DP02_07a_DiaObject_Samples.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><b>DiaObject Sample Identification</b> <br>\n",
    "Contact author: Melissa Graham <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": "9da1a210-d858-42fe-8591-570965b8be1a",
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   "source": [
    "**Description:** To use the DiaObject table parameters to identify a sample of time-variable objects of interest, and to investigate the forced photometry lightcurves of a specific object of interest with the ForcedSourceOnDiaObject table. **Adapted to use the dp02_dc2_catalogs_frdf catalogs produced at CC-IN2P3.**"
   ]
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  {
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   "source": [
    "**Skills:** Use the TAP service and the DP0.2 DiaObject and DiaSource tables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393da88f-7978-4920-aa4a-a9830df6eed9",
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   "source": [
    "**LSST Data Products:** TAP tables dp02_dc2_catalog_frdf.DiaObject, DiaSource, and ForcedSourceOnDiaObject."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c67fab9-136a-4adc-bb42-142b91ab69dd",
   "metadata": {},
   "source": [
    "**Packages:** lsst.rsp, astropy.cosmology, astropy.stats, numpy, matplotlib"
   ]
  },
  {
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   "id": "8f72b27f",
   "metadata": {},
   "source": [
    "**Credit:** Originally developed by Melissa Graham and the Rubin Community Science Team for Data Preview 0. Updated for ForcedSourceOnDiaObject by Ryan Lau."
   ]
  },
  {
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   "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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   "source": [
    "## 1. Introduction\n",
    "\n",
    "This notebook demonstrates how to use the DiaObject table's lightcurve summary statistics to identify samples of objects for further study, and how to retrieve and plot lightcurves for DiaObjects of interest from the DiaSource table.\n",
    "\n",
    "As an example, a sample of potential low-redshift Type Ia supernovae are identified and retrieved from the DiaObject table.\n",
    "\n",
    "> **Notice:** This notebook does not produce a pure or complete sample of SNIa, just a sample of *potential well-sampled low-redshift* SNIa. This is decent enough to use as a demonstration, or a starting point for a more rigorous classification process.\n",
    "Different science goals will have different requirements on sample identification.\n",
    "\n",
    "There are three different types of catalogs built from the difference images:\n",
    "1. `DiaSource`: astrometric and photometric measurements for sources detected with signal-to-noise ratio > 5 in the difference images.\n",
    "2. `DiaObject`: derived summary parameters for `DiaSources` associated by sky location, including lightcurve statistics.\n",
    "3. `ForcedSourceOnDiaObject`: point-source forced-photometry measurements on individual single-epoch visit images and difference images, based on and linked to the entries in the `DiaObject` table.\n",
    "\n",
    "Learn more about the contents of the DiaObject, DiaSource, ForcedSourceOnDiaObjects tables in the <a href=\"https://dp0-2.lsst.io\">DP0.2 Documentation</a>.\n",
    "\n",
    "This notebook uses the Table Access Protocol (TAP) service to access the catalogs, but they are also available via the Butler."
   ]
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    "### 1.1. Package Imports\n",
    "\n",
    "**lsst.rsp:** The LSST Science Pipelines package for RSP functionality such as the TAP service (<a href=\"http://pipelines.lsst.io\">pipelines.lsst.io</a>).\n",
    "\n",
    "**astropy.cosmology:** An open-source package of cosmology tools (<a href=\"https://docs.astropy.org/en/stable/cosmology/index.html\">the astropy cosmology documentation</a>).\n",
    "\n",
    "**astropy.stats:** An open-source package of astronomy-related statistical tools, used in this notebook to look for outliers in the forced-source lightcurves."
   ]
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   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.cosmology import FlatLambdaCDM\n",
    "from astropy.stats import sigma_clipped_stats\n",
    "\n",
    "from lsst.rsp import get_tap_service"
   ]
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   "source": [
    "### 1.2. Define functions and parameters"
   ]
  },
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   "source": [
    "Start the TAP service."
   ]
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   "source": [
    "service = get_tap_service(\"tap\")"
   ]
  },
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   "source": [
    "Set the cosmology to use with the astropy.cosmology package."
   ]
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   "source": [
    "cosmo = FlatLambdaCDM(H0=70, Om0=0.3)"
   ]
  },
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   "source": [
    "Set a few parameters to use later, when plotting lightcurves."
   ]
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   "source": [
    "plt.style.use('tableau-colorblind10')\n",
    "\n",
    "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'}"
   ]
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   "source": [
    "## 2. Understand the DiaObject table's contents\n",
    "\n",
    "**DiaSource Table:** Measurements for sources detected with a signal-to-noise ratio > 5 in the difference images (i.e., lightcurves).\n",
    "Note that these measurements are not forced photometry at the locations of all DiaObjects, but detections only.\n",
    "\n",
    "**DiaObject Table:** Summary parameters for DiaSources associated by coordinate (including lightcurve summary statistics).\n",
    "\n",
    "Option to print all of the available column names for the DiaObject table."
   ]
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    "# results = service.search(\"SELECT column_name from TAP_SCHEMA.columns \"\n",
    "#                          \"WHERE table_name = 'dp02_dc2_catalogs_frdf.DiaObject'\")\n",
    "# for column_name in results['column_name']:\n",
    "#     print(column_name)"
   ]
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    "### 2.1. Review these descriptions for selected lightcurve summary statistics\n",
    "\n",
    "The DiaObjects table includes the following lightcurve summary statistics, which are derived from the contents of the DiaSource table.\n",
    "\n",
    "**nDiaSources:** The number of difference-image detections in any filter (i.e., number of DiaSources associated with a given DiaObject).\n",
    " \n",
    "The following statistics are all based on _difference-image point source (PS) flux values_ for each filter [f].<br>\n",
    "**[f]PSFluxMin:** The faintest flux. <br>\n",
    "**[f]PSFluxMax:** The brightest flux. <br>\n",
    "**[f]PSFluxMean:** The average flux. <br>\n",
    "**[f]PSFluxSigma:** The standard deviation of the fluxes. <br>\n",
    "**[f]PSFluxMAD:** The mean absolute deviation of the fluxes (i.e., the average distance from the mean). <br>\n",
    "**[f]PSFluxChi2:** The Chi2 statistic for the scatter of the fluxes around the mean. <br>\n",
    "**[f]PSFluxNdata:** The number of data points used to compute [f]PSFluxChi2. <br>\n",
    "**[f]PSFluxSkew:** A measure of asymmentry in the distribution of fluxes about the mean (where 0 means symmetric). <br>\n",
    "**[f]PSFluxStetsonJ:** A variability index developed for Cepheids (defined in <a href=\"https://ui.adsabs.harvard.edu/abs/1996PASP..108..851S/abstract\">Stetson 1996</a>). <br>\n",
    "**[f]PSFluxPercentile05, 25, 50, 75, 95:** Derived from the cumulative distribution of flux values. <br>\n",
    "\n",
    "The following statistics are all based on the _direct-image total (TOT) flux values_ for each filter [f]. <br>\n",
    "**[f]TOTFluxMean:** The average flux.  <br>\n",
    "**[f]TOTFluxSigma:** The standard deviation of the fluxes.  <br>\n",
    "\n",
    "> **Notice:** The DP0.2 DiaObject table is missing some variability characterization parameters (<a href=\"https://dmtn-118.lsst.io\">DMTN-118</a>) and host association parameters (<a href=\"https://dmtn-151.lsst.io\">DMTN-151</a>) which will exist for future data releases."
   ]
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    "### 2.2. Plot summary-statistic histograms for a random sample of DiaObjects\n",
    "\n",
    "In order to learn a bit more about these lightcurve summary statistics, histograms of their values for a random selection of DiaObjects are plotted.\n",
    "\n",
    "First, retrieve a random sample of DiaObjects. This query takes about 1.5 minutes."
   ]
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    "results = service.search(\"SELECT TOP 100000 \"\n",
    "                         \"ra, decl, diaObjectId, nDiaSources, \"\n",
    "                         \"rPSFluxMin, rPSFluxMax, rPSFluxMean, rPSFluxSigma, \"\n",
    "                         \"rPSFluxMAD, rPSFluxChi2, rPSFluxNdata, rPSFluxSkew, \"\n",
    "                         \"rPSFluxStetsonJ, rPSFluxPercentile05, rPSFluxPercentile25, \"\n",
    "                         \"rPSFluxPercentile50, rPSFluxPercentile75, rPSFluxPercentile95, \"\n",
    "                         \"rTOTFluxMean, rTOTFluxSigma \"\n",
    "                         \"FROM dp02_dc2_catalogs_frdf.DiaObject \")\n",
    "DiaObjs = results.to_table()\n",
    "del results"
   ]
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    "**Plot the number of DiaSources per DiaObject**\n",
    "\n",
    "Plot the distribution of the number of DiaSources per DiaObject (i.e., the total number of difference-image detections in any filter; at left), and the distribution of the number of r-band DiaSources per DiaObject (at right).\n",
    "\n",
    "Notice how the distribution is peaked at small numbers of DiaSources (or r-band detections) -- these are time-variable sources that were only detected in a few difference images, and are most likely to have a faint time-variable flux (e.g., high-_z_ supernovae that were only detectable for a short duration)."
   ]
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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].hist(DiaObjs['nDiaSources'], bins=50, log=True, color='gray')\n",
    "ax[0].set_xlabel('nDiaSources')\n",
    "ax[0].set_ylabel('log(Number of DiaObjects)')\n",
    "\n",
    "ax[1].hist(DiaObjs['rPSFluxNdata'], bins=50, log=True, color=plot_filter_colors['r'])\n",
    "ax[1].set_xlabel('rPSFluxNdata')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360c31db-1ac4-45c7-b0b6-499d5d99292c",
   "metadata": {},
   "source": [
    "**Plot distributions of min/max PS fluxes**\n",
    "\n",
    "Plot the distribution of minimum and maximum r-band PS flux from the difference-image detections.\n",
    "\n",
    "Notice how the PS fluxes can be negative because they are measured on the difference images.\n",
    "Any source that is fainter in the direct image than in the template image will be detected as a negative-flux source in the difference image. \n",
    "\n",
    "> **Warning:** Take care when converting difference-image fluxes into magnitudes, because they can be negative.\n",
    "\n",
    "Notice how these distributions are peaked at low time-variable flux components -- likely the same types of faint sources which were only detectable for a short duration (as shown above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b119d72-6caa-4c7c-b122-d834d58b1314",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:40.273666Z",
     "iopub.status.busy": "2024-04-29T13:55:40.273446Z",
     "iopub.status.idle": "2024-04-29T13:55:41.092767Z",
     "shell.execute_reply": "2024-04-29T13:55:41.091984Z",
     "shell.execute_reply.started": "2024-04-29T13:55:40.273646Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].hist(DiaObjs['rPSFluxMin'], bins=100, log=True, color=plot_filter_colors['r'])\n",
    "ax[0].set_xlabel('rPSFluxMin')\n",
    "ax[0].set_ylabel('log(Number of DiaObjects)')\n",
    "\n",
    "ax[1].hist(DiaObjs['rPSFluxMax'], bins=100, log=True, color=plot_filter_colors['r'])\n",
    "ax[1].set_xlabel('rPSFluxMax')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c40eb33-9331-4365-9dde-85bfa1e178e2",
   "metadata": {},
   "source": [
    "**Confirm that DiaObjects with few DiaSources have min/max PS fluxes close to zero**\n",
    "\n",
    "Plot the correlation between the number of r-band DiaSources per DiaObject, and the minimum (left) and maximum (right) difference-image PS flux values. \n",
    "\n",
    "Notice that it is, indeed, the fainter DiaObjects that have fewer DiaSource detections, as hypothesized above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "862a4ba2-cff1-42a8-9676-a5625a90d74a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:41.094011Z",
     "iopub.status.busy": "2024-04-29T13:55:41.093793Z",
     "iopub.status.idle": "2024-04-29T13:55:41.430623Z",
     "shell.execute_reply": "2024-04-29T13:55:41.429787Z",
     "shell.execute_reply.started": "2024-04-29T13:55:41.093991Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LuPvuu/Hggw/iJ3/yJ/Hd734Xn/70p/HpT38aAGlq9913Hx588EEcP34cx48fx4MPPohgMIi3ve1t21l1AwODaxHr0Z7tVrSNgufgsNLSZL5QX6Az16nsQzthN4DiNo06c3GPTfMH4OdwrPfuhMFAMPbawGCLMIhN2cguq7Fd249Bn8GodtW3oxy7tlaKVIB3WCTGAuFrMkxiW53uf/JP/gn+7M/+DB/+8Ifx7/7dv8Phw4fxyCOP4Kd/+qevnvOhD30I5XIZ733ve5FOp3HXXXfh61//OiKRyDbW3MDA4JrEMGqcRsFzcFhpaTLXqVWkTfbh+Az/1mO6AXtxG+u1BiOBsdcGBluErbIpxnZtP66nZ2DX1j1Hgfz37I9fg2ESjna73V7/tN2LXC6HWCyGbDaLaDS63dUxMDDYyTjzFHdarXA6geN3jO4ag06aWfI8KeIBzTlLLQKL54D4JCnjk7Okk0tht0lBFV88b9//lSIF0y6f5S66ywUk9irq+Q7H9Wq7rtd2GxhcxVbZFGO7th/6MyjnqV1Sr3CX947Xd3e816Ok75SwAb0ei8LOA0q8zesHmg3A4QCWLgIeH3DwFuDGV+6qRYd+7da27nQbGBgY7CgMo8ZpFGSHg05Ls/ZhapGTEa8PaDaB3Cp/bvuBtVRxu/6Xk5dSDjj9lDouywJ2heNtYGBwHWKrbIqxXdsP+QzK+U4mV6vdnWa+HiV9p4QNWOvRbgPzQhdEipvKRYFX/vCucrKHxbbm6TYwMDDYUehGZ+pFcxrmGoNOWPtq4Rx/h2Odxy+cXP9agA53NAFcPtd5XKqk2pVjYGBgsBOwVTbF2K7th+xra3YOGTplR8nuRUnv5/utgvV+sQRtsFWtPJbY+rptE8xOt4GBgQGgaFCVIpUzA2HSk9ejZQ2i4LlTKF87DdY+bNVIB790RuXiTuzjsfWu9QVISW+1gOQ5oFalwnkgTAobYJ+ixMDAwGAnYFTq0oPep1Hn8cXzu88+bbVtTc7ZZ8wYFPIZXJmjffL66XDLNF52Ct7dVL3l8fW+HxaD9rH1foEIEBkDihnA6aA4aizB49egUrkdjNNtYGBgoNOg/CFFferXcPej4LlTKF87FXofvvQ08Px31XelIlA6DUTi618LAMUcMH8acLmBdhWo14F6GkiIfNChmF0pBgYGBjsDW5X7Wd5Ht092GSR2MrbatibngGf+Xn3OrarPwzreh27pn+q/XljAZoQNDNPHdvWIjPNn5tDo6raLYOjlBgYGBltBx9oplK/rCRP77I8fOrG19TAwMDDYydjN9mmr694tPGkjYUuDUP3XO3czwgaG6WO7+0UTa7OUdDv3GoTZ6TYwMDDol461EQrbZlG+RoWdRH33+oEDN1KpvFoCfEGujudTFFhbr361CtBuUhU1EgfqVbIXomMUYzMiagYGBgYK1TJQyneqSkcTVDLf6dhq2yrDk6pFoJAFGjXA7aXqeL+ws7f9hhSsF36wGeEJw/SxXT32HeV3/dZtJ81LRgDjdBsYGBj0Q8faKIVtJyvF7jTqeyhGpfHYBD9Xi8DqEuD0rk99LGSAzDLgcAHje/gDAFOzNNjG4TYwMDDoRKPO1IoS1TI/+4PbV6d+sdW2NRQDludpkyRqVaBcov3ZSKjZgZv6q8N64QejDk8Yto+71aOfuu20eckIsAuWsAwMDAw2Gf3QsTZKYdvJSrE7jVpopX8XxM7C3iOdx7spu0Zt6GvZ1M7oawMDAwOD0WGrbeuhE8om6dh7pD+budPsbT/YjvnLbuyndWB2ug0MrhdcYzSdkaIfOtZGKWzr3WM7n892Ud+7tVnuRkt1WK8fOHgLkNizfv2qZSq/Ts52UiXjk0owyPwfGBgYGCi4PWQDZVOkSUtlaben87zNHD+HLXur1N4lZg7S8b58lvm1AxE63Ik9/dnMYe3tdtqure5jgPcp59e+k7sh5KELjNNtYHA94Bqk6Ywc69GxRkFh63aP7X4+20F9X6/NMweV833xxcGVXYMRlXYFAAKh7e9nAwMDg50IX4ChO4HI2uMSmzl+brTsrVJ7l9h31H6Xtx+bOYy93Qm2a6v7uFEHliwhD0uXgP27IOShC4zTbWBwPaAXTcc4G/2tII/PdBo9/fhGsd3PZ5i2bWTVvZABnnscKKQ7c3UCqs1nnwFOfhvIr3KnenwPMHt8/frZtaWU5z3/8RtAtQKMTQPTB5RTbv4PDAwMrmf0YwM2004NW/agdmiQ83vl4t7IfKDbtd4AF5jt6mbXP6U87Whiz/aytrZyB75aBJIXyMDYhUy13btHb2Bg0D92unL2dkKuIJeLnSJdhUzneZJeFQiR3hQIjW6Vebufz6Bt67fPel2bTwOttlq9Luf5fbVMh/ubfwGkl4BGAygVgHPPUNhnvfpZ29JqAuUCkFrk5KlWAa7MAfOnOGmR9zQwMDC4XtGPDdhMOzVM2YPaoUHOl7m4c6sU9ZS5uJNz/H4j8wG7a2MTQHale92s/VDK0x4W0oPb4FFiI3OB9eD2MEzMFwAcDqDdAtrgDvh2tnkDMDvdBgbXA3aycvZ2Y5AV9s2iV+2E5zNI2zay4yGv9fo7JxLZFHe7fQHg6cfWXucN8NrXvWX9+ultufgisDTPv91eqswCdMBzKe52m/8DAwOD6x1bEWI1yrIHtUODnN8rF7fc7d7IfMB67cUXe9fN2j+5FH97/PbnbxU2k/0gQx4kIy15AXA4OXcY9b22CMbpNjC4HrCZ1OjdCJ0OtXieOaD1+F+gv9X7UdGqdsrz6bc9G9nxkOdEE50pamSO0/EZUsoBoF7mLnezDrg83A3vRr/r1pYzTzHfdzAKhGMqzUu9xl1veU8DAwODnYCdKva4mXZKlm0Vzrrxzu7XDGqHBjletFEn73V8WOh2yu1VoVayHxo1nue1ON3SdsUsmTq2mrVlvZ+13ht5d63vm5wjRBOdOeV30v/IOjD0cgOD6wGbSY3ebbDSodptOn+Saiyx3ur9KGlVO+H5DNKebn3Tr4gMoBTGJXUsPKbaHBmnw51NA/U60AKfT2aFE5T16qe3xe0ljV062+NTgNfHn8jY9ft/YGBgsPOwmXTdjWIz7VQ4LijWwpHy+OlcZVe6t31QOzTI8VDM/txux4eB1U7JUKvUIn9XyzxeLrIfYhOq7yNjVJvvJXy3FdDvV86vrfdG3l3r+xYe45wB4JytWub8rd3eOf8j68DsdBsYXC/YauXJnQorHSqWoKGQVGOJ9VbvR02r2u7nM0h7RiUioyuM65O3E69ZS++rloGjL1c09F7109siJ261JeZWTewFfCEa7+O3m/8JAwODnYPtFtVcD5tpp2plYObQ2uPd2j6oHRrk/EMnGMNtd3xUsNopyfxaPAtEEuq4RK0MHLiJf+85vDPYcXqfZlPquF7vjby7+vsm25y80HmO3O3fKf8jPTC0033p0iV85StfwcWLF1Gr1Tq+e/jhhzdcMQMDA4NNQXoJyCx35n2cmiWluVKk6FYgrAxit0F8EKraTqILdqtLr/bYXTNszs5+8n0evQ249bUUVCtmgFAcGJsEXC7g+e8AZ74HOFxAbFKlbtHLSy8B/pCioDkcFGWpVwF/kA73/ht2vIEeJYzNNrgusJPG2mGw3aKawPb1Yb9t1+vXavJYP2rWg+SalnHbUr3c6aIjmV+l8zuKPtHbJZlfuRSwXAImZgGPj59XLjOOOT6pnO7NzJu93vMvZID50wzbaoO7/8EIRd3KJV4jY86DkdG9u5INcfJxoJTjPG3PUbUIvwsEUYdyuv/6r/8aP/ZjP4bDhw/j1KlTuPXWW3HhwgW022284hWv6Luc+++/Hw888EDHsenpaSSTnOy222088MAD+PSnP410Oo277roLv//7v48TJ0a40mRgYHD9oJChwy0HZ0nnmpqlkXO66KwB6+fB7Ff4ZSfk1+ynLt3a06h3v0ZOAAZFP7slx14O7Dsm7pkHXvguJwIrC6SZAUC7SUPfbjNtiqxfZhnw5BTdzBcEJoNs4ytev7sm4SPAKGy2sdcGOx47aawdFtstqrmdfdhP2631c7r4e8/h0efznjnIH/2eOuV/o31iba9kfvkCtFnLlhzVmWXWRd5zM1gH6z3/QgZ46enO/NmVkqCUe4DouKrv8iUuJCRGtPteyJC1ltir5gCFDBAMKxHWHY6hYro//OEP41d+5Vfw3HPPwe/340tf+hLm5+dxzz334Cd+4icGKuvEiRNYXFy8+vPss89e/e4Tn/gEHn74YXzyk5/EE088gZmZGdx7773I5/M9SjQwMDDogtVkJ+1JQqdF2V1jh14Utn6u73Z8M9GrLoPS0ja7/np9simupmeWAL9mWEsF/l600OxiCWDh3NoyY4nt6fdtxqhstrHXBjsaO2msHRb92pXNwnb2YT9t3476bdY9u7X30Am1U6xjK+zXem1dTdrPl+zsLcB2jOrdlXWwzuFkfXaBIOpQO90vvPAC/tt/+28swO1GuVxGOBzGv/t3/w5vectb8J73vKf/CrjdmJlZ21HtdhuPPPIIPvKRj+Ctb30rAODRRx/F9PQ0vvCFL+Dd7373MFU3MDC4npFeohGoloCqUL30+vh54WyneqhEN8pSv/Quu/yauRGpew6KXvS9bu1ZPM/VfaBTWXazaYd6fQppkerLwZ13OEgtC0W401EudF4biAABsbOthxEENKrbbqehDoBR2Wxjrw12NHYCNXuj2Cza8KCZKXR1aCu1ebPQT9uHoaBvtA83673q1d4rc2vD4NoALjxv36ZRtXe9tlbLSkVcRzkPxBPAxL5O9fn45Pr1GPTd1Kn4tQrDx3YJm2UopzsUCqFaZa7TvXv34uzZs1cpZCsrKwOVdebMGezduxc+nw933XUXHnzwQRw5cgTnz59HMpnEG97whqvn+nw+3HPPPXj88ceNETcwMBgMOrXcF+RPtcjPsQmeo9PNpePdi7LUD71Lp5CV8ooyJo9vJf1xPfqeXXvkNVKZVKLV3vy6X6XRjdGAj80oB9sfBoJCSdbrW3ttYi8wNr32uC9wbdBQB8CobLax1wY7GttNzR4VRk0bHmS88wWAVHJ9avNmYRS5wkc9vm/me9WtvWNTKtQNUHMHmbtabxMwuvau11ZfgLbY6pwHIjweiHRuWgRC6IlB301ZN12ENRDaNXZ7KHr5q1/9anzrW98CALz5zW/Gr/zKr+C3f/u38a53vQuvfvWr+y7nrrvuwuc//3l87Wtfw2c+8xkkk0ncfffdSKVSV+PEpqc7J016DJkdqtUqcrlcx4+BgYEBVpNrc1oWshRJiSY6KUs6fWqjlCX9ep0yptdlq+iPw1AX5XdWSpnsr62gu8m+mtynjpcLzLsdilFMxYpuKrNSdK3bva5BjMJmb4a9BozNNhghtpuavVMxyHg3PrN91OZ+sB0U9O14r6xly2dincOsJkfb3vXaOj6ztg4AsPeI/fGNZIEZtG67AEPtdD/88MMoFLjbcP/996NQKOC///f/jmPHjuE//If/0Hc5b3zjG6/+/bKXvQyvec1rcPToUTz66KNXJwIOh6Pjmna7veaYjoceemiN2IuBgYEBqmWuwIbjjAEuF7jKGh2nQcutKtq3PwhM7VcK18k5pWIaitGhk+qm60GnkDVqXK3tl8I+atjR2bwBfl48353a1WoCyfPc3R6bBqYPqFXmQevei0pm18/pJZG7tQw4HcDMAa76u9zA5H4gFKW42j/8Jalmk/uBG15Bhzwcs7/X4nn7uu0mGuoAGIXN3gx7DRibbTBCbKaisx12S4jKIPTocJyUYEltbjZ4fOUylbvt2rgR+zgoRklB13H2GeDkt9nGyDjTVh69be0900sqw8mp73HOUMoBzSYpzzKbRr/vgd07BPCYnk3F6bDPy92rTcPYs/X6NxwHjt1OpqBUL5fZQGS9+/1/KGSAOUGX10PAutV9q/+/NwGOdrvd3u5K6Lj33ntx7Ngx/Oqv/iqOHj2KJ598EnfcccfV79/ylrcgHo/j0Ucftb2+Wq1epdEBQC6Xw/79+5HNZhGNRje9/gYGBjsUF1/kYK1TpBfO0mjuPQIUNcGn6YPA4RMc4AtZ+3ydt/3A4BOLiy/aU7cCoc2Pl7ODldoloSuVyu+TF5QhnJztpHb1W/de97Pr52qRVDrrRGNqlsbWG+A1lSInQxLH76CaeTd63U57DjbI5XKIxWI72nZt1F4DxmYb7FKsN3buJAw63snzrSFFvgDzaOttTM6Nzj6OCoO29+wzwDf/Yu3x171FOd5A5zNPLQJnnqJNdALwCPr1erZHh907VMoDDqy1ea2mUmq3tgnY8fZsDWTb9XkFoBYWdnLdbdCvvR6KXn7kyBGkUmvpJ5lMBkeOHBmmSAA0vi+88AL27NmDw4cPY2ZmBt/4xjeufl+r1fDYY4/h7rvv7lqGz+dDNBrt+DEwMDDA+Iy96mYgDCxd7jwmN+dWk1zBt0O34+vVYZDjm41+lEoldOqYTj8cpO697mfXn4Usf6zICkVUeU3Rco5UM9+o8vw1gs2w2aOw14Cx2Qa7FLspRGXQ8W6QkKJR2sdRYdD2nvx2f8f1dku17nJBZdEA1rc93cqTyKV6Z1OxYnxmd9qza0CJfBgMRS+/cOECms3mmuPVahWXL1+2ucIe//pf/2v86I/+KA4cOIClpSX81m/9FnK5HN7xjnfA4XDgvvvuw4MPPojjx4/j+PHjePDBBxEMBvG2t71tmGobGBhcz9Bpc1KRNbEXaLeAiy+QvtVs0uFemud5yXPAwnnA7Wbc8IGblLiJ1dHrtw47iR7Vj1KpRCDCVeisoOAHQuurpwKdx9JLneIw+v2s/VktktLYbPA5AUC9KsIEgiw3tUBa2uoVIH2F1MCaEMprO7rnEt9pz2GTMQqbbey1gYGGnaCU3i+9XY5386c7KcE6rBTxyVmqQjsctJXRBNlNpTyVteU95RhsxTD2cVQYdHzPr6q/q2U60s0GUFjtFI+rlrnDvXAOeO6bzHYCMIuGhBT67Oc90M+RavEXXwQ8XqVWLhW6fQHghlfSvtm1abfZM6sS+ZWLtOH1Cnf1K0WKyfXTjt0S5oEBne6vfOUrV//+2te+hlgsdvVzs9nEX//1X+PQoUN9l3fp0iX81E/9FFZWVjA5OYlXv/rV+M53voODB0lJ+dCHPoRyuYz3vve9SKfTuOuuu/D1r38dkUhknZINDAwMbGBVBAU4UO85zImDpChnV4DLL9HJa7eZrursM0C9RrqZP8SJyTAYtTLtRtCPUqn+vVQmtVK/7BRIzzzdSZMrF7mQEW0parp+v1CMcfUAHe7VJfa9LwA4nTTCAGPwZb0q4ryVy5zkZYUSd71G2lq9Ahy+1Z7muJOewyZhlDbb2GsDAw3brZQ+jEK30wVMaWOhPN8a2pNb5U9iD0OtJOwUtCtFLlZb7eqw9nFUGGR8j4zT9lfLQD6tjnsDnX2aWyWlHAA8PqBSZjy3W3OlAmH+7uc9kO+QntXE7aHdu3iK9tMn+rXdpn3r9nx3mz2z/v84ndRnKbZpv69cVO9Yr3d6l2UiGSim2+kkG93hcMB6mcfjwaFDh/A7v/M7+Gf/7J+NtpYbwG6IizMwMNgiWAdoaezCcQp61ERs6aUXgfQyU1G12zSuAJ3Nl72OO6/bGbM2KgwS0233vYRdDF3yAn/PHFLHZJ5v/ZgsT5/4pRb4LOplIDpJhkJqgd8l9qq4r9Qi8Pdf4vH5U0C9zr9jE9yZue0HKIj36jet0xE7D6OwXcZmGxhsErY7pnvYOG278xfOqQVPHR4vsFfLDCHjb3VBr9QibadkI0nsJvsoY7ozy0Cjro7ffg8we1z16d/8DzrDAJ9/ck4Ir/qBQy/j8WFiuvW45kqRzrYMq5L9uktjnbvCTi8mtUDxU7nQIDUEerV5h+iz9Gu3BtrpbrVaAIDDhw/jiSeewMTExMZqaWBgYLCVsNLOEjPc5a6VgbRQC/UFgfkXSKlzeUmvC8W4Ai5pXrtpQtEL/SiV9kNbs6PS1SoqNl4iEAEcTtLHli/x+2CMtEe3h5OVXIrfh2LA3juAYJiOerNOOp8+4UvsAWKTDBHw+LiTE4gAwSif22oSOPt9CuaF48D+G5Ui/XUAY7MNDDYJ2x2iMii9vdfxblTwVrOzjXYK2ok9ioa+Ferlo4SkJbeaZLA983cAWrQfR26jww2ovms1gfEpOsTRMS5KlAtcIB6bGly9XL5Dc8+rGOexae68F3P22U7051jIrA0Z2A32Tfa7VGcvpIHwGBAZI7tAolbh72EU2ndoJpKhYrrPn++SbsXAwMBgp6MXDUuumC6e5+p/owG4XEAgyp/YGHDnvbtjQtEv1qOl9UNbs6Naem3i/ORxp4u0RamOm1rkhEHea+ZQp1KrPsmzqrruPaqojSVRh0aFfycvMDYvn+FPtQRUSsDx23f+xGSEMDbbwGATsJ2U3kHp7b3O10N7dIRinW3sVkY37YydDCtTYXIWOHG3il3XIfs0FCOV3meh0kfHN8am8gWB8T2ddYtPc7fWygqTdSlkgJee7lSXnz+98+2b3u/+EH+qZSXUqjvLcg7Ri6q/3WEeA2IopxsAisUiHnvsMVy8eBG1Wq3juw984AMbrpiBgcEuxHYJWowiT+j4jDIGe4+StlTIMc6oXuVO6uHbNkdVc6cJgQxaH73vJKIJtdMtaeX1CuDycJJRr1KortXmjkFuFYgnGFtvN5mVwjISUngmHKewjzfACUizQec6FGP9J/dp7crymtXkzp2UbBKMzTYwuEZQyHAn9NKZTpEzoLeStR0dXk+9KFEp0pZGxmgH5fjfq4x+6rzZNm6QeYCdcngsQTslBeOkiNnscdb/0InOfqoWaVOs/WSHbu1fTfK+uvOs18cKXaDUTuV8J9u3QgZ44uu01wDgD3NhoVbhbv2eo51Ot1Q27/V+beSd3AYMlaf7qaeewpve9CaUSiUUi0WMj49jZWUFwWAQU1NTOHfu3GbUdSiY+DADgy3CdsW4jTJPqKRrXTpDw7VwFihmmIPzFa8HXvPm0bdlu2MDrRi2Pt3Uy2V/ysnh5TNAKkk6XnaZDnI2DcTGgQM38xqHg7HzVqVWgPdILzH+TtLuLp8BTv2jiAOvAbUSzw3FuGMg4XQAM4eBQ7cw9m4HY5S2y9hsA4NrBPr4rC9m7ju+PrW4l+MrHdbUAp3uPUdJHZfQdT4GdZ63wsYNOg848xSFuqyoFGmrLp/hArBO7ZbaI7KfyiVg7xH7ftLRq/2L54VgmPYsPX7qmBy9rXtfn3mKtPSWxYVzOHamfZM78898U4jTloFcmgsWew6y3pEE+97hoCDdLlIv35SYbolf+qVfwo/+6I/iU5/6FOLxOL7zne/A4/Hg7W9/Oz74wQ8OXWkDA4NdjF55SzdzAOyVJ3RQpzsc5872gZv4c/sPqu8Coc1px3b1WzcMW59uVEvZnxJSlK6YZYy2VIyvV9U5Xj+Ncrd0X0CnWq7LAxy9XQmvJC9wwSS93Hmt28uydyj1bLNgbLaBwTUCfXyW2SSA/uxTLzr8zEH+dBOmkuP/MJT6rbBxg84DutGSx6b4e7+N7VlN0ib100/WY3ZYTap66M8SUM+zW//4AnTOrbHLO9W+yZ15j5eL4zK1WrnAhYy9R9cXTeuGXaTcPpTT/fTTT+MP//AP4XK54HK5UK1WceTIEXziE5/AO97xDrz1rW8ddT0NDAy2C/1StvTBX1+13eyVx24iMP3mCS1kgFPfYxubNaDRZBv11WuAK9UL5zrzmHq83VdX+119tRpN2XcNQQEeVd91q4/1eK9c2oO2zXpdOU/mQGqRfbfnMAXSAMDtEynAklzpTp4nhdJu98baZ3UhuLI0Dyxf5g56QaZ+ibK+mSWGCLSbSiDnOoGx2QYG1wjsBKJSixwvzzzV20br47ZU6XZ7OsfwUQpTyfudeYp/F7IUIwtEuEPcEfqTGXy3Up+bnHmKcdHxyc5ziln7snvRkhctGhiSaq7bZLv+KOe5+zz3fKewWa8+3XN4OHr04nng2/8f02UGInzm+28km2wnUqurZdrpUAyoLan3r9lgv1pjuq3PrF6j+OpuE+uzwDnMRR6PBw4Hg/Wmp6dx8eJFAEAsFrv6t4GBwTUASdnKrVJAJLfKz8m5tefK1VUpjlUtk/rUatOoFDKbU8du+UD7yRNayJDu9NzjnBCUy3Tann+cExmJ1CKNu+yH5XmmGLn0ksolqbdR0snKRfvvdeir0nrfub29rxsE3eqTnFt7PLPMenSr5yBt06+TbfP6qVTq9gClAidJiRk6xKtJsePtYNnzp0lJs5ZtXcn3+FnvxXNqBz0oKF71KpBdAvxBpg8LjzNFjN07fI3C2GwDg2sE1rEvtUiHs1rubaP1cbuQ5dg6f5rjpT6G9xJiGwQd98sAL4kxt1xUTrIUbxvUpgBr5yZtYS8yFnaT02VfNkB6dyDEHNGBkKKG622VaUWtNllPLQbQvs2f4vMoC8HO+dPAmafXnivhCygFc7t6dMOz3wS++RUATsAfYZ1e+C7DqnaqiJrcmfeHyCbwh6n5EhB2We7y+wJr34fLL3G+tTS//jx0h2Mop/uOO+7AP/7jPwIAfuiHfgi/+Zu/if/6X/8r7rvvPrzsZS8baQUNDAy2Eb0oW1bI1VWruIcUw+hGsdooDp0Y7LiO1SR3r3UEwnQGF7XV58WznU68zKFpvVa2sRedzAp9VVrvO9lvvcrrF92ut3uO0YS9QIseUz3IPazvRThGw7rvOOlke48papkvoFKGhGPqOmvZ1pX8WAJIXeazk/AGgCMvo3N/5z8FXvYDnUrn3d7taxDGZhsYXCOwjn3STlkXma3jmz6G5rTxXR/rV5O9hdgGgX6/gsY6k7RivR6D2hRgbfsmZvk7dbnzuG5HrWWH46QyH7+Dv6WzqrdV7ys7YTOJbIrttD6HnI0tlZD36VaPbvjeX/O3x8cUY3uO8GdlcWc63ADbKvvPHwJmjzEH+b7jzGSin2d97pfFPMvKXtyFNnwoevmDDz6IfJ47IR/96Efxjne8A+95z3tw7NgxfPaznx1pBQ0MDLYRg1C35YrtlTnSg62qqoPQ0wahmkmKkR0F3kqnK+V5jgM00nJ3tJTvjCl2Alj2k+48NkUj4fGr7xs1oF4GkikqbjeaLNPt4ff9ULSt/baaVHk5rWlL+um7Xn3W7fpilkZbRzCiVtx7lSUpd/lVxmkHgjxufVbW9yI6AUzsE6JnFcAXBo6/EjjzPWClDnh93FEoZPl8vD7uhutxXrJMmaO0VGR9HA5ORCLj3D33hYDsCp1tHZUi87In9uwMtfhNhrHZBgYjwkZFm/q9vtt51vzgjbqyUTqstOrF8xxX61XGI7s9tJVOh7qmWh4s/3g38UxJKXd7RdaJJtNtlgpAq8ExPRwj1VzeV4cdndt6fzkHkQrijRoQiXNsd7nUPCC/ai+Y1sum6n1QSHPn2h+gY90GbaTbQ2q4bL/Dwft7g51l1Sprz92ozcmu9D6+lcJivd4B6zFfEECb14TjwK13q77U62ml91cE865eU8r69Rpt+K13299vh9rzoZzuO++88+rfk5OT+Mu//MuRVcjAwGAHoVf+TjuE41TO3EjeRKvSp6Rz9aJcSRGYbuWU88DFU3SG5QTlzFMctCslZbQrRdKQ/SEgOsbf4zNckdX7odWk4nYwxImEFAObOaAo2rHW2pzS3fpAFwIZpu/W67NuojHdnuPYVHcxE1+A6uPLlzjhWZVCaBUeLxfXPqte70UgxGdXE+EIuRVVJsBjmWVlqHU4XaSLl0vMo14usDyHU6UXi010XlMp8nmFo510w+1Si98CGJttYDACDGObhrl+vfN0e5FatLfRklYtUSpw4XNsik5OrcrYWq9mW6Sd6UeYyq6OZ54WlOEIHe5qmSFFDhczgMQCdLgTe3mNtD+6fZJ0bv24XR+FYgzz0m2FzHd91xvVubXycDZVXh8eUwvu1TLrNjnLRV1rDvPzJ7sLm41S7Cs2QVtrd3yj7+ggsLvXS0+rhQl5TH8vDt6izu9WJ+t8xR+ho91qqrkWALi89vfbwfZ8KHq5gYHBdYJhqNsbpacNQzVb7/xsSq2MW3/LhX4pxlWvclU7FFfldGtvYp/9rv96FO1uGLbv1uuzbtd3a9d6eTElZU6nDYZivemC67VtfIb9VrD0ZyjG49Yy5Wd5TynKUxL0RflcXvnDndfJ43ssu9+bFf5gYGBwbWCjtqnf6we5T7cx3EqrlnZOssGsx4HBKOR2dcmllN3T7x/W7qffW9a9Hzq39X6HTqy1FQAF2vRzNzIfkTm0rcil1l6v06d1bIawmdWm6cdHNX/qB3ZlZlNrKfX6e9FPnaz9te+I/Xn7jtjfr1fZ24y+d7rvuOOOq0Is6+HJJ58cukIGBrseOyBnYF/op546dTu1QBGLyVkgfYU/VloQMBg9zQ7DUM1ke+ZPcyXaAa6IeoOkmi2cJY28VlX3aNRJ/SrmgWYVyKdZ1/gEcOTlFOJKLfK6+KRS2ZZCYLM3sv0XX+TqfSgGuMSQuh5FuxuG7bv1FGd7lRuODXa/cJz9kVlmn8i2+0PsT2Ct0ruk+/dqWzhOEZileaFgDubYnj7A/rS2UX6W94xNAAdANVeXk+XLHK2T+9U7vHqFyunzLwD5FOPJAjbl73IYm21gMGJsVNnbGppTq9CeeLWd1H4zSEh0C696/jvAU39NxzQc4xg3PgWUckAwDPjEPd0e2qlB5yl2dalVlBMfjHCukBNZTKZmgfkXgRcvsG16Hmm7ECs9P7a8nzVczOUGSiu8ZnwGOHwrQ4b0c9NLSqAzEGJY2Xr5zAFe/+IT6n6SKh8Zp/2zXh+OA8du5277yqVO9fKNzv+sc7XDtwJv+Gng2/+LTIJAGHj