eQGV/z58/b3Q/a1JNH6OqZs2aJX8uu/FBET9LNfPe9+7dK1+7dOLEiUZ/jgDb+yxZUm06NzHosgH16tWDr68vACAlJUVr2ezsbPkvi+KAQn0oDi7U1Y7i4MKq7cjq0VWHYj2G9tUaWeo4qbNy5Up8/PHHAIAuXbpg//79JvvW365dO/nz2j4IuCaPkTq6frb8LNXMcVK8a1GfS4v6sqXPkiXVpnMTgy4b0bZtWwDArVu3UF5errHc9evXVfbRl+IfBMV6tLWjbjoCWT25ublIS0vTWMfDhw8hkUiq1VdrZYnjVNVXX32Fd999V17XkSNHTDpI19Zuc6+JY6SJrp8tP0uWP07p6ek4cuQIgMovMO3btze6Thlb+yxZSm06NzHoshF9+vQBUJnyvHDhgsZyipcoevfubVAb3bp1kw8wVHepQ6a0tBQJCQkq+1Ttq656jOmrtbLEcVK0ZcsWvP766wCAFi1a4NixY/IMgalcvXpV/rxRo0YmrbsmWPoYaaPrZ8vPkuWP09atW+VBnimzXIDtfZYspVadmwxaNIis1pkzZwxah8zLy6ta65ANHTpUvsCsPutbrVq1SmX7w4cPBTs7O73Xt7KzsxMePnxocF+tkaWOkyAIwi+//CJfRywoKEi4c+eOET1XLycnR/Dx8REACK6urkJxcbHJ27A0Sx4jXaZNmybvy5YtW1S287Nk+ePUuXNn+d/A9PR0o+uTscXPkjrmWnuxtpybGHTZkL59+8p/6U6fPq2yfdWqVfJfuCVLlqhs37Rpk9btgiAIx48fl5d55plntK7k7uXlJWRlZamt5+WXX5bX8/PPP6tsV1zJ3dYWiLXEcTpy5Ijg5OQkABD8/f2F69evG9zPQ4cOCYWFhRq3SyQSISIiQt6XN954w+A2rJW5j1FiYqJw8+ZNrX1QXEg5ICBAyM/PV1uOnyXzfpYUXb58WV5+5MiRevezLn+WqqpO0GVL5yYGXTbkzz//FFxcXAQAgru7u7BixQohPj5eOHHihDBz5kz5L0qrVq0EiUSisr++f4DGjx8vL9e/f39h7969wrlz54TvvvtOCA4Olm9bt26dxjru3bsn+Pn5yf9gvvPOO8KpU6eEU6dOCe+8847g4OAgABD8/Pw0fmuprcx9nOLj4wVXV1cBgODo6ChER0cLSUlJWh/Z2dkq9fTr10+oX7++MH36dOH7778XTp06JVy8eFGIjY0VVqxYITRu3Fjej9atWwuPHz82w0+rZpj7GG3atEmwt7cXBg0aJHzyySdCTEyMcOHCBeHMmTPC5s2bhcGDB8v3t7e3F/bu3auxr/wsmf9vnsy8efPk5Xfu3Kl3P+vyZ+nUqVPCpk2b5I/Vq1fL32vv3r2Vtm3atEltHbZ0bmLQZWP27dsniMVi+S9X1UerVq00fsPW9xe7sLBQGDZsmMY27Ozs9PoDlpCQIAQEBGisJyAgQEhISKjmT8K6mfM4LVmyRGO9mh7q/tj169dPr32feuopISUlxcQ/oZpnzmOkuF3bw8fHR9izZ4/OvvKzZN6/eYJQeakyMDBQACB4e3sbdPmvLn+WJk2aZNDfInVs6dzEGeltzMiRI5GYmIi1a9fiwIEDSElJgZOTE1q2bImxY8fi9ddfh6urq1FtuLi44MCBA9i6dSu+//57XLp0CTk5OWjQoAH69u2L119/HT179tRZT48ePZCUlIS1a9diz549uHv3LgCgefPmGDVqFN5++235ZIG2xhLHyVhr1qzB8ePHER8fjxs3biAzMxM5OTlwdXVFo0aN0KNHD0yYMAEREREQiUQ12ldzMOcxGjZsGDZu3Ij4+HhcvHgRjx49wuPHjyEIAurXr4+OHTvi6aefxuTJk3UuLwPws2SJz9Lx48fl0ziMGzdOaWZzXer6Z8lSasO5SSQIvEeViIiIyNw4ZQQRERGRBTDoIiIiIrIABl1EREREFsCgi4iIiMgCGHQRERERWQCDLiIiIiILYNBFREREZAEMuoiIiIgsgEEXERERkQUw6CIiIiKyAAZdRERERBbAoIuISI24uDiIRCIcPny4prtCRDaCQRcRERGRBTDoIiIiIrIABl1EREREFsCgi4iIiMgCGHQRERERWQCDLiIiIiILYNBFRDZB3RQPR48exZAhQ+Dh4QEvLy+Ehobi448/Rl5ensH1FxQU4NNPP8VTTz0FX19fODo6okGDBnjuuecQHx9vUF1//fUXFixYgH79+qFhw4ZwcnJC/fr10b17dyxevBiPHj0yqL7U1FQsWrQI3bt3h6enJ+zt7eHk5IQGDRpg4MCB2LBhA3JycvSqKzc3F5988gkGDBiARo0awdnZGT4+PggNDcX7779vcN+I6H9EgiAINd0JIiJjxcXFoX///jh06BAGDRqEf//739i0aRNGjx6N8PBw2Nvb48yZM9ixYwf8/Pywc+dOdOvWTa/62rRpg0GDBqGkpASjRo1C27ZtUa9ePfzzzz/45ZdfcO3aNXz66ad46623tPbx2rVreOONN3D8+HG0bdsWAwYMwBNPPAFPT0/k5uYiKSkJ+/fvR15eHtatW4eXX35Z5/uOjo7GzJkz4eTkhJEjR6Jjx46oX78+ysvLkZWVhaSkJOzevRszZszAZ599prWu3bt3Y+bMmSgsLMSIESMQGhoKX19fFBQU4OLFi9i5cycEQcCWLVswatQonX0joioEIiIbEBsbKwAQDh06JERGRgqtWrUS/vrrL5Vyd+/eFXr16iV4eXkJly5d0lnf9u3bhZYtWwqLFi0SSkpKVMqVl5cLs2fPFgAIsbGxWvu4cOFCoX///sLp06c1likqKpLXt3XrVq31/fnnn4K9vb0wYcIEITc3V2O5R48eCcePH9da19atWwU7Ozth5syZQlZWltoyWVlZwogRIwQHBwet74GI1GOmi4hsgiwztXLlSqxfvx5nz56Fj4+P2rLFxcUIDw9HYWEhLly4AEdHR431devWDYMGDcKKFSs0tl1RUYHQ0FDUq1cPp0+f1lguNTUVgYGBer2fF154AXFxcUhJSYGTk5PaMtOnT8euXbuQkpICV1dXvepV5+7duwgJCcHMmTOxZs0arWVLS0vRt29flJSU4K+//qp2m0R1Ecd0EZFN+eSTT7Bu3TqNARcA1KtXD9HR0bhy5Qo2b96stb5bt25h0aJFWsvY2dlh2rRpiI+Pxz///KOxnL4BFwB8+OGHyMjIwMGDBzWWuXDhAnr06GFUwAUAq1evhpubGz788EOdZZ2cnLBixQpcunQJv/32m1HtEtU1DLqIyKZ4eXlh8ODBOssFBwdj2LBhWL9+vdZyzzzzDFxcXHTW17NnTwDAuXPn9OuoDq1bt0aTJk1w9uxZjWWKiorg7OxsVDvl5eWIjo7GuHHj9HqfADBgwAB4eXnhwIEDRrVNVNcw6CIimzJgwACDyl64cAG5ubkay3Tp0kWvupo3bw6g8lJddZSWliIrKwuZmZnyh5+fH/7++2+N+4SGhuKPP/6ARCKpVpsAcOXKFeTm5qJ///567yMSidChQwf8+eef1W6XqC5yqOkOEBGZUseOHfUu26lTJwiCgIsXLyI8PFxtmYCAAL3qcnd3BwCtAZyiEydOYNu2bTh37hxu3LiBoqIiteW8vb011rFs2TIcOnQIERERWLduHTp16qRX24qSkpIAAH5+fsjMzNR7P3d3d6SmphrcHlFdxqCLiGxK/fr1DS6bnp6usYyHh4dedckG45eVlWkt9/DhQ7z88ss4fvw4unfvjuHDhyMyMhJeXl4ql/dmz56NgoICjXUFBwfjjz/+wOTJk9G1a1eEh4dj2LBhCA8PR8eOHeHgoPtPfFZWFgCgd+/eOstWJcvuEZF+GHQRkU1xc3MzuKy2yVJFIpHRfZLJz8/HwIEDIZFI8Ntvv6Fv3746+1dRUaG1TJs2bZCQkIBTp07hl19+wQ8//ID58+fDzc0NERERmDZtGoYOHapxf9l737dvn95jumQMLU9U1zHoIiKboi0zpKmsvtksY3300Ue4efMmLly4gJCQEJ3lMzMz4evrq1fdffv2lQdxWVlZOHLkCLZs2YLhw4ejf//+2LFjh9o7OmWBU/fu3dGgQQMD3g0RGYoD6YnIpsgulxlS1s/Pz1zdUfLDDz9gxIgRegVceXl5uHPnTrXaqV+/PiZMmICDBw8iPj4ely9fxjPPPAN10zLKLrEaMp6LiKqHQRcR2ZRLly7pXfavv/6CSCRC586dzdijSjk5OUhJSUGPHj30Kh8TE6Pz0qI+evTogVWrVuH06dP4448/VLa3a9cOAHD58mWj2yIi7Rh0EZFNiY2N1bvsiRMn0KVLF3h5eZmvQ/+vsLAQgP5jzjZv3mz0pKcyoaGhAICbN2+qbOvcuTPc3Nxw4sQJk7RFRJox6CIim5KdnY2jR4/qLJecnIxDhw5h1qxZFugV4OPjA3t7e72mWbhy5QquXr2KsLAwk7Qtm8fL09NTZZujoyMmTpyI7du3IycnxyTtEZF6DLqIyKZERkbiX//6Fx4/fqyxTHFxMSZOnIg2bdpg0qRJFumXs7MzunXrhn379mm9bCiVSjFjxgzMmTPHZHdO/vTTTwAqB8urM3/+fJSXl2PGjBmQSqV612tIWSJi0EVENqZjx44YNWoUevXqpXZ81z///IOBAwfi2rVr+PHHHzUuJm0Os2fPxrVr1/DGG2+onQw1Ly8PY8eORUVFBWbOnKmzvp49eyIuLk5rmW+++QZffvklnnvuOQQFBaktExwcjPXr1+OXX37B0KFDcf36dY31FRYWYseOHejevTuuXbums49E9D+cMoKIbM6qVasgkUgQGhqK0aNHo1+/fnBwcEBCQoJ86oSYmJhqzeBujBdeeAEXLlzAqlWrsGvXLkyYMAFPPPEESktLcfXqVezYsQPNmzfHoUOHYG9vr7O+a9euoX///ujQoQN69eqF1q1bw9vbG6Wlpbhz5w4OHTqES5cuITQ0FBs2bNBa18SJE+Hu7o7p06fjySefRO/evdG3b180atQILi4uSE1NRUJCAuLi4lBYWIiwsDC9p7MgokoMuojI5jg4OODbb7/FCy+8gDVr1uDdd9+Fvb09WrZsiffffx9vvPGGxebmqmrlypUYPHgwvvrqK/z00094/PgxfHx8EBISgrVr12LChAny2e11efToEQ4ePIiff/4Zx48fx48//oji4mI4OzvD398fHTt2xNy5czF+/Hi9ZqcfNWoUBg0ahM2bNyMmJgbR0dHIzMxEWVkZGjVqhI4dO+LDDz/E8OHD0bp1a2N/FER1jkhQN3ELEVEtExcXh/79++PQoUN4+umna7o7REQqOKaLiIiIyAIYdBERERFZAIMuIiIiIgtg0EVERERkAQy6iIiIiCyAQRcRERGRBXDKCCIiIiILYKaLiIiIyAIYdBERERFZAIMuIiIiIgtg0EVERERkAQy6iIiIiCyAQRcRERGRBTDoIiIiIrIABl1EREREFvB/yq4+6szgb4AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in objid_index:\n",
    "    fig = plt.figure(figsize=(6, 4))    \n",
    "    findobj_fsodo = (fsodo_sources['diaObjectId'] == objids[i])\n",
    "    findobj_dia = (dia_sources['diaObjectId'] == objids[i])\n",
    "    obj_mjd_days_dia = np.array(dia_sources[findobj_dia]['midPointTai']) * u.day\n",
    "    obj_mags_dia = np.array(dia_sources[findobj_dia]['dias_gmag'])\n",
    "    obj_mjd_days_fsodo = np.array(fsodo_sources[findobj_fsodo]['expMidptMJD']) * u.day\n",
    "    obj_mags_fsodo = np.array(fsodo_sources[findobj_fsodo]['fsodo_gmag'])\n",
    "    obj_period_fsodo = 1 / LSfreq[i]\n",
    "    obj_period_dia = 1 / LSfreq_dia[i]\n",
    "    print('DiaObjectID: ', objids[i],\n",
    "          '\\n RA: ', fsodo_sources[findobj_fsodo]['ra'][0],\n",
    "          '\\n Dec: ', fsodo_sources[findobj_fsodo]['decl'][0],\n",
    "          '\\n period (DiaSource): ', obj_period_dia,\n",
    "          '\\n period (ForcedSourceOnDiaObject)',obj_period_fsodo)\n",
    "    \n",
    "    t0 = 0.0\n",
    "    obj_mjd_norm_fsodo = (obj_mjd_days_fsodo.value - t0) / obj_period_fsodo\n",
    "    obj_mjd_norm_dia = (obj_mjd_days_dia.value - t0) / obj_period_dia\n",
    "    obj_phase_dia = np.mod(obj_mjd_norm_dia, 1.0)\n",
    "    obj_phase_fsodo = np.mod(obj_mjd_norm_fsodo, 1.0)\n",
    "\n",
    "    plt.plot(obj_phase_dia, obj_mags_dia-np.nanmean(obj_mags_dia), plot_filter_symbols['g'],\n",
    "             markerfacecolor = 'none', mec=plot_filter_colors['g'], ms=10, \n",
    "             label='DIA (N=%i)' % len(obj_phase_dia))\n",
    "    plt.plot(obj_phase_fsodo, obj_mags_fsodo-np.nanmean(obj_mags_fsodo), plot_filter_symbols['g'],\n",
    "             color=plot_filter_colors['g'], ms=6, label='FSODO (N=%i)' % len(obj_phase_fsodo))\n",
