{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pvsystem tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial explores the ``pvlib.pvsystem`` module. The module has functions for importing PV module and inverter data and functions for modeling module and inverter performance.\n",
    "\n",
    "1. [Angle of Incidence Modifiers](#Angle-of-Incidence-Modifiers)\n",
    "2. [Sandia Cell Temp correction](#Sandia-Cell-Temp-correction)\n",
    "2. [Sandia Inverter Model](#Sandia-inverter-model)\n",
    "2. [Sandia Array Performance Model](#SAPM)\n",
    "    1. [SAPM IV curves](#SAPM-IV-curves)\n",
    "2. [DeSoto Model](#desoto)\n",
    "2. [Single Diode Model](#Single-diode-model)\n",
    "\n",
    "This notebook requires pvlib >= 0.8. See output of ``pd.show_versions()`` to see the other packages that this notebook was last used with. It should work with other Python and Pandas versions. \n",
    "\n",
    "Authors:\n",
    "* Will Holmgren (@wholmgren), University of Arizona. 2015, March 2016, November 2016, May 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "We suggest you install seaborn using conda or pip and rerun this cell\n"
    }
   ],
   "source": [
    "# built-in python modules\n",
    "import os\n",
    "import inspect\n",
    "import datetime\n",
    "\n",
    "# scientific python add-ons\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting stuff\n",
    "# first line makes the plots appear in the notebook\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "# seaborn makes your plots look better\n",
    "try:\n",
    "    import seaborn as sns\n",
    "    sns.set(rc={\"figure.figsize\": (12, 6)})\n",
    "except ImportError:\n",
    "    print('We suggest you install seaborn using conda or pip and rerun this cell')\n",
    "\n",
    "# finally, we import the pvlib library\n",
    "import pvlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nINSTALLED VERSIONS\n------------------\ncommit           : None\npython           : 3.8.2.final.0\npython-bits      : 64\nOS               : Darwin\nOS-release       : 19.5.0\nmachine          : x86_64\nprocessor        : i386\nbyteorder        : little\nLC_ALL           : None\nLANG             : None\nLOCALE           : None.UTF-8\n\npandas           : 1.0.3\nnumpy            : 1.18.2\npytz             : 2019.3\ndateutil         : 2.8.1\npip              : 20.0.2\nsetuptools       : 46.1.3.post20200330\nCython           : 0.29.16\npytest           : 5.4.1\nhypothesis       : None\nsphinx           : 1.8.5\nblosc            : None\nfeather          : None\nxlsxwriter       : None\nlxml.etree       : None\nhtml5lib         : None\npymysql          : None\npsycopg2         : None\njinja2           : 2.11.2\nIPython          : 7.16.1\npandas_datareader: None\nbs4              : 4.9.0\nbottleneck       : None\nfastparquet      : None\ngcsfs            : None\nlxml.etree       : None\nmatplotlib       : 3.2.1\nnumexpr          : 2.7.1\nodfpy            : None\nopenpyxl         : None\npandas_gbq       : None\npyarrow          : None\npytables         : None\npytest           : 5.4.1\npyxlsb           : None\ns3fs             : None\nscipy            : 1.4.1\nsqlalchemy       : None\ntables           : 3.6.1\ntabulate         : None\nxarray           : None\nxlrd             : None\nxlwt             : None\nxlsxwriter       : None\nnumba            : 0.49.0\n"
    }
   ],
   "source": [
    "pd.show_versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib\n",
    "from pvlib import pvsystem, inverter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Angle of Incidence Modifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "angles = np.linspace(-180,180,3601)\n",
    "ashraeiam = pd.Series(pvsystem.iam.ashrae(angles, .05), index=angles)\n",
    "\n",
    "ashraeiam.plot()\n",
    "plt.ylabel('ASHRAE modifier')\n",
    "plt.xlabel('input angle (deg)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "angles = np.linspace(-180,180,3601)\n",
    "physicaliam = pd.Series(pvsystem.iam.ashrae(angles), index=angles)\n",
    "\n",
    "physicaliam.plot()\n",
    "plt.ylabel('physical modifier')\n",
    "plt.xlabel('input index');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure()\n",
    "ashraeiam.plot(label='ASHRAE')\n",
    "physicaliam.plot(label='physical')\n",
    "plt.ylabel('modifier')\n",
    "plt.xlabel('input angle (deg)')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sandia Cell Temp correction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PV system efficiency can vary by up to 0.5% per degree C, so it's important to accurately model cell temperature. The ``temperature.sapm_cell`` function uses plane of array irradiance, ambient temperature, wind speed, and module and racking type to calculate cell temperature. From King et. al. (2004):\n",
    "\n",
    "$$T_m = E e^{a+b*WS} + T_a$$\n",
    "\n",
    "$$T_c = T_m + \\frac{E}{E_0} \\Delta T$$\n",
    "\n",
    "The $a$, $b$, and $\\Delta T$ parameters depend on the module and racking type. Here we use the ``open_rack_glass_glass`` parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``sapm_cell`` works with either scalar or vector inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "43.509190983665746"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# scalar inputs\n",
    "thermal_params = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']\n",
    "pvlib.temperature.sapm_cell(900, 20, 5, **thermal_params) # irrad, temp, wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# vector inputs\n",
    "times = pd.date_range(start='2015-01-01', end='2015-01-02', freq='12H')\n",
    "temps = pd.Series([0, 10, 5], index=times)\n",
    "irrads = pd.Series([0, 500, 0], index=times)\n",
    "winds = pd.Series([10, 5, 0], index=times)\n",
    "\n",
    "pvtemps = pvlib.temperature.sapm_cell(irrads, temps, winds, **thermal_params)\n",
    "pvtemps.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cell temperature as a function of wind speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "wind = np.linspace(0,20,21)\n",
    "temps = pd.Series(pvlib.temperature.sapm_cell(900, 20, wind, **thermal_params), index=wind)\n",
    "\n",
    "temps.plot()\n",
    "plt.xlabel('wind speed (m/s)')\n",
    "plt.ylabel('temperature (deg C)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cell temperature as a function of ambient temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "atemp = np.linspace(-20,50,71)\n",
    "temps = pd.Series(pvlib.temperature.sapm_cell(900, atemp, 2, **thermal_params), index=atemp)\n",
    "\n",
    "temps.plot()\n",
    "plt.xlabel('ambient temperature (deg C)')\n",
    "plt.ylabel('temperature (deg C)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cell temperature as a function of incident irradiance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "irrad = np.linspace(0,1000,101)\n",
    "temps = pd.Series(pvlib.temperature.sapm_cell(irrad, 20, 2, **thermal_params), index=irrad)\n",
    "\n",
    "temps.plot()\n",
    "plt.xlabel('incident irradiance (W/m**2)')\n",
    "plt.ylabel('temperature (deg C)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cell temperature for different module and racking types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "models = ['open_rack_glass_glass',\n",
    "          'close_mount_glass_glass',\n",
    "          'open_rack_glass_polymer',\n",
    "          'insulated_back_glass_polymer']\n",
    "\n",
    "temps = pd.Series(dtype=float)\n",
    "\n",
    "for model in models:\n",
    "    params = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm'][model]\n",
    "    temps[model] = pvlib.temperature.sapm_cell(1000, 20, 5, **params)\n",
    "\n",
    "temps.plot(kind='bar')\n",
    "plt.ylabel('temperature (deg C)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sandia-inverter-model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          ABB__MICRO_0_25_I_OUTD_US_208__208V_  \\\nVac                                        208   \nPso                                    2.08961   \nPaco                                       250   \nPdco                                   259.589   \nVdco                                        40   \nC0                                    -4.1e-05   \nC1                                    -9.1e-05   \nC2                                    0.000494   \nC3                                   -0.013171   \nPnt                                      0.075   \nVdcmax                                      50   \nIdcmax                                 6.48972   \nMppt_low                                    30   \nMppt_high                                   50   \nCEC_Date                                   NaN   \nCEC_Type                   Utility Interactive   \n\n          ABB__MICRO_0_25_I_OUTD_US_240__240V_  \\\nVac                                        240   \nPso                                    2.24041   \nPaco                                       250   \nPdco                                   259.492   \nVdco                                        40   \nC0                                    -3.9e-05   \nC1                                   -0.000132   \nC2                                    0.002418   \nC3                                   -0.014926   \nPnt                                      0.075   \nVdcmax                                      50   \nIdcmax                                  6.4873   \nMppt_low                                    30   \nMppt_high                                   50   \nCEC_Date                                   NaN   \nCEC_Type                   Utility Interactive   \n\n          ABB__MICRO_0_3_I_OUTD_US_208__208V_  \\\nVac                                       208   \nPso                                   1.84651   \nPaco                                      300   \nPdco                                  311.669   \nVdco                                       40   \nC0                                   -3.3e-05   \nC1                                  -0.000192   \nC2                                   0.000907   \nC3                                  -0.031742   \nPnt                                      0.09   \nVdcmax                                     50   \nIdcmax                                7.79173   \nMppt_low                                   30   \nMppt_high                                  50   \nCEC_Date                                  NaN   \nCEC_Type                  Utility Interactive   \n\n          ABB__MICRO_0_3_I_OUTD_US_240__240V_  \\\nVac                                       240   \nPso                                   1.95054   \nPaco                                      300   \nPdco                                  311.581   \nVdco                                       40   \nC0                                   -3.4e-05   \nC1                                  -0.000256   \nC2                                   0.002453   \nC3                                  -0.028223   \nPnt                                      0.09   \nVdcmax                                     50   \nIdcmax                                7.78952   \nMppt_low                                   30   \nMppt_high                                  50   \nCEC_Date                                  NaN   \nCEC_Type                  Utility Interactive   \n\n          ABB__MICRO_0_3HV_I_OUTD_US_208__208V_  \\\nVac                                         208   \nPso                                     1.76944   \nPaco                                        300   \nPdco                                    312.421   \nVdco                                         45   \nC0                                     -4.5e-05   \nC1                                    -0.000196   \nC2                                     0.001959   \nC3                                    -0.023725   \nPnt                                        0.09   \nVdcmax                                       60   \nIdcmax                                  6.94269   \nMppt_low                                     30   \nMppt_high                                    60   \nCEC_Date                                    NaN   \nCEC_Type                    Utility Interactive   \n\n          ABB__MICRO_0_3HV_I_OUTD_US_240__240V_  \\\nVac                                         240   \nPso                                     1.84378   \nPaco                                        300   \nPdco                                    312.005   \nVdco                                         45   \nC0                                     -3.5e-05   \nC1                                    -0.000227   \nC2                                    -0.000526   \nC3                                    -0.041214   \nPnt                                        0.09   \nVdcmax                                       60   \nIdcmax                                  6.93344   \nMppt_low                                     30   \nMppt_high                                    60   \nCEC_Date                                    NaN   \nCEC_Type                    Utility Interactive   \n\n          ABB__PVI_10_0_I_OUTD_x_US_208_y__208V_  \\\nVac                                          208   \nPso                                      46.8638   \nPaco                                       10000   \nPdco                                     10488.3   \nVdco                                         320   \nC0                                   -2.7759e-06   \nC1                                      -3.6e-05   \nC2                                      0.000305   \nC3                                     -0.002351   \nPnt                                          0.1   \nVdcmax                                       416   \nIdcmax                                    32.776   \nMppt_low                                     220   \nMppt_high                                    416   \nCEC_Date                                     NaN   \nCEC_Type                     Utility Interactive   \n\n          ABB__PVI_10_0_I_OUTD_x_US_480_y_z__480V_  \\\nVac                                            480   \nPso                                        67.7909   \nPaco                                         10000   \nPdco                                         10296   \nVdco                                           362   \nC0                                    -1.38839e-06   \nC1                                        -4.9e-05   \nC2                                        -0.00052   \nC3                                       -0.003855   \nPnt                                            0.4   \nVdcmax                                         416   \nIdcmax                                     28.4419   \nMppt_low                                       220   \nMppt_high                                      416   \nCEC_Date                                       NaN   \nCEC_Type                       Utility Interactive   \n\n          ABB__PVI_12_0_I_OUTD_x_US_480_y__480V_  \\\nVac                                          480   \nPso                                      62.5547   \nPaco                                       12000   \nPdco                                     12358.8   \nVdco                                         370   \nC0                                    -1.009e-06   \nC1                                      -5.6e-05   \nC2                                     -0.001437   \nC3                                     -0.007112   \nPnt                                          0.4   \nVdcmax                                       416   \nIdcmax                                   33.4022   \nMppt_low                                     250   \nMppt_high                                    416   \nCEC_Date                                     NaN   \nCEC_Type                     Utility Interactive   \n\n          ABB__PVI_3_0_OUTD_S_US__208V_  ... Zigor__Sunzet_3_TL_US__240V_  \\\nVac                                 208  ...                          240   \nPso                             18.1663  ...                      36.0334   \nPaco                               3000  ...                         3180   \nPdco                             3142.3  ...                      3315.65   \nVdco                                310  ...                          375   \nC0                         -8.03949e-06  ...                 -7.98467e-06   \nC1                             -1.1e-05  ...                     -7.5e-05   \nC2                             0.000999  ...                     0.000544   \nC3                            -0.000287  ...                    -0.000338   \nPnt                                 0.1  ...                        0.954   \nVdcmax                              480  ...                          400   \nIdcmax                          10.1365  ...                      8.84174   \nMppt_low                            100  ...                          100   \nMppt_high                           480  ...                          400   \nCEC_Date                            NaN  ...                          NaN   \nCEC_Type            Utility Interactive  ...          Utility Interactive   \n\n          i_Energy__GT260__240V_ iPower__SHO_1_1__120V_  \\\nVac                          240                    120   \nPso                       2.5301                22.0954   \nPaco                         230                   1100   \nPdco                      245.63                1194.09   \nVdco                          40                    182   \nC0                       6.2e-05               -2.1e-05   \nC1                      -9.8e-05                5.7e-05   \nC2                      0.000231               0.002001   \nC3                      0.121032               0.000623   \nPnt                        0.069                   0.33   \nVdcmax                        49                    380   \nIdcmax                   6.14076                6.56096   \nMppt_low                      30                    100   \nMppt_high                     49                    380   \nCEC_Date                     NaN                    NaN   \nCEC_Type     Utility Interactive    Utility Interactive   \n\n          iPower__SHO_2_0__240V_ iPower__SHO_2_5__240V_  \\\nVac                          240                    240   \nPso                      24.4658                42.7765   \nPaco                        2000                   2500   \nPdco                     2161.88                2632.84   \nVdco                         199                    218   \nC0                      -1.3e-05               -1.4e-05   \nC1                       5.5e-05                6.1e-05   \nC2                      0.001703               0.002053   \nC3                      0.000315                0.00153   \nPnt                          0.6                   0.75   \nVdcmax                       380                    400   \nIdcmax                   10.8637                12.0772   \nMppt_low                     100                    100   \nMppt_high                    380                    400   \nCEC_Date                     NaN                    NaN   \nCEC_Type     Utility Interactive    Utility Interactive   \n\n          iPower__SHO_3_0__240V_ iPower__SHO_3_5__240V_  \\\nVac                          240                    240   \nPso                       31.682                64.7742   \nPaco                        3000                   3500   \nPdco                     3205.93                3641.84   \nVdco                       222.5                    263   \nC0                  -8.21046e-06           -9.08073e-06   \nC1                       3.6e-05                3.5e-05   \nC2                      0.001708               0.001417   \nC3                       0.00086               0.001218   \nPnt                          0.9                   1.05   \nVdcmax                       380                    400   \nIdcmax                   14.4087                13.8473   \nMppt_low                     100                    100   \nMppt_high                    380                    400   \nCEC_Date                     NaN                    NaN   \nCEC_Type     Utility Interactive    Utility Interactive   \n\n          iPower__SHO_4_6__208V_ iPower__SHO_4_8__240V_ iPower__SHO_5_2__240V_  \nVac                          208                    240                    240  \nPso                      54.5701                85.1457                62.4867  \nPaco                        4600                   4800                   5200  \nPdco                     4797.81                4968.03                5382.86  \nVdco                         254                    263                    280  \nC0                  -5.99928e-06           -6.16035e-06           -4.63524e-06  \nC1                       2.8e-05                3.4e-05                4.4e-05  \nC2                      0.001381               0.000586                0.00126  \nC3                      0.000889               0.000195               0.000367  \nPnt                         1.38                   1.44                   1.56  \nVdcmax                       400                    400                    400  \nIdcmax                    18.889                18.8898                19.2245  \nMppt_low                     100                    100                    240  \nMppt_high                    400                    400                    400  \nCEC_Date                     NaN                    NaN                    NaN  \nCEC_Type     Utility Interactive    Utility Interactive    Utility Interactive  \n\n[16 rows x 3264 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ABB__MICRO_0_25_I_OUTD_US_208__208V_</th>\n      <th>ABB__MICRO_0_25_I_OUTD_US_240__240V_</th>\n      <th>ABB__MICRO_0_3_I_OUTD_US_208__208V_</th>\n      <th>ABB__MICRO_0_3_I_OUTD_US_240__240V_</th>\n      <th>ABB__MICRO_0_3HV_I_OUTD_US_208__208V_</th>\n      <th>ABB__MICRO_0_3HV_I_OUTD_US_240__240V_</th>\n      <th>ABB__PVI_10_0_I_OUTD_x_US_208_y__208V_</th>\n      <th>ABB__PVI_10_0_I_OUTD_x_US_480_y_z__480V_</th>\n      <th>ABB__PVI_12_0_I_OUTD_x_US_480_y__480V_</th>\n      <th>ABB__PVI_3_0_OUTD_S_US__208V_</th>\n      <th>...