5D/C4lZotsRn2za7MekWxzCT096KfOsn3Qc8qEx1nVhKXEI/bd4SshvkX196vV9nbjL6d7h//8R+/+nelUsEf/MEf4JZbbsFrXvMaAMB3vvMdnDx5Eu9973tHXkkDg12DraT2bASD1HPmII22TtWeP82/J2eFuqTl2o1QqYahmhUypDDJcytF4MLznFzEpuiIrSwI9cwgqVmlLI2BLwgkZhn/22hQkCQ2wXRVC+foVDabzAUNACfuXktZkwO8Hvfdi6LdC8P0XTf6uE6h61buMPfTYwh14+b1c6Fi7nlFIZRKo4B4l3rcKxwHbv4n/dEBZZu9flWH2ASVUGW+Tzkhle/wmadZn1oVqFSA5EW+A/tv3JlpVjYAY7MNDEaMfsbZ9a6XoTkS2RU6y/4QHVUZnhRtdep69LqPNbzq7DPAc98C6mKhOJumGNXR2zjWzRxS5w6TF1nWxdoXXn/n52BEOVseHx3EaSF22aittQu9QqwadTUHkfOCZoNzAWmLpJCmPFefP8BBh9npWr9tcj6RWhQL9Q6W2W7R8Za5vK0Ix4GbXwXgVevfo190m6tN7gfu+YnOcy+fJQXbro2bkbvb7h3w+NeeZ30v+qlTIcv+j4yrY5Ui6el6Ole7+61X9jaib6f73/7bf3v175//+Z/HBz7wAXz0ox9dc878/PzoamdgsNvQi9qzk5zuQetppWpL5FJqYjCqNur5M3tRzfR7rSY7zy1mlQp5TUw8PD46WHFRztWd7Sm1gtzIK3VVSUPWqXEAV15vvVvVMZpQhl2v41Y6cb1yjm7m/WIJTqQkogng3PftY8UvnOwvr2a/bZHn6f0PqGdgPV++IzJPqEQhy3f62O3r120XwdhsA4MRY6Pj7PgM4351FMVOtG5LYyI8yep093ufk9+m/aun1TFfELh8RglPDVqmFXZ9EU2s3dHMpnj87DOdxwtZLnRb7UK3PtYhbX04xnKk052z9JmVdpxNcfd8vbnKTrIVvTKPWEVQe2Ez5gJ2zyqWWLvzbPderFcnO2XyUIxq/brTbXe/9creRgwlpPanf/qnV9OP6Hj729+OO++8E//5P//nDVfMwGBXYqP0s63CoPXUj8vYZ0BRe3tdux6s1ClvgKu1K5eA5HlSuSJiEnLpDFAtKSMrB9a550kzcnlIl1u5THXLWgWoLlE91R8AQhHA7eOKabMOeMZIXWrUgEYVaNVZVrXE3YbpA9zpBjhBAARFOckyygVObvbfwO/s6Pa92irP22hIQjf6OED6e7/lJufsVeDt6h6bUKJlsh/GpoCFl+xXn7sp4du1JTYBvPAPwNJFLpYcvIUTHitdEBBq5WNUj5V1sGtntcz3QeYJlQqoDoc9XfAagrHZBgYDoF/l8EHGallmIU3b4QtyFy8yRpun29JAhOOqDE+SeZ4XzysbWSt3r0N+ledEwbG5USfry+vnfa/MqQweclxdrz12tsHaF/uOsjz9PLnLmRThXW4Px2mpmq7bBRkiNn+Kf7s8pBWPz5AREBmnUy37yifsjy/AeYnTwTqcfQZYvAA8/22gUiAN3R9iWBiwNhuGFd1sRa3CuYF8DoPY6fVsfLfvB8k8AoxeKb0X7P4fYhMMPVyaozMciqlnlr7S20Zb26cr07u9XGTxh9hXc89zDjB1gM/S4935IZ0Y0ukOBAL45je/iePHj3cc/+Y3vwm/v8tWv4HB9YCN0s+2CoPWUz/fo9F5ddrQMG20Uqck9W5qFpg6qNSsVy5zkiCVSmtVnrua5MDeatNA5zPcwUaLRrJaphHyxwA4SQ+LjDNlVbsBNFtM9xWOAekKd8ZDUapiNuqkH3t8vN+4oJRVq+wLf0g5//2ED3SjicUmOlOADBuSYKWJDxrqkJxTdD+gkxauhxfIsqRKuXXysnBuMMV7KwoZTmrqNWBMLBxcfonO/MQ+vgvyPRibAuAgW+H47b37yxdQVHT92fkC3emC1wiMzTYw6BODKIcPU6auhi1ZOdXyWgquDE/Sr221Om1kIGI/rkfGhaZJoFOdPBAm9VjmRS7nOcZPzipqu52N6GYbbvuBzvG/kKEtG5tWDuH5k1xAd7mBdpWU93paLWZLu9ARIuagk5VeoiMdnwLabaUcrocURcYVXT4gxvTFC8ClFzkXqJS4yBEI8t45La1Wt+doZytkqipfcPCsF+u9U72+HzTzyKiV0teDfi/ZDqeL8zdJ73e5+K7qmyXr1c/p6lSmr1X5OTpO2rlcQKnXuMhifRd3KIZSL7/vvvvwnve8B+9///vxJ3/yJ/iTP/kTvP/978f73vc+/NIv/dKo62hgsHuwUeXurcKg9dSP6xRqXaF0mDZaqVOSDqYroBayIueojVK2VK6MJnhMUsPbEAbTxqGS58Sn+T0AZJaEsw5Fg4qMi9VZMfAXRB322ihp9qOU2YsmNsj5/WJQFdNu9bhwcjRqur0U761lWmmB8vlfPtf5Hshdklxq/f6S6uhWxDZBXXaHwdhsA4M+sRnqz/q1+hiUTSl7ah2b9HzHOqw20u4eJ15jXw+r7ZJlWMdb6z172YZe10kUssz0YQdpF+xCxOS1Oa2fpL2XsIZ0yTqUCmoHHFAiqoUsr+n1PO1shQwDsCqUb8T2y+O9vh9l5pHNxjDvajfY2WrAXrEe6P6O7jAMtdP967/+6zhy5Ah+93d/F1/4whcAADfffDM+97nP4Sd/8idHWkEDg12FjSp3bxUGrad+vtMJ7A/y+Hp0ah129CkrdSq/SudXKoXHEkAkDuSzpIFblbIdUDka99/IFfnledLJgzEgNk61y1IeQJO0OqeLK6+VIulJ2RRF0xp1roanV7iS7Q3wcz4FXHyBdT50Qom16JDt0NuYW6XRaTVZ53bb3iB2o4ltNCRh0BACK/1bruwvz3PiEhnvVJMt50nxevEJUiZDY8CBG0m1v+0HlFp4Kc++O/090st6qblK2pgMFQjHSCGs1/ism3X2Y7XMxZNWg9f1ouHpz8QfZDxYMavolaNQl93hMDbbwGAdyHHizFPcZY0mOuODNzIe69cGIxxvFs6RPut7ObDnEG3Q6qKibssxqVruVDtPLTCzhqRnW+9RyJBqu/8Ghha1mmQInXgN/261OHZnU2qcjYx1iqtZlcJPf487xtUSx82IoHwXXfZ1ANQ9VuY5Vru9DOEqZphVpBTkfWviXtWysv+lHG2120tavKxfOM6Qs0aNbZSL7focZPE87XtIPDtvQMwn2kCtxL5MXgSuzHef+8hMGv6gyoaS2EORTt0GWtvcDevZ4l7fy3ArnbI/Oct+00Pc+qFsbzbSSwzLk6r8uRSZAXpIItBfn7k9bPfiOT6/sRmqs59/Vp0j5yj1GpBOKr2CHTz/HsrpBoCf/MmfHKmxfuihh/Abv/Eb+OAHP4hHHnkEANBut/HAAw/g05/+NNLpNO666y78/u//Pk6c6HPHxMBgO7CV1J6NYNB6bqRd3ehTutJmOU+DW6vSua6WKdLl8akVeqtSthWRuKCG+dR9K8IABMPCua7S+auVAbhIJ280AKeI42q3uEperwHVCjA+CRy4WaiYN4BLp5guTJ+Q+QKdbUwtcvIG0Bg2m5wstdudIiBAb5rYRjBoCEEopmjhlaLa4Q9HSd9f0imNecbdrSZxlTCVz3JSVilxwnLr3cBLTyuhtUqJ8Xrye+u7JPtPhgpIOtn4FCdYAGmZhbRiJXjEeYWMinu0K1PC6eJ9b3zl7vgfHSGMzTYw6AJ9nHCL2FBJZZbj/EbGY2tGjkKGNFnfPjp2ixd4L7n4ml3hgmM4znFNF4pstzk2+/xr76G3Y98x/gCKxnzxRY7ZckyW42w+zTFdOpS6Ung5T3u5kgTCEcDtB6pJLlgfvd2+neW8ukezIeyKkw7Q+AyQS3PhwB9Sc4FChvUoZPh9vQ6Uy6Sl59O0qYUMs2LoCwR7DneO5ZIaHoyxrrLPq2UuDLvcdOi9PjrRdtlXAKVEfrNQIr/44vChg+vZ4l7fWyn7pTzp1NIWD0LZ3kwUMnS49YWEQobva2yi89z1+kyWFYwAR1+ujgfCSs1cn6MAdNLPPM0FEvke78DsQUPRy0eNJ554Ap/+9Kdx2223dRz/xCc+gYcffhif/OQn8cQTT2BmZgb33nsv8vn8NtXUwMBgKPRDJ8qmlANqdUSjibW0LnlM0pBy2vXhmKKbZ5aUkrk/ALQdjP1ui52C3Cqd9TZo6OUkq5TjAO4LqjqFYorupkOntQFU2JTQVdAXzq1t92bRxAYNIdDroe967zmq+ljSxLIp9oNVNbSYVVTv1eRaWhnQnQouj8ndC4mCWN0Px5ibU9/gkayDbgsXm0EVNTA22+Dagj4e6HZGH+c3Mh7r11ozcvRL8ZboNtZZbZBdWeMznWOybm/txmpAKH6LcbaiLXqXCt0p8XpZuo0oFzpDwPQ2S/stv9cdM6laDay9p7XNkhous44EwirULBBW9kMqxncrx4qNhA6ud22v70dJ2d5MrCbXztFCMc4JrMfX6zO7sgC2XYZOWJl5e46qkEO78nYIBtrpPnz4MBwOO913BYfDgbNnz/Y8R0ehUMBP//RP4zOf+Qx+67d+6+rxdruNRx55BB/5yEfw1re+FQDw6KOPYnp6Gl/4whfw7ne/e5CqGxgYbCe60Yl0pc1GjSuiE/u4ii4pSvFJ5hddTa5Vytbj3ho1UsniE0BBOMz+EI/7glwZHdsDwAEsL5CCFh4HCm3A4eJ5Lhd3wj1+roRHE1wx1/NSO51AdpmCXs0aMHmA7Vh4iZS19BXS0f1hobwuVmZ9Ie60SkXaDlXc2OgpUYOGEEiV8gsnSSkPR2nI5M785Czpf04nFb8jcfZZS5tVXVV4Fc/bSisDOr/XIY8FI3wH8mnW3eMFbngld21qZR5z+8RzFTH70wf5DLqV2e/xawzGZhsY9AF9PAhEuIuYTXGMCYQ2Ph7rY7Ect2IJ3mvlMs+pWcZKWSe3R9WnXqGNnNzHsdbpXEut7tW+cJw2afECkLlCxzc2Qbsp2+oVoUC5FJ3s7BKvT0wBxTxp7f4g7UF0vPM+kgp98lvckfULKn29xs/NBnfXfQHakosv8rpogovc3gDbubpAmxwZo+0OhLl7rjMPrG3T6yCp4VIF3eEg0y06QXp7MEp7PEj2lY2EDq53ba/vrc9U1nkYyvZmolru/N+5+q7OqlDCfvvMriyPn/NBj5fvxcUXOB+MT6h5ysUX7VOTbXffaBjI6b7vvvu6fnfhwgX84R/+IarV6kAVeN/73oc3v/nN+JEf+ZEOA37+/Hkkk0m84Q1vuHrM5/PhnnvuweOPP24MuIHBbkIv+pROW7c7JxBS53RTpwzHSR2fP83BWtKL2m0VA+XxkkoXCHOgjo4DsUnA0QKyaTrXoahSxRzXHG35G+DEoZQnzQ0g3fmbf0HDviwnUFWeVymTriiR2Gvfhs0KSRi03JmD/Nl7ZO2zCEaAxAzr7wtQlTafUQI1APvY61e7FLrSvYT+vQ75jkj65eQsf66+IzEgLFV3+6T57ZZsApsEY7MNDPqAdZyQNiQQGp0icjc7J8dIa7iUTj1utdbGEtvVrZ/xzuunsz5uCXPae1TlXS7nqZIO0K5Wq7xm9hgdHFmmdRyVVOjEPiAsxv3UAuD1At5JFTaWSjI0yO3h5/lTQGGVdPBWEwiLXc5gWGmiRMbWOtzWtkn0ooYnLwyffWUjdnq9a7t9b32mUlXdmpZzu22arKc+/wKG+x+yK0vqGpSLfE+Pv4L9IGn2gH3IoSxvh2Agp/uDH/zgmmOrq6v46Ec/ik996lO466678PGPf7zv8r74xS/iySefxBNPPLHmu2SS//DT050CQ9PT05ibm+taZrVa7ZhE5HK5vutjYHDNYKN5n0ddl2IOuHyGhkLShrIprroDKtZLxpGV8sCVi1yNH5/h9VbBK2sbU4s08HUhsiLp4OWCEPTKsZxySdDcRAx5Q4hxReOknBfSjMFuCap5IQOce5bGI7GHEyDpcANKTfPySyo9S2Rc0Np9zBMqYUcll7lJVy5x52FyB4h76c/Celz+Xk0y/cqqJa6qLGKtvH41ydIFT6YOdKaxsd5Tp/xVixTFm3+RDIJb715bNynYo79Lsu/Wa8c1DmOzDQz6wFaME9JeSbEpudMdSzD+uRtVW3eEsynuEGdTZBrNPd8pBmnXjlKejuuZp2gnS/nO8VjaSsCehhsIq4VVnSquZ32QbZt7nswnj085tqEY/y6mGSueWqT2x97D6r7FLBCKAwtnSQMv5LnznroCTDiAk98GXv/Peb0uKtdscjH2wvOdwpiyLXJuUK/R2a5XuBheq3Cne9jsK1s5v7I+02iCMf4en2qTxw/ceOfm3L9fjOJ/SM/TvppkhpnpA/zu7DMq20w0ofohm1JOdzRhv9O9g+z90EJq5XIZDz/8MP79v//3OHToEL785S/jTW96U9/Xz8/P44Mf/CC+/vWv98wTaqXGtdvtnnS5hx56CA888EDf9TAwuOYwaH7mraiL08XV71wKuHSG9ODpA51CKvuO8mf+NB30fFrEZTnWCnDZ5fc+96yKPWvUaMinDnAH2uvn74oQeZma5XnNBg3X9GEgEuOESNLM8xkas3abxr4gJgatZufA3qjxd7VEZ7xcIH3N7RF5Qx3cVT90QlG49f7RxcaA3mJjW4V+6HDHbif1b/4UFyrcXqrSyucK8Hk0m0B6kf0/vZ/f60JB1nsuzZHC2GxwkudwcSKXT3e+J3ICm03R2FrfJblzsBuyCWwBjM02MOiCzR4ndHvlDwHRFscthxAXmznM0JluY21sgnYqvwqsLNAJbtQBOLiIXC1xPLa2o1GnrXK6VG7pxXM8H1B2zAE6ojJEx+Vm2E4xy3F7MsA6VEW+a93R19tWLXOslqrb9aqgdkfYvlaTO9zBaS521wQFWdLIvQE6UI06kCtxQcAf5i57o8765QTduNVgf1yZU+Ff86dpEwJhtSsuheNkf7YrgCfABfRwbPBnvdXzK+szTcxwPnHpjArBiybsbepWYqP/Q9Y87aEoFckLaS4w+QN8P3SRQz3kzRfg/YEdbe8HdrqbzSY+85nP4IEHHoDf78fv/d7v4e1vf/u6cWNWfO9738PS0hJe+cpXdpT9d3/3d/jkJz+JU6dOAeDq+Z49igaztLS0ZiVdx4c//GH88i//8tXPuVwO+/fvH6huBga7Gr3EVLZ68NHrEox00sOsVLHVJGlIoSh3iq27oVKAKxy3FxfxeOkA67vQ6SXGv80cotP77LcYx+31qfOkox4Z5+o3wJ2ISp6DeTCqKHVen8rXLc91C9p6eKyTcifvER0HXt3FuVlPbGw7jUU/dLj11F0DES4g3P76tdfbtS8cBw7ewnKSF+hwS0jqmHxP5LU69d+u7N2STWCTYGy2gUEf2MxxwmqvpC3sl3pbKyvF7nyazjpAp9gfEnHQSdUG2Y6LL6rsIBLVEh1Y3U4CXCwem+LY6/ED/rYaW30B3t+uvnrb9HCielXVuVwADt3Kv1MLaufcgU4l8tgk7WkbjAf3eHgsHOX3y5fU+ckLwOqVzn4AKLg2ocV+S/uq1wcYPnRgO+ZX1nfz4oudbdmKOvSDjfwPWfO0+0L8ya1y4cWPzlC1XIp9IEPerPXYoRjI6f4f/+N/4N/8m3+DbDaL3/iN38B73vMeeL3e9S+0wQ//8A/j2Wef7Tj2zne+EzfddBN+7dd+DUeOHMHMzAy+8Y1v4I477gAA1Go1PPbYYz3pcD6fDz6fb6g6GRhcExhWPCo515kL0m5ndpi6SFqcpEHlV5UiuF39qmWeWy3SuW3UaIgjY93zWtYqdLplDk+3l6u+lXwnhawsVJSv7k4XKexy8XnSl10eDvBlocxasTiR9RowNqkUVqtFJe41tZ95QD3C6ZbqqYdOdKejpZeA5DlOQmSdpcDLVoh/jJIm1yv/tz/Y+Q7EElzQ0O8vU37VKlz0WL7EiZJ8ntMHONGSz06Ks+h0Q6+ftLtGbceudG8ljM02MNgBGMYm62Pj4nkuCtcrtEES8u96F3thd8wXpCCalWLu9qgwsGaD6TKlIyttqB1NV7fxcice4G55Zok2fPEs5xSJffxdEyFJ0o7GEnS0PT7S4JsNoFEB0AKuXABas8CZJ4GSsCWBSGdf6H1SLnQKpEnBsW5Cdf3YQP2cpHgW3fJ1dytvlLbWmg9b5pTfjDnDZlLprf2aX1WbLXL+d+kMc71PzHI+oM+RgB1FHe8HAznd/+Jf/AsEAgH81E/9FObm5vDrv/7rtuc9/PDD65YViURw6623dhwLhUJIJBJXj99333148MEHcfz4cRw/fhwPPvgggsEg3va2tw1SbQOD6wvDiEcl54Bn/l59zq2qzxtxvBv1Tup0tcyVerfNJFsXjmk2OmOFZS5R6ZhZ29hqciIRHqeDVq9xwE7s7dxRD0Q40XB7OaivLgFwAPEp3rOc50RjfJrGrKEZc4CTk8n9NDq5FKltoTHm7XZ7gKV5AG0hOraXDnc4Zk9Hi03QcLba/NHzUkcnNl/8Y9Q0uW7vndO19h1YugQkmqQ8AnScZS7aqVlOvpbnyRYIxrgYMn9G9Y2e2zW1qMrOrnBCMnNA0Sl3WJ7OrYSx2QYGOwCD2mTr2Nxuc3xstZQgKMC/AS5k9iskGRmnrdMdbo9X0LSnVRiY/G58RmUKsRtDdRvfajH7R6nAEK30FYqheXxAqQiUTpOWLqnrvgB3nHVasMMBPPv3QEYstvpDgNPNEDK3h/eammWbZV94tIXEQLhTUKuXUF0/NtB6Tqut6qA73r3Ki03QNvW6T7+wy4ct6daJETugm0mlt+vXfBoIiznB6hI3NGploBbgglAownmdw8kMJ7vQrg/kdP8//8//s256kUEpa73woQ99COVyGe9973uRTqdx11134etf/zoiERsFQwMDA2IYQYsLJ7sf3+hutxVyB9iK9XJW6vlJrW1sa8d1urFMdyWx9whX0sMxJYBWLgAHT9Boppfo6IVijCeS1HO9DrEE4+dWk1R81WFHwZNpUay4cFLFYtW0BYZClk78Zq/gjpom1+29iyY6HWOJXErdR6eVSTrgvuOcmCX2kpIIsG/239hZhg5r7k6J7abdbROMzTYw2AEY1CZbx2YptuZA506xtIm6qNl6940llDK0RGqBZUnKbjDSnU7eC8UsmV6xAGNxY0LcMtCmRgoApC4DR2+nnb7tBzrnFzJDSXyS8dk6FV1vbzYlcpwL26nPDfYc7bTbvYTq+rGB1nPsBLzWK+/CSS5o9LpPv5A5rPWFbIDP7vjtg5XVz726Hd+oPbXr1+yKmpcBnJvFp9SCSV2ED/oCFFXdhTZ9IKf7b//2bzepGvblOxwO3H///bj//vs39b4GBtuOUVJ4hhG06OasdDveL9wersDq9N/JWVLb0ldoVJtNHms1ueNZylK5vFwAli4yj2mrRQe6UqTxqglFbJmzOxgGjt/Rmd/b42P5z/8DcO77Im9oCJjYC7RbigZXKzPV1+Q+rsCXcizPf0Q5fPk06e37b1QCMuvlRLX7rFPtU4vADXeyTG9A5U0dn9m4iFo/79OwYQjdyu723p19hs8vc4XH3V4a2HSSBjUY6aT+5Vd5j3qVz6nd4m6K18dnoE90Wk3uOMy9QIGglctAJEFdAL2vr1OaubHZBgYjxjC2elCbbB2DZc7i/Cqd4cgYx8dAqFPUzK5+sYlOkbZ9R7lTmFlW4T6RMdqgbhRsK5JzwAv/QBHN5AU6vfEpZuoIRvk5I+Kt62XGkQdCQD4FLGfYnsgYy3j2mzxer7EO/qBwyBycC/hCSmG9WiHd+MoFzgOmDwJHb1Pipg4X75VOcl7h9tB21KucL0zOsv395jW364NgZK2A13rlFbPK6dbDofp5f/SwP6eL9w1GmNWjIjKxOACMbcIi/bBzhGHKDkY4ByumGavv9Ijc6pOck2VXKLA6PsNFmW59tpMy99hgKPXycrmMQMCeFrO4uNghomJgYLAONoPCM6igRShGSrnd8Y1A5hjVKd7lPA1ONCEoYuDK8aUzNEhjU7xm8RyPu910oHOrwJP/lxOFE3fTcMtdba+/UzCmlOeE4OKLwJV5UsmzK4C3wJXzAz4asYl9LLtcAC6eAg7cCOw50t8qf7+UQXleOd+5Ou3yKlrY4RMARDoxmZd8WPT7Pg0ThrBe2db3TlLhpCBdtcJJULvFCZFsv8w9KuO4ANLNvH5OEKf287e1bqEYqYyZFSAQBSJC1XfueVIyYxOqrOuYZm5stoHBCLARWz2ITbYbmwMROhC9bJJd/crFtfWTat86euUK15GcA777Vf7Op1l+PouritOhGMsORulwZ9Mc66MxAG2gfkXtyj//HSB5EdhzkON18iIQjtBB9YlsFLEpOlmZZaaOhJP3CEQ4J5g+CNx+D+2GDInz+IHiMnfLD9yo7EA+Tcde9kU/NtDunGDEXsCrW3lyHqWHUQFsc6/3Rw/7k+FwmWUy9nwB2r3xKSE6Fhi9jRtmjjBs2eU82RB7j/OnWmYKudyyYE9McOHd4eyej3snZe7pAucwF91xxx148skn1xz/n//zf+K2227bcKUMDK4r9KLwbBXs8kf3Ot4v7ChvMsWTTg0uZpmnWv7tAFerqyWgXlfn1SvApZfsFb915FIsJ7PMzyWR+7dRYxkvPckUFJmlTira8mWVR3w9ene3763H5WdrnfcdUXXtp9x+0e/71G/9hylbPy77U2dNFLNKET6XUtS/QlblVwfUbxk+IMuSOHSCIj0S+rNc0SY3svyt/J/aQTA228BgBNgqWz3M2NyrHuuN/XJc7ZYrXIfcdZUiaDJ3ckZQ3uU4f+Q2LogCalxOL3FHvJgV9nlJHZe2ulJWZQfDpKMD6rc8LrFwju2zhsjJ81csNGz9vH76eZBn0e1cOY+y2vrYOnZJr6tcjA6EOU+R/SyPb4aNG/Y9HKZsOT+SObgl5DsEdA9NlNgJc+l1MNRO97333ou7774b999/P37t134NxWIR73//+/Gnf/qn+NjHPjbqOhoYXNsYFYVnI7QaGVc1SvVySYvSKeT7jnLV2h8iFViiXqODHYzxb4eLq8DVMvNxulxcXW6I3JyL5+g8Nxu83u1lfHUpz/pfOc/d9MIqy1i9Qoe7UadQS6POVe+xKVKa0OL9nQ4asfQS8NJTnfX2BtbmUu2HMljIcsX2+W+TMrX3MHDgZpEyJqxoalIkbvH8xmhR8r2xqsbHJztX5vuhPBYy3C1YuUTHt1LkrvN6yq3pJU6cUgtMpRaOM1e306GU6BN7OHnKr9KY+oMA2mole2IfaYGSijc2TTaCy8VdlKhQQC/l+PwcLubzjE9yRyS1wGvi0yof7VYowu9AGJttcE1gu6mjm0m31SHH5vnT3Bl1gDRyiW79IOshKcxLl+jYulzALa9WNl3m/tbt/dHbSONeT3X78ku03elFjrn+ALfvLp0GFl6i6OX+G4Ab7yRjrF4j/TsQ4TjfbJCS7vKwnk4PbZRcWG02yIQan6LtLBeYdtMX5N/tFpDLAC4RWuZ00t7MPQ/MnaLtb9Rpqyb2Ac1J2gKZBUOqmev9rLfPG+Bn3Q5bc58D9na6l00Nx4ClOdpAmcFD2tFemT8kGjUyByoFzlUcALxewOfnHCUYEWy6udH9f6w3R9jI/6O1bIdDtUN+n10BVhe5Yz0+TQZkqwWce4bfBcKdIn9b9f+5AQzldP/e7/0e3vzmN+Od73wn/vf//t9YWFhANBrFE088gVtuuWXUdTQwuLYxCgrPKGg1MwdHJ5qm06IkhVxSu2QuUEkpBmjsZRoxafgdTp7vC7CMep3Gu9WiQ14uKIXz6YOMb0otMvat3QbOP0e6UrXM68oFGkyXE3B66ew1G4wBnjkAeGbp4J99hs6gXGFNL9GYNRqacdP6txfdT++H8T2ktmdWgMmCcLoFZVAXvNmo6rZPTBqsiuGZZb4nenm9KI+FDPDS053lpBbZzwdu7K7cqtPoc6tcuPCHuMMh827Ld9tKmQxFO/8XSnnW3ePns/OHSLPLpzmZc7qAQEzEzE2SZlcpUh0/EWGfA0ptdpelFxkVjM022PXYCdTRzaTb2sHpom2TWE8J2xegyvfyJUHHPs1zPCKzhgwhC8dYxti0ijVu1JmW0broqvf5alKEa1U4ljcawEqSMdnVEtNtBvx0iudPA7PHgOOvoK0r5VmHcoH1QZuOpLMN+MUOZqXMVGO+oMrTHB0HXv0mXvfsNzkPaLfVwrnbw7YuzQNnn1Z1rVZEPuuy6sNaFSiXOu2gbgN7vWMHbur8vpud7mZTw3Hg4C2DvT962F+rSaq+LCs+yfaMTWkO9yUV0jeq/49u7RnF/6Netv6/VcpzYcfhoK5ObJILDkvz/LtZ55wgt8pFGBlCsdX/n0NgKHo5ALzhDW/AW9/6VnzrW9/C/Pw8PvaxjxnjbWAwDEZB4dlptJpeauiyXTqFKBRTK/mSUjw+DQSCwkALtAFM7+9UH5eQ1C2pbOoAd0Tlbrj83XaIXVVxLL/aSWECOnfhAeDss533kFivf/V+0KlRC+fU3+upqA6K8Rl7+n00MVh5q8m15YSEg2s9rrdB/06eb1cX/Vq7vwHV37rAdiGrysylGN8mjwP8LhDu3BmS9bpOnW7A2GyDXY6dYOM2k25rRT+UY+v54zNqzLTSseWYeeFk/31p/ZxNkRnWhlokL+VYtstDW+0V6UAzyzwu65NLAVP7+HcgzJ/IuGAxibhtgDvnE/vUPSU1O5ropJVLOEB7L+npEi43F3xL+c7je48MFybVz/frYdD3p1t4n8x3rkOnZw9Tt0Ex6v9HvQ9keKAUyQU4RysX+F7rbZftlu//emVvM4ba6T579ize9ra3IZlM4mtf+xoee+wxvOUtb8EHPvAB/PZv/zY8+iTZwMCgN/pVNu1F5dlqWo2kHevUN11BtZcaut5ep5MD6fQB7nxL9fJyHrjj9Rx8L75Ig+oWCqYzR+goy1XdSIJqpqlFUq9cHmD2Bu4oOx004uUCjXC7ReG0UIT5nnMrTG1SyAAHb6YzXK+S0uQPc/W+3WI/xie4WztI/+r94AuRMreaJAVuelY5hqMOMSikuaLv86sYqWBksPKq5U4qHsCdZoeTK9BSuVXS8c48xWeUTwHeoHa+gyvv2WXuqnh8vLbV5Dlnn1Eq9GNTnWq7Tgd3qC+dEXnaayzHKWxMKUehHbebfe318/f4NGnt7RZ3i7z+3oqn1ziMzTbY9eg2dqWX1PebTTkfJjPIsOjW3tQCv5NZOvSxXe6AyjzOHqEA7QmQGQRwfJQx1HZlWOsgqeq5VdJ6G3WgWaPT3RDClQ4nKextUX6zSTscTwCHb6M4aKPGsKLZY9RradSBfcfIRirnRXqoSWBiD21DIc2dYblYHR1nWS9+l9fLXM3tNh2vWpm2o5TjTqgUJG03aG8CETrciT2d7dTnVYvnuRCgC7/qz2Kjdlp/f2QIViCsHFXre6SH/S3Pc/7QhgiFcwGzx8kMdDrX0rMHqdswNHG7cgeht9vdU/ZNo8b2yPYXsmSw1cXxtoPUereXn/UMJf2GAmwThnK6b7/9drz5zW/G1772NcTjcdx7771405vehJ/92Z/FN77xDTz11FOjrqeBwbWN9ZRN16PybCWtppABzjzdqcI5f5qG4NjtrM96auiyvd2o2RdfVO152esUXXnlEunNAB3gRpUOGVqkk1WLpFzVazzPE6BjG4gxtq0pKMn+MB3SyBgwfQg4fCsN12qSDlyjwbRlAHfFHQ7g4un+1F2t7V3TD05g+jAwJQzK5bOcZOjq6/2Wr0N/R8JjpF8BakI1aHmS1m81rtFx4NAta+l2bhEWkM8AobZSx/X42bczhxnjB9A4y+co3yNJFQtEOt/r1SQdbpmvtVoBCldEKrEGqeQAd2ACUV7n0Ehck/tYZsCi1nsdwdhsg10POxsndSvkWLMVlPNBM4MMC7v2lvJ0PuTYXi2rDBAJsZsnlclnb+xc9PV4+dvpUk55tzIkGnV+VynSmcosc8yNJegMh2K0oZdf4rhfEYwxp5vOYaNBx358hpor86c7Ke25FY7Vx27n50qRzuj+m+gcA+p5Nupkph1/hVLyBkgvr5YBOGi7x8R1buHehGPAq/5/a/sWWDuvardVX+jOqzx/FPMs+e6Ui/29tzLsLzahwgUkmg0uJBy/Y/i6DUsTt1Mf75fevh6NH1DzCYDvkj/EjZNykYsVuQo3fAIRLrJ7/IOHAmwDhqKX/8Ef/AG++MUvIh6PXz12991346mnnsIrXvGKUdXNwMBAYj0qz1bT3qw0a4CTH1mfjaqh6/XW6cpBC6WqJERFqmXuaNeqpLflV0XeTqHO4vNz0G5D0eBKeZHzcUrR1eOTjCvTUa8Ck/v5t5XSvl7/Wtsrr5eU6PUwbIiBrvKtP6tByhufWasWDtCJl+XY3TPchVKul5VNid0Ty3ukU8VkHbKptVQ6X4ATQF2tXN6z3Xnq1TJ3EMVsq2FstsGuR69MGFbsILXioWHX3lxKZX2wHpfny9/7LDZGjqHRhP24rpdhRTFLW+vXHLhyQe3URsZpUxsN/gAM7QqEe4c1LVnCuOQYrmekANZer9th2a5DJ9TCLMCdeH8AuOlVa+9rZ78A1S/dMoqMap61GaESm614v165g9Db+5nP6uUUs4pmLtmG1TLnasEw3werevtOCEexwVA73T/zMz9jezwSieCP//iPN1QhAwMDG6xHaxol7c2qQG2nEFmrrL2uXtHqE+NK9YWT3F2ePADcfNdaobZetKZWk6vOV86TQjx1gPE8TicH4YagkKXF6rTHC+TS3EGtFrjrOX2Q4lztKqluvgCd7mYNcDtIX5s6wFXQK3P8fvYGKrE6ANQbGn0LwOUzpJkn9tLI26m7Wvu9WQfmXhBqo0GquspVfEnda9S4EwBw5X6Y56e/I4EIadlZUXYgZK9Mbq030Hls5jAnL/OnSPcLjal4eOs92+DuczHHhQqPV8QYrnDVOptiqhOvnxR0n83Os6Sz6++1jPVzOESe7yqfYSrJunhE2MHyvFDKrwMT+1V5+VWW0Y3Cdx3A2GyDXQ87GyczYVixg9SKh4atsraftrdW5mKzL0iHVw+dkdcFQgActI0ul7JZV8SOtV6G28e/F8+TcZRLCeGuFQpX1SoM68mlaXuX54Vq9j7g6A8BYzN8Dqf+kfRuj4/032IWePEJhgkFwkoAtQ06S5UCM4yUClwsL+dZTtnZmX3DF2A42OQs61bKs/5uH8uY2k+qdfICfxo1wHUA+MGfBO68t/+wPGk3ZUYROzvcatIe5tPc5d9/4+DPdhiautujbLrMSBJL8Li05ZWi/Zxt1HUBVFaWpYu0wW4vGQr90Nv1Y3IOVKt09vfx22nfly9R/Hb/TUA8zblFMcs5nsfHdyCfBk49wfKkMO0OVTIfyun+/Oc/3/U7h8PR1cAbGBgMiX6oQ6OgvUlKTim/lvarK0TqyuMSHn+ninU4Dtz6Wq1+lt3KbhQjqc4qlVtl6jCHuIdfUJdbLcZ2eUUf1Cp0DOs11hctDubVIrD3GJ3adot0trEptSu+ssBYqemDYqW8xYlGeEwtJJRL3DWPJkQdgkpBtpuSbCFL5XKXhzlLATqFEnof+wKKXr7n8HDP0fqOBCKKVm2l8dv1/UtPi8lQRB0rF0kFtFLfdbXcclG1xeFkjJ4vQCq5THuj0+IkBd3tZ93090jSJvX3WtIlJTU9eUHEL46R6g5wslGtAm7xTJ1OHgMY9+cP7Rh62XbA2GyDawJ2Nm6HqxVvCFZl7fMnOfZ5A8ruxRIcI+2usxv3Ty13llEpUhk6OsFF0TMi1GRsiuedfwZoCfqQ3AF2ukSqrhU6QDOHeVyKlvmEfbx8lgvNoSjDtQJh2lkHSA9vNvj8XF6VDqxWoyOpZ81otblQEBO79MEInTyA84PUZTqjkTEumkvkVmmHu4Wx2c2rrBk19L67fJaLE3BwsQPgIv+gdmUYKrikbVvTdTbqypb7Q2oRqt9F+2HqomdlGROL9akFPkOr021XjnXeINFud/blza/ijww3lLY/sZcL7dUy52yVohL2Sy1yLuMLbjxkbxMwlNP9wQ9+sONzvV5HqVSC1+tFMBg0BtzAYNTQU0pZj48ScjfQjvYbiCiFSJmWREcssb4St24EeqmzypgvgI7u8iWlSq4b47AYeIMRYGWRq9DtFldeG00RV1YDaqXOexSzjEu+8Dw/F7Isa3WJ14TCTHtSLXPwrorrj8x29oe1rnrbdIVyiVCM1LnEns4+1ul+1n7qF4O8I3Z9L+lhVqPZq43yntb3Rban2zO+Ko6T6HyP5HVWRXO9XfId2HuEEyGAz3NqH1DMq7IlVfGAZSdi2P7dxTA22+CaxFbZxZ2A1eTa8RLguC1joocpQ46T+28Ezn6/83goxnPbbS5oS0iH1+unw3vDncDJ7yj6eaUkRNp81FIJR1UaTj30yIH+nCBJHZY2StpqgHUsZMlCkyki9TZcONk9FeowNtOauSOXos0cxK4M8952u6Yb+q3PMHWxU9APxzjnSViegV05/cwb9LrL8/X5n3S6ywXO1yRCMT6jKY2R12+7tgBDOd3pdHrNsTNnzuA973kPfvVXf3XDlTIwMLBgq1RT5a6jlT5upf36gwDanRSrselOFWtpKO2oQ3pZOr3I66dxH5sWeT0vclU1s0SK2o3/hLFi9SqpTQ5QMMUX4opvo8HdVo+f39WKQKUJLF7gSunKAqnloTh3yZsNTgQqRe6wN5sUa2uDO+PBGB32zBXuWJ99BoiN07l/7lssb+8R7mTPHu+ki69cpmOu0x/9IVL9pJqrL0BDoq9eD0t/Wk8ZFVDvT1KotOr3rVfWxkMDnLj4gp3PKJrgToe859Icn4+kvMlyq2VFi5t7AVg8x/72BRib5fOTHlgXyrbNJvvWqjaqv/vjM6z7xReBiy9whb2YAyb2UhTP62ddHA5ea90ZuBaopwPC2GyDHYVh1JLtsJVq4sNiVG2Vi8uSYp1LMdwmELQf42VmiVpZ2YPUAhlCkTh3CGsVwe5q014tnudY7wDDgho1pRjtcHMsb9Z5vO0GAgDOP087mTyncmyjTQfcG+C5F17gTnVdiLaNTSvnJ79K+1yv0GG68U4hnFnmOO/1cZc8c4Vzg/SysNkF7oAHw7xHuaDGeskCqNe6Z1IBBnt/pN2wZvSQc6VBVcJl9g5rOFm396VbXRfPcwccWEvJ79YW/R6NOuctxax9Jho72PWpL8SdZcle00PWLr64NlwxNtF73qBDb/vCOeDMk2RENIW4XiENtBpka4QibL/bQ9bgDhsbhnK67XD8+HF87GMfw9vf/na8+OKLoyrWwMBAYitUUyXtx0ofl7RfSWVyuhgPLSEp4YBSnZw/xc/S6bRSh3yBtTvm1TId4NQiV9CTc4wjAyjUklnhrvP+G2mUkxfpZPt8QHQMaNWpnOry8HuPj0axWgIWLjAOyOkEmi3g1JM03NOHgWAIuDLPe/uDNBz1GmOJXG5OVMoFOnfLF1lmfIYOdC4NPP2YUJX1qX50e2hoJD1aIrFXUddGTY3spoxqpY632lwxnppVhk4+YyucrrXPaPmSiu0Ox/ku9GrL8mU+O7eH9aiUOVnw+vnuHBAMgqVLvF8wspYOrtMsX3qahrbdJk3dVeZELJfiJGz/jd3bc61QTzcIY7MNtgXDqiV3w1apiQ+DUbZV2uag5pj4guq4dYxfTXI8jcQZzgOQbt1s0jZMiTF3RYiZtdq0dauLLCcQ4TGPl+yxcIxjaqPGdjVqIrWTE0hdod31BXmO2wuEwTE5u6QWt50usTMZpQ25/BK/czmBpottyizROWqJ9J7Ll2lHq2VgdYEL3e0WF94BxpCHY7T32RRQrwNR0PGWeh+90O/7I/vZmtFDZjQZVCXcLpxsvffFrq6yXlI9XKLVtn/X9Hvo19ilG+uGbtlp9LmNfi/9PrlVPttghA5+vxTwcJyLL8uXgbhg3i2cZfaa6f1AZIKbLnOn+N74AjtybBiZ0w0ALpcLCwsL659oYGCw+RhkhV0XT8ssc9DKroh0Tavc2SwXKZyilyFXVp/7FmNnZczV0iW1GiqdPyt1aHyGMWoS1SJpYh4fcPLbFGIpF9T3gTAd8fhUJ8VrbIor6cGIEFip0BC3W/zscHAVtlIE0KKRbDW569puc+U8OsZjDieQd7Ku2RUVM+7yKOpcLkXnEaDTLVdZv/2/KP7hdFHYJT4NFHJsry9AJ97jBY6K+G4rrSu1SOq5P8TV3EMnutPieqEf6rikGCYvcnU7fYXvSWxSrTjLZ5tf5WJDOKbEzyoivkqu0nu7xIXJle4nvsbfJe15tkEDGptQ9css89mX82LyNKZYBFLcZ/E82QirSzzmFRoDjRrbl0oqZkWzKXK3u9Vq+j4b9d/rFMZmG2w5+g0/2g1Yz8bqbdUZXekrwK13dzpb69lq3V7YhSZZx3j5+fI5pX0REpkl/CH1vdvD8X3+RX6XnOPusswQURXOcjAKVJfVIjbaDOma2k8H2BcUucH9ijGVX+UCd3ZFLAQHmK6zWqLdRZv9UkjT+XZ76DS1wcWAZoPft5oARC5qt5vfOd1cQHd7gLqP/ZNPc96wNM/j/hBjyC++qHb915sPdXsWdhRnQDH6xmd6P8f11LRXkwx3a7eVDS7l6WiefJzleEQ7dZE0WS8r7T1qmW9Z76e/I4CiydtdY8WhEyqmu1Lke1MX9vd7fw2UsnzemRUugldLXASQCzW5VeDYbZ1x2Itn1U747T/Evpw/LbRiQAf9uW911qNa5pzt0hkgnuMxf4hzk2KOrMsdssMtMZTT/ZWvfKXjc7vdxuLiIj75yU/ita99bZerDAwMtgyDrLDr5/pDQKxFZyy/SucnGKFTUy9zYNx3g9iJ1FYvS3kOgHL3dGoWSC9yILZSqK3K1Jll3qsgYsj8IRrp7Cpjw/xBDsTegFphr1foSI1NibixZTrAngApS+UiHWUXaLBrJV7ndnGQrpaE+EaAhryYJVXPF1SpT3IpnuMNsOxGHWgLx9zpBHxeoFZnXxTTpO5N7Sfd3OHihGFiLyczbjdpT3uOspxCppMydfksMPc82+/x0yhJozao421Hc7NSx4MR3v/Mk4KO71Vx28mLfF61Cg13rQLAwefjcFIt1AFSu2T+y7Kg53eb1PhDpDG2xMQqEGYfVIuqfpkV4PQ/ctckEKB4XWaJE6xLZ1iWyw2cfpLPu1Wnuny5IFRcfWKC1uA7s/confrVK7iat9yOPn8dwNhsgx2DHaoqPDD6sbF6CJXOFiqk1blAf7Zatxd2oUnWMV7SoCt55XT7Q2IRWtC+mw3avDa4mOkUjmq7xTF2fAY4fBup5qU8dVBeegqkj4eEc+5Xi7KTswActLdT+2kvz58UOilROsqlHMd3f5AaHLWKEjhtNpUtDYQ4frdaanG3lOU432ywrGZDfT97nLarKXKDh2O0xY26yiMumV3d+ni9Zyr73+FcqxK+3nPs9n6nl9SCdbVMp1syFOQuf6Oq2Ar5tLK7er2uzPHZyvCvoGW+JaF/1qnyekjhev+Lck7ywj9wvuWPMAtMOgmcXWK/6OEMjRrrGx3jfKqUYxunD7D+c8+rTQmPH3j+21yAaDbUPedP81h8UgsfEHWu1wC02f5mnZszlZI9Y26bMZTT/eM//uMdnx0OByYnJ/H6178ev/M7vzOKehkYGGwEg+wmWM+Vitf+oMpPLSHTiQQjnauketxsNkWl6RmRJ1SqTkvYKVMDaiAFuOMajHA1PBRVx/1BUtckdVgqmXv8nFikFoCLcbapVmM6knKBBsXp4kqx0wnUW0ox3BugE9locJAPhOiEe/0A2ow1drmpwuoPcEe/0eAKvsul0lZM7GW96zVV33IRuPnVa/tAPgf5s3CO1CwreonAdIOdGqkd1bpe5cQkkug8LhciZJ29fk4E/CH17JzOtWXWyt1VYhN71fkyl2q5oOj4Hj9j9vJpUg31fKuri9xlCceBy6f5LFoNUtQ9Pi6qlPLAZByAg3Hde46wDKnmKtXUgd25o7ZBGJttsGMwjFryTkQ/Nla21SoYJcfCXjmD7cYpnS673hgvadB+C2U4muBYGBCMKohFZ5ma0e3mgvrMETrVsQnBIopyITM2ASy8RCZSpUCnttWgPRmb4ljvCwCHTwD/97+T3ZQXtHYJlxtITAHV02yP28OF8HaL9rNWFjbXx+s8PnEfqO/0kC2Hmwvu0weBgzdzniIXGgC12y8FULv18XrPtJsqPMDd9F7XdnvvywVtDqRR1y+fU05lrcI5BsBnZRVvC8e5INLP/5VeD50q7/V3v8YOMwf5LGSq0+QFlT89dZlx/m4P21fJMwysXFC0f9n2cmHt3Ce1wAWUgzd3HpeLHXKu2G7xJ5ZQ+esLacDp7dy5B3aM3R/K6W7JoH0DA4OdiX52EyQVShc+k4NUrSJyeVbVCmQoBqBNWnatwoExGKXB0JWkpaHw+Gj85l/sFMrwBoAXvsuV/0qRDl29QqdWUpVkXDjEWFMrC2pejUY8GOEKamqBE4LouEh9UqOjvnSJDjdEEQ3RjlYTcIqV9XCcA3hLGPqGiAePJejEuT007rkUV+p9fvbBnsMc2MsFGvvsClfgj7yc92toTvfyPCcT8y9y9XfPUap7Wp9PN7GXXiIw3WCnRhpLrN3llelUinn1fP0hPttGTe18e3zAhed4vkOkSglFuRihP9uKtlovKeeXX+Lu9eVzpIS73ZysOV28bvYYr5uaJTugXFTpvhxOvge1GuP4Wg1SHyVtrt3kIonMGz/e4Ap6KafE2fR0chK7bUdtBDA222DHwDo+yTAWr59O4CA5hq1IznGhUipvDxui0w/6sbGyrfr4k1kGihkymTxeLhAeuGn9/MZ622oV/oSiavyNJWiTkhdoT+WO774japcU6KRDp5c4rusLxYGwSMFYpm2r1zjWBqIqbCcQBuppPqf8Ks+rtlnHuRd5j1ZTMcryqypFp9sDNEO0L3MvCIq4G6gIA+X2iEVXp0opVshw/He6AbSpUu4RIV5tB9XRAVKZLzwHXDxFGxYIM52ZWzjwq4udYqBWm5VeWpv3vZTnLrKVwSXpz1J4tVzk7n6352h972W4QSHNc5p1CsotC50aycRzupVaPMDnL+dgdvR3K6xq3fp5OlU+muh+TTdYd83l3KdSotMdCPPda9S5SN5qsq/2iBRzgbDSFAD4v5G6zP6ulPksmnUx9wDfgeUVluf2iFDBFuteSHOBppTlM7hwkv3kAEMSZw533xTYQow0ptvAwGCHYL3dBJ1GJR3c5UtKTKPZ7FxdrVWB4hw/7znMwc4BGoCJfXQkA2EaEaeDg2sgrI7VKiKP5jhpbFepdg4a5nwa8JbpHEvnzxdgfJbbT5oxHMDeQzT+6SUau4lZxhm7XLxnpUhHF+AKfy5NY+DxsF0uFynhTgedPDmoO508x+FiGS4X695qieMOOuCNGnDjK9kP3/sGzw1E6DSGorzG6eL5pRwXCxp10ec5lQN19ljnc+kmTLKeCIwd7FROJY1RVyz1+vgca1Xxs6RUReV7UsrTCNaqXBwBaBgLGZ7jCynRPE9ATVhSSdIQl+ZpPMt5PuNGjf0TGQfCYrLo83OSERmnMny5yHPabaDdEOEBZT43l5sGvdXiZDMQ5rNyiPMT02RnOFy8X6glaJADruIbGBhsDqxZFrIpjkV5IZgphZZ0+mw/0HMHy3KGDdHpB/3s2Mu2pq/QKSjkgJV5oCyclXaL1NpahWm/dIdNL0dvW7VIPYtqmQJSQbAPZ4/ToalV+dsfJospPsXFcSsdOhzn360W+6qUE2FGU3RS8xnaMY9XpWF0ezhme70chysFzh9cbo7P+VXaQa+HC6WLF8R4PwbksyIszEN7+YrXs67nnuH1DhfLr5QobOrxsYxahfVxesmCcns454iMKUGu2AQVty88z4wnlSLnMNUK5xdhcW44TjshbZZXs1nlIm1btKWegwwLkDmyJVU5NsEMIHps9+qiEnm1e47W9z6XUvH4y/OMZY+IMKiVBcHe2gvE4nx29TKfa6nAeUG7vVacth8ldv08pxPYH1TPdtD4Z+uuudsrGGaiTG+ANrheVgsHoQjnXPL9Kxf4/mWWuYgB8F1oNYHnv8PzA1GgUeFcYmyKjMNalXOGsWluHtQrnEt4PAyHgFOJBSYvAnBQT2e3qJf/8i//ct+FPvzww32d96lPfQqf+tSncOHCBQDAiRMn8Ju/+Zt44xvfCIBxZw888AA+/elPI51O46677sLv//7v48SJE33XxcDgusR6q546jUpf7dQpOROzHPglSgUg6mPsTjCirpMqosEIf64O6C51XGL50toVfH+Ig2U+20kz8oeYY7lSYuyPfl2zDiAgVjAPqePP/j0wvlcMumA6FWebq8VHbhNCYC069ysLgrLWAnyC+ub1c7Iyc5CrrU4n4BKTjvg0DfvYDHf2J/epfqsUaUibdeCGO+iIP/stUq908TCAcfG3va7zmC5MYj0+DLqpdspjF1/kc6yVVb5TgIso+2/kwkp2RazEZ4VYWYAGbzXJSV4hq4TVCllgTJsk5lJcwc4ssz/zGUHjd3BStu84qfguL3DoVlIdj94mxN2EkE9Z7JIEQlw1B2hgc6ucrI5N8x0FgIgQeYtPqElUOMZ6+UPDreLvcozaZht7bTAy6OOTFD7SMUzuY7vcwfL4Zjjd/e4shuMUTbt8ljahrsWpBkV+4eXLa3dJ9XL0tkkKry9A1pDcvZOpuKw7rYEQcPOrurehXFRj79XjexjfPT6lxniAtnb/DZ3tTl5Qi/ZygRngorg3wAXXPceAPVC5vg+9jDbyth8Qei0+OpIXTtK2ONpkYDXFjqbLQ1vvdjHd5813AXfeq+4l5xyL5xn2Je0HIBaUy3Tu9PegmF27ABtL8P6yD2VYQCzRed6FkzZprYS9sdKa9edofe8lTn6Hv8sFbmaExxiu1qgrtpacRwTCajHeKk7br1r3qFS9rbvmuRXOJxL7lAiuL0AWYCnfGU5Yr/J6b4Bzn5S24+0PcO6VWeHudiAqdr7FosDhW/kulvJ0zKWaOcD3rlphP+poY0dQzPt2up966qm+znM4rC3tjtnZWXzsYx/DsWPc9Xn00Ufxlre8BU899RROnDiBT3ziE3j44Yfxuc99DjfccAN+67d+C/feey9OnTqFSCSyTukGBtc5Wk1F4Zaql9LZ1g2G3KnNpgQlKwTsOQTMHKDjmVnigIe2oPqkOKDJ6/KrQlhMWyU9+4zIy1lR9Lc2uKpfyHD1utXmjnO7zZ1OOXb4/Vyhl3T01CINTmqBDlfqMo0rHKzjXW9WQiKnnxRxQnnSkgHuhDZbYudZUNzqFUFLripRMY+XgjBOBxcB4tNsr8NJQ7H/Bsa9pRZ4fTal+swXoMMnDaIvQIff46dQzWpSUaTshkg5GdgMaqSdoqrM+Tp7I41e+grPHZ+hAns4zknE0hz7QE6KALajKnYFwnE6tJE4+1lCKtyW85w45FOkKRYyfJ4ri7y/7JtIjOVIuvml0yrHd2yCiy8yFCAc566C00kD6/JQfGd8D+/tdPGdrNfZ1x4vzw2EdpSK6WZj1Dbb2GuDkWMUuY8lRhmisx7kmFop2u8gWyF3F7//N2TveIQjKfNNl0vAXJQ2cNImT7LehvQVikPWqxzP9x4h06yYVSFBEt2o0dZ6yV3P1STLrRZpzzyBzjzKMmtHq8md4kKGP1MH2ReVIucLtSrH/8iY2LGeV2Py9EGgWaOzXqtwfC/nuBgbHactqZSA2jk6W/UqgJbYdQ/y98UXOK7XKqxLOM7+LBf4u9nkO1WvciHd42UfRSfUnMTjY1+6XOpzbpU2L7XAMiXFe+VyZ4hcMcuFXx2+kBBZdaj5kDegFgP0/q+WaQcvPMtneeEFQYEXu8XBqJaeLc0yXE4uMu891Dk/AjY/ZKpX2IacZzoA7D3GH6le7vGJlG7iPahXVf/HJ5XzX8wC3/7/hCq9iw51NMHzK0WK1iLOeVh+VcXPRxN8Txt1shykirpP9J3cfImMc6NlB4SW9e10/83f/M3Ib/6jP/qjHZ9/+7d/G5/61Kfwne98B7fccgseeeQRfOQjH8Fb3/pWADTy09PT+MIXvoB3v/vdI6+PgcE1AUkdlzTeSknEZWn0qFazMz9iIKIE1PQc0oduVSrlkqKrq5QHIjQk1tyMmWU1wFXLjLGSYmXFHCcbxRxparUaEBfx5F4fY7ujmkFxumgEU0kqos+fFnHmHlISv/XnvH+zKeJ5Bd3J7eaxVhvw+zjQ10o8x+tj30BT+2y16ARGx4H9NzE23OvhxMPro5GoFoFqlTu0KckWELu3PuGYH7+DhxfOKcq4w8nJBcBz7dQ0Zw6OfkemmxqrfP7BCA2o3FEPhFSdZP7tVlvlT08vAWiLXeVJ0V+WXQBAhB84aTQdQmU2v8qJmMvDhZaSMODnnxNx7hVBQXMAJ17Nfs8KZfv4NN9FgPU4/5yIH3PyWOYK2zVR4u5RXey4TB1UQi/XkcMNjN5mG3ttMHJsNPexjlGG6PSCNduH3Llbb3wJx4EDt3ABuJgBsmmOj8Ucd/A8Ptoeu7zFsm2ZZe6KN8RuebutQpas7exGjbZTRZcxwbItMv0XwF1LuXPbqLOMagmAg85UtSwYU0mqV7t8qm5X5vg7PgOM7aVznU8Bp5/iM/aFWPbYFHDjnZqWC2j/qxU6xU4Ps1WUS4AzxPPOPccd8fE9XNDPLtPpkuFhHj9/XGJ33OnqTIe5IJ5hq03nbHFO7DLH2a5KkU75+IwKo5LznlCMn61OXGScNvPATb2V0HOrwHOPq5CKdou2dXxGzBOcdP4bdeDwy1gfr+jX8b20lzo2M2SqW9hGMSt0cFxcSJGwvl8XX7QPwwhIllyGadHcXr4nAFAqkukQipJ1d/R2MgRXkpz3uT2KXeF0Ac4mmRDxKb4Hy2K3e3yP6pt8upOJsU1wDnLyuXPn0JZxfSNGs9nEF7/4RRSLRbzmNa/B+fPnkUwm8YY3vOHqOT6fD/fccw8ef/zxruVUq1XkcrmOHwOD6wo6dVxXTbXmcbSDpELplCh5XSjWadjlcSudbjW51hErZkm9CgtxjaqgfukCLxOzakVfr7delqRzy11VGcN25mmusOv1k+JRrboWt1WiYQ1GFR0e4GQBoNGMT7GcyX2K0iXLLWS5s2CFpPzpkI6sdadFqmz2Uq4dFQa9h53oiqSwyXbIWH0Z5ydj0/TnFE3wnIhUkG3zeThc6FB0q9c6d7kuneFv2e9BsYOkU8+KWVLWrahVVH5XO2xFf+8wbJbNHpW9BozNvq4hxxsrfVcX++oX3UJxhg3R6Yb18i33wqETtCVyfJN2UNocaVOtZck2pC7TQZeIT/H3wrm17exGje6n/vo1VuV1oHMuIXN/O9CZdSI6LnY2NZE2ma2iWuq0mbEEd1Kt84bIGMvUy3a0+blaEnohmq2oaJRyCa+PoUx6/2ZTnfOZQlYpactjxSztidW2Z1Psa2u/Aqy/XQifjtUk+1TWW9YR6BTcy6dVbDRAexuK0fm0YjNDprqFbZz8tv1xa7u71U3vp8WztPNWVMsqhExCp9dL6M8oqM0N9D4e9eLbkBhISO348eNYXFzE1BQ755//83+O//gf/yOmp6fXubI7nn32WbzmNa9BpVJBOBzGn/3Zn+GWW265aqitZU9PT2Nubq5reQ899BAeeOCBoetjYLAlsKP8jmoXTl991Qdx3blxe7iCqNfBSoWSeZclfXpSDH5SGM3h6FzVtKqhu90UUllN0oBM7KURnTmo8nLXWtzlDkS485lKUnyrXgMmZrjiXsgy/rqYY5yxy0lxtXqVdW3UuJqeT3OQdfu4I92oC2qxjzsDhVXS2txulSO61eJOe73Mc/bfwHvOn2Z9JH1u8SVS0xL7OOgXszQSks4kaYGSfgeoXevHv8JdB0nzn3+Bq/0bVdPs5x3S88TK5+YV1K6jt/UnunLsdvZJ8jxZAqEIJzFev6DSOXhOIasoaE4XEJvi5OfyS+zbQIi7PJL253QBTcF4WL7E3fBSjtS5VoOr/24f+3ZpgcJsMp9rbILvQUnQ1+EAfD7upFTyioGhU953ALVsqzFqmz1qew0Ym31dY73cx4PYxM0M0dFhHUfk2CqVm7vVu5ChPZ2cBS6dEuEvDuYu9vvFuOkktbZa5vlPPwa8+F06mP4wHT5/gH+73KSq51ZUyFKrqdS0qyXGiespsvT6S/Xt+VNC9Owc7ah0UuOTANpAJqnsW63CBex6hbvui2fZ9lab7fGHSOMt5YCCE4CDyucNEU7m9QNwApdeYohQIMJ5wd6jtN/7jtNGZpe5kFDKsU2FDMf5UIy71oUMRel8PrV76QmIeYwfSBaFsFaAC+vVIunsC2fVQsf+G0Ve6WeBy2d4/uQBLea4RvsRibMc3XbOHKQT7Auq/raGBfRSt281uWNbEKrurTZF06pCdDQQZrsySyJjSEKFePlDtKWbMXe0Q7fwjPwqKfKLZ9X/7Z6j3KzQsZ7AW7XM2O1Gne92dpmLJw4hhNas0/avJvnsfUHa+VKO40Qpz2cjF4ei48wIUC4Imr+fTLnpA53zs23CQE63dcX8L//yL/HQQw9tqAI33ngjnn76aWQyGXzpS1/CO97xDjz22GNXv7fGm7Xb7Z4xaB/+8Ic7BGRyuRz279+/oToaGIwUvWhHoxg8dUVJPe+jnsfTF+gU07Crk1SPPX5HJz1I7g7rVGSrGnp2hbQkJxR1u1IkTXx8RuVRLmXpGGdT4h4txrgVMpzEhMeAKxcY1xQI02kv54FylhMEp5sGrNXkYN1uC+GNGOBr0OC3HRyIJ/bTeS7kOBA3hWPXqJI+FkvQOJ/9PmlNbQhaXgnYc5ADfiXfqXp6lY4njLuV5jVzkKIvZ54S1GwAlcrG1TT7fYd8AS5k6CI51TInTEB/Tn84zsnE0vzaicTEPrVinV1hTKE/SEZCOc/73/QqITKTYV3qXhVz12iIneuAWJTw01AGozS6APur1WD/y4WO5Uvs+9gkJ7L5tIj/nuT7JK+9zlXLR22zR22vAWOzr3v0yn08KDYjRMcK3b5KCrd+3G4c1sfr8RngxGvpLKeSaqyqVcXCcZ6OxT98lU63RKVCZ2T2OG2VLoDp8pDyOzXLkBpgrTidXv9ChuywS6dop1NJLqq2GiqVZiFLB/jATYo+LNW9i1ng/DNAQQitut10glxOjsEy60m9KrJeOIDxSTqyi6u08YEg23zxNPtwaj8dK7cLSM7zGpcPiAkb63RygdztZb3abVKIZWx1rUwned8xOn4rSTr7ksLvcAGJGZbbavFel87QRsj0adkV/sQmVC7pSKJTrFXSosNxIVDXRaSul7q90yWczAh/nGnOPUJjXAyAU4iypTl/KRcBb4b9deDGrU191S1sQw9tAFR2Fjs720u4rVHnc6zX+R5L4byw0Ma5fI7zvtkb+CyXLwMHb+QmSKVItfpghJ+v1lmELOw92vnsdsAcYCB6+WbA6/Xi2LFjuPPOO/HQQw/h5S9/OX73d38XMzOcyCWTnVSFpaWlnqv0Pp8P0Wi048fAYEdhI/S0fqDTeXS6lk6HsqOEd6vTevQg6/XRBI1yudCpuBkMK7qWpPokNAq3RKVMQ1Mti1RhUDTyyDgNd1VQiVtNJXTmcPAzIHb123Tu/CGVrqJR53eSftZsatdUlZBMPq3irQDlMAP2K7+yb7v1ld01bQz/zPt9h8Zn7OmBscRg97YLGQC4WKIL9MljAHdfJL0rPqX63OVRDHNJN2yDjvfYJD/rtLDMkth1gdrBCobVOeUCr/UFeXxinzr3OlQt30yM2l4DxmYb7DLo44g+tur21Tq2Wj/HEvYhSToF+twza7/3Brgra71W2lOd9h1N2IeUyfFaZqYoF9Tub73OcboibG/ZYptjCV5XzApxVQGZ3rOU5w6oRDBG21sXZeZXuZjuDShmQKPG+F2JSy/xt6yTX+xg6/Y4PiUE0nzqWLnAnfJQTNmXShnIZTop+RLntf4Nhmk//AE6dQDLCcfW0sj7tSO95k0y/ErCF2C/+4Kq7r6g2DQR/SDnSXZ2eDPRLTyjG13bbr6xHuTueLXUGRog+0K39QCwJJ5RMcs5pF0EVSi2tq92wBxgoJ1uh8OxZtV6ELXyftBut1GtVnH48GHMzMzgG9/4Bu64g8JEtVoNjz32GD7+8Y+P9J4G1zg2k8rdD6zKj+22SNWRp1HU1Ry7rWDKNqSX1qfgWRVJZVxQrzyMvahQsrz50xREK6a5A13IcIXR7SHNOzKu4qQ9PrFbXeGKsTfA3er8Klck45PADa8Q+a9dXHFu1LlaXq9zxb3aVBOCZkPkwJa7AmVB63YB4Yig2rVp4B0Ont9qcSe1IvJlVwo03NUyjbFXrJjLlBy1MoVTPD6uRF/NHepWBkZSmZfm2G6Hg2nOfMG1uxvyuZ/9PtW6A2Jn3ONleW6XcubXezet73B6qTPlCNBdqTY+uVZJPhDpfOZ2/yOAOpYUzzcS58pzdomUuPFp9V5epeVVuAKdFcJppSz7KRhhH5dyfObeKBc9mk3S1Io59tHkLCeDq1dUKECjwTJjE+zvqYOcFM4cIsPBHwFCYardyhRhDodaLdf7dbvHgy3EZttsY68Nrjvo9lWGXulK0kAnhVsPuYomFFOsVqFNDI/TJkQTZGTFJ2lbShZtg3pVCFN6Oc5nVzie7zkknFonKduSBh1NsCw7KvLZZ0gnv3KBu6m1Em2d0yn0VzJcjJ7YR3XomQOsQzbFOufStLfNGm1tbpk796UibezkLNviHWN52WXOd+p18ZMHrmQ5vsNJe5tbYT/WqsCBm8lSC0a4wy/bPj5DGzKxD7j9HhFyJmyb28Pf86dII2+3aTecDtqHQFiJ1LlcZMl5fZxHhOJ05AtZitwFgiI96DTvN4ytsObllvO2+dNk/I1NiywfZZHqMkDR0cwS7xVN0OZmloWCvI+sw+j4urfuiUHmkkBn2EZqgfZ6cpbU/ECY9O96Tc1r5CZGv/ddPM9+WF2kKn2lzJ1qv59zNq+fbESnQ6QrFYzGdpO/pa0PBIHUFcGQjPH87/0VGQSxSS4eWLPsbAMGppf/3M/9HHw+ri5VKhX84i/+IkKhzsnfl7/85b7K+43f+A288Y1vxP79+5HP5/HFL34Rf/u3f4uvfvWrcDgcuO+++/Dggw/i+PHjOH78OB588EEEg0G87W1vG6TaBtczNpvKvR7slB9TCyKVkpavU1J+C5m19ZJtkCrishypSGrXlkHzMPaiQklUSsKRGacD9NzjzOM5eyMHv+VLKu67XuX9HWA9L7xA5zg6wXNcHpFr8SDwwnc56NaqQNbLQTmzyv5ptQSN3CUoSBVSv1tNGvtGg0bAUedqZ2KGxqyUp9PWaqkUIqtX6AS6PXS0222l4t1qqJi0dpvtc/s44NcFnW41SWMdS3CyI3OKT86uVZzVn7v8Lp9h3aSKebMh8lhLkbc+qInyPEnzkxO4Xkq1em5Mu2drV/5LT7M/ZfmttlKg9/oUfbBWUXm7ZX0aTU3lPMjJZi5N43iVcVDjs8suM77d7SNNsJTjAoxMNVOtKIe7IZ7xVJHtueGVzH+rK+1KTM7yXbAuYm33eLDFGKXNNvbawEBAt6/d7KY15EqqLUfitAVeYUN0OxKMKPpyMMpxE1AUabebY5/TRTscHaPzePEFhkglZgTtWtxr/w32Y2BmWYVntdt0bBtVtfvsdAMeN8ff5cukovsDvEd4jPe9cpGOU/qKWNQVeiker2AfBWk34FBhP6kkHalKCUBbCGmKLBRuN9tcLpAyHEvQTjscHPsDIapRR+LsM11jJpoAnnmMGjLFvLC5TtWOQJjPRFLGvX467la7GJ2gU/vqNw30OnSF/o74Q7TT86e5yByOAcdfwe8jcaa8kqhVaRsDYeDIrWJhP0DV8o1QpIeZSwIqhl23nQsiVM86v7DbAZf31cMxcqvUEjj5OO17SWixNBt8fpmUoNYXRBhhi+XACbidXPyplEEV/RjDAGePco6QPA+cegqIjbG/rsyLclzsw220+QM53e94xzs6Pr/97W/f0M2vXLmCn/mZn8Hi4iJisRhuu+02fPWrX8W9994LAPjQhz6EcrmM9773vUin07jrrrvw9a9/3eT8NOgfvWi4W/EPZ6f8GI5RvOPgzZ3Hown7esk2WKliuRSN9CjaMj7TOaDqx2UddNqQpEsXskq9eumSOicUAwIZBrBcuchjlTKNphyUL5xUq6ihGFBbErRhQTVr1DixqApKUSHDCYLLTee31aKhrtVIMff5ObmpVXFVWUbujEvBNKeLK/oeLw15q6EWCDweMYlYFMZei74JRoHleRrANjoNi91z0J97OMZJVj7NCYx0ugF7qpj1edq9wzFBHZROcS+l2n6erRXyXZPlRxMqxYoOSYfU6yM3UiV9TlLCHA5F8yvU1I54swn4RFqxdlvkr83zOXsFxa5e4bNtQ8SAh7hyvZpU756OXIr5xq3Y7vFgizFKm23stYGBBb3GVqsauByjLp+jYxeOddJipR2R4/KR21RMt6QY+wJ0TNwuphwrFxRTK7PEWNf1IMfM7Ap3dUt5jrElYdPboC30hZXw1PJlhv74BIsoE+dYnrqi4n1dLo7R/hAduOVLYmc/qhx4gPa53eL4XqvQMXeBTj9Am5JZVrZRLqhOHWB75ZxBt3nZlBCPsyy+OsTCb7nAvpN2O5pgbPxZGwr/Zqrd6/OjQpb9CfCdkHVzgPH6ARFCJePp1wthG6Quw8wlrbZzz1EyOKQ9lrDrP3mtlXp+7hnO+c4/i6vRzi432QXhGO2+wwn4PYKd6KNz7fHzsz/A9z4i+k6GXcg5hx66WC5QnE+mW9smmz+Q0/3Zz352pDf/4z/+457fOxwO3H///bj//vtHel+D6wi9aNNbAbtYXl+IBkpXxJS0M7t6yWO6+jiglMlH1ZZWk4bSAaZpsCpxdiiha/FYtQonAlOzpI/XqnQsJ/eRYnzxNAdRR0DFYFWKpCcl9ogd8n2kCy2eV1T7XJp5sqsOro5nU4rOHZukEyt3370+9mGzxc+FtFgdj3ACsZqkUXe76Yg3m3S2my3hrLu5mhqM0jDU60CryvpeNYZOYPYmUua9QbZB5qoEOsMD9OfuC1GIzevjJAdtllvKKSMU1BwT6/O0e76BCOsjqYNOh8qbroctSFX6VpO0u0KaYi0Hbuws36punk+pCYGsXzguFl9W2QaZDqxe6ayP26PoiCsL3CFvt/hz5DY618uXuSNRE0bV4eQzLub5vyHj8yf3sT6xCZbdbrNNt/0AJ19SyKXdpPFtgyvv8Ul7g7rd48EWY5Q229hrg12DQUJINhJu0kuZefG8Ok/ax2yKdFffPu7UOqDGaqeD45wsa+8RjoFzzwP5FYbP7L+BO8vuIHfxKkVe5/ML5zZAmi5AVlVmmXmS9bqll3hPp5PhOAnQ8XF76KSWClRHDwlthVKONiUkwrjOPkMBNpeHZbSbvDYo5jBOF8so5mjLQxHa1kJG7Ya7PQBaQLVNX8vlATNQBGkbHU5eFwwDmSsA2twddnuBK2HFCPCJ0LmVS0IJvKzigr0+lhmOA7FxLhrUq9yB9wepZh2KqdA/Ob85+TiP9VK+7/bO2B23ZpOpinlDKQ94U2xDswbsv43n5FLCHpaBcQgF+jDtYKupssv0G4pmVQoHhptLWr+TzuuFk5zLeXzMUS5TidpdW7Pct5SjsnijLhgXBUFPd3Ju5gY3OkpFYHmOGybj02Lhpsn/q5hgxC2cY9+6vWQJyHfKJSjlvpBI15fvL9vAJmEgp7sb5ubmUCwWcdNNN8HpdK5/gYHBVqEf2vRmopvy48S+TlVFCbt6yTZ4/J0Dn1Rm3mhbJPXH6VKrqnZ16FBCFzvKbq+qh0zTBKg+94eZGqtcoPPrcDIerQ2eK+njK5c5oB55Oa9bOMtUU14fB9zQGMtsNjgwu4WTLXcLxvdwtbVa5OSj1eIKelPE3IXHVGovp0vFCYXH+Bx8ftKZ3G5OfLIrQNPB1eWZQ0xR5g/QUOdS/F7GY3t9a8MDrM/dF6KSpsfL3wDVZSUNUFIL7Z5nt3d4bEo5+fIcnTYGAMUCae7hODgBEY5ypaQoVo36WnXzfIZt1uHxC1reXiV2kl5SOy16fcpFTpKSF+k41+us28olTjIi4/zxeOjgVwtCQK9N57wF9nMoyhX1fUcVxe6wNhlq1NlehwsY26Pq6rXUfb2+3AGqplsJY7MNrlkMEkIyinCTbqFc1rEmEOGPL0CHWD8OiAwcK+q408Xx9GWvA079o7K9uRR3uWNjFJFK7KUdbrXoBI+LcbBSpEMUCInFWBE2lLzI87wBRdGenKWzWi3T9kpmFsAx2O3j+Hz2GaW+7WipdFotuWXfAuCkY9RqCO2XLODyivSdTjqQLrdinVUrgNetnCK3FwiGgAO3iFA0NzB/RvVLtcIF+WO3sfzcCnDprIj7ddGxq9eFnodoe3yafanH3cvn/Oo3dYaDNZu03fKz1fHu9s7IeYP1uAxhk89Yqs4HI1RGB4Q4aEQdl3PDQIj11u9pDR9bLxRNP28jc0k72xkMUy1en8t2y6JSLnbOIQHO1WplLsI0G3SUHU5+jiaYTnb2GBdxQlG1g91Ks89ik3zPUkk+N7eX720qqbSEJKPC6eamw6VTfC96ZRvYRAxkbR999FE88sgjHcd+4Rd+AUeOHMHLXvYy3HrrrZifnx9l/QwMNoZ+lLc3E92oSideY3/crl7ymJU6LOlXG21LP0rYUnFTQu7+hmNrFaKt6q5SmTKo0Y3LBWDfEX6WSq5WVkB8spPSHIxxAAU40AKM940kgGicq63RcTrHXp+iPFVKQmTNrcRTStq9wmOMHWrUOFh7fTQAbnEvqdI6sU9R6fW6yr7QFcG7PfduavI67cr6PPt5h6+m7UrZn7tooUDK+3V79nar1fK4NWZL0sllHeRveU9JM49PsS+XLipF2rEZGtpCXuyYiD4PhPisdToewP7byPu+3ePBFsPYbIPrDoNkB9nMTCLdxpRB6csXTnbajYSwp6WCGqftxuVilsd1m5BNqfFaRy7FesXEQrauWh4I04ZXCuq4HNPHppRQqw6vTy0uA3R6ghE6ReMztK8AF7v9fjrfkTG1A3voVtU2vS6+oMpoUsiquYOsTxt0uN0epQYeCAsxNxvbKJ+zXRhgt+Pd3o1uZejQwwn059VNkbxXCJjd8fXO28hc0u67bKp7iJzdtdZzj9zG57v3qEgZJuy/P8R3Zt9xJUCs5//2B9R73MZaeAPivWlzYb8u2IiBsKKg98o2sIkYaKf7P/2n/4Rf+IVfuPr5q1/9Kj772c/i85//PG6++Wa8//3vxwMPPIA/+qM/GnlFDa4BbIdqcC/61yjquN75uvKjHDwkbanfe+ltcDj7U5zsVVeremR6iaunOr04mlDx0LIOx2+ngV2+RKXIfcf4XX6V1K5gjCIhbo9SsCykSX+bOUBaT6NGgxwZp1E8/SR3OudP0xDn0zS2zYaI865xabDZoto4oiIOrQCM+1mWy6lyNsPBMlpNxvmkFoFSkgvwjjb7L5ehWqs/yPpcuSBi01foxAfCvN7nZ794AqQGoqVyenr96tlllljW4jm2dUKszleK/F5S7gDgxe+KXe8jpGdJymGjJoRibJ6n3TvsDfCzTjXbd5RiLKtJGqT4NOl0PmGAIpqR0elkbo+qR14ojvuCbEurqVTv9xziLvSVOQBtPk+3m308c0gZLlnfp/9GCO/F+Wyadaqep/N0wP0B7og06+zDBgCPE/AE2fdtcJHE52P/7r+hM+QBYN0mZ6kbkL6i2m2lsfXqy2tYvdzYbIPrDnYU2XKeGSes//ObGW7Sa6wJx9YeXzxPe2NFMcud8clZ2ugxkS6rnONOYSgGTN5GpeZTT9BxDY/RwW3UKCBaKZDpdek0dzlnj9E+yZ3LQESMn05eG4pwrK6UOaYGBdW8WSPVvZBTmUBade6uF7LA0jzb4PXy2tUloLjKe/sCtNP+gNBTabEv9gi71KjS3hy7jU7Yi/9AFlPyAu2dL0D7VMqQ+Za5oujC2WVF3W/Whap6nfMPl4tzlpywb5Fx5XDJd+Li84KFEFrb9xK9lOj152SF28N7XjgJpC7zczimKOOSsbfn8Pq0cCus2U/sspro129kLmn3Pscne99P77dKkXOLyJhQGxf3dTi4YVIqcC4I8V0sQabkyiW1KRKNc97oDHMuMjbJsLlGje9sakGEO4ZE+GCTDrfXJ/7fK4CvokLx7Oq7yRjI6T59+jTuvPPOq5//4i/+Aj/2Yz+Gn/7pnwYAPPjgg3jnO9852hoaXBvYTtXgfpW8B61jv+fPHLSPDRpEYVye2y2l2Hroph7ZbpFuViurwVNSnq0r2OE4cPOr+KOXGY4rWnNqUVGly3nSyiL+ToevnBeGcoVGLytis71+0tpkjHa9SuPtj9CIZlN0XqMTHFinDjHOzRMQQltu/jjcgLPBeHKXS+S+bHPQbzUBr0vFexWzPCZVVFtNDuAOB/umXlMCMa22WJQIMMVIeIzO4UqSKcF8Xp63tEDq3/RBOpsutxIbcbr4t4xFTuwRtMNQ72ervyu96G3+oIq1AjjpaLc6c4ICa+lkcrJXLSu6uKQR7jms7r2a5K7EniMiLn9J9ZX1/T94SyfFvlLk5MPtAaYP8VitTAd8zxHhxHvJYmiDMWL7jgK33UOn3u7/0BfgZM/p7Gx3t0wA1r68xmFstsF1BysNVtomu8wOmx1u0m2ssTverS5yR1Sm45TQqcfPfJPOqdsHxj9fAdCiDZX0WgfosFSrtI3lArOPABxD50/Tdu89KhhGbWBC9ENDLIx6A9Qzya1Q0KpZU7bE5SIFuFbhz8UXOb47XQActPH1CuCa5Fgdn2K8cjhOW+kPCYXqVTqo+Rzr1WrSNtTKyv42a1wkOPVdLiLk0iJlVZT2u1RgvzlcXHSoCScxlaStyq3QxsQmBC3fywWC8alOx1v2fTclej0srFve6kadc52xaWD2BuXgTezrDCnrZZfs3g2p3aJnP7FmNdGvl9jIXNKujr3+f/R+84dUXa22vHyMmxWyb6pFPnuvj89LpmqVNHb57GYOcc64sqjCCOXzjyXIpHMIZfzpg6pe1t3xLQwvG8jpLpfLiEajVz8//vjjeNe73nX185EjR5BMbt02vcEuwm5QDR60jjutTdZ84PqO+nOP0wG7Ms8VZang3WrRGFsVKPu5RzFHyk8gzFzU0mlqt3hvSefKrlA4q1qkAS5kaNiqJaFULnZUi1mmPMmlhBjGCh3SlcsqntvtBcpZwOUDzn1frDiLXXOpSN6sc9DNpWjonSKOu9mkg1h30JmvVUltrpXZH60Gd1brVZ5XE7vU6SvAgtjZT14Anv17PuNijmXXqtxJz9TEQB9g22sirmx5nnF3e4+KHQ6xOq0raQ5Cb7a+d9L4Pvctll+t8FnKnfbly8DBm/gZ4lk3m3wnpABPKMpnorMbJBVMvs/jM8D5k6SHX36JImluL3DsdvWsr8yxHlOzIl6vyHIrReDyGRHb7+eExeniyna9Rge7sapyuTqdvL/LRce9XGSowexxkc89zxCBcpHti4x1vr+S6r9TxpZtgrHZBtcdrIricmyyUlv7yeywlehWl0MnVKywFIHKpbh4+fTfit3g8/zcqHMHulblGFotM7ezTgtvQ+y4eoFzz9Ie+EK0d+krtJ/f/zteE44rm1oRscC5FPuuVlapsFJLQLPKvxs1Lr6Xs0CzzfmF0yUEMl10kgJROvGri3S+M8u0GdUqnfVWHcis0D406kIw1anShRZzdPYvnWXdZN7telmEk4m+8keEo+5T/VnICvHQAu18WeyOpxZpU/YdV7ZEhgJ0U6KXqt/y3MXzaxmD+uaF9Vr52+vnfKDbrrN8N1KLDNsqF1jvI7d1viteH+PuY4lOgd5h3+f12JjDZEaRx2U5sgy9bwpZsg6/91d8Ryolzmlnj3GhwxsA9t/Ic8MxkTa0ROe6UuK7Gk2QFVkpcE508RS1AuKTPHbH69Vu9xb+vw/kdB88eBDf+973cPDgQaysrODkyZN43eted/X7ZDKJWKzLao/B9Y3doBo8aB13Upvs8oE/8/dKXTuf5kAtRcxiYyK39kUavcgYB9V6hYY1llDpQrrdI7PM8uKTNADtNo39lYsUHKtXSOdKL9GRrVdYj+wqcOQEkC3S+PtDdD7L4GDbbJLKHEuIyYObZaMt8ps3OXLVKhw8223GA3sDrHOlSLpZvQKgBbSdSmHc4eAkwOPnRKPZ4CKEx8PPrSadb4eLRr4tjHJDOIIXniOFzhfizvfSvHAQx+mAl/I0AA0x6QlFudjhcHFg94e4ml7I0iHtRinvBf390oXTSnnuJDvE8VyKz3TvET7j5Uvi2jYXD5o1oOLn52KWv2cO01jp1DmdmlbM0qg3m+KZgJR2B7hyL3e+yyLFTSjG63MrPL7vOFARiwS1Mo26P8xrKyUg2wAgJlEeHydnktLudLBvVy7zPnJXolLg+zWxV1EHA5GdNbZsE4zNNrjuYKXBOhydO5IS1fLOCjdZj44+f5r2stGkg5Fc4g5vU1DIpR6GzHsts0J4fBxn3R4llPrS00B9Vd0HDpafT3N3sF7jfGFlgaFk0QT7MbdKu1vOC2e8QYe1URICZjUer5W4EN1ucb4hbYULvGc5LxYJmrSvVy5yEaBeoaNdzNEpbtZU6s56jfdrNcggq5Z5vtxkcDT4vcfP37UK7xGKkJkWmxB6LjnhkIdZrytzLH9qlu3PrXA+dPNdiqGo2xJdiV4PCwNUnLH+u1ZRTrx+bUEI1Xl8SrSuW87scJxtmXteLFJ7WfilM3TUE3vYpnxGaNr42Te5VCdTbRD0w+Rc7/+nnzmyHeV95RLbllpge9ttzukaNTrjx27XGAZR4OhtwIUXGHIRigHRSaDd4CJSo8G+aLUZqljwc/MgkqC2kjVkbZMxkNP9sz/7s3jf+96HkydP4v/+3/+Lm266Ca985Suvfv/444/j1ltvHXklDa4B7AbV4EHruJPa1E3E4+S3gRvv5CC8IuKJ6mInOBYQVKkKd6ytaurWdljv4fGKWNwanTqpaO3x0qh4/HTMfSIeKws6Y4EQY8L8QRpDgDHbgQiNpd9DJyyzxLL2HOYkIyeMcWGVht3tBloiZYnXL6jkftZHqmFCUJWdDgAO0u/Cce48u1w8p14jBS99RUwiGjSSLiep6+EYJ23LlxmnJncUWk2xsi92zL0uOun5VQ78LrFo4Q91Usx94ic6PhzFS3/vdHEYuWrrC3HiIincvkDns12+rNKyAOJdmKQTHE30fg+W5knhAth/DSGIM39KUdg9XnW+ywOMRbkin7zA3WpPgFSyYk4ZzuV5ocS7jxM2gDRGmV4F4Lu6cE6J7cg8pzLeMDLeWfedNLZsE4zNNrguodNg17PTOyncpBcdPRQF9t/EcTSdVLvX0gmTO9ZeH+AN0T5KwUo5LkrnLzJOJ02O2fU67Va5wPHU7aU9kM6dy8OFzaCIpY0k6LxChBbJNI/1KucBDhfgFJlJHOB5cIjjLj6PsWkg4GGdQlFRHlQGi2aDC9cOl8hI0mR9QzG2yeHkfWtlkcs5xAUAr5+Lwk4HcOgWzkEc4HWJvZyXSJuhx2y73MAtr2ZfBUKdIYHdlOj1sLCLL6rjOtJXOllY8px0gH2QvNB5frec2cuXWH8dtSp3vhN71M651Q7Whlx87pfJOSgtXh7XYaW8L82rMDOAz8blFu+DeJckPH4uqBw+wTlVaoH9kjzHc1eTAEQIhNMtQg32sIxQdMv/9wdyun/t134NpVIJX/7ylzEzM4M//dM/7fj+W9/6Fn7qp35qpBU0uEYwChpXN/r0dtVxp1DTChkKgVQrNJYyVQZAQwrQmTrzJI1iVlDTAmER0+XsVHKUdOX4JD/LlctiVuWmLmWBK5eYfsHh5IAXFKlF2i3gpadY/tI804q43HSisytiIM4LCpRPxIY1aeDnTuGqIXe6uALscHJ3NHmB9200AbS56+kPc9X06qp7k05bTYhotFtC6VwM0o0aAJFaZeoAY869ARrmpozndvL8UJx92gYnI6nLIlXZOFNxVYosu1IBPDlgcj93zJcu0/mvFHjtxKwQ9Kh1PjdJXetXUE8Xwsss85nq+Tb3HuE5gKJxF7PKSZa5t8t5pR4LcKICqHQrOkp5TnbOPMW65cQKfbPJxY6GoPzLmHigM7atXuH5Mgbu/LM83+Xm7karyboU80B8gpO7ckHkHncBzUmR79XHd/bc06TnN+vso1u8vF9mGWtyj16jiuSDwNhsgx2Pfu36sEKso7bTdvUAetdtFCKycncwv8oF9NVFOqRuD3dmMytAW9ikdkosFt/OcTk+2alaHp+kbfX7mZIzt8pdUpdT5ZDOLHO3Wqb6BGhzK0UVOtRuc+EbDu7QVkpchG6DY3tbpBBrix3qVkPZDYdgLuXSdD6dDu5GppKkBddEPLqM1fb6ee9AWMV4e4Psz0YVaHjZxnqFjl4gTBZWoybSnonFYH+AdSjm6BC73DzX4VDzIOsOrTfA3VGdCainSLW7RsKqp2I93m/ObLlAkFvhHKO4SqFZWQd5nVWZfFjGl/W6Up42O7vEfp2c5S4xsPbdLmSFcNwCn9meo52aK3b/e/r/SGZJLMIIHR2ZDk8uAvn9qk6VIhf9izkgLRZnVpPcLc8skQXicInn3hLvR45OeXppeJ2kITGQ0+10OvHRj34UH/3oR22/txp0A4Or2CiNqxt9Ghid4z1oHXcCNe1qjm0v0CrTUOmCIJFxda7bS+clFAXQFrk6/cCBmzl4SodOpoHwhzopRU6XoIqX+TxSSeV05TJq9drhoBGc2g+ExQp2uaDo1nACbidX42MAQlXudK8mqYDu9oiY7BU68vWKisOSgl8Op8jZnafatdOlHOyW/C2XSR24umTqdAlD7uNgffAEd9w9Xk4uGoLK5PGrRYdAmOfLhQyXD4h4hDBMDYiOkSkQSYiBvc12eIWDPjbDyWQxK8RmLPH2/YjxWQVJYi0+p1aLExdJBw+E6RjLnXW5K6Dn3g5E+L2k3Msd+fgEY6YDIbWQIePxWi0+71JB0Rhbbf4dEv0TjHamagMEfbCkxeEFSROTQnrj04AnzO9kLL/Hy7Y3G7yukCV1fPkSRetaYnEln2Zc/5GXcwElPMZ37BpXJB8ExmYb7Gj0a9c3IsQ6SjttV48zT3OclDuc1rqNSkTWF2Ab8mmxg6wxjSb2cXxfXaCdik+K0J4CQ7WO3yFo4UKtOjrOsfXKHMfM2DgXiTPLQPUK5wUeH/urVqYTPDZD+9Zs0FY2G6R/1+u0oV4vndlmnbZYMtFkHaWOTBuC7tukzapW6Bw53YzldjqEPktNhHp5hdK3j8/N5ab9CIaB8b3s31IOcLtoP/xBwOlhWT4/cz17fbxnZAyoCN0Zf5AL0JUi+2Vqv8YW03ZiCxnaqmhCxWtnUwzFsstFbYWM0ba+f6vJwXJmh2JUn587xc9OL+Bqc560mhS6JsG1O+3DMr709pTydGzTS+zLSonhCJklbnwEtXf/mW9yg8IXElT/JmnxDgffebv/Pev/SCjKOV6rKd6jlmI9TM4CR2/nd6nLANpk6tWrQHpRzf8aDRHm5mDmmnqN50QnyLZzuXsLrm4SBnK6ARrpP//zP0e9XseP/MiPdKQjMTDoiY3QuHrlURzlbvegddxuapqkAO09ohSxAUW9PfEaOk+5FAc7h5MDlHTKfQEV0yLbYSeotppUQjSlgopB8gn6dWyCDrDTzc9yt33vUeD09xhjgxYNaSHP3d9wDMAUjdKlU9xZboid5WqZE4N8mk6lA0LJsi3ish0i97aHxt7pAFx+5SA2m0C7zsHW6edEwOmmgUhMA5MHOLB7vMArXg9UX8OV+9Ul1hNO7qrL1XFvgEb58hkl6jG1n87izEHgwA1sb24VuOOH1Y6zhNsDvP6fr31X+qVwWc+TFLVWU6UjA5TCbThO0RUdMu/p3iNq8gao/Ol7jnbGN118sbPsXArYfxx46ftq9xzgBOyHfpJGTirjSzjAfOwXnufnaIKLAbUqHe5Wi6rvM4c5YXQ61f9zIc9Uc6EYd+nTS2RNlApAsyiMf5kr2sduZ0o742ivgbHZBjsW/dr1jYqWjspO29VD0nqtzo6s26gEV6WQZShG+1LOc4z0B2jfJvZx0Xt8ptPRik+qjCMShQx3xisldcwfBupPCmGxFheO0aYD6w/Rga2WgcMvEzvFGaV30hAU92CUzn8hBcAhRNAE/TsYEsw04Qh5/RzvgxHh2Oe5gOvy0JZE/bxPICLaNQ3EZ1iPelUolnu4GHDlPFlo+46IheElLtSOzyhKti9Ap6+YA+YFK2vfcZV2Sxc803di5fOzqsdbadu9GBXd3j+rgBjQPWf2oRPAk3/Teczjo812OIBb7x4to0NvTy6ldtrlPAIALp/jhoPeLzIETG5SSNVyh6P7rrL1f2TPUS5mFTMANKbA+DTFduUmkQy3kDvZ50+SCeKPiA2OiFg8KgkhXg/nNP4A/1+iiS0XXB3I6f70pz+NX/zFX8Tx48fh9/vxpS99CefPn8dDDz20WfUzMCD02Jt+jl9L6EVNk1TjWoWOr0yB5fUDt/2A2k1dmqNz5Q2o3Uuv2M3VB5xewhfRca6Yf/8xGlOvlwMgHHS4l+Ypkpaf4SAcGef343voSBczdNbCcd6/XqMieD7DfN0OJ3dbA1HSvrLLNJCxSa5Et8EB1ClWQBtNUbk2qNAihMwqYoBtN3nM7aQf7QJ34DMrrH8wylXUsUmV73R8Clic48r2wmka9KmDXC0PxbianL7Cfna6SauPi1XWlXn2QTHT6Qy7vWyXnse6n/7u9VnC7bHP77l4nkYnm1KUuKlZ9rHMjTl/mqvkANswNgWcf04ZrnKBZbWEgF0hyzyZE3uE41xh/9x8F/Cy1wlRtCAdbwe4sCJFZFILXASoFvlTynKhxO3juaUsJ1Fo8fxAmN/PvUDHPCBEbyJxTsKuxg6Cz+X47azfc49vXvjJLoSx2QY7Gv3a9UFFS0dB5+73fjIdV7dz5W8ZtiXH4/ikSvnVT13DccW+cjhoszJLQnxU7OqOzdCmXY3zzgOPfwV44mscz2MJOpqhGG3ulTnu9IbjwJGXiQwdc6xrdEzNKa46vyHWf3WRNtQvnHtpj9ttEVPdEiFeddoPj4zFBe2hZJzVa9x1rlWAYlrLNFJXFHVHQaWZTIhY3NQCgBb7bnKWC+jVqnDo23SowvHOkK5Lp4HnHydjy+HgOfuO0bavXqG43GqS+jSVotqh7vaOvfR94OnHVO7vE6+xZ1QAXMBeI44XHyxn9sxBYHo/s4dUSrS1viB3exfP8ZzJWT73Ubz3ev0aNT4ba0q1Sn5tSFo5zz7JLKl6JvZxPmJFIcN5yBNf41wvFKHYXyTBuaOcZwCcT9xwB1lt86eBZ/4OQJuLOuPTrJfby82EepU/0wfZr9lVIVwb5jt906vI8AxGtlxwdSCn+/d+7/fwkY985CpV7XOf+xz+1b/6V8aAG2w+QrHOnL/68WsZvahpgKCDiUFDrsROznLAkg6HzJlsR30KWHa11xO+SOyh2IiMc65XxAAvKNoOiNXzNuON6lVS1yLjnBzIVWcZG+4NAOVLdLQrRa7cX7nEgbvVYv2kiroDPN8XoDGulOgExxLM9Z26pGKynQ6g6QRcDnF9Wwi0tHlOucBjgYiIXROTBKeLq7pOFzCxnyvzxRzbcmAfV1Zln8l+KgvqVb3Be6+IHWZ/gBMhOESqMxtaYb9CI73O65bztdVau/sihV8O3AS87LWd79blM8BTj3Gy5Q0wbvDkd5hubGyGjvHCOVLQb3+lum7PYf625nEHONkoF7lYAqcSkgtFuWjhdArF8zafJdpq98Tt4aTJ6SJ1sO0QyvsJlUvc6wMO38p3a7PDT3YhjM022NHo164PIlo6Kjq3HezqIenA3eomaeH6bma1TLuTnFPpwPqp69gUx9CZQ1xglswiX4CMn0IOCInQsCsXqYFRKtAuNeocvxfP85pWizvK/jAAp9IB2X8Dz6+L8KNCmmPx5CxFu6p1IDRG575Roy5KOC5o2nllPwNhfl+vcffa7eNxh0Pl9Pb4eV9fUImllQt0yH3aGB8Xjmg0wT5yOOmESVZaIAbMJMisSy2oOZEU9Vy6CJz6R8VcazSYbszjZZlON5l2tRqfU6OmVMStbDKAytonH+eGAEAb9s2/AF73FippS6z3Lg6aM3vfMRUymF0GLp7m34Ew/49yq9xsGVWcsj63kPnJdfgja9//eo1irZINVyoCpdNcMNdRyHCh4+IpockiBH7zGc6bPH7ONeWccWqWv5MX6VjLOVt2hfPQ2BSfW2RMpYpzuMlIPHgL37+Q2Fy5SZujbLHgqnOQk8+dO4d3vvOdVz//zM/8DKrVqsnzabD5kKJT/R6/VtCLmraaXCuaASgquY5eYnD9nie/iyVIKQKUw10tcZCMjAuxFbm6XxB0uGznREruRp57hp9nDvB3KUfjLVc349OkkLks64PNhoof3nOEsWW1KlfN5a6D00FHTaqTy3hwSTFqNtVALpVgF86oe8hdBYATGkkj1PsCEOk/xM6MLpqSWaYRAZQKJ9D5TEfxXIY9bn23zj7L37IvpIFdusjfsg2Vgn3Z/dSjXKCRGxPHyzkxMauLRaMoP+dTdPIBGnVfUL1nZe3+cke7F031Ooax2QY7Gv3a9UHGuV42c6Owu180YW+H5bnjM51ZJvTruo1P3eqq3z+nlRlLkJILKJbAwhmVUQQQIqLgeF4UabMAJeSVXqL9CoRVyBHAcTcu2Gb6LqcvSJsgbbXXJ9KH1UW6zaYI//IBDhFa5nLRWfcFFH1dYuaw2DG3CI5GEnTUi9m1udYldLur05/lnOOysOv+AOstcf4kRcmsKGQ7+9eKc8929pHEyW93fh71u6j/X6xo9ZbzMWBzbN74jP07vu/I2mfigGJA9MJqkv8Xxaxl3rSk5k36nDGb6vw/kt8Fwjx/5RL/jk/x+OR+/q6UeVz20b4ja9u2hRhop7tcLiMcVp3jcrng8/lQKpV6XGVgMALI3arNVC/fiViPVqfnfdRpa9ZV8nCctBrZf04XB8vF8zSA3gBp0yuXuDIpRUusVKd9R0ntiSVo3FKXhNiJU8TPeGl0223uclcLXPUMRsROspOOeatJQ3j2GcZrxSdFXPrTKvd2dIbOVz5Dw+7xCXq8EJCJxOl0F/OCTt7iwnerSXobAHGAxt/lZrslPd0dJiVu4Szbm75CYzs2KYRoxEptZplpyqYPMv2atS+unCe9y+UWeWHdgjKdVyv9+RR3c6MJftbpX/2I/OhUr/SSoqLZUdat58tyvWLHZfE8n9GZJ9l3Mp/p3Ekwv6uD5S9fVsyApYvAwks0YFcucEIysY/PTE66dJpko872F7M0opfOKAqdUwjqOB2MoXe5Gbqw7xid6kYVSOdFzL6fu9xOpwhTaIv3DcKQzjK2LrXAd19CKrzKCeX1MFbYwNhsgx2Nfu36IGJo/VDRh6Wf29VDss66lSdp4TIMzOtXwperi7R/EpKC3qjRMQZ43fIlOqyJvYpC3KjxXlJJW7KakiK0qN2ijbwyL0RHxa5tfpVOitPFuGd4gFYNyJfZ75GEGFOT3FH0BmjLly6qMC+XB0CLTrbLrWjdTUExR5t90WzT7rUaXFhtNkk33nNUCJKtiBhbwWjzh0W+7wrtkNwJXzhLll21xAX2mQMMEzv3fYpl+XxAeJy7q/4A7VJiL98pyZALi3AmgDuehQxTr3n9dKDrNbVr26gpu2YXwtWss+xijn3j8jBmvbDaKcw1aFjEetD/X2pVqsK3AVw+TXs9tY99Z0dn3wjCceqm+IKcI7bRXb18aj+fw8olPi9fkKwE6454eontOPsMM87IuOxykc8+FOU7vHxJMRNdbobwORzc0fYEhE5QhoyMQESkkhOubbsBlMrqPfP6yd7z+oD9NzI0bienDAOAP/qjP+ow4o1GA5/73OcwMTFx9dgHPvCB0dTOwEDHzMHrb+K8Hq2uXFybG9JKGQeUAufYNI3e0iXG2DpddDTnT9E4+YTgRV3Qq+wGbKeLAyscIn1WBsiIFUtPW9DO6jT4jYZakfQJp7lRJwW7UVdiWJdeotN+4EZOClaTKm7M7QbgZb0SM8y9uXCWiwP1Kh1Aj9gNrZVFujFJ4mkrJy8QYdvbLQ7MHi8d7XabBj08TiPhdAPlMg1LpcyJQCBMI55dEUJxcdUX0SnAfZGTinJRrfJezSfpYF/OiT4+cKOirkmqWT90MHnPclGJ3fWiI+rUMJ3mJpVILzzPCUW5pI7X63RymzXlcMv82I0G+7vVYgxcJM6JT/I8FyQkTVJXOg1F6Ng36mIHJUaj6fUCwTgnffUaJ6FNsTMSGuPfgTDVzgHW0e0llXxqfyfdr1zkhLLZZL/kVpTCazB03VPNjc022NHo1673K4a2ns3cKP28Vy7tbpC0cCv0nbxyXlHQ2y2hDr1MGrEMp2k1FYX4+B1r25nYA8weoz1ZTQLP/wNtYb0hlMgrYh26Jexbkfat3eZiuMtDhzKaoK3MLDF0y+2jyngxL3apwzyvIJzy8WnSzAtZ2uCqYJ2hzTrDKZzqIG1+KEynKbfKfolO0DlvN8h2ksJbTWFzAhFFKQ6PCYG2Eu9dKgGVKtBYJuvNK8Rh9TCn9DIdPEmZBzgHGJ9hvLHDyf5vt9k+t7dTRdxqS5sNznkaNfYhwP4NxTvfpUHCIvqF/H9JXwGe/y7nFM02HdOXnmHc99XQtxGGVsjwMbzK/juJhXOAZ5WbPDr0d72QoQDaymWyG5tCn6dR5/viC/JdzKyIOR0AtNiWRg2AU7zPNRFnHuBiQ61GZ71eJYsuNiEWtZxclIgITaE2+O4mz3fO57YAAzndBw4cwGc+8xk4HA6026SFzMzM4L/8l/9y9RyHw2EMuIHBqLBejtF+1Sp1OpNO0ZEUKklJ0+ljuVR3FW1ZRijGlcg26GxKWrkvwAnD5H412EpF9f8/e38eZddV3Ynjn3vvm8eaqzRLtuVJHgA7ODYk0Enjb5yhIelfOlkQwNBJxwtCICRNJ02vjslK44R0WKQz0AEWDkk3K8NKSOiEEJwABmImG4/CtmTNKlWpql7Vm4c7/v747PPOfU+vSiWpBql0P2uVrXp1373nTmefvfdnf/bRZ+hQ1ZYoUlaa5YRZc1kzjYDHccTxM00RXknSIZ89SWe/usRtYwkaadOUyTkksGYYktl2Ne3c8yiu5tq63ltlRMd3A9V5OvHlOdYGAYwgKxrVIFXaTI6Lk/A1ALQDrihQgwR3LkQ982KVcMPfU0qkmRyzIClxyBKyQIilGUXP5nXWJZHi/z1XU8HmTgHD2/jv4wd1xiasdDo3TSOYkfNX9Zu1Mg3r8BTbfIR7lRrgMxyLc/EG6Ou4HL1w27VsS5LK9tIFR/tod1eZ0x3Z7AhXHc5nM9dKTXwtxrT3gA5Whu1yAM6DpWnJwKqAgdjQ4wfPr1a97Rra2kxBZ81Vq6y41Fi7LplFdgfYMa6FKRsVOqquzL/luVAW2NG0ddemEzw0ASyc0S3B7JaUcNno2mHfZ0A2OwSUzvI80hnt+BsQp7+jL4An7cYSSdqdmSNsEfn4F/Q1UVCiYuGyMIUDd1M0zlni7x0JNO87IB01atpGq44sy6mIL86S5TV7sldEzGnzc7VNbmjte8SfD+q8+rGRCt17D/Tqq4Q/D49HVRbEU+xGAvD5KAz3fk+xK+BzHVCX8gO1hpg/zWBTIgO4dV4DJSjcbkmW3BSWhBMKnlT4zl3O6uXHjx+Hbdu499578cd//Me44YYb1mtcESKsP2ZPXDhdfb1UUVeC7/UqQofbOq2WcqdUzp02jaOKJlcXddQxFud1UBF5u61pUOq8Dz8pCpGLNHqpLJ0zwwBSYxK1dLmNY/N6qv2Zhgi+TNMRVsqmpsEJULUvGRqnkTUtMboGqUcIOF7TAlLXkkJXOgMEZ2X/Qo12JcKuMs0B6LCnsiIQ09C076ZQ05fmGckfGuNiJJUGagvsA7p9H5UuAV4nVXO2NMd9WhaDB/EkxxdPMTvfaZP616rrFi/FSR539vi5KrarQfg+xlOaWng+ulr473Zb1G3THHM8LqwDyViUzjBSPLGH78QL3yb9yw9IiTRMRonrZWBnhWNSfdTtNr/fqHB8dkMEc9JA1mQWOl9kr/e8PGtD4yJ+I7SyXddToKbd4Lm6HY7TMLlgtNtcPIZLKoqjHKth8LNMlg53eAF2NXQ66ENksyNcdThfKc5aU36XQ/9aoTgmWeB+FeuiVohOpunsTR+iU3H2BMuV0tJ5RNmeRoXfjcVZRxxW0Fb2Pz8M7L2F2d3KAgPPcbFtsSTH165xTPkRCmSaYmcXpjknmzHa5/Ii52zfE/vR4X4Ko1QVNyza0nqV83Wnycy1q3RUJONtGMxmL53lGsEyafcrImbqeyKo5lLTo9WQLhxzFIbrNKnzosTgGlVel8Cjw70wTZvitGl31DVW4mbf+RfS5L0sxbVuuIOO9rHngNmjEmTukInle4MzxJ0WGXc3vAJ4/pu0U8kMMLlburmEnqW17BHf/0xVS2R91Re1Sngqo8Xj+se8FljN+nc1JSOdFtvDwuBaybSEZZDiGnfPTfxubYHPRrbIAFEsBaQkUDRfApJ1BoBch/uMx/k8eI6I+WWF1eHz2SnXRXPA4HuXH7q81csBIJFI4ODBg7AGyb9fIB566CH8zd/8DV544QWk02ncc889+O3f/u2ehUEQBPjABz6Aj33sY1haWsJdd92FP/zDP8SBA1tcQCvC+mL2xIWrHa+nKupKxzMt0ncHYTWUu3q5V+U8CLgYyeS1Ebdi+nNFhUukOLGGz1spWNaWgJzPaHWmyJ/R7Zo21Wlp6phCflS3/IqlRAG9xrpvywRyeUYrTanJyRZF6TTGOm+7LTXZFrPcjQoXHWrSdV1O4ErtXFHMDZOTcTzFBYSfZQAj8OSaSMi1PMfjTO0lBXxyL3DdbXRq+5ViW+IQFnxepyBgtnXbtfoaTB8irU+xB8oxLnIWzugovVKxDdeBrfY+dlqkI07sPH/kPExzS6RomO0OjU6myIVXrQRYaS3IY1r8rDDC+96u07gn0/xbMsV9VkusjY9LYCMImDnIBEAidP8zBV7beJoLwowwIOwWF3DXvqz3+Rnfyfs0F7rupsWARaelny11HcKUwqux08EyWCubHdnrCFcMVirFGaRGDaytivGgtUKrcf4yoFaDtqZW5vwcSzIoXF0CCqHzyha5hjnyjO6DDPD3bJEORXme7KNX/hCdzukjdGBchyVhmTz3l86LcneHwe7yAufwuMzvjQqdllZDM53GtktgvcM1RCYvNPaW2OEEv2MJnVvNPYbJYLTv0RkyTR1gT+c4tmRaRN4MZsB9lxnLuRP83lOP0o6nizx+eU66nCS57eIc91ua7b3m196mnW/VVaNV471KpLgmmdyrNXFKMwwmD+oMEk+xdZUV01n/RFLrioSfpbXqEd//TMXivC479uv7XzrDcfRjLZ7tC1n/nq9kxHX4jMcTDKIMCUsuZvG65od4b/NjDPYHcjynLYr7WWEiBHxW7LYE4z2K52bykjzJ6tZ5jRqZF66wJ2pl3mPXGTjE9cIFqZcrvOUtb8EnPvGJSz74o48+ine+8534xje+gUceeQSu6+Lee+9Fo6FrID70oQ/hwx/+MP7gD/4A3/72tzE1NYXXve51qNVql3z8CFcxLkbteD1VUdfzeIuzvbRcpe45P00DnS32KpaqjGBhlM5c+HhqP0qRXO1POTRhJdftfSqRxVHSw7bv5+/KeWzWmI1QTli7Rdqx6zBiGUZ+iJNoZYFUNNfVquRKkdwwwMI1gWWIuEsfTBGb8T10qWxqQq5XOH5F+etXilX/r5bOpTwX5Rr0O3mKlt1PMS+Mru6e9t9HhUrp/E53+O+F0NjGQurtSiE8mZZM/yIXFIo2HkswqKGeD7XPVp30e/W5ep7U5wq5IrBPFj1h2nerrml5xdDzU+1TK1XnHmDlrPXV2ulgBayFzY7sdYQrChdqJ9eS8nsxtlsdv1rSNnpoQpfWtOr68/N1bFjO5gOSqRa7Ghd18VSa64FGmZ+nc3Q+DbGbyn7GYvwOoMcVyNjsNoOvpsVtlHYKIA62xWCzUpNOpGivLdmfEyr3Avg3JdRmxTjmmtDDlZMG0OlvVuiQ5UPUZGWzB11zda2VfZmf1ucN6LXNoGscVvIO2/hsUX++HvTx/vPoV6tXY1Cfh7EW41nr9W+ueK4CfDqnr2lYnVw9j9VFXcJXGCKzMi/XPJbgM5JI6XLHTI5rDVWukSnoY61GYX0dcMGZbgCwbRuf+MQn8Mgjj+DOO+9ENtsrEPHhD394Vfv5/Oc/3/P7ww8/jImJCTzxxBP4/u//fgRBgI985CN4//vfj5/4iZ8AAHzqU5/C5OQkPv3pT+Pnf/7nL2b4ESIsv2hfaTG/FrS0C6GnrxUNrtPihOP7QPksJ67yPNUly2eBbXuBa24jZWf2BCnAmQIjyM/+q6h7p3QUtybf9xwayaTQzD0HmD/FbTyXFGSAGdARcaLPHOHfOg2tStksMyqZSAilqEQjb7cYsbZtwHcY9U5mSQdrVJkdbwlV3Bbqmy814L4PBEIxMuLS87kjkf0c65GrS5LpN2jgXZtjPP4CM7qnj3Bi7zRJh0tleJ6nDwHtNidtz+NxnTb/PS5Z50Sa9dJY4HhjFo2I5/D3TosZZKVi239PBz0nnZbuw14taSXcQWr1/QjT3EyTIjxKXbzdYBZ7aY7jS6RYp2eK2Nz4DlLxjj4FQHpjFkalRdwQF0SJNO/J0owYOINR5JmjzD6MbKeRHS8AN95JqmB9SSvoTuzWVHmA2Xvl9CvKZUb+FrO0SE11Ua5Ris/Truuv3k4HK2AtbHZkryNsOlZjP/tLocJzBzBYjfpiKb/LjWdpjiJNS3O0vUMTdA7qSxSwHFQqpubouRPiZPi0lY0yUDlLgcncMBWXp/awT3Q/2g0qcjcqPPfcEO2eYdBuFEcoWlpb0hodnk9nuroINCfotA2NkVruukBigfO1I7Xf9Qpw9jTF0FwXsA4BtsOssSoZ80RVO/D5u+9I9jwOwKTtCXwy2wyD9tZusVTN6dB5R8CxxcH9KZ0YwxD2WozZbKXEnilKAN6nA2+3OaYT3x18zXeIDsjiLKnp6bxOQ4Zp/P0IK3mfepHnZBhcHyQza8N8XM7+hzEqeionDvKex5O00zuuG1zGcKlYy7KMWJzq4YkUED/B+5TO87287fuBQ0/w+VOMQKdNhoTn8llRrMNOm7o76SzXI80Kn7VOne/87AlmtOFzvVU6wy4sVoxrjoldmhm5Qbgop/u5557DK17xCgDAoUOHev5mGIOUglaHSoUP+MgIm78fO3YMs7OzuPfee7vbJJNJvOY1r8Fjjz0WGfEIF49s8cIpqJeqRHmh9PS1Ur50HdJvTZMT2dIsUK/xXJNZYHEeyM6wpsuwNO2rtgScfBFdR0sliw3IZJigkUllOf56mUZfqaADdJ7SeU5sR55hbXWzRuOpKG5BQMf2zFFGvTstGjHl0MPgpJpI0eD7ro62ew6NtDLuCGT8Brc3LQYQ8kNAYZz0o3ZDhNXEeAc+v+N5nMQR0IE88xJQLbJVVSpL2l26o/tEl2cZLEjngO3X6YVdIs1MfCpDMZt2g4uv/LCmvPc7kuF7utxzomiRmXzvInKQWv0gLEdzmz1BB7nT0hkQz9PlAmM7+G/L4iLHbQNTIRaD7/L8EikKq82dBI49w3s5NM5nrV7l4sj3eX67b2D7NYB08bDDDfDf3Ux63zsQlwBQYVQvLAA+R+p9uho7HayA9bDZkb2OsKFYjf0cVAo1f5oOZ3iuXQvK73LjKY4BM8fpqCqcepG2JVsEtksm8tQh2r3rXtbreO+5mQ5Xs07nYGof/5ZIsvRLZa371zDKzuQK+txVmU4mr4XQYhLcVhocTkfaTe3kPvPDtHfjO+nInX6BTpwnrLJajXOt15Ggs8+AeRDwHI1A2jZZVNW2pETMMkGNlYC/GyZtl+swYxlPCJ3ep9OUFI2ReEKow4HY7zoznWptkC3Qrqq2mMqpb1aBk3We6+j2c685wPXLyDZg7Cy7oVSWgCJIt1fXeBByQ3TeTYv3ay1xPvsfhlp7TO3Vn1UW1qfkcS2V2JNpPjd7b+GPQjpLu223Bh/LsoCjz/HZdF0G+WslIH8dHfaqCL+2m3oNmkjzntcq8g4O8fN2k/vafweTEBuEi3K6v/SlL631OBAEAd773vfi1a9+NW65hTdhdpa0hcnJyZ5tJycnceLEiYH76XQ66CjFWwDVanXNxxphC2A1Cov9uFQlygtVTV0P5cvStFbXVu2Y1OeWqak6QG+Ut6sgiZDTLduksnSYCyPnqqBXSnSgDn5dDPkOZsObVTpl7abUAftUqvbrOqOeKVKIC4ZuzWHG+GNIZrpLDw+kVZgEDQLJXpsx/l3R2doNrWxpxbgPw+RCwXO4QIgXtTgKQEZAYUxfJ0WDb9aBgtRPVUt6UaeUvIujDHaoa9KocMGkrmP4O+F7utG0yOMHRbG2zEVHGOlcLzURYA2ZavEB6PNR200fZpAmHe9Vcj9zmPsrjOjnAqDzHP69/7z634HiKI8Zpvyrz4ENVyO9ErDWNnut7DUQ2ewIq8Rq7Gd4GzX/AsvPtesxnkGU5GadQex+J26QevLIFHDs4LlZ1myxd/v+NYzaftu1pNX2n3thVJc2JZJa0VwhADPByqar+bUpQWal6xH4wkRztc115N+KUWcEUovrM5BvQEROHcCUwHq2QJtrd1hrmxZ7G4vTuXI60o9cAsHtOlCc0C26XIfB91qF6wqAzlU8yfOvV+iML3fNAV6T+dOkIDcP6fNVGjorrQfXSwX/Qux/taTt3lqOYRDWcj16vn0t93f1jKfSDOZnCgwy1JeAlqjvd1p67QaQsel2KMrmubqHt3quzhy98PFfAi7K6V4P/MIv/AKeeeYZfO1rXzvnb/2R+CAIlo3OP/TQQ/jABz6wLmOMsEHYCIXw1VJQw2NxHU3JHUQPOx8ulJ5zPuXL1aqvx+KaktxpATBEtExoxJUSxb18jxQ2p81JyxGKlWPTOVUTXjzNif7sKRpGpTIKnxNYs8L/B+L0br+GaqPX3q6j0a0GjxdPcMyzJ+iI2206wckMKJJRl4i5y2M5jlDJRcCs09EUcQTMugL8u+3SoHea4gRmuFioVzk2GLqViRcwCGDGGLW32xSFOVsmJSk3zEyA6zFSGhehmDMvkaKfytLYj27jdRqepBM5sZPRWFOOtUtEp1QrrnT23Od7uedhJVrkSs+CeobDSr7DE7wPdgs48rT0Gx/nOSzMkP6fzpPK6HR4rwojHH8sTor9zHFZgPnAgXs4DpU5SaR4H9X9ALjonD3K/pmJZG/t9vRhKsj6HjPr+w7o8/Q9ff7JNN8JAHjyi7yuYRX3la5fhDXDWtlrILLZm4bN6MRxKViN/Qz/W82/K821F4MwfV0xgyyLc15hlHOwZXGObVRoPw2DNqM/U1krAU9+GXj8Ee7XtDjPludF7LTQq5kyc1RTn0emKAym1MtbDWDfLXQ4jz9P6nRlnk7G/ldwDZDKch7OFJglNwItglldBA4LrTfwGDg3JDM9f4bzdr0iwW5P6OOBrts2TV377brSdxukbAcAYHFtEfMBeEBHnG/PpQRLpwN0zmobEgTA2eMS+B2hnfEcKV0r8ziFETLKsgVZo9m0XdUF2pi9B3rFXNsNYHGG28WTVG0f38nn3zB02VwAZvzt1vIip+pZa9V6O2koRsH0ETr0lsVM+2pLnC7E/g+ND+7/vh42cC2V2M/XYSA3RMZI/5rGMHhfWnWh9Kf4LDsiCltf4rjmT/PfnsuxJlIMMqn1Xiwu79ZQb7vSDcBl4XS/613vwmc/+1l85Stfwc6dO7ufT00x6jE7O4tt27Z1P5+bmzsnmq7wa7/2a3jve9/b/b1arWLXrl3rNPIIa46NVAg/HwU1PJZWTUeOw1S1C8HF0HNWogSvVn1dUXkyeWDnDYD5EhcLVlzqb01Geid30nDWy1J/lWA9TLvOKHdxnJNbvcKJsEuzXqRzdeRpMYYG0JxmFjtXZN9Fu0WqejLFuq2x7XSELZPHz+QAcyfQapK+3HWkITVivu4papiA4fP4viuRdEM7Z+GUvAEa63aDP/G41HwLrdyKsUYYARcbyQyPF0sADen5mM7y2lWWuK0SWykv8