    "    plt.hlines(0, 0, 1, linestyle=':', color='Gray')\n",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.legend(loc='upper right', labelspacing=0.2, handletextpad=0.2, fontsize=11)\n",
    "    plt.xlabel('phase')\n",
    "    plt.ylabel('mag - mean')\n",
    "    plt.minorticks_on()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780089e3-b3e7-403f-909a-fd3491d016e2",
   "metadata": {},
   "source": [
    "Note that the number of measurements is always greater from the `ForcedSourceOnDiaObject` results.\n",
    "\n",
    "The plots demonstrate the possible deviations in this analysis using the different catalogs.\n",
    "Not only can the periods differ from using the different catalogs (e.g. 1567762843720221100), but the phase may also be offset due to additional measurements provided by the `ForcedSourceOnDiaObject` catalog. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798f11b3-af08-4c60-91b7-6dc244430cd0",
   "metadata": {},
   "source": [
    "Lastly, we can estimate the fraction of candidates that will have different periods between the `ForcedSourceOnDiaObject` and `DiaSource` catalogs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "331f9016-f9f5-4727-b89f-5943cf0387a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:40.601976Z",
     "iopub.status.busy": "2024-04-29T13:59:40.601747Z",
     "iopub.status.idle": "2024-04-29T13:59:40.607503Z",
     "shell.execute_reply": "2024-04-29T13:59:40.606548Z",
     "shell.execute_reply.started": "2024-04-29T13:59:40.601958Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571428571428572"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-(LSfreq_dia-LSfreq).tolist().count(0.0)/len(LSfreq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dcc9f22-77b8-454a-9862-3b659538736a",
   "metadata": {},
   "source": [
    "We see that **~70%** of the identified candidates exhibit different periods from both catalogs.\n",
    "\n",
    "Plot a histogram of the absolute difference in the periods measured from `ForcedSourceOnDiaObject` and `DiaSource`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30f20541-aebc-42bc-8e84-e7ab5a000134",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:59:40.608807Z",
     "iopub.status.busy": "2024-04-29T13:59:40.608598Z",
     "iopub.status.idle": "2024-04-29T13:59:41.443396Z",
     "shell.execute_reply": "2024-04-29T13:59:41.442091Z",
     "shell.execute_reply.started": "2024-04-29T13:59:40.608789Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SB0hERESWR3IXhp+fH+Lj4zFlyhSdycOaNWvE93v37i1ddERERGSRJCcQwcHBJW7fuXOn+N7FxUV6RNaiatVny3cXfVWtau6oiIiIjMLgxziLIwiCsZssn2xsgGrVzB0FERGRSRh9LYznl/YmIiIi68PFtIiIiEgyJhBEREQkmV5jIIYNG6ZX14QgCLh8+XKpgyIiIiLLplcCsWjRIr0blFLXquXlAQkJmmUdOgD29uaJh4iIyIj0SiC8vLxMHYf1SU8HgoI0yx4+5JMZRERkFTgGgoiIiCRjAkFERESSMYEgIiIiyZhAEBERkWRMIIiIiEgyJhBEREQkmVETiMzMTMyfP9+YTRIREZEFkpxApKSkaK24mZubi6VLl6J9+/aoU6eO0YIjIiIiy6RXApGdnY2xY8eiUaNG6NOnD7y9vbFq1SoAwLZt29C0aVM8efIEJ06cwKhRo0wZLxEREVkAvWai/OSTT+Dt7Y3169cDAFQqFSZPnowJEybg2LFjOHDgABo2bGjSQImIiMhy6JVAnDhxArGxseJnOzs7LFu2DJUrV8aVK1fQoEEDkwVIRERElkevLgwbG+1qzs7OaNasGZMHIiKiCkivBCI/P19nubu7u1GDISIiovKB80AQERGRZHqNgbh37x46duyoUSYIAlJSUjTKBUGATCZDfHy8caMsj6pUAS5e1C4jIiKyAnolEFeuXDF1HNZHLgdee83cURAREZkEuzCIiIhIMqMnEOnp6cZukoiIiCyM0RKI7OxsfPLJJ+jSpYuxmiQiIiILpXcCcePGDURGRmLChAlYsWIFHj9+DODZI56rV69GixYt4OjoiISEBJMFS0RERJZBrwTiyJEjCA0NhUKhQJ8+fWBra4s+ffrgr7/+QsuWLXHx4kUkJCRg6tSpsLOzM3XM5YNaDVy6pPlSq80dFRERkVHo9RRGZGQk9u7di9q1awMAunfvjvbt28PX1xdz587F5MmTTRlj+ZSaCrz+umbZw4dAtWrmiYeIiMiI9LoDoVKpxOShUNu2beHg4IB//etfJgmMiIiILFepprJu1KgRbG1tjRoQERERWT69EghBEHTvrGORLSIiIrJ+ek9lvWnTJo0yQRB0lgPAqFGjjBMdERERWSS9EohZs2ZJKiciIiLrplcCMWLECFPHQUREROUIBzEQERGRZBaTQJw+fRqLFy9Gjx49UKdOHTg4OMDV1RXe3t4YOXIkjhw5Yu4QiYiI6L/06sIwtYCAAMTHx2uV5+Xl4dq1a7h27Rq+/fZbDBs2DBs2bIC9vb0ZoiQiIqJCFpFAJCcnAwA8PT0xYMAA+Pv7o27dusjPz8fx48exYsUKJCcnY8uWLVCr1fjhhx/MHDEREVHFZhEJROPGjbF48WKEhoZqTUzVrl07DBs2DL6+vrh69Sq2bt2K9957D/7+/maKloiIiCxiDMTevXsxcODAYme1VCgUWLFihfh5586dZRUaERER6VCqBCIvLw/37t0zViwlCgwMFN/fuHGjTI5JREREuhnUhbFp0yZs3boVDg4OSEpKwvnz5wEAcXFxUKvV6Ny5s1GDBJ4lK4U4hTYREZF5SU4gVq5ciTt37mD//v2ws7NDUFCQuK1ly5aYOHGiSRKIuLg48X3jxo312kepVBp8vGqlXXa7UiXgt9+0y4iIiEwkJSXFoP0M+b6UnED8+OOPOHbsmHgXQCaTidvc3Nzw4MEDyUG8SEFBASIjI8XPAwcO1Gu/V1991eBjFreAmN7s7YEi3S5ERESmVr169TI7luS+ADs7uxK7EJ48eVKqgHT57LPPcPLkSQBA//790bp1a6Mfg4iIiPQn+Q5ETk4OBEHQuPNQSBAEFBQUGCWwQnFxcZg5cyaAZ5nV119/bdT29SWb+L3R2xS+HGL0NomIKhJTXJsBXp/1IfkORHBwML744gud27Zu3Yru3buXOqhCly5dQv/+/aFWq+Hg4IAdO3agRo0aRmufiIiIDCP5DkR4eDiGDx+Oo0ePokePHnj06BH27t2LhIQE3L9/H+vWrTNKYDdv3kS3bt2QmpoKW1tbbN26FQEBAZLaSExMhEKhMEo8RERElu7hw4cG7adUKiWPG5ScQNjZ2WHr1q24cOECjh07hkGDBuHWrVsYOHAgmjdvLrU5ne7du4cuXbrg3r17kMlk2LRpE/r37y+5HYVCUfqnKQwkEwpQNTdTo+yRoysEGR9BJSIi0yjL7zyDp7Ju2rQpmjZtasxYADzLgrp27Yq///4bAPDFF19g+PDhRj+OqVXNzUTKpvc1yqqNWgOlk7uZIiIiIjIeyX8OHz58uFTbS5Keno7g4GAkJiYCACIjIzFhwgSD2yMiIiLTkJxALFiwoMTtCxcuNCiQ7Oxs9OrVC6dPnwYAzJkzBx999JFBbREREZFpSU4gSppgqaCgwKB5IPLy8tC/f38cO3YMADBp0iQsWrRIcjtERERUNvQaA3Hr1i38/fffEAQBaWlpOHTokFadrKwsbNq0CaGhoZKDePvtt3Hw4EEAQKdOnTB69GhcvHix2Pr29vbw9vaWfBwiIiIyDr0SiNu3b+PIkSMAgLS0NBw9elTrToSjo6PB62Ds2rVLfH/o0CE0a9asxPpeXl64deuW5OMQERGRceiVQAQEBIhzMLi7u+PDDz80aVBERERk2SQ/xmmK5KHUC1cRERFRmeKsRkRERCSZQRNJJSQkIDo6GklJScjNzdXarmuQJREREVkPyQlEXFwc1q1bhyVLlmDo0KHYv38/Ll26hH379qFatWoICwszRZxERERkQSR3YSxfvhzr1q1D7dq1YWtrC2dnZ7Rp0wYRERGoU6cOVqxYYYo4iYiIyIJITiCysrLg4uICAMjPz9fY1rdvX3ZfEBERVQCSE4iCggLxvZOTk9YYiLy8vNJHRURERBZNcgJRvXp1qNVqAEDv3r2xYcMGcdv9+/eNFxkRERFZLMmDKMPCwhATE4NBgwZh/PjxGDx4MI4ePYoaNWrg999/x+rVq00RZ7mTYe+EAcH/0iojIiKyBpITiE6dOv1vZ7kcO3fuxNWrV5GVlYUlS5bA0dHRqAGWV3m2dtjZsK25wyAiIjIJg+aBeB4XtiIiIqpYOBMlERERSabXHQh/f3/IZDLxc0FBAf788094eXnBw8MDjx49wu3bt9G6dWvY2NggPj7eZAETERGR+emVQBQu5Q0ADx48QHh4OLZs2YL69euL5Tdu3MCyZcuwYMEC40dJREREFkVyF8a0adMwc+ZMjeQBAF5++WXMnDkTU6ZMMVpwREREZJkkD6JMSkpCvXr1dG6rV68ekpKSShuTVVDkZCBl0/saZdVGrYHSyd1MERERERmP5DsQOTk54kRSz1Or1cjJySl1UERERGTZJCcQw4YNw+zZs3Vumz17NoYOHVrqoIiIiMiySe7C+Ne//oXIyEj07NkTHTt2RNWqVaFUKhEXF4eOHTti0qRJpoiTiIiILIhBE0nNnDkT77//Po4fPw6lUomWLVtiwoQJcHdn/z4REVFFYPBMlO7u7ggODjZmLERERFROcCZKIiIikowJBBEREUmmVxfGpEmTxGW6n5/WuihBECCTyTiVNRERkZXTK4EoTB4AzWmtiYiIqGJiFwYRERFJxgSCiIiIJNOrCyMoKEgc91A4zqEkhw4dKn1kREREZLH0SiB+/fVX8b1arcbSpUvRoEEDdOrUCR4eHnj8+DFiY2Nx8+ZNfPTRRyYLloiIiCyDXl0Ytra24mv+/Pnw9/fHkCFDUKtWLTg4OKBWrVoYOnQo/Pz8EBERYeKQiYiIyNwkz0R59OhRLF68WOe2oKAghIeHlzooa5Bp54gJHUdolREREVkDyQlEbm5uidu5nPczuXJ7rGna1dxhEBERmYTkpzDatm2Lbdu26dy2fft2tG3bttRBERERkWWTfAdiyZIleOedd3DlyhV0794dVatWxaNHj7B//36cO3cOW7duNUWcREREZEEkJxDOzs7YvXs3fv31V/z0009QKpWoVq0agoKCsGDBAlPESERERBbG4OW8u3Tpgi5duhgzFiIiIionOBMlERERSWbwHQgqWdWcJ7j8wwyNsibvLMUjJzczRURERGQ8BiUQMTExiImJwf3796FWq8VyLuf9PzIIqJb7RKuMiIjIGkhOID7//HOcP38eERER8PLygoODgyniIiIiIgsmOYGIiYlBXFycKWIhIiKickLyIEpbW1tTxEFERETliOQEQqFQIDMz0xSxEBERUTkhOYGYO3cuFixYAEHggEAiIqKKSvIYiGXLliEpKQleXl7w8fGBs7OzVp3o6GijBEdERESWSXICsWjRIlPEQUREROWI5ATCy8vLFHEQERFROcKprImIiEgyg6eyFgQBKSkpyM3N1dpWt27dUgVFRERElk1yAqFSqTBp0iTcvXsXp06dQseOHXHx4kWkpqaiU6dOsLGx4SBKIiIiKye5C2PFihUIDAzEnj170LhxY2zfvh2XLl1CTEwMnJycsHTpUlPESURERBZEcgJx6NAhDBw48NnONv/bvX379li4cCFmzJhR3K5ERERkJSR3YRRdfVOlUmlsq1mzJu7fv1/6qKxAttweEW36a5URERFZA8kJRNEZKOvVq4ebN2+ifv36YpmuQZUVUbadI+b7hJo7DCIiIpOQ3IXRqlUr3Lt3DwAwbtw4TJ06FdnZ2QCArVu3olGjRsaNkIiIiCyO5DsQU6ZMweXLl+Hp6Qk/Pz8olUoEBwdDJpOhRYsWWLVqlQnCJCIiIksiOYHw9PSEp6en+DkkJAQhISHGjImIiIgsHGeiJCIiIskkJxB79+7F77//rnPb+fPnsWPHjlIHRURERJZNchfG+vXr8e9//1vnttdffx2zZ88W54moyKrkZuLIroUaZf5vfoxUR1czRURERGQ8khOI7OxsjQmkirKxseFjnP9lKxTgtdRkrTIiIiJrILkLo+hEUroUFPBLkoiIyNpJTiDkcjlycnJ0bsvOzoatrW2pgyIiIiLLJjmBGDNmDKZMmaI1jbVKpcK0adMwevRoowVHRERElknyGIhBgwbh7t27aN26NZo3b46aNWvi3r17OH/+PEaOHInBgwebIk4iIiKyIJITCACYOnUqJk+ejCtXriAtLQ2VK1dG48aN2X1BRERUQRiUQACAra0tXnvtNWPGQkREROUEZ6IkIiIiyZhAEBERkWRMIIiIiEgyJhBEREQkmV4JxPnz500dBxEREZUjeiUQH374ofh+5MiRpoqFiIiIygm9Eoi8vDzxfVJSksmCISIiovJBr3kg+vbti3feeQdBQUH4559/sGnTphLrjxo1yijBlWe5tnb46vUuWmVERETWQK8EYvr06Th79iwSExOf7SSXQxAEkwZW3mXaO2FiwEhzh0FERGQSes9E2bx5czRv3hy//fYbhg8fbsqYiIiIyMJJfoxz/fr1pogDDx8+xN69ezFv3jz06NEDCoUCMpkMMpmMAzeJiIgsjMFrYRhbjRo1zB0CERER6cmgiaTOnj2L0NBQtGrVCr6+vmjVqhVCQ0Nx+vRpowRVp04ddOvWzShtERERkfFJvgMRHx+Pjz/+GKtXr0bz5s3F8jNnzmDSpElYtGgROnbsKDmQefPmoU2bNmjTpg1q1KiBW7duoX79+pLbISIiItOTnEDMnz8fMTExUCgUGuUtWrTArl27MHDgQBw6dEhyIPPnz5e8jyWr9DQLu/d/plEW0vNDpDu4mCkiIiIi45GcQBQUFGglD4UUCgUf7/wvu4J8BN67olVGRERkDSSPgVCpVCVuLzprJREREVknyXcg2rZti6ioKJ2PVm7ZsgVt2rQxRlxGoVQqDd63WrVqRoyEiIjI9FJSUgzaz5DvS8kJxOLFizFixAjs3r0bffv2Rc2aNfHPP/9g3759cHd3x9q1ayUHYSqvvvqqwfuyK4aIiMqb6tWrl9mxJCcQDg4O2LZtGy5evIj4+HicP38eL730Ej799FO88sorpoiRiIiILIzBE0m9/vrreP31140ZC1kw2cTvzR2CXoQvh5g7BCKrYIr/8xX9/6e1/U4NmkiKiIiIKjaLmcraFBITE4t95JSIiMjaPHz40KD9lEql5HGDVp1AKBQKPk1BREQVRll+57ELg4iIiCRjAkFERESSSU4g+vXrZ4o4iIiIqByRnEBkZmaaIg4iIiIqRyQPoqxSpQqys7Ph7Oxs1ECOHj2K69evi5+LTqt5/fp1REVFadTXNZU2ERERlQ2DlvP+6KOPsHr1atjYGG8IxYYNG/Dtt9/q3Hbs2DEcO3ZMo8zSE4g8Gzl+fNlHq4yIiMgaSP5Gi4yMxJ07d+Dp6QkfHx9UqlQJMplMo050dLTRAiyvMhycMbD7B+YOg4iIyCQkJxCLFi0yRRyIiorS6qYgIiIiyyQ5gfDy8jJFHERERFSOcB4IIiIiksygUX1paWlYt24dzp49CwD44YcfAABXr16Fq6srPD09jRYgERERWR7JdyBOnTqFHj16oGnTpli1ahX++ecfcZtMJsOXX35p1ACJiIjI8ki+AzFnzhzs2LEDderUAQCNJzAaNWqEc+fOGS+6csz9aTY2/LZBo2xM0BhkOBh3/gwiIiJzkJxAZGVlicmDLtnZ2aUKyFrYF6gx4MZJjbL3A0aaJxgiIiIjk9yFoVKpStzOBIKIiMj6SU4g6tSpgzNnzujclpiYiPr165c6KCIiIrJskhOIxYsXIywsDAcOHNC4G3H16lXMmDEDCxcuNGqAREREZHkkJxCvvPIKYmJi8OuvvyIwMBAPHjxAcHAwvv76a3z99ddo1KiRKeIkIiIiC2LQPBC1a9fGihUrjB0LERERlROciZKIiIgkM3h96czMTJw4cQIpKSlQKBRo164d3NzcjBkbERERWSiD7kAsX74cgwYNwqlTp5CRkYFTp05h4MCBWLp0qbHjIyIiIgsk+Q7EmjVr8ODBA+zbt09r2/Tp0/Hll19i4sSJRgmOiIiILJPkOxDR0dH49NNPdW5bvHgxoqOjSx0UERERWTbJCYSdnR3kct03Luzs7ODg4FDqoIiIiMiySU4gvLy8cOvWLZ3bbt26hbp165Y2JiIiIrJwkhOIFStWYMmSJbh9+7ZG+c2bN7FkyRJ89tlnRguOiIiILJNegyj9/f01lu3Oz8+Ht7c3GjRoAA8PDyiVSty+fRutWrXCW2+9hfj4eJMFXF6obGxx2LOxVhkREZE10CuBOHLkiKnjsDrpDi4I6j/X3GEQERGZBGeiJCIiIsmYQBAREZFkBk1lnZqaiuPHj0OpVEKtVmttHzVqVKkDIyIiIssl+Q7E3r17ERwcjLNnz0KtVsPW1lbrRURERNZN8h2IyMhIxMbGcuEsIiKiCkxyAmFra8vkQQ+ueTmIPL5do2xm+0HItHcyU0RERETGIzmBcHR0RFZWFlxcXEwRj9VwzFdhwsVfNcoifN5EJphAEBFR+Sd5DMSMGTMwdy7nNyAiIqrIJN+B6Ny5M5KTk9GqVSsEBATA1dVVq86CBQuMEhwRERFZJskJxPfff4/vvvsOH374ITw9PfnUBRERUQUkOYH46quvEB8fX+yS3kRERGT9JI+BsLOzY/JARERUwUlOICpXrgylUmmKWIiIiKickJxAzJ8/Hx999BFUKpUp4iEiIqJyQHJfxIoVK5CUlIQ6deqgQ4cOOp/CiI6ONkpwREREZJkkJxCLFi0yRRxERERUjkhOILy8vEwRBxEREZUjksdAEBEREUm+A3Ho0KEX1unUqZNBwRAREVH5IDmBOHLkiFZZVlYWEhIS4Obmhvbt2zOBICIisnKSE4jw8PBit0VGRqJdu3alCsha5MtscKnKS1plRERE1sCoU0pOnToVoaGhCAwMNGaz5VKqoytef2eJucMgIiIyCaP+SWxnZ4esrCxjNklEREQWyKgJxNOnTzlDJRERUQUguQvj77//1irLzc3FrVu38MUXX+CDDz4wSmBERERkuSQnEAsXLtRuRC5HrVq18Mknn6Bly5ZGCYyIiIgsl+QEYvPmzaaIg4iIiMoRoz6FQf/jrMrF9DP7NMqWteiFbDtHM0VERERkPEwgTMRZnYeIU//WKPuqaVcmEEREZBUMSiBiYmIQExOD+/fvQ61Wi+WCIEAmkyE+Pt5oARIREZHlkZxAfP755zh//jwiIiLg5eUFBwcHU8RFREREFkxyAhETE4O4uDhTxEJERETlhOSJpGxtbU0RBxEREZUjkhMIhUKBzMxMU8RCRERE5YTkBGLu3LlYsGABBEEwRTxERERUDkgeA7Fs2TIkJSXBy8sLPj4+cHZ21qoTHR1tlOCIiIjIMklOIBYtWmSKOIiIiKgckZxAeHl5mSIOIiIiKkeMupw3ERERVQxMIIiIiEgyJhBEREQkGRMIIiIikoyrcZqIABlSHN20yoiIiKwBEwgTeeTkhuqjvzZ3GERERCbBLgwiIiKSjAkEERERScYEgoiIiCRjAkFERESSMYEoQUpKCmQyGWQyGfDV0GevnAxzh0UkSdHzuPCVkpJi7rCIJOF5bHn4FIaJOKrzMOpynEbZpiYByJXbmykiIiIi42ECYSKuqlx8Ff+tRtmOhm2ZQBARkVVgFwYRERFJxgSCiIiIJGMCQURERJIxgSAiIiLJLC6BSEpKwrRp09CkSRO4uLjAw8MDPj4+WL58ObKzs80dHhEREcHCnsLYt28fhgwZgvT0dLEsOzsbp06dwqlTp7Bhwwbs378fDRo0MGOUREREZDF3IM6dO4eBAwciPT0drq6u+OSTT5CQkIDY2FiMHTsWAPDXX3+hV69eyMzMNHO0REREFZvF3IGYPHkysrOzIZfLcfDgQbRv317c1qlTJzRq1AgzZszAlStXsHLlSsybN8+M0RIREVVsFnEH4tSpUzh8+DAAYPTo0RrJQ6GpU6eiSZMmAIBVq1ZBpVKVZYhERERUhEUkELt37xbfv/vuuzrr2NjYYPjw4QCA1NRUMeEgIiKismcRCcSRI0cAAC4uLmjVqlWx9QICAsT3R48eNXlcREREpJtFjIG4fPkyAKBhw4aQy4sPqXHjxlr7FCooKNCqf/XqVSiVSoNiUigUuvfNeaLX/gU5T/D8OnEFz+1brlaSKyerkJar32kZ0XUeG/r/wmhMcD6Z5N++vMRpChb2sxd7Hpvo2lRezydD/28/fvxYq0zX96oGwcxycnIEAAIAoVevXi+s7+LiIgAQ2rVrp1GemJgotsMXX3zxxRdffJXulZiYWOL3sdm7MJ48+d9f5a6uri+s7+LiAgB8lJOIiMiMzJ5A5Obmiu/t7V+81LWDgwMAICcnx2QxERERUcnMnkA4OjqK7/Py8l5Y/+nTpwAAJycnk8VEREREJTP7IEo3NzfxvT7dEllZWQC0uzsaNWqExMREAP8bDFK5cmXY2BiWIykUCoP2IyIiMhdDB1EWFBQgLS0NAODh4QHg2fdqScyeQDg6OopPPNy9e7fEuqmpqWICUadOHY1tcrlcnGiKiIioIqpWrVqZHcvsXRgAxC/+69evQ61WF1vvypUrWvsQERFR2bOIBMLPzw/As+6JP//8s9h6cXFx4ntfX1+Tx0VERES6WUQCERISIr7fvHmzzjoFBQWIjo4G8GxsQ1BQkKRjJCUlYdq0aWjSpAlcXFzg4eEBHx8fLF++HNnZ2QbH/rxt27YhODgYtWrVgqOjI+rVq4dhw4bhxIkTRjsGVVymPI8jIiIgk8n0enEqeZLq4cOH2Lt3L+bNm4cePXpAoVCI59PIkSNNckxej02sVLNAGZG/v78AQJDL5UJCQoLW9qVLl4qTW4SHh0tqe+/evUKlSpWKnSzjlVdeEW7cuFGq+HNycoTevXsXewwbGxthwYIFpToGVWymPo/Dw8P1nmDmt99+M94PRhVCSefTiBEjjHosXo/LhsUkEKdPnxacnJwEAIKrq6uwePFi4fjx48KhQ4eEcePGif/w3t7eQkZGht7tnj17VnB2dhbb/eSTT4SEhAQhNjZWGDt2rNhu48aNhSdPnhgc/zvvvCO2FRQUJOzevVs4efKksHHjRuHll18Wt61fv97gY1DFVRbncdEE4sKFCyW+MjMzjfwTkrUr+gVep04doVu3biZLIHg9LhsWk0AIgiDs2bNHcHd3LzZr9Pb2Fq5duyapzcDAQL3vbMyfP9+guA8fPiy20adPH0GtVmtsT0lJEerWrSsAEKpUqSKkpqYadByquMriPC6aQBAZ27x584Sff/5ZuH//viAIgnDz5k2TJBC8Hpcdi7tS3Lp1S/jwww8Fb29vwdnZWahcubLQunVrYcmSJUJWVpaktk6ePCmeSOPHj9dZJz8/X2jSpIl4MuXl5UmOuWfPngIAwdbWVrhz547OOlu3bhVjWb58ueRjUMVVVucxEwgqS6ZKIHg9LjsWMYiyKC8vL6xcuRJ//fUXsrKykJqailOnTmHGjBlwdnaW1Nbu3bvF9++++67OOjY2Nhg+fDiAZ/NMSB0clpmZidjYWABA165dUbt2bZ313nzzTbi7uwMAdu3aJekYVLGVxXlMZA14PS5bFpdAGNORI0cAPFuAq1WrVsXWCwgIEN8fPXpU0jFOnjwpTq9dtJ3n2dvbo127duI+KpVK0nGo4iqL85jIGvB6XLasOoG4fPkyAKBhw4aQy4ufdLNx48Za+0g9xvPtlHQctVqNa9euSToOVVxlcR4/r2vXrqhatSrs7e1RvXp1BAYGIjIyEqmpqaVql8iUeD0uW1abQOTm5opzghd3G6tQlSpVxGXC79y5I+k4Reu/6DhFp9+WehyqmMrqPH7er7/+isePH0OlUiElJQVxcXGYNWsWGjRogJ9++qlUbROZCq/HZcvsa2GYypMnT8T3zy+8pYuLiwuysrL0WtDL0OMUXtwB/RYOIyqr87hQ06ZNERISAh8fH3h6ekKlUuGvv/7C999/j4MHDyItLQ2hoaH4+eef0aNHD4OOQWQqvB6XLatNIHJzc8X39vb2L6zv4OAAAMjJyTHZcQqPYchxqGIqq/MYACZPnoyIiAit8rZt22L48OFYu3YtwsLCkJ+fjzFjxuD69etwcnKSfBwiU+H1uGxZbReGo6Oj+D4vL++F9QsH3ki9IEo5TuExDDkOVUxldR4Dz6aIL8n48eMxZswYAMC9e/c4ep0sDq/HZctqEwg3NzfxvT63pwqXCdfnNrGhxyk8hiHHoYqprM5jfY0fP158X3RxOyJLwOtx2bLaBMLR0REKhQIAcPfu3RLrpqamiidT0YE1+ig6UOdFxyk6UEfqcahiKqvzWF+vvvqq+D45OdkkxyAyFK/HZctqEwgAaNKkCQDg+vXrUKvVxda7cuWK1j76KnpBLdpOSceRy+Vo2LChpONQxVUW57G+BEEwSbtExsDrcdmy6gTCz88PwLNbVX/++Wex9YreivX19ZV0jDZt2oiDdUq6pZuXlycuIVt0H6IXKYvzWF+JiYnie09PT5Mcg8hQvB6XLatOIEJCQsT3mzdv1lmnoKAA0dHRAJ4NIgsKCpJ0DDc3N3Tu3BnAs2fni7tttmvXLmRkZAAA+vfvL+kYVLGVxXmsr7Vr14rvS5rpj8gceD0uY+ZejMPU/P399V7FMDw8XGv75s2bS9wuCIIQGxsr1unbt2+Jq79VrlxZePz4sTF+NKpATH0enz9//oUr3X7zzTdiGzVr1uSS3lQqhiymxeuxZbHaeSAKrV69Gr6+vsjJyUG3bt0we/ZsBAUFIScnB9u2bcO6desAAN7e3pg6dapBx+jUqRMGDx6Mbdu2Yc+ePejatSsmT54MT09PXLhwAZ988gmSkpIAAJGRkahSpYrRfj6qGEx9Hv/5558YM2YMgoKC0KNHDzRt2hRVq1aFWq3GlStX8N133+GXX34BANja2mLt2rUaE/EQvcjRo0dx/fp18XPhDKvAs/E9UVFRGvVHjhxp0HF4PS5D5s5gysKePXsEd3d3MSt9/uXt7V3sX1/6ZLyCIAjZ2dniMrK6XjY2NiXuT/QipjyPi24v6VW1alVh9+7dJv5JyRqNGDFCr3Os8KULr8eWxervQABAnz59cP78eaxevRr79u3D3bt3YW9vj4YNG2LAgAGYOHGi5KXCn+fk5IR9+/bhhx9+QFRUFM6dO4e0tDTUqFED/v7+mDhxItq3b2+kn4gqIlOexz179sTGjRtx/PhxnDlzBg8ePMCjR48gCAI8PDzwxhtvoHv37hg5cqS4DDKRpeL1uGzIBIHPZREREZE0Vv0UBhEREZkGEwgiIiKSjAkEERERScYEgoiIiCRjAkFERESSMYEgIiIiyZhAEBERkWRMIIiIiEgyJhBEREQkGRMIIiIikowJBBmdn59fmR/z8OHDmDt3rkH7pqWlYdeuXUaOqGRz587F4cOHxc9RUVH4888/NeoIgoApU6YgICAAQUFBuH//fonlFdXkyZORn59v7jAMFhkZieTkZKO2ef36db1Ws3zllVcQGBiIIUOG6N22Of5/P+/dd99FYGAgGjZsaO5QKrQKsZgWUUkKE4g333zTbDHoutjv3r0bdnZ2iIuL06u8olq1apW5QyiVmTNnmu3Y1apV00hky4vNmzcDsIxkpiLjHQgiC5WYmIhOnTrpXU5EVJaYQJBesrOzsXLlSnTp0gUBAQHo0qULxo8fj4cPH+qsn5mZibCwMPj5+aF169YIDw/Xus2cl5eHadOmoUOHDggICEDnzp0xduxYrbaOHDmCTp06ITAwEB06dMDkyZORm5v7wphHjhyJ69eva5VHRETg119/BfDsy3jw4MH45ZdfEBgYiMDAQMyYMUOj/oEDBxAYGIh27dqhbdu2ePvttyV1G8TGxsLf3x++vr4IDg7GyZMnS4wJAHr37o3169djxowZGjEVVw4869pYuXIlfHx84OvrCx8fH8ycORNqtVrr93L16lUsXboUQUFB8PX1xZ49ewAA6enpmDBhAlq1agU/Pz+0b98e0dHRWvH6+flBqVTi/fffR1BQEAIDA/Hee+8hKytLq+7BgwfRq1cv8d+4R48e2LRpk0ade/fuYdiwYWjdujV8fX3h7++PAwcO6PX7DQwM1PgZb926haFDh+LcuXMYMGAAgoKC4O/vr/ediqioKGzYsAEHDhxAUFAQ2rdvj27duuHs2bM660dHR6NDhw7o0KED2rZti3HjxiEzM1OjTuG/76ZNm9C5c2d07NgR33zzDQDd52lubi6mTJkCX19fBAYGIigoqNg7TikpKRg2bJj47758+XK9fk59fP7552jTpg38/f0xZMgQned9bGwsBgwYgI4dOyIoKAi9evXSuqvxf//3f8V2MX799dca58PFixfRp08f+Pv7IygoCN26dcOOHTuM9jOREQlEerhw4YJw6tQpjbJz584JgYGBglqt1ij39fUVpk6dKpw+fVoQBEEoKCgQVqxYIUyYMEGj3scffyx88cUXGmV3797V+Hz69GnB19dXePDggVgWFRUlDBgwQKPeb7/9JsyZM0ejbMSIEcK1a9e0fpbw8HDhl19+ET/fvHlTGDJkiM6f++effxZ69uwp3Lt3TyyLi4sTunbtqrP+8+Li4oQePXoI9+/fFwRBEJ48eSK8//77wsiRI4Xffvut2JiKKyupfPr06cKMGTOEnJwcsWzp0qXC3LlzNeqNGDFCePfdd4WDBw9qlKvVaiEoKEj44YcfxLKsrCzhrbfeEn799VeNuu3atRMmTpwo3LhxQyyLiooS3n//fY16P/74o9CvXz9BqVSKZQUFBUJ8fLz4OSMjQ/Dx8dH4mZRKpRAUFCRcuXJF6+d8XkBAgKBSqcTPN2/eFPz9/YWpU6cK6enpgiAIQn5+vjBq1Chhx44dL2xv8+bNQlhYmDBnzhwhLy9PEARBuH79uhAQEKB1Pn311VfC8OHDhdTUVLFs27ZtwrBhwzTqhYeHCwMHDhS+++47rePpOk8HDhwobNy4Ufz88OFDwd/fX/jzzz816uXm5gqBgYEav7tt27YJM2bMEEaMGPHCn9XX17fYbYsXLxamTp0qPH36VBAEQbhx44Ywbtw4oX379hr1fvjhB7GOIDz79xw8eLBw5MgRsaygoEDw9fUVf59FdezYUcjKyhI/t27dWrh+/bpGneevC/rET6bHBIJKZcGCBUJCQoJGWcOGDTW+HAsNHz5c40LZt29fjQuvLqGhocIff/yhVd6vXz/h7Nmz4mdTJRDt27fX+PIrNG3aNOHEiRMlxi4IghAcHCykpaVplKnVasHb29uoCYRSqdS6sBfq0qWLkJubK34eMWKE8Nlnn2nV++mnn4SpU6dqlSuVSiEkJESjzMvLS7h8+bJGWUFBgdCyZUuNMh8fH43kT5fVq1drJZKCIAgXL14UwsLCStxXEHQnEC+99JJGmSAIwpUrV4TBgwe/sL3NmzcLnTt31iq/du2aRmKgVquFZs2a6fxSHDRokMaXXnh4uDBp0iSdx3v+PD1//rzQp08frXqnT58W+vfvr1G2bt06jUSj0MKFC0uVQKSlpQnBwcFa5Tdu3BDs7e1f2G5SUpIwbtw4jbJly5YJW7du1Sg7cuSIxu/l8ePHQr9+/V7YfiEmEObFLgzS261btzBp0iQEBASIt/u/++473L59W6OeXC5Hx44dtfbv1q0bDh06JH4eNWoUlixZonW7t6gbN26gVatWWuV9+/ZFfHx8KX6aF8vKyoJcLkfVqlW1trVp06bYW9qFsrOzIZPJUKlSJY1yW1tbBAQEGDNUnDx5Ep07d9a5rVGjRrh165ZGma7BZ8eOHUOPHj20yqtWrYqMjAyNMg8PDzRu3FijTCaTwd7eHoIgAACePHkCBwcHVK9evcTYizvua6+9hmvXrpW4b3F8fHwgl2uOEffy8sI///yj1/66fpcNGzbU+D1evXoVzZo1g52dnVbdli1b4uLFixpl+g74i4+PR9++fbXKW7RogZs3b2qUxcbGonv37lp1SztG5vjx4zr/Dzdo0ABeXl4aZSqVCl9++SW6d+8udmMMHTpU67owatQocfBjoW+++QZhYWHi5ypVqqBGjRr4z3/+U6r4qWzwKQzSy82bN/HOO+9g2bJlWL16tVj+1VdfaY1HqFy5MmxstHPTqlWralyA+/Xrh2bNmmHDhg148uQJnJycMGLECFSrVk2s4+zsrDMeT09P/P7776X8qUqWlpaGK1euIDAwUGtbbm4uBg8eXOL+jx8/RuXKlXVu8/DwMEKEmsfavn07jhw5orUtLS0Nw4cP1yh7PqkpbGPWrFk6f+cPHjxAQUGB+O9a3L+Lg4MD8vPzIZfLkZ6ejho1augV+7vvvqvznClMRqTSFZ+jo6PWeJDiFPfvUzSex48fIy4uTuf5kZmZiQYNGmiU6fqd65KRkYGmTZvq3Obo6KjxWalU6kxwS3t+FdeurrYHDx6MgIAA7Nq1S/y9Z2VloVevXlr71apVCxcvXsTrr7+OlJQUPHr0SCsRXbt2LX755Rd8+umnePr0KZo0aYIBAwboPD/IvJhAkF7WrVuHjz/+WOuvqEuXLsHHx0ejLC0tDfn5+bC1tdUof/ToERQKhUZZ/fr1MXnyZADAw4cP8cEHH2DFihXw9PQEgGIvGvfu3XvhRVIul+scbKlroJ8u7u7u8PPzM3iOCA8PD6Smpurc9vjxY4PaLI67uzsmTpyIDz74QK/6MplMZxsbNmxAs2bNjBJTpUqV9Bps6u7ujs2bN8Pd3d0oxzWGR48e6Swv+ntzd3fHW2+9hZUrV+rVpq7fuS4vvfRSsXdKnj+fFQoFHj16JP5/KVTa80uhUCApKUnntqJt//XXX7C1tdU67y5duqRz3/feew9r1qzBmjVrsHHjRowaNUpnva5du6Jr164AgLi4OLz33ntYu3atIT8KmRBTOtJLVlYWatasqVGWnZ2t8xlytVqts3vhP//5T4m3VqtXr44OHTrg6NGjYlndunVx+vRprbp79uyBv79/iTHXrFlT6zYqACQkJGh8dnR01JlUuLm54enTpwZPUuTs7AxBEJCenq5Rnp+fb/Q5HNq2bYs//vijVG34+fnh+PHjRoro2e8vLy+v2Cd1THVcY4iNjdUqu3btGurVqyd+btKkCa5evWr0Y/v4+GD//v1a5WfOnNE4PvCsq0LX0yq64peiffv2Os/RGzduaNxF1HVdAIB9+/bpbLdt27a4fPky0tPTsWfPHoSEhLwwloCAACQmJuodO5UdJhCkl4CAAGzYsEH8Mn369CmmT5+OQYMGadWtUaMG9u3bJ44REAQBn332GVxcXNCoUSOxXmxsrMYt5cePH2Pr1q144403xLK5c+di0qRJSElJEcuio6Mhl8vRvHnzEmPu378/Pv/8c/Gvtry8PHz66adwdXXVqFezZk3cuHFDZ397WFgYxo0bB6VSKZYJgoBjx44Ve3ehqFmzZmHw4MHil2hmZiY++OADdOjQ4YX7SlG9enV4enpi1apVyMvLE8tzc3Pxyy+/6NVG3759ceDAAa0vL6VSafAX/PTp0zFu3DiNv1oFQcCZM2fEz6NHj8bq1au1Ers7d+5o1CtL3t7emDt3LlQqFYBnX5yjR4/Gxx9/LNaRy+Xo0aMHZs2apTGOR61W4+DBgwYnno0bN4abmxu+/fZbsUypVOKDDz7AnDlzNOoOHz4c0dHRGo8Ab9++vcRxRfqoVKkS/P39MW3aNPF3cPPmTSxduhStW7cW6zVp0gQnTpzQuGPy008/aXRDPm/o0KEYNmwYOnfurDV+5MGDB1r/5ocOHdI5zoTMj10YpJfQ0FBcu3YNfn5+cHJygouLC2bPnl3srcrw8HBMnz4dFy9eRE5ODnr27Ikvv/xSo87NmzexcOFC5OTkwNbWFvn5+Zg4cSJeeeUVsU6TJk2wcOFCDBw4EIIgIC8vD61bt8aWLVteGHOrVq0wevRodO3aFXK5HC4uLpgyZQqePn2qVXft2rUYM2YMBEFAu3btsHTpUgBAnz59YGdnh3feeQepqalwd3eHs7MzOnXqhJYtW74whsDAQOTl5SE0NBQFBQVwdXXFwoULxXkXjOnTTz/FF198gaCgIOTn58PNzQ0eHh5466239Nrf1tYW0dHRmDdvHiIiImBvbw8XFxfUrVtX5/wc+njrrbfg5uaGoUOHIjs7GzY2NrCzs0Pv3r3RokULAM+6AjZu3Ig5c+Zg0qRJcHR0hIuLC7y9vfH+++8bdNzSatWqFV566SV069YNubm5cHV1xapVqzQSYACYMGECvv/+e/Tu3RvZ2dlwd3eHm5sbevbsWao++7Vr12LmzJlYu3Yt7O3tAQALFizQ+PIGnt0927FjB6ZOnYrZs2fD3t4eISEhGDt2LBYtWmTw8YFnyfuqVavQvn17ODo6om7duli5cqXG+eTk5ITVq1fjnXfegSAIsLe3R6dOnTBt2jTs3LlTZ7tvv/02pk2bpnU9AAB7e3usW7cOp0+fhlwuh0qlgkKhwPr160v1s5BpyARDRykREVmhqKgoqNVqjBkzxtyhmJyfn59Gl2FZuHbtGmbMmIF///vfpW7LHPHT/7ALg4iogpLL5ZIX0yqNvLw8fPDBB5g1a1ap2ilcTEvfgalkGuzCICKqoMpqIa3MzEyxm2fixIlaT25J9fx8EmQe7MIgIiIiydiFQURERJIxgSAiIiLJmEAQERGRZEwgiIiISDImEERERCQZEwgiIiKSjAkEERERScYEgoiIiCRjAkFERESS/T9WxRTBFgsUrgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "period_difference = np.abs((1.0 / LSfreq_dia) - (1.0 / LSfreq))\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "plt.hist(period_difference, bins=20)\n",
    "plt.axvline(1.0 / 24.0, ls='--', color='red', label='1 hour')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('absolute difference in period [days]', fontsize=12)\n",
    "plt.ylabel('number of candidate RR Lyrae', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f797101-397f-4332-8cdc-2d355956b729",
   "metadata": {},
   "source": [
    "Above, we can see that only 5 of the 14 candidate RR Lyrae (which have not been confirmed to _be_ RR Lyrae) have\n",
    "periods that change by less than 1 hour when the incomplete `DiaSource` photometry is used instead of `ForcedSourceOnDiaObject`.\n",
    "\n",
    "> Note: because of the source association issue pointed out at the end of Section 3.1, there are two `DiaObject` IDs from\n",
    "the search that correspond to the same candidate.\n",
    "This means that the fraction of unique RR Lyrae stars with different periods should actually be _greater_ than ~70%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a6fd31-0176-4250-8831-9aeb7c341c23",
   "metadata": {},
   "source": [
    "## 4. Exercises for the learner\n",
    "\n",
    "There are many further explorations one could try as extensions of this notebook. Some examples:\n",
    "\n",
    "1. Experiment with ways to identify periodic signals in the data.\n",
    "2. Refine the selection criteria for finding candidates.\n",
    "3. Compare results from candidate search to the true star lightcurves from the DESC truth tables.\n",
    "\n",
    "...and many more. Have fun!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LSST",
   "language": "python",
   "name": "lsst"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}