</th>\n      <th>Zigor__Sunzet_3_TL_US__240V_</th>\n      <th>i_Energy__GT260__240V_</th>\n      <th>iPower__SHO_1_1__120V_</th>\n      <th>iPower__SHO_2_0__240V_</th>\n      <th>iPower__SHO_2_5__240V_</th>\n      <th>iPower__SHO_3_0__240V_</th>\n      <th>iPower__SHO_3_5__240V_</th>\n      <th>iPower__SHO_4_6__208V_</th>\n      <th>iPower__SHO_4_8__240V_</th>\n      <th>iPower__SHO_5_2__240V_</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Vac</th>\n      <td>208</td>\n      <td>240</td>\n      <td>208</td>\n      <td>240</td>\n      <td>208</td>\n      <td>240</td>\n      <td>208</td>\n      <td>480</td>\n      <td>480</td>\n      <td>208</td>\n      <td>...</td>\n      <td>240</td>\n      <td>240</td>\n      <td>120</td>\n      <td>240</td>\n      <td>240</td>\n      <td>240</td>\n      <td>240</td>\n      <td>208</td>\n      <td>240</td>\n      <td>240</td>\n    </tr>\n    <tr>\n      <th>Pso</th>\n      <td>2.08961</td>\n      <td>2.24041</td>\n      <td>1.84651</td>\n      <td>1.95054</td>\n      <td>1.76944</td>\n      <td>1.84378</td>\n      <td>46.8638</td>\n      <td>67.7909</td>\n      <td>62.5547</td>\n      <td>18.1663</td>\n      <td>...</td>\n      <td>36.0334</td>\n      <td>2.5301</td>\n      <td>22.0954</td>\n      <td>24.4658</td>\n      <td>42.7765</td>\n      <td>31.682</td>\n      <td>64.7742</td>\n      <td>54.5701</td>\n      <td>85.1457</td>\n      <td>62.4867</td>\n    </tr>\n    <tr>\n      <th>Paco</th>\n      <td>250</td>\n      <td>250</td>\n      <td>300</td>\n      <td>300</td>\n      <td>300</td>\n      <td>300</td>\n      <td>10000</td>\n      <td>10000</td>\n      <td>12000</td>\n      <td>3000</td>\n      <td>...</td>\n      <td>3180</td>\n      <td>230</td>\n      <td>1100</td>\n      <td>2000</td>\n      <td>2500</td>\n      <td>3000</td>\n      <td>3500</td>\n      <td>4600</td>\n      <td>4800</td>\n      <td>5200</td>\n    </tr>\n    <tr>\n      <th>Pdco</th>\n      <td>259.589</td>\n      <td>259.492</td>\n      <td>311.669</td>\n      <td>311.581</td>\n      <td>312.421</td>\n      <td>312.005</td>\n      <td>10488.3</td>\n      <td>10296</td>\n      <td>12358.8</td>\n      <td>3142.3</td>\n      <td>...</td>\n      <td>3315.65</td>\n      <td>245.63</td>\n      <td>1194.09</td>\n      <td>2161.88</td>\n      <td>2632.84</td>\n      <td>3205.93</td>\n      <td>3641.84</td>\n      <td>4797.81</td>\n      <td>4968.03</td>\n      <td>5382.86</td>\n    </tr>\n    <tr>\n      <th>Vdco</th>\n      <td>40</td>\n      <td>40</td>\n      <td>40</td>\n      <td>40</td>\n      <td>45</td>\n      <td>45</td>\n      <td>320</td>\n      <td>362</td>\n      <td>370</td>\n      <td>310</td>\n      <td>...</td>\n      <td>375</td>\n      <td>40</td>\n      <td>182</td>\n      <td>199</td>\n      <td>218</td>\n      <td>222.5</td>\n      <td>263</td>\n      <td>254</td>\n      <td>263</td>\n      <td>280</td>\n    </tr>\n    <tr>\n      <th>C0</th>\n      <td>-4.1e-05</td>\n      <td>-3.9e-05</td>\n      <td>-3.3e-05</td>\n      <td>-3.4e-05</td>\n      <td>-4.5e-05</td>\n      <td>-3.5e-05</td>\n      <td>-2.7759e-06</td>\n      <td>-1.38839e-06</td>\n      <td>-1.009e-06</td>\n      <td>-8.03949e-06</td>\n      <td>...</td>\n      <td>-7.98467e-06</td>\n      <td>6.2e-05</td>\n      <td>-2.1e-05</td>\n      <td>-1.3e-05</td>\n      <td>-1.4e-05</td>\n      <td>-8.21046e-06</td>\n      <td>-9.08073e-06</td>\n      <td>-5.99928e-06</td>\n      <td>-6.16035e-06</td>\n      <td>-4.63524e-06</td>\n    </tr>\n    <tr>\n      <th>C1</th>\n      <td>-9.1e-05</td>\n      <td>-0.000132</td>\n      <td>-0.000192</td>\n      <td>-0.000256</td>\n      <td>-0.000196</td>\n      <td>-0.000227</td>\n      <td>-3.6e-05</td>\n      <td>-4.9e-05</td>\n      <td>-5.6e-05</td>\n      <td>-1.1e-05</td>\n      <td>...</td>\n      <td>-7.5e-05</td>\n      <td>-9.8e-05</td>\n      <td>5.7e-05</td>\n      <td>5.5e-05</td>\n      <td>6.1e-05</td>\n      <td>3.6e-05</td>\n      <td>3.5e-05</td>\n      <td>2.8e-05</td>\n      <td>3.4e-05</td>\n      <td>4.4e-05</td>\n    </tr>\n    <tr>\n      <th>C2</th>\n      <td>0.000494</td>\n      <td>0.002418</td>\n      <td>0.000907</td>\n      <td>0.002453</td>\n      <td>0.001959</td>\n      <td>-0.000526</td>\n      <td>0.000305</td>\n      <td>-0.00052</td>\n      <td>-0.001437</td>\n      <td>0.000999</td>\n      <td>...</td>\n      <td>0.000544</td>\n      <td>0.000231</td>\n      <td>0.002001</td>\n      <td>0.001703</td>\n      <td>0.002053</td>\n      <td>0.001708</td>\n      <td>0.001417</td>\n      <td>0.001381</td>\n      <td>0.000586</td>\n      <td>0.00126</td>\n    </tr>\n    <tr>\n      <th>C3</th>\n      <td>-0.013171</td>\n      <td>-0.014926</td>\n      <td>-0.031742</td>\n      <td>-0.028223</td>\n      <td>-0.023725</td>\n      <td>-0.041214</td>\n      <td>-0.002351</td>\n      <td>-0.003855</td>\n      <td>-0.007112</td>\n      <td>-0.000287</td>\n      <td>...</td>\n      <td>-0.000338</td>\n      <td>0.121032</td>\n      <td>0.000623</td>\n      <td>0.000315</td>\n      <td>0.00153</td>\n      <td>0.00086</td>\n      <td>0.001218</td>\n      <td>0.000889</td>\n      <td>0.000195</td>\n      <td>0.000367</td>\n    </tr>\n    <tr>\n      <th>Pnt</th>\n      <td>0.075</td>\n      <td>0.075</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>0.1</td>\n      <td>0.4</td>\n      <td>0.4</td>\n      <td>0.1</td>\n      <td>...</td>\n      <td>0.954</td>\n      <td>0.069</td>\n      <td>0.33</td>\n      <td>0.6</td>\n      <td>0.75</td>\n      <td>0.9</td>\n      <td>1.05</td>\n      <td>1.38</td>\n      <td>1.44</td>\n      <td>1.56</td>\n    </tr>\n    <tr>\n      <th>Vdcmax</th>\n      <td>50</td>\n      <td>50</td>\n      <td>50</td>\n      <td>50</td>\n      <td>60</td>\n      <td>60</td>\n      <td>416</td>\n      <td>416</td>\n      <td>416</td>\n      <td>480</td>\n      <td>...</td>\n      <td>400</td>\n      <td>49</td>\n      <td>380</td>\n      <td>380</td>\n      <td>400</td>\n      <td>380</td>\n      <td>400</td>\n      <td>400</td>\n      <td>400</td>\n      <td>400</td>\n    </tr>\n    <tr>\n      <th>Idcmax</th>\n      <td>6.48972</td>\n      <td>6.4873</td>\n      <td>7.79173</td>\n      <td>7.78952</td>\n      <td>6.94269</td>\n      <td>6.93344</td>\n      <td>32.776</td>\n      <td>28.4419</td>\n      <td>33.4022</td>\n      <td>10.1365</td>\n      <td>...</td>\n      <td>8.84174</td>\n      <td>6.14076</td>\n      <td>6.56096</td>\n      <td>10.8637</td>\n      <td>12.0772</td>\n      <td>14.4087</td>\n      <td>13.8473</td>\n      <td>18.889</td>\n      <td>18.8898</td>\n      <td>19.2245</td>\n    </tr>\n    <tr>\n      <th>Mppt_low</th>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>220</td>\n      <td>220</td>\n      <td>250</td>\n      <td>100</td>\n      <td>...</td>\n      <td>100</td>\n      <td>30</td>\n      <td>100</td>\n      <td>100</td>\n      <td>100</td>\n      <td>100</td>\n      <td>100</td>\n      <td>100</td>\n      <td>100</td>\n      <td>240</td>\n    </tr>\n    <tr>\n      <th>Mppt_high</th>\n      <td>50</td>\n      <td>50</td>\n      <td>50</td>\n      <td>50</td>\n      <td>60</td>\n      <td>60</td>\n      <td>416</td>\n      <td>416</td>\n      <td>416</td>\n      <td>480</td>\n      <td>...</td>\n      <td>400</td>\n      <td>49</td>\n      <td>380</td>\n      <td>380</td>\n      <td>400</td>\n      <td>380</td>\n      <td>400</td>\n      <td>400</td>\n      <td>400</td>\n      <td>400</td>\n    </tr>\n    <tr>\n      <th>CEC_Date</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CEC_Type</th>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>...</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n      <td>Utility Interactive</td>\n    </tr>\n  </tbody>\n</table>\n<p>16 rows × 3264 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "inverters = pvsystem.retrieve_sam('sandiainverter')\n",
    "inverters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "vdcs = pd.Series(np.linspace(0,50,51))\n",
    "idcs = pd.Series(np.linspace(0,11,110))\n",
    "pdcs = idcs * vdcs\n",
    "\n",
    "pacs = inverter.sandia(vdcs, pdcs, inverters['ABB__MICRO_0_25_I_OUTD_US_208__208V_'])\n",
    "#pacs.plot()\n",
    "plt.plot(pdcs, pacs)\n",
    "plt.ylabel('ac power')\n",
    "plt.xlabel('dc power');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to put more effort into describing this function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DC model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows use of the Desoto module performance model and the Sandia Array Performance Model (SAPM). Both models reuire a set of parameter values which can be read from SAM databases for modules.\n",
    "\n",
    "Foe the Desoto model, the database content is returned by supplying the keyword `cecmod` to `pvsystem.retrievesam`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "           A10Green_Technology_A10J_S72_175 A10Green_Technology_A10J_S72_180  \\\nTechnology                        Mono-c-Si                        Mono-c-Si   \nBifacial                                  0                                0   \nSTC                                 175.091                          179.928   \nPTC                                   151.2                            155.7   \nA_c                                     1.3                              1.3   \nLength                                1.576                            1.576   \nWidth                                 0.825                            0.825   \nN_s                                      72                               72   \nI_sc_ref                               5.17                             5.31   \nV_oc_ref                              43.99                            44.06   \nI_mp_ref                               4.78                              4.9   \nV_mp_ref                              36.63                            36.72   \nalpha_sc                           0.002146                         0.002204   \nbeta_oc                           -0.159068                        -0.159321   \nT_NOCT                                 49.9                             49.9   \na_ref                                1.9817                          1.98841   \nI_L_ref                              5.1757                          5.31615   \nI_o_ref                         1.14916e-09                      1.22524e-09   \nR_s                                0.316688                         0.299919   \nR_sh_ref                            287.102                          259.048   \nAdjust                              16.0571                           16.419   \ngamma_r                             -0.5072                          -0.5072   \nBIPV                                      N                                N   \nVersion                   SAM 2018.11.11 r2                SAM 2018.11.11 r2   \nDate                               1/3/2019                         1/3/2019   \n\n           A10Green_Technology_A10J_S72_185 A10Green_Technology_A10J_M60_220  \\\nTechnology                        Mono-c-Si                       Multi-c-Si   \nBifacial                                  0                                0   \nSTC                                 184.702                          219.876   \nPTC                                   160.2                            189.1   \nA_c                                     1.3                            1.624   \nLength                                1.576                            1.632   \nWidth                                 0.825                            0.995   \nN_s                                      72                               60   \nI_sc_ref                               5.43                             7.95   \nV_oc_ref                              44.14                            36.06   \nI_mp_ref                               5.03                              7.3   \nV_mp_ref                              36.72                            30.12   \nalpha_sc                           0.002253                         0.004357   \nbeta_oc                            -0.15961                        -0.130681   \nT_NOCT                                 49.9                             50.2   \na_ref                               1.98482                          1.67309   \nI_L_ref                             5.43568                          7.95906   \nI_o_ref                         1.16164e-09                      3.34415e-09   \nR_s                                0.311962                         0.140393   \nR_sh_ref                            298.424                          123.168   \nAdjust                              15.6882                          21.8752   \ngamma_r                             -0.5072                          -0.5196   \nBIPV                                      N                                N   \nVersion                   SAM 2018.11.11 r2                SAM 2018.11.11 r2   \nDate                               1/3/2019                         1/3/2019   \n\n           A10Green_Technology_A10J_M60_225 A10Green_Technology_A10J_M60_230  \\\nTechnology                       Multi-c-Si                       Multi-c-Si   \nBifacial                                  0                                0   \nSTC                                 224.986                          230.129   \nPTC                                   193.5                            204.1   \nA_c                                   1.624                            1.624   \nLength                                1.632                            1.632   \nWidth                                 0.995                            0.995   \nN_s                                      60                               60   \nI_sc_ref                               8.04                              8.1   \nV_oc_ref                              36.24                            36.42   \nI_mp_ref                               7.44                             7.58   \nV_mp_ref                              30.24                            30.36   \nalpha_sc                           0.004406                         0.007857   \nbeta_oc                           -0.131334                        -0.130748   \nT_NOCT                                 50.2                             46.4   \na_ref                               1.67178                          1.68048   \nI_L_ref                             8.04721                          8.10361   \nI_o_ref                         3.01424e-09                      3.09549e-09   \nR_s                                 0.14737                         0.152058   \nR_sh_ref                            164.419                          340.983   \nAdjust                              20.6984                          21.5544   \ngamma_r                             -0.5196                           -0.493   \nBIPV                                      N                                N   \nVersion                   SAM 2018.11.11 r2                SAM 2018.11.11 r2   \nDate                               1/3/2019                         1/3/2019   \n\n           A10Green_Technology_A10J_M60_235 A10Green_Technology_A10J_M60_240  \\\nTechnology                       Multi-c-Si                       Multi-c-Si   \nBifacial                                  0                                0   \nSTC                                 235.008                          240.538   \nPTC                                   208.7                            213.3   \nA_c                                   1.624                            1.624   \nLength                                1.632                            1.632   \nWidth                                 0.995                            0.995   \nN_s                                      60                               60   \nI_sc_ref                               8.23                             8.32   \nV_oc_ref                              36.72                            36.84   \nI_mp_ref                               7.68                             7.83   \nV_mp_ref                               30.6                            30.72   \nalpha_sc                           0.007983                          0.00807   \nbeta_oc                           -0.131825                        -0.132256   \nT_NOCT                                 46.4                             46.4   \na_ref                               1.69698                          1.69423   \nI_L_ref                             8.23464                          8.32177   \nI_o_ref                         3.24284e-09                      2.97878e-09   \nR_s                                0.151504                         0.150077   \nR_sh_ref                            268.701                           706.27   \nAdjust                              21.8719                           20.881   \ngamma_r                              -0.493                           -0.493   \nBIPV                                      N                                N   \nVersion                   SAM 2018.11.11 r2                SAM 2018.11.11 r2   \nDate                               1/3/2019                         1/3/2019   \n\n           A2Peak_Power_POWER_ON_P220_6x10 Aavid_Solar_ASMS_165P  ...  \\\nTechnology                      Multi-c-Si            Multi-c-Si  ...   \nBifacial                                 0                     0  ...   \nSTC                                219.978                164.85  ...   \nPTC                                    195                 146.3  ...   \nA_c                                  1.633                 1.301  ...   \nLength                               1.633                 1.575  ...   \nWidth                                    1                 0.826  ...   \nN_s                                     60                    72  ...   \nI_sc_ref                              7.98                  5.25  ...   \nV_oc_ref                             36.72                  43.5  ...   \nI_mp_ref                              7.26                  4.71  ...   \nV_mp_ref                              30.3                    35  ...   \nalpha_sc                           0.00399              0.001575  ...   \nbeta_oc                           -0.12852             -0.170955  ...   \nT_NOCT                                47.9                    45  ...   \na_ref                              1.59703               1.96463  ...   \nI_L_ref                            8.00023               5.27415  ...   \nI_o_ref                        7.85133e-10           1.19571e-09  ...   \nR_s                               0.229644              0.595855  ...   \nR_sh_ref                           90.5774               129.523  ...   \nAdjust                             12.2172               7.16388  ...   \ngamma_r                              -0.46                -0.519  ...   \nBIPV                                     N                     N  ...   \nVersion                  SAM 2018.11.11 r2     SAM 2018.11.11 r2  ...   \nDate                              1/3/2019              1/3/2019  ...   \n\n           Zytech_Solar_ZT275P Zytech_Solar_ZT280P Zytech_Solar_ZT285P  \\\nTechnology          Multi-c-Si          Multi-c-Si          Multi-c-Si   \nBifacial                     0                   0                   0   \nSTC                    275.014             280.329             285.326   \nPTC                        248               252.6               257.3   \nA_c                      1.931               1.931               1.931   \nLength                    1.95                1.95                1.95   \nWidth                     0.99                0.99                0.99   \nN_s                         72                  72                  72   \nI_sc_ref                  8.31                 8.4                8.48   \nV_oc_ref                  45.1               45.25               45.43   \nI_mp_ref                  7.76                7.87                7.97   \nV_mp_ref                 35.44               35.62                35.8   \nalpha_sc              0.004014            0.004057            0.004096   \nbeta_oc              -0.144275           -0.144755           -0.145331   \nT_NOCT                    46.4                46.4                46.4   \na_ref                  1.81027             1.81485              1.8201   \nI_L_ref                8.32377             8.41015              8.4867   \nI_o_ref            1.24062e-10          1.2341e-10         1.21696e-10   \nR_s                   0.566493            0.552584            0.543536   \nR_sh_ref               341.758             457.468             687.561   \nAdjust                 5.42178             5.27464             5.06509   \ngamma_r                -0.4308             -0.4308             -0.4308   \nBIPV                         N                   N                   N   \nVersion      SAM 2018.11.11 r2   SAM 2018.11.11 r2   SAM 2018.11.11 r2   \nDate                  1/3/2019            1/3/2019            1/3/2019   \n\n           Zytech_Solar_ZT290P Zytech_Solar_ZT295P Zytech_Solar_ZT300P  \\\nTechnology          Multi-c-Si          Multi-c-Si          Multi-c-Si   \nBifacial                     0                   0                   0   \nSTC                    290.036             295.066             300.003   \nPTC                      261.9               266.5               271.2   \nA_c                      1.931               1.931               1.931   \nLength                    1.95                1.95                1.95   \nWidth                     0.99                0.99                0.99   \nN_s                         72                  72                  72   \nI_sc_ref                  8.55                8.64                8.71   \nV_oc_ref                 45.59               45.75               45.96   \nI_mp_ref                  8.07                8.16                8.26   \nV_mp_ref                 35.94               36.16               36.32   \nalpha_sc               0.00413            0.004173            0.004207   \nbeta_oc              -0.145842           -0.146354           -0.147026   \nT_NOCT                    46.4                46.4                46.4   \na_ref                  1.82278             1.83125             1.84441   \nI_L_ref                8.55196             8.64154             8.80531   \nI_o_ref            1.17172e-10         1.21851e-10         1.31413e-10   \nR_s                   0.538499            0.521134            0.515735   \nR_sh_ref               2348.68             2917.76             552.455   \nAdjust                 4.66051              4.9013             5.41555   \ngamma_r                -0.4308             -0.4308             -0.4308   \nBIPV                         N                   N                   N   \nVersion      SAM 2018.11.11 r2   SAM 2018.11.11 r2   SAM 2018.11.11 r2   \nDate                  1/3/2019            1/3/2019            1/3/2019   \n\n           Zytech_Solar_ZT305P Zytech_Solar_ZT310P Zytech_Solar_ZT315P  \\\nTechnology          Multi-c-Si          Multi-c-Si          Multi-c-Si   \nBifacial                     0                   0                   0   \nSTC                    305.056             310.144             315.094   \nPTC                      275.8               280.5               285.1   \nA_c                      1.931               1.931               1.931   \nLength                    1.95                1.95                1.95   \nWidth                     0.99                0.99                0.99   \nN_s                         72                  72                  72   \nI_sc_ref                  8.87                 8.9                9.01   \nV_oc_ref                 46.12               46.28               46.44   \nI_mp_ref                  8.36                8.46                8.56   \nV_mp_ref                 36.49               36.66               36.81   \nalpha_sc              0.004284            0.004299            0.004352   \nbeta_oc              -0.147538            -0.14805           -0.148562   \nT_NOCT                    46.4                46.4                46.4   \na_ref                  1.84915              1.8574             1.86502   \nI_L_ref                8.87402              8.9948             9.10661   \nI_o_ref            1.30106e-10         1.34888e-10         1.38664e-10   \nR_s                   0.506611            0.495904            0.488376   \nR_sh_ref               1119.07             767.958             682.292   \nAdjust                 5.24155             5.44634             5.57874   \ngamma_r                -0.4308             -0.4308             -0.4308   \nBIPV                         N                   N                   N   \nVersion      SAM 2018.11.11 r2   SAM 2018.11.11 r2   SAM 2018.11.11 r2   \nDate                  1/3/2019            1/3/2019            1/3/2019   \n\n           Zytech_Solar_ZT320P  \nTechnology          Multi-c-Si  \nBifacial                     0  \nSTC                     320.42  \nPTC                      289.8  \nA_c                      1.931  \nLength                    1.95  \nWidth                     0.99  \nN_s                         72  \nI_sc_ref                  9.12  \nV_oc_ref                  46.6  \nI_mp_ref                  8.66  \nV_mp_ref                    37  \nalpha_sc              0.004405  \nbeta_oc              -0.149073  \nT_NOCT                    46.4  \na_ref                  1.87378  \nI_L_ref                9.21845  \nI_o_ref            1.44659e-10  \nR_s                   0.475581  \nR_sh_ref               604.221  \nAdjust                 5.83833  \ngamma_r                -0.4308  \nBIPV                         N  \nVersion      SAM 2018.11.11 r2  \nDate                  1/3/2019  \n\n[25 rows x 21535 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A10Green_Technology_A10J_S72_175</th>\n      <th>A10Green_Technology_A10J_S72_180</th>\n      <th>A10Green_Technology_A10J_S72_185</th>\n      <th>A10Green_Technology_A10J_M60_220</th>\n      <th>A10Green_Technology_A10J_M60_225</th>\n      <th>A10Green_Technology_A10J_M60_230</th>\n      <th>A10Green_Technology_A10J_M60_235</th>\n      <th>A10Green_Technology_A10J_M60_240</th>\n      <th>A2Peak_Power_POWER_ON_P220_6x10</th>\n      <th>Aavid_Solar_ASMS_165P</th>\n      <th>...</th>\n      <th>Zytech_Solar_ZT275P</th>\n      <th>Zytech_Solar_ZT280P</th>\n      <th>Zytech_Solar_ZT285P</th>\n      <th>Zytech_Solar_ZT290P</th>\n      <th>Zytech_Solar_ZT295P</th>\n      <th>Zytech_Solar_ZT300P</th>\n      <th>Zytech_Solar_ZT305P</th>\n      <th>Zytech_Solar_ZT310P</th>\n      <th>Zytech_Solar_ZT315P</th>\n      <th>Zytech_Solar_ZT320P</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Technology</th>\n      <td>Mono-c-Si</td>\n      <td>Mono-c-Si</td>\n      <td>Mono-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>...</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n      <td>Multi-c-Si</td>\n    </tr>\n    <tr>\n      <th>Bifacial</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>STC</th>\n      <td>175.091</td>\n      <td>179.928</td>\n      <td>184.702</td>\n      <td>219.876</td>\n      <td>224.986</td>\n      <td>230.129</td>\n      <td>235.008</td>\n      <td>240.538</td>\n      <td>219.978</td>\n      <td>164.85</td>\n      <td>...</td>\n      <td>275.014</td>\n      <td>280.329</td>\n      <td>285.326</td>\n      <td>290.036</td>\n      <td>295.066</td>\n      <td>300.003</td>\n      <td>305.056</td>\n      <td>310.144</td>\n      <td>315.094</td>\n      <td>320.42</td>\n    </tr>\n    <tr>\n      <th>PTC</th>\n      <td>151.2</td>\n      <td>155.7</td>\n      <td>160.2</td>\n      <td>189.1</td>\n      <td>193.5</td>\n      <td>204.1</td>\n      <td>208.7</td>\n      <td>213.3</td>\n      <td>195</td>\n      <td>146.3</td>\n      <td>...</td>\n      <td>248</td>\n      <td>252.6</td>\n      <td>257.3</td>\n      <td>261.9</td>\n      <td>266.5</td>\n      <td>271.2</td>\n      <td>275.8</td>\n      <td>280.5</td>\n      <td>285.1</td>\n      <td>289.8</td>\n    </tr>\n    <tr>\n      <th>A_c</th>\n      <td>1.3</td>\n      <td>1.3</td>\n      <td>1.3</td>\n      <td>1.624</td>\n      <td>1.624</td>\n      <td>1.624</td>\n      <td>1.624</td>\n      <td>1.624</td>\n      <td>1.633</td>\n      <td>1.301</td>\n      <td>...</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n      <td>1.931</td>\n    </tr>\n    <tr>\n      <th>Length</th>\n      <td>1.576</td>\n      <td>1.576</td>\n      <td>1.576</td>\n      <td>1.632</td>\n      <td>1.632</td>\n      <td>1.632</td>\n      <td>1.632</td>\n      <td>1.632</td>\n      <td>1.633</td>\n      <td>1.575</td>\n      <td>...</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n      <td>1.95</td>\n    </tr>\n    <tr>\n      <th>Width</th>\n      <td>0.825</td>\n      <td>0.825</td>\n      <td>0.825</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>1</td>\n      <td>0.826</td>\n      <td>...</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n    </tr>\n    <tr>\n      <th>N_s</th>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>60</td>\n      <td>60</td>\n      <td>60</td>\n      <td>60</td>\n      <td>60</td>\n      <td>60</td>\n      <td>72</td>\n      <td>...</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>I_sc_ref</th>\n      <td>5.17</td>\n      <td>5.31</td>\n      <td>5.43</td>\n      <td>7.95</td>\n      <td>8.04</td>\n      <td>8.1</td>\n      <td>8.23</td>\n      <td>8.32</td>\n      <td>7.98</td>\n      <td>5.25</td>\n      <td>...</td>\n      <td>8.31</td>\n      <td>8.4</td>\n      <td>8.48</td>\n      <td>8.55</td>\n      <td>8.64</td>\n      <td>8.71</td>\n      <td>8.87</td>\n      <td>8.9</td>\n      <td>9.01</td>\n      <td>9.12</td>\n    </tr>\n    <tr>\n      <th>V_oc_ref</th>\n      <td>43.99</td>\n      <td>44.06</td>\n      <td>44.14</td>\n      <td>36.06</td>\n      <td>36.24</td>\n      <td>36.42</td>\n      <td>36.72</td>\n      <td>36.84</td>\n      <td>36.72</td>\n      <td>43.5</td>\n      <td>...</td>\n      <td>45.1</td>\n      <td>45.25</td>\n      <td>45.43</td>\n      <td>45.59</td>\n      <td>45.75</td>\n      <td>45.96</td>\n      <td>46.12</td>\n      <td>46.28</td>\n      <td>46.44</td>\n      <td>46.6</td>\n    </tr>\n    <tr>\n      <th>I_mp_ref</th>\n      <td>4.78</td>\n      <td>4.9</td>\n      <td>5.03</td>\n      <td>7.3</td>\n      <td>7.44</td>\n      <td>7.58</td>\n      <td>7.68</td>\n      <td>7.83</td>\n      <td>7.26</td>\n      <td>4.71</td>\n      <td>...</td>\n      <td>7.76</td>\n      <td>7.87</td>\n      <td>7.97</td>\n      <td>8.07</td>\n      <td>8.16</td>\n      <td>8.26</td>\n      <td>8.36</td>\n      <td>8.46</td>\n      <td>8.56</td>\n      <td>8.66</td>\n    </tr>\n    <tr>\n      <th>V_mp_ref</th>\n      <td>36.63</td>\n      <td>36.72</td>\n      <td>36.72</td>\n      <td>30.12</td>\n      <td>30.24</td>\n      <td>30.36</td>\n      <td>30.6</td>\n      <td>30.72</td>\n      <td>30.3</td>\n      <td>35</td>\n      <td>...</td>\n      <td>35.44</td>\n      <td>35.62</td>\n      <td>35.8</td>\n      <td>35.94</td>\n      <td>36.16</td>\n      <td>36.32</td>\n      <td>36.49</td>\n      <td>36.66</td>\n      <td>36.81</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>alpha_sc</th>\n      <td>0.002146</td>\n      <td>0.002204</td>\n      <td>0.002253</td>\n      <td>0.004357</td>\n      <td>0.004406</td>\n      <td>0.007857</td>\n      <td>0.007983</td>\n      <td>0.00807</td>\n      <td>0.00399</td>\n      <td>0.001575</td>\n      <td>...</td>\n      <td>0.004014</td>\n      <td>0.004057</td>\n      <td>0.004096</td>\n      <td>0.00413</td>\n      <td>0.004173</td>\n      <td>0.004207</td>\n      <td>0.004284</td>\n      <td>0.004299</td>\n      <td>0.004352</td>\n      <td>0.004405</td>\n    </tr>\n    <tr>\n      <th>beta_oc</th>\n      <td>-0.159068</td>\n      <td>-0.159321</td>\n      <td>-0.15961</td>\n      <td>-0.130681</td>\n      <td>-0.131334</td>\n      <td>-0.130748</td>\n      <td>-0.131825</td>\n      <td>-0.132256</td>\n      <td>-0.12852</td>\n      <td>-0.170955</td>\n      <td>...</td>\n      <td>-0.144275</td>\n      <td>-0.144755</td>\n      <td>-0.145331</td>\n      <td>-0.145842</td>\n      <td>-0.146354</td>\n      <td>-0.147026</td>\n      <td>-0.147538</td>\n      <td>-0.14805</td>\n      <td>-0.148562</td>\n      <td>-0.149073</td>\n    </tr>\n    <tr>\n      <th>T_NOCT</th>\n      <td>49.9</td>\n      <td>49.9</td>\n      <td>49.9</td>\n      <td>50.2</td>\n      <td>50.2</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>47.9</td>\n      <td>45</td>\n      <td>...</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n      <td>46.4</td>\n    </tr>\n    <tr>\n      <th>a_ref</th>\n      <td>1.9817</td>\n      <td>1.98841</td>\n      <td>1.98482</td>\n      <td>1.67309</td>\n      <td>1.67178</td>\n      <td>1.68048</td>\n      <td>1.69698</td>\n      <td>1.69423</td>\n      <td>1.59703</td>\n      <td>1.96463</td>\n      <td>...</td>\n      <td>1.81027</td>\n      <td>1.81485</td>\n      <td>1.8201</td>\n      <td>1.82278</td>\n      <td>1.83125</td>\n      <td>1.84441</td>\n      <td>1.84915</td>\n      <td>1.8574</td>\n      <td>1.86502</td>\n      <td>1.87378</td>\n    </tr>\n    <tr>\n      <th>I_L_ref</th>\n      <td>5.1757</td>\n      <td>5.31615</td>\n      <td>5.43568</td>\n      <td>7.95906</td>\n      <td>8.04721</td>\n      <td>8.10361</td>\n      <td>8.23464</td>\n      <td>8.32177</td>\n      <td>8.00023</td>\n      <td>5.27415</td>\n      <td>...</td>\n      <td>8.32377</td>\n      <td>8.41015</td>\n      <td>8.4867</td>\n      <td>8.55196</td>\n      <td>8.64154</td>\n      <td>8.80531</td>\n      <td>8.87402</td>\n      <td>8.9948</td>\n      <td>9.10661</td>\n      <td>9.21845</td>\n    </tr>\n    <tr>\n      <th>I_o_ref</th>\n      <td>1.14916e-09</td>\n      <td>1.22524e-09</td>\n      <td>1.16164e-09</td>\n      <td>3.34415e-09</td>\n      <td>3.01424e-09</td>\n      <td>3.09549e-09</td>\n      <td>3.24284e-09</td>\n      <td>2.97878e-09</td>\n      <td>7.85133e-10</td>\n      <td>1.19571e-09</td>\n      <td>...</td>\n      <td>1.24062e-10</td>\n      <td>1.2341e-10</td>\n      <td>1.21696e-10</td>\n      <td>1.17172e-10</td>\n      <td>1.21851e-10</td>\n      <td>1.31413e-10</td>\n      <td>1.30106e-10</td>\n      <td>1.34888e-10</td>\n      <td>1.38664e-10</td>\n      <td>1.44659e-10</td>\n    </tr>\n    <tr>\n      <th>R_s</th>\n      <td>0.316688</td>\n      <td>0.299919</td>\n      <td>0.311962</td>\n      <td>0.140393</td>\n      <td>0.14737</td>\n      <td>0.152058</td>\n      <td>0.151504</td>\n      <td>0.150077</td>\n      <td>0.229644</td>\n      <td>0.595855</td>\n      <td>...</td>\n      <td>0.566493</td>\n      <td>0.552584</td>\n      <td>0.543536</td>\n      <td>0.538499</td>\n      <td>0.521134</td>\n      <td>0.515735</td>\n      <td>0.506611</td>\n      <td>0.495904</td>\n      <td>0.488376</td>\n      <td>0.475581</td>\n    </tr>\n    <tr>\n      <th>R_sh_ref</th>\n      <td>287.102</td>\n      <td>259.048</td>\n      <td>298.424</td>\n      <td>123.168</td>\n      <td>164.419</td>\n      <td>340.983</td>\n      <td>268.701</td>\n      <td>706.27</td>\n      <td>90.5774</td>\n      <td>129.523</td>\n      <td>...</td>\n      <td>341.758</td>\n      <td>457.468</td>\n      <td>687.561</td>\n      <td>2348.68</td>\n      <td>2917.76</td>\n      <td>552.455</td>\n      <td>1119.07</td>\n      <td>767.958</td>\n      <td>682.292</td>\n      <td>604.221</td>\n    </tr>\n    <tr>\n      <th>Adjust</th>\n      <td>16.0571</td>\n      <td>16.419</td>\n      <td>15.6882</td>\n      <td>21.8752</td>\n      <td>20.6984</td>\n      <td>21.5544</td>\n      <td>21.8719</td>\n      <td>20.881</td>\n      <td>12.2172</td>\n      <td>7.16388</td>\n      <td>...</td>\n      <td>5.42178</td>\n      <td>5.27464</td>\n      <td>5.06509</td>\n      <td>4.66051</td>\n      <td>4.9013</td>\n      <td>5.41555</td>\n      <td>5.24155</td>\n      <td>5.44634</td>\n      <td>5.57874</td>\n      <td>5.83833</td>\n    </tr>\n    <tr>\n      <th>gamma_r</th>\n      <td>-0.5072</td>\n      <td>-0.5072</td>\n      <td>-0.5072</td>\n      <td>-0.5196</td>\n      <td>-0.5196</td>\n      <td>-0.493</td>\n      <td>-0.493</td>\n      <td>-0.493</td>\n      <td>-0.46</td>\n      <td>-0.519</td>\n      <td>...</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n      <td>-0.4308</td>\n    </tr>\n    <tr>\n      <th>BIPV</th>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>...</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>Version</th>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>...</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n      <td>SAM 2018.11.11 r2</td>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>...</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n      <td>1/3/2019</td>\n    </tr>\n  </tbody>\n</table>\n<p>25 rows × 21535 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "cec_modules = pvsystem.retrieve_sam('cecmod')\n",
    "cec_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Technology            Mono-c-Si\nBifacial                      0\nSTC                     219.961\nPTC                       200.1\nA_c                         1.7\nLength                    1.602\nWidth                     1.061\nN_s                          96\nI_sc_ref                    5.1\nV_oc_ref                   59.4\nI_mp_ref                   4.69\nV_mp_ref                   46.9\nalpha_sc               0.004539\nbeta_oc               -0.222156\nT_NOCT                     42.4\na_ref                   2.63593\nI_L_ref                 5.11426\nI_o_ref             8.10251e-10\nR_s                     1.06602\nR_sh_ref                381.254\nAdjust                  8.61952\ngamma_r                  -0.476\nBIPV                          N\nVersion       SAM 2018.11.11 r2\nDate                   1/3/2019\nName: Canadian_Solar_Inc__CS5P_220M, dtype: object"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "cecmodule = cec_modules.Canadian_Solar_Inc__CS5P_220M \n",