P9ewAXD4Sf5eziyns6Tiq2MlHIM01KHNKhV2ErPyaDnYaVnIVfkMxx+fquLNED1MhelqQwDEegA6QIwJouoXIHPOqANZKship+u/luzwqDN8CRpbse/y9p9Bd8lFTEAr7cvWZBTL/KzTouLuqFxPg92C/jOl4DrX07qn1qobtvXe+57bl47uluEVWMt7TUQ2exNwUZ34lgLrMZ+9m+Tzq88114owtfNtTWFfHhCnNPTQpMWurVyiOIJZtzC7ZzaDZbWtBvicMxI2VeBtGrl8Cp7sjRHRzKW4Dkefor2LVymU11gwHvuFO2o49LpP/YsqAaeod1LF+igOw5w9hgdXT8AWi3Wdy/NAzmbteitGgPQjismORCqt8zjkHI03wAggfaUUL89m/bZikkfbhV0MPjvWIpsNseWNqIux+K5mm5uxnjMRIrbWSExtyBge8hkluuNWEKLrLXrwJnjDC4XxzT9Xl1Ddb/Gd9JuTe1lUqVa0nTtld6LZJo2UdlVgNe/XuF+Th/Wn/ueLgU4n+N9ofZ/I23gWimxq30BgzsMFMeYwR6e1EKulQU+A67U92Nc72t4ghTxp74MvPSUBFYcea5dYWgEfBYSSdaBJ1N0vvMja3M+q8SmOt1BEOBd73oXPvOZz+DLX/4y9u3b1/P3ffv2YWpqCo888ghe/nJy7m3bxqOPPorf/u3fHrjPZDKJZDK57mOPsE5YL8rOxSA8lrCKcpiqdiHjOh+lJpytNC1GzQsjgyOKyymXPv9NOi0qehgENMYnX6CRiyel9zXo6DbEcS4M0xDNHqeBqC1xgps5SsctneN3VX/OM8e4f9fm9YjFOcG1pW7XEZG1WJwZ8UQKePHb6NLAaxXuK5sHlhYYOXdtjrdVFyVxT/f8DAJoQx0X4TMHXZXy4Jwrge7iwPP1OTsd9MiHmwaNejxJh9qT/VkxGmnLkiy2RK9NCzj6DKPhrq1p9ukcFyYHH2NG//RLuh9qp8HnZ2I3jfH5aI4XSuNaScVWKcifPQEsnqWzbXcY3Z3Yxeu+7VodMJg5wqCG61BMpyWq0889pmumjz/HexJLUNxuZIrPzMwRZvqvvRVYPCPX26YIXW2JpQvlOdaFp9Js4RGLc7/DE1LHVee4Y3Fu+7LX6ufu7Alg7836XVjuOiXSfN6vlAzeFYL1sNdAZLM3BZeTnV0tVjMvhrcJZyB37O/NWK6U5V/pb4uzer+zJ+gIqAxdMk2HK52VXtEdOsypLG2o63IeXJjmHFxdZDYuU6DtcV0AJufW6gLn4aU5oV7n+XuuqFXJTz5P5zqdpWMbi1NPY+6kaKxUtC2120DqCG1QOqft7+Ic2WTNKoPqC6f4+8Gvc7tUhjarXRd770jHECnpUvA8AKrTiEtHR5V6mdCCqKoczHTQdaTtDmA3tXJ6IBlyzwXcMzTx6SzXKOrcHZs/7aaopotQ25A4Ym6btrs2Dxx1gJvv0ky9XJH2e/aYiJ2C5x5PcC0ztkOLlCrdlUHvhSoDCKMuWdmjz+qMt/o8maVtm9qz8jN2IfZ/ZIpOpnrOXY/LGxU0uJxtX73MdYVaa4Y1bo4fZAKkWhKGZpvXyXO5znAkiJ9Icn2w63quXT1XAk4u3yPPk4CQR+aj4zGxkyyL3swSWXUbiE11ut/5znfi05/+NP7u7/4O+Xy+WxNWLBaRTqdhGAbe85734IMf/CD279+P/fv344Mf/CAymQze+MY3bubQI6wX1lIh8VIRPmaYgmK3B29zPqxEzwlnKzsNGkOA0bvRbedGWwepanYadEyGJhjB7TSAEy8CDanTjUlv5kSKmWbT4OQ0tYcOeDKr+1uW54Cd14ljBiCZIJ0+keLfTh3iJJlI0RjXSoAhEWkvECc64KIkW2T0vNOkcU9keOxUhpl032N0vzDGhYgvwm2urR1xAIDQwD1Xst1YxtleCYFkEMS59g2qoCaTpI3HRK273eQiJp2jIUhmeL61RYnIG7wXVhzIF1kLXq/pGrREktlfz+Giasd13F9pGkjtX7ks4UJpXCsp8XdakuU+xXOqyrNQXdB92if38Dk7/izpdRO7KACXzpOdYIDXO5HmvTp7gkEIFXlXNfOuw2tz3cuYPXj6K8BLT/IZGZkQddE6t283SVfMFGQB5zNAYaixDfGdmz9F53x4QtqQ9WUe+q+TErFTuBIyeFcIInu9hXA52dnVYjXzotrm1CGKgyVSZHGZlp4HgOWz/Cv9TamSq8ymYmiVS5zDJnYJpdpnFi2eoL1LZURos0h2T22J82x9SUq5UkC9Bfg25z1f6Ns796PbyrKxRJbazhvECZwBjjyLrgFU83rMEsq2J5TdLNDyxME+yzGkcsx0n3qeY0ylOXfXy6L63eK47CZQl2B6MsVz9FTttjG4DWeXS25KIMASsdJAWnfGNQ3dcfijAupBgG6gHAA8SPvPgONpNbi2CfxQe6gkt1EOmtMG4HMI8TSQNemU1au8BlN7eM6nJAttGMD0S1zTjO+UAEOLYyyO60y4EkDtfx6HxnkvlZOeH+L6plEGEiEnOayGfj6WyYXa/yC0n3qZ6y3VV/1ytX3qGtSXRICv1St4WDrD9YJiJwAUAHZs3mvX0ezFa28nM+T0YQaHTIPvpesAbkuU7uNAIM9xs65ZjpN7uWZYroRgHbCpTvdHP/pRAMBrX/vans8ffvhh3H///QCA973vfWi1WnjHO96BpaUl3HXXXfjCF76AfP4i6L0RLn+spULiWo4lntILknAE80LHtRw9J5ytrIecqDNHdWuIcLR1kPp6vUJHSQmg1Cs0Jp7LmuR4nIZE1Tp97w8zI3jsoD63ZJbntOsGqqzuQOhvaRq9gzVSfVN5TpqxpFB4WszMB6D6qOcLXbgpxtNgttOyeD2dlvTWdkgJc9p0vp2WRJ0XtWhaEEg7EAOA7DcAnXBAour99PJBMIRSL5T0hLSUGZrghLzjOmD3TbxGc6eoaApoOtvQOBdQRiCUbHBbQKhyEpBJyXVEmvVcsbiOPqezq2vxtVojsJISfzLNZyiWAFpz+m/xpPR9HWKUfGqv1GJneQ8VunX8KRonz+G1ch0+U4pB4DmkK4ZVQItjfI46LeDIU7yW9SXS0TNFID6m23VUSrz3gP4sngTmpvl5o8L9Kah3of86nXxh8DW6nDN4Vwgie72FcDnZ2QvBaubF3BDnjEF08pVEqs73t9yQLqkCRNcj4DwVT0pdsMm5VtnRG1+pu0vUy7SpyTS3UUFYVWbleLSBZkzmxzSZSkrtHNCZwOmjmrIdHlO7wfVCW2qlrSRgdWS8STLHlODlvlv5//nTpJ+3W0D7LAPkDngudou20guE7WXoDDYMIHDRbRHZbYYMnls2LwF7CdC2W9ym3aT9UMHvTpP7tOJSt+1yOwNa9CqWlPafJoP/ZkyzCOJJtkIzTekRnkS391cqR5t+66vIuBqeZBtUhUaFdiw3TBZYOkdnfmGaayWA66nRZVhmwxPn1lR3WlohWyEmZQXZ4upYJqu1/4uzvd1MlIBYWJj0crR96hqE19WAZpF6HlvF9Yv5tmos11NrKbvD9VelxOcrnmbypmCSNr54ltupNWVumGvNwggwsUc/Xxt4jTadXn4+GIaBBx98EA8++OD6DyjC5mM9FLvXYixhJVRFcVrLcYUnFxUVBTS9F9CTU71Mw3L2uPTiLNLIuzbrXNW+XFsLkgBcZDkdRt8XZ+jEJFIUt7I7el+OTeeyVuLk99LTNOaxGOlpZw5zcl86y2ylIdHkdpMTYGBw8eC0OdEZFh1v3+N+mhWteBozAZhCB2pw4myUARi9IlzdtLaci1KeVOjS3ZYXbCLUPkwuHPyAdKNYGfALNIqzxynWcfowr1M8Cey4gY53YVhTthUlW2UHGjXu+8hT/E6zzojr6PZepsRaZ5NWUuLPFdnz0nMp+tZucFGRTFNkJ5UBasM0YnYb2HGN9LWEPjcDvI/HDzK70qry8x0WYBT0dmGl13qZ9VXPPcZ/V+ZlEdeWSLPHZ8sw+D7ZLcDfweenMAK0O0I5b0ggxu597wZdw3qZ9eRKOCVMV7ucM3hXCCJ7vYVwOdnZ9cBqMvmqfldlKYfGe7Vawn9X2cZ0Tgc4s0XAnuOc6nWAU4ucp1QNalyCsUq0a/60lMkcpzPgdBiYbtSYITWlvCkRY2BTHUOpjs9Pc0xnTwLPf4M6Itk8nc2YOpZLR/PMc5xTFSMpIbXRtSU6Y47N8qFGldlBV2q/PRuAyX/7fqi7h8HjuOIQey7odKtAdxiGdp6tGO24FeOP53B/6lgB+JkZkwy4j66ND3xRKE/qMi+nrdt9qkB4YYhrl/ywZhG4Do83tl3ba6XF0q7pUqbFWbKt3DZ/H5kCnCUGAhTs9upLwdQ68Zpbe2u6VaBj7wGy5QbhQmzUoF70YQbmeq43VsJyJZJqvabEUKePcLy1Ra3qb4DrrdqirsmulqRE0AFK4kCnJMiQTPPcKgsicFzmGjeZ5hrX6XBfnqc1AxZnhYmS5nNyza1cYw9iMqwTLgshtQgRulhLhcS1HItpArtCLS3WelzhbKWigQO9bZSSaU3LyQ0B176MWczyArBzGLj5blFj7PDaxRK6htttSwbdpPNsWqT+BgCSOTqfjs1tlKp0aUbqf20apHYTsBKMJiqBCivG47gu6XXDkzTkyTQnS9OU1lxiPGuLUmNmA/AAzyK1uy1ZUNdB12j3YDU8ciP00//90DaGwUCAafBaJBISHACzucmsGNJAxOMctg275lbg5ns08wBgfVGjynMd3UZj0WwAaHAMzYDnFs7SrnU26XxK/KkcSwZMS/fi9n1SudpN3udrhc5oWtxeLTYzBV0reOowF0qW1GEvneU9zRXYazOslv7M1+gA15b4/thtHVhpNZiISGQYLGnK9fMdIJZnNHrPdj43ZVGhHZ7qXRD3X0P1XiiBn3662uWewYsQYSNxOdnZ9cD5MvmlWc4PCp0WF+GJFOfAZq3370rPIpHSnUCUEnJ5jnbL6XCubDWAYJZ2Lpli5q1Vo7hkKkcnoFGTjhDDnE8dm3Z3cg/vQTJLWzq5u1cD5OxJYP4k50QV4G43gCR47EQKSKeZXVfOqWVyHZHN0Zmf2sNMuSr92ncbxz79EgBTnGAzlM32+bkBXR8bAOR/D4Kw0kxfuktkuJ9kWvRZlEMrgXTDQg+tPAxDdFzSeWabC+N0rpW4WjItPzlmu+dPibYMeD3Hd2lne3iC1/CZDB3jWJxOoeuxNCyVoP0vyNBUZ4yJXasvBRuZYl91u8X7Nki9fLke1Ku1Ucv1ovd97TwqDZoL2e+lYrkSyZ37NSNuYifXmKpNaSoL5HxZI9i8Zr7PpE5DtGA6La4FEkm5tzV5dDxqE8AHGqpEAXw+PI/3WPWBd9pc60CCR50OdWfywyyhu9z7dEeIsK5YS4XES8VGjSWcrcwV9YSlorQAJ/QwNWl0m3YAVTuU6SO692SuSCPbWOrtl5xKs85N0dj7HRrfZ/0MQGdb0efyQ7oHZjMgTSfwJXLdofOVHwXGwBpeZUMDn1Tts8c44SnxFCPGbfyABg7gvixTT9Jd57u/gFu28wMdhU9luGDw1fcCZg5MWRyp/cfjpMul0rxGI1Oc5IfGpF2IR8cPIUo+IFnrkMOtrr/vcTHx/LeEnrakt8nk6AjvvqH3Pq41VlLidzrM4jgd6YkqiyVL6tam9ohirjw/Ybpas0aHW1EY4ynAa/CdSGWBndcDt7xa10MCfEbPHOW1UNH3WEw711YoqhwTSuHwJJXKb/9+Ppej2/n3vTcz866U0hX6r6F6L8KMFEDT1bZKBi9ChLXC5WRn1xrny+T3C2ABvf2OVYnWoL+F58fZ47Q9S7NcyNdk7lfaGPUK67Db9V7h0oTUq1oJYM+NdPamdgOvej3/PmjsmTzt/OReOu8nX+Tn8bjOdKczZG5t28ffYykeOz/MOfSmV0og3+CcqebZ67+HDpwZYxY+AO1ELE77lkjpjHOzysCE3caywfB4gs5ePE3Nk+I4g6oI6HQGPuDFdabbsaVLhcljBAYdrWRaMtoGsOcm4Lbv5z1s1YFDT+rjqfZsd/0w7c5yNiA3BFxzm+49bsWAWVGgLwq7IJHWnTOAXts2CMu9R8up5V8qy2S5XvRhhJ/XjbJ9y5VIHn2W9w7QgsS5IrdJifJ8QvRYhqf4PhngGjY7hC57MZUGnAzXsiPb+BwqqMCK4/BZazdZ2qbahznPaz2CWIyBHtfVLYA3EJHTHeHKxWb1GV2P4+aKdOBUttKyWJPi2TR6Snhr5hi3X643ZLvByHu7zkktV2Amd/4095MfolPebjDy3m6SopYpcD/5Yf6eLfBYc6fo1NodoJOmimQqSwe1tkSDVhij4uf4Dhq3pTn+pHKkd514nqJi1bL04mwC8EUxPMmoqOfqwEDXSQ4b9H7j7mul8UCo6G2hjRuG9BhXkXpLDDn4eywJmB7/7dgca3FcHG2D2dVyidS6WJKGOTfMjPCZI3RC40lS+W+4g9fJzEvNnE0hHC/gAild0DSnZObC+roPwsU8e77HRUl1QURIpO2KaTE4ojLgqjfm899kNsVKMPMyd4o0tkaF38vkuXDqtKigu+cmUQdtC+XtKSq8222KrqlyCbvNRZwZ4z2KxYV1IHWQ9Qqj5Z7LKPj4Ti54VD/xlc5ZUejCvXmdNp+ly1FIJkKECBeP882D58vkJ1JkL6n+wNuu5dxhmnRY506I0Kinqd2Kgn7tbXq/nSaz2a7LjFo6K0Qrl8fKD9EWH32O9lKVXI1vp1NrxWhzhydpl08dotjawhnOeYkUbeuuGzh/2m3Ox9OHGJjvNHluO64jjVeJabVqzGQ7HWYQRyd5rpV57XR7DnD4O9yf7+sMezxF1p2j1MYBBC3JgMeBjgi9rcQ+czosH0vY2gHvNPkVWwV/HWkf6Un2PBDBTqHEB774WxYdpaEJCRJLJhlGbyY52KkdzNwQWWitOtc2N9yp731hhE719FHNAjDkmdh+7bldYwAG1OdPc7uxnZdmx3vE/kL7XC36e9Grc2039fiVnssgW7ke69d6GTj5Xa4PYyIgqAJBSpUe4PMfgM9Zp80SxladzJNYnM99p01xW+UMW7JeGN3BbRplPju1Ja4Raks8fjILTO6SLPcJPmO5YQbtO20+X64kc1xZG5w9wfdE6chsACKnO8KVic3qM7oex+2hjN+2cj/wQb0hKwucoJRgi2MDMCQbuYOf7bmZzqHbYfS5tkixENfRxrTdkmh8lceNxWkw6xVRw/REwEzqtdKS2VZtOmIxGsSpfZqat3CGNWzNuhjeOpCIA7C4wGnVdM2w3aIj7Ysx76qeWsyK+25fnbeCUNQMi4sB3+CEGghtTf2kcqLSnkFXxVwJw5gG686XztLpdsRR9B1GwgtVLgwcm9FYgE54vcLzKs1yoZEp8sdp8Zzzw3RkJ5bJQl8ILvbZyxZpiMZ2aNVYgIvNZFYvcupltlFxbGBoioGZpx7lAs9zuJ3vsgwgledCLpMHjjzD7wVgwGdGqIvls1ITbkr02uQ9QMB7nBPlchVgMQ3dR3P3DVxQrHZBEKaTqt68wOpE6yJEiHDlYLXz4HIZyHqZzmt+lD/qs0xOzzd7bu6loKuSlfI8f999I79z5ig/z+S1WNbwBBf8hRHqXxx6krbBipONBdBGDo1z3lL9oc+eZCBgcY72o7JE+nluWDpPlGjDDz/FfVgyp3oug6PFceDQ43Ru1bh9KS07e5J2NpZktr1VIaU6VxB7a+jspGkwsG6A43bF+VYlQP2ks2Uh64TaEsc6PEWbUy/zc9/Vzrtp6d7emQKD+qbF3STSdKa2X0eba1nskNGfST75Ap+FVo3HUPc3maY9yhUlsJBmplRlshUG9XKvl3m9w6UGpw7xGl/3skuzLabF+6aw2nVk2NaFz3VspxYY27Zv+Wd/vdavZgLwW3z2G1W+T/F0r6icor1XJSA0up2fnT3JdyuZ0mwQ+HxH4kkGErZfK6UUaX6vMMq1TKfF5yqRlja5Qk/3fL43jgj4WSa7zvgelfRNScS8+G0qoG8QxXzjqscjRFhLrKQAeaUdd6V+4P3bjEz1bgPoOt5KSf+tUemlzSgjqWhV5Xlpt9E3lumXNPUnkzt3rIHBCc2Wuqx0aJtGRfdVVChNa1EST5w95RzbnVAdkiEtx8KDhc6EwtW1ZYNgQEfLA+WYB3pXfoBuq5O+Q8CKiRhaRxZjQ/pvSpn8zBFg+/5zj3v8IO9JtcR7oNCUDEqj0isAtlbPyWo+V1ACZ9li7/2akIBMYVSXLoSfrUaFTrQS2QOkLrtN4xcY4kSDKuMqc5RK8+8ADZsrojypNICAQQl1XV2XC7wArL9T41TjWO31WknoJkKECFsHl2qDF2d752SFSknPF2pO70dxVB8nvJ9caO5vVHRZmLLBypamJfBbmubv6vvVEm2YcnyVnW7VtV0tjErdtUDpvsSTDKCr/S+FroPK7BkQoU9BVbLuig6v0KrRBnq2DnoDkiWUAGkspu3o+WD4AHwJ6BcYXFCtx7y+bLkvmW7X5rjVn1wHKE7q7Sqlwfda3bv+9ZG6xuE11CAM+nxxdvBzsNwYVotLeYbD4wyf62rWGeu5fg2XQqZz+hm+5lb9eXGU41T9zAH9jiQzg0Xf+teY6l3LqP/ndPlbq86f8d16bWvF6dirEsMuTDI6A5ACv0GIMt0RrkxsVp/RSz3uIGrPoH7g7Qaj6GeOcvJI5xk9jMWFpiXR4USKkfBkmt8tz9Mwn5Aalm3XANv2Sv3xdt3ao76kDWe9AtKuYzR49QqPXxgHguepNt5uUajEinES7LR4vIVTzJbmh4FSmdHFWEK3f5o5TqqPIVnteFKronpyro7DmSgpmVSlRK7o4KoWZyX4HpYVdvHByKbvM4qq2oBlcmLkHWYgJveQ6p7IkGWgBHKyeSA+RYNROkMHPZ6g0bAs3a8TEJGOJs8vI5TzuZOs/WvWeF2+5/87Pz3tfM9JGCs9e/UyGQT5YVILAdb9ZfI0fruuJ7VRKaHOn2ZwZOYYcOQ5YGmGz1pG7metxGviuwy8zJ3g3xTFqzLPxWBlnvR0CFMhkWIGyLB4zOmXyCrwhGkRT3NfnTYXkPUy6YTD46u7XltdGCpChKsZ4flw9hhLr8Iio8DqbHC4w4Hvi7BjnbYm7MyEezDXSkC7zaBhpUSbsftGneHuCquZtJPpDLDzOmbeHv0rOqvZIc5nji3KyjZp58ee1XOU06GtLS+QHabakbk2xxGLA6df1LXP8IGsMKmcDufZyT26tZaj9DsMOh6dJlXWlXCb79ImWzUew+5ICzNRFjdDTrfnAnAAF6GWYedDALg+EHToUB9/lsd1bLH/yrH29f0zTY6rWdU15MVx2rDZ47S7+aFz77V6PtoNnkMiLW0xG8xkArxXag2l2ouGRXEBZsv7bW5YFVzBaa/+eeu3ScClddmoV3SZ29Ics9q7b+plRXZaa7eGAHpVyfuFWtV3ld7NmaN89uJx2m9VXqDE+jyP67n6ItcApRmeh90CFmZEoyDB57owQm2E8gK3d4SpOb5TntU235l6WTLsZd6bjATul2b5ThoGkwSqpMGygFRSWH4pnSTYAEROd4QrE5vVZ/RSjrsctcf3NJUqniIVavYEjXJxnPQYVVu96wYdERzfoRcenRb7Rp94nhOXGuPZk5yoAGByHydKw6JDuTALmB1SxlyhpKcynLzqS5yIDIMTWH6EdPJ0hkYzWxDVUXCSW5oVqrpLQ3ZWHKh6mdsrkZZYQvdGtCXSbhi6nYhpaXXxbs/OVUbVz4F8z7JIn1f9vU1TK7Jn8ppybpoMYJhi6Hdcp3dVK9EwKNgdtovJD/N31a9T0btmj/M+Ls1SmEZlFJwOnduV6GmreU7CWO7ZC+8nneU5Fcd7yxaKYxwnwHtTLbFFXL0irWQcjlUpmafzvG6qzt/uANUTpIt5LqPbniMUdhG3K4zy+Rkao5G75hYu/Ea3k4oeS/AYS3PA/Bka9bgo4rtyvdpNYP8y10thKwtDRYhwtaJ/PvQDlldN7Dy3u8dq9qM6HNgtLubV3K1aGSmq7fCEOOYtBmIBTTGvl/VaQAmrqbk/TFPec7PuSgLQybeFVqtYQgBt3cIZzn+NGs/RFpvuOCL6OSTtHuO0samMrpvNDnFubdXpqMCig700L51MZHs/AAKbY+w0aaOdOh3tZlVqX31+37AAwxXHW0qxEGiHFdCfnQMDgKn1WXxXqL6OFkm14uJ4i502DL07K841hgoQLJ2lDQkC1uiGW4aGn49UlrW/1QU6XSoD2ulbQ6nnRtGwV7K5idS5TqlqxbYSBu3z8FNyjsHFddkIK4QPT4l6/gIwXu91ul1nbdYQ/ccE+Dyr36f29K6JR7cxKTN/WpdOtGp8X/NDkrAAgwFqXWq36CzXylJukJA1W0x3wNl7M/c1e1zfC8/l/bUSvIeKWdhu0fGvlZkEMgxR8rd47jGT64tEksdu1nVp5gYgopdHuDKxWXTSSznuaig8xVFOHErgBeilKiuaGaCpRer3ptB0nLZEC0UcItx3WUHtO5GikVMToFLwTOfomCuoWhxFFQ/b2U6LE91wqLdoq87vJ1K6jq0wzChnqy4R5ozUjafE8WpLT1CZllR2exX9gQdD6Gqq7YkVE8qcx6y+GrvraiOcyvXSmRRGdww+hLr2/fdf3ccA2vAD+j6uRE+7UKrXcs/easoWwoqjhVGOtVnl4lCJrcHUbTc8Vwcx1AKpVeP/XYd/86UPZrj+PpHic7jjOlLRAQZ24nEJ+ATMQngOa8EV/RzQz/16l45EiBDh8kP/e99v/xTOZ4PDHQ4ATWtV/1efh2nIK1HMV7MWUKU9Coo+Hi5FArQ9VfY1kRQGmIhSBQZt7PhOLUypWHGdFim8BbE5e2/h57ZkG5X9HZ7UtnpyF+1uPE77pwKs8aR2TFTNte/rFlrnBMBXss2qY4h8L5DtDdFc6bYJC/Tnyvar9Ui7IesYQ9vRXN+1G/R81Cuawg/o9VS90vvchEsFlsOgUoTi6OqftzCqUgJYHD33c+D8+wzba0BfizNHV/7e+bDScfuP2f95/3fVuRT73tPpvjEqKng75ESrhIwj2gEtEQRW90DtM/xeqnurStuaNa4l3Y4IAXqiku/qkglfPvc8vk/7QhT4dUaU6Y5wZWKz6KSXctylOUbJleJ4cZTOXizOiKvqBz66Tfoa20LtFkPs2DRMc6dFnbHNiPreA8Bt3wd85a90T8t0Tvpht7if2hKp5vlRyVrmSYFr19nbMggYtRydIlXp8HekR3eHRtsSUbLFs5zUDJPnHASMfBrgtWjWaVBbNWaHPU87ZI70Mm3UOEEqerzrkPbtSkY8gHbELhWBp4W8YtKPOyUK3p2OiMakeb3iCbIHtl1DI9aq8Vpuv0b/WymipnNsmdaoMBObTDNr3FXZzjBSXzoDVOeZJS6Mkvbm2OfS01ZDn7TbvF7zp7kYiSfPbSN36hBrq5sNHrswwvOaFYp/PMFsfnGUxvD0IR1QsCzut9WQe2rwnqm6bEcWZckcAJ+MisVZLtpU2UK6oA2cJRH12hKPG0+Kka3zfsyren8plXAcMYhSmw+P44in+Kytd+lIhAgRVqaSbgb633tF6a4tch5ZrQ3utHTXD7vFEph4EkilerPm6ng9FPNFfs+1gcUZjmH/KzgnK3ug+kCHx6Gu2/GDnI8bFbKNWnXdU3lxlj9LZ8lMQ6ADzukcYPucP2MWgBRtie/Qfo3uELuSkCxmjGNKJoGFCm144DNjrIL5QcDM4sKMCFq1aaeNuJQCJdl9w2uLorihx9pvkw1zmdIvmdNVEDYIpEuJCFoZlu4o0lUuFxq8aivmdHh/JnYJLb8sbKm0pnzXy8CJ7/K6Lc1znMVx7rPVoB1s1yW4PiostxBdXN3r5WxLLE6GVSpzrnp5vQI899jy78mgfdptXS54MV02wjo97QZ/d9tAqcbf1TM4c2xwCUB4rbna9WtDyg0XZ/keBOCaKZXlWrV/7dNp8jl78dvcttMCRiaByhzP1bHJ6nBsrhFVhxOnI/24hYput0ihX5zjczC+U3rTe1wjAXweAF6/doPjmTkuJQQAVM/4wOczbhpAYAKBw88TIqo3sWvl676GiJzuCFcuNotOejHHrZdpvMOTvKLIqUlP7TOZphGaPUH6mGNL9K7FCactRiMe52RUmmEGcffN0pc6RgfGbVOhNJUinS2Z4aRfHAVwox7LxG7+f3Q7j53J0bmb2qOP3agx+u25rFGLSeS6WedEGIvLNjFmNK0Ex25XRC3cAJwSJ7pMlpNrswq0HG6bSmvj7UpwYWCGWyLtpikB8v7WYn3bWhZrgAxD15pnCprylsyQ5l0clwkdXOjc+qreXS2ZzBSouqVmjUY4Fue+WuKsqp6e00e4z9Eq74nr6CRBPNFLT1sNfVItFqf20pFuN6Uuz9e0tVad+2o3dIBn6SxrlhRbIgiAxTO8bqksr9HZEzxGJs/v+uIwq3vgBrw2Q2OsIWyUtdCNmQQglLVA2sCl5DkLPAAmvzu8jYvG6hK/X1+SSLSnmRbxBMfge1IbaQh9MuB5hSmFESJEWHucj0q6GRhU0pXJS4D4xtXvx3V0149EWmqFO6y1Xo6mPjzBea26IPOPwTls7hT/vfsG2gVgeedlag8zktNHGPDstOiAz54QMbOynhOdNh3PTI52PJUWVlhKsncu5/9Umv8+cDcdntwIW421a9q+lc7QTppxzr0qAO+5wFJHHKhASq8gFNwEf7dAx90wGfj3Q6VenrK3hnxX6OYBdDbcMIVZBl27HZiA6WtbnEjQ+fYcEeuMaSfcjNORzY/QfmTyVK9WfcXL87x+lQWgdBY48aK+3oFHW9lpUW8mleO9K0t2dGdIEFXd65XKBnND7G9+0yv156t5TwbtU7V2BS6uy0a2yGMpGw+wF/uQlEioZ/B853Mh61fT4hqhKiVybpsB87EpBh7UGkStfY4+S8q7QmVB7Lcta8KWduTrsk8zxjauqixNBWBch8Gxl55m0Mb1+E5O7eP31D1WSRW7w5K/TpP7d0SYz5OgTgDq+8TkmRqe4PM1e4ydgzbAn4jo5REibARWo5iqMDLV60cq4bJUmlE/hUxORz6PH2SkNVfU9GhF2xma4OeFUf7005uyRU13UxnQbJFRdHVsgDXgmYKIf4wwg2m3eyPHqbRuqxKmorUbop4a0Bj7HgCLvTw7TXECJQOtKOHLQdHVAKyYDVe14b4vvVRtWdAUdUZ9eFJfr2wR2HHNYEphP02wn0KloLIWgO6XGS4TUMcJ09NWQ5+sDCgrCI+jWmIWHtDPRDrHe9Gs9yp8BqFtwlS9+WkGIay4DniYJu9VLCFGUKLRSn3WimkqeHZIDJ2tj4NAi7AlheZYX+SzkAkd27REvd7QbXzsjmYh9NMxI0SIsPY4H5V0M7BepWTLzSnh/apuIYoWDujMtioTUliJotxPbe+WYM33lmypOmfV1lH1Glb2H6Dj1G4xC6+6Y1RLvVlQVRrWbobo6A5rxD2Hzk43e+2Rvq5ERQHogHWSx1clWoB2rGMxdCnk8pWuS6Go4p7sW9lp00LXdisbDHGCFLXcijFR4NrsaKHWMeH7VRzVz2T4vAFtZ8OZZmV7+hFWq1/p7/1YzXsy6LuF0XPXDCsdpx9qHdJ/ztvE4b0YhfbzQZWdKaj7kc6f22FncfbcJZlah6h1kKKBq6x3KiMivlKO1qrq5Z3qe99pAqdFuV898/3XUT0few/w2UrlpDRCGHemKmsAuvo+gH42Nqh8Lcp0R9iaGKTcuNZRrAs5Rr/SqaIKD42f+53ckFbKXpzlBDK2jU7NS0/RWMYTNC6GRPiOPsPJKJZgP+ZkmgqkQ5PAnhuZzVZCG6YpStImJ8DJ3ToCG4szkrrrBv5+4nlSrauLpAcZE5zwzBgj1Y6hhUFaVWZR60t0nDotRs/jSVGVtPkdABIC5xhVFN00xOifh1Ye+OdXM1fbqci+KYbcd0O15lke7/QhTtCxGLMGniuqmme4aBjfScezWeM1VkZgYtfKCrqZPK9jIk012ladSvK7buhV4x5EQ/N90uZK07LoAQAxMuEgh6LZKZpjIiX91+W6NiqSwTBo9Cb3UHFeidfNHGUE22710jXtNDPuXofPRDrH+6ho7YaM0YrRMCrBmUYZaCzxevueKLzLta8uMKszc5TXOjfM57Nd4/ZxoXlWy3zWPIfUwLoozo7v7L1GG/GOR4hwNaF/MX++zy8EF/u+nq+ka7X7VXOIsr/FMc5P5TnaANOibZs51rufoXE6U9VSqPQJukxIYTlF7aU52m2lVJ0f4v4TSdpMGNxnMgW4ac53nSbnzdFtnLPLc7Q/zQq7RiRSXAO0GsDjjwDTh8kWmtjF+u5T35XabCmp6rRFq6MOeHGWcymlcM8VwSnQ3nVaPM9EXATmMqASuSfOkNhudR2gPgtAeXOFAXbNkDHFRHMmnadzbwvF3fPIRhuZ4i5L08CJF3g9Dn6dpV63vpp2olFh0NwX2vD8aW6XzHC9kxsiG6I0w3PM5nhs16HNSqR5f9T97ikPO8/zOeh96DSAk7Oacj0yde5zuyPkHF/IexB+xke38boomn6uyOfC6fBZVSVn7cbg0ofVvC/hbZoVHnPhDG14WwLm9SXg1AvcvjjK4509Abz0HS3IZ5hcL8QSuuTu7DEdfFG6MV4ooBMEIqImz4Pf5LrSl9r/whjfxaK8SzNHteO8+3rud3GGSvS1EroBJM/gmkLVebeawEic76QV27DytcjpjrD1sJwS5WpqZtbrGP1KpwqDVBPrZRqh8Z3a0Sid4cSVG+YkBjByPXeaTlZhVDKPQsu6+0dpQAZRjIYnSMtbjpqnxtqscazb9gqd2NGR+KYoqxsmx2sY3L5e5t8bFUbZDYMTY3aIxk7RkFWvTtNANzruuljR4TYtWSgEuq3YeSHbBK5QkJJ0ePPDwOnDXDSlpNZ69iQnfCtG1dl4ikqb86dZJ53MapXR2ePLXztAX/dMHth3gD9hVdtB1xvQtHW7JVmGERm/p1VO4yltIFSwJJGiAV6c4/XptJlNdl2pJx/jIm9qN8dRnuP5dzq8lo0q71l+lEbad6UmL0aDqIIlpkljp+qzrZh26M0Y/59I0Sg3KgyqlM/qgIXT0Qr26tnYcyMAg89Lfkgr97ZqYpBt1nYZJrDvFtL3NuIdjxDhaoOirw76/FJwqe/rcpTYC9lvMi3lTWJ/1Vw7toNz2txpOmiqJZbaTyIlDrPUj5bnGagcTWqBUbX//nEp5Wa7zWx5ENCR7baZDKgcXp7jPGnbwuwpkFqdyvPYYzvpvCsWEHwGw9sN6Uks7cpOHSIFe3RHiFIrTkejzMS0F9DkOm3wHyYAl+29YnGt4eG5tCOe1JT7qj7WpQMDQNvgVdhiw5LuIZLZzo1w7h/dptt4QkqUrBjPZXFOypACXstp6XZhWNRfARj8WJxjmVomwYBuZYFsvJEpnUEHaANzw7T3qmuHem4URfpCaN4KnQbHkC1qurV6fgbZ/AuxUf3PeG4I2H4dz0WJ5PoB1wQzx7m+Suf5nKVkfRl2uM/3vgwqd1MtUIcmuBYqlwC/zHroTgs4+SKP26xJS9gOSxtzeT7HcWHPJVPArhvJ4GhWeSxTkh2ZnNR1V0Xp3OF+XBEUVLA7vHeVBQZmRnfozgHNGgP1O68X6rgkIeoV/k09p4ZQ2Nt13XllvTsfCSJ6eYSth7VShV7LY1wI3WdxdjB1Zn4a2LW/93O7SWM71qeuffzgxVOM1N8VdUhRi9TnSllUIZUhXagm2ydSdJosEUtZmuPkn81zUaEWKr6vRc5UC42V4Pur7A/aj0BnvQ3QUM2dotFq1pjhVZh+iZHjsBpoo6LpheqaKJp+P0amLvy6hz9X+29UzlVpVX8PPxuKcl4Y1duHSwIKQ3rbbFGXFjTKvds6HTrKTltq9BOMBDuOpocH4L/tDg1wOqPbwMRTorILXSaQynLBV69wQelJZqQwLFkdl4GBTlNH3RXaDS6eHKn3a7d47xR9byPe8QgRrjb0l9Gc7/PVYr3e1wvZ70oqy8t1d1D7Cc/F4e4W4bk4vH/1PbVf9X2VIT1zlPPx2E7uT1HHDUh2OsnsnfJtB1VblWZpTwFum0hx7p4+zP2m0pyXw3XEuTwDqKpNFxRrTDLVgSdMMgjTyZFsuAS6A1+ykqsJePeh243EB3yxxUMT565dFB3Zbuv+yYoKb7d4bUvTg8v1ABH5StPx7M9Iq9Ku5ejhq30el1Ol337Nxe1vJQzaR3H0XDVwhUHrkvOptIc/H1TuFqCXYg70UvbVGilbpPOsKOjtFr83uoP3ux5a1ySz0hpUnuFMXtYYRd1m1nV127jhKdF6kdIOdczwO1gt8brkiqI3JM++bYMRJ6nrNk2Ox2nr8o717nwkiDLdEbYelqOJKOGJtaCjLneMlSgqvscoYQBmLHddz89PvtA7pk7rXHXL4hgdo7Ed7BlanmPEuCOKlcXx0BhCNCdFiz59GDhzmE7M+A5GG9M5UofC46lXtNLqyecZPa4t0mEGaAgbVSDR4b/3HWBvUddmlNxK0FC3JAppSP1vPEVDHwR0tJymNvauKEme15CrDPcFIhBnvVkHzhwhTSo7BIxITVz5LDPXXYEXJaiWASpL+vytOK91s8bJfWGa0VbLYnZh7wH9PC1Hh1yO2qW2d21+nh/mmM8c4fENg/Vtnstt4gntaCvammky0q2E0WIW1cTzw6Qouh3gzEv8+xNfpLFJZbUgn2EAtgcUximE0ljSLVvyw3Swy/PSi13oYXabznG7QQPpuyw5aNV071dFwTQtGuriGPdpt/kcew7pgPUyf6+XaajjCSDtAyhy+1OHgENPAk88wnO9/nt6RXGASOE8QoRLQVhtey3Vyy/GXq7lftW8G6bcmgZtLADMHhUxrwTnOpU5W5pjJkz1+q0uMVs7Mk4nYe4UbWW7yWDj6Hb+TB+mnViQ8qCRKWBkgrbTbnEeVnTm+dPcz+Ks6JWYQNXlWA0wG4dAHGGXNjSRYuDSMEVZfU63WfJ84KYUS6aWvsu/d1ryvQT3r/Q4VOcJ1bvZ96haruyxa5BWHnicp3twoY53IDXewsTqtGmLPJfpv3aLtmhonPfrzBFeV1Xr3h2fw0x1qLJx/wAAbiFJREFULM7tzh5nQDcIdHu0/Cidv0wBsIWanymISC00Nf18z81y6H9PEimy45TQKsBjnvguf8JrrJXKIoBzPxs0pnSe97q6SLp3Kk89mnqlt+Sh/7z6O+jEk1xX1pcYCErn+LyHu6co53RJbLNj873JD/NdOvE811COzax+u8X1VKMCIOB1bovIq2ISVhc41k5TMxeTabLwrAQ/NwJdumi3uaZIpti558RzAExu32rwmXBdqQc3KI4b+FxrNmpAu6qfPRjidBs8Vn6EwmwbxJCLnO4IWw+DlBuV+rOi21wqHXUldch+KLqOaQETocVLvXIuxWn6iNQoWb3qlmrfw5N6QQAAz/5rX11ZH82p3QSOPENnvyURvZMvAEeeZZR59w28JqcOcdJtVhiB9D0a8aY4Zp5LulBxTBRETaqQj26n4bc7nNhK81JvJCIrpsFFQKcFIBDnNQbUAQQtHsewANWrc81h6No1QBYZbTrNrRodZtcB4uLM+z7wwuM0Pum89A9vch+nDkkWIOCCyG5zwgZo7CsLdIIVFXJQndRK1C61vaK6nXpBK4bCp5DI6BQNu3outoWMxfAEx2+KYrgtjIREklS70jSz/EuzPCfH1uqxsSQXPMOToiQfUKm8MMIFTOk0x5RIimKtwWdieEJokuC1LIkiqd2WbRy5/j6NqNvhwtOUeq7iKA2ea0s7urIWYHMdjtE0uFCol3m9220eoyIMhUFqtBEiRLg4TO1Ze6XyC7GXa73f8LwbptwmUpwH507Lwj/gnFlbom0AdNbQlzrToXE6zwF0i89ahd8BOIe99CTHVBgVFfFZOjaTe3Sv7HiCc+TZU2x3ZQTcd6vGOTNX5PHqS3Tqk1kJxkvQMp3ncZ22rCFMXXbmduiwthrcvtOivQp8bmcanMfTYovtprR5aqLr3KsWXoohBnFWLtlGG7pdmNPmdUumeT3GttNpm9jNYIUSm2s36fTHEkAiywRDOssAs2nyuiUyVNUGSHlOpJkxTWbYrmpbKAM9f5rHG4QLeR7D78nJF3qfw2aNavKNilZcP3WI57L/Zfy9fy1w+CndTkx9Fl4PhtGq8VYURvgD0LZ3u5IMOK/+DjrVBa4VM3kJ5KfoxAc+nwUVkDr5IseikjuVeRE/E7XwTIHXv1EDnv6KqNyDwfhWnc66Fee29TLVw1M53RYuluC9Sqe16GthmN9JpHnuzSqvXbuu68XjSakzL3HboTHJsAfAS8+wrlux+GCiux5VQSYzBgyPs+vP8MTq7/slInK6I2w9jEz1TmhAr/pzGIuzF+d0h49RmgGOPUdnZnQbaeA33KH3uxyl5/jBwdHW5bD3QK+T3qzRUaqc1RNpbZELhJ37OTEfeYbCFvWyZBhTnJiaVW4/OqUn6Re+RSOWG+LioVXjpOk6nPD8gE55OkdD4E9S5CSRpPBYpQIce0ZTeQKhq/kBj2fG0I12e87FZa0vGFKHppTMnY7Ujtf5u2ly8RUEjJwHvlaVTed4bWKWOKFDXAwMT/JeA3SM7TbFPPYe4DmFW4uEoZ4DlSlXYnrh74SfqzCdy+nQ2CiDcu1tNJbh53dkCvjutzi2ekWCCnEgVwD++f8yq1Ar0+iZcQZoluYBeIDtMINTHNPibKYl4kE+jbHnyiKtLQ6xPEe+KJk36ywxqJXF0XZ478Oid1aM55HOiVBaUwILBs9XRbZVX83A58I0nmCUOx7jeXRkEflMutfp3iCKWIQIES4Ag2yy+vxS93v4qd75tDCqBavqZfZSri/1ZvZUa67qAuetZgVYEnZWcYxzbG6IIqMAnViFsJL53LQut3LbwPRprYFRX+I812qiW6scTzILGHicU0szFE4zDR7bFaGnmQqZYaYJNBO0XyrTvDRHGnM8wYx5LK7nTN/nNfjmP/Lf1UVxWsUGtuqyHUQUVNhng+xxt9zrYkq6lkHgMXPeMYFYjU630+GYKyUes1YWwblhydx2+B1HWGCJtJQkNeigju4Amoc0pblV5zbbrwHmT/He5YZEE6dIZ9Vz6Cir5wXgM6QCyOcT6gPoRKve3Zki7aXSC6iW9Brh7HF97Grp3PVgaYYJj5mjdGYP3NObMQ9DJY9mjtJxrczr4HQ8weuhntnwd3wPeOHbknxpCAOgBcDkezk0xucyntBB8sUZ2uCF07wPtSWyH31hKFoxHVBQDLzSDN+tWIJrKNcGYi3RqEkzMQOTY2g3GfAJQIe9OML7OnOUay43YHCl3dRie6bFdcDoNt2GtVETzQFhbwQenfFYnMw5JRao2pMCgFfnvxdmyJJIbFywPnK6I2w9DFI7HRofHAG8WHqbOsaLTwDPf4PZu0yODudzj3G/t72a2y13jOUoTrE4M5iDaMi5olZErZaAPTeRvnbocaHkWOxn2ZSal2aVTk67BVg1IAc9QTod3d6p3aCBKo5xAivNoNt3027T2XFcTnStOh1QM0YKcmyM9LiGOE+myWi7F6oV8z2ZJKWWbEMcboFqMWYCgCUq6QF7hgZS4+N7gJHgNckWSVPyXFmcZUUtNs4FWiALgCDQmehWgxF1u91LIQuj09LiPeHPpg/r76jnau4ExXRscbTnynwODINjU6Jq3bYvAlcWgKk0x1lbomE+dlBnF5od3v9EiufryflkcjTQjZpQvTLcv60U4C2hJDo6mOIIhbzToZNtWMzaKEe8H57U87Ul6JGO6WdK9eqOieEPfBHNgQjwxHk+VoLPnuvQQC/O8ppF6uURIlyeOJ8C+aWgv+5Z/V9luGtLMldLZm94gmuBdoUB8liiW+7J7xsi4CRzaSavS4Mcm3/PDvHfdoPfddtkgrVbdOicmAimDdNxqJe5H8PQ/YrbTS2aGhe6eKvBOTqAdjBUgLxbn9pm+c/kbmZyHZs2wYpRnKwltPaYMJJ8V1Sb5dxUwjoA53GlqbKRsOSYtUWuPbJDUqtb5t9iKaqwT+wCDn+H650AwLUvA266S2vFqEw3wOSC3WZgY//Lac8qJa2e7dhkHai647Ed/Pv0Yd6Pqd28Z2EGGjA4I92uaxEzgIGV3JBkgOPCJHN4bMWgWJwjKzC8HizNAIef5L8dm8+c+l053mo9eOoQn9dEiucQBHRsE0ndFisWI3NMqbC7ct87TfYWDwLNPFw6yzVGp8lxBQGvf3WJQmStmqw1z2pxXFeYa67U+g+Nybti89wtE6jWeT1VZtnt8DkzLBHk9fX60xcGRacBLLQkez8c0ntpaKFW16bdN6Qk0Yzx+W/Wha0npSHNmpSKSBLFdQY/34YSDoxRwV6xFNcZm+p0f+UrX8Hv/M7v4IknnsDMzAw+85nP4A1veEP370EQ4AMf+AA+9rGPYWlpCXfddRf+8A//EAcOXKKoR4Stj0H03rWmt+WGRICqIJNMCGeOAjuv4zbLUeCWU4RNppdXaw1/roIIsRKNEcBMY36UlDSATkw8qelU7RaNc6zv84ZksC1xLFW9V6tBGnp+mIsX9X/Vz9OKM9qfytIQjW0TAzNDpxaGrrdSln5N7btBA2wAgKnp34ZF46R6ijodGgsz1ksxMgwuimIJMTwur0siI0Y0y+1yRYrTBD4jzNlir/McizMaXBhbnj2RTPcKtCnEU73fyQ2xPkwpkpbO0IA7jrRakXtWLZGpoLA4S/q1itoqWuKzX+GCz/d4rZoiYtOqS22YjW7ruVROC855Ab8/so1GzZBzMC2pN5SIuOty3+kCgDq/47qA1wJXpKEbbpn8nlpAqsxT4IsGgKkV0E2TC6ORKS5wymeZ8bDk/FXE2zCWV+KPsKaIbHaEi8ZyNu1SsDh7bhmW+lwhkeI8qjLUjYrYq5a2wcl0qBNEUtORqyXOnYVRLRyl1gz1JdKdnQ5QE0cqFqeTofQr4knajWaFKuMA5zdVf51Iih0yZd5eou2KxWiDfUXzDkLZ2oKITXWo8myYQEPGlsrS9ipquIokqH2YpnawFHMJJhALdJZ9zaHGYAIItN01THQzpokEr5eiIcdSdOLuvBd47X/Q3UJUad3scQYs6lIONzQO7LuVf9t+Le2CytzGEzojWzqjqdT9z81Kz1AY1RKdX0XrVnA6dGJ330hnOlvQJV4K7ZBgaKvRux6wYjrwM3NEO91qPaj2rXDmCJ+ZRFKfXzJNh1ttd/IFXm91LeyOblc3tZeZ4HzoPJp1PsOew0BIQoI+Z09Iq7cEg0L5YWFeGFLWII54Oq8z0oEn/wbXHfAlKC/rLPXcKyVx1THFiAl9PMH1RTwu757BdUamIPX7Up+v3t38MAP9yQwwOsnxV+bphFsddDWFVBImnuHaz7U1A2GrO92NRgO333473va2t+Hf//t/f87fP/ShD+HDH/4w/uRP/gTXX389fvM3fxOve93r8OKLLyKfzw/YY4QIy+Bi6W2zJwaLyijK0Xe/Acwe40SVyjGCGk8zSqgimssdO0wXV7Qhpw3s2M/9908A4bFUFhjRHN2m6cCAXhi4NiekoXE6zQgkGylOpWkxsjp9mBN/o8LjWXFSflJpRoUXzwBmwP01a4w81xbpAC3NcXHQKGtnLT/KcTqS6VbG3cRFKo+fD0JXNwxO8uEWJnaTDlrgCR3eBAyv+2c63nHJyHb4WTwpPVxHRRTOBjoO0MxwAr/uZbxH2SJrndoNnl9xlMapMCoLvLJmJCjhnkSKk3tY9RPgd/vZECNTrOE+c4S1Ya0axzWyjcao3QCOf5f0wi//FfdfL3O7M8d4zMoCz21xhsbF87j4a9b1dVIUdlPoW50mz8du837NnxIDGkgGIs4AU+AzgBGLk+XREqEUV+qnPKUW2hdhUfVeDcmwqMWn43B7RV234uwT60hGHoGuWVN167lhvitr0UM4wqoQ2ewIlxVWI6RWGGWANNxfGwBSKTof8yc5J7o2bZZpcAGvbMHUXr0PgPN1ANZix2O0h7VFOnL5EWHmxOgMKEp4V/hMsnbOksxxGcAuU1gUECfNANJJjsWWwKUK6huyjbOgy6xnjtA2GyYdcteRoLmsAZSAVOADgal7FQc+4Ku093pC9h94HLBhijK6B5gOHbKOlGjNiBM6NM7A8/HnyAg49SIdzOnDvK7tFu2x72rKuN3SLatqJdoFle0vneF9r8wziBJWvFbMgn4szdHZnDlGZgGgxe/qSxKE97S97zQpmnbgHtHNeVqeO4fXwIozKG9ZfKbsNgXwfClpa4vD2ygD9RKP5bk8TqfFceRHdBDo8HekRVaTZWLJBDPUR57Wa7q5k3RSj3+X69Rmjc77xB4yCc4eB3ZcL9nhFjPnVozMRYDrpsVZOueWlKR1Wnxus3na5GyeDm2zyutsi2ifFQ8x3gJpPebq58E0ZA0R4/ZOJ5QUkcx9pyHljW3uy5csfWWeAfnA53hVp5xuZ5UcxxZPapZHIAkftXYJXAZ2HFvWPhfJer1AbKrTfd999+G+++4b+LcgCPCRj3wE73//+/ETP/ETAIBPfepTmJycxKc//Wn8/M///EYONcKVjouht82eAJ75qv69usjfFc2mJXQyxwFaLfoYjgMUAQxN6YjmSsfOFXtpQ6M76OT0i7z1j6XT0jQkFckHOCkXR2l0bJsT9yt2cBI98V3tzO++SdcdBeAkVRgGkjmg5ssixAXyY0Aih25Es9PiOQcgjdjzuDhwbXRbjSQS0utTJtiYJVTztYbF/RtCaffCTjc4yVseJ3/D4qRtQChK4OexZEhIJElnenyn7lVaLTGSnh9h9iMrFCRX6HuWUJxiSTkO+GyEe7QCfHbGd3KR5/u8JvEU71U6P5hxocTcXJvnmhvlOCoLjJJ3mgxmVJZ4rNK0zpo7HY49luD3G54Il4SMHkCjbli63sl39f+798xgHbgRBzqepnblM9LSqy3Ra7n/8EWoxF++jCDweU6B1JMjEINrchHk2UCsyGe7XhFBt2E+10bAf2+/louPS+0hHGHViGx2hMsK5xNSazU4v4/v5BzcrNIJGd/J+bG6wOBqLK4dmFxRmDciSOl7/EwpY8finAN37mcdaiItc5ZLinLsOs6/7RZt4dQesbHCTAt87rdd1wFEVVoDn3YrlqATkUjKHG3QWeq22PS5f7stDkQgAeIWkEwCNtDNDPoene1AnO8wK2zdHe4wJKttmcxeZnLSNcMU6nBSj7e6KAJ3Qg+2TC0CCkjwOQlYOaB0Ft32Y5miiHLO8VjxGO2FQiyuldAVwr3WFVo1YPYkHdWzx1k6AND+tFuSZMmSil2dp9OZynDM9X9gwqK2yPu6dJbfTeW4Vjj6HB29nfvpVNYr/NvoFO/n/DTPxZVMcWmGz0IQ6LIyQLLOHQbgUxle04VpYGGWXWUA7qv0ONcEsQSfrUad3Wy27QNu+B6+D/OnRUQ3xQBDW4LxrstnqtXQVG1DmIONgO+LafJ6VRdp97vrxIZmpXmeJEckUuQHZNLBBQI5t25AxmXwPibrtsDntVBRJl/+3apz/VQY5VrJsLSwnGkK9b0lz5HX23I2kZBMvpThJVIbJsJ62dZ0Hzt2DLOzs7j33nu7nyWTSbzmNa/BY489tqwB73Q66HQ0paNara77WCNcIbhQettyvRwPfh244U5mpsd3ALUFTsqdJiecZh24+ZreLPpKdPF+2pBCmO7SP5Zska0wZo4A19zeG4VP54Frb+ccpYQ9RrdRrEspYc4eZxZVUYvUIiMWI1X96LNiJNLaoU+lGeEdGhPjLfXEhVFGHuNZRodVW6jyArexRDhjUI3vxSKeYIDANHXdMQKd9VcRUNNiJDaW0LSneEIrYBbHgLEpGrLhSfaCVFHfuVP8PT/MhVMqy3NNCS1p/8t1GzqAqraVEjAhi7P+fpnVEhVsK6VeBXrgXMbF4izv3fAEcMur9XESkvFYOCNjFeOoeo3PndJKpYmUiM9kaFA9TyjcljY+XdqhZDwCQ0eCIduHKY6qjiqdZaDBtHT2WdUHuqH6b99j9DkQWqEynKaiFsYl+23w2UskuIkVk+c3x3E26yzXMPbz+VYt8hoVYO8PreqRibC+iGx2hA3H+Rhs6m+ZPHDdbVyAj++U+mrQBo5MAZgC7IOc0wrDmrY8uWewfVa03dFtwN5bgNu/n/tOpnuz4hM7OV8ptWmA9nSbaFAcfU6C28Myby/RNltx2ma3DZRLQCoJNBu0UQY4PtWjOxbXVOdYQtfxJoR267qsPXdcnSl3bQZsXXdt7sMgdAMEkvVUJUMBqFQ+NAaMbCdrrlll26t2g86jlWArLNNg1rssjmtYLK04zvujghnq2ir4vnbSFf1atWGrlDSdXDEXwgjb7nZon7Ul2uBkmjbZdTSFfEiy50tzvI++0KlTOX09VBC6NM2a/B3Xs+WWsmfNOhlce27ieSnNlkqJ45w7rfvIj0zx39v26ZKy0y9xXaDYX+kcr4FrCz08zud2fDud5Nf8/zTbsjDK9WRpVu5FTWfpU2nWWCekRt00+PwN5bieqguD0oqJaJkBWCLOZ8R1FxiAx1frD0vWBOkcr6MB0r6NjlDJpSwu8Hm8eIzPUTrHZ37vAT5LiTTvsbrndodrJ8MAOjYz8I2yaAtJWcXQON+7QM79au/TPTvLl2VysldoanJyEidOnFj2ew899BA+8IEPrOvYIlwClutTfDliOdqqcnBU/+xrbqfxXpxlhK4wcmEtCFZDkVNjaTf470aFlJ8TIiKRH2WrjWxR920G9LVWNdhHn+E+ygsUX0llSYc3LU5CzSojq6dfBGpVOszpPCevZpVGJzskIhhCrbKlJjeTF4qUZH3jMcADM6Zr6XBD7dNhBN8V6ryhKHTQipUqMJDO6gyFovBlcsDwGM9nci8XUKPbqURfLemWWOM7OCkXRnmOYfEW0yD1qdNgZHz6CGlfQxPMPGQK6PYFdR0u3jyX0e9wyUJuSJcPlM6QSjY0zu9kCxxHo6IFQ0yTBqos6qWLMzQmlQXe/0aF97TT0vTtnl7oivIX6Ou5EnwAcAHD4TPerosRlyCAUqR3bV7bWJx1ecpYwtdt2wAAlhhAW4/FD0SgJSC13LT4nGYLPFbxACllC2f4XKbyfM4vtb3RlTQnXcaIbHaENcNq38l+FpmyczPH+D1FB+60uI+wyNTIlJR4lUQsymHgzzAZoA08Zg5ri1zUL52lM91ucA4qjHD+M6CDj50mYI5zX+U5jsOSDFwqK6JecRFl60iGUbKoybQWgqoucH5LJGl/hic5t6v+07UlOp6NMm1PKstxOy2qoSstlcoCx5aQuVi1z/R9iLLo+kCxpzxlb1zt+BnQ65nCKJ1a1+F98YRunorzHrSbzF77PoMh5TnaHtPgd51OSNOlD5ZkXedPc+0S+MDQJJ1a1QY0vFY6dYhK3QF4XKetg8eNKsdvt4HCHmBiB5Wvq4sMqKTyst82f6+XeT+WZhgsMaAZDYYpTmtHZ4+VunZLNFFmjtIBBnQp3K79kkVu8v7PHmfGulWXLiVF3u+lOdp1FQDyJUGSyWknutUAThwEvvjnfBcWZziWSonrP0/WSfG4ZIgDGaOw1yxZS1WXRKi3wX0qB1kx3tSzYAnz0TQ0xdsA14e+DxSSfLZbVV6vjiRpYk0AkrhRQmzoaGHeeIr3JTfMtdP8aZ7f0CTf5fwIcMe/YSngoSd4Tr7DtUm2yIDI8CTbuEV9ugnD6H2bgiA457Mwfu3Xfg3vfe97u79Xq1Xs2rVr3cYX4QJwvj7FlxuyRU6q/VDZ4bjQuotjnOAmdtFpU2JTqz231fQazRaZxVyao3EqzTDymEpzwrfinNjCfZsBrbo6LXVf1UVOPI2KtKGySYfPDolyZ511vIk0YDVFIfo4J18lttWu0wghkIyxx8WH53KyL45yInU6NFJOG2tCYzMsbbxMoYg54uQZkGys/B+mpp0joNPpOoyQjmzjeSRTwOQ+YMdeYGwXx1wvMxMyvlOL0qnMSPi+JNO8XlN7QwqkkmnvtNlDOj9Eo2SAgY1EkoupegW48ZVaub6ywPtx5BndZ71Z4z5yRe5vZEI/WwCPWV1Ct2d4s66DDIEYpEDVqoUoiZC/XxQkqOGDBjERAyA1VvEUnxFlVDMFBjqU2Fq7LrQ0B90ae0jgQDn+BkLG2BPapM97nM7prP3N36uZAuEsx8XgSpuTrgBENjvCJeFC30nFIgt/z/f5vVZj5Xe5IYHk0W3g/N2ROupAGEEB59ZvfV5/Z2mOwc7yWc57iTTn52SWc1uzSnucHQLqJzhPt+oMDrYl2ze2gw6T3RF1Z4PO1PxpyRb6QL4IWEmuN1I5BmyvuY224YVv0fFvVDnfxhO69tWK6bZOpiGBAUMHvpMZwGyTsusJw+lSoXQ/VD9p06RDJcSmbtcS09KZ+XiKAVnV9UKJgkG0PlIpTekPU/tzw8wMG6a0gktqOxBOVHge1yDpnA6+N6q0+4Wx3rVSvcyxTUgAd/Y4HdG22NVkRoTxmnLdPJ5DOgcsydqgLcEcpXDfbmqtEs/n9WjVpBbdE0bguLARHAbWY3HSwxtVrgksiwmFoVHS6IcndDuv+hLXVnYb8OuiI9Tks5YfkiB4h89kOkdWgdMWlkSLDuzMcV6jyjzPs1XjNo5o3PgWz8f3JJNt6fcD4D23WzyuUvvvRyDnDtEQUKw6JXYL8N7HJEiScMjMsCw+PIEHuD6QMOVZDqQMzieDb/olfu50dMa/VQdOHyILZXw3f3bfpN/3eFyzC1IbQytXWMdQ16VhaoqpfhU9V5ibmzsnkh5GMplEoVDo+YlwmWA5NcjlPt9s7F1GcffA3fx/WIxDZS37P1/NuS1Hawl/vveAjg636tq4DE3ozyulwcdTn1VLeozpnP57s64/X5jm38KCbHZLBx+U2rnncfJW4jSA9ElNMxrt2jqy2mljcCj6QmDouh+lWK7UWANPWpCoCGuILq2UKiHCMYbBCde1pdUGlq8J3iYRcEXnUhiZ6r03SoG0Vec5q2vbqvNHUcCzRV6zbPHcfR78Ov+vFHbVPlRtlfq8OMqfsR38PZXmIk8ZG0XHMgwaeqUaqmhll6xOG7qmhikZckuLEykBFAQ8pt3uDYT0rO/85dd7Sr3U87gYSKSlpUxF91UFLp0SdqXNSZcxIpsdYU1wse/kpb7Lyg4ocUkFxVRSP4A4W3N623Dv7mmxB42K/nurzm3UPmaOkGJcGJKAsK0dvGaF7RoVbbk83zueaonB2ESaweO02OrqQkidXLLZhkGRtsDnal+1u1QZ70u2yyEoxXTl4HfndrEVSizLgGZs2W1SyQcNY3wPNW4A/r9VlyRDWtvH8JorvO5SyBbP3Xe9wm3Dz0X/M1IUrRB1TIXCiCRJ6hQ4S+f0mACulQoj7OThOaLsDandl3K32lKvmGomJ8mO6d4xJNPcNozuc1jvpb27rgR7YlqsNJ4SYbEiAw2AXjfaHbIqAGlv1uZz59oMACmofQF8zlwX3XadVhzdVmD+eQI3PS27JPOuVOyHx3mc6iLXKyowZamcsDD0nBArDqYcH6Jfs8AxJVK6FWq7xfWsQq6o38fw+hfYUHt/2Wa69+3bh6mpKTzyyCN4+ctfDgCwbRuPPvoofvu3f3uTRxfhorAaGvV64GLpo4q2Oki9fPYE6WZOW7J+KWa8VU11aYaGtd1kffXeA7rHthpHIq2pbqrWJxYfPMapPdzHmZckwh6n45Ub0g7PkaeBx78gVNwx1nBv2wc8+zXSdFXbh5Qce2icEfRmlVHTxRluWy9zwoqLo+Y5+hzdjkzEvjhU4oQFNT0BBz6pPabFSHbYMb9oBPzxJdOqrpcV0xnuIPQ5DDriKrPrGTSArk36XSzO/TQqrJkb3QGcekGU1k1mKTpNTtpnT4qACBghH57gT6YgauUNOvDZQm/ruE6TTnC9zME1q7xmsTjpY05bi6mpkoUzR7koMOQcaiV+1mmyfGByD+/b7DGhFjZFsVWi2p0Gus6tFUNXtMTydQYFF5vplkBGAEb7mxX5zCSV0bHR7d/dafI84il+xxQ6uRGc63h3/+nr+2aYfObsNuu8EbA2P1vg/Vk6S8PZv3C60Hd8s+akLYirzmavdVlCVOZAXOw7eTHfi8VZd10pMVCbSAMIJKgrQcHpw7rlUipHhlMyLe0ObWacOw2tJ+LadLwcW9PdW3XO+cppzw0JPTbJebs8x7VCLA4KTIL7TWbplJkG8NJT7JayeIZOnesyo1lbAuDw93SSTrbtsnuHsr3K2bBiotcByXxfahBWoOyuoWyOOErd9mRSfmRagJuU7Hyctrs4Jo66q9ll26+hUObIFG1jXZh56bwEtgsUscuP0h5eexvfHcPU3UKqCxS267SBhVO8TmrNUp6nLTlwD230mSOybhrVrcR238D7orY3wOxoKsu11MI0M8Suq7VPfBdIFoBYVQIPvs6yK10Uz+W9L53h3wojcp98IDkB7LkBeOHbLE1YOMNrlslRmXxiF6/D4qxW3UagO460GrTF5bN8dopjwPUvJ7vPMJlB9hypZQ6oGeBJIMZxJCgS8DmplbUSuWGIsy3rP9+n8rwZ4/VdaY2n2HeIAYbov1imZkU4wtCwa3wHOm1ZJ7ghpz/g9ek0RYwvoUX2IOUgZpxrIqVdY8aBxALf24XTwLGDXJebFgNcw1MsE7BiG2rvN9XprtfreOmll7q/Hzt2DE899RRGRkawe/duvOc978EHP/hB7N+/H/v378cHP/hBZDIZvPGNb9zEUUe4aKyGRr3WuFT66NSec2tG62VSgocnNUV49rioKOZDdGMIpXiR9LTxHZz8AIpVzJ/WQitqAuqnh4exQ4zQ2E4aCRUJjyfYUuP5b5KKlhtmUODIs8DLvp8O9cIs/5+UFgl5k5P7xB46lXaLE31pRrfQ8B1OmPGEKHSLEraKYscSNLZOB92JPxC6dxBQXLynjncNEfg08IFE0ZWB7zrhSqjD1HR/GHQCs0UuqkxIpNwX581gK4xWHTh7igZmaZaGL5WjcZ09Sdrfzv3Arht4LUemtCKquifhRfPSrBZymTkCnDxEpVLVh3vuNK/X2RPSdkvq38qzpI5PXctFxPw0FwFju0gDc10uOOanJUiS5KLR93uDEkrozG7rAMWFwlTUcVUL6OvotW93CQjdfuy+1HB3GsIukDr/ZvVcA60WaKalMzWmxWuYyXOBlcwBuRFRG/X1e9dqcDEaQJcAXMg7vhlz0hWMyGYL1rosISpz0LjYd/JivpeU+bKnX3NWymFMzl9Kj0O14lJB7mRaFv2G3CODTljgiyCYUkWX2utYQh+jKSyoWpllYlYCiLnct9cUxycuDtS8zgy7DsdWrUjgPkGBycCQvzVkjvYHi6QpJXQzRtuoaL9rBSPQwXpVO67EwAID3d7MnRavV34YZEVJuyvVc7rd4PltvxZoTdBGGobU4QqVXJV9pbO6xCAseHfyBa6z3I60DvV5rZ02j9OsAt/5F67v4mmua+ZCa7J0Hrj+Dm1rAN2RpFllGVg8DcTBLHBtUbrHjPM42QKdxERSeo9LiZ4rAmOBlAHUyrwGYzvZIeXUi1xztGoifGuQFj13gtc0XeD181yuZ6yYONsuHVYlLhv4fMYMi2UJN9wJvPg4147zp0Xwrcbr47q6PMzzqQ0QCEPOdbVGTgBdSqDKzXpatQ56JuR5U8wMxUa0JDnQaVHDxU+iq64fILR2FEajERNNAEM0Y2K8xq4N+BJMUAx302LHFSXa1mxIGz2XZXHtFsfdqGkRww3CptLLH3/8cbz85S/vRsXf+9734uUvfzn++3//7wCA973vfXjPe96Dd7zjHbjzzjsxPT2NL3zhC1G/zysVq6FRrzXWgz466LvFUU0ZngktnhT9qVHRtDNAb9uvcL3SuNR1UtSn8DGOP0fHRk0eSinyua/p7VJp/bmKfFdLWthk5oimQxnQjmunxSw9IK1MYgAC1lkpcY0gQLcPogEaMF/EQdYDAdClHaka4W4Nt8lBBNCOYVe5M6adPuUgA5rJUBI6UqtOo6Ro9U5H06/sjhisEKUQ0NdIYfs1gz8fhH4qmy21V1Zc6rZkrI0aMPOSvs9n5FlzJMuiKFmmpcVZ1GIv8C5yxleZC0idtaeP0aUYyKJPGePucdRzIY5/MimRb+hACH/R/1TUdAXFzgiXUYRRKZ1L1wfWrrQjQheRzRastV2Jyhw0LvadvJjvDfpbuHylUWGWMZXupQSrbKqC6rKgyouUPUnntM1V+0ilWRaGgIFVSwLusQTnVivO+d409bxfK2s6NkA71Kry722pqVXZ9VhCO96D4AXi9AVryi4HoMuIVPA0HAA3VVDcpB2plaRWWl0P9K6XlB+n5vtcsVfcNqziPQhK3VvZVtViLZHsVUHvt+Nh+9JfYqj+1u9jLs317jMlDmanrVlnKgObTIvDHLJxgZxfcZTMCoDfDbczq1fQrf0vjEi7NUjdOOTei/1XWioGqNFTGCVjU7XYVM80wGuSyZOGHktQALfd7M3S+yEH2w89V74qI1hhYeF7EvwXRzuZAODrcftuiPXhakXz8IUOAn0tVFs5QIvz5Yq9CZ5Ygu/Gjv0UD1ZIpPRaKVy2sYH2flMz3a997WsRBP1Pr4ZhGHjwwQfx4IMPbtygIqwfLrRX9lrQ7daSPqrGc/jJXioSIG1BGqS9nn5JIrY7dFTWsRmJa9ZoCE6+QMciP0RRqFaNE7prM6rZrNHAGGAEdNf13I/qYZnKMCJrxoBMVveLblSA2nFSqQyDE1FMKGyuOKaxmAh0tYDjz9Nhr1dIwTFjnIBbNYnsB4xQbr+OkfvZ44wsOzYn0LZEWD1HT4TKz/bWgla+HILQhA9GaK0YJ+4wnS2WpJGJi9Bbs8ZMgRXXDAO7QZpaLAG0msxCnDnGRU27QRaAos0nUrwO5QX27+y0uJjY/wpuG4vzGWg3eQ0NUxZhL5E5UF0i5f70i8C3/4lGJpXRbIJqSZTNq9x3KiPPQlUi5EKnOvUi11RKoCwIeI5xiea6Dr/fcKEd40u41vCkV2YITqAXV6plmDqMu0ymJYye/QWa7hgYDHK4Lu9xOsvnWWUY1OJEvXuKmaHeLbvN+zQ0PrgVXxgXOicBVzUNOLLZgku1K/3P0NKcthUXs7+NxGqf/9VsN2gbgO/9/Ole+7fcO6a6PqiODYVRXXu73DHDStWmpbtxqBIy09SdIbJDzF4unaXwVLumVbXjCc5Dc6c4XxXl2Ai4XbZA+16e475SkpVVNaedDudsKyYaKLZmKikqsieMM1vmRGWTfB8YCmUG7Q7H06xrh2YQAhdwuxHOtYdSSYewrDyPjpQqqUtmuC6xbc7lw5O87ouzZC05HZ5L8ZSw9E7zvDJixwOP97xdJ7Pg6DN6HaSelXoFePKLwLe/wLK5SkmrbueKtBHKzsZkzVOcAEYnddleJk/22eIsRe/mTjFQML6b66n8MPdbm+f7G0uQGVEYJWOwNE17b1lk0GVEWLRZ5f+dDp+xZpXK5O0Wz2XuNAP8qjzB6Qijwud7kikCU7uYaa/My7MAoFMHYIkaupRyLVncX3mBz8n4LiCXB2oVHttui40tcOyVkiiR17XT7nmXmDwJNCW96QIw9fNtNzh2S7LYnqirq++FnynXEcfaBIIO2aZOB5jcBYzs5fnUSgwQxJOafXDwG3we61Weh+tKqUiGiZHC2Iba78u2pjvCFsVqe2WvFd1ureij4fHEEpoWHO7DabdpYHfs1yIqqYy0CpEaFNW/MybR7FqZRqFW5ue+T6d+aU4rop46xN/TORqCyRDdXV2P578FHPsux2m3aBTsNg3Kwml+10pyUTA0wXHNnqATbdu6lZbfouhGLAWkE1qlO5unA7N9H6nqcydoJJp1LXARYP0y28tBUY1MoYYnUjw3RYUfnuCkujhDAxtPSqTU4aKoVefvni9Kqq44f4Gm63UjyCYdO9MUurlH+lo6R8dw9w1cNCTSpJnZLfbg9F0+E7BI5e80pE2KrYVkrBi6dfIIdGuQRlmi+xJAUYEGVesUeKGMMTTbwLbF6fWx9umMEDwP3V6sl4IuS0ECCIbBxSgMUXGNswarXuW/w+9ePMV3XL1bAN/P8jzfh/PNF6udk4CIBhyBuBS7MugZKs8DBb+3S8Jq97eRWO3zv5rtBm1z+ClOV+k+O7ccZk8Az3xV/+55dHh2XDe4lWC9zDl6TuaKdoO2dXiC83c6z8V8IqU7JHRa3A4mbaPrAkN5vb/SLJ2g7JBux3n7a6ipArCWNJVGNxtYL7N8KVcEtu3h5+0GbYpja5XxeEIHs12PDoSV0MHsmJRS1avsee2oDLdkFFeMea6Tw21ZvHYGpE47zjVNIi21uBbvf36E9bTX38HgdqeNbn9xBOKMyhoqluA1UoFlpf2RzNKeVpeA4jBL5VpNaqDMHOU1ri3x/jodXcI3f4q1zp02nXorLrZfAiXKabZbXJedPQkcepLOqmEB9UVg8ayUCvpAYFLfpdOi09psksHgODqYrgTs8iNSV+7z3qaK/PzMMZ7X/lfw+izO8jr4itrtcI2j2uJ5bdZBJ1J04BtVSar4WnMnAFkNgc/ESmFElLtlvTE0yWu4NMtgdTLFZ8pzNFPCD9ZmTaeYiYEp9doe961U7X2PGgQ+aPdNEVH1fW7fVTGXZ8xM6LWeIeuy/DAVyp02GYG1Bc4nKoDRkTVXPEFl/EyO51pdWN06YY2wqfTyCBGWxVrR7daKPho+bph+puhGlZL+fMc1+u+KDpUt9tKMFY0qV+ylnYe/E6Y9zRxZmUK771ZONoo6rRQo03IcRVNTtnZhWnpCpjl5xRKMygceFz+KCmbFdZY9rASZHxaxCnEWDUMUwzcYqu4onkTXUfZcnc0flvvcVgtkce4CoNsnOpHsVQntNHUrC8vSNWmKuu05VJ1VGN/B+18pAXPTvYq1MHjty/PMdhiQlh1tHf1VdKdGlRkUX6j5ipKmIs2Kxm8gRN0XR7WbHfEZPe69SJd6lQcjkGh6mGJ+0fsSA6/G6rlc4Kha9HZdFuMhSqd69wap1qrP15qeG9GAIwCXZlfOV550ofvbSKz2+V/NdoO2qZbOLR9ZaX/HD17Y54uzvftXtrZRGXxcZdNVEF0xD5RidUs0KlQZUrslHSUq5+5LoTzH/yczukNGPM4sXaaos8IAnVUzxsC3ZTFga1n8yRSldjwueh2QulWnlwK8kVAq1oZkXM241uFwlfp0QPu6Q9YVjQqzwsputlv8u6LOKzTr/HoptA5R32nW9b089oymaKv7YMVoH1U3kEaVTrVp8bopwTu7wxZUgF6XnTmsx5VK8/+FETr2anxqPZQp0ilv1bluUKVdnk/Hv1ERqrWM33N1qYC6FsmMjFdq/JXdD7trtbIkSkLlBqYp7LHwvQ+g6/2bWiXcFdZfq8ogjt3isdWaRAW/LzWYDnBfvsf/K3G5sK0HeBxfsTaUky3n0dXtCW0LyBpVKPrh963LDjL4TqVy2oEPfN4LV+rhVWeZSL08wlWPC6Xv9dPUwsrg86cZ/Zw9JhG9HDC+HdhzIzC1j99fTjV80HEzeYp4VIUOns6KErjQA0e2AdeDk3anwQn62tuoLr44Q9XK2hIngYXTdBjHX0MDvzCto3LVBd1Pe+40o3pj20mDUj0d60s8N7vFLMKxZ0n7TWUYSW5WOUErwxKLMUN49gQj9IZMaO26GOtAMuWS3c2NUEWzJRnfTJ7OtWHJQqTKCc1dx/rtlRD4IrqVlXG0gboSusmQht2sSj/TQC9ODJOTbnWR2Q1lsJtV7ci3FfUpLgsBySI3aszQxCQgUZkHXnoSmD/DZyzw+CzYtqjbu8x2dGufXRo6lbk2LOnv3RBxNTNklGUx0MfEHggXun5woxCsUSQckMBCyCSpGjIbvOYLp4HCMDNKybR+93Zcy/tZnu9VhE/n156eG6mdRwAurixBYdCzks5LKUr28i5bWO3z3/97uHwK4LkN2pean1d73EaF8+biLOdyA2Q8pTLsva2cKWXfX3qaHToqC7RvrkebZlla/dnpaMeu09S13LkiUJ0HchkywQBmnuOK6ROQYbZQZuZy9gSz7Yk0xcHiCVHD9oBsDoCUesHn3NWqsSRtx34yyZTwZX6Ea4h2k58nkuLU2rT/rTptTLvJbKFtr5GzdBFQHSdMk86NIlrFE0KpdsRR9ph9fe4xoDLHMrD8EMetgvt2h/bcNIDJvVq4bmmeWd75U8zSOiLeOjzGIPuxg3weTJPXJJ5k5t+2tSCdL+ypVJZ2uFUDSlJGtTAtAfQT4uSfAWDyXiXE7lhxrpMWZyShMizK7G2uC1sNQOmZGELlVyJqVkz6YAdSptDhWtCKsZPK/EmyGqpLEpCX77sdPo8qCWDGhO1gaYbcoM4kgcu1Qa3MdYzv89qePclzcR29pjOF9n7JrUV7BiDPhqrhNtENDPQcx5BkkQHA1eucbo952daHOOLg895pMhhvtxjwWDjDrH2rQTaDbVMYzu5IwkLWXm1R93c6V496eYQIy+JC6Hv9NLXFWTqp4zs5wR95hpOjopSVZpkNPHucBm5iN7f1/eWpov3jyeS1cqaqGw3/fWQbf9JZLjCmj9B4ew4we4rb5PIATE6wrsP9JVJ0xGpLIN3YBloVGpjhSRqndpO05aldnHiV8zg8xchyNsfJuzQDBBUanWyBdTumwXrydoMTUKvK66cE0pJJTrzxJI1IoyxUdaHlKDXOIND1Z+Eo5EZDtSRxHWknJZl9z9VR7XaL18w0QcoWALiAJYqc5TmeEwI+L6r/I0AaejrHBYHTYWQ0nWdku1bmfZgTGlurQePdaetaLkVZTyR5Te22VrxVSqHwhOIumeNAIsPnq4nealCtbAzoBY4vQinq+tUq/LxwOxXg1bs3PDG4Jnat6bmR2nkEhQspSwhjuWdoeOL8GgSbjdU+/+HtlOJz+PPpI6LMbPV+TzGMzrd/BdOic1sVppLbBs5OA2NT7OpQL/Pz8Z38/ckvioCSBJqX5nkPh6foiCsBy2qJjlRhWOt5jG7nwr4ZOv9sgXZ9aIz/Xpjmd+MJ7ttpc/667mWkDgN09E+/KCVGHdqgdJ4B7u3XSEAgAYwP0ak3Lf4+Oc7jmHEGd32Xf2/XmbWE2DcDa+w0XQACj3bSEnXpuFDgUxlRnI6zvK1RB45/V9Y8aV6Hky9wvWRa4hC5uiuHusbNGhXbF2eZxSzP6TWI59CRLC/w/C1L1i5NUZIXMVX4DNKbprTLkkCtJ7XkCGjXa4scS6cpdhqiQA59T4KAawJD2AcGwIyuONcGaNMdh8dQ+ivdLLIvLbLA56VWZq256+iEgh92pAOdNQ4kaG+Jpsr5GG0GdLlCzaG6dzf7C9AzN86/n0tG+Nk0pSuKJDRMU8rKTD3WrqK5GptcXyem1wy1Rd7L0W2syW9U+O5bpmTEE4Dl8bk0oJkjQaBZAxuEiF4e4fLEhdD3+qkhiiZWLWkl8fnTogItdJzqIid0RSUKU/sGUU3ON56V/q72VxilyJqCoiYNTwBHn9Xb9KNapkFXUPS00y9pmrr6fzqnKTN2W8S14uJUgpHx2iINvKpD8j1x7mtiPKQeWlGymzUaU6W+2ZDsdqfVK6K1GVDR40DVMgkcqXkCRAgNQtuWWiUYuvWLLYa9UdOOW6epbU9HqPkqGmrFeU2U8NrMcX3cwNc/doeGMej7G38JZSPE6FgWjYgyNOtu/C5DKHqZGQMXkBYXHtmh3stRLfW+cxulQh6pnUe4VFzJz9Bqxx7+PUzbHmTfwiiMDi4XWe64hVFNMQa0TU3neruIVEtkhIVhd2gbWzXagEyO+5o/pZWd1b4NcCE/tqN3H+O7uK2yu2p9oUQe2y3OW+FSsVyRnycztO1K9CxToG1u1jgHqs+V4nJtEdhzC7O7AJ1Slc1VfYwVNXczEW5ZCXAO77QBx9UB7YqsYTotcYYT0qe7rL+n1iyKvVWv8NopO+DYctoi+GW3hNGGUPcLg793OhxHXOje8STvWbspZUxSc20KA662KO2ybG2LA49OPCCswYQO7rcb/Ju6Z6o0DJBaa1NK2dKyFvF1CZVnayXyaon7dYTlsOwSQNGv5Z6bq3CWu91GDABeaPPw87LRaw5VjiCuaCCMAEP+r2q5B41VaRu4rnSlcULlHVUmkAIIGwByPzv6uVL/X013mTVElOmOcHlgkIrpaul7/dQQVSNrtxn1bFQYgfZcXafse4y+1pbYdmlxhpngTI6U80HH8j3g8HeoZKlahoztYNZT1fvGk/zu8ITex5FnGF2fP01RNKfDibUwwr6MyTQN/9FndOY0neXEnM1z0gh8Zp3tDs9vPE66baUk9DJHjJDQkNI5YRs50opBVFgr8zx+bZH7alRokEyTk3I8VH+jaEeOQwPQqQIVydwCMtlbWLde3KuB7wIdl+fcqktLK1fXsNdlMjbMwYGBhdOkdJ89Kb0d492EQdcRNmKy/wYXQJ7HaHSrwcndc4BSQgu0eQOO48oz2RNJDtHOus54iHp1taH7HMmz5zo6G9FpkuZXHOV7OzTe+35eCt33QrBRx4mwdXE5PEMrKYuv9LfVjj28nWtzu3iSTsXCtARwDdKvlS0dnuB36hWtRp4tMku9OMsyp/7SsUaFjLJaiba806TTtDDNjPHO67kPw9AdOVyHNrS8QCesMCyK4waD0tVFoSHHmRHN5MQRMIHCDmC0wn0bJvU8xnbSjjRr3N/UXtqCekm6SEgni4XTuvdyKst5zG6SHu95kq0OdNYtleF3a4tSSpMDXvwWa4kdKefyPRFAFfbU5RCoDTw6mq6Il6leyQCwJOKsijoP0CG3YryHjsNnRHVjSSSA0z7veSpLtoEBUdpekBIwH2iJ0+y74qymJXDRCY3J1ir1nbP83OloBz2RBnIFPo+qv7Ufro+Xa+u6PKdmjbTldpvPp6KSq+39gJlWw9QtrEozmmYdtvsqUNFuyLZKm2e5ZEboGKut31elHd1dXA5MOpW5VzB1vbdpUAgOwLlrooAdUtpN3r90lu/KyefJnqgLw7Pd0uUHiRS/F09JOZ+wDhMpHSzbAEROd4TNx0pKp6uh2/VT3uIpncHttMXQ+fLT0dQjuyOUpOMSkTa04Tv8FLD/ZVw8qPEtzQGzUm9Tr/GYpw7TMZ7cI2qpEoFUC5F6mQ733Eng5KFQPY7FCd8RalG7QQpeOk+HwnEAwweKk5yoy2KIEmlG+GaPcpGRHaKxqi5y0i+OcptmgwqNqYxWLnfkfG25JvGk1AV5IvoRpyGwVfQ5pkU/2q2QyEZoArwsJm5Ia5UALAD2ej9XUeouQosTlR13fQAuYNg0nJbUS7kuYEpWOh4nBa1WorF1XXSFObzWKil9Qd+/jb7vXaUOt4Lq4RpL8P+WGEsrxgh2dYnbDE+c+92LpfteKDbqOBG2LjbzGVrJ3gLnVx1f7djD25VmdXcBpRieSAITu1g2BehsdmWBnw1P0h4feUZ3KgiXjmXyOjA6uoMiVmaMgWhHlLDrZdr5RJo2t16mHUwX0RV2yhRJeVaOnin1v7bNgGtphvPRngPA3pvZoig8BoBz1MgU1yLVEs8BYlNrS8xON6eomu10yJBKJElhr5e1kwep8/V9TV2vlsQJM8RxFEEy3+X6Bn1O4eWEcE9lRQFvS+mUJc6oEvBSgRgYuubZAD9rijM6f5piparDR7shDrXDzwIf8A2u/6yYOP0mEMR7nfAeR9XQfaQ7HWm9FQh9exnGmVLb7kAz1sIsNhjicBuh47ocW7dDh7o+vpy3xa86bU09XxUuk2DLpUBlug1D9633PS3MNxByfV1HWr8l+J7H04Bzltc1lWG7v2ZFhOZywNAUn5l0QboItVff6WQNEDndVwPWqq/sevWnXUnpdDX7T6QpnqFqc+JJjjEAI9HzpyhGUp6jwWpUgSL4sk7sFqqX0MkceQFf+Da3L4wChx7nwuP0YS4CfE8UJiWKa7f48rcbzHy3RIV6dBsNdiLJbHp9SepSlzipprOkt5txOhedFpUWYzERHKkB3/ocr3u9wu2zRW63cIZR9apkul1xiFs1CskELgCpjwmEpht4NPJKxbLd5HcdW9cjKapVuyWRRoff74lGXq4YMMaBjvCgiVxRyOSfvguEy3xWUqO9JFzhxnKtEfgMKjVtMZQWaZcA6+oBPvsjUyuLJ4Z7/l7qnHUV9+WOsAVxMQr8q7XFCuF3xnUYdFYLayVamSvSfinH9cUnaA+rC2KHXAqSdZrMcL7832in6cTz3G9lnnbV7tCWK6TSLKFS9PDSGR63PCeaGwuSBWvz83ZLqOpV2t96hfavUQFONjj2Rk2cI5/B9tnjIoxlaeFG1+E5KHGzbv1ph9t7PvcfS9DWW1Zvv2TP5baGQd2ZeBpdo9QoiwPXDgXAryD70Q1Qy++uh4G1tMqBVuKirSaZC0qVXdGvO21NMQYQoqhRrMvt9GaT7WVERr0LZQn0BeyX28bzMHBNouD7gK2C/vby2612PFcyAin5CwKdQFPv2rLwpf1aIIKEYNnC7HG+u64jDneNpR+tBuea0y9wLvMc6h/VK8DOGy58jrtIRE73Vsda9ZVdz/60l6IIXC8zqlwYlYiw0ESuvY3Gz7KAbfskKu7T6MYTHLMZ0wY/mdK1Qb4HNMvAkQq6Bra6SAq6GpOisamWE9miqImWKXJixhipV+qi4dZhpkWVReWsT+4mXbleBRBQGdV3GNWtlES0I85r3hbDofocui2ORfU8VLToQKK+tgQGYjGpqYpp2rnv8/+qxggBELcAGBSwMkSowrkUgxAhwiVA0SebVS5Gi6MMPgHLiydm8uf2/AUubs6K+nJH2Gq4GHt7Ieq+/e+MKayyhIh1mgYwMkHqtrLXpRn2zwZo486e4DudSDKIPHeCStcjE7R1pw/REVetLkuHmEVOp4HCftpz02IgPJGkvTSENdNuynxi066aJvsqq1ZWqRztYb1C+xuLUcSrXQO+9re04eVZijrGE5xjSjOcD/bciG55jO8zSNeoCBPNoz31XHECaixNczt0Hj1faOIBEJiA7Wgqs2UJsyrozR5vSYijFQSAZ0gSw9IswXZDU7QHfQ8hZ23VgnJbwHHdCugGSYLz3xLD4jvtSdIokRTF+46Ue5oM3AU+ui1WLRPw43zXbJvzQDzBNcMGKZhHTvdWx6Vmkdd6P4NwKYrAalxKTVwhniAVTNXhnjnC/TkOacLFcWa8lVp0tsCoOaT3n93h526HUXEowTBfnFaPVORYgi+yqhVX6tcdWUzEU1RNtWJsVQbw2O1xGtx0nhH/eoUU5XYLyKVIv1Pb+p6ug2rVaVMS6ZDoRAwwPej+hoGubTKFrpvKAUYbXeGRRFovDHyhYBsm6eqNChDPi5EzAdQksr6VDX2EyxNSE2fFpLXeTlLLVxJPVPOAEkdM53u3vZA5az3nvQgRNgPns7eXqs4/6J0pjPD/U3t1+Reg1cpnjmiRq6U5lnABtHeJDG1Yqw4siU3uCmWB2eCpfaztHt/Ru3iulYD8KMd/+AmgMIZuRlQpH/s+MDzOmmHXJlV9dAfw0hNs0QnowAHAutG4OOvhLGmzRht93csorhmTbh8Lp2k7FdUY4lT44NyWTAuN1kaXYmxAVJeFcq7o0OH2U5ulUL5hkHNU7DvDkPrwcBKgL0OtHDEA59YLR7isoZJDniS/+sVx+xGPizCfJ++JwXWtFdPlpPlRbhdPMtGVygEZS0rWLAb+lJ74BnUgiZzuKxmroVeuVV/Z8+3nfBTMlf6uWmr1Y5Biaf9+luZoLJs1nelOpFgXPbmHtPPqIh3fdp21zVacDrYVp4BWvQwKM9jA0CSwaz+6KomdOg2iCV5fpyMTgkcKU31RhJ7abGFkxXn82aPAV/9GaG01OvdmTFpXxGkc0hmeY7bAmrfSDPebyEgLBFecZXCh4vvSvkIc4I4IqPQblnAU2PMAy+FCxfeBpkxo6vx6ouYGz8MXanq3yWYUBY6wWQhYR98xSSs/+Bgwvh048hQwsQfYdwvLOMLiiQqq56/qEazKT4bGV9+aSc1vl7KPCBEuByjbuTRHSnNhtDdQrextvy1WJVWHn1xdeUWn1WuPPRGIKp2hA9pu8m+JFLDjemD6MKnlSlNk7hS3VbTRVAZAmkwzz6GdVnXgjbIOUFfOcv1z9FkRcOtwUa10TqYPC2umRrutAulqgT93mn9PH+OaYuksa709l9+ZlzrjZl0Lpyp6eSxJDZXDT1FDpV6W/t4i9qTYYqbY3SDgsTotdAXRwnY2AD+7TCRTNgdKLExRsAc50H1rkx6l6whXFHwXcAMddDpfwMTzuN6GwXe01ZCMtwijxeLcZ9thFlx1ncmP6LKGeEJ3Ktig7hGR032lop/CpYRKlODISr0wgQuP6qwUHT8fBfN8f1+tIuqg/ZTngUS1t9VEp8VIc+Dr9iNFqdeyLBrutNRyVBfoDCczfBGrJSBxGynfi2f5kmbynAScnKY4OR0RZMlrJc7FWS7I3Q6pYK06FwGOI9QyyUonUnpSSaRJeW/VuZ3v0kl3bC42TIP/Vj2cPRGIicd5fPc80e7A5yLFlNpuEyLm4Q+Y1IKQMNplLM4S4epCADAgJAJEvk9BtU6H7/TN9+jsWbjPbyLFQJvqEQxwmwsRTUmmNXX9YvcRIcJmI2w7U1mg6EvpktnbaQPotcWulB8pBerVlFe4zrmiabYITdbKtKvZAhfBpWmte+K5tIWVeamHDVgS60tttBUjO0w5540Kg+mpNEuy6kvAtz6PrhiZ51KhfPEMkBnSLZ1qi7TZVozn7zjMiHtiozvSycMLqL9ixrSAmSciYJ4NLeTkiyq2tC6yYrTTrZrWlrEMmtSezhaGiHVdYbXZESKsFwKI8N0qWBxBIN1mDN1/2w/4k86x60AyK+0Ai8Jkcelouw7ZN5N7qMO0geVikdN9paKfwqWolJXSuXTKQbjQqM5K2ejzUTBXQ9FcjSLqQNraKNVNFX1NwQCvxdReXo/iKDAySTG0TJHbzD8LpPLAkIigpHOymG/zZey0GUkf38WFQ36UxymOcSyFES4gbKkNazfoyPvSu9GWaFrXuRWqrGlJD0FP6sArnGRSaRFQacnk4dFIxxNaxTE/zOO5orIdi3NxYZqk5agwoWmGjhuEEtbyHdUWLPA5ruCqDqlHuFwRS+gWYUPjfG+U8GGnyazTzBHg2tvpGIf7ABdG+R72ozC6enr4yBTZMpeyjwgRNhv9tjOdl5/suYyNsC0++cLgoP1qn32lZdKq64zz6DY62aPbKYhWHOe7PHOM2d9kVi++kylQBTxBbZYD93B/J75LpzY3rI/le1qNODckDrIIpyYy3EetrANzVky3M2s1SEHNFPn3eBzYtodMODhkpqkMWiKp+zdbki1X9aNq3wkJAiYzFIJsGZLJgxbqhPQh7rHVESJcbTC1toJqEeqpdPeAYJRq35cTRzqe5Psei8m7LuKDw1Ncu6dSmm1igAE6ABibAm7/fpaDbKAdj5zuKxX9dG9Fq3TavdSuZBrYcR0jz6rv5d4Dq+vJ2f+34ti59PXcEI1lmH6p6jBioRqtwO/9e7tJAbRskU5lsya9Dn2qkL7iBymGBgCzJ9i388jTNMjDE3yBzh7n90oz/Ky7OJ+kYrlr60W3Y9OhXjzL/8cTIsw0xpfQc/iyFkDHvFpi9NuweI5DItTmejTcC2doqJ0OX/bFElCdl1YgCUbPAd2KoivmEFCRU9WkNeqA0wA6dpfx3YVqKwGAkX6hsfmeZLlDtU1eODIYqgHr/l3uiRf6dxdbvTYswhULtwO0fAaoFucYHIonRcTQ5AI4P8w5KDdECqmq6auVgVMvCuNlHNj/cuD6O84vmhKm4bbqVM4NwDmvMMJjVkukuwKRmvlWwHoq1G+E+v2gYwD6s5ljpFWG6eTAue+BsrVqrRAEel/NGlXIl87yHWtUgV3X82+nDnGNYYB2MTdM+1k6w+B1+SxtZixOG5ZI8hhHnyaFW2myzJ3kWiII6JyXF4DKou7s8dy/8hjlBWD+JJlj49uB3TcDT5zkHNGuCZPP4JqjVed6I5GizfQkM20EuhUXAKQN2uZGVTLzkjFXXT1iJnVewkJeHQNd2+x0gDNHmXnzhZWGIGSb++2sF5neCBEMQ0pIIEwQadVmWIOTQd31tOgr5Uc4VwDSIcHgPjM5YGSc+8oN0zcoz3N+SecZxNtghxuInO4rF/10bxVZdT1N7QKYCVJ9LlUdVGWBUSLgwvp1thqDaRiuo+mXnQYNH8BscatBCf9Oi1GpdkOUSaX3ZadFJ3fxLJAv0glut0XUDDT8z3yV/zYt9rs88iTFU4IAcNs07vE4o+Kez/0pazZ7kga0iwAYneT3m1VSVGMdvqAA6W1LM8xkpXPsx11eAG76HmBsJylstTIj4p0W961qwoNAWhV4cnxL94kG+PIHAWD6miberOisteob2hPdkwWBYXBCcdoigBbR0SJcJfB89mlFIP3tbQA1LuAzBRrf734T2HuLZJM6nHdqiwx+xRJArAocf45Uskx++fIaRcNt1vQ8arc5XybTdBZUKYuagyM18ysb66lQvxHq94OO8dJTNCPKyQ4CPs/h3tJA73swe0LbWoBaKKq2Op0DTr+obXsiSUd7aY4mKlzeVZqhs5tMc7HbOM4xBWBAuj3HRbaye/UqF9H1KvdrxqQsLkZnNj3MwHZ5jgEw1S0kO8T9GDGOtVkTlXKT64x2U/ex9hzqlKlaTiWIahha4MxuAVURIPWkzjrwdYcS2xZnekDQ2hPBL9jMZvsBVmwXFSFCBAASOM/mdelGPA6YUq7h9K+HIe1tTQbfRybpZ+SF6To0xvkgHudcN7qdc9jIJNcHYaSzm2KzzQ0/YoS1QT89XNEq+7Ol6nel7quwOLsy7ftienkCpEr3I4CmmTUqEnmWWqdWnY60a5Na1hYHuVkHDn6dUXeFXFF/d/4UP2u3mH1qt/id0rQ+pkKrrn+GJrgdwH8DpLYpp/vMEdJS1L4Vnvoy/1+e5/YqgNFpcewdUTZV/bsNVWMiLT8MQ2rjxHlWyuNd9VOlbNrvTMtngXzuuSEF1AgRrgZ4epHcZXEENNK2tAdpN4EzL/F9rJbpYDgOHXPXZrCq3QKOPcNdLldeo+a3ami+zBb1vDZ9VH8eprKfb16McPniYm3dZu97pX1VSr3PsNI2qfatA8LvQdjWKuSKzN5WS722PStB+zNHe98J9bcFYdYZ4HuXSAo9W+yk0+a7qnprd1rMPAN8XzN5BsUDaEaX0+ZnzSrtbDKjbfSJ59hOzBLNlABcwMPnZ4ZouRiGZMg8rbPi+7TZgS+9n+V76rtqDOFOH12Exc98cbYjhztChNVB2XVRpg88JudiMR1A60cQADEpeQkLoYWRzuk5Sv2/HxsknNaPKNN9IVgrmthqVMdzQ+dSvfYeAKb28PunDpE6OX2EdKrMELPZtSVRtm7xgWw1xOgYzNj4nhbiSiQZIeqvAT/yNPD0V9jnutOSmugxip/E46R4L83RAKXzjCrbTRrlVoNU79wII9Pzp3gezSqA79DB9lwKH9RK3P7sCWaH49IWKAAN5Lc/L2MzhCouqqftOiPLpkVDbUhrq2RKqKcilNBuhkRPoFsSWFLTbLfFUCoDeR6l7m9//sLv9UCo44Ui5udNXAdaoTlChKsOsphVVE0fABy+E/VFdiXIFYHGEvvnls+KMrERYoUEzJ599W9Ji732ZcBt3wfccIeex5Wy88kX6LBnpW7MFkbN9EtAZ4c4FtNc4BdGOQcC60shjnBhWK29vtDOHIPsNUCbvHCaj+r4Tj5jnRbpjLMn+UxWF8nOSuWBm7+X29ltTc3OFOlwxuI6KxuLL98R5MUngC/9JTBzlPTmbJEBYadFga+EsMlUsNa1gcIwkMhyLXHmWgAGneGnv8yxuA7tr5Vghw0/AF56krbccaQ9psVzqS3RBqfyUlNZ4/m2m9IZQ2jWBjRbK5GQzLAEmj1X6N7yd1MW4LaDZZ3Xg49dwIOwCrid0L8v1s4GkXp2hAgXCsOgHc0Nc35yhQWqWobZNt8rGDI3xKnRkMqyzCyeps/hObTJo+NkwBkG583xnQyupbOXhX2+IpzuP/qjP8Lv/M7vYGZmBgcOHMBHPvIRfN/3fd/GDmKtaGKrVR2PxUkLV6gukvrVqEgLrNOkiS+e5d9jSTq9s8dlEWDQQC7Osa9hOs8HsVnj9pN7SLucCx0bIDX7W58H5k6QMtZpsnY6I463LQ6sFeMxayUtIBJP86VZnCWlozjOl+XMYRrjoXGhk1WYpQpcUkGUSqpjAajpjG8nBZRLdJpNi1Fo14ama/dle92O9PSNc9/9VG1FPV0WkbWMEOGKhNehw+126EComssegSKT/ezPHpeMWV1KTFrAba/mJuV5/h6Lcz/2HKPmnkOq2pjMVZUFUVkNGPxMpLkIACLKOS4Dm30h9vpCOnMMsteHnyKLql7W2506pB3P6ZcYzGksATMn+fehUW7z7NdoF4vjDNwcPciM7egOvb/xnbR1/R1Bnvka8OQXgRPSFtP3WDc9ewyALDhdR9Ohfcne2tslS1xnUH9kCkBA0bBWgw57IsWAdTLDwFZxXJdsqNaVdkcri1dLUvYh1GxVz3wODAkeC7srzNjq1moCgDPguxEiRNhSUOv1id3Ara8ClhZYWlpb5P9dlxoLvst5LJZgUs0wOTcVR4Ht+3Q2e2SSgscKqi3iIMHITcJlTy//i7/4C7znPe/B+9//fjz55JP4vu/7Ptx33304efLkxg5krWhiK6mOh3Hw64O/f/Dr+jsL0/rzVp3tM9I5igkpKHpXJscofBiKHhk+9tFndJsLz9U9oZt1ESlp0OC6qs2GKyrfVTrE8TgXGEpIbO6UHkdT6GmxmIiRuJKplmMHHuC42pn25d+ex2OeQ+3qR6AVwbtK3hEiRLgq4AfSF9jVJRy9G6DrFLQanL/K86TIqpIaRcMNU9JK01oDIxf6PFwy01/WA1y1lPPLwmZfiL1ejmY4qDPHIHtdLVFBvx9VoXir50T1k1aYn9ZCfYDerl7p3V+YEq7GszjLbcohW+t5dHybNb4H9bJkitpSXiFlVdVFZpZrS1zcLs3xJ5GiJovrUhAUACpzXOjaHWGYedxXq6bb+vgux+AJq6y/53QPQqyTqEQqQoQIiaROBmZzpIsnkkBhjMm9VE6EDBMMdFsxHeROZmirGxX+bLu2d99q7twkKvkgXPZO94c//GH8x//4H/GzP/uzuOmmm/CRj3wEu3btwkc/+tGNHcj5KGgXu5+w6ngYtcXB31c9JgE6ogquQ4OZSJNCmUiSipFMMVKeHdLtLoYnSA/L5Pk3w9D0CytGY6oiUDDEPoqBDDxSwX1fDKzURykhMCvJrHRKaptdhxmBlNDUTFEDT6WZGY8lgXSBn1kJAH6oVQj4hJpCQztnET0IiiIeOdwRIlxVMOQ/piW9fAe0OjJlfvBd0ZFo6366qpRmYidZPaNTQK5Aw14YExGqAjORiSSzeck01dMHtVW6UNuwRXBZ2OwLsde5IWaQ01ltB1VGeTX22m7Tee6HKufKDfN5UZ0ucnna105D7LbYcUdYZK7duz87dCw1nk6L29gdAAHLqgBdq6x6UKvP/CAk1qkUvG06y06bY4unGLQ3pYVkXLJKsQSPYcm/A4jQmcW1hnrPgiAyuxEiRFg9YnHdeWjbNcD2a4GdNwD7bgH23MhWoNfezNa/u66n4nhxjB2Zbr6Lv2dytOv5YaqYT+ykXTaFjn6ZMc4ua3q5bdt44okn8Ku/+qs9n99777147LE1ruk5H1aioF3KfpTqeDzVu51S4+tHfqS3B2RT9qVacQAUCBvdLp9L267R7VLDPaqPC9DxHp3S1IuxHawPsywa3KT0vLNiXIz44nCbpjjJUp+dSFOBMF0A4HEMwxM04u0WFw6pHBetrqvHkCmSEufaoh66gG4sKGhL7XacT6rnanGUfhimViM1DW4SDIq4n6duO0KECFceTEvqbTOkmMcSgNUG2iHhNVPaiQB0FmIJzkHpkJp5q6F7GE/t5WdLZ7VwYiJF5yKZ5XfUNoNwobZhC+CysdkXaq/DvalX2s8ge50QZ7UfiRS/HxM7OrnnXHvtdmhnAf7flmc3kezdT//4k2keM5GUcqqAn6ma6GSKzrDSc1G0blM+syRjhEDOJaBtzRaYQbJifJfqJa4FzJgIHMXJZnM7Mn6hlBtGKPMtgmLBoEy2Efp/OOsdIUKEKx6GyXV+MimaFCbX/nZbXnnRYLJMrusLQ8xCT+0Dbvwe7mPQvB22waobEqBtcHg+VvYb2DSF8pVwWWe6FxYW4HkeJicnez6fnJzE7Oxg+lin00G1Wu35WROsREG7lP0oindxtPfzA3cP/v6Bu/V3xnboz9M5YFx+v/ZW/Xm2qCmRYepFWHk3PKYDd9NhTiRl4RrjT67ITE9hlM5+LE4HXv2tMKKj7duuZSY7nQN27JesdoqRKKU0uP1aBhCSGb5M8ThfkPyoZOlNjj2R4t+yQ0IvMcDHNsTnNExZTEh2Pp6ShUL/420MVkOMECHClQ0rRvHG8e2cVwxTsnRxzeQxhbljWXquGRoHtl/DOXC5uXzvAf3v8LxZDM3dhb75G7isKG0bhcvGZm+kvS6MnktrVJ/vPbCyvU7n9OeqpCFXPL+tHpniNkMTDHYD4ignucjMjzLjE0twEZwSGmYspm11fpjvyrAEyJNpCerL3wDaafV72E4Xx/lOxURsLZHi+6bWDUbYRqu2l6a2y7E4x2IMqsuIbHSECFckYgnOL8Vxzm2xGAWS1btumjr4HY8B+THOGTe98qqxwZd1plvB6JuYgyA45zOFhx56CB/4wAfWfhCKgnap6uX9+xmdIkVikHp5tri8enkqw59kmt/L5JnNHt9J47c0R/rZ5G4dKVdUDvXvQedw7W3AD90PfP3vgaPPsvY6V+TCID/M7LaqA/Nd4JpbeOxA2vdk8jTUmQIpa75Hyke7KbVkbWDfKMcZBBRca9WB9jXSB9MDlualxsPXNFG7xRo01brEc4QGL+244gm9iAg89vxslEmhc20ey4pzUshkWYferJBep2rHDenHqQRnBsGKi0BbVI8WIcLGw0L33VOL+HSOdLMD3wvsuJ59gg8+RtGr/JjoU7Q5P5kGkBsFdlwzWL18uTk+V+Tnpsl5F9Bz6A5xkNais8UWwabb7I201+r+D1IvV8/Ocvb6FT+g1cvTGT7HSr18JVudG6L4XzJNW3vieY6pILa1MEzbVl+ifXWlfV43y50ChseBse3oqpd3Ghyb3aHtTiRJjY/FQ60+Q3Z67jRtaADpGpKk+FG1xHWD4wgzzgYCg/tJZZlNRyBaMHVmzZU9NyWTnkjx+62q9MxWNlqx4FbSbInYbBEibAyERROLkbV6zQH6EOkCsDgDnHiBc5An3YJ8ETi24pxL9x4A7vi3wN0/ctXYYCMIVlUouymwbRuZTAZ/9Vd/hR//8R/vfv7ud78bTz31FB599NFzvtPpdNBRPZMBVKtV7Nq1C5VKBYVCYUPGHSFChAgRIlwKqtUqisXiFWW7IpsdIUKECBGuNqzWXl/WPJ5EIoE77rgDjzzySM/njzzyCO65556B30kmkygUCj0/ESJEiBAhQoT1RWSzI0SIECFChMG47Onl733ve/HmN78Zd955J+6++2587GMfw8mTJ/HAAw9s9tAiRIgQIUKECCFENjtChAgRIkQ4F5e90/1TP/VTKJVK+I3f+A3MzMzglltuwec+9zns2bNns4cWIUKECBEiRAghstkRIkSIECHCubisa7rXAldiXVyECBEiRLi6cbXarqv1vCNEiBAhwpWJ1dqtyz7TfalQMYU1ax0WIUKECBEirDOuVpsV2ewIESJEiHAlQdmr8+Wxt7zTXavVAAC7du3a5JFEiBAhQoQIEVZCZLMjRIgQIcKViFqthmKxuOzftzy93Pd9nDlzBvl8ftk+oZsN1SLl1KlTW5pOd7WcJ3D1nOvVcp5AdK5bEZfzeSrTXCgULlvbtR643G325fzMrBW2+jlu9fMDonPcKtjq57hVzi8IAtRqNWzfvh2muXxjsC2f6TZNEzt37tzsYawKV0u7lKvlPIGr51yvlvMEonPdirhazvNKwJVis6+GZ2arn+NWPz8gOsetgq1+jlvh/FbKcCtc1n26I0SIECFChAgRIkSIECFChCsZkdMdIUKECBEiRIgQIUKECBEirBMip/syQDKZxK//+q8jmUxu9lDWFVfLeQJXz7leLecJROe6FXG1nGeEtcPV8Mxs9XPc6ucHROe4VbDVz3Grn18/tryQWoQIESJEiBAhQoQIESJEiLBZiDLdESJEiBAhQoQIESJEiBAhwjohcrojRIgQIUKECBEiRIgQIUKEdULkdEeIECFChAgRIkSIECFChAjrhMjpjhAhQoQIESJEiBAhQoQIEdYJkdO9yfijP/oj7Nu3D6lUCnfccQe++tWvbvaQ1gVf+cpX8GM/9mPYvn07DMPA3/7t3272kNYcDz30EL7ne74H+XweExMTeMMb3oAXX3xxs4e1LvjoRz+K2267DYVCAYVCAXfffTf+8R//cbOHte546KGHYBgG3vOe92z2UNYcDz74IAzD6PmZmpra7GGtG6anp/EzP/MzGB0dRSaTwcte9jI88cQTmz2sCJcxtrq93up2+mqw0Vebbd6KNvlqscVXow2OnO5NxF/8xV/gPe95D97//vfjySefxPd93/fhvvvuw8mTJzd7aGuORqOB22+/HX/wB3+w2UNZNzz66KN45zvfiW984xt45JFH4Lou7r33XjQajc0e2ppj586d+K3f+i08/vjjePzxx/EDP/ADeP3rX4+DBw9u9tDWDd/+9rfxsY99DLfddttmD2XdcODAAczMzHR/nn322c0e0rpgaWkJr3rVqxCPx/GP//iP+O53v4vf/d3fxdDQ0GYPLcJliqvBXm91O3012OiryTZvZZu81W3xVWuDgwibhle+8pXBAw880PPZjTfeGPzqr/7qJo1oYwAg+MxnPrPZw1h3zM3NBQCCRx99dLOHsiEYHh4OPvGJT2z2MNYFtVot2L9/f/DII48Er3nNa4J3v/vdmz2kNcev//qvB7fffvtmD2ND8F/+y38JXv3qV2/2MCJcQbja7PXVYKevFhu9FW3zVrbJV4MtvlptcJTp3iTYto0nnngC9957b8/n9957Lx577LFNGlWEtUSlUgEAjIyMbPJI1hee5+HP//zP0Wg0cPfdd2/2cNYF73znO/EjP/Ij+Lf/9t9u9lDWFYcPH8b27duxb98+/PRP/zSOHj262UNaF3z2s5/FnXfeiZ/8yZ/ExMQEXv7yl+PjH//4Zg8rwmWKyF5vTWx1G72VbfNWt8lb3RZfrTY4cro3CQsLC/A8D5OTkz2fT05OYnZ2dpNGFWGtEAQB3vve9+LVr341brnlls0ezrrg2WefRS6XQzKZxAMPPIDPfOYzuPnmmzd7WGuOP//zP8d3vvMdPPTQQ5s9lHXFXXfdhT/90z/FP/3TP+HjH/84Zmdncc8996BUKm320NYcR48exUc/+lHs378f//RP/4QHHngAv/iLv4g//dM/3eyhRbgMEdnrrYetbKO3um3e6jb5arDFV6sNjm32AK52GIbR83sQBOd8FuHKwy/8wi/gmWeewde+9rXNHsq64YYbbsBTTz2FcrmMv/7rv8Zb3/pWPProo1vKuJ86dQrvfve78YUvfAGpVGqzh7OuuO+++7r/vvXWW3H33Xfj2muvxac+9Sm8973v3cSRrT1838edd96JD37wgwCAl7/85Th48CA++tGP4i1vecsmjy7C5YrIXm8dbGUbvZVt89Vgk68GW3y12uAo071JGBsbg2VZ50TJ5+bmzommR7iy8K53vQuf/exn8aUvfQk7d+7c7OGsGxKJBK677jrceeedeOihh3D77bfj937v9zZ7WGuKJ554AnNzc7jjjjsQi8UQi8Xw6KOP4n/9r/+FWCwGz/M2e4jrhmw2i1tvvRWHDx/e7KGsObZt23bOAvSmm27aUqJYEdYOkb3eWtjqNnor2+ar0SZvRVt8tdrgyOneJCQSCdxxxx145JFHej5/5JFHcM8992zSqCJcCoIgwC/8wi/gb/7mb/DFL34R+/bt2+whbSiCIECn09nsYawpfvAHfxDPPvssnnrqqe7PnXfeiTe96U146qmnYFnWZg9x3dDpdPD8889j27Ztmz2UNcerXvWqc1oFHTp0CHv27NmkEUW4nBHZ662Bq9VGbyXbfDXa5K1oi69WGxzRyzcR733ve/HmN78Zd955J+6++2587GMfw8mTJ/HAAw9s9tDWHPV6HS+99FL392PHjuGpp57CyMgIdu/evYkjWzu8853vxKc//Wn83d/9HfL5fDcrUiwWkU6nN3l0a4v/+l//K+677z7s2rULtVoNf/7nf44vf/nL+PznP7/ZQ1tT5PP5c+r9stksRkdHt1wd4K/8yq/gx37sx7B7927Mzc3hN3/zN1GtVvHWt751s4e25vilX/ol3HPPPfjgBz+I//Af/gO+9a1v4WMf+xg+9rGPbfbQIlymuBrs9Va301eDjd7qtvlqsMlXgy2+am3w5gmnRwiCIPjDP/zDYM+ePUEikQhe8YpXbNnWFV/60pcCAOf8vPWtb93soa0ZBp0fgODhhx/e7KGtOd7+9rd3n9vx8fHgB3/wB4MvfOELmz2sDcFWa0+i8FM/9VPBtm3bgng8Hmzfvj34iZ/4ieDgwYObPax1w//7f/8vuOWWW4JkMhnceOONwcc+9rHNHlKEyxxb3V5vdTt9Ndjoq9E2bzWbfLXY4qvRBhtBEAQb6eRHiBAhQoQIESJEiBAhQoQIVwuimu4IESJEiBAhQoQIESJEiBBhnRA53REiRIgQIUKECBEiRIgQIcI6IXK6I0SIECFChAgRIkSIECFChHVC5HRHiBAhQoQIESJEiBAhQoQI64TI6Y4QIUKECBEiRIgQIUKECBHWCZHTHSFChAgRIkSIECFChAgRIqwTIqc7QoQIESJEiBAhQoQIESJEWCdETneECBEiRIgQIUKECBEiRIiwToic7ggRNgCvfe1r8Z73vGfdj3P//ffjwQcfXPfjrAYPPvggXvayl23qGO6//3684Q1vuKDvPPjgg7j//vvXZTwRIkSIEOHyRGSnr1x8+ctfxt69ezd7GBEirIjI6Y4Q4SrE/fffD8MwYBgG4vE4rrnmGvzKr/wKGo1Gd5u//uu/xl133YVisYh8Po8DBw7gl3/5l7t//5M/+ZPuPsI/n/jEJ9Zt3A8++CAMw8AP/dAPnfO3D33oQzAMA6997Wu7n/3e7/0e/uRP/mTdxhMhQoQIESKsB64WOx0hwtWC2GYPIEKECBsHx3EQj8cBAD/0Qz+Ehx9+GI7j4Ktf/Sp+9md/Fo1GAx/96Efxz//8z/jpn/5pfPCDH8S/+3f/DoZh4Lvf/S7+5V/+pWd/hUIBL774Ys9nxWJxXc9h27Zt+NKXvoTTp09j586d3c8ffvhh7N69e0PHEiFChAgRIqwlrjY7HSHC1YIo0x0hwiZgaWkJb3nLWzA8PIxMJoP77rsPhw8f7tnm4x//OHbt2oVMJoMf//Efx4c//GEMDQ1d0HEUdeyTn/wkrrnmGiSTSQRBAABIJpOYmprCrl278MY3vhFvetOb8Ld/+7cAgL//+7/Hq1/9avzn//yfccMNN+D666/HG97wBvz+7/9+z/4Nw8DU1FTPTzqdHjiWQdS9N7zhDV0q9wsvvIBMJoNPf/rT3b//zd/8DVKpFJ599tnuZxMTE7j33nvxqU99qvvZY489hoWFBfzIj/xIz/776eWvfe1r8Yu/+It43/veh5GREUxNTV02NL8IESJEiHD5ILLTxHrb6W9/+9t43eteh7GxMRSLRbzmNa/Bd77zne7fv/zlLyORSOCrX/1q97Pf/d3fxdjYGGZmZs5zdSNEuHwQOd0RImwC7r//fjz++OP47Gc/i69//esIggA//MM/DMdxAAD/+q//igceeADvfve78dRTT+F1r3sd/sf/+B8XdayXXnoJf/mXf4m//uu/xlNPPbXsdul0unv8qakpHDx4EM8999xFHfNicOONN+J//s//iXe84x04ceIEzpw5g5/7uZ/Db/3Wb+HWW2/t2fbtb397D238k5/8JN70pjchkUic9zif+tSnkM1m8c1vfhMf+tCH8Bu/8Rt45JFH1vp0IkSIECHCFYzITp+L9bDTtVoNb33rW/HVr34V3/jGN7B//3788A//MGq1GgAdCHjzm9+MSqWCp59+Gu9///vx8Y9/HNu2bVv3c44QYa0Q0csjRNhgHD58GJ/97Gfxr//6r7jnnnsAAP/3//5f7Nq1C3/7t3+Ln/zJn8Tv//7v47777sOv/MqvAACuv/56PPbYY/j7v//7Cz6ebdv4sz/7M4yPjy+7zbe+9S18+tOfxg/+4A8CAN71rnfhq1/9Km699Vbs2bMH3/u934t7770Xb3rTm5BMJrvfq1QqyOVy3d9zuRxmZ2cveIwK73jHO/C5z30Ob37zm5FIJHDHHXfg3e9+9znb/eiP/igeeOABfOUrX8Edd9yBv/zLv8TXvvY1fPKTnzzvMW677Tb8+q//OgBg//79+IM/+AP8y7/8C173utdd9LgjRIgQIcLWQWSnl8da2+kf+IEf6Pn9j//4jzE8PIxHH30UP/qjPwoA+M3f/E388z//M/7Tf/pPOHjwIN785jfjx3/8xy/6HCJE2AxETneECBuM559/HrFYDHfddVf3s9HRUdxwww14/vnnAQAvvvjiOQblla985UUZ8z179gw05H//93+PXC4H13XhOA5e//rXd2lp2WwW//AP/4AjR47gS1/6Er7xjW/gl3/5l/F7v/d7+PrXv45MJgMAyOfzPTQw07x08swnP/lJXH/99TBNE8899xwMwzhnm3g8jp/5mZ/Bww8/jKNHj+L666/Hbbfdtqr992+3bds2zM3NXfK4I0SIECHC1kBkp1fGWtrpubk5/Pf//t/xxS9+EWfPnoXneWg2mzh58mR3m0Qigf/zf/4PbrvtNuzZswcf+chHLvkcIkTYaEROd4QIGwxVqzXoc2W4wv8+3/fOh2w2O/Dzf/Nv/g0++tGPIh6PY/v27V3hljCuvfZaXHvttfjZn/1ZvP/978f111+Pv/iLv8Db3vY2ADTe11133arGYZrmOeegaHJhPP3002g0GjBNE7Ozs9i+ffvA/b397W/HXXfdheeeew5vf/vbVzUGAOecp2EY8H1/1d+PECFChAhbG5Gd1lhvO33//fdjfn4eH/nIR7Bnzx4kk0ncfffdsG27Z7vHHnsMALC4uIjFxcVlr1mECJcropruCBE2GDfffDNc18U3v/nN7melUgmHDh3CTTfdBIB1U9/61rd6vvf444+v6Tiy2Syuu+467NmzZ6Ah78fevXuRyWR62pVcCMbHx3tETzzPO6cWbXFxEffffz/e//73421vexve9KY3odVqDdzfgQMHcODAATz33HN44xvfeFFjihAhQoQIEfoR2WliI+z0V7/6VfziL/4ifviHfxgHDhxAMpnEwsJCzzZHjhzBL/3SL+HjH/84vvd7vxdvectbomB5hCsOkdMdIcIGY//+/Xj961+Pn/u5n8PXvvY1PP300/iZn/kZ7NixA69//esBsFbrc5/7HD784Q/j8OHD+OM//mP84z/+40AK13rgwQcfxPve9z58+ctfxrFjx/Dkk0/i7W9/OxzHueja5x/4gR/AP/zDP+Af/uEf8MILL+Ad73gHyuVyzzYPPPAAdu3ahf/23/4bPvzhDyMIgm693CB88YtfxMzMzAWrxUaIECFChAjLIbLTG2enr7vuOvzZn/0Znn/+eXzzm9/Em970ph51dc/z8OY3vxn33nsv3va2t+Hhhx/Gc889h9/93d+9qHOMEGGzEDndESJsAh5++GHccccd+NEf/VHcfffdCIIAn/vc57qR7Fe96lX43//7f+PDH/4wbr/9dnz+85/HL/3SLyGVSm3I+F7zmtfg6NGjeMtb3oIbb7wR9913H2ZnZ/GFL3wBN9xww0Xt8+1vfzve+ta34i1vecv/v707xFEkDMIAWpugaI8GbsAJAIHnAiQtwBIOgMBgCAqBIJ1JgGA4AFg0J0D2DRAcYFZsMsnM7s7O7mwPiPfsn+6Uq3yiqqLZbEatVot2u/3yvtls4nA4xHa7jVKpFOVyOXa7XWRZFofD4Zf/TJJE4Abgv9Onv6ZPPz09xfV6jUajEb1eL4bDYVQqlZf36XQaeZ7HarWKiB9b27Msi/F4/O6md3g0357/dQAF+FKDwSAul8urW5VvpWka1WrV7elPmEwmkef5q1MnAPAn+vR9nE6nSNM08jy/dynwWxapwYOaz+fR6XQiSZI4Ho+xXq9juVzeuywAIPRp4OOEbnhQ5/M5ZrNZ3G63qNfrsVgsot/v37ssACD0aeDjhG54UPv9/q+/6Xa7Zpw/qdVq/bQ4BgDe0qcfQ7VajdFodO8y4F1mugEAAKAgtpcDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAryHYftvEpBgi36AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].plot(numpy.log10(numpy.absolute(DiaObjs['rPSFluxMin'])),\n",