    "cecmodule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Sandia module database is read by the same function with the keyword `SandiaMod`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                                    -0.355   \nC3                                                         -13.0643   \nA0                                                           0.9327   \nA1                                                          0.07283   \nA2                                                         -0.02402   \nA3                                                         0.003819   \nA4                                                        -0.000235   \nB0                                                                1   \nB1                                                        -0.006801   \nB2                                                        0.0007968   \nB3                                                       -3.095e-05   \nB4                                                        4.896e-07   \nB5                                                        -2.78e-09   \nDTC                                                            2.58   \nFD                                                                1   \nA                                                           -3.7566   \nB                                                            -0.156   \nC4                                                              NaN   \nC5                                                              NaN   \nIXO                                                             NaN   \nIXXO                                                            NaN   \nC6                                                              NaN   \nC7                                                              NaN   \nNotes             Source:  CFV Solar Test Lab.  Tested 2013.  Mo...   \n\n                                    Canadian_Solar_CS6X_300M__2013_  \\\nVintage                                                        2013   \nArea                                                           1.91   \nMaterial                                                       c-Si   \nCells_in_Series                                                  72   \nParallel_Strings                                                  1   \nIsco                                                         8.6388   \nVoco                                                        43.5918   \nImpo                                                         8.1359   \nVmpo                                                        34.9531   \nAisc                                                         0.0005   \nAimp                                                        -0.0001   \nC0                                                           1.0121   \nC1                                                          -0.0121   \nBvoco                                                       -0.1532   \nMbvoc                                                             0   \nBvmpo                                                       -0.1634   \nMbvmp                                                             0   \nN                                                            1.0025   \nC2                                                           -0.171   \nC3                                                         -9.39745   \nA0                                                           0.9371   \nA1                                                          0.06262   \nA2                                                         -0.01667   \nA3                                                         0.002168   \nA4                                                       -0.0001087   \nB0                                                                1   \nB1                                                         -0.00789   \nB2                                                        0.0008656   \nB3                                                       -3.298e-05   \nB4                                                        5.178e-07   \nB5                                                       -2.918e-09   \nDTC                                                             3.2   \nFD                                                                1   \nA                                                           -3.6024   \nB                                                           -0.2106   \nC4                                                              NaN   \nC5                                                              NaN   \nIXO                                                             NaN   \nIXXO                                                            NaN   \nC6                                                              NaN   \nC7                                                              NaN   \nNotes             Source:  CFV Solar Test Lab.  Tested 2013.  Mo...   \n\n                                              LG_LG290N1C_G3__2013_  \\\nVintage                                                        2013   \nArea                                                           1.64   \nMaterial                                                       c-Si   \nCells_in_Series                                                  60   \nParallel_Strings                                                  1   \nIsco                                                         9.8525   \nVoco                                                        39.6117   \nImpo                                                         9.2473   \nVmpo                                                        31.2921   \nAisc                                                         0.0002   \nAimp                                                        -0.0004   \nC0                                                           1.0145   \nC1                                                          -0.0145   \nBvoco                                                       -0.1205   \nMbvoc                                                             0   \nBvmpo                                                       -0.1337   \nMbvmp                                                             0   \nN                                                            1.0925   \nC2                                                          -0.4647   \nC3                                                         -11.9008   \nA0                                                           0.9731   \nA1                                                          0.02966   \nA2                                                         -0.01024   \nA3                                                         0.001793   \nA4                                                       -0.0001286   \nB0                                                                1   \nB1                                                          -0.0154   \nB2                                                         0.001572   \nB3                                                       -5.525e-05   \nB4                                                         8.04e-07   \nB5                                                       -4.202e-09   \nDTC                                                            3.05   \nFD                                                                1   \nA                                                           -3.4247   \nB                                                           -0.0951   \nC4                                                              NaN   \nC5                                                              NaN   \nIXO                                                             NaN   \nIXXO                                                            NaN   \nC6                                                              NaN   \nC7                                                              NaN   \nNotes             Source:  CFV Solar Test Lab.  Tested 2013.  Mo...   \n\n                                              Sharp_NDQ235F4__2013_  \\\nVintage                                                        2013   \nArea                                                           1.56   \nMaterial                                                      mc-Si   \nCells_in_Series                                                  60   \nParallel_Strings                                                  1   \nIsco                                                         8.6739   \nVoco                                                        36.8276   \nImpo                                                         8.1243   \nVmpo                                                        29.1988   \nAisc                                                         0.0006   \nAimp                                                        -0.0002   \nC0                                                           1.0049   \nC1                                                          -0.0049   \nBvoco                                                       -0.1279   \nMbvoc                                                             0   \nBvmpo                                                       -0.1348   \nMbvmp                                                             0   \nN                                                            1.0695   \nC2                                                          -0.2718   \nC3                                                         -11.4033   \nA0                                                           0.9436   \nA1                                                          0.04765   \nA2                                                        -0.007405   \nA3                                                        0.0003818   \nA4                                                       -1.101e-05   \nB0                                                                1   \nB1                                                         -0.00464   \nB2                                                         0.000559   \nB3                                                       -2.249e-05   \nB4                                                        3.673e-07   \nB5                                                       -2.144e-09   \nDTC                                                            3.27   \nFD                                                                1   \nA                                                           -3.7445   \nB                                                            -0.149   \nC4                                                              NaN   \nC5                                                              NaN   \nIXO                                                             NaN   \nIXXO                                                            NaN   \nC6                                                              NaN   \nC7                                                              NaN   \nNotes             Source:  CFV Solar Test Lab.  Tested 2013.  Mo...   \n\n                                      Solar_Frontier_SF_160S__2013_  \\\nVintage                                                        2013   \nArea                                                           1.22   \nMaterial                                                        CIS   \nCells_in_Series                                                 172   \nParallel_Strings                                                  1   \nIsco                                                         2.0259   \nVoco                                                        112.505   \nImpo                                                         1.8356   \nVmpo                                                        86.6752   \nAisc                                                         0.0001   \nAimp                                                        -0.0003   \nC0                                                           1.0096   \nC1                                                          -0.0096   \nBvoco                                                       -0.3044   \nMbvoc                                                             0   \nBvmpo                                                       -0.2339   \nMbvmp                                                             0   \nN                                                            1.2066   \nC2                                                          -0.5426   \nC3                                                         -15.2598   \nA0                                                           0.9354   \nA1                                                          0.06809   \nA2                                                         -0.02094   \nA3                                                          0.00293   \nA4                                                       -0.0001564   \nB0                                                                1   \nB1                                                          -0.0152   \nB2                                                         0.001598   \nB3                                                       -5.682e-05   \nB4                                                        8.326e-07   \nB5                                                       -4.363e-09   \nDTC                                                            3.29   \nFD                                                                1   \nA                                                           -3.6836   \nB                                                           -0.1483   \nC4                                                              NaN   \nC5                                                              NaN   \nIXO                                                             NaN   \nIXXO                                                            NaN   \nC6                                                              NaN   \nC7                                                              NaN   \nNotes             Source:  CFV Solar Test Lab.  Tested 2013.  Mo...   \n\n                               SolarWorld_Sunmodule_250_Poly__2013_  \\\nVintage                                                        2013   \nArea                                                           1.68   \nMaterial                                                      mc-Si   \nCells_in_Series                                                  60   \nParallel_Strings                                                  1   \nIsco                                                         8.3768   \nVoco                                                        36.3806   \nImpo                                                         7.6921   \nVmpo                                                         28.348   \nAisc                                                         0.0006   \nAimp                                                        -0.0001   \nC0                                                           1.0158   \nC1                                                          -0.0158   \nBvoco                                                       -0.1393   \nMbvoc                                                             0   \nBvmpo                                                       -0.1449   \nMbvmp                                                             0   \nN                                                             1.226   \nC2                                                         -0.09677   \nC3                                                         -8.51148   \nA0                                                           0.9288   \nA1                                                          0.07201   \nA2                                                         -0.02065   \nA3                                                         0.002862   \nA4                                                       -0.0001544   \nB0                                                                1   \nB1                                                         -0.00308   \nB2                                                        0.0004053   \nB3                                                       -1.729e-05   \nB4                                                        2.997e-07   \nB5                                                       -1.878e-09   \nDTC                                                            3.19   \nFD                                                                1   \nA                                                             -3.73   \nB                                                           -0.1483   \nC4                                                              NaN   \nC5                                                              NaN   \nIXO                                                             NaN   \nIXXO                                                            NaN   \nC6                                                              NaN   \nC7                                                              NaN   \nNotes             Source:  CFV Solar Test Lab.  Tested 2013.  Mo...   \n\n                                     Silevo_Triex_U300_Black__2014_  \nVintage                                                        2014  \nArea                                                           1.68  \nMaterial                                                       c-Si  \nCells_in_Series                                                  96  \nParallel_Strings                                                  1  \nIsco                                                          5.771  \nVoco                                                        68.5983  \nImpo                                                          5.383  \nVmpo                                                        55.4547  \nAisc                                                         0.0003  \nAimp                                                        -0.0003  \nC0                                                            0.995  \nC1                                                            0.005  \nBvoco                                                       -0.1913  \nMbvoc                                                             0  \nBvmpo                                                        -0.184  \nMbvmp                                                             0  \nN                                                             1.345  \nC2                                                           0.3221  \nC3                                                          -6.7178  \nA0                                                           0.9191  \nA1                                                          0.09988  \nA2                                                         -0.04273  \nA3                                                          0.00937  \nA4                                                       -0.0007643  \nB0                                                                1  \nB1                                                        -0.006498  \nB2                                                        0.0006908  \nB3                                                       -2.678e-05  \nB4                                                        4.322e-07  \nB5                                                       -2.508e-09  \nDTC                                                            3.13  \nFD                                                                1  \nA                                                           -3.6866  \nB                                                            -0.104  \nC4                                                              NaN  \nC5                                                              NaN  \nIXO                                                             NaN  \nIXXO                                                            NaN  \nC6                                                              NaN  \nC7                                                              NaN  \nNotes             Source:  CFV Solar Test Lab.  