    "           DiaObjs['rPSFluxNdata'],\n",
    "           'o', ms=5, alpha=0.2, mew=0, color=plot_filter_colors['r'])\n",
    "ax[0].set_xlabel('log |rPSFluxMin|')\n",
    "ax[0].set_ylabel('rPSFluxNdata')\n",
    "\n",
    "ax[1].plot(numpy.log10(numpy.absolute(DiaObjs['rPSFluxMax'])),\n",
    "           DiaObjs['rPSFluxNdata'],\n",
    "           'o', ms=5, alpha=0.2, mew=0, color=plot_filter_colors['r'])\n",
    "ax[1].set_xlabel('log |rPSFluxMax|')\n",
    "ax[1].set_ylabel('rPSFluxNdata')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc0a428-ae54-4ff0-a3cf-d81ca60b1f55",
   "metadata": {},
   "source": [
    "**Other PS Flux statistics**\n",
    "\n",
    "It is left as an exercise for the learner to investigate typical values of the other PS Flux summary statistics, such as rPSFluxMean, rPSFluxSigma, rPSFluxMAD, rPSFluxChi2, rPSFluxSkew, and/or rPSFluxStetsonJ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63b9b17-e4d8-4a03-91a4-5c59f18f5668",
   "metadata": {},
   "source": [
    "**Plot distributions of TOT flux mean and sigma**\n",
    "\n",
    "Plot the distributions of the DiaObject parameters (mean and sigma) derived from the total fluxes from the direct-images. \n",
    "\n",
    "Note that the TOT fluxes _cannot be_ negative because they are measured on the direct images.\n",
    "The TOT fluxes are safe to convert to magnitudes.\n",
    "\n",
    "Notice how the distribution is not so sharply peaked at low flux, compared to the PS Flux plots above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8970a042-5155-4652-a161-617a2bd3acab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:41.431804Z",
     "iopub.status.busy": "2024-04-29T13:55:41.431594Z",
     "iopub.status.idle": "2024-04-29T13:55:42.449450Z",
     "shell.execute_reply": "2024-04-29T13:55:42.448717Z",
     "shell.execute_reply.started": "2024-04-29T13:55:41.431784Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].hist(DiaObjs['rTOTFluxMean'], bins=50, log=True, color=plot_filter_colors['r'])\n",
    "ax[0].set_xlabel('rTOTFluxMean')\n",
    "ax[0].set_ylabel('log(Number of DiaObjects)')\n",
    "\n",
    "ax[1].hist(DiaObjs['rTOTFluxSigma'], bins=50, log=True, color=plot_filter_colors['r'])\n",
    "ax[1].set_xlabel('rTOTFluxSigma')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb77e8c5-79a0-485d-b1e1-d07e4971efe2",
   "metadata": {},
   "source": [
    "### 2.3. Investigate the DiaSource data for one random, bright, well-sampled DiaObject\n",
    "\n",
    "Choose a DiaObject that was detected >40 times in an r-band difference image, had an average total (direct-image) flux > 1e6, and an average PS difference-image flux > 5e5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c45cc504-1e97-492d-80e3-69d8ce913a2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:42.450550Z",
     "iopub.status.busy": "2024-04-29T13:55:42.450337Z",
     "iopub.status.idle": "2024-04-29T13:55:42.456967Z",
     "shell.execute_reply": "2024-04-29T13:55:42.456236Z",
     "shell.execute_reply.started": "2024-04-29T13:55:42.450531Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tx = numpy.where((DiaObjs['rPSFluxNdata'] > 40)\n",
    "                 & (DiaObjs['rTOTFluxMean'] > 1000000)\n",
    "                 & (DiaObjs['rPSFluxMean'] > 500000))[0]\n",
    "\n",
    "use_index = tx[0]\n",
    "use_diaObjectId = DiaObjs['diaObjectId'][tx[0]]\n",
    "del tx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0aec16-2c06-4533-8168-6ca66f2239c0",
   "metadata": {},
   "source": [
    "Retrieve the DiaSource data for this DiaObject."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "704b36c9-1929-468a-9337-bb71f50dcb5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:42.457986Z",
     "iopub.status.busy": "2024-04-29T13:55:42.457776Z",
     "iopub.status.idle": "2024-04-29T13:55:43.377353Z",
     "shell.execute_reply": "2024-04-29T13:55:43.376066Z",
     "shell.execute_reply.started": "2024-04-29T13:55:42.457969Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = service.search(\"SELECT ra, decl, diaObjectId, diaSourceId, ccdVisitId,\"\n",
    "                         \"filterName, midPointTai, psFlux, totFlux, totFluxErr, \"\n",
    "                         \"apFlux, apFluxErr,psFluxErr, snr  \"\n",
    "                         \"FROM dp02_dc2_catalogs_frdf.DiaSource \"\n",
    "                         \"WHERE diaObjectId = \"+str(use_diaObjectId))\n",
    "\n",
    "DiaSrcs = results.to_table()\n",
    "del results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00ba02e-5ab8-4cab-80e7-657cdeb4d6e1",
   "metadata": {},
   "source": [
    "Sort the DiaSrcs table based on the mid-point time of the exposure, and create an array `fx` that indexes all observations obtained with the r-band filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b08bfb4-e9b3-4808-9468-49ced614bd7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:43.379969Z",
     "iopub.status.busy": "2024-04-29T13:55:43.378954Z",
     "iopub.status.idle": "2024-04-29T13:55:43.396255Z",
     "shell.execute_reply": "2024-04-29T13:55:43.395324Z",
     "shell.execute_reply.started": "2024-04-29T13:55:43.379927Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of r-band observations:  42\n"
     ]
    }
   ],
   "source": [
    "DiaSrcs.sort('midPointTai')\n",
    "fx = numpy.where(DiaSrcs['filterName'] == 'r')[0]\n",
    "print('Number of r-band observations: ', len(fx))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de36bdd9-555b-4a14-90ef-015ddf1a0738",
   "metadata": {},
   "source": [
    "**Plot the lightcurves for one DiaObject**\n",
    "\n",
    "Plot the difference-image (PSFlux) and direct-image (TOTFlux) lightcurves. \n",
    "Mark the DiaObject minimum and maximum flux with solid lines; average flux with dashed lines; and the standard deviation in flux with dotted lines.\n",
    "\n",
    "The purpose of this plot is to serve as a visual aid for understanding the lightcurve statistic summary parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "57166c37-81f7-478d-a2db-5aad76820427",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:43.399814Z",
     "iopub.status.busy": "2024-04-29T13:55:43.399343Z",
     "iopub.status.idle": "2024-04-29T13:55:44.927452Z",
     "shell.execute_reply": "2024-04-29T13:55:44.926504Z",
     "shell.execute_reply.started": "2024-04-29T13:55:43.399783Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, figsize=(10, 8), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].plot(DiaSrcs['midPointTai'][fx], DiaSrcs['psFlux'][fx],\n",
    "           plot_filter_symbols['r'], ms=15, mew=0, alpha=0.5, color=plot_filter_colors['r'])\n",
    "ax[0].axhline(DiaObjs['rPSFluxMin'][use_index])\n",
    "ax[0].axhline(DiaObjs['rPSFluxMax'][use_index])\n",
    "ax[0].axhline(DiaObjs['rPSFluxMean'][use_index], ls='dashed')\n",
    "ax[0].axhline(DiaObjs['rPSFluxMean'][use_index] - DiaObjs['rPSFluxSigma'][use_index], ls='dotted')\n",
    "ax[0].axhline(DiaObjs['rPSFluxMean'][use_index] + DiaObjs['rPSFluxSigma'][use_index], ls='dotted')\n",
    "ax[0].set_xlabel('Modified Julian Date')\n",
    "ax[0].set_ylabel('PS Flux')\n",
    "ax[0].set_title('Difference-Image PSFlux r-band Lightcurve')\n",
    "\n",
    "ax[1].plot(DiaSrcs['midPointTai'][fx], DiaSrcs['totFlux'][fx],\n",
    "           plot_filter_symbols['r'], ms=15, mew=0, alpha=0.5, color=plot_filter_colors['r'])\n",
    "ax[1].axhline(DiaObjs['rTOTFluxMean'][use_index], ls='dashed')\n",
    "ax[1].axhline(DiaObjs['rTOTFluxMean'][use_index] - DiaObjs['rTOTFluxSigma'][use_index], ls='dotted')\n",
    "ax[1].axhline(DiaObjs['rTOTFluxMean'][use_index] + DiaObjs['rTOTFluxSigma'][use_index], ls='dotted')\n",
    "ax[1].set_xlabel('Modified Julian Date')\n",
    "ax[1].set_ylabel('Total Flux')\n",
    "ax[1].set_title('Direct-Image TOTFlux r-band Lightcurve')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592a9baa-74a0-41cc-810e-3c8b3ac4df07",
   "metadata": {},
   "source": [
    "**Plot the distribution of flux measurements for one DiaObject**\n",
    "\n",
    "Plot the distribution (left) and normalized cumulative distribution (right) of difference-image fluxes (PSFlux), along with the relevant DiaObject characterization parameters (e.g., mean, sigma, and percentiles).\n",
    "Mark the average flux with dashed lines and the standard deviation in flux with dotted lines.\n",
    "\n",
    "The purpose of this plot is to serve as a visual aid for understanding the lightcurve statistic summary parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "560af696-3468-4c77-aa47-71027f2c131d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:44.928655Z",
     "iopub.status.busy": "2024-04-29T13:55:44.928442Z",
     "iopub.status.idle": "2024-04-29T13:55:45.317904Z",
     "shell.execute_reply": "2024-04-29T13:55:45.317134Z",
     "shell.execute_reply.started": "2024-04-29T13:55:44.928637Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].hist(DiaSrcs['psFlux'][fx], bins=20, color=plot_filter_colors['r'],\n",
    "           label='skew=%5.2f' % DiaObjs['rPSFluxSkew'][use_index])\n",
    "ax[0].axvline(DiaObjs['rPSFluxMean'][use_index], ls='dashed')\n",
    "ax[0].axvline(DiaObjs['rPSFluxMean'][use_index] - DiaObjs['rPSFluxSigma'][use_index], ls='dotted')\n",
    "ax[0].axvline(DiaObjs['rPSFluxMean'][use_index] + DiaObjs['rPSFluxSigma'][use_index], ls='dotted')\n",
    "ax[0].set_xlabel('r-band PS Flux')\n",
    "ax[0].set_ylabel('Number of DiaSources')\n",
    "ax[0].legend(loc='upper left')\n",
    "\n",
    "ax[1].hist(DiaSrcs['psFlux'][fx], bins=len(fx), color=plot_filter_colors['r'],\n",
    "           cumulative=True, density=True, histtype='step')\n",
    "ax[1].plot(DiaObjs['rPSFluxPercentile05'][use_index], 0.05, '*', ms=10, color='black',\n",
    "           label='percentiles: 0.05, 0.25, 0.50, 0.75, and 0.95')\n",
    "ax[1].plot(DiaObjs['rPSFluxPercentile25'][use_index], 0.25, '*', ms=10, color='black')\n",
    "ax[1].plot(DiaObjs['rPSFluxPercentile50'][use_index], 0.50, '*', ms=10, color='black')\n",
    "ax[1].plot(DiaObjs['rPSFluxPercentile75'][use_index], 0.75, '*', ms=10, color='black')\n",
    "ax[1].plot(DiaObjs['rPSFluxPercentile95'][use_index], 0.95, '*', ms=10, color='black')\n",
    "ax[1].set_xlabel('r-band PS Flux')\n",
    "ax[1].set_ylabel('Cumulative Fraction')\n",
    "ax[1].legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28be602-fdbb-40cb-b4b1-56a2259a39ae",
   "metadata": {},
   "source": [
    "Clean up by deleting a few arrays that are no longer needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "656f5166-84ce-415a-a9e4-eadcb90ccc13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:55:45.319059Z",
     "iopub.status.busy": "2024-04-29T13:55:45.318809Z",
     "iopub.status.idle": "2024-04-29T13:55:45.323493Z",
     "shell.execute_reply": "2024-04-29T13:55:45.322666Z",
     "shell.execute_reply.started": "2024-04-29T13:55:45.319038Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "del DiaObjs, DiaSrcs, fx\n",
    "del use_index, use_diaObjectId"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88e53a69-f6dd-4a04-8d00-93d54b325668",
   "metadata": {},
   "source": [
    "## 3. Identify DiaObjects that are potential Type Ia Supernovae\n",
    "\n",
    "For this example, a sample of ***potential*** low-redshift, well-sampled Type Ia supernovae (SNIa) are identified.\n",
    "\n",
    "Compared to other types of supernovae, SNIa have homogenous lightcurves with very similar peak absolute brightnesses (about -19 mag in B-band), and similar rise and decline times (i.e., similar observed lightcurve amplitudes and durations, for a survey with a given limiting magnitude).\n",
    "\n",
    "In LSST-like data sets with an r-band limiting magnitude of about 24.5 mag, such as the DC2 simulation, low-redshift SNIa (0.1 < _z_ < 0.3) lightcurves would look approximately like those plotted in the image below.\n",
    "\n",
    "<img src=\"data/template_lowz_Vband.png\" width=400>\n",
    "\n",
    "In this example, the expected peak brightnesses, amplitude, and duration for the lightcurves of low-redshift SNIa are used to construct a TAP query on the DiaObjects table to identify a sample of ***potential*** SNIa.\n",
    "\n",
    "> **Warning:** The following is a very rough, back-of-the-envelope way to identify a bunch of DiaObjects with parameters that suggest they are SNIa-like. The resulting sample is not scientifically useful (not pure, not complete) – it is just a few simple cuts that will get you a bunch of DiaObjects for which we can demonstrate how to plot lightcurves using the DiaSource table. Real SN-finding algorithms are photometric classifiers that fit the lightcurves with templates, rigorously, and provide probabilistic classifications. The text in Section 1 of the notebook describes this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec51ac0b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3.1. Establish lightcurve parameter constraints to identify potential SNIa\n",
    "\n",
    "Define the desired redshift boundaries, the known approximate peak absolute magnitude for SNeIa (-19 mag), and the desired peak range to use to create the TAP query. This range roughly includes the intrinsic diversity in SNIa brightness and color, and host-galaxy reddening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23eb3470-bd49-45a1-a4a2-ab36697c63c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:25.648410Z",
     "iopub.status.busy": "2024-04-29T13:56:25.647745Z",
     "iopub.status.idle": "2024-04-29T13:56:25.654090Z",
     "shell.execute_reply": "2024-04-29T13:56:25.652954Z",
     "shell.execute_reply.started": "2024-04-29T13:56:25.648369Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "redshift_min = 0.1\n",
    "redshift_max = 0.3\n",
    "\n",
    "snia_peak_mag = -19.0\n",
    "snia_peak_mag_range = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8e4f0a-4b8e-4847-9989-b7fac84f66f5",
   "metadata": {},
   "source": [
    "Use the astropy.cosmology package to convert redshift to distance modulus. \n",
    "Define the minimum and maximum peak apparent r-band magnitudes -- allowing for the intrinsic diversity range specified above -- to use in the \n",
    "TAP query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "862cec58-1dc6-44b7-afed-34a3bbeb12c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:27.506116Z",
     "iopub.status.busy": "2024-04-29T13:56:27.505428Z",
     "iopub.status.idle": "2024-04-29T13:56:27.514623Z",
     "shell.execute_reply": "2024-04-29T13:56:27.513589Z",
     "shell.execute_reply.started": "2024-04-29T13:56:27.506072Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum and maximum apparent r-band magnitudes to use in the TAP query are 18.82 and 22.46 mag.\n"
     ]
    }
   ],
   "source": [
    "snia_peak_mr_min = cosmo.distmod(redshift_min).value + snia_peak_mag - snia_peak_mag_range\n",
    "snia_peak_mr_max = cosmo.distmod(redshift_max).value + snia_peak_mag + snia_peak_mag_range\n",
    "print('The minimum and maximum apparent r-band magnitudes '\n",
    "      'to use in the TAP query are %5.2f and %5.2f mag.' %\n",
    "      (snia_peak_mr_min, snia_peak_mr_max))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab83fd6f-8215-4af7-be4a-d25b07d6fea7",
   "metadata": {},
   "source": [
    "Define maximum magnitudes in the g- and i-bands to use in the TAP query. The point of this is to simply enforce detection in at least the three filters g, r, and i. With knowledge of SNIa colors, this could be made more constraining, but for the purposes of this example these values are fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1df6cb93-53ee-4f09-9581-455bdd0f5466",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:28.006961Z",
     "iopub.status.busy": "2024-04-29T13:56:28.006018Z",
     "iopub.status.idle": "2024-04-29T13:56:28.011917Z",
     "shell.execute_reply": "2024-04-29T13:56:28.010758Z",
     "shell.execute_reply.started": "2024-04-29T13:56:28.006911Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "snia_peak_mg_max = 24.0\n",
    "snia_peak_mi_max = 24.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cadc9f1-0d97-4724-a97a-8a98e0733712",
   "metadata": {},
   "source": [
    "Define the r-band minimum and maximum lightcurve amplitudes to use in the TAP query (i.e., the difference between the brightest and faintest detections in the difference image, in magnitudes). Well-sampled, low-redshift SNIa should be observed to change by at least 1.5 mag (_z_=0.3), and up to 5.5 mag (_z_=0.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "61f81c3b-e7ab-4d66-b534-97a0b477d3ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:28.305324Z",
     "iopub.status.busy": "2024-04-29T13:56:28.304616Z",
     "iopub.status.idle": "2024-04-29T13:56:28.611781Z",
     "shell.execute_reply": "2024-04-29T13:56:28.610897Z",
     "shell.execute_reply.started": "2024-04-29T13:56:28.305278Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "snia_ampl_mr_min = 1.5\n",
    "snia_ampl_mr_max = 5.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a336a81-cceb-4488-bad7-6b6278c7720a",
   "metadata": {},
   "source": [
    "Define the minimum and maximum number of DiaSources (i.e., difference-image detections) to use in the TAP query.\n",
    "The goal of this example is to identify potential _well-sampled_ Type Ia supernovae, and here a minimum of 15 detections is used.\n",
    "Since the DC2 dataset was simulated using a baseline observing strategy (and does not include deep drilling fields), there are no more than 100 visits per year per field.\n",
    "Any DiaObject with >100 DiaSources had a duration >1 year, and is not a SNIa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d83e88cb-2032-4641-a152-092670595fa6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:28.697047Z",
     "iopub.status.busy": "2024-04-29T13:56:28.696258Z",
     "iopub.status.idle": "2024-04-29T13:56:28.992891Z",
     "shell.execute_reply": "2024-04-29T13:56:28.991418Z",
     "shell.execute_reply.started": "2024-04-29T13:56:28.697000Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "snia_nDiaSources_min = 15\n",
    "snia_nDiaSources_max = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2c49cb-bcb1-4deb-aad9-42d6530c24be",
   "metadata": {},
   "source": [
    "Define the minimum and maximum lightcurve duration, in days. \n",
    "The duration is the time between the first and last difference-image detection in any filter.\n",
    "As seen in the template lightcurve plot above, SNIa at redshifts 0.1 < _z_ < 0.3 will have durations of 50 to 300 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a77ce538-57cf-4bb8-97d8-a2a5c787b0b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:29.908352Z",
     "iopub.status.busy": "2024-04-29T13:56:29.907614Z",
     "iopub.status.idle": "2024-04-29T13:56:29.914284Z",
     "shell.execute_reply": "2024-04-29T13:56:29.912778Z",
     "shell.execute_reply.started": "2024-04-29T13:56:29.908303Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "snia_duration_min = 50\n",
    "snia_duration_max = 300"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41d9032-612e-4134-8a2e-4b82470b6268",
   "metadata": {},
   "source": [
    "> **Notice:** Of the above parameters defined to identify potential SNIa, only the lightcurve duration is _not_ represented in the DiaObjects table, and cannot be included as a constraint in the TAP query used below.\n",
    "Instead, the lightcurve durations are calculated and used in Section 3.3."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2c05d1-7f62-458c-b5e9-cb6a9f399a6d",
   "metadata": {},
   "source": [
    "### 3.2. Retrieve a sample of potentially SNIa-like DiaObjects\n",
    "\n",
    "Only retrieve 1000 DiaObjects for this example.\n",
    "When the TAP query completes, transfer the results to an astropy table.\n",
    "\n",
    "This TAP query takes about a minute.\n",
    "\n",
    "Note the use of the function `scisql_nanojanskyToAbMag` to retrieve magnitudes directly rather than having to convert them from fluxes after the query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "81b8cd59-1ba3-4eaa-846f-6478ed0c3cf5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:34.422387Z",
     "iopub.status.busy": "2024-04-29T13:56:34.421686Z",
     "iopub.status.idle": "2024-04-29T13:56:36.657362Z",
     "shell.execute_reply": "2024-04-29T13:56:36.656517Z",
     "shell.execute_reply.started": "2024-04-29T13:56:34.422344Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = service.search(\"SELECT TOP 1000 \"\n",
    "                         \"ra, decl, diaObjectId, nDiaSources, \"\n",
    "                         \"scisql_nanojanskyToAbMag(rPSFluxMin) AS rMagMax, \"\n",
    "                         \"scisql_nanojanskyToAbMag(rPSFluxMax) AS rMagMin, \"\n",
    "                         \"scisql_nanojanskyToAbMag(gPSFluxMax) AS gMagMin, \"\n",
    "                         \"scisql_nanojanskyToAbMag(iPSFluxMax) AS iMagMin \"\n",
    "                         \"FROM dp02_dc2_catalogs_frdf.DiaObject \"\n",
    "                         \"WHERE nDiaSources > \"+str(snia_nDiaSources_min)+\" \"\n",
    "                         \"AND nDiaSources < \"+str(snia_nDiaSources_max)+\" \"\n",
    "                         \"AND scisql_nanojanskyToAbMag(rPSFluxMax) > \"+str(snia_peak_mr_min)+\" \"\n",
    "                         \"AND scisql_nanojanskyToAbMag(rPSFluxMax) < \"+str(snia_peak_mr_max)+\" \"\n",
    "                         \"AND scisql_nanojanskyToAbMag(gPSFluxMax) < \"+str(snia_peak_mg_max)+\" \"\n",
    "                         \"AND scisql_nanojanskyToAbMag(iPSFluxMax) < \"+str(snia_peak_mi_max)+\" \"\n",
    "                         \"AND scisql_nanojanskyToAbMag(rPSFluxMin)\"\n",
    "                         \" - scisql_nanojanskyToAbMag(rPSFluxMax) < \"+str(snia_ampl_mr_max)+\" \"\n",
    "                         \"AND scisql_nanojanskyToAbMag(rPSFluxMin)\"\n",
    "                         \" - scisql_nanojanskyToAbMag(rPSFluxMax) > \"+str(snia_ampl_mr_min)+\" \")\n",
    "\n",
    "DiaObjs = results.to_table()\n",
    "DiaObjs['rMagAmp'] = DiaObjs['rMagMin'] - DiaObjs['rMagMax']\n",
    "del results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1768203c-de36-4aeb-a447-f3f7e40cdb9c",
   "metadata": {},
   "source": [
    "**Option**: display the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "85db3b35-5317-4bb5-888a-56bb56290e2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:43.451506Z",
     "iopub.status.busy": "2024-04-29T13:56:43.450760Z",
     "iopub.status.idle": "2024-04-29T13:56:43.456867Z",
     "shell.execute_reply": "2024-04-29T13:56:43.455569Z",
     "shell.execute_reply.started": "2024-04-29T13:56:43.451459Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# DiaObjs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff735a9-9a1a-4b5f-8a8a-dfa02f6e2593",
   "metadata": {},
   "source": [
    "**Plot histograms of brightest magnitude and lightcurve amplitude**\n",
    "\n",
    "Plot histograms to characterize the brightest detected r-band magnitudes (left) and the r-band amplitudes (right) for the DiaObjects in this initial sample of potentially SNIa-like time-domain objects.\n",
    "\n",
    "Notice that the brightest r-band magnitude distribution (left) is bimodal, and that the distribution of r-band amplitudes declines until about 3.5 mag and then extends as a tail of brighter time-variable objects to amplitudes of 5.5 mag.\n",
    "The underlying cause of both of these effects is likely the fact that the only time-variable sources in DC2 are stars (mostly brighter) and Type Ia supernovae (mostly fainter) -- but confirming this is left as an exercise for the learner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "89c95b1b-159f-461d-9057-c4ea95255ca6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:45.162758Z",
     "iopub.status.busy": "2024-04-29T13:56:45.162070Z",
     "iopub.status.idle": "2024-04-29T13:56:45.522952Z",
     "shell.execute_reply": "2024-04-29T13:56:45.522161Z",
     "shell.execute_reply.started": "2024-04-29T13:56:45.162714Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].hist(DiaObjs['rMagMin'], bins=20, color=plot_filter_colors['r'])\n",
    "ax[0].set_xlabel('Brightest Detected r-band Magnitude')\n",
    "ax[0].set_ylabel('Number of Potential SNIa')\n",
    "\n",
    "ax[1].hist(DiaObjs['rMagAmp'], bins=20, color=plot_filter_colors['r'])\n",
    "ax[1].set_xlabel('Amplitude in r-band Magnitude')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424d4c96-66ca-4b0d-a4c8-9aa684981737",
   "metadata": {},
   "source": [
    "### 3.3. Calculate lightcurve duration and identify potential SNIa\n",
    "\n",
    "The lightcurve duration -- time between the first and last detected DiaSource in any filter -- is not included in the DiaObject table.\n",
    "It is calculated below, using all of the DiaSources for each DiaObject.\n",
    "\n",
    "Time is reported in the DiaSource table as `midPointTai`, which is in the SI unit of \"TAI\" (<a href=\"https://en.wikipedia.org/wiki/International_Atomic_Time\">International Atomic Time</a>), and is presented in days (in particular, as \"<a href=\"https://en.wikipedia.org/wiki/Julian_day\">Modified Julian Days</a>\").\n",
    "\n",
    "First, create a string list of the `diaObjectId` that is formatted like \"(1249546586455802795, 1248684569339625641, 1248684569339626125)\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2dae27d2-353d-4c6d-b623-b2c0bb0500f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:46.981949Z",
     "iopub.status.busy": "2024-04-29T13:56:46.981208Z",
     "iopub.status.idle": "2024-04-29T13:56:47.026186Z",
     "shell.execute_reply": "2024-04-29T13:56:47.025158Z",
     "shell.execute_reply.started": "2024-04-29T13:56:46.981902Z"
    }
   },
   "outputs": [],
   "source": [
    "all_ids = DiaObjs['diaObjectId']\n",
    "temp_list = '('\n",
    "for i, id in enumerate(all_ids):\n",
    "    temp_list = temp_list + str(id)\n",
    "    if i < len(all_ids)-1:\n",
    "        temp_list = temp_list + ', '\n",
    "    elif i == len(all_ids)-1:\n",
    "        temp_list = temp_list + ')'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df6731f-5ef8-4c89-9d4f-f4fbbf5578f6",
   "metadata": {},
   "source": [
    "Option to print the 1000-element long list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "de12d5ba-cafd-463d-bcbe-20bf2ba673a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:48.128744Z",
     "iopub.status.busy": "2024-04-29T13:56:48.128052Z",
     "iopub.status.idle": "2024-04-29T13:56:48.133977Z",
     "shell.execute_reply": "2024-04-29T13:56:48.132760Z",
     "shell.execute_reply.started": "2024-04-29T13:56:48.128697Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(temp_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1997fd-472b-45e4-a23c-b475b5521e04",
   "metadata": {},
   "source": [
    "Retrieve the `midPointTai` for all observations (`diaSource`s) for all the `diaObject`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5962ca13-03f9-452b-9623-98f98e3ad012",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:49.744893Z",
     "iopub.status.busy": "2024-04-29T13:56:49.744235Z",
     "iopub.status.idle": "2024-04-29T13:56:54.848340Z",
     "shell.execute_reply": "2024-04-29T13:56:54.847570Z",
     "shell.execute_reply.started": "2024-04-29T13:56:49.744835Z"
    }
   },
   "outputs": [],
   "source": [
    "results = service.search(\"SELECT diaObjectId, midPointTai \"\n",
    "                         \"FROM dp02_dc2_catalogs_frdf.DiaSource \"\n",
    "                         \"WHERE diaObjectId IN \" + temp_list)\n",
    "results = results.to_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49286229-60c9-4c01-9326-4d49d7b3f3d8",
   "metadata": {},
   "source": [
    "Calculate the durations for each `diaObject` and add them to the `DiaObjs` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "910d465f-94ca-4b91-a7ac-9717a4415541",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:54.877399Z",
     "iopub.status.busy": "2024-04-29T13:56:54.877218Z",
     "iopub.status.idle": "2024-04-29T13:56:55.634279Z",
     "shell.execute_reply": "2024-04-29T13:56:55.633468Z",
     "shell.execute_reply.started": "2024-04-29T13:56:54.877384Z"
    }
   },
   "outputs": [],
   "source": [
    "DiaObjs['duration'] = numpy.zeros(len(DiaObjs), dtype='float')\n",
    "\n",
    "for j, DiaObjId in enumerate(DiaObjs['diaObjectId']):\n",
    "    tx = numpy.where(results['diaObjectId'] == DiaObjId)[0]\n",
    "    DiaObjs['duration'][j] = numpy.max(results['midPointTai'][tx]) - numpy.min(results['midPointTai'][tx])\n",
    "    del tx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2353c54e-de16-41d2-af88-be7bc2dcb143",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-31T20:39:35.064380Z",
     "iopub.status.busy": "2024-01-31T20:39:35.063650Z",
     "iopub.status.idle": "2024-01-31T20:39:35.068845Z",
     "shell.execute_reply": "2024-01-31T20:39:35.068162Z",
     "shell.execute_reply.started": "2024-01-31T20:39:35.064356Z"
    }
   },
   "source": [
    "Clean up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3ef761c3-b610-4393-b5a5-9067afe7c616",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:55.636053Z",
     "iopub.status.busy": "2024-04-29T13:56:55.635795Z",
     "iopub.status.idle": "2024-04-29T13:56:55.639680Z",
     "shell.execute_reply": "2024-04-29T13:56:55.638959Z",
     "shell.execute_reply.started": "2024-04-29T13:56:55.636033Z"
    }
   },
   "outputs": [],
   "source": [
    "del results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16aafdfa-e78d-4345-982e-c4ee37b44489",
   "metadata": {},
   "source": [
    "Select only DiaObjects that have lightcurve durations within the specified range for SNIa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aed21a5f-101c-44bb-868c-a3b18e97fe27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:55.640705Z",
     "iopub.status.busy": "2024-04-29T13:56:55.640496Z",
     "iopub.status.idle": "2024-04-29T13:56:56.162608Z",
     "shell.execute_reply": "2024-04-29T13:56:56.161338Z",
     "shell.execute_reply.started": "2024-04-29T13:56:55.640688Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n"
     ]
    }
   ],
   "source": [
    "tx = numpy.where((DiaObjs['duration'] > snia_duration_min)\n",
    "                 & (DiaObjs['duration'] < snia_duration_max))[0]\n",
    "print(len(tx))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23cdc39f-3fbb-4bfb-87d3-0e07f5c34096",
   "metadata": {},
   "source": [
    "**Plot a histogram of lightcurve durations, and a scatter plot of duration vs. amplitude, for the potential SNIa**\n",
    "\n",
    "Plot a histogram of lightcurve durations (left) and a plot of duration versus r-band amplitude (right) to further characterize this sample of potential SNIa. \n",
    "\n",
    "Notice the spread in duration, and how it is correlated with lightcurve amplitude, as expected for SNIa. \n",
    "\n",
    "Tracking down the origin of the two outliers is left as an exercise for the learner: if they are truly SNIa observed for >100 days, then they should have a brighter amplitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "508ae771-c983-4299-89a4-97b497490b95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:56.165056Z",
     "iopub.status.busy": "2024-04-29T13:56:56.164257Z",
     "iopub.status.idle": "2024-04-29T13:56:56.913103Z",
     "shell.execute_reply": "2024-04-29T13:56:56.912272Z",
     "shell.execute_reply.started": "2024-04-29T13:56:56.165010Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), sharey=False, sharex=False)\n",
    "\n",
    "ax[0].hist(DiaObjs['duration'][tx], bins=20, color='gray')\n",
    "ax[0].set_xlabel('Lightcurve Duration [days]')\n",
    "ax[0].set_ylabel('Number of Potential SNIa')\n",
    "\n",
    "ax[1].plot(DiaObjs['duration'][tx], DiaObjs['rMagAmp'][tx], 'o', color='gray')\n",
    "ax[1].set_xlabel('Lightcurve Duration [days]')\n",
    "ax[1].set_ylabel('Amplitude in r-band [mag]')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6216ee-2188-43e0-b188-a70f42d2f11e",
   "metadata": {},
   "source": [
    "### 3.4. Plot multi-band lightcurves for the first 20 potential SNIa\n",
    "\n",
    "Notice that all of these lightcurves do resemble Type Ia supernovae!\n",
    "That is a promising sign that the methodology of using the DiaObject summary parameters, along with the lightcurve duration (which must be calculate separately), can identify an initial sample of potential SNIa.\n",
    "\n",
    "> **Keep in mind that this is not necessarily a pure or complete sample of SNIa,** just a sample of *potential well-sampled low-redshift* SNIa - decent enough to use as a demonstration, or a starting point for a more rigorous classification process.\n",
    "Different science goals will have different requirements on sample identification.\n",
    "\n",
    "Below, the lightcurves plotted are based on the signal-to-noise ratio > 5 detections from the DiaSource table only.\n",
    "In Section 4, lightcurves based on forced photometry are used instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "41bb4666-7c90-4f35-b9e8-b3dbd59b0185",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:56:56.984364Z",
     "iopub.status.busy": "2024-04-29T13:56:56.984192Z",
     "iopub.status.idle": "2024-04-29T13:57:06.238266Z",
     "shell.execute_reply": "2024-04-29T13:57:06.236719Z",
     "shell.execute_reply.started": "2024-04-29T13:56:56.984349Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(5, 4, figsize=(10, 10), sharey=False, sharex=False)\n",
    "\n",
    "x = 0\n",
    "\n",
    "for i in range(5):\n",
    "    for j in range(4):\n",
    "        results = service.search(\"SELECT ra, decl, diaObjectId, diaSourceId, \"\n",
    "                                 \"filterName, midPointTai, \"\n",
    "                                 \"scisql_nanojanskyToAbMag(psFlux) AS psAbMag \"\n",
    "                                 \"FROM dp02_dc2_catalogs_frdf.DiaSource \"\n",
    "                                 \"WHERE diaObjectId = \"+str(DiaObjs['diaObjectId'][tx[x]]))\n",
    "        results = results.to_table()\n",
    "\n",
    "        for f, filt in enumerate(plot_filter_labels):\n",
    "            fx = numpy.where(results['filterName'] == filt)[0]\n",
    "            ax[i, j].plot(results['midPointTai'][fx], results['psAbMag'][fx],\n",
    "                          plot_filter_symbols[filt], ms=10, mew=0, alpha=0.5,\n",
    "                          color=plot_filter_colors[filt])\n",
    "            del fx\n",
    "\n",
    "        ax[i, j].invert_yaxis()\n",
    "        ax[i, j].set_title(DiaObjs['diaObjectId'][tx[x]])\n",
    "\n",
    "        if i == 4:\n",
    "            ax[i, j].xaxis.set_label_text('MJD (days)')\n",
    "        if j == 0:\n",
    "            ax[i, j].yaxis.set_label_text('mag')\n",
    "\n",
    "        x += 1\n",
    "        del results\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251cbca7-8b24-437f-9442-e140133ef96a",
   "metadata": {},
   "source": [
    "## 4. Forced photometry lightcurves\n",
    "\n",
    "Above, the DiaSource table is used to plot lightcurves, but it only includes SNR>5 detections in the difference images.\n",
    "If your science goal requires lower-SNR measurements, use the forced photometry in the ForcedSourceOnDiaObjects table instead, as demonstrated below.\n",
    "\n",
    "Let's say there is a specific object whose lightcurve looks particularly interesting. In order to explore the flux of the object in all sets of differences images (not just those with detected sources), the ForcedSourceOnDiaObject catalog can be utilized. \n",
    "\n",
    "**ForcedSourceOnDiaObject Table**: point-source forced-photometry measurements on individual single-epoch visit images and difference images, based on and linked to the entries in the `DiaObject` table.\n",
    "\n",
    "For more information, see <a href=\"https://dm.lsst.org/sdm_schemas/browser/dp02.html#ForcedSourceOnDiaObject\">the schema of the ForcedSourceOnDiaObject catalog</a>.\n",
    "\n",
    "**Option**: print all of the available column names and descriptions for the ForcedSourceOnDiaObject table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f7a14ffd-9999-47d7-8e5a-3f71c3f329a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:06.339704Z",
     "iopub.status.busy": "2024-04-29T13:57:06.339424Z",
     "iopub.status.idle": "2024-04-29T13:57:06.343614Z",
     "shell.execute_reply": "2024-04-29T13:57:06.342836Z",
     "shell.execute_reply.started": "2024-04-29T13:57:06.339679Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# results = service.search(\"SELECT column_name, description from TAP_SCHEMA.columns \"\n",
    "#                          \"WHERE table_name = 'dp02_dc2_catalogs_frdf.ForcedSourceOnDiaObject'\")\n",
    "# for column_name, description in zip(results['column_name'], results['description']):\n",
    "#     print(column_name, '-', description)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0bae65-b864-4938-9a7d-ee2509db6bda",
   "metadata": {},
   "source": [
    "### 4.1. Retrieve photometry for a DiaObject\n",
    "\n",
    "As an example, let's look at the forced photometry on the difference images of the object 1250953961339360185, which has an interesting lightcurve. Note that it is unlikely this object is shown in the 20 SNIa candidates above since they will change every time the search is run. That object should however be in the list of 1000 SNIa candidates that we started with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "298f86a6-d019-4a93-81c4-7bf0ccdc014b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:06.344777Z",
     "iopub.status.busy": "2024-04-29T13:57:06.344524Z",
     "iopub.status.idle": "2024-04-29T13:57:06.747402Z",
     "shell.execute_reply": "2024-04-29T13:57:06.746279Z",
     "shell.execute_reply.started": "2024-04-29T13:57:06.344754Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1250953961339360185\n"
     ]
    }
   ],
   "source": [
    "DiaObjID = 1250953961339360185\n",
    "print(DiaObjID)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3259766-b214-4564-8739-2efec82fdaa4",
   "metadata": {},
   "source": [
    "Here, we will define the query to extract the information from the ForcedSourceOnDiaObject table on our DiaObject of interest in order to construct a lightcurve that includes forced photometry (i.e., sub-threshold measurements with SNR<5).\n",
    "\n",
    "Below, a JOIN to the CcdVisit table is used to obtain the exposure time mid-point in modified julian date (MJD) format (expMidptMJD), which is necessary to construct the lightcurve.\n",
    "\n",
    "The ForcedSourceOnDiaObject contains forced photometry on both the difference image (psfDiffFlux, psfDiffFluxErr) and the processed visit image (PVI), also called the \"direct\" image (psfFlux, psfFluxErr).\n",
    "Both types of fluxes are retrieved below for our DiaObject of interest.\n",
    "\n",
    "This query should take less than a second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6c605360-f988-4ecf-a18b-fb807ec25266",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:06.749804Z",
     "iopub.status.busy": "2024-04-29T13:57:06.749137Z",
     "iopub.status.idle": "2024-04-29T13:57:15.556107Z",
     "shell.execute_reply": "2024-04-29T13:57:15.555076Z",
     "shell.execute_reply.started": "2024-04-29T13:57:06.749758Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = service.search(\"SELECT fsodo.coord_ra, fsodo.coord_dec, \"\\\n",
    "                         \"fsodo.diaObjectId, fsodo.ccdVisitId, fsodo.band, \"\\\n",
    "                         \"fsodo.psfDiffFlux, fsodo.psfDiffFluxErr, \"\\\n",
    "                         \"fsodo.psfFlux, fsodo.psfFluxErr, \"\\\n",
    "                         \"cv.expMidptMJD \"\\\n",
    "                         \"FROM dp02_dc2_catalogs_frdf.ForcedSourceOnDiaObject as fsodo \"\\\n",
    "                         \"JOIN dp02_dc2_catalogs_frdf.CcdVisit as cv ON cv.ccdVisitId = fsodo.ccdVisitId \"\\\n",
    "                         \"WHERE fsodo.diaObjectId = \"+str(DiaObjID))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282cbca1-d5e3-4499-837c-d3d7d25b2d3c",
   "metadata": {},
   "source": [
    "Save the results in a table \"FrcdSrc\" (short for forced source, to distinguish it from the results from DiaSource saved as DiaSrc, below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "81212c62-e601-43dd-ad2a-b74039004981",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:15.559461Z",
     "iopub.status.busy": "2024-04-29T13:57:15.559059Z",
     "iopub.status.idle": "2024-04-29T13:57:15.566685Z",
     "shell.execute_reply": "2024-04-29T13:57:15.565944Z",
     "shell.execute_reply.started": "2024-04-29T13:57:15.559423Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "FrcdSrc = results.to_table()\n",
    "FrcdSrc\n",
    "del results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2609a6c3-c9fa-49c4-a70a-48a4dc4723af",
   "metadata": {},
   "source": [
    "Also retrieve the DiaSource measurements for the DiaObject of interest.\n",
    "Recall that this will only return flux measurements from the difference images with SNR>5 detection.\n",
    "\n",
    "This should also take less than a second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "663f204d-66ff-4177-9841-bb81f03f7360",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:15.750960Z",
     "iopub.status.busy": "2024-04-29T13:57:15.750689Z",
     "iopub.status.idle": "2024-04-29T13:57:16.127820Z",
     "shell.execute_reply": "2024-04-29T13:57:16.126962Z",
     "shell.execute_reply.started": "2024-04-29T13:57:15.750937Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = service.search(\"SELECT ra, decl, diaObjectId, diaSourceId, psFlux, psFluxErr, \"\n",
    "                         \"filterName, midPointTai, SNR, ccdVisitId, \"\n",
    "                         \"scisql_nanojanskyToAbMag(psFlux) AS psAbMag \"\n",
    "                         \"FROM dp02_dc2_catalogs_frdf.DiaSource \"\n",
    "                         \"WHERE diaObjectId = \"+str(DiaObjID))\n",
    "DiaSrc = results.to_table()\n",
    "del results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce60a0dd-b89f-4d96-a2c5-9134661e1540",
   "metadata": {},
   "source": [
    "Unlike the DiaSource catalog, the ForcedSourceOnDiaObject catalog does not provide the SNR for the flux measurements. Since it provides a useful diagnostic, we define 'diffSNR' for our object by the ratio of the absolute value of 'psfDiffFlux' and 'psfDiffFluxErr.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8a701ae4-874f-4558-a1b1-f63270ef64e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:16.275101Z",
     "iopub.status.busy": "2024-04-29T13:57:16.274561Z",
     "iopub.status.idle": "2024-04-29T13:57:16.281825Z",
     "shell.execute_reply": "2024-04-29T13:57:16.280853Z",
     "shell.execute_reply.started": "2024-04-29T13:57:16.275068Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "FrcdSrc['DiffSNR'] = abs(FrcdSrc['psfDiffFlux']/FrcdSrc['psfDiffFluxErr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a5d68f-1aaa-4067-8218-4722805b6803",
   "metadata": {},
   "source": [
    "Show that the DiffSNR column has been added to our \"FrcdSrc\" table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6ff11d98-a048-418d-a9f4-179fed460b79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:17.938581Z",
     "iopub.status.busy": "2024-04-29T13:57:17.937866Z",
     "iopub.status.idle": "2024-04-29T13:57:17.967364Z",
     "shell.execute_reply": "2024-04-29T13:57:17.966426Z",
     "shell.execute_reply.started": "2024-04-29T13:57:17.938533Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=940</i>\n",
       "<table id=\"table140552027860176\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>coord_ra</th><th>coord_dec</th><th>diaObjectId</th><th>ccdVisitId</th><th>band</th><th>psfDiffFlux</th><th>psfDiffFluxErr</th><th>psfFlux</th><th>psfFluxErr</th><th>expMidptMJD</th><th>DiffSNR</th></tr></thead>\n",
       "<thead><tr><th>deg</th><th>deg</th><th></th><th></th><th></th><th>nJy</th><th>nJy</th><th>nJy</th><th>nJy</th><th>d</th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>688121184</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60598.1694092</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>688121184</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60598.1694092</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>688323004</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60598.2610902</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>688323004</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60598.2610902</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>688237049</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60598.2216632</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>688237049</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60598.2216632</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>712268050</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60631.0857602</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>712268050</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60631.0857602</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>712267086</td><td>z</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60631.0853092</td><td>--</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1145186140</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61259.3408422</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1145186140</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61259.3408422</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1189102101</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61323.1876552</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1189102101</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61323.1876552</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1184868164</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61318.1991772</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1184868164</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61318.1991772</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1006061032</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61066.0930942</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1006061032</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61066.0930942</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1155583172</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61272.3273902</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>1155583172</td><td>r</td><td>--</td><td>--</td><td>--</td><td>--</td><td>61272.3273902</td><td>--</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=940>\n",
       " coord_ra  coord_dec      diaObjectId     ... psfFluxErr  expMidptMJD  DiffSNR\n",
       "   deg        deg                         ...    nJy           d              \n",
       " float64    float64          int64        ...  float64      float64    float64\n",
       "---------- ---------- ------------------- ... ---------- ------------- -------\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60598.1694092      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60598.1694092      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60598.2610902      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60598.2610902      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60598.2216632      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60598.2216632      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60631.0857602      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60631.0857602      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60631.0853092      --\n",
       "       ...        ...                 ... ...        ...           ...     ...\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61259.3408422      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61259.3408422      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61323.1876552      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61323.1876552      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61318.1991772      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61318.1991772      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61066.0930942      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61066.0930942      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61272.3273902      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 61272.3273902      --"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FrcdSrc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1b6da40-19e7-4191-a363-6c8beb0f3714",
   "metadata": {},
   "source": [
    "### 4.2. Plot the lightcurves\n",
    "\n",
    "For our DiaObject of interest, compare the lightcurves that include forced photometry from the ForcedSourceOnDiaObject table (open markers, left plots) with lightcurves that include only SNR>5 detections from the DiaSource table (filled markers, right plots).\n",
    "\n",
    "> **Notice**: The forced photometry lightcurves (left) include flux measurements from before and after the source was identified in the DIAsource catalogs. The shaded gray regions indicate the dates spanned by the detections only (DiaSources only).\n",
    "\n",
    "Define the MJD ranges for the \"full\" lightcurve from the forced photometry, and for the \"detected\" part of the lightcurve from the DiaSource table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9fe9873e-2da6-46f4-9677-9213847c9585",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:22.105305Z",
     "iopub.status.busy": "2024-04-29T13:57:22.104612Z",