Tested 2014.  Mo...  \n\n[42 rows x 523 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Advent_Solar_AS160___2006_</th>\n      <th>Advent_Solar_Ventura_210___2008_</th>\n      <th>Advent_Solar_Ventura_215___2009_</th>\n      <th>Aleo_S03_160__2007__E__</th>\n      <th>Aleo_S03_165__2007__E__</th>\n      <th>Aleo_S16_165__2007__E__</th>\n      <th>Aleo_S16_170__2007__E__</th>\n      <th>Aleo_S16_175__2007__E__</th>\n      <th>Aleo_S16_180__2007__E__</th>\n      <th>Aleo_S16_185__2007__E__</th>\n      <th>...</th>\n      <th>Panasonic_VBHN235SA06B__2013_</th>\n      <th>Trina_TSM_240PA05__2013_</th>\n      <th>Hanwha_HSL60P6_PA_4_250T__2013_</th>\n      <th>Suniva_OPT300_72_4_100__2013_</th>\n      <th>Canadian_Solar_CS6X_300M__2013_</th>\n      <th>LG_LG290N1C_G3__2013_</th>\n      <th>Sharp_NDQ235F4__2013_</th>\n      <th>Solar_Frontier_SF_160S__2013_</th>\n      <th>SolarWorld_Sunmodule_250_Poly__2013_</th>\n      <th>Silevo_Triex_U300_Black__2014_</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Vintage</th>\n      <td>2006</td>\n      <td>2008</td>\n      <td>2009</td>\n      <td>2007 (E)</td>\n      <td>2007 (E)</td>\n      <td>2007 (E)</td>\n      <td>2007 (E)</td>\n      <td>2007 (E)</td>\n      <td>2007 (E)</td>\n      <td>2007 (E)</td>\n      <td>...</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2013</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>Area</th>\n      <td>1.312</td>\n      <td>1.646</td>\n      <td>1.646</td>\n      <td>1.28</td>\n      <td>1.28</td>\n      <td>1.378</td>\n      <td>1.378</td>\n      <td>1.378</td>\n      <td>1.378</td>\n      <td>1.378</td>\n      <td>...</td>\n      <td>1.26</td>\n      <td>1.63</td>\n      <td>1.65</td>\n      <td>1.93</td>\n      <td>1.91</td>\n      <td>1.64</td>\n      <td>1.56</td>\n      <td>1.22</td>\n      <td>1.68</td>\n      <td>1.68</td>\n    </tr>\n    <tr>\n      <th>Material</th>\n      <td>mc-Si</td>\n      <td>mc-Si</td>\n      <td>mc-Si</td>\n      <td>c-Si</td>\n      <td>c-Si</td>\n      <td>mc-Si</td>\n      <td>mc-Si</td>\n      <td>mc-Si</td>\n      <td>mc-Si</td>\n      <td>mc-Si</td>\n      <td>...</td>\n      <td>a-Si / mono-Si</td>\n      <td>mc-Si</td>\n      <td>mc-Si</td>\n      <td>c-Si</td>\n      <td>c-Si</td>\n      <td>c-Si</td>\n      <td>mc-Si</td>\n      <td>CIS</td>\n      <td>mc-Si</td>\n      <td>c-Si</td>\n    </tr>\n    <tr>\n      <th>Cells_in_Series</th>\n      <td>72</td>\n      <td>60</td>\n      <td>60</td>\n      <td>72</td>\n      <td>72</td>\n      <td>50</td>\n      <td>50</td>\n      <td>50</td>\n      <td>50</td>\n      <td>50</td>\n      <td>...</td>\n      <td>72</td>\n      <td>60</td>\n      <td>60</td>\n      <td>72</td>\n      <td>72</td>\n      <td>60</td>\n      <td>60</td>\n      <td>172</td>\n      <td>60</td>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>Parallel_Strings</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Isco</th>\n      <td>5.564</td>\n      <td>8.34</td>\n      <td>8.49</td>\n      <td>5.1</td>\n      <td>5.2</td>\n      <td>7.9</td>\n      <td>7.95</td>\n      <td>8.1</td>\n      <td>8.15</td>\n      <td>8.2</td>\n      <td>...</td>\n      <td>5.8738</td>\n      <td>8.8449</td>\n      <td>8.5935</td>\n      <td>8.5753</td>\n      <td>8.6388</td>\n      <td>9.8525</td>\n      <td>8.6739</td>\n      <td>2.0259</td>\n      <td>8.3768</td>\n      <td>5.771</td>\n    </tr>\n    <tr>\n      <th>Voco</th>\n      <td>42.832</td>\n      <td>35.31</td>\n      <td>35.92</td>\n      <td>43.5</td>\n      <td>43.6</td>\n      <td>30</td>\n      <td>30.1</td>\n      <td>30.2</td>\n      <td>30.3</td>\n      <td>30.5</td>\n      <td>...</td>\n      <td>52.0042</td>\n      <td>36.8926</td>\n      <td>36.8075</td>\n      <td>44.2921</td>\n      <td>43.5918</td>\n      <td>39.6117</td>\n      <td>36.8276</td>\n      <td>112.505</td>\n      <td>36.3806</td>\n      <td>68.5983</td>\n    </tr>\n    <tr>\n      <th>Impo</th>\n      <td>5.028</td>\n      <td>7.49</td>\n      <td>7.74</td>\n      <td>4.55</td>\n      <td>4.65</td>\n      <td>7.08</td>\n      <td>7.23</td>\n      <td>7.38</td>\n      <td>7.53</td>\n      <td>7.67</td>\n      <td>...</td>\n      <td>5.5383</td>\n      <td>8.2955</td>\n      <td>8.0822</td>\n      <td>7.963</td>\n      <td>8.1359</td>\n      <td>9.2473</td>\n      <td>8.1243</td>\n      <td>1.8356</td>\n      <td>7.6921</td>\n      <td>5.383</td>\n    </tr>\n    <tr>\n      <th>Vmpo</th>\n      <td>32.41</td>\n      <td>27.61</td>\n      <td>27.92</td>\n      <td>35.6</td>\n      <td>35.8</td>\n      <td>23.3</td>\n      <td>23.5</td>\n      <td>23.7</td>\n      <td>23.9</td>\n      <td>24.1</td>\n      <td>...</td>\n      <td>43.1204</td>\n      <td>29.066</td>\n      <td>29.2011</td>\n      <td>35.0837</td>\n      <td>34.9531</td>\n      <td>31.2921</td>\n      <td>29.1988</td>\n      <td>86.6752</td>\n      <td>28.348</td>\n      <td>55.4547</td>\n    </tr>\n    <tr>\n      <th>Aisc</th>\n      <td>0.000537</td>\n      <td>0.00077</td>\n      <td>0.00082</td>\n      <td>0.0003</td>\n      <td>0.0003</td>\n      <td>0.0008</td>\n      <td>0.0008</td>\n      <td>0.0008</td>\n      <td>0.0008</td>\n      <td>0.0008</td>\n      <td>...</td>\n      <td>0.0005</td>\n      <td>0.0004</td>\n      <td>0.0004</td>\n      <td>0.0006</td>\n      <td>0.0005</td>\n      <td>0.0002</td>\n      <td>0.0006</td>\n      <td>0.0001</td>\n      <td>0.0006</td>\n      <td>0.0003</td>\n    </tr>\n    <tr>\n      <th>Aimp</th>\n      <td>-0.000491</td>\n      <td>-0.00015</td>\n      <td>-0.00013</td>\n      <td>-0.00025</td>\n      <td>-0.00025</td>\n      <td>-0.0003</td>\n      <td>-0.0003</td>\n      <td>-0.0003</td>\n      <td>-0.0003</td>\n      <td>-0.0003</td>\n      <td>...</td>\n      <td>-0.0001</td>\n      <td>-0.0003</td>\n      <td>-0.0003</td>\n      <td>-0.0002</td>\n      <td>-0.0001</td>\n      <td>-0.0004</td>\n      <td>-0.0002</td>\n      <td>-0.0003</td>\n      <td>-0.0001</td>\n      <td>-0.0003</td>\n    </tr>\n    <tr>\n      <th>C0</th>\n      <td>1.0233</td>\n      <td>0.937</td>\n      <td>1.015</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>0.99</td>\n      <td>...</td>\n      <td>1.0015</td>\n      <td>1.0116</td>\n      <td>1.0061</td>\n      <td>0.999</td>\n      <td>1.0121</td>\n      <td>1.0145</td>\n      <td>1.0049</td>\n      <td>1.0096</td>\n      <td>1.0158</td>\n      <td>0.995</td>\n    </tr>\n    <tr>\n      <th>C1</th>\n      <td>-0.0233</td>\n      <td>0.063</td>\n      <td>-0.015</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>...</td>\n      <td>-0.0015</td>\n      <td>-0.0116</td>\n      <td>-0.0061</td>\n      <td>0.001</td>\n      <td>-0.0121</td>\n      <td>-0.0145</td>\n      <td>-0.0049</td>\n      <td>-0.0096</td>\n      <td>-0.0158</td>\n      <td>0.005</td>\n    </tr>\n    <tr>\n      <th>Bvoco</th>\n      <td>-0.1703</td>\n      <td>-0.133</td>\n      <td>-0.135</td>\n      <td>-0.152</td>\n      <td>-0.152</td>\n      <td>-0.11</td>\n      <td>-0.11</td>\n      <td>-0.11</td>\n      <td>-0.11</td>\n      <td>-0.11</td>\n      <td>...</td>\n      <td>-0.1411</td>\n      <td>-0.137</td>\n      <td>-0.1263</td>\n      <td>-0.155</td>\n      <td>-0.1532</td>\n      <td>-0.1205</td>\n      <td>-0.1279</td>\n      <td>-0.3044</td>\n      <td>-0.1393</td>\n      <td>-0.1913</td>\n    </tr>\n    <tr>\n      <th>Mbvoc</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Bvmpo</th>\n      <td>-0.1731</td>\n      <td>-0.135</td>\n      <td>-0.136</td>\n      <td>-0.158</td>\n      <td>-0.158</td>\n      <td>-0.115</td>\n      <td>-0.115</td>\n      <td>-0.115</td>\n      <td>-0.115</td>\n      <td>-0.115</td>\n      <td>...</td>\n      <td>-0.1366</td>\n      <td>-0.1441</td>\n      <td>-0.1314</td>\n      <td>-0.1669</td>\n      <td>-0.1634</td>\n      <td>-0.1337</td>\n      <td>-0.1348</td>\n      <td>-0.2339</td>\n      <td>-0.1449</td>\n      <td>-0.184</td>\n    </tr>\n    <tr>\n      <th>Mbvmp</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>N</th>\n      <td>1.174</td>\n      <td>1.495</td>\n      <td>1.373</td>\n      <td>1.25</td>\n      <td>1.25</td>\n      <td>1.35</td>\n      <td>1.35</td>\n      <td>1.35</td>\n      <td>1.35</td>\n      <td>1.35</td>\n      <td>...</td>\n      <td>1.029</td>\n      <td>1.2073</td>\n      <td>1.0686</td>\n      <td>1.0771</td>\n      <td>1.0025</td>\n      <td>1.0925</td>\n      <td>1.0695</td>\n      <td>1.2066</td>\n      <td>1.226</td>\n      <td>1.345</td>\n    </tr>\n    <tr>\n      <th>C2</th>\n      <td>-0.76444</td>\n      <td>0.0182</td>\n      <td>0.0036</td>\n      <td>-0.15</td>\n      <td>-0.15</td>\n      <td>-0.12</td>\n      <td>-0.12</td>\n      <td>-0.12</td>\n      <td>-0.12</td>\n      <td>-0.12</td>\n      <td>...</td>\n      <td>0.2859</td>\n      <td>-0.07993</td>\n      <td>-0.2585</td>\n      <td>-0.355</td>\n      <td>-0.171</td>\n      <td>-0.4647</td>\n      <td>-0.2718</td>\n      <td>-0.5426</td>\n      <td>-0.09677</td>\n      <td>0.3221</td>\n    </tr>\n    <tr>\n      <th>C3</th>\n      <td>-15.5087</td>\n      <td>-10.758</td>\n      <td>-7.2509</td>\n      <td>-8.96</td>\n      <td>-8.96</td>\n      <td>-11.08</td>\n      <td>-11.08</td>\n      <td>-11.08</td>\n      <td>-11.08</td>\n      <td>-11.08</td>\n      <td>...</td>\n      <td>-5.48455</td>\n      <td>-7.27624</td>\n      <td>-9.85905</td>\n      <td>-13.0643</td>\n      <td>-9.39745</td>\n      <td>-11.9008</td>\n      <td>-11.4033</td>\n      <td>-15.2598</td>\n      <td>-8.51148</td>\n      <td>-6.7178</td>\n    </tr>\n    <tr>\n      <th>A0</th>\n      <td>0.9281</td>\n      <td>0.9067</td>\n      <td>0.9323</td>\n      <td>0.938</td>\n      <td>0.938</td>\n      <td>0.924</td>\n      <td>0.924</td>\n      <td>0.924</td>\n      <td>0.924</td>\n      <td>0.924</td>\n      <td>...</td>\n      <td>0.9161</td>\n      <td>0.9645</td>\n      <td>0.9428</td>\n      <td>0.9327</td>\n      <td>0.9371</td>\n      <td>0.9731</td>\n      <td>0.9436</td>\n      <td>0.9354</td>\n      <td>0.9288</td>\n      <td>0.9191</td>\n    </tr>\n    <tr>\n      <th>A1</th>\n      <td>0.06615</td>\n      <td>0.09573</td>\n      <td>0.06526</td>\n      <td>0.05422</td>\n      <td>0.05422</td>\n      <td>0.06749</td>\n      <td>0.06749</td>\n      <td>0.06749</td>\n      <td>0.06749</td>\n      <td>0.06749</td>\n      <td>...</td>\n      <td>0.07968</td>\n      <td>0.02753</td>\n      <td>0.0536</td>\n      <td>0.07283</td>\n      <td>0.06262</td>\n      <td>0.02966</td>\n      <td>0.04765</td>\n      <td>0.06809</td>\n      <td>0.07201</td>\n      <td>0.09988</td>\n    </tr>\n    <tr>\n      <th>A2</th>\n      <td>-0.01384</td>\n      <td>-0.0266</td>\n      <td>-0.01567</td>\n      <td>-0.009903</td>\n      <td>-0.009903</td>\n      <td>-0.012549</td>\n      <td>-0.012549</td>\n      <td>-0.012549</td>\n      <td>-0.012549</td>\n      <td>-0.012549</td>\n      <td>...</td>\n      <td>-0.01866</td>\n      <td>-0.002848</td>\n      <td>-0.01281</td>\n      <td>-0.02402</td>\n      <td>-0.01667</td>\n      <td>-0.01024</td>\n      <td>-0.007405</td>\n      <td>-0.02094</td>\n      <td>-0.02065</td>\n      <td>-0.04273</td>\n    </tr>\n    <tr>\n      <th>A3</th>\n      <td>0.001298</td>\n      <td>0.00343</td>\n      <td>0.00193</td>\n      <td>0.0007297</td>\n      <td>0.0007297</td>\n      <td>0.0010049</td>\n      <td>0.0010049</td>\n      <td>0.0010049</td>\n      <td>0.0010049</td>\n      <td>0.0010049</td>\n      <td>...</td>\n      <td>0.002278</td>\n      <td>-0.0001439</td>\n      <td>0.001826</td>\n      <td>0.003819</td>\n      <td>0.002168</td>\n      <td>0.001793</td>\n      <td>0.0003818</td>\n      <td>0.00293</td>\n      <td>0.002862</td>\n      <td>0.00937</td>\n    </tr>\n    <tr>\n      <th>A4</th>\n      <td>-4.6e-05</td>\n      <td>-0.0001794</td>\n      <td>-9.81e-05</td>\n      <td>-1.907e-05</td>\n      <td>-1.907e-05</td>\n      <td>-2.8797e-05</td>\n      <td>-2.8797e-05</td>\n      <td>-2.8797e-05</td>\n      <td>-2.8797e-05</td>\n      <td>-2.8797e-05</td>\n      <td>...</td>\n      <td>-0.0001118</td>\n      <td>2.219e-05</td>\n      <td>-0.0001048</td>\n      <td>-0.000235</td>\n      <td>-0.0001087</td>\n      <td>-0.0001286</td>\n      <td>-1.101e-05</td>\n      <td>-0.0001564</td>\n      <td>-0.0001544</td>\n      <td>-0.0007643</td>\n    </tr>\n    <tr>\n      <th>B0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>B1</th>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>-0.002438</td>\n      <td>...</td>\n      <td>-0.01053</td>\n      <td>-0.00261</td>\n      <td>-0.007861</td>\n      <td>-0.006801</td>\n      <td>-0.00789</td>\n      <td>-0.0154</td>\n      <td>-0.00464</td>\n      <td>-0.0152</td>\n      <td>-0.00308</td>\n      <td>-0.006498</td>\n    </tr>\n    <tr>\n      <th>B2</th>\n      <td>0.0003103</td>\n      <td>0.00031</td>\n      <td>0.00031</td>\n      <td>0.0003103</td>\n      <td>0.0003103</td>\n      <td>0.0003103</td>\n      <td>0.0003103</td>\n      <td>0.0003103</td>\n      <td>0.0003103</td>\n      <td>0.0003103</td>\n      <td>...</td>\n      <td>0.001149</td>\n      <td>0.0003279</td>\n      <td>0.0009058</td>\n      <td>0.0007968</td>\n      <td>0.0008656</td>\n      <td>0.001572</td>\n      <td>0.000559</td>\n      <td>0.001598</td>\n      <td>0.0004053</td>\n      <td>0.0006908</td>\n    </tr>\n    <tr>\n      <th>B3</th>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>-1.246e-05</td>\n      <td>...</td>\n      <td>-4.268e-05</td>\n      <td>-1.458e-05</td>\n      <td>-3.496e-05</td>\n      <td>-3.095e-05</td>\n      <td>-3.298e-05</td>\n      <td>-5.525e-05</td>\n      <td>-2.249e-05</td>\n      <td>-5.682e-05</td>\n      <td>-1.729e-05</td>\n      <td>-2.678e-05</td>\n    </tr>\n    <tr>\n      <th>B4</th>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>2.11e-07</td>\n      <td>...</td>\n      <td>6.517e-07</td>\n      <td>2.654e-07</td>\n      <td>5.473e-07</td>\n      <td>4.896e-07</td>\n      <td>5.178e-07</td>\n      <td>8.04e-07</td>\n      <td>3.673e-07</td>\n      <td>8.326e-07</td>\n      <td>2.997e-07</td>\n      <td>4.322e-07</td>\n    </tr>\n    <tr>\n      <th>B5</th>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>-1.36e-09</td>\n      <td>...</td>\n      <td>-3.556e-09</td>\n      <td>-1.732e-09</td>\n      <td>-3.058e-09</td>\n      <td>-2.78e-09</td>\n      <td>-2.918e-09</td>\n      <td>-4.202e-09</td>\n      <td>-2.144e-09</td>\n      <td>-4.363e-09</td>\n      <td>-1.878e-09</td>\n      <td>-2.508e-09</td>\n    </tr>\n    <tr>\n      <th>DTC</th>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>...</td>\n      <td>2.03</td>\n      <td>3.03</td>\n      <td>2.55</td>\n      <td>2.58</td>\n      <td>3.2</td>\n      <td>3.05</td>\n      <td>3.27</td>\n      <td>3.29</td>\n      <td>3.19</td>\n      <td>3.13</td>\n    </tr>\n    <tr>\n      <th>FD</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>-3.35</td>\n      <td>-3.45</td>\n      <td>-3.47</td>\n      <td>-3.56</td>\n      <td>-3.56</td>\n      <td>-3.56</td>\n      <td>-3.56</td>\n      <td>-3.56</td>\n      <td>-3.56</td>\n      <td>-3.56</td>\n      <td>...</td>\n      <td>-3.7489</td>\n      <td>-3.5924</td>\n      <td>-3.5578</td>\n      <td>-3.7566</td>\n      <td>-3.6024</td>\n      <td>-3.4247</td>\n      <td>-3.7445</td>\n      <td>-3.6836</td>\n      <td>-3.73</td>\n      <td>-3.6866</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>-0.1161</td>\n      <td>-0.077</td>\n      <td>-0.087</td>\n      <td>-0.075</td>\n      <td>-0.075</td>\n      <td>-0.075</td>\n      <td>-0.075</td>\n      <td>-0.075</td>\n      <td>-0.075</td>\n      <td>-0.075</td>\n      <td>...</td>\n      <td>-0.1287</td>\n      <td>-0.1319</td>\n      <td>-0.1766</td>\n      <td>-0.156</td>\n      <td>-0.2106</td>\n      <td>-0.0951</td>\n      <td>-0.149</td>\n      <td>-0.1483</td>\n      <td>-0.1483</td>\n      <td>-0.104</td>\n    </tr>\n    <tr>\n      <th>C4</th>\n      <td>0.9974</td>\n      <td>0.972</td>\n      <td>0.989</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>0.995</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>C5</th>\n      <td>0.0026</td>\n      <td>0.028</td>\n      <td>0.012</td>\n      <td>0.005</td>\n      <td>0.005</td>\n      <td>0.005</td>\n      <td>0.005</td>\n      <td>0.005</td>\n      <td>0.005</td>\n      <td>0.005</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>IXO</th>\n      <td>5.54</td>\n      <td>8.25</td>\n      <td>8.49</td>\n      <td>5.04</td>\n      <td>5.14</td>\n      <td>7.8</td>\n      <td>7.85</td>\n      <td>8</td>\n      <td>8.05</td>\n      <td>8.1</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>IXXO</th>\n      <td>3.56</td>\n      <td>5.2</td>\n      <td>5.45</td>\n      <td>3.16</td>\n      <td>3.25</td>\n      <td>4.92</td>\n      <td>5.08</td>\n      <td>5.18</td>\n      <td>5.39</td>\n      <td>5.54</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>C6</th>\n      <td>1.173</td>\n      <td>1.067</td>\n      <td>1.137</td>\n      <td>1.15</td>\n      <td>1.15</td>\n      <td>1.15</td>\n      <td>1.15</td>\n      <td>1.15</td>\n      <td>1.15</td>\n      <td>1.15</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>C7</th>\n      <td>-0.173</td>\n      <td>-0.067</td>\n      <td>-0.137</td>\n      <td>-0.15</td>\n      <td>-0.15</td>\n      <td>-0.15</td>\n      <td>-0.15</td>\n      <td>-0.15</td>\n      <td>-0.15</td>\n      <td>-0.15</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Notes</th>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>Source: Sandia National Laboratories Updated 9...</td>\n      <td>...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2013.  Mo...</td>\n      <td>Source:  CFV Solar Test Lab.  Tested 2014.  Mo...</td>\n    </tr>\n  </tbody>\n</table>\n<p>42 rows × 523 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "sandia_modules = pvsystem.retrieve_sam(name='SandiaMod')\n",
    "sandia_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Vintage                                                          2009\nArea                                                            1.701\nMaterial                                                         c-Si\nCells_in_Series                                                    96\nParallel_Strings                                                    1\nIsco                                                          5.09115\nVoco                                                          59.2608\nImpo                                                          4.54629\nVmpo                                                          48.3156\nAisc                                                         0.000397\nAimp                                                         0.000181\nC0                                                            1.01284\nC1                                                         -0.0128398\nBvoco                                                        -0.21696\nMbvoc                                                               0\nBvmpo                                                       -0.235488\nMbvmp                                                               0\nN                                                              1.4032\nC2                                                           0.279317\nC3                                                           -7.24463\nA0                                                           0.928385\nA1                                                           0.068093\nA2                                                         -0.0157738\nA3                                                          0.0016606\nA4                                                          -6.93e-05\nB0                                                                  1\nB1                                                          -0.002438\nB2                                                          0.0003103\nB3                                                         -1.246e-05\nB4                                                           2.11e-07\nB5                                                          -1.36e-09\nDTC                                                                 3\nFD                                                                  1\nA                                                            -3.40641\nB                                                          -0.0842075\nC4                                                           0.996446\nC5                                                           0.003554\nIXO                                                           4.97599\nIXXO                                                          3.18803\nC6                                                            1.15535\nC7                                                          -0.155353\nNotes               Source: Sandia National Laboratories Updated 9...\nName: Canadian_Solar_CS5P_220M___2009_, dtype: object"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_\n",
    "sandia_module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate some irradiance data for modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pvlib import clearsky\n",
    "from pvlib import irradiance\n",
    "from pvlib import atmosphere\n",
    "from pvlib.location import Location\n",
    "\n",
    "tus = Location(32.2, -111, 'US/Arizona', 700, 'Tucson')\n",
    "\n",
    "times_loc = pd.date_range(start=datetime.datetime(2014,4,1), end=datetime.datetime(2014,4,2), freq='30s', tz=tus.tz)\n",
    "solpos = pvlib.solarposition.get_solarposition(times_loc, tus.latitude, tus.longitude)\n",
    "dni_extra = pvlib.irradiance.get_extra_radiation(times_loc)\n",
    "airmass = pvlib.atmosphere.get_relative_airmass(solpos['apparent_zenith'])\n",
    "pressure = pvlib.atmosphere.alt2pres(tus.altitude)\n",
    "am_abs = pvlib.atmosphere.get_absolute_airmass(airmass, pressure)\n",
    "cs = tus.get_clearsky(times_loc)\n",
    "\n",
    "surface_tilt = tus.latitude\n",
    "surface_azimuth = 180  # pointing south\n",
    "\n",
    "aoi = pvlib.irradiance.aoi(surface_tilt, surface_azimuth,\n",
    "                           solpos['apparent_zenith'], solpos['azimuth'])\n",
    "total_irrad = pvlib.irradiance.get_total_irradiance(surface_tilt,\n",
    "                                                    surface_azimuth,\n",
    "                                                    solpos['apparent_zenith'],\n",
    "                                                    solpos['azimuth'],\n",
    "                                                    cs['dni'], cs['ghi'], cs['dhi'],\n",
    "                                                    dni_extra=dni_extra,\n",
    "                                                    model='haydavies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run the module parameters and the irradiance data through the SAPM functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "module = sandia_module\n",
    "\n",
    "# a sunny, calm, and hot day in the desert\n",
    "thermal_params = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']\n",
    "temps = pvlib.temperature.sapm_cell(total_irrad['poa_global'], 30, 0, **thermal_params)\n",
    "\n",
    "effective_irradiance = pvlib.pvsystem.sapm_effective_irradiance(\n",
    "    total_irrad['poa_direct'], total_irrad['poa_diffuse'],\n",
    "    am_abs, aoi, module)\n",
    "\n",
    "sapm_1 = pvlib.pvsystem.sapm(effective_irradiance, temps, module)\n",
    "\n",
    "sapm_1.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sapm(sapm_data, effective_irradiance):\n",
    "    \"\"\"\n",
    "    Makes a nice figure with the SAPM data.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    sapm_data : DataFrame\n",
    "        The output of ``pvsystem.sapm``\n",
    "    \"\"\"\n",
    "    fig, axes = plt.subplots(2, 3, figsize=(16,10), sharex=False, sharey=False, squeeze=False)\n",
    "    plt.subplots_adjust(wspace=.2, hspace=.3)\n",
    "\n",
    "    ax = axes[0,0]\n",
    "    sapm_data.filter(like='i_').plot(ax=ax)\n",
    "    ax.set_ylabel('Current (A)')\n",
    "\n",
    "    ax = axes[0,1]\n",
    "    sapm_data.filter(like='v_').plot(ax=ax)\n",
    "    ax.set_ylabel('Voltage (V)')\n",
    "\n",
    "    ax = axes[0,2]\n",
    "    sapm_data.filter(like='p_').plot(ax=ax)\n",
    "    ax.set_ylabel('Power (W)')\n",
    "\n",
    "    ax = axes[1,0]\n",
    "    [ax.plot(effective_irradiance, current, label=name) for name, current in\n",
    "     sapm_data.filter(like='i_').iteritems()]\n",
    "    ax.set_ylabel('Current (A)')\n",
    "    ax.set_xlabel('Effective Irradiance')\n",
    "    ax.legend(loc=2)\n",
    "\n",
    "    ax = axes[1,1]\n",
    "    [ax.plot(effective_irradiance, voltage, label=name) for name, voltage in\n",
    "     sapm_data.filter(like='v_').iteritems()]\n",
    "    ax.set_ylabel('Voltage (V)')\n",
    "    ax.set_xlabel('Effective Irradiance')\n",
    "    ax.legend(loc=4)\n",
    "\n",
    "    ax = axes[1,2]\n",
    "    ax.plot(effective_irradiance, sapm_data['p_mp'], label='p_mp')\n",
    "    ax.set_ylabel('Power (W)')\n",
    "    ax.set_xlabel('Effective Irradiance')\n",
    "    ax.legend(loc=2)\n",
    "\n",
    "    # needed to show the time ticks\n",
    "    for ax in axes.flatten():\n",
    "        for tk in ax.get_xticklabels():\n",
    "            tk.set_visible(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_sapm(sapm_1, effective_irradiance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, here's the SAPM for a sunny, windy, cold version of the same day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x720 with 6 Axes>",
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P7+/lyU8O8K+lk5g9sv8U3XBXzW12rnp8Iw0tNv7741n4e59+1tFAat86k7ZO9BcOh+Zb//iSnJJj/PfeWcQE985oluc2HuR373zNH745jkWT3KsWiSu0cwUFBVx55ZXs3r27R863fv16Hn30Ud55550zHtfdtk56VIWp7A7NA6t3Ee7vxT3zhnDHh3dQ01LD3y/9+3klqQAJQQk8d9lzhPqEcvuHtzNrjCJjSCj/887XVMswESH6tbzyelZsyOcbE+IkSXURPp5WHvlmKiXHmvnzf/ebHY4Qohe9vOUQWw4e5f9dMbrXklSApdMSyUwM4+G1OVTVt/TadYS5JFEVplq9o4g9R47xyytS+OOO35Jfm8+fZv+J1MjUCzpvlF8U/7j0HwDct+EnPHj1cOqa2/jrx7k9EbYAsrOzSU9PP+ExefJks8MSA9wf1u3Fx9PKLy4feL1yrmzikDAWTUzghS8KKa5pMjuccybtnBDnrrqhlT+s28u0YeFcn9G7I9iUUjy0cCz1zTb+b+3eXr1Wf5SYmHhKb+rChQtPae+6Wp6rK7Nnzz5rb+r5kKq/wjRNrXYe/WAf6QkhNPl8xn8L/8tPJv6EqYOm9sj5E4ISeGTGI9z54Z2sLnySxZO+wb+/KOQ7U4aQHBnQI9cYyFJTU8nKyjI7DCE6fH3kGB98XcbdFw8nIsD77C8QfepHlwxnzVfFPPFxLv/3jXFmh3NOpJ0T4tw9/nEe9S02fn3VmB6p+Ho2w6MD+d6MJFZsyGfZtCRGDwrq9Wv2Z2vWrDE7hFNIj6owzXOfH6TsWAu3zQ1h+fblXBR7ETeNualHrzE9bjpLxy7ljdw3mJlWjbeHheUy9EyIfulvnx4g0NuDZdOSzA5FdCEuxJdFk+J5Y3sxlTJUT4h+pbCqgX9/WcCijARGRAf22XXvnD2MIB9P/vi+e/WqunKNoN5yPu+5TxNVpVSBUipbKZWllJLZ9ANYU6udZzceZNbICFYf/jNWZeV3037XK3fg7ky/k8SgRB7L+j+WTInh3ewSDlTU9/h1hBDmqapvYd3uEq7LiCfYz9PscMRp3Dw1kVa7g5VbD5sdihCiBz32YS4eFgv3XjqiT68b7OvJHbOH8sm+CjbnV/Xptc+Xj48PVVVVAypZ1VpTVVWFj0/35i2bMfR3jta60oTrChfy6tZDHG1oZVpqKY/v2czPM39OjH9Mr1zL2+rNb6b+hpvX3Yx3wga8PYbz1CcH+NOitF65nhCi763aXkSbXXND5mCzQxFnMCwqkKlDw3l58yHumDUUi6X3hwcKIXrX4aONvLnzCEunJpqyZvXNUxN5buNBHv84j8nJ4X1+/e6Kj4+nqKiIigr3WLKrp/j4+BAf3725yzJHVfS5VpuDFRvymZQYxJuHHmFo8FAWjVzUq9ecGD2R+YnzeT33Ra7N+BOvby7mnkuGkxDm16vXFUL0jde3F5ExJJThfTjkTJyfRRkJ3LMyi+2HqpmUGGZ2OEKIC7RiQz4WBbfMSDbl+j6eVpZNT+KRtXvJLqolNT7YlDjOlaenJ0lJMkXlXPT1HFUNfKCU2q6Uuq2Pry1cxNs7j1BS28yYUXs4VHeI+ybdh4el9++Z/GjCj2hztGEPWgfAi18W9vo1+6upU3um4JUQPSGvvI688nquTh9kdijiHFwyOhpvDwvv7DxidihCiAtUXtfMym2H+eaE+F5djuZsbpg8mEBvD57ecMC0GETP6+tEdbrWegKwALhLKTXz5AOUUrcppbYppbYNtC7xgeKFLwtJjvRiQ/lrTIqZxPS46X1y3YTABL418lt8cPgdZo5WvLr1ME2t9j65dn+zadMms0MQosP7e8oAmDe6d6YPiJ4V4O3B3FFRvJtdit0xcOZoCdEfvfhFIW12B9+fNdTUOIJ8PLlhymDWZpdwqKrR1FhEz+nTRFVrXex8LgfWAJldHLNCa52htc6IjIzsy/BEH9hVVMPOwzWMS9lPRVMF3x/3/T69/rKxy/BQHvhFfkZtUxtvZhX36fX7i4CA0y/vs379embNmsU111xDcnIyDzzwAC+99BKZmZmkpqZy4IBxt/Pmm2/m9ttvJyMjgxEjRvTK+ltiYFi3u5Txg0NMvZsvuueyMTFU1rew50it2aGc4IEHHuDJJ5/s+P43v/kNjz766CnHSTsnhDGV65Wth5kzMoqkCH+zw2Hp1CSUUry0WUbM9Rd9NkdVKeUPWLTWdc6v5wG/66vrC9fw4peF+Hlpdjf8h/TIdDJjTrlX0asi/SJZOHwhq3NXMzx2Cv/aVMDiSQl9st5Xr1j7AJRm9+w5Y1JhwSMXdIqdO3eSk5NDWFgYycnJ3HLLLWzZsoXHHnuMxx9/nL/85S8AFBQUsGXLFg4cOMCcOXPIy8vrdkU4MbBVN7SSXVzLT/q40qS4MNOHRwCwYX8F4+JDuj7IhPZt8eLF3HPPPdx1110AvPbaa6dd8F7aOTHQffB1KRV1LXxnyhCzQwEgJtiHS1OieW3bYe69dAQ+nlazQxIXqC97VKOBjUqpncAW4F2t9bo+vL4wWW1jG29mHWHimEOUNZZy27jbzj1BtLXAnjXw1g/hb9Nh+Wh4cgq8fjNsfx6aas45jpvH3IxDO0hI2sre0jp2FrnWHf3+YNKkScTGxuLt7c3QoUOZN28eAKmpqRQUFHQct2jRIiwWC8OHDyc5OZm9e91rHTRhvi+dyxFMHeb6lR7FcREB3oyNC2LDftdaBGD8+PGUl5dz5MgRdu7cSWhoKAkJCV0eK+2cGOj+/UUhCWG+zBzhOiMgb5wyhOrGNtbtLjU7FNED+qxHVWudD8h6IAPYmzuLabE5qPNaT5J30rnNTW2uhU2Pw9Znoeko+ARD/CSIHWfsO/SlkcCu/RmMux5m/QyCz1z6Oj4wnnmJ89hQ9CE+XhN5fdth0hNOc0ff1V1gz2dv8fb27vjaYrF0fG+xWLDZbB37Tr5R4bY928I0mw5U4edlPX2vnHBZM4ZH8o8N+TS22vDz6uLjiEnt2/XXX8+qVasoLS1l8eLFpz1O2jkxkO0vq2PzwaM8sGAUVhdaZmrq0HCSIvx58ctCrh0fZ3Y44gL1dTElMYC9saOY5PgqDhzLYcmoJWf+Y6017FwJj6XDhj/CkKlw42r46UG48Q249in41kvw4xy47VNIW2wc/9cJ8OkfwG47/bmBG0bdQENbPWNHHuCtnUdobpOiSmZ4/fXXcTgcHDhwgPz8fEaOHGl2SMLNbDpQyaTEMDyt8ufM3UxKDMXm0Ow87FqjWhYvXsyrr77KqlWruP766y/4fNLOif7ota2H8bQqFmV0PeLALBaL4obMwWwrrGZ/WZ3Z4YgLJH/ZRZ84UFHPzsM1hMVswd/Tn6uHXn36g9ua4I3vwZrbIGK4kYh+6yUYdjFYTppvoBQMSoerHoMfboNRV8AnD8E/F8CxktNeIi0yjVFho6jz+pS65jY++Lqsh96p6I7BgweTmZnJggULePrpp2XeluiWmsZWDlQ0kJkka3G6o/EJoQDsOFRtciQnGjNmDHV1dcTFxREbG3vB55N2TvQ3NruD/2QdYe6oKML8vcwO5xQLJ8RhtShW75CCme6uz4b+ioFtzY5iLB51HGj8nMWjFuPveZrqcI1H4eXFULQV5v4Kpv/41OT0dEIGw/X/NJLVt34Ez15q9L5Gnnr3WinFklFL+PWmXxMddYTXt0VydZqswXiu6uvrT7tv9uzZzJ49u+P79evXn3bfJZdcwtNPP90LEYqBILvY6IlLk2G/binU34vkSH++crFEFSA7+8xFnPpjO6eUSgBewKgpooEVWuvHlFJhwEogESgAFmmtq5UxLOox4HKgEbhZa73DjNhF39qYV0llfQsLx595qpVZIgK8mTUikjezirn/spEuNTRZdI/0qIpe53Bo1nxVzIihudi0jUUjFnV9YPMx+PdCKNkJi56Hmfefe5LaWep1sPQ9owDT81fB0fwuD1uQtIBAr0Ci477i87xKKupaun8tIYRpdjkLoaXGBZsciThfEwaHsuNQDVrLeqouwAb8RGs9GpiCsd79aOAB4COt9XDgI+f3AAuA4c7HbcDf+j5kYYbVO4oJ8fNkzijXKaJ0sm9MiKOktrmj4J5wT5Koil63peAoxTWN2Py2MC5iHMkhyaceZG+Dld82liJY/G8Yfc2FXXRQOtz0Nthb4YVroeHUypK+Hr4sSFxAcetWHKqZdbtPP1RYnCo7O5v09PQTHpMnTz7n1//rX//iuuuu68UIe59SqkApla2UylJKbXNuC1NK/Vcplet8DjU7zv4qu6iWxHA/gv08zQ5FnKe0+GCONrRSeqzZ7FC6NJDaOa11SXuPqNa6DsgB4oBrgOedhz0PXOv8+hrgBW34EghRSl34WGnh0uqa23h/TylXjRuEt4frLv9ySUo0gd4eMvzXzUmiKnrdu7tK8A0opay5gGuGnSYB/fA3cHADXPMEjLisZy4cNQq+vQrqSuGNW8BxasGka4ZdQ6ujhbi4/by9SxLV7khNTSUrK+uEx+bNm80OywxztNbpWusM5/en630QPSy7uJZUGfbr1lJigwDYW+KaRU8GajunlEoExgObgWitdfsfyFKMocFgJLGHO72syLlN9GNrd5fSYnOwcIJr/6h9PK1cnhrL2t0lNLaeucCmcF2SqIpeZXdo1u0pJWHwbrwsXsxPmn/qQTnvwBdPwKRbIf2Gng0gPgMu/yPkf2JUDz5JakQqiUGJ+IZ9xdaCo5S56F194VZO1/sgetCx5jaKa5pIiQ00OxRxAUbEGD+/nNJjHdsG2jBgV3u/SqkA4A3gHq31sc77tBFstwJWSt2mlNqmlNpWUVHRg5EKM7yzq4TBYX6Md4Nl/a4ZP4jGVjvr98n/O3cliaroVdsLq6mob6DGsoWLB19MkFfQiQc0VMHbP4LYNLjsod4JYsJ3YdxiY9makp0n7FJKcc2wayhrzQGPSt7Lll5V0S0a+EAptV0pdZtz2+l6H0QPyis3CnoNj5JE1Z0F+XgSF+Lb0aPq4+NDVVWVyyVvvUVrTVVVlctUAlZKeWIkqS9prVc7N5e1D+l1Ppc7txcDndcmiXduO4HWeoXWOkNrnREZ6bpzGsXZ1Ta2sSmvkstTY91iPeDJSeGE+3vJZzs3JlV/Ra9au7sEn6D9NNnruHpYF0vSvP8LaK415pN6eJ+6vycoBQt+D/nr4c274NZPwHp8TttVyVfx+FePExu3h3d2DWfptKTeiUP0R9O11sVKqSjgv0qpvZ13aq21UqrLT9zOxPY2MJavEN3TnqgOiwowORJxoVJiA8kpMTru4uPjKSoqYiD1vPn4+BAfb371VGcV32eBHK318k673gJuAh5xPr/ZafsPlFKvApOB2k436UQ/9N+cMmwOzYKxMWaHck6sFsVlY2P4z1fFNLfZ8fF03Tm1omuSqIpe43Bo1u0uJWbQPuzeIUyJnXLiAQc+gV2vwoz7IHpM7wbjGwpXLDcKNm1ZARfd1bEr2j+ajOgM9lfuYnv2TMqONRMd5Bp3t4Vr01oXO5/LlVJrgEycvQ9a65KTeh9Ofu0KYAVARkbGwOg+6kEHyuvx8rCQEOp77i9qqYfcD+DAR1C+F2qLjHWb0eAdCD4hEBwPoYkQlgxRKRCTCn4DeJ1WreFYsTEapWQnVOYa8/7rjhg3GR0OsFggIAbCkiBhMoy+2vj3O0fDowNZv68Cm92Bp6cnSUlys9Ak04DvANlKqSzntl9gJKivKaW+BxQC7aX738NYmiYPY3mapX0bruhra7NLiAvxZVy8+1Rav3xsLC9vPsSn+yu4bIx7JNjiOElURa/JKqqh5Fgd4fE7uWrwFXhYOv13c9jh/V9CyBBjGZq+kHIlDLsEPv09pC054cPnvCHz2FL6v1i8y/gwp4xvTx7SNzG5qalTp7Jp0yazwzCVUsofsGit65xfzwN+x+l7H0QPyiuvJznCHw/rOcxgsbXA548Zc+Gba40bVzGpMPwS8AoAFLQcg6YaqDkEhZ9Da6e1goMTjONjxhnTFAalQ2CsMVqjP9EaqguOJ6UlWcZzo3N5B2UxkvigOIifZCT2FqtRtb2+DMpzYN978OGvYfg8mPcQRI44650LdpEAACAASURBVGWTwv2xOTRHapoZHO7Xq29RnJ7WeiNwuv/UF3dxvAbu6uJY0Q/VNbfxWW4l371oiFsM+203OTmMUD9P1maXSKLqhiRRFb1m3e5SvIP20+po5rLEkyr57loJ5XvguufAsw97L+f9L/xtGqx/BC7/Q8fmi4dczMNbHiY8OocPv06VRPUsBnqS6hQNrHH+wfYAXtZar1NKbaXr3gfRg3LL60k9l7v61QXwyg1GezPyCmM0xeApZ16jWWtoqICy3caSWSW7jOd9a+moI+MfZSSssenHk9egOPdJXh12qDoApbvgyFdGQlq6y0jkASweRo/yyMuN9xebbox88TpLIllbDF+9CF88CX+bClf+GSZ854wvGeJMTg9WNUiiKoSL+nhvOa12BwtS3SvZ87RamDc6hveyS2ix2V16SR1xKklURa/58OsyomL2obxDmRQz6fiOtib4+H9h0AQYvbBvg4pKgYk3wbZnjQ+soUZCGuEbQUZ0Bjkqm8/3zaWhxYa/t/x6nE5AQAD19fVd7luzZg1PPPEEH374IaWlpcyaNYsNGzYQE+Nef9zORmudD6R1sb2KLnofRM9pszsoqm7kmvRBZz6w6gD860poa4QbXjv3pa+UgoAoCJgLQ+ce397a4Excd8KRLKPHMe9D0A5jv1+EM3l1JnaD0o3eWLOT17ZmKP/aSETbk+6yPdDWYOy3ehtJ6NhvOmNPg6jR51c3IDgOZv8MMpbBmtvgrR9ASx1cdOdpX5IU4Q9AYVUDIMV2hHBF72WXEB3kzfgE91safH5qDCu3HebzvErmjpL6hu5EPomLXnGwsoH8qmrConZx7ZCrThz2u/15Y87TwqeNuU19beb9xh3/jcvhqsc6Nl+WeBlbSv8Hm7WEz3IrmD/W9dct//2W37P36N6zH9gNo8JG8bPMn5336xcuXMgbb7zBk08+ybp16/jtb3/b75JUYa7S2mYcGuLPND+1pQ5eWQK2Zlj6Xs/Mg/fyN3pjB3eab9/aaCR9JVnO5HUn5D8GDue6fb5hEDkSwoYaczjDhxrzN4MTjCHIPZXEtjZCXYkx77Yqz3hU5kJVrjGcuT2Z9g4yhjFP+M7xocyRI08oMNcjAiLhhtdh1VJ4/+fGTcFRV3R5aGSgN35eVg5WNvRsDEKIHtHcZmfD/kquz4jHYnGTUSOdTB0ajr+XlY9yyiVRdTOSqIpe8fHecjwC9tGmm5mXOO/4DlsrbHocBl8ESTPNCS5okLFkzfbnjUJOIUZ1/YsHX8xDmx/CP2w3H3w90S0SVVf1+OOPM3bsWKZMmcKSJUvMDkf0M0XVTQDEh55hmOgHvzKStO/8p3eLtXn5QcIk49Gurfl48lqy00ga8z6E+tITX2v1goBo5yPKSCK9/IyE2NPfSB61diaZ2kh+W+qN+bTNtcZzfcXxwkadefoZSfGg8ZC6CGLGGglqSGLf3SC0esA3/gHPFcJbPzTmtQZEnXKYUooh4f4UVjX2TVxCiG75Mr+KpjY7c0ed+vvrDrw9rMwYHsnHe8vRWrvVHNuBThJV0Ss+3ltGaORefL1DyYjOOL4j+3U4VmTMWzLTtHuMRHXjn+FKowp/uG84GdEZ7CGHT/aWY7M7zq1Qi4kupOezNxUVFWGxWCgrK8PhcGAxo+dc9FvFNUaiGhdymh7Vou3G7/dFd0HyrD6MzMnTB+InGo/OWurhaD4cPQDHjhgFiOrKjOfqQmitM3pGWxvA1tTFiZVRndg7yFmlOMhIRhOnQ1AsBA4ybsSFDzW+doXfO08fWLgCnp5uTPm4+q9dHjYkzI/c8ro+Dk4IcS4+2VuOr6eVKcnhZody3uamRLFuTyk5JXWMHhRkdjjiHEmiKnpcXXMbm/MrCBq1l5nxlxwf9utwwOd/gehUGH6puUGGJEDatyDrJZj7q44KwLMTZrOl9A/Ut5WyvbCayW7cKJvFZrOxbNkyXnnlFZ5//nmWL1/OfffdZ3ZYoh8pqjZ63mJDTlOIbf3D4B8Bsx/ow6jOgXcAxI4zHmfjsBsPZTGGB7c/u6OoUcac1a3PwNQfQsTwUw4ZFOLLhtwK6e0QwsVorflobznThkW49Tqkc0YavcEf7y2TRNWNuMDtVtHfbMytRPscpE03MCdhzvEd+9dB5X6Yfo9rfOCacqcxf237Pzs2zY6fDYBXUA7r9w+cBed70sMPP8yMGTOYPn06y5cv55lnniEnJ8fssEQ/UlzdRHSQd9fVG0t2GcNsp9xh9Dq6K4sVPLyM4bMWq2u0mRdi5v3GUOYvnuxyd0ywN42tdupabH0cmBDiTPLK6ymqbnLbYb/tIgO9SUsI4aO9XS5tLlyUJKqix320txy/kH14Wjy5aNBFx3ds/YcxHG30teYF11n0aEieDVueMdYBBBKCEhgWMoyQ8Dw2SKJ6Wqer+Avw4IMPsny5MZw6MDCQvXv3kpKS0lehiQGguKbp9MN+tz1rzM/M+F7fBiXOLCASxl4Hu147dT4tEBNs/DxLa5v7OjIhxBl87Ezs5oxy/4rcF4+KIutwDZX1LWaHIs6RJKqiRzkcmk/2leETvJfM2Ez8PJ3FTqoOwIGPIWOp0UPgKibfYRQiyXmrY9Os+Fk0Wfezp7SU8jr50CSEqymvayEmuIthv23NsGcNpFwFviF9H5g4s0nLjCVx9vznlF2xzp9niSSqQriUj/aWkxIbRGzwGaqsu4m5o6LQ2phzK9yDJKqiR+0+Ukt1azHNlDMnvtOw363PGgvIT/iuecF1Zfg8Y6mIzSs6Ns1OmI3GgUfAfj7bX2licK4tOzub9PT0Ex6TJ082OywxAFTVtxDu38Uan7kfGL114xb1fVDi7AZNgNDEE24MtosJMhLV0tquikgJIcxQ29jG9sJqLnbzYb/txgwKIibIh0/2SaLqLlyoa0v0B5/lVuIRYMxHnJXgrLbZ2ghZL0LK1RDoYutpWiww8Wb474NQsR8iR5AakUqYTxg1IfvYkFvBNyfGmx2lS0pNTSUrK8vsMMQAY7M7qG5sI8zf69Sd+94z1iZNmt3ncYlzoBSMvsaYp9pUbfysnKI7ElUZkieEq/gsrwK7QzOnnySqSilmDI/gg6/LsDs0VjdcE3agkR5V0aM25lYSGLaflLAUYvydSeme1UYvx6RbzA3udNKWGL29X/0bAKvFyqz4WVj897IhtwyHQ5scoBCi3dHGVgAiAk5KVLU2phckz3Gt6QXiRCOvMNaDzf/0hM1eHhYCvT2odv58hRDm25hbSaCPB+kJ/WcqxfThEdQ2tbG7+NS58sL1SKIqekxTq53th4to88w/3psKkPUyhA+DIVPNC+5MAqJgxHzY+QrYjA9Js+JnYaOJY45cdh+RxkwIV3G0wfgdDQ84aehv2W5jPdJhF5sQlThncRPAKwAObjhlV4i/JzWSqArhErTWfJZbydSh4f2q53HasAgAPsuVgpnuQBJV0WM2H6zC4bMfjWZ63HRjY3UBFH5u9Fq68vIKE26ChgpjCR0gMzYTi7LiEbCfT/dJYyaEq6iqdyaqJw/9PfiZ8Zw8B+HCrJ4w+CIo+OyUXaF+XlQ3tpkQlBDiZIVVjRTXNDF9uPtX++0sIsCbMYOC+CxXapC4A0lURY/ZmFuJV2AegZ6BjAkfY2zc+SqgYNxiU2M7q2EXG0vnOIf/BnoFkh6ZRkDoAT6VZWqEcBntywqEnzz0t2grBMVDcJwJUYluSZpprKldV3bC5mBf6VEVwlVszDMSuenOHsj+ZPrwCHYcqqZB1m12eZKoih7zWV4F3kG5TBk0BQ+LhzFnbOcrkDQDQhLMDu/MLFZIWwx5H0GD0ThPHTSVVuthso4USWN2kqlTXXQYt+j36p2/i4E+nifuKN4G8RkmRCS6LX6S8XzkqxM2h/p5UdMkPapCuIKNuZXEhfiSGO5ndig9bsawSNrsms0Hq8wORZxFnyeqSimrUuorpdQ7fX1t0Xsq6lrIPXoAm6ph6iBnEnPoS2Pob9oNpsZ2zlKvB2031mEEpsVNM7b77mdrwVETA3M9mzZtMjsEMUA1ttgB8PfuVDCpvgJqDkmi6i5iUgEFJSdWDQ/186S6QXpUhTCb3aHZdKCS6cMiUK48bes8ZSSG4u1hkeG/bsCM0oh3AzlAkAnXFr3k87xKrAH7AY4nqjtfBk9/SLnKxMi6IXoMRI2G7FWQeSspYSkEe4VgD8zliwNVzB7peuXZSx9+mJacvT16Tu+UUcT84hdnPCYgIID6+vou961Zs4YnnniCDz/8kNLSUmbNmsWGDRt45ZVXyM7O5rnnniM7O5slS5awZcsW/Pz6391a0Xvae1R9Pa3HNx7ZYTzHTTQhItFt3gEQMQKOnJioBvt6cqzZhsOhsfSj4i1CuJvs4lqONduYNrz/DfsF8PG0kpkUJomqG+jTHlWlVDxwBfBMX15X9L7PcivxCcojMSiRQQGDjOq5X78Fo64wPpS4i9Tr4PCXUF2I1WJlatxFeAXmsSlf5qmeq4ULFxIbG8uTTz7Jrbfeym9/+1tiYmK4++67ycvLY82aNSxdupS///3vkqSKbmtsteHraT2xCmX518Zz9BhzghLdNyj9lB5VP2cveYvNYUZEQginz53zU6cODTc5kt4zY3gEeeX1lNY2mx2KOIO+7lH9C/BTILCPryt6kdaajXklWOLymRa3yNh48FNoroExC80NrrvGXgcf/Q52r4IZP2HaoGmsPbiWnMp91DZeRLCf59nP0YfO1vNplscff5yxY8cyZcoUlixZAoDFYuFf//oX48aN4/vf/z7Tpk0zOUrhjhpa7ScO+wWo2GcUQ/MJNico0X1Ro2HXSmiqAV9jjUYfD+PeeVObHV8v65leLYToRRtzKxkdG0TEycuA9SMXJRu9xZsPVnFNuhThc1V91qOqlLoSKNdabz/LcbcppbYppbZVVEgvljsorGqk0r4XB23Hh/3u+Q94B8HQueYG112hQyBhCux6HTg+jNniv18m3XdDUVERFouFsrIyHI7jvSO5ubkEBARw5MgRE6MT7qyhxYa/90lJTMVeiBxpTkDi/ESMMJ6r8jo2tSenTW12MyISQgDNbXa2F1YzbVj/7U0FGD0oiEBvD77Mlxokrqwvh/5OA65WShUArwJzlVIvnnyQ1nqF1jpDa50RGdm/1m7qrzYfrMLDfz8eypOM6Axj2O/ed2DkAvD0MTu87hv7TajIgYp9RPpFMixkOJ4BeWw6IInqubDZbCxbtoxXXnmFlJQUli9fDkBtbS0/+tGP2LBhA1VVVaxatcrkSIU7amix4+fVqUfV4YCK/RA5yrygRPe1J6qV+zs2+TjnHTe1SqIqhFmyDtfQancwOal/J6pWiyIzKYzN+fLZzpX1WaKqtf651jpea50IfAv4WGt9Y19dX/SezflH8Q7MY3x0On6efu477LddypXGc87bAEyJnYzVt5Av8ktNDMp9PPzww8yYMYPp06ezfPlynnnmGXJycrj33nu56667GDFiBM8++ywPPPAA5eXlZocr3Exjqw3/zsNCjxVBW4P0qLqb0CFg8TwhUW0vkNUsPapCmGbLwaMoBZMSw8wOpddNTg4jv7KB8mMyT9VVmVH1V/QjWmu+KDiEjilhSux1xkZ3HfbbLmiQsc5fztsw8z4yYzJ5MedF8o59TWX99H49Z+Ncna7iL8CDDz7Y8XVgYCB79xpViZ977rmO7QkJCeTl5Z3yWiHOprHVTqBPpz9d7UNHI4abE5A4P1ZPCEuGytyOTTL0VwjzbT5YxaiYIJerydEbpiQbvcZfHjzK1WmDTI5GdKXP11EF0Fqv11pfaca1Rc8qqm6iwmZU3JwUM6nTsN/LwcONE7qUq4yKlDWHmBgzEYUFq98BvpQhIkKYyuZw4Gnt9Ker5rDxHDLYnIDE+QsfCkfzO771laG/Qpiq1eZge2E1k5P6f28qwOjYIAK8PWT4rwszJVEV/ceX+VVY/fLxtvowNnwsFGwwhv2Ovsbs0C7MqOPDf4O8gkgJS8ErIJ8vZJ5qh+zsbNLT0094TJ482eywRD9ns2s8Oi9NU3MIlNWo+ivcS3CCcaNBa+D4HNVGSVSFMEV2cS3NbY4Bk6h6WC1MSgyVTggXJkN/xQXZfPAoXgEHmRA1AU+rJ+xbC55+MHSO2aFdmPChED3WGP570V1MHpRJTtULbCkoBVLNjs4lpKamkpWVdfYD+zmllBXYBhRrra9USiVhFIwLB7YD39Fat5oZY39ic2g8rCclqkFxYJU/Z24nJAFa64ybm76heDuXp2mzyzqqQpihfXWDzAGSqAJMTg7nk30VVNS1EBnoxiMB+ynpURUXZFNBAXiVkhk7ybgrvm+dMTfV09fs0C5cylVw6EuoKyMzJhOt7OTX76Gm0fycQzt7IPozN3qPdwM5nb7/PfBnrfUwoBr4nilR9VN2h8bD0ulPV+1hGfbrroITjGfn8G2rs6fc7nCb330h+pUtB48yLCqA8AFUi6N9nqosQeiaJFEV5624pony1k7zU0uzjQqcI+abHFkPSbkK0LDvXSZETcCqrFj9DrCtoNrUsHx8fKiqqnKnRK7btNZUVVXh4+PayxsppeKBK4BnnN8rYC7QvvbO88C15kTXP7XZHacO/ZVE1T2FOBPVWiNRbZ97bJNEVYg+Z7M72FYwcOanths7KAh/LyubZT1VlyRjpcR525xfhdU/Hx+rL6PDR8OG5YCCEZeZHVrPiBoNoYmw/338MpYxJnwsWY0H2FpwlEtGR5sWVnx8PEVFRVRUVJgWQ1/w8fEhPj7e7DDO5i/AT4FA5/fhQI3W2ub8vgiIMyOw/sreeeivrRWOHTme8Aj3Euy8wXBKj6oM/RWir+WU1FHfYhtQw37BmKc6YUgoWwskUXVFkqiK87Y5/yhe/vlkRE/E0+IJ+94zlnUJiDI7tJ6hFAy/DHa8AG1NTBk0mV0Vz/BlQTGQYlpYnp6eJCUlmXZ9YVBKXQmUa623K6Vmn8frbwNuAxg8WHoEz1WbXWNtH/pbdwTQx4eQCvfiHwEevh09qu095dKjKkTfax/6Ojkp3ORI+l7GkDD+8tF+6prbCPTp/8vyuBMZ+ivO25eFBeBVTmZsJtQWG8u5jFxgdlg9a8Q8sDXBwc/IjMkE5WBvzU5ZPkEATAOuVkoVYBRPmgs8BoQopdpvAsYDxV29WGu9QmudobXOiIyM7It4+wW7w4Fne49qXZnxHBhrXkDi/CkFQbFQVwLIHFUhzLStoJqEMF9igl17yk1vmDgkFK3hq0M1ZociTiKJqjgvVfUtFLfsBpzzU/evM3aMvNzEqHrBkOlGFePc90mLTMOqPNDeB8k6LI3ZQKe1/rnWOl5rnQh8C/hYa/1t4BPgOudhNwFvmhRiv2Sz646EhnpnotpfRnEMRAHRUF8O0FEky2aXRFWIvqS1ZvuhajKGDKxhv+3SB4dgUbC90NwaJOJUkqiK87LjUA1WvwP4Wv0ZFTbKSFRDEyFypNmh9SxPH0ieA/s/wMfqzeiwMXj4HZS5DOJMfgb8WCmVhzFn9VmT4+lXbA7dUXSHBiPBkUTVjQVEddxwsFqlR9VMSqnnlFLlSqndnbb9RilVrJTKcj4u77Tv50qpPKXUPqVUPylOMTAVVTdRUdfChMEhZodiigBvD0bFBLHjkCSqrkYSVXFethUexcOvgAnR4/GwtUL+pzBigTGUq78ZMQ9qD0F5DhmxE7D6FrO5oNTsqIQL0Vqv11pf6fw6X2udqbUeprW+XmvdYnZ8/YnN4ejUo1oOKPCLMDUmcQECojsSVZmjarp/AV2V7f+z1jrd+XgPQCk1GmMkyRjna55yrikt3FB7gjZ+cKjJkZhn4pBQvjpUIzfKXIwkquK8bCk8jMW7nIyYiVD4OdhbYPilZofVO4bPM55z32di1ERQdrLKdmGTRemF6HM2h8az89Bf/wiwSl1AtxUQBc210NYsVX9NprXeAJzrcKFrgFe11i1a64NAHpDZa8GJXrWjsBo/LyujYgLPfnA/NXFIKPUtNvaV1pkdiuhEElXRba02BznVuwAYHzUe8j4EDx8YMtXkyHpJ0CCISYX9H5AelQ5Am+cBckqkMROiL9kdGq05XvW3vtzokRPuq/3n11COVUmPqov6gVJql3NocHuXWxxwuNMxshSXG9txqIa0+BA8rAM3LZg4xPivvV2G/7qU8/ofqZSKUkotVErdpZRappTKVEoN3P/dA8zuI7Vo74NYlSdjI8ZC3kcwZBp4+podWu8ZMR8ObybY4SAxaChWvwKZp9pPSHvmPmzOnraOdVTry2R+qrtrT1TryztmjmjJU13J34ChQDpQAvypuydQSt2mlNqmlNrW39f/dkeNrTa+LjnGhCEDc35qu/hQX6ICvdkun+1cSrc+jCml5iil3gfeBRYAscBo4FdAtlLqt0qpoJ4PU7iSHYXVWH0LGRWagnddGVTlwrCLzQ6rdw27FLQd8j9lcmwGHn6H2H6oyuyoxAWQ9qwfkB5V99d+o6G+DOXMVCVPdR1a6zKttV1r7QD+wfHhvcVA5wWMZSkuN7WrqBa