     "iopub.status.idle": "2024-04-29T13:57:22.114291Z",
     "shell.execute_reply": "2024-04-29T13:57:22.113086Z",
     "shell.execute_reply.started": "2024-04-29T13:57:22.105263Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mjd_full_start = numpy.min(FrcdSrc['expMidptMJD'])\n",
    "mjd_full_end = numpy.max(FrcdSrc['expMidptMJD'])\n",
    "\n",
    "mjd_det_start = numpy.min(DiaSrc['midPointTai'])\n",
    "mjd_det_end = numpy.max(DiaSrc['midPointTai'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d82867-4ade-4c86-98fe-fbeea609d833",
   "metadata": {},
   "source": [
    "#### 4.2.1. Lightcurve for the full survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7dfb35a4-d68c-4fd0-a482-0402142d0e67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:24.294830Z",
     "iopub.status.busy": "2024-04-29T13:57:24.294112Z",
     "iopub.status.idle": "2024-04-29T13:57:24.706730Z",
     "shell.execute_reply": "2024-04-29T13:57:24.705991Z",
     "shell.execute_reply.started": "2024-04-29T13:57:24.294784Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharey=True, sharex=False)\n",
    "\n",
    "for f, filt in enumerate(plot_filter_labels):\n",
    "    fx = numpy.where(FrcdSrc['band'] == filt)[0]\n",
    "    ax[0].plot(FrcdSrc['expMidptMJD'][fx], FrcdSrc['psfDiffFlux'][fx],\n",
    "               plot_filter_symbols[filt], ms=10, mew=2, mec=plot_filter_colors[filt],\n",
    "               alpha=0.5, color='none', label=filt)\n",
    "    del fx\n",
    "\n",
    "ax[0].set_xlabel('Modified Julian Date')\n",
    "ax[0].set_ylabel('Difference-Image Flux')\n",
    "ax[0].set_title('Forced Photometry (ForcedSourceOnDiaObject)')\n",
    "ax[0].set_xlim(mjd_full_start, mjd_full_end)\n",
    "ax[0].legend(loc='upper left')\n",
    "ax[0].axvspan(mjd_det_start, mjd_det_end, alpha=0.3, color='gray')\n",
    "\n",
    "for f, filt in enumerate(plot_filter_labels):\n",
    "    fx = numpy.where(DiaSrc['filterName'] == filt)[0]\n",
    "    ax[1].plot(DiaSrc['midPointTai'][fx], DiaSrc['psFlux'][fx],\n",
    "               plot_filter_symbols[filt], ms=10, mew=0, color=plot_filter_colors[filt],\n",
    "               alpha=0.5, label=filt)\n",
    "    del fx\n",
    "\n",
    "ax[1].set_xlabel('Modified Julian Date')\n",
    "ax[1].set_title('SNR>5 Detections (DiaSource)')\n",
    "ax[1].set_xlim(mjd_full_start, mjd_full_end)\n",
    "ax[1].legend(loc='upper left')\n",
    "ax[1].axvspan(mjd_det_start, mjd_det_end, alpha=0.3, color='gray')\n",
    "plt.subplots_adjust(wspace=.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28bf1e6-6362-4a9a-83c3-48107b923b94",
   "metadata": {},
   "source": [
    "#### 4.2.2. Lightcurve for epochs with SNR>5 detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "64eb4993-4684-41f8-a5a5-72197c41f066",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:28.034114Z",
     "iopub.status.busy": "2024-04-29T13:57:28.033448Z",
     "iopub.status.idle": "2024-04-29T13:57:28.853926Z",
     "shell.execute_reply": "2024-04-29T13:57:28.853187Z",
     "shell.execute_reply.started": "2024-04-29T13:57:28.034071Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharey=True, sharex=False)\n",
    "\n",
    "for f, filt in enumerate(plot_filter_labels):\n",
    "    fx = numpy.where(FrcdSrc['band'] == filt)[0]\n",
    "    ax[0].plot(FrcdSrc['expMidptMJD'][fx], FrcdSrc['psfDiffFlux'][fx],\n",
    "               plot_filter_symbols[filt], ms=10, mew=2, mec=plot_filter_colors[filt],\n",
    "               alpha=0.5, color='none', label=filt)\n",
    "    del fx\n",
    "ax[0].set_xlabel('Modified Julian Date')\n",
    "ax[0].set_ylabel('Difference-Image Flux')\n",
    "ax[0].set_title('Forced Photometry (ForcedSourceOnDiaObject)')\n",
    "ax[0].set_xlim(mjd_det_start-50, mjd_det_end+50)\n",
    "ax[0].legend(loc='upper left')\n",
    "ax[0].axvspan(mjd_det_start, mjd_det_end, alpha=0.3, color='gray')\n",
    "\n",
    "for f, filt in enumerate(plot_filter_labels):\n",
    "    fx = numpy.where(DiaSrc['filterName'] == filt)[0]\n",
    "    ax[1].plot(DiaSrc['midPointTai'][fx], DiaSrc['psFlux'][fx],\n",
    "               plot_filter_symbols[filt], ms=10, mew=0, color=plot_filter_colors[filt],\n",
    "               alpha=0.5, label=filt)\n",
    "    del fx\n",
    "\n",
    "ax[1].set_xlabel('Modified Julian Date')\n",
    "ax[1].set_title('SNR>5 Detections (DiaSource)')\n",
    "ax[1].set_xlim(mjd_det_start-50, mjd_det_end+50)\n",
    "ax[1].legend(loc='upper left')\n",
    "ax[1].axvspan(mjd_det_start, mjd_det_end, alpha=0.3, color='gray')\n",
    "plt.subplots_adjust(wspace=.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "052bd8aa-a6eb-4e93-9110-48670550078c",
   "metadata": {},
   "source": [
    "> **Notice**: In the above plots, the forced photometry (psfDiffFlux from ForcedSourceOnDiaObject, open symbols, left panels) is nearly identical to the detection photometry (psFlux from DiaSource, filled symbols, right panels), but the ForcedSourceOnDiaObject contains additional measurements from epochs in which the source in the difference image had a SNR<5. No such cutoff is applied to the ForcedSourceOnDiaObject catalog, in which photometry from all visits is included.\n",
    "\n",
    "### 4.3. Identify \"missing measurements\" from SNR<5 epochs\n",
    "\n",
    "Within the \"gray zone\" above (defined by epochs in which the source was detected with SNR>5 in the difference images), identify how many measurements there are in the DiaSource and ForcedSourceOnDiaObject tables, and compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0c4681de-b970-4c00-8f50-55a68907d611",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:30.488037Z",
     "iopub.status.busy": "2024-04-29T13:57:30.487309Z",
     "iopub.status.idle": "2024-04-29T13:57:30.501701Z",
     "shell.execute_reply": "2024-04-29T13:57:30.500592Z",
     "shell.execute_reply.started": "2024-04-29T13:57:30.487989Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within the date range of SNR>5 detections, there were:\n",
      "27 measurements in DiaSource catalog of 1250953961339360185\n",
      "80 measurements in ForcedSourceOnDiaObject catalog of 1250953961339360185\n"
     ]
    }
   ],
   "source": [
    "cov = numpy.where((FrcdSrc['expMidptMJD'] >= mjd_det_start)\n",
    "                  & (FrcdSrc['expMidptMJD'] <= mjd_det_end))\n",
    "\n",
    "print('Within the date range of SNR>5 detections, there were:')\n",
    "print(len(DiaSrc), 'measurements in DiaSource catalog of %.i' % (DiaObjID))\n",
    "print(len(FrcdSrc[cov]), 'measurements in ForcedSourceOnDiaObject catalog of %.i' % (DiaObjID))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0fbe561-56c3-4fa2-b233-b93cdbfc1b94",
   "metadata": {},
   "source": [
    "Identify only the SNR<5 measurements from the ForcedSourceOnDiaObject table by looking for unique \"ccdVisitId\" values that are not in the DiaSource results.\n",
    "Note that we are still applying the condition where the measurements are taken within the date range of the DiaSource detections.\n",
    "\n",
    "First, identify the unique visit IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "88694b90-e64b-4f3b-bd8e-9748ac9b6cff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:31.640558Z",
     "iopub.status.busy": "2024-04-29T13:57:31.639829Z",
     "iopub.status.idle": "2024-04-29T13:57:31.653289Z",
     "shell.execute_reply": "2024-04-29T13:57:31.652235Z",
     "shell.execute_reply.started": "2024-04-29T13:57:31.640513Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "UniqueVID = set(FrcdSrc[cov]['ccdVisitId']) - set(DiaSrc['ccdVisitId'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee4c7bd-b501-4f82-91b4-0def50993753",
   "metadata": {},
   "source": [
    "Next, construct a table of ForcedSourceOnDiaObject \"missing measurements\" that are not in the DiaSource catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f5bcb312-3e8c-449c-bdb6-8422b976857e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:32.730828Z",
     "iopub.status.busy": "2024-04-29T13:57:32.730130Z",
     "iopub.status.idle": "2024-04-29T13:57:32.741798Z",
     "shell.execute_reply": "2024-04-29T13:57:32.740640Z",
     "shell.execute_reply.started": "2024-04-29T13:57:32.730780Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "UniqueObj = []\n",
    "for x in UniqueVID:\n",
    "    UniqueObj += numpy.where(FrcdSrc['ccdVisitId'] == x)\n",
    "UniqueObj = numpy.concatenate(UniqueObj)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3c0e76-dd5f-4888-8f11-21e507a852cd",
   "metadata": {},
   "source": [
    "Inspect the table of \"missing measurements\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "80f12a04-b2bc-4f9b-8409-6909381fb8cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:33.523020Z",
     "iopub.status.busy": "2024-04-29T13:57:33.522309Z",
     "iopub.status.idle": "2024-04-29T13:57:33.554918Z",
     "shell.execute_reply": "2024-04-29T13:57:33.553955Z",
     "shell.execute_reply.started": "2024-04-29T13:57:33.522977Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=26</i>\n",
       "<table id=\"table140552211731664\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>coord_ra</th><th>coord_dec</th><th>diaObjectId</th><th>ccdVisitId</th><th>band</th><th>psfDiffFlux</th><th>psfDiffFluxErr</th><th>psfFlux</th><th>psfFluxErr</th><th>expMidptMJD</th><th>DiffSNR</th></tr></thead>\n",
       "<thead><tr><th>deg</th><th>deg</th><th></th><th></th><th></th><th>nJy</th><th>nJy</th><th>nJy</th><th>nJy</th><th>d</th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>960109025</td><td>g</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60993.0627302</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>960109025</td><td>g</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60993.0627302</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>948985095</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60970.1579432</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>948985095</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60970.1579432</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>936368072</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60952.1606072</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>936368072</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60952.1606072</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>936421079</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60952.1842852</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>936421079</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60952.1842852</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>936422155</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60952.1847302</td><td>--</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>948940154</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60970.1377562</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>948940154</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60970.1377562</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>936431155</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60952.1887502</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>936431155</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60952.1887502</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>963902134</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60998.0695872</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>963902134</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60998.0695872</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>956243158</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60980.0924622</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>956243158</td><td>y</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60980.0924622</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>961630074</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60995.0636662</td><td>--</td></tr>\n",
       "<tr><td>60.2901401</td><td>-44.142051</td><td>1250953961339360185</td><td>961630074</td><td>u</td><td>--</td><td>--</td><td>--</td><td>--</td><td>60995.0636662</td><td>--</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=26>\n",
       " coord_ra  coord_dec      diaObjectId     ... psfFluxErr  expMidptMJD  DiffSNR\n",
       "   deg        deg                         ...    nJy           d              \n",
       " float64    float64          int64        ...  float64      float64    float64\n",
       "---------- ---------- ------------------- ... ---------- ------------- -------\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60993.0627302      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60993.0627302      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60970.1579432      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60970.1579432      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60952.1606072      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60952.1606072      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60952.1842852      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60952.1842852      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60952.1847302      --\n",
       "       ...        ...                 ... ...        ...           ...     ...\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60970.1377562      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60970.1377562      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60952.1887502      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60952.1887502      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60998.0695872      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60998.0695872      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60980.0924622      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60980.0924622      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60995.0636662      --\n",
       "60.2901401 -44.142051 1250953961339360185 ...         -- 60995.0636662      --"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FrcdSrc[UniqueObj]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0721e50-1c9c-46fd-b1ad-8434502e5adf",
   "metadata": {},
   "source": [
    "Finally, verify why these \"missing measurements\" are not in the DiaSource catalog (their SNR is <5) by printing the maximum SNR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8cd157fe-4d8a-42d2-8be7-2f16265bf704",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:34.758787Z",
     "iopub.status.busy": "2024-04-29T13:57:34.758158Z",
     "iopub.status.idle": "2024-04-29T13:57:34.769335Z",
     "shell.execute_reply": "2024-04-29T13:57:34.768288Z",
     "shell.execute_reply.started": "2024-04-29T13:57:34.758749Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.max(FrcdSrc[UniqueObj]['DiffSNR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73bca2c0-4872-4173-8ee3-5fb44befdfde",
   "metadata": {},
   "source": [
    "### 4.4. Investigate potential SNR<5 variability\n",
    "\n",
    "Going back to the forced photometry lightcurve of our object of interest, it looked like there may have been some past variability in the z band (purple star) at around MJD 60250. Let's investigate this point. **This is for demonstration purposes only since there are no simulated precursor outbursts for the supernovae in DP0.2**. \n",
    "\n",
    "Let's check if it is at least greater than 3$\\sigma$ of the baseline (median) measurements using \"sigma_clipped_stats\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac4a87b-7d8d-4def-b764-9db39353ee73",
   "metadata": {},
   "source": [
    "Define the filter in which flaring (precursor activity) is investigated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a5e75997-954e-443a-b846-fa65051474e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:36.225789Z",
     "iopub.status.busy": "2024-04-29T13:57:36.225179Z",
     "iopub.status.idle": "2024-04-29T13:57:36.230732Z",
     "shell.execute_reply": "2024-04-29T13:57:36.229627Z",
     "shell.execute_reply.started": "2024-04-29T13:57:36.225751Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "flare_filt = 'z'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30219e89-92d3-4ee1-a602-c011e8471561",
   "metadata": {},
   "source": [
    "Specify the median and standard deviation in the \"flare\" filter band for the flux measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9f9b613e-b48e-4585-862a-dca1c939a154",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:37.313913Z",
     "iopub.status.busy": "2024-04-29T13:57:37.313192Z",
     "iopub.status.idle": "2024-04-29T13:57:37.321457Z",
     "shell.execute_reply": "2024-04-29T13:57:37.320347Z",
     "shell.execute_reply.started": "2024-04-29T13:57:37.313864Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "_, med, std = sigma_clipped_stats(FrcdSrc['psfDiffFlux'][numpy.where(FrcdSrc['band'] == flare_filt)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d66f2b38-54d1-42b1-a340-1ba0fa8c7013",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:37.773220Z",
     "iopub.status.busy": "2024-04-29T13:57:37.772556Z",
     "iopub.status.idle": "2024-04-29T13:57:37.780010Z",
     "shell.execute_reply": "2024-04-29T13:57:37.778954Z",
     "shell.execute_reply.started": "2024-04-29T13:57:37.773176Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median psfDiffFlux for all z-band forced sources:  -- nJy\n",
      "Standard deviation in psfDiffFlux for all z-band forced sources:  -- nJy\n"
     ]
    }
   ],
   "source": [
    "print('Median psfDiffFlux for all z-band forced sources: ', med, FrcdSrc['psfDiffFlux'].unit)\n",
    "print('Standard deviation in psfDiffFlux for all z-band forced sources: ',std, FrcdSrc['psfDiffFlux'].unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2dc868-9912-41d2-b732-05273003eeef",
   "metadata": {},
   "source": [
    "Define where any outlier variability (above 3$\\sigma$) might be in the \"flare\" band, BEFORE or AFTER the \"gray zone\" defined by the epochs in which our DiaObject of interest was detected with SNR>5 in any filter.\n",
    "\n",
    "First, index the flare-band epochs \"before\" and \"after\" within FrcdSrc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cfee369c-8866-4e67-afd8-14adda14ab94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:38.209647Z",
     "iopub.status.busy": "2024-04-29T13:57:38.208940Z",
     "iopub.status.idle": "2024-04-29T13:57:38.220024Z",
     "shell.execute_reply": "2024-04-29T13:57:38.219089Z",
     "shell.execute_reply.started": "2024-04-29T13:57:38.209601Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "154\n"
     ]
    }
   ],
   "source": [
    "ex = numpy.where(((FrcdSrc['expMidptMJD'] < mjd_det_start)\n",
    "                  | (FrcdSrc['expMidptMJD'] > mjd_det_end))\n",
    "                 & (FrcdSrc['band'] == flare_filt))[0]\n",
    "print(len(ex))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03377608-7760-4170-88a4-f44e83954202",
   "metadata": {},
   "source": [
    "Next, index which of _these_ is >3$\\sigma$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cfab7f63-20a4-41c4-9cde-bbf7505d3645",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:38.869740Z",
     "iopub.status.busy": "2024-04-29T13:57:38.869055Z",
     "iopub.status.idle": "2024-04-29T13:57:38.878445Z",
     "shell.execute_reply": "2024-04-29T13:57:38.877274Z",
     "shell.execute_reply.started": "2024-04-29T13:57:38.869695Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of >3𝜎 z-band measurements (outside SNR>5 detections): 0\n"
     ]
    }
   ],
   "source": [
    "ix = numpy.where(FrcdSrc['psfDiffFlux'][ex] > med+3*std)[0]\n",
    "print('Number of >3𝜎 z-band measurements (outside SNR>5 detections):',len(ix))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41250d3-4058-4fdb-8c1f-214747f99ff3",
   "metadata": {},
   "source": [
    "Inspect the single >3$\\sigma$ measurement in the forced photometry that occurred outside of the \"gray zone\".\n",
    "\n",
    "**As a reminder, 3$\\sigma$ is defined from the distribution of forced photometry measurements and is _not_ the photometric signal-to-noise ratio (i.e., DiffSNR)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4542760b-ea27-46ea-9138-b9bcfc9a60b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:39.688678Z",
     "iopub.status.busy": "2024-04-29T13:57:39.688036Z",
     "iopub.status.idle": "2024-04-29T13:57:39.701003Z",
     "shell.execute_reply": "2024-04-29T13:57:39.700110Z",
     "shell.execute_reply.started": "2024-04-29T13:57:39.688638Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=0</i>\n",
       "<table id=\"table140552213160016\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>coord_ra</th><th>coord_dec</th><th>diaObjectId</th><th>ccdVisitId</th><th>band</th><th>psfDiffFlux</th><th>psfDiffFluxErr</th><th>psfFlux</th><th>psfFluxErr</th><th>expMidptMJD</th><th>DiffSNR</th></tr></thead>\n",
       "<thead><tr><th>deg</th><th>deg</th><th></th><th></th><th></th><th>nJy</th><th>nJy</th><th>nJy</th><th>nJy</th><th>d</th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=0>\n",
       "coord_ra coord_dec diaObjectId ccdVisitId ... psfFluxErr expMidptMJD DiffSNR\n",
       "  deg       deg                           ...    nJy          d             \n",
       "float64   float64     int64      int64    ...  float64     float64   float64\n",
       "-------- --------- ----------- ---------- ... ---------- ----------- -------"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FrcdSrc[ex[ix]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd561917-30cd-461a-a585-cb3e8f8be2d6",
   "metadata": {},
   "source": [
    "**Statistics check:** recall that $\\pm 3\\sigma$ encompasses 99.7% of values in a normally-distributed random sample. So out of the 77 z-band forced photometry measurements we could have reasonably expected 0.23 at the $\\sigma$ > 3 level, and the fact that we found one is consistent with small-number statistics, i.e., consistent with noise.\n",
    "\n",
    "**Again, since this is from DP0.2, we _know_ this \"flaring activity\" is just noise but we use it to demonstrate how the ForcedSourceOnDIAObject catalog can be applied to search for pre- or post-transient variability.**\n",
    "\n",
    "As a final demonstration, plot the point(s) on the lightcurve that exceed 3$\\sigma$ and are outside the DiaSource date coverage.\n",
    "Below, we plot the 3$\\sigma$ flux range in red and the apparent variability outlier(s) as a yellow-filled plot marker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "255bf00e-0331-4c15-8126-43efbf23fd48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-29T13:57:40.427767Z",
     "iopub.status.busy": "2024-04-29T13:57:40.426561Z",
     "iopub.status.idle": "2024-04-29T13:57:40.704859Z",
     "shell.execute_reply": "2024-04-29T13:57:40.703531Z",
     "shell.execute_reply.started": "2024-04-29T13:57:40.427719Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/conda/miniconda3-py38_4.9.2/envs/lsst-scipipe-8.0.0/lib/python3.11/site-packages/matplotlib/cbook.py:1072: UserWarning: Warning: converting a masked element to nan.\n",
      "  x = np.asanyarray(x)\n",
      "/opt/lsst/software/stack/conda/miniconda3-py38_4.9.2/envs/lsst-scipipe-8.0.0/lib/python3.11/site-packages/matplotlib/cbook.py:1699: UserWarning: Warning: converting a masked element to nan.\n",
      "  return math.isfinite(val)\n",
      "/opt/lsst/software/stack/conda/miniconda3-py38_4.9.2/envs/lsst-scipipe-8.0.0/lib/python3.11/site-packages/matplotlib/patches.py:1151: UserWarning: Warning: converting a masked element to nan.\n",
      "  xy = np.asarray(xy)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "\n",
    "for f, filt in enumerate(plot_filter_labels):\n",
    "    fx = numpy.where(FrcdSrc['band'] == filt)[0]\n",
    "    ax.plot(FrcdSrc['expMidptMJD'][fx], FrcdSrc['psfDiffFlux'][fx],\n",
    "            plot_filter_symbols[filt], ms=10, mew=2, mec=plot_filter_colors[filt],\n",
    "            alpha=0.5, color='none', label=filt)\n",
    "    del fx\n",
    "\n",
    "for i in ix:\n",
    "    ax.plot(FrcdSrc['expMidptMJD'][ex[i]], FrcdSrc['psfDiffFlux'][ex[i]],\n",
    "            plot_filter_symbols['z'], ms=14, mew=2, mec='black', alpha=1.0,\n",
    "            color='yellow', label='z-band >3$\\sigma$ FrcdSrc psfDiffFlux')\n",
    "\n",
    "ax.hlines(y=med, xmin=numpy.min(FrcdSrc['expMidptMJD']), xmax=numpy.max(FrcdSrc['expMidptMJD']),\n",
    "          linewidth=2, color='black', ls='--', label='median z-band FrcdSrc psfDiffFlux')\n",
    "ax.axhspan(med, med+3*std, alpha=0.3, color='tab:red', label='stddev of z-band FrcdSrc psfDiffFlux')\n",
    "\n",
    "ax.set_xlabel('Modified Julian Date')\n",
    "ax.set_ylabel('Difference-Image Flux')\n",
    "ax.set_title('Forced Photometry (ForcedSourceOnDiaObject)')\n",
    "ax.legend(loc='upper left', ncol=2)\n",
    "ax.set_xlim(mjd_full_start, mjd_full_end)\n",
    "ax.axvspan(mjd_det_start, mjd_det_end, alpha=0.3, color='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a79e66e-9e2b-4c13-b6bc-18260e203d18",
   "metadata": {},
   "source": [
    "## 5. Exercises for the learner\n",
    "\n",
    "From here, there are a variety of exercises that a learner could undertake.\n",
    "\n",
    "1. Change the query constraints and identify a sample of high-redshift SNIa.\n",
    "2. Tighten the query contraints to identify only SNIa with pre-peak observations.\n",
    "3. Add error bars to the lightcurves. Magnitude errors can be retrieved during a TAP survey with, e.g., `scisql_nanojanskyToAbMagSigma(psFlux, psFluxErr) as psAbMagErr`.\n",
    "4. Apply a lightcurve-template fitter, or try a photometric classifier on the sample.\n",
    "5. Investigate the forced source photometry lightcurves of other SNIa candidates."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "LSST",
   "language": "python",
   "name": "lsst"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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  "toc-autonumbering": false
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