7Q3f0KA5USikmDgmVHlUX092JPZcDt2qtD528w7lu4JXApcAbPRCbcFFbCsqw+haROWiu0ZsKMOySPru+1hra2sBiQXn00dy0uIngHQT5nzBhzGWs3LeSHSV7gIy+ub7oDdKeuTOtjUTVXwopubX2QliNnW78SZeqy1BKxWqtS5zfLgTaKwK/BbyslFoODAKGA1tMCFFcoI5CSgkDO1EFY/jv2t2llB9rJipo4K0n64q69Slfa33/GXaHa63/c4HxCBentWZ76S6IsjM+cjxsegaC4iFiRO9cz+Ggec8eGr78kqbtO2g5mE9bUTHY7WCx4BERgdfQZHzT0/HPzMRv0qTeSV6tHpA4Aw58zIQ5DwBQadtHeV0zUYHSmLmpP2mtuyzfrLW2AdKeuZgT8pe2JqOIm+/AXPev3/B1fjhuNIbbKSU9qhdCKXURcCMwA2OUSBNGcvku8KLWuvYMr30FmA1EKKWKgF8Ds5VS6Rg/lgLg+wBa6z1KqdeArwEbcJfW2t5Lb0v0oh2FNSRH+hPq72V2KKZr71Xecaia+WNjTY5GQPd7VE+glAoBvgncAKRg3FUT/VhRdRN15OINpIePgYMbYMy1Pb4sTVtZGdWvvsqxN9+i7cgRALySk/FJGU3QZfOx+PmiW1tpKymled9eqv6+gqq/PY01JITA+ZcR9t3v4p2c3KMxMXQO7HuXmJZGwr1jKPUr4KtDNVw2JqZnryP6SpZzvcBXgDe01jVmByTOjVJAs/PH5Su9AG7Nyx+sXtBk9OpYlJIO1fOklFoLHAHeBB4CygEfYAQwB3hTKbVca/1WV6/XWi/pYvNp14HWWj/kvI5wU1prdhyqZu4omesPMHpQEJ5WRdbhWklUXUS3E1WllC9GWfIbgPFAIHAtsKFnQxOuaHthNVa/AuL8EwmtPAAtx3p02K+tuprKJ56k5rXX0HY7/hddRMSPfkjAtGl4nGFui6OhgYYvvuDYuvepXfMfal5dSeCllxB1//14DR7cM8ENnWs8H/iYzNiJvNfwKTsKqyVRdV9xwCUYawE+rJT6EiNpfVNr3WRqZOLsnImNJKpuTinjZ9jk7FEFHJKpnq/vaK0rT9pWD+xwPv6klJKx8qJDYVUjRxtamTCA10/tzNvDSkpsEDsPy31rV9HdYkovA/sx5m09DiQC1c7F7mXhswFga0ElVt9DTI6daCxLo6yQNKtHzl37zrvkX34F1StXEnztNQz94H0GP/sMIddee8YkFcDi70/gJZcQ9+gfGfbJx0TceSf1n28i/8qrqHz6abS9B0YkhSVD8GDIX09GzASURz2bi/Zf+HmFKZwFQt7XWi/FKAryHMZNuINKqZfMjU6clSSq/YdvWMfPU4b+XpAblVKTnHPsu9RFIisGsK8OO+enDh7YFX87S4sPIbu4Focsk+USursEw2igGsgBcpzzEeQnOYBsLtqLsjYxIXo8HPgY4jPA98IaOEdrKyX/70GO3HcfnoMTSHrjDWL/53/wio8/r/N5hIUR+aMfMnTtewTMnUvFXx7j0PduwVZ5gX+flTKG/x7cwISINAD21ezCZpd7NO5Oa92KMdcqBziGMZVBuCiFkkS1P/ENhUZnoooM/b0A8cBjQLlS6lOl1MNKqSuVUjKRW3RpV1Etvp5WhkcFmB2Ky0hLCKG+xUZ+Zb3ZoQi6mahqrdOBRRjDfT9USm0EApVSsj7AANDUaudw4x4AJgQPhZIsSJ5zQee0HzvGoZtupub11wm/7TYSX34Zn5E9U5jJMzqauD8vJ/ahh2jKyqJgyQ20FhRc2EmHzoGWYyTXH8Xb4ofDq5C9pXU9Eq/oe0qpBKXU/UqpHcA7GG3i1VrrCSaHJrpwQgLTkahKT4Db8zveo4oCLfe/z4vW+j6t9VQgBvg5cBRYCuxWSn1tanDCJe0qqmVsXBAeVlk6vF1afDAAWYdPW3dM9KFu/8/UWu/VWv9aaz0KuBt4HtiqlNrU49EJl7LnSC3K9yCBHqHEVxaAdkDSzPM+n626msKbb6Z5927i/vJnon58L8pq7bmAMdbFCvnmNxjy/L9w1NdTsOQGWnJzz/+ESbMAheXgp4wOG4vV9xBfyZpbbsnZZm0EojCWqRmptf6N1nqvyaGJs1AKaJJiSv2Gb8gJc1QlT71gvkAQEOx8HAE2mxqRcDk2u4M9R2pJjZObfZ0lRwYQ4O0h81RdxAXdQtFab9da3wcMAR7omZCEq8o6XIPV9zBpkWmogs/Aw9cY+nse7PUNHFr2PVoP5BP/1JMEzZ/fw9GeyDctjSEvv4Ty8DCue/jw+Z3ILwwGGcOeMweNx+pdytbCLlc4Ea7vASBRa32/1nq72cGIbmqqBosHeMmQNbcnc1R7hFJqhVLqc2AlcBGwCbhe2b8yfwAAIABJREFUa53hnIsvRIfc8nqa2xykJQSbHYpLsVoUqXHB7CySRNUVdLeY0q+6muugDRuUUnOVUlf2XHjClWw9XITFq4pJg8bDwc9g8GTw8O72eXRbG8V3303L/v3EP/E4ATNm9EK0p/JOSmLwc8+iW1s5tHQZturz7AkdOgeKtpEWMgKUZntZds8GKvrKTOC0t5KlPXM9JwwJbao2elN7eGksYQLfULA1Q2ujc46qpKrnaTDgDZQCxUARIJ+2RZd2OROxcfHSo3qytIQQckqO0WKTpYHN1t0e1WzgbaXUR0qpPyqlfqqUelAp9W+lVDZwFTK8pN/aVb4LgNSAwVC+57yH/ZY+9BANn39O7O9+22dJajvv4cNJWPF3bOXlFN9zL7qtrfsnSZwB2k5qk7GCSUXrfo42tPZwpKIPZAPvSHvmfhQcT1SF+2v/OTbXGD2qkqeeF631fGAS8Khz008wpmZ9oJT6rXmRCVe0q6iWQB8PhoT5mR2Ky0mLD6bNrskpkRokZutuMaU3tdbTgNuBPYAVo0Lmi0Cm1vperXVFz4cpzHa0oZUqWy4KC2OOOavnnseyNLVvv03NqysJv+V7hHzzm/+fvfuOj/OqEv//OdPUJcu2LFnusVVc45ZiOyHNIQmEJPQNJYFlYWHZXdrCF75LWdjl92VpCyyQJYEsvSyEEpJAkg1xSJxC7MTdlltc1GzZltXbzJzfH88ztuxYtjSamWfKeb9eY2memfE5o9Hc0X3uvecmOMvRKbj4Yqo+91l6n3uOI1/60tj/gxmXgi/IhKYXqCyYga/goK1TzUDWnmW4/pOQbyMBWSGvxPk60IVgU3/Hw53htg14CPgDsB6Yi1NTxJhTtjR2sGR6GT6fzUo528UznM8WW6fqvRH32jofVd0DjKkijYjkA3/GmZYSAH6lqp+JJ75Jvc2NzvrUaUVzKDz0HIRKYOrSMf0fA/v20fKZf6Fg5QoqPvjBJGU6OhNuu43+7Tto/+GPKFq1ipJrxlC9OFQE01bAgadYUXsZrV2Ps/nwSa6bb8WvM1E87ZnxxplVf09C8RTPcjEJlFfqfB3oQsS2p4mXiPwjsNq9DOGsUX0aZ49oW6NiThkIR9jV2snfXHmR16mkpall+VSU5FlHNQ2ksh71AHCtql4MLAVuFJHLUxjfjMOmQyfwFxxmZdVSZ33qrNXgH/15Dh0aoumjH8WXn8+0r3wVCcR1jiShpnz0n8irq6Plnz859j1WZ18BzS+yYnI9EuhmQ9Pe5CRpjHkZEWCw2wopZYtTI6qd7oiq9VTjNBv4JXCZqs5V1ber6l2qullVbcNvc8quli6GIsqSaVZI6VxEhIunl7HJCip5LmUdVXc6Smz33KB7sU+jDPFcYwPi72d56Sw4vmfM61OPf+97DOzYSdVn/4VgZXqMgvhCIaZ9+UtEu7tp+dSnx1bAY/YVoBGWDDmf/Tvbt1oBEGNSaaAb8qyjmhWGTf3F1qiOx6dV9T5VbRnpDiJibxpzupDSDFs+MZIl0yewv62Hrv44apmYhImroyoia0Zz7Bz38YvIJuAo8KiqWqGSDKCq7GrfDsCSvh7n4JzRF0Hq372btm99m9JX3UTp9dcnI8W45dXUUPGhD9H9+ON0PfzI6B844zLwBZl3dC9ByadXXqKloz95iRpjzjyzOdjtLEEwme+sNaombr8Tka+IyCtEpCh2UEQuEpF3icjDQHL3gjMZYUtjB5OLQ1SX5XudStpaNM1ZkmAFlbwV74jqf47y2BlUNaKqS4HpwKUisujs+4jIe0Rkg4hsaGuzOibpoLG9jz7ffvJ8hcxp2eEUMKlcPKrHajRKy6c+hb+khMpPfjLJmcZn4tvfRt78+Rz5/OeJdI2yQQoVwvSVBA4+zbyy+fgLDp06Q2kyi4jUupV/t7nXl4hIev6yGgBE1emo2ohqdhjeURXbniZeqnod8Bjwt8B2EekQkeM4BeKqgDtV9Vde5mjSw5bGDhZPK0Nse68RLap2pkVva+rwOJPcNtZ9VFeJyEeAChH58LDLv+BUzBwVVT0JPM45zuyp6t3u5tQrKyoqxpKeSZJNh51CSjUTFuA78JQz7dU3ul+djt/+jv7NW5jysY8SmPiyLXjTggQCTP3cZwkfO0bb174++gfOvgKaN3Fp5QJ8+c28cPho8pI0yXQP8Amc4iOo6hbgrzzNyJxXMOJsDWVrVLPEGR1VWxM0Hqr6kKq+VVVnq2qZqk5S1dWq+nlVbfU6P+O93sEwe4522f6pFzClNJ/JxXlsb+70OpWcNtYR1RBQjFO1t2TYpRN4w/keKCIVIjLB/b4AuB7YNdaETeptPHQEX14rl02aBycPwqwLzvIGINLVxdGvfpWCiy+m7JZbkpzl+BQsXkz5W95C+09/Sn9Dw+ge5K5TXS55iER5vtmKKmaoQlX9y1nHwp5kYs4rNtIWjLhLEGxENTv4gxAosKm/xqTAzpZOogqLrZDSBS2aVsr2ZhtR9dKYSq+q6hPAEyLyfVU9OMZYU4EfiIgfp4P8P6r6wBj/D+OB51u2IHlRlkbdPyFmrRrV4459+y4ix49Tede3kVGOwHqp4h/+no4HHuDoF7/EzO9998IPmO7sp7q43albsa9zF6pqU2kyzzERmYs7kCMibwBGLEZivBeK9Lrf2BrVrJFX4hRTwoopGZNMO9wRwoXuGkwzsoXVpTy55xj9QxHyg6OeOGoSKN7eQ56I3C0ij4jIn2KX8z1AVbeo6jJVXaKqi1T1c3HGNikUiSovde0EcDpkoeJRrU8dPHiQEz/6EWWvey0Fi0e3ntVr/gkTmPy+99Kzfj3dTz514QeECmH6JVQcep6SwCQG/Qc5dKI3+YmaRHs/8B2gXkSagA8C77vQg0QkX0T+IiKbRWS7iHzWPT5HRJ4Tkb0i8gsRCSU3/dxjI6pZyO2o2ok+Y5Jre3Mn5YVBqkqtkNKFLKouIxJVGlqtoJJX4u2o/hJ4Efgk8NFhF5Nl9rd1Ew0dpDxYxaTGF2D6JaPaP7Xtm99CAgEqPvCBFGSZOBPf8haCM2dy9ItfRCORCz9g1mpo2UxdWS3+/Ea2NNoUkUyjqvtVdS1QAdSr6hWqemAUDx1pb+h/B/5DVecB7cC7kpR6zokNtAXDsRFV66hmjWEjqiZ+7u4KtqzKjGhHSycLqkvtpNAoLHKnR2+z6b+eibejGnY3kf6Lqm6MXRKamUkLW5s68BccZmF5HRzZ7nTMLqB/9246H3iAiW97K8Ep6bFn6mhJKMSUD3+YgT176Pj97y/8gJmXg0a4tGgivrxjbDxsM0YzTawoHE6lzHe7198lIkvP97jz7A19LRCrrPkD4LYkpZ6zbEQ1C8VGVAG1ckpxU9UI0CAiM73OxaSfcCTKrtYuFlbb+tTRmF5eQGl+wAoqeSjejurvReTvRGSqiEyMXRKamUkLGw4fxhfs4NL8EkCdjtkFtH3jG/iKipj4rswcSCq54ZXkzZ/PsbvuQsMXqKkz/RJAWDLg7KG6oWVL8hM0ibYSeC8wzb38LU5F8ntE5GPne+DZe0MD+4CTqhr7xWl0/0+TALG1i0Fbo5p98kpgwP4YTJBynO1pHhOR+2MXr5My3tvX1sNgOMqCqbY+dTREhIXVZWy3LWo8M6ZiSsPc6X4dPt1XgYvGl45JN5uOboUQLOrtBF8Qpq087/37tmyh+38fY/I//gOB8vIUZZlYIkLF37+fxvc7xZUm3HaeAbGCCTBlAQuO7gfgpa5dRKOKz2dTajLIdGB5bHRURD4DPAi8AtgIfHGkB7qjF0vdiua/AepHG1RE3gO8B2DmTBv8GItg2EZUs06oCAad19WKKY3bp7xOwKSnHS1Oh2thtXVUR2thdSk/fPYgQ5EoQX/6FwbNNnH9xFV1zjku1knNMtGocqh7DwDzW3dD9VKngNB5HPv2XfgnTGDiHXee937prvjaa0c/qjrzcsobX2BCsJJI8DD7j/WkJkmTKFNw1pvGDAGVqtp31vERDdsbehUwQURiJwGnA00jPMb2jI5TMGr7qGadYAEM9WHL5sbP3aHhABB0v38eeMHTpExa2N7USV7Ax5zJRV6nkjEWTStjMBxlX1v3he9sEi6ujqqIFIrIJ0Xkbvd6jYjcnNjUjNcOnuglHDzEpGA1xc2bLjjtt79hN93r1lH+9rfhL87sRjA2qjp08BAd919grerMy2Gwi0XF092CSidTk6RJlJ8Az4nIZ9zR1PXAT0WkCNgx0oNG2Bt6J06HNbav9J3A75KZfE6JTf0N9wDijMKZ7BAsgiHnBIQNqI6PiLwbZ538d9xD04DfepeRSRc7WjqpryohYCODoxYbfd7WZEsTvBDvb+p/A4NArLJOE/BvCcnIpI1tTR3485tYWFgJkUGYef5CSse/+12ksJCJb31rijJMrtio6vHvfheNRke+o9uBXxnIwxdqZ+PhxhRlaBJBVf8VZ13qSffyXlX9nKr2qOr5fpmnAo+LyBacEYtH3b2h/w/wYRHZC0wCvpfcZ5B7QpE+CBZiw29ZJFgAQ72AvaYJ8H5gDdAJoKp7cGaOmBymqmxv7mSBFVIak4sqiskP+thulX89Ee8a1bmq+mYRuR1AVXvF6lxnnQ2HD+ELdrDS525yfJ4R1cHGRjofeoiJb387/gkTUpRhcokIk/76r2n+6EfpXvcEJddec+47ls2AkmoWdx0HYPPRbcCFi06Z9KGqz4vIQSAfQERmquqhCzxmC7DsHMf3A5cmJVEDgD/aD0HbAzCrBAshOoRfLrDUwozGgKoOxv4sc5ci2EB1jmvu6Kejb4gFtj51TPw+YcHUUrbbiKon4h1RHXSnuimAiMxllGu5TOZ44chWABZ1HIGKeigcubDz8e99D3w+Jr7zHSnKLjVKb7yBQPVUTtx778h3EoGZlzO/2ZklerCnAbVqIBlDRG4RkT3AS8AT7tc/eJuVOZfYtiX+yAAECjzOxiRU0Hk98xm0Ykrj94SI/F+gQESuB34JjGK/NZPNYpVrreLv2M2fWsrO1k77284D8XZUPwP8EZghIj8BHgPOu42DySyqysGu3QDMb9p23tHU8IkTdNz3a8puvYVgZWWqUkwJCQaZeMcd9G7YQN+W82w9M/NySjqamByoYihwiMb2vtQlacbrX3GGwHer6hxgLfCstymZ8wlEB2xENdu4HdUCsXPeCfBxoA3YirOs4SHgk55mZDy3o6UTEZg/1bb1Gqv6qaV09Ydp7uj3OpWcM+aOqoj4cPboeh3wDuBnwEpVXZfQzIynGtv7GAwcYnJgCsX9HTDjshHve/J/fokODjLpHe9IXYIpNOENb8RXUsLxe/975Du5HflFeeX48xttLUNmGVLV44BPRHyq+jjO3qomTQWiNqKadYJORfl8BrBZquN2DfBjVX2jqr5BVe9RGwrKedubO5kzuYjCULyr/nLX/Cqnc7+rxab/ptqYO6qqGgU+pqrHVfVBVX1AVY8lITfjoe3NTiGlRUF3ven0S855Px0aov3nP6do9Sry5s1LYYap4y8uovyv3kzXI48w2HjOnUZgykIIFXOJRvEFO3n+8IGU5mjG5aSIFAN/Bn4iIl8HbI+hNBT7U9sfsTWqWSc2omqriBLhDmCziDwrIl8SkdeISGZubG4SZkdzJwutkFJcamMd1dYujzPJPfFO/f1fEfknEZkhIhNjl4RmZjz1/CGnkNJyDUP+BJh07k5o12OPEW5tpfxtb0txhqlV/ta3gggnf/GLc9/BH4Dpl7D4xGHg9PpekxFuBXqBD+EsadgH2HZbacwZUbWOalaJjaiqdVTHS1XvVNVanJlvh4Fv4UwFNjmqo3eIppN9Nu03TqX5QaaXF7DTRlRTLt6O6ptxyp//GdjoXjYkKinjvY2tTkdrcXuLM5o6QlHnEz/+McHp0ym+6qpUppdywaoqSq69hpO/+hXRgRH+kJpxKXVH9gDCga6GlOZnxuXTqhpV1bCq/kBVv4GzxYxJU/7IwKkROJMlThVTGrBiSuMkIm8Tke/g7KW6FvgmcKW3WRkv7Wp1OljzrZBS3OqrSm1E1QPxrlH9uKrOOetyURLyMx5QVbejJcw/smfEab/9O3fSt2Ej5W95C+L3pzZJD5TffjuR9na6Hn743HeYtpLCaIQK30T65TDHum1kIENcf45jN6U8C3NBsf6LP9pvI6rZxh1RLZBBjxPJCl8DlgL3AP+oql9U1Wcu9CARuVdEjorItmHHJorIoyKyx/1a7h4XEfmGiOwVkS0isjxpz8aMW8MRp4NVX2UjqvGaP7WEl4710D8U8TqVnBLvGtWPJiEXkyaOdA7Q7z/IFN9EijQKM87dUT3xk58gBQVMeP3rUpyhNwovv5zQ7Nm0//Rn577DdKf+zgJ/Eb78ZnY02xSRdCYi7xORrUCd+4dW7PIScJ4Sz8ZrTtVfG1HNKu7rmWcjquOmqpOBv8bZF/rzIvIXEfnRKB76feDGs459HHhMVWtwdnj4uHv8JqDGvbwHuCsBqZsk2dXaRWl+gKpSO8EXr/qqUiJRZe/Rbq9TySm2RtW8zLYmp5DSQn8RIDBtxcvuE+nupvPBhyh99avwl+XG4nzx+Sh/y+30bdpE/44dL79D4USYeBHLIoP4gh1sONyY+iTNWPwUeA1wv/s1dlmhqtm96DrDOfuo5nmdhkmkWDEltRHV8RKRUmAmMAuYDZQB0Qs9TlX/DJw46/CtwA/c738A3Dbs+A/V8SwwQUSmjj97kwy7W7uorypFRljGZS6sfqoVVPKCrVE1L7Ox8SC+YCfLw/1QUQf5L++Idj70ENrXR/kb3+hBht4pu+02JD+f9p+NNKp6CQtPHAJgQ4sVVEpzfqATpy3rGnbBTrylp9gOG7Y9TRY6Y3saM05P4Zx02wK8WVXrVPXOOP+vSlVtcb9vBWKbpU/DKdQU0+geM2lGVWk40kVtVbHXqWS02ZOKyAv4bIuaFItrMyVVnZPoREz6ePHIdgAWHTsAc8+eBeQ4+av7yKuZR/6SJSnMzHv+0lJKX/UqOh98iMpPfAJfYeGZd5i2kvnbfgkTprO/Y7c3SZrR2sjpZY9nn2ZWwNbdpyl/dMC2p8k2gRAAeQyhto/quKjqEgB3261E/r8qImN+cUTkPTjTg5k5c2YiUzKj0NzRT1d/mLoqK6Q0Hn6fUFdVYiOqKRbXiKqI3HGuS6KTM9440LkHgLqu4zD90pfd3t+wm/4tW5jwhjfk5DSSCa97LdHeXjoffuTlN05fSVk0ygSKORk9QM9AOPUJmlGJFYFzL1YcLkMIUfzRQRtRzTZ+Zyp3kCGPE8l8IrJIRF4EtgM7RGSjiCyK8787EpvS63496h5vAmYMu99099jLqOrdqrpSVVdWVFTEmYaJV4Nb8dcKKY1ffVXJqQrKJjXinfp7ybDLlcC/ALckKCfjod7BMCejB5lICSWq56z4e/K+XyHBIKW35OZLXrBiBcFZM+n49a9ffmPlIvDnUS/5+PKabc+tDCEit4jIl92L7aGaphRnxA2wEdVsEzjdUbViSuN2N/BhVZ2lqjOBj7jH4nE/EJs2fCfwu2HH73Cr/14OdAybImzSSGwEsLbSOqrjVV9VyrHuQdq6bIlCqsTVUVXVfxh2eTewHLDJ71lg95Fu/HnN1BGCUImzRnWY6MAAnb+7n5Lr1xIoL/coS2+JCBNe+1p6n3+ewcOHz7wxEILqpVw81I8vdIwXG494k6QZNRH5AvABYId7+YCI/H/eZmVGko9bbMdGVLOLzw/iJ4TNQkmAIlV9PHZFVdcBRRd6kIj8DHgGpxJ6o4i8C/gCcL2I7MHZk/UL7t0fAvYDe3G2wfm7hD4DkzC7W7uYWpZPWUHQ61Qy3umCSjYIkSrxjqierQewdatZYHPTUSR0nCUD3TB9hfPHwzDdjz1GpKODste/3qMM00PZrbeCCB2/+c3Lb5y2kgXtjYgof2na9vLbTbp5FXC9qt6rqvfibM9go6ppSHVYR9VGVLNPIM+m/ibGfhH5lIjMdi+fxOlUnpeq3q6qU1U1qKrTVfV7qnpcVa9T1RpVXauqJ9z7qqq+X1XnqupiVbWCmmlqV2sXdTbtNyHq3XW+u1psnWqqxLtG9fcicr97eQBoAM7xF7vJNM83bUdEWXCy+ZzTfjvu/z2BykqKVq3yILv0EZw6laLVqzn529+i0bOq/k9fyYK+HgB2tzd4kJ2Jw4Rh3+fGfksZKijuiJvftqfJOv4QIcJWSmn8/hqoAH4N3AfE9lU1OWYoEmVfW7d1VBNkYlGIytI8dtqIasrEVfUX+PKw78PAQVW1TSOzQMOJBghB/WA/TFt5xm3h9na6n3qKiXfcgfgSNRifucpe91qaP/JP9D77LEWrV5++YfpKKiMRCjVE2+B+wpEoAb/9vNLY/wNeFJHHcar/voLTm9qbNHNqaqjfprFlnUAewSEbUY2XiOQD7wXmAVuBj6iq/UBz2EvHehiKqBVSSqD6qlIbUU2hMf31LCLzRGSNqj4x7LIemCUic5OUo0kRVaW1fx/5GmRqOALVy864veuPf4RwmLLX2KxIgJK1a/EVFdHx4INn3lA2AymupFZDEGrmwPEebxI05yUi33Lbs58Bl3N69GGVqv7C2+zMuShKgIhzxTqq2cefR0itmNI4/ABYidNJvQn4krfpGK/FCinVVdrWNIlSV1XC3rZuIlFrqFJhrMM8XwPONd7d6d42IhGZISKPi8gOEdkuIh8YY2yTZI3tfUSCTdREQ0jpNCipPOP2jt8/QGjeXPLq6z3KML348vIoWbuWrkceJTo4ePoGEZi2ksUDPfjyWtnW3O5dkuZ8dgNfFpEDwIeAw6p6v6q2epuWOZ9gbETVZx3VrBMInX59TTwWqOrbVPU7wBtwZoeYHLa7tQu/T5g75YK1tMwo1VaWMBiOctAGIVJirB3VSlXdevZB99jsCzw2jDMNZQHO6MX7RWTBGOObJNrRchJfXiuLBntfNpo62NhE3wsvUHbza3Jy79SRlN78aqJdXfQ8+eSZN0xfwaKuNsQX4bnGHd4kZ85LVb+uqquAq4DjwL0isktEPiMitR6nZ0ZweupvyNtETOL5rZjSOJ364amq9fgNu1q7mDO5iLyA/8J3NqNSW+lscrL7iE3/TYWxdlQnnOe28+4VoKotqvqC+30XsBOYNsb4JomeO7wb8Q2xoPvYyzqqnQ88AEDpzTbtd7iiyy/HX15O59nTf6uXU++Osm4/ttODzMxoqepBVf13VV0G3A7chtM+mXSj2NTfbBYIEWIItXJK8bpYRDrdSxewJPa9iFj1lxzUcKTTCikl2LwpsY5qt8eZ5IaxdlQ3iMi7zz4oIn8DbBztfyIis4FlwHNjjG+SaGubM/JXPzh4RkdVVel44PcULF9OaLqdWxhOgkFKbnglXY+vI9rbe/qG6qXMGgoTUj+NPXu9S9BckIgEROQ1IvIT4A84Vcxf53FaZgSnq/5aRzXr2IjquKiqX1VL3UuJqgaGfW+LFHNM90CYwyf6qK+0jmoiFYYCzJhYYCOqKTLWjuoHgXeKyDoR+Yp7eQJ4FzCqNaciUoxTsOSDqvqyM3wi8h4R2SAiG9ra2saYnhmPg9178aswd3DojI7qwO49DO7dR+nNr/Ywu/RV9upXo319dP3p8dMHC8rxl8/homiQPt8hOnrtj690IyLXi8i9QCPwbuBBYK6q/pWq/s7b7MxIgqdGVG3qb9ZxR1RtQNWY8Yt1pGptRDXh6ipLrKOaImPqqKrqEVVdDXwWOOBePquqq0ZTgEREgjid1J+o6q9HiHG3qq5U1ZUVFRVjSc+MQ+9gmK7oQWZEgwTLZ0PhxFO3dT3yCIhQ+spXepdgGitYsYJAVdU5pv8uY/FAL/68Fna0nPQmOXM+nwCeBuar6i2q+lNVHXV1hJEKxInIRBF5VET2uF/Lk/UEco0yvJhSvLurmbTlz7NiSsYkyG634q9tTZN4NZUl7tY/Ua9TyXpxbe6oqo+r6n+6lz+N5jHiVOD5HrBTVb8aT1yTPA2tXfjyWlgw2A/Vy8+4reuRhylcsYLA5MkeZZfexOej9Kab6H7qKSKdwyYJVC9jYU874h/gL4dt+m+6UdVrVfW7qhpvWeaRCsR9HHhMVWuAx7A9WRMqaMWUspc/RMi2/TQmIXa1dlEY8jOjvNDrVLJObWUxQxHlwDGr/JtscXVU47QGeDtwrYhsci+vSmF8cx4bDh/CF+xiYW/HmdN+97/EwJ69lNho6nmV3vBKGBqi+4knTh+sXnaqoNILR7Z5lJlJlvMUiLsVZz9D3K+3eZNhdgrY1N/s5Q8SIGIzf41JgIbWLmqmFOPz2U4NiVbrrvttsOm/SZeyuVOq+hRg75Y0taFlO/DyQkpdjzwCQMkrr/ckr0yRv2QJgYoKuh79X8pe8xrn4NSLmTcURhT2d+7xNkGTVGcViKtU1Rb3plagcoSHmTFShdCpYko29Tfr+IP4YycijDHjsudoF9fUTfE6jaw0t6IYn1jl31RI5YiqSWN7TjYAUDcYhqkXnzre+cjDFFx8McGqKq9Sywji81Fy/Vq6n3ySaH+/czC/lLxJNcyIBjkxdIBI1MYJstH5CsSpqjJCaRgrHBcfG1HNYr4AfiI4bxtjTLxO9AxyrHvw1MifSaz8oJ9Zk4rYYyOqSWcdVYOq0jbwEpMifsomzoV8p4r94OHDDOzYadN+R6lk7Vq0r4+e9etPH6xexsKBfgg1cfC4rWXINiMUiDsiIlPd26cCR8/1WCscF5/TxZRse5qs4/PbiKoxCRCrSFtTWexxJtmrZkqxTf1NAeuoGhrb+4gEm1gwNHhGIaWuRx4FoOQG66iORuEll+ArKzv1cwOcjmpfJ75gJxsOH/IuOZNw5ykQdz9wp/v9nYBtdZMgihLC9lHNWu6IqjFmfGIjfXVW8Tdp6qpKOHi8l4GwtVmFiuYIAAAgAElEQVTJZB1Vw5amNvyhNhb0db9sfWr+ggWEpk/3MLvMIcEgJddcQ9fjj6NDbuXKYQWVnmuygkpZZqQCcV8ArheRPcBa97pJEJv6m8V8AQJqxZSMGa+GI12U5AWoKs33OpWsVVNZQiSq7G+z2XLJZB1Vw7ON20H0jEJK4bY2+jZvpuT6tR5nl1lKrl9LtLOT3uefdw5ULaZ+yBkB2nVil4eZmURT1adUVVR1iaoudS8PqepxVb1OVWtUda2qnvA612yhOnx7GhtRzTo+K6ZkTCLsPtJNTWUxzsQfkwy17rTq3Tb9N6mso2rY1rYTgLqhCFQtBqD7z38GoPiaazzLKxMVrVmDFBTQ+ag7/TdURNmkeiZH/LT07fM2OWOyQFDcjozPqv5mHZ8fP1GslpIx8VNV9hzpskJKSXbR5GICPrGOapJZR9VwuGcvBVFh2sQaCDkbQ3c9/jiBqVPJq6vzOLvM4svPp/iKK+h+fN3pypXVy5g/2E+/r5HOftvM3pjxCBImIgGwkYLsY2tUjRm3Y92DtPcOUWMd1aQKBXzMnlxkW9QkmXVUc1zPQJheDlM7NITPLaQUHRig5+lnKL76Kps2Eofiq68i3NrKQIOz5Q/VS1nU34s/1MbWJtuGxJh4KeBDUfF7nYpJhtj2NF7nYUwGixVSqrWKv0lXW1lsI6pJZh3VHLeztQN/XjML+vtOrU/t/ctf0N5eSq6+2tvkMlTxK14BQPe6dc6B6uXUDQ6CKE8dsoJKxoyHjyiIfXRlJV8AP1Fs7q8x8dt9qqNqI6rJVjOlhEMneukbtJkgyWKf9jnu2UO7wT90RiGl7sfXIQUFFF5+ucfZZaZARQX5ixbRve4J50DlQuqHogBsPrrdw8yMyXw+oqh1VLOT31l37LPpv8bEbffRbkrzA0wpyfM6laxXV1WCKuxrs+m/yWKf9jnuhdYdANSGFSoXoqp0rXucolWr8OVZIxev4quuom/zZsLt7RDMp3pSLYVR4WDXHq9TMyZjqaoz9dc+urKTWyDLFw17nIgxmStWSMmWbiVfbHp1Q6tN/00W+7TPcfs7duNTmFdeA4E8BnbvIdzcQvE1V3udWkYrvvoqUKXnyScBkGkrmD84SEfkINGoTWszJl6CovYHWHby2YiqMeOhqu7WNDbtNxVmTSoi6Bd2H7WOarJYRzWHqSrHB19i9lCE/Ni0X3ddZfFVV3mYWebLX7gQ/+TJw9apLmPBQB8SaubACWvQjImXjyj20ZWl3I6qqHVUjYlHW9cAHX1DVkgpRYJ+H3MritljlX+Txj7tc1hjex8SOkz9YP+w9amPk79oEcEpUzzOLrOJz0fxK15B91Pr0XAYqpdRPzgEvjBPvrTT6/SMyUiqOPts2hrV7OR2VP1qU3+NiUdsqxQrpJQ6NZUlNvU3iezTPodtONyIBrudQkpTlxI5eZK+LVtOVa0141N81VVEOzvpe/FFqJhPnfu31/PNVvnXmHj5UNtDNVv5nG2HbC9VY+JjFX9Tr3ZKMU0n++gesBNsyWAd1Rz2bKPTYaoLK0xZQM+zz0E0StEVV3icWXYoWrMagkG61q2DQIiLJtURUNh9ssHr1IzJWIKi2D6qWckXdL5EraNqTDx2H+mivDDI5OKQ16nkjNoq56TA3qM2/TcZrKOaw3Yed6ag1pXNhUCInvXr8RUXU7BksceZZQd/cTGFy5fT89R6AILVy5k3GKZtYJ/HmRmTuXxWTCl7WTElY8Zl95Euaqzib0rVuaPXDa2dHmeSnayjmsOae/czORxl0tTlqCo969dTePllSCDgdWpZo2jNGgYaGhg6ehSqlzF/sJ+I/zAdfYNep2ZMRrJ9VLNYrKNqa1SNGTNVZc+RbiuklGIzJhaSH/TR0Gojqslgn/Y5qncwTFj2Uz84ANVLGTp4kKHmZorXrPE6taxStGY1AD1PP+0UVBoYRAN9PHvwJY8zMyYz+USxj64sFVujah1VY8astbOfroGwrU9NMb9PqK0sObU+2CSWfdrnqG3Nx4nmHafOLaTUvd6ZnlpkHdWEyp8/H//EifSsfxom11EXcabjrD+8xePMjMk8qjaimtXsdTUmbrGKvzVTrKOaarWVJTRYRzUp7FMhR60/uAMVpW7ILaS0/mmCM2YQmjnT69Syivh8FK1eTc/TT6Pio3ZSPQDbj9kWNcbEw6r+ZjG3oyoa9TgRczYROSAiW0Vkk4hscI9NFJFHRWSP+7Xc6zxz2Z5TFX9t6m+q1VWW0NY1wIkeW9aVaNZRzVGbjuwAoK5sNqpC77PPnpqmahKraM0aIsePM9DQQEn1CmYMhWnq2eN1WsZkHEWdEVWr+pudYh1V1ONEzAiuUdWlqrrSvf5x4DFVrQEec68bj+w+0sWkohCTivO8TiXnxCr/2vTfxLOOao460LmHvKgyq3I5fZs3E+3ttWm/SRI7AdD91FPOOtXBQQb0JSJR+2PMmLESq/qbvWJTf21ENVPcCvzA/f4HwG0e5pLzdh/ppsZGUz0Rq/xrHdXEs45qDlJVesK7qRscxD9tmbM+1e+n6LLLvE4tKwWnTCGvttZZp+oWVAqHOtjectTr1IzJOM72NPbRlZVOTf21k3hpSIFHRGSjiLzHPVapqi3u961ApTepGVVl79FuK6TkkcrSPErzAzS0Wkc10ezTPgc1tvcSCbVSOzgI1cvoWf80BUuW4C8t9Tq1rFW0Zg19GzcSLaimNuq87dYd2ORxVsZkFqeYklX9zVpuR9WHjaimoStUdTlwE/B+EXnF8BtVVeHcc7ZF5D0iskFENrS1taUg1dzT3NFP90CYGuuoekJEqKuyyr/JYJ/2OeiZg/sI+4eoG4oSCVXTv3WrTftNsqI1a9ChIXo3vkB9eR1wep2wMWb0/ERt6m+2OjVSbh3VdKOqTe7Xo8BvgEuBIyIyFcD9es5pQqp6t6quVNWVFRUVqUo5p8Q6SLVTbOqvV2orS2ho7UJtRkhCpayjKiL3ishREdmWqpjm3J5r2gpATfF0ep5/AVQpWm2FlJKpcOUKJC+P7vXrqaxeSXkkwoGOBq/TMibjCFHbxiRbuScgrJhSehGRIhEpiX0PvBLYBtwP3One7U7gd95kaE5X/LURVa/UVZXQ2R/mSOeA16lklVR+2n8fuDGF8cwIGk40IKrUVy2nZ/16fMXFFCxZ7HVaWc2Xn0/hypX0rH8ambac+QODdId3e52WMRnFmfTr1P41Wci2p0lXlcBTIrIZ+AvwoKr+EfgCcL2I7AHWuteNBxpau5lcnEd5UcjrVHJW7CTBrtZOjzPJLin7tFfVPwMnUhXPjOxk3w5mhsMUVq+kZ/16ilZdjgQCXqeV9YrWrGFw3z6G/NOpGxyiP3CMo109XqdlTEaxYkpZLDaialPn0oqq7lfVi93LQlX9vHv8uKpep6o1qrpWVe1vPI80HOlk/lQbTfWSVf5NDvu0zzF9gxEGfI3UDg4xGK1kqLnZ1qemSOzn3LO9kdqID/Upj+/f6nFWJl7nWs4gIhPdje/3uF/LvcwxG/ls6m/2OrWPqo2oGjNa4UiU3Ue6qa+yjqqXyotCTCnJo6G12+tUskrafdpbdbjk2tTUSn+oh7rBCD27nLoH1lFNjbzaGgIVFfQ88zQ1E2oBeK7JlmxnsO/z8uUMHwceU9Ua4DH3ukkQVXX3UU27jy6TCKdeVxtRNWa0DhzvZTAcpa7Kdm7wmlX+Tby0+7S36nDJ9cSBzQDMLaii55nnCM6cSWjGDI+zyg0iQtGaNfSsf5q51ZeQH42y54RV/s1UIyxnuBVn43vcr7elNKkc4CeKYlV/s9KpEVXrqBozWrE1kTai6r3ayhL2HO0iErU2LFHSrqNqkmtTqzOCt2jyUnqfe46iNVbtN5WK1qwh0tHB0EAltYNDnOzf6XVKJrEqVbXF/b4VpwiJSSCfqE39zVZWTMmYMdvV0oXfJ8yzrWk8V1dZQv9QlMMner1OJWukcnuanwHPAHUi0igi70pVbHNaW9eLTA5HKB2YSbS3l2Kb9ptSRatXAdCzv5v6wUG6fa0MhiMeZ2WSQZ3N1EY8rWrLHMZOwab+ZrNTHVUbjTBmtHa1dnLR5CLyg36vU8l5te6odoNN/02YVFb9vV1Vp6pqUFWnq+r3UhXbOIYiUXo5wMLBQboP9IPfT+Fll3mdVk4JTJpE3oL59GzcwbxIgLA/zLOH93qdlkmcI+7G97hfj450R1vmEB8rppTFrJiSMWO2s6WL+qm2PjUd1Lij2rtbraOaKPZpn0O2Nh2lJ9RN3RD0vLiLgiVL8JfYmoZUK16zht5Nm5ibNweAJw9s8jgjk0D342x8j/v1dx7mkpVsH9UsFtuexjqqxoxKZ/8QTSf7bH1qmijKCzBjYoGNqCaQfdrnkD+9tAkVqPdNo3/bNqv265GiNWsgHGZO70x8qmw9YlvUZKIRljN8AbheRPYAa93rJkFUbR/VrOa+rj4rpmTMqMRG7qyjmj7qKkvYZSOqCRPwOgGTOptbXgSgrmsmPXqU4iuso+qFguXLkfx8Ik0wuzTMsbB1VDORqt4+wk3XpTSRHONM/bWqv1kpdgLCiikZMyo7Yx1Vm/qbNhZUl/GnXUfpG4xQELJ1w+Nlp6VzyLGuF6gIhwk0+fCVlpK/aJHXKeUkXyhE4aWX0LPtEPWDg3TSRDhif5gZMxo+ojb1N1tZMSVjxmRXSycl+QGqy/K9TsW4FlWXElXY6W4bZMbHPu1zRCSqdHKIBQND9Gx5iaLLL0cCNqDuleI1axg81MjC9iB9wQFeaGzyOiVjMoA600Jt6m92smJKxozJrtYu6qtKEJtlkjYWTisDYHtTh8eZZAf7tM8RO1qP0hXsZdmxIOGjbRTZtF9PxdYH1xxzqr3+6SUrqGTMaNga1Sx2qqNqI6rGXEgkquxo7mRhdZnXqZhhqsvyKS8Msr3ZRlQTwT7tc8T/7nvRKaR0dBIARauto+ql0Ny5BKqqmHK0CIDtzc97nJEx6U/V7cRYRzU72YiqMaO2v62bvqEIi6dZRzWdiAgLq8vY1mwjqolgn/Y5YlPjMwBUtuQTmj2b0PRpHmeU20SEojWrCe8+RtVgmBPdL3qdkjEZwdaoZrFTa1Sto2rMhWx1p5Yunm4d1XSzcFopu1u7GQxbWzZe9mmfI9q6NjCzL0x4bxtFV1zhdToGZ51qtLuX6w6Fafc1MRCOeJ2SMWnPb1V/s5cVUzJm1LY0dlAQ9DO3otjrVMxZFlaXMRiJsueobVMzXtZRzQG9A2FO+JtZeyCMDgxStGa11ykZoHDVKhDh4sNFdAWHeObAfq9TMiatKc7UX1ujmqXcExA29deYC9vW1MGC6lL8Pjtxl24WVTvbBdk61fGzT/sc8MRLu+kJhFncWATBIEWXXup1SgYIlJeTv3Ah01vyAHhs73qPMzIm/VnV3yxmxZSMGZVIVNne3GnrU9PU7ElFFIX8Vvk3AezTPges2/NnAKY2BilctgxfUZHHGZmYojVrCBzuZkJfhD0t67xOx5i05yOKim2inpWsmJIxoxIrpLTIOqppyedzCiptbrSO6nhZRzUHHDj6JDPaI/hauym+5hqv0zHDFK1ZDdEoN+4L0za02+t0jElrquATRbGpblnJ1qgaMyqnCilZRzVtLZs1ge3NHfQPWf2R8bCOapZTVY5H9nHTrjAAJddc7W1C5gyFS5fiKy5mxb4Ax0Nd7Dt2wuuUjElrPqI29Tdb2dRfY0Zl48F2SvICzJtihZTS1fKZ5QxFlO22Tc242Kd9ltvZ2kZbqIcl+4OE5swhNHu21ymZYSQUoviqq5h+wEdUld/veNrrlIxJa7ZGNYvZ9jTGjMrGg+0sm1VuhZTS2PKZ5YDzWpn42ad9lvvttkfJG1QqmtSm/aapkrXX4e+NUN+obD3woNfpGJO2FMWHTf3NWm5H1WdrVI0ZUUffEA1Hulg5q9zrVMx5VJTkMWtSoXVUx8k6qllux+GHWL5PkYhSfPVVXqdjzqHoyiuRYJAbdkVo7t/idTrGpDWxqb/Zy4opGXNBLx5qRxXrqGaAFTPL2XjwJGrr7uNmn/ZZTFVpjezm+l1h/GVlFC5f7nVK5hz8xcUUrl7Fkr0+WoIdHGy3darGjMSHWtXfbGVTf425oI0H2/H7hItnTPA6FXMBy2aVc6x7gMMn+rxOJWNZRzWLbW5uol36qN3vo+SGG5BAwOuUzAhKrruO4g5l2jH4+YY/eJ2OMWlJNdZRtam/WcnnnICwqb/GjOy5l06wYGopRXn2N126u2zORACe3nfM40wyl3VUs9h9G/6H5XuVwBCUvuomr9Mx51Fy3XXg8/GK7RG2H7rf63SMSVt+m/qbvWJrVG1E1Zhz6h4I8+Khdq6omex1KmYUaqYUU1max5N7rKMaL/u0z2K7jvyBq3dE8U+cQOEll3idjjmPwKRJFF2xhmu2QWO0gaGw7btlzLk4W5fYR1dWcqd02xpVY87t2X3HGYooV1pHNSOICFfWVPDU3mNEorZONR72aZ+ljvf0cizcwpJ9UPqqmxG/relKdxNuu42SbpjSPMQD25/zOh1j0o4z9TdqU3+zlU39Nea8ntzTRkHQzworpJQxrqyZTEffEFubbD/VeFhHNUv9+Jlfctl2JRCBCW98o9fpmFEovvZaKMjjmi3KI5vu8TodY9KS7aOaxdwRVZ/ajBJjzqaqPLbrKKvmTiIvYIMPmeKKeZMRgT/tOup1KhnJPu2z1LP7f8T1L0bJW1hLfl2t1+mYUfDl51N+622s3qW0tr9g03+NOcupfVSt6m92io2o2hpVY15mc2MHje193LSoyutUzBhMKs7jsjkTeWBLs21TEwfrqGah/W1HKDzUQvUJmHTHX3udjhmDie94B/4IrNgyxE+euc/rdIxJO0IUtY+u7CRCFLE1qsacwwObmwn6hVcutI5qprnl4mnsb+the3On16lkHPu0z0J3PfxZbnk6SmRSMaWvepXX6ZgxCM2eTcEVl3DjC8oTL37L63SMSTt+ouCzNarZKooPHzabxJjh+oci3PdCI9fWT6GsIOh1OmaMblpURdAv/OL5w16nknFS2lEVkRtFpEFE9orIx1MZO1cc6+oksukJapth2j98GAlag5Zppn7kExQMwKK/tPHw5nVep2PiYG1dcqiCX6zqbzaL4rOpvxnE2rrU+N2mJtp7h7hz1WyvUzFxKC8KcdvSafxy42Haewa9TiejpOzTXkT8wLeAm4AFwO0isiBV8XPFl77/Dt74eJT+GROZ8MY3eZ2OiUP+/Pnk3XQNN2xUHrnvIwxFbHQhk1hbl0Tu+h61YkpZKyo+m/qbIaytS42+wQhf+989LJ5Wxqq5k7xOx8Tp3a+4iIFwlK8/tsfrVDJKKj/tLwX2qup+VR0Efg7cmsL4We+en36Kq361k6JBmP/N/7YtaTLYnM/+O/3lebzp9738+xduJhq1P9wyiLV1yRIbabPtabJWFL+NqGYOa+uSLBpV/vm3W2np6OeTr56PWNuXsWorS3j75bP4wTMH+NOuI16nkzECKYw1DRg+ObsRuCwR//HRpn2s+/YngNhm8E51SPeb2D9nfjmj8lbsBj3z/mc9KHZU9KwY7jUZdp9z/f8K7n3UzZUzK4CdUQxMT3/Vc9zhrDyHurpYuqmLUBimfOFzVuk3w/lLSlj0o1+y+fbbuPUnB/jplmWE5tVl7YfU2g98lfKKaV6nkShJa+uikQgvPJi7Wxe19/SxCKyYUhZThCn9B9hw/395nUpSzFt9GxMmZ00xnKS1dQAPbW1hwK1+r8P+TIs546+sYTec808pzvybbeT/Z2z3Z4S4Z9x/NPc54/jpa+sa2nhq7zE+tLaWyy6y0dRM97Eb63nhUDvv+eFG7lg1myXTy7xOKWkunTOR6gkF4/5/UtlRHRUReQ/wHoCZM2eO6jHN+7ex+L6tyUwrIxyaFWDZv32bKZdc6XUqJgHy59aw7ME/8eg/vJYlW9sJbs7e3/Ejr23Ipo7qqMTT1kWjEVa+8H+SmVZGCJZO8ToFkyRdgYmsHHoRXnjR61SSYs+MBdnUUR2VeNo6gE//bhvHunN7Pd+EwiD/etsi3nbZ6H9uJn0V5wX4yd9czud+v4MfPnOAcDR7t6u5++0rMq6j2gTMGHZ9unvsDKp6N3A3wMqVK0f1CtYsvZod3/j06QNuRUifu47JGYVyLz732KnjONPI3PuIe11ia6B8EjsK4nPv6nPvK6cefmoD+th9ENz/7NRtMiwfEffx4tweGycTNxc5ddyN4Yv9/6dz9cVyFiEYymP+tOE/XpMNQhWVvPrnT3P4pa207NnCWedys8bi+pVep5BISWvr/P4Ah9/2VCJyzFh5oSCzZ9R5nYZJkvIPPsXhluytjDlj2kVep5BISWvrAH79vjVEh40uDp9QdMbfX+dwxn2HXZGR7jPsljOPc84ro7n/WOOe8a37fUHQT9BvM0iySVlBkK+86WI+ffMCTvRm74mYKSV5Cfl/UtlRfR6oEZE5OA3ZXwFvScR/XFRSxiWvvD0R/5UxaWnGnMXMmLPY6zTM6CStrROfjxnz7PfAZK+8ognMmDfB6zTM6CStrQOYOakwUf+VMWmnrDBIWaHtzHEhKeuoqmpYRP4eeBjwA/eq6vZUxTfGmFSwts4YkwusrTPGJFtK16iq6kPAQ6mMaYwxqWZtnTEmF1hbZ4xJJpv4bowxxhhjjDEmrVhH1RhjjDHGGGNMWrGOqjHGGGOMMcaYtGIdVWOMMcYYY4wxacU6qsYYY4wxxhhj0oqojnrv5ZQTkTbg4BgeMhk4lqR00immxbW42RR3rDFnqWpFspLxQoa0dV7FzaXnanGzO661ddbWpWPcXHquFjc9447Y1qV1R3WsRGSDqq7M9pgW1+JmU1yvnmsms98Pi2txMy+utXVjl0u/H17FzaXnanEzL65N/TXGGGOMMcYYk1aso2qMMcYYY4wxJq1kW0f17hyJaXEtbjbF9eq5ZjL7/bC4Fjfz4lpbN3a59PvhVdxceq4WN8PiZtUaVWOMMcYYY4wxmS/bRlSNMcYYY4wxxmS4jOqoikiBR3GLPIp7kYjUeRA35T9nD3/Gs0RkggdxPdtyQETEg5ievHczlbV1KYtrbV3y41pbZ0ZkbV3K4ubMz9naupTFTMnvVEZ0VEWkWES+CXxXRG4UkbIUxv0acK+IvF5EpqQobr6IfBt4GJgjIqEUxS0Wkf8AviEiV6fi5zws5o9F5G0iMivZMYfF/SrwIFCdipjD4n4F+KOIfF5E1qQobomI/KeI1GkK5/t79d7NVNbWWVuXpLjW1iU/rrV1Y2BtXfa2dWfFTVl7Z21daqT6vZsRHVXga0AI+DVwO/DxZAcUkZuB9cAQ8DPgb4EVyY7rehMwSVVrVPWPqjqY7IAiUgzci/N8fw+8GvhokmNeATwJ9Lmxr8R5fZNKRFbivLYTgWWquiPZMd24AeBbQAC4A1DguhTEnQf8HHg38LlkxztLyt+7Gc7auiSzti75rK2ztm4UrK1LMi/aOjduyts7a+tSKqXv3UAy//PxEBFRVRWRyThnRt6kqt0ishf4kIi8W1XvSUJcn6pGgZeAd6nqBvf4m4DORMc7V3ygCvixe/0aN+5+VW1PQjxxz8RUA/NU9U3ucQU+JSLbVPXniY7rOg58O/Y6ish04KKz8kqGfmAf8B+qOiQiS4GTQKOqhpMUE6ACmK2qVwGISCGwOYnxYnqALwG3AptE5EZV/WOyfsZevXczlbV11tZZW5cw1talMWvrcqKtA2/aO2vrsrStS7sRVRGpF5H/Av5RREpV9RgQxTlrALAL+A1ws4hMTGLc7aq6QUQqROQPwOXubW9yz1IlNK6IfMCNGwVqgStF5P3AvwN/B/xIRKYmOi6nn+9u4KCI/K17l16cRv0NIlKeoJhzReSdseuquhP4qcipufVNwCz3toS90c4RdxvOmbd/FJF1wH8C/wF8UUQmJTFuC6Ai8t8i8hxwM3CLiPw2wa9tjYh8XUTeKyLlbtzn3cb668Cn3XwS2ph59d7NVNbWWVvn3mZtXfxxra3LANbWZW9b58ZNeXtnbV3utHVp1VEVkTk4Z5z2ARcDd4lzVuRLwA3uizMAbMF5sy1PQtwlwDdF5DL35hPAT1X1IuB7wGrgtiTEvRj4LxGpBf4f8BagXlUvxWnQ9gCfSlLcb4ozp/5rwP8VkbuArwIPAIdwzgSON+bfARtxzry83j3mU9WeYW+spcD28ca6UFzXDwE/8BtVvRL4rHv9XUmO+xrgB8BOVa0F/gY4iNvIJCDux3EajSbgauA7IuLH+YDCPeMVFZEPJCLesLievHczlbV11tZhbd1441pblwGsrcvets6Nm/L2ztq63Grr0qqjCtQDx1T1SzhrBxpwGo9+nKH0TwCo6kvAbJyh72TE3Qu8WkTmqmpEVX/kxn0EmAB0JSnuLuBOoBu4H2deP+4vwpNAaxLivhfn+d6E88u2GvgDcJX7vK/EWWcwXvtw3ryfAt4iIvnuWUbcNxzAVOBp99h1IlKZjLgAqtoG/JOqft29vgnndT2egJjni9sFzMD5nY69tk8BR8cbUJzqet3Am1X1i8A7gEXAInfKRtC96yeBd4lIUEReI4kpcuDVezdTWVtnbZ21dXGyti6jWFuXvW0deNPeWVuXQ21dunVUtwH9IlKvqkM4b6xCnCkTdwO3icjrRORynHnhiSrHfHbch9y4q4ffSUSWAHOAY0mMWwBcBXwEKBeR14rIdcA/4ZxNSXTcQU4/35tVtUlV71fVkyKyGues0LgbcFV9GGfh9Sacs5nvg1Nn3iLirOGYCtSJyEM4i9KjSYwr7hQG3OtLgGuAlvHGHCHue4fd/AjO1JAbxCkA8GES89r2Avep6nYRyVPVfuAFnDOKuL9jqOo6nA+pTuD9QCLWb3j13s1U1jkYrq4AACAASURBVNZZW2dtXfysrcsc1tZlaVsH3rR31tblVluXbh3VPGAncAWAqj6P8wt2karuAz4GXArcA9ylqk8nKe4GoBGYLSI+EZkjIr/FeWHuUtX1SYx7GOdMSR/OG3oqztmbr6vq95IY9xDOGRFEZLKI3APcBfxSVRNyNso9y9aE80ZfKyI1sTNvwFzgFuANwA9V9U737Fiy4iqAiEwUkV8B3wX+U1UfSkTMc8S9XkRq3ONHgH/GOct6D/A1Vb07AfFUnXULqOqAezZzOXCqWIOIhNzpK1XAO1X1RlUddWMqZ+2JJnJqDYpX791MZW2dtXXW1sUfz9q6zGFtXRa3dW6slLd31tblUFunqim94CwivwPwjXD73wBfBla51y8HtnkUd4v7fQHwjhTG3erl83WvvyrRMYfdrwpnvcYn3es17tcPJOO5nidurfv1jSmOG3u++UmOeyXwwPA83K+L4oz7aeAJnFLkV7nH/Bf4nRr3ezdTL9bWjSqutXWpiWtt3djiWluXwNfJ2rrMbetGE3fY/RLW3llbN+L9cq6tS10gKMepTHUU+CMw56zbxf06E2efp4eAYuCvcBa7F3oUtyjHnm9xomOO8Jg6nEICPcDHkvFcRxH3ox7F/ciFGqPxxB32Gt+Mc9b29cAO4v9Qnu3+ntzrNlKfwNmDrsS93ZeM3+VMvWTwe9/aunHGHOEx1tYlKS7W1nl6yeD3vrV1CYg7wmPG1d4lIKa1daOLO5sMaeuSHwAKYl9xho99wH+7Tzw00guCU1XqtzhzpC+1uOkXN86YPmAK8BzwLHBlip5rTsV1738PzlqQX8YZt9D9Ogl497DjK4Dv457JS/TvcqZecum9n2txM+m9n2tx3ftbW5fCSy699y1uat7/mdTmeBXXvX/OtXXJ+4+dH8J3gB8B1zGs9w1cAvwJWHaexwtQYXHTL24CYuYTx7QMizv6uO7r+i7iONt2Vty1QMj9/2Jn2KbjNNDnPEsb7+9ypl5y6b2fa3Ez8b2fa3GtrUvdJZfe+xY3Ne//TGxzvIqbq21dbCg54UTkZ8AR4C/AK4HDqvqpYbd/BQgAn1LVToubOXHHE9OtyhbXL53FHV3c8cQcZdxrgb9V1TfHGyOb5NJ7P9fiZtp7P9fiWluXWrn03re4qXn/Z1qb41XcnG7rktH7BSpx5lrHOsLLgZ8Abxl2n2qcUsercPblucLipn/cXHquFnfEuO8E/tX9/npg6XjjZuolzV8ni5thMS1u2sW1ti4zXieLm4Fxc+m5ZkDctG3rxr09zbAyxqeoU6a5wH3i4JQ3vh94Q6wMsqo2u8cfAz7LGPf8sbjJj5tLz9XijipusXtsOVAqIvfilCePjCVupsqg18niWltncccX19q6s6Tp62RxMyRuLj3XDIub/m3dOHvp+cO+j/XUY/OdXws8wOkFw/OAb+KeHcDZhPcg8EGLm35xc+m5WtzRx8WZkrIZp6F731jjZuol014ni5veMS1u+sfF2rqMeJ0sbvrHzaXnmolxSfO2Lv4HOmWZjwL/5l73n/VDqcDZbPczwx7zAO5wMs7wdoHFTb+4ufRcLe6Y4i5zv38HcZb3z8RLBr5OFjeNY1rcjIhrbV1mvE4WN83j5tJzzdC4ad/Wxf9AqAE2AMeAqe6xwLDbZwH1wF7gRuAqYD2wYlwJW9ykx82l52pxxxT3kvHEzdRLBr5OFjeNY1rcjIhrbV1mvE4WN83j5tJzzdC4ad/WjeWHMPwJC84GsW8CvgA8POz4LOA+4L/cY28CvghsBV4fxw/f4iY5bi49V4uburiZesm11ymX4ubSc7W41tal68/L4mZv3Fx6rrkY14vLqH4YwJeBrwNrhx2/DrjH/f4Izr481cDNwOfHnZjFTXrcXHquFjd1cTP1kmuvUy7FzaXnanGtrUvXn5fFzd64ufRcczGul5cL/UAE+DbwY+CtwKPA+4Gg+0N5p3u/XwBR4AtnPd4X5wthcZMcN5eeq8VNXdxMveTa65RLcXPpuVpca+vS9edlcbM3bi4911yM6/UlwPmVAEuBG1S1S0SO4fTOXw0cAr4tIne6P5A9wC4AEfEDUVWNXuD/t7jexc2l52pxUxc3U+Xa65RLcXPpuVpca+suJNdeJ4trbazFzeC27rz7qKpqJ3AApxoUOAtvNwKvBIqBZ4Efqeq1wB3AR0XEr6oRdbvv8bC4yY+bS8/V4qYubqbKtdcpl+Lm0nO1uKmLm6ly7XWyuNbGWtzMbusuNKIK/P/s3Xd8FNX6x/HPSSEBEnqV0EFqQBQQKQKK0hRFvSgKgqjI72JXrijFiuWq2AAVr9gQCyqoqKiogICAgEhQeg+995Cy5/fHLJsEA6RsMrvZ7/v12tfOmZ3yxOBknzlznsMUoIsxprK1drsxJgFoBJyw1vYDMMYYa+0C73p/0Xnz/7yh9LPqvAV33mAVar+nUDpvKP2sOq+udWcTar8nnVfXWJ03SJ2xR9VrDk654/4A1trFwEV4k1xjTEQ+Zeo6b/6fN5R+Vp234M4brELt9xRK5w2ln1Xn1bXubELt96Tz6hqr8wapsyaq1trtwJdAV2PMv4wxNYAkIMX7eWp+BKbz5v95Q+ln1XkL7rzBKtR+T6F03lD6WXVeXevOJtR+Tzpv/p83lH7WUDyvq2z2q011BSbgDM69M7v75fWl8xbOc+q8hf+8wfoKtd9TKJ03lH5WnVfXukD976XzFt7zhtLPGorndeNlvD9wthhjIgFrCzhj13kL5zl13sJ/3mAVar+nUDpvKP2sOq+cTaj9nnTewnlOnbfwylGiKiIiIiIiIpLfslNMSURERERERKTAKFEVERERERGRgKJEVURERERERAKKElUREREREREJKEpURUREREREJKAoURUREREREZGAokRVREREREREAooSVREREREREQkoSlRFREREREQkoChRFRERERERkYCiRFVEREREREQCihJVERERERERCShKVEVERERERCSgKFEVERERERGRgKJEVURERERERAKKElUREREREREJKEpURUREREREJKAoURUREREREZGAokRVREREREREAooSVREREREREQkoSlRFREREREQkoChRFRERERERkYCiRFVEREREREQCihJVERERERERCShKVEVERERERCSgKFEVERERERGRgKJEVURERERERAJKhNsBnEm5cuVsjRo13A5DRALI4sWL91hry7sdhz/pWicip9K1TkRCwZmudQGdqNaoUYNFixa5HYaIBBBjzCa3Y/A3XetE5FS61olIKDjTtU6P/oqIiIiIiEhAUaIqIpJLxphSxpjPjDErjTErjDEXGWPKGGN+NMas8b6XdjtOERERkWCjRFVEJPdeAaZba+sDTYEVwFDgJ2ttXeAnb1tEREREciCgx6hmJSUlhcTERJKSktwOJV9FR0cTFxdHZGSk26GISBaMMSWBi4H+ANbaZCDZGHMV0MG72XvATOChgo9QRCTwhcr3ulPpe57I2QVdopqYmEhsbCw1atTAGON2OPnCWsvevXtJTEykZs2abocjIlmrCewG3jHGNAUWA/cAFa21273b7AAquhSfiEjAC4XvdafS9zyR7Am6R3+TkpIoW7Zsob6YGWMoW7ZsyN1dFAkyEcD5wOvW2mbAUU55zNdaawGb1c7GmIHGmEXGmEW7d+/O92BFRAJRKHyvO5W+54lkT9D1qAIhcTELhZ9RBCApJY3oyHC3w8iNRCDRWrvA2/4MJ1HdaYypbK3dboypDOzKamdr7XhgPEDz5s2zTGZF8pvHY0lO85CS5iElzZKS5iE51WmneSypHkuax+KxFmvT77pYa7GAtXByrfV+eHL9yW0yf5Z+EGMMxkCYMYQZpx0RZgj3vjIuO+0wwsIgIizsn9t4j6W/ncEpFH9vofgzS2jw5/e6oExURST47T58ghajZgCwdlRXIsKD6wEPa+0OY8wWY0w9a+0q4FLgb++rH/Cs9/1LF8MUl6SmeTh4PIX9x1I4cCyZ/cdS2H8smQPHkjlwLON6p33gWApHTqRyNDnVl9RJcChWJJziURHERkUQEx1B8SLO+8l2jPe9efUytKxZxu1wRUTyxaGkFJo89gMAM+6/mDoVYvN8TCWqudC6dWvmzZvndhgiQeu3dXvp/dZ8AGqVKx50SWoGdwEfGmOKAOuBW3CGVHxqjLkV2AT0cjE+yYGDx1PYtPcom/cdY/uBJHYeSmLHIed956ET7DiURHKqx+0w/coYiAwLIzLcEBkRRpHwMCLDnXZ4mCEyPMzp8Qxzej4BTIadjfcYxrfKWQozYE6uzfBmvOst3h5a6/Syeiy+ntvUNKcXN81ab6+uB48HUj1OL2/Gnt6MbbccS07jWHIauw+fOOu2i4Z3olxMVAFEJSJScKYv386giUt87ZrlYvxyXCWquaAkVST3XpmxhpdmrAbg1rY1GXFFQ5cjyj1r7VKgeRYfXVrQsUg6j8eyZf8xVmw/zModh1iz8wgb9hxl096jHE1OK7A4SheLpHSxIpTyvpf0vpcuFkmpYkV8yyWLRVKyaCQlikZSLDI8mG/chAzrTaJT0pxHp5NS0jiclMqRE6kc8b4fPeFtn0jlcFIqTeJKKkkVkULFWsuVY+awfOshAG66sBqjesb77fhBnag+/vVf/L3tkF+P2fCcEjx6ZaMzbhMTE8ORI0ey/Gz79u1cf/31HDp0iNTUVF5//XXatWvH9OnTeeSRR0hLS6NcuXL89NNPfo1bJNBZa+ny8q+s2nkYgAn9m3NJfRXElexLSknjzy0HWLx5P39sPsBfWw+y7aD/ipEULxJO9bLFqVamGOeUKkqlklFULBFNxRLRVCoRTYUSURQrEtR/NsVPjDFEhBsiwqEo4ZQsGknFEm5HFfzc+l63ceNGunTpwgUXXMCSJUto1KgR77//PsWKFfvHtjVq1KB379589913REREMH78eB5++GHWrl3LkCFDGDRoEDNnzmTkyJHExsaydu1aOnbsyLhx4wgL000oKTw27DlKxxdm+trT7mpL4yol/XoO/cX1s0mTJtG5c2eGDRtGWloax44dY/fu3dx+++3Mnj2bmjVrsm/fPrfDFClQx5JTaTjye1973tBLOKdUUe7++W4OJx/mrcvfIiJMl6NQt+tQEjNX72bu2j38vmFfnpLQKqWK0qByLPUrleDcSrHUKlecqmWKUbKo5iwUkX9atWoVb7/9Nm3atGHAgAGMGzeOBx98MMttq1WrxtKlS7nvvvvo378/c+fOJSkpicaNGzNo0CAAFi5cyN9//0316tXp0qULX3zxBdddd11B/kgi+ealH1fzyk9rAKgQG8W8oZfky9NAQf3N8Gx3yNzQokULBgwYQEpKCldffTXnnXceM2fO5OKLL/bNlVWmjIopSOhYvfMwl78029deM6orHlKIfy/90ZBwE5RVfyUX9hw5wfd/7eDnFbuYtXp3jscWnle1FBdUL02zaqVoUqUUcaWLEham6pkihYGb3+uqVq1KmzZtAOjTpw+vvvrqaRPVHj16ABAfH8+RI0eIjY0lNjaWqKgoDhw4AEDLli2pVasWAL1792bOnDlKVCXoHU9Oo8HI6b72M9fE07tltXw7X1AnqoHo4osvZvbs2XzzzTf079+f+++/n9KlS7sdlogrPl20hf98tgyATg0q8L9+LdhwcAM9pvbwbbPwpoUq018IHT2RyvTlO/h62TZmrsrePLEli0Zy8bnlaVenHBfWKkO1MsX0b0NECsSp15ozXXuiopyxxmFhYb7lk+3U1NQcH08kGMxevZubJyz0tX8f1onysfk77l6Jqp9t2rSJuLg4br/9dk6cOMGSJUsYNmwY//73v9mwYYPv0V/1qkphN/jDJXyTsB2Ap3vGc+OF1Zi6dioj5o4AoEWlFkzoPMHNEMVPklLS+G75dibO38ziTfvPuG2RiDC6x1emU4OKtK1TjpLF9CiuiLhv8+bN/Pbbb1x00UVMmjSJtm3b5ul4CxcuZMOGDVSvXp1PPvmEgQMH+ilSkYJlraXfO78ze7Vz07lbfCXG3nh+gdx8UaLqZzNnzuT5558nMjKSmJgY3n//fcqXL8/48eO55ppr8Hg8VKhQgR9//NHtUEXyRWqahzrDvvO1v727HQ3PKcFdP9/FzC0zARh24TBuqH+DSxFKXh04lsyEORt49ee1Z9zu8oYVueq8KlxSvwJFi+jxbhEJXPXq1WPs2LEMGDCAhg0b8n//9395Ol6LFi248847fcWUevbs6adIRQrOzkNJXPh0egHYTwa24sJaZQvs/EpUc+F0FX8B+vXrR79+/f6xvmvXrnTt2jU/wxJx3akXtOWPd6ZIhCfTeNTPrvyMemXquRGe5FKax/LRws2M/HI5pxtS2rFeefq0qk6HehUI15hREQkyERERTJw48azbbdy40bfcv39/+vfvn+VnJUqUYNq0aX6MUKRgfbxwM0O/SPC1Vz7ZhejIgr3prERVRPwi49iFWuWK89MD7dl8eDNXfHyFb5sFNy6gWOQ/y/1L4Dl4PIVR3/zNp4sSs/y8b6vqDOpQmyqlihZwZCIiIpJf0jyWDi/8wpZ9xwG4r9O53NOpriuxKFHNpYSEBPr27ZtpXVRUFAsWLHApIhH3PDd9Ja/PXAfA4I61GdK5Pl+v+5pH5jwCQLMKzXi/6/tuhijZkJzqYfSPq3lj1rp/fNaubjkevbIRdSrEuBCZiEj+qVGjBsuXL8+0rmfPnmzYsCHTuueee47OnTuf9XgdOnSgQ4cO/gxRpECs3XWETqNn+doz7m/v6t99Jaq5FB8fz9KlS90OQ8RV1lraPz+TzfuOAfDBrS1pV7c89/1yHzM2zwBgaMuh3NTgJjfDlLNYvvUgV42dS9opz/Xe3q4m919WT+NLRSTkTJkyxe0QRArUyzNW8/IMZ27UGmWL8fMDHVyf/k2JqojkyuGkFOIf+8HXXvDIpZQpHp5pPOqnV3xKg7IN3AhPsmHKH4nc98mfmdZ1b1KZp6+OVzVeESkw1tqQm77F2pzNIS2SX06kplFvePrcqP+9tgm9WlR1MaJ0BZqoGmM2AoeBNCDVWtu8IM8vIv7x17aDdH91jq+9dlRXth1N5PyJ3X3rNB41cGWVoL57Sws61KvgUkQiEqqio6PZu3cvZcuWDZlk1VrL3r17iY6OdjsUCXGLN+3j2td/87UXDruUCrGB8+/SjR7VjtbaPS6cV0T8YOL8TQyf6ozl6d6kMmNvPD/TeNSm5ZsysdvZKydKwVu98zCXvzQ707rZQzpSraxuKIiIO+Li4khMTGT37t1uh1KgoqOjiYuLczsMCWFDJv/J5MVOwcT255bn3VtaBNzNIj36KyLZNuDd3/l55S4AXvhXU667II57f7mXnzY7U9I81OIh+jTs42aIkgVrLde/OZ+FG/f51ilBFZFAEBkZSc2aNd0OQyRkHEpKoUmGoVvv3NKCjgH6RFVBJ6oW+MEYY4E3rbXjC/j8ftG6dWvmzZvndhgiBSY51cO5w7/ztX+872JqlIvONB518pWTqV+mvhvhyRms232ES19Mr+D3Rp8L6NK4kosRiYiIiBtmrtpF/3d+97WXP96ZmKjA7bcs6MjaWmu3GmMqAD8aY1ZaazM9h2aMGQgMBKhWrVoBh5c9SlIllCTuP0bb537xtf9+ojN7krZpPGoQmPrHVu79xKlOXqlENHMe6khEeJjLUYmIiEhBu3PSEqYt2w7ADS2q8uy1TVyO6OwKNFG11m71vu8yxkwBWgKzT9lmPDAeoHnz5mcuifbdUNiR4N8gK8VD12fPuElMTAxHjhzJ8rOZM2fy6KOPUqpUKRISEujVqxfx8fG88sorHD9+nKlTp1K7dm369+9PdHQ0ixYt4tChQ4wePZorrrjCvz+LSB7N+Hsnt72/CICGlUvwzd1tmbZ+msajBoEnvv6bCXOdOQCfuroxfVpVdzkiERERKWhHTqTS+NHvfe1P77iIljXLuBhR9hVYomqMKQ6EWWsPe5cvB54oqPMXpD///JMVK1ZQpkwZatWqxW233cbChQt55ZVXeO2113j55ZcB2LhxIwsXLmTdunV07NiRtWvXqgKcBIzHvvqLd+dtBOD+y87l7kvrZhqP6pf5UT0eSD4M0SXzGK1kNGxKAh8u2AzAV3e2oUlcKZcjEhERkYI2b90ebnxrga/99xOdKVYkcB/1PVVBRloRmOKtJhUBTLLWTj/zLmdxlp5Pt7Ro0YLKlSsDULt2bS6//HIA4uPj+eWX9Ecoe/XqRVhYGHXr1qVWrVqsXLmS8847z5WYRU7yeCwtn57BniPJAHw8sBUXVC/h//GoWxfDW5c4yyP2QLjm7fSHMT+v8SWpv/6nI1XL6JFsERGRUPPQZ8v4ZNEWAK5pVoXR1wdfjlFgiaq1dj3QtKDO56aoqCjfclhYmK8dFhZGamqq77NTS0AHWkloCT0Hj6XQ9In0SnCLhnfimGcn50/s4Fvnl/GoUwfDUu8jw7U6Kkn1k7lr9/DCD6sBmPlgByWpIiIiIeZ4choNRqb3BU689ULa1i3nYkS5p6oaLpo8eTIej4d169axfv166tWr53ZIEsKWbjngS1KLhIex7ulu/LbzB7pPcYomNSnfhIR+CXlLUo/tg8dKpiepvT+Gm6fmNXTB+cN00/+cx3vevaUFNcoVdzkiERERKUiLN+3PlKQue+zyoE1SQfOouqpatWq0bNmSQ4cO8cYbb2h8qrjm7TkbeHLa3wBce34cL/Zq6v/5UZd+BFMHpbcf3gpRMXk7pvg0fNT5w9Sj6Tl0CND50ERERCR/ZKwt0rlRRd7s29zdgPxAiWounK7iL0CHDh3o0KGDrz1z5szTftapUyfeeOONfIhQJHustfR+az7z1+8D4NXezejauLx/x6N60uCV8+CgM26SNvfCZY/nJWw5xazVu7HeGumv9m7mbjAiEpSMMVWB93FqilhgvLX2FWNMGeAToAawEehlrd1vnPFKrwDdgGNAf2vtEjdiFwllJ1LTqDc8vRf17X7NubRBRRcj8h8lqiIhKikljfoj0i9sPz/QnoiofZw/8XzfujyPR92xHN5ok94e/DuUPzf3x5Ms9ZuwEIBpd7V1ORIRCWKpwAPW2iXGmFhgsTHmR6A/8JO19lljzFBgKPAQ0BWo631dCLzufReRArJ860GueG2Or7105GWUKlbExYj8S4lqLiUkJNC3b99M66KioliwYMFp9sjs3XffzYeoRLJn456jdHhhpq+98sku/Lj5W9/8qE3KN+HDbh/m7STfDoGF453lCg1h0FwIK1zD4o0xG4HDQBqQaq1tfrreh/yK4culW33Ljatomh8RyR1r7XZgu3f5sDFmBVAFuAro4N3sPWAmTqJ6FfC+tdYC840xpYwxlb3HEZF89t/pKxk3cx0A7eqW44NbC999IiWquRQfH8/SpUvdDkMkx75Ztp3Bk5yns5pXL83kQRdx38z7/Dc/atJBeLZaevu6d6DxNXkJOdB1tNbuydAeSta9D/nino+d69CsIR3y6xQiEmKMMTWAZsACoGKG5HMHzqPB4CSxWzLsluhdp0RVJB+lpHmoN/w7PN4hP+NuOp9u8ZXdDSqfKFEVCSH/+exPPl2UCMCwbg3o36YqTd5v4vs8z+NR/5oCk/untx/aBEVL5f54wel0vQ9+t2HPUd9y9bKq8isieWeMiQE+B+611h7KOHWetdYaY2wOjzcQGAhOEUkRyb21u47QafQsX3vR8E6Ui4k6wx7BTYmqSAhI81gajJhOcpoHgC/+3ZpypQ77bzyqtfB6G9j1l9NucTt0fyGvYQcDC/zg/eL2prV2PKfvffC7wR86PeP/vbbJWbYUETk7Y0wkTpL6obX2C+/qnScf6TXGVAZ2eddvBapm2D3Ouy4T73VxPEDz5s1zlOSKSLr//bqep75ZAUDTuJJMHdyGjDeSCiMlqiKF3O7DJ2gxaoavvXTkZczZ8QP9pjwMQNPyTZnYbWIeTrAaxrZIbw+aA5XiT7994dLWWrvVGFMB+NEYszLjh2fqffBHL8Pf2w8B8K/mcbnaX7JgLSQfheQjkHIc0pIhNQnSUiD1hHc52bt8wllOO+FUt/akgTGAARPmLPva3nWZPguDsAjnFV4EIqIhMhoii0JEUYiI8i5Hp78X8i8l4h5vFd+3gRXW2tEZPvoK6Ac8633/MsP6O40xH+MUUTqo8aki/metpeMLM9m49xgAz10bz/UtQuPpBCWqIoXYb+v20vut+QCUj41iwcOX8sCs+5mx2Ulc8zw/6ozHYM5LznLJanDPUggLz2PUwcNau9X7vssYMwVoyel7H07dN0+9DFv2HfMtF/Y7qqdlLRxMhD2r4MBmOLQNDm6FQ4nO8qFtkHLs7McJVeFREF0CokpAdEkoWvqfr2JloFg5KF4WipV1lovkoRK4BLI2QF8gwRhzsgjHIzgJ6qfGmFuBTUAv72ff4kxNsxZneppbCjZckcJv1+EkWo76ydf+9T8dqVomdK7BSlRzoXXr1sybN8/tMETO6OUZq3l5xhoAbmlTg0e6nUvTD/w0HvXEEXimSnr76tfhvBvzEm7QMcYUB8K81TGLA5cDT3D63ge/Gv3jagD+06VefhzeXUd2Q+LvkLgQtvwOWxc5PZkFJbI4RMU6vZvhURBRxPse5e35zPju/TwswttjCliPk0RbD2DT277lk9t4wJMKnhRvj20SpCRB6nHvu/eVcsxpp53w78+ZdgKO7nZe+cmEQ0wFKF7e+14BYsp7372v2MoQU9FJmEP1xovLrLVzgNP9x780i+0tMDhfgxIJYdOX72DQxMUAxEZHsHTk5YSHhdb1UYlqLihJlUBmraXzy7NZvfMI4Ez8XLfKCf+NR101HT66Pr09ZL3T2xJ6KgJTvL2ZEcAka+10Y8zvZN374FdT/nCG2dl+IgAAIABJREFUgt3SumZ+HD5/pSbD5nmw5kdY8wPsWZ37Y8WeA+XqQukaUKIKlKwCJc5xlkuc4ySc8k/WOo82nzgESYecat1JB+D4/vTXsX1wfB8c3QPH9jjto3tynjDbNDi83Xn5iwmDmEoQ633FVHR+37GVoURl5/cfW1mJr4gEpUEfLGb6XzsAuOuSOjxweSG8KZ0NQZ2oPrfwOVbuW3n2DXOgfpn6PNTyzAU6Y2JiOHLkSJafTZkyhTFjxjBjxgx27NhB+/btmT17NpUqVfJrnCJZOXoilUaPfu9rzx16CUv2zqD7FGd+1DyNR7UWJnSGLd65gpveCD1fz2vIQctaux5omsX6vWTR+5BfihYJ4EetrYXERfDnJFj6kdNTmB3RJSGuJcS1gKotoMoFzjrxH2OcR3iLFHMSvfyUkuQkukd2OYnu0V3e5d1wZKezfHiH80o+nL1jWg8c3ua88iq6JJSIg5Jxzo2OknHetnc59hyn11xEJJ8dS06l4cj073FfDm5D06ohN3uCT1AnqoGoZ8+efP7554wdO5bp06fz+OOPK0mVArFyxyG6vPyrr71mVFeGzL7fNz9qnsaj7tsAr56X3r7tZ4i7IC/hSh4cS051O4SsHd4JC16HOS/jPN96BlUvhLqXQd3OTvEt9XoVXpHR3iTQj0W/Uk84Ce6Rnd7eWm+ie3g7HNoKh7Y7Y5Szk/gmHXReJ6uW51TRMlC2NpSp7X2vld6OLpG7Y4pIyFm65QBXj53ra694oktg34wuAEGdqJ6t59Mtr732Go0bN6ZVq1b07t3b7XAkBHy8cDNDv0gA4LKGFRl3U1POn5je2Zen8aiznodfnnKWi5aGB9dCeFBfOoLetD+dRyg7N8q3mW+y58Rh+PXF9IJaWSlXD87rDU2udx7NFPGHiCgoVdV55YW1zmPOh7Y6hblOvg5tdQpzHUx0inNZz+mPcXwfJO5zxlWfzSPboIjmPBaRzF74fhVjflkLQLf4Soy7SZ0BEOSJaqBKTEwkLCyMnTt34vF4CAsLczskKcRue28RM1bsBJyS5RfVwz/jUVOOw6gMTwN0fxFa3JbXcMUPpiU4iWrPZlXOsmU+2LsOvhjoFDjKSqt/w0WD/dt7JpJfjPFWNi6Tu2m1rHUeYd633vl/Y9862LsW9q53llV1WkTOIM1jafLY9xxNTgPgzb4X0LmRnsQ8SYmqn6WmpjJgwAA++ugj3nvvPUaPHs2DDz7odlhSCKWkeag77Dtfe/q97VhzdJZ/xqOu+wU+uDq9/cBqiHW59058Zq92qrS2rlOuYE54dC9MHeQUPjrV+f3gkhFOFVeRUGNMeuXiaq3cjkZEgsiWfcdo999ffO3fh3WifGyUixEFHiWqfvb000/Trl072rZtS9OmTWnRogXdu3enQYMGbocmhcjWA8dp8+zPvvZfj3dm2LwH8z4e1VqYeC2s887Z1aAHXP+BP0KWfFAiOjJ/T7B2hvPv4VQ9XoNmfTWuVEREJBc+/X0L//l8GQC1yxdnxv3tQ3dO9DNQopoLp6v4CzBy5EjfcmxsLCtX+rcqschPK3Zy63vOY5f1K8Xy1Z2tuODDZr7Pcz0e9WAivNQovX3Ld1C9dV7DlWC08C349pQnQTo8Ahc/CGGhXdhBREQkt6y1/OuN31i0aT8AI65oyK1tg3CauQKiRFUkiDz+9V+8M3cjAPd1OpeeLaO44MP0Afe5Ho86bwz8MMxZDouER7Y6xUoktCydBFP/L/O6gbPgnPOy3l5ERESy5eDxFJo+nj6EZsb9F1Ongub6PhMlqrmUkJBA3759M62LiopiwYIFLkUkhZm1lhajZrDnSDIAHw9sxW47L+/jUVNPwNNVwJPitC9/Clrf5a+wJR94PGeZ9iU39m+EVzJMCVskBu5cBCUq+/9cIiIiIWb++r3cMH6+r736qa4UiVCx1bNRoppL8fHxLF261O0wJAQcOJbMeU/86GsvGt6JUb8PZcbmGQAMbTmUmxrclPMDb/oN3umS3r7vL1VqDQKJ+48DUKVUUf8ccHJ/+GtKevueZVC6un+OLSIiEuKemvY3/5uzAYDeLavyzDVNXI4oeChRFQlgSzbv55px8wCIjgzjz5GX0nxS+qO+uRqPai18ejOs+Mpp174E+nyhwjhBYuWOQwDUq5THx4UO74QXz01vXzUOmuXihoeIiIj8Q5rHEv/Y9xzzTj3z3oCWtD9XFfJzQomqSIB6a/Z6Rn27AoDrm1dl8OWlMiWpuRqPempy0neKk6hK0FizyynmVrdCTO4PsmE2vHdlenvYDoj0Uw+tiIhIiNt24DitM8zOsHh4J8rGqPZHTilRFQlA174+j8XeinDjbjqftGKL6D7F6e3K9XjURRNg2n3pbSUnQWnT3qMAVC9bPHcHmP86TB/qLLe5Fy573E+RiYiIyHcJ2/m/D5cAUL1sMWY+2EFTz+SSElWRAJKUkkb9EdN97VlDOvDSsmG++VFzNR41LRVerAfH9jjtDo9Ah4f8FbIUsC37nDGq1crkorrzr6PhJ29i2vtjqNfVj5GJiIiEtjsnLWHasu0ADOlcj8Ed67gcUXAr8ETVGBMOLAK2WmuvKOjz+0Pr1q2ZN2+e22FIIbNu9xEufXGWr53w+CW0/rilr52r8ahbl8BbHdPbd/8BZWrlNVRx0c7DSQBUKpnDR4jmvpqepGrKGREREb85kZpGveHpHQ1fDm5D06qlXIyocHCjR/UeYAVQwoVz+4WSVPG3L5du5Z6PnSrSrWuX5elelTIlqbkaj/rVXbDkfWe5SnO4bYYKJhUCuw+dAKB8THT2d1r7E/w4wllWkioiIuI3a3cdptPo2b72X493pniUHlr1hwL9r2iMiQO6A6OA+/N6vB1PP82JFSvzHFdGUQ3qU+mRR864TUxMDEeOHMnysylTpjBmzBhmzJjBjh07aN++PbNnz+ajjz4iISGBCRMmkJCQQO/evVm4cCHFiuXi8T0pVO77ZClT/tgKwGNXNqR0xWVcOfV2AJpVaMb7Xd/P2QGP7YP/1kxvX/8hNAjKhxckC4dPpAJQomg2L98HE2HiNc7yTZ8rSRUREfGTD+ZvYsTU5QC0qlWGjwde5HJEhUtBp/svA/8B8jivQuDq2bMnn3/+OWPHjmX69Ok8/vjjVKpUiXvuuYcOHTowZcoURo0axZtvvqkkNcSleSy1H/nW1/76zra8uWoYs+Y6j/8+3PJhbmxwY84OumwyfHFbevvhRIgqtP+7hbRsFWawFl5q5Cy3Hwp1O+VvUCIiIiHimnFzWbL5AADPXhPPDS2ruRxR4VNgiaox5gpgl7V2sTGmwxm2GwgMBKhW7cy/8LP1fLrltddeo3HjxrRq1YrevXsDEBYWxrvvvkuTJk244447aNOmjctRipt2HU6i5aiffO3fR7Tnks/S78J9duVn1CtTL/sH9HhgbAvYu9ZpX3QndB7lr3AlWE263nmPLAYdH3Y3FhERkULgcFIK8Y/94Gv//EB7apXPw5RxcloF2aPaBuhhjOkGRAMljDETrbV9Mm5krR0PjAdo3ry5LcD4/CYxMZGwsDB27tyJx+MhLCwMgDVr1hATE8O2bdtcjlDcNHftHm763wIAqpQqygeDamRKUhfetJCiETmYNmbXShh3YXr7/36Dig39Fa4Eq10rYM33zvJDm9yNRUREpBBYsnk/14xLr1Wz+qmuFIkIczGiwq3A/staax+21sZZa2sANwA/n5qkFgapqakMGDCAjz76iAYNGjB69GgADh48yN13383s2bPZu3cvn332mcuRihte+H6VL0m94+Ja3H/Nfq768ioAWlZqSUK/hJwlqT8MT09Sy9aBkfuVpIpjXCvn/foPIaKIu7GIiIgEudE/rvYlqdc0q8LGZ7srSc1nKknlZ08//TTt2rWjbdu2NG3alBYtWtC9e3eef/55Bg8ezLnnnsvbb79Nx44dufjii6lQoYLbIUsBsNZyyYuz2LDnKADvDWjJpE3DmfSbc8Eb0WoEver1yv4BTxyGZ+LS29e8BU1ysL8Ubks+SF9WIS0REZFcs9bS+tmf2X7QmR7uzb4X0LlRJZejCg2uJKrW2pnATDfO7Q+nq/gLMHLkSN9ybGwsK1c6VYknTJjgW1+1alXWrl2bfwFKQDl1LMPMIW24clo7X3tKjynUKZ2DCaFXfQcf3ZDe/s8GKFbGH6FKYfHVnc77vcvdjUNERCSI7TuazPlP/uhrL3jkUiqWyMH0cJIn6lEVyUd/bTtI91fn+NrfPVgnU5K6qM8iosKjsncwa+HtyyFxodNu1geuGuvPcKUwONmbGhYJpaq6G4uIiEiQylhTJCYqgmWPXk5YmOajL0hKVHMpISGBvn37ZloXFRXFggULXIpIAk3GubWuaFKZ1s1W0uub6wC4OO5ixl6agyRz33p4tVl6+/ZfoMr5/gxXCouTval3/+FuHCIiIkHqmW9X8Obs9QAMaFOTkVeq/ocblKjmUnx8PEuXLnU7DAlQ/SYsZNbq3QC8+K+mTNk5lGcW/gnAU22e4qo6V2X/YLOeh1+ecpaLlYUHVkO4/tcNFMaYcGARsNVae4UxpibwMVAWWAz0tdYmF0gwO/9OX1ZvqoiISI5Ya2kxagZ7jjh/tj+4tSXt6pZ3OarQFZTfdq212ZvsPohZG5Qz84S85FQP5w7/ztf++u4LuPHHy3ztaT2nUb1E9ewdLOU4jMowWL/7aGhxq79CFf+5B1gBlPC2nwNestZ+bIx5A7gVeL1AIpnQxXm//sMCOZ2IiEhhcep41N+HdaJ8bDaHZ0m+CLqaytHR0ezdu7dQJ3LWWvbu3Ut0tAZrB5Mt+45lSlI/v6dqpiR1SZ8l2U9S18/MnKQ+sFpJagAyxsQB3YH/edsGuAQ4Of/Ue8DVBRKMtXDioLOsSr8iIiLZ9tu6vb4kNSYqgvVPd1OSGgCCrkc1Li6OxMREdu/e7XYo+So6Opq4uLizbygB4fu/dnDHB4sBaBJXkqs7rKb/D0MB6FKjC8+3fz57B7IWJvWCNd4qwfWvgBvUOxbAXgb+A8R622WBA9baVG87EahSIJEkTHbeKzQqkNOJiIgUBi98v4oxvzizcfRvXYPHeujvaKAIukQ1MjKSmjVruh2GiM+Iqcv5YP4mAIZ0PpcfDw7hpcXrAHi+/fN0qdElewc6tA1GN0hv9/8WarTxd7jiJ8aYK4Bd1trFxpgOudh/IDAQoFq1ankP6IvbnffrJpx5OxEREcFaS9vnfmHrgeMAvNO/BR3rV3A5Ksko6BJVkUDh8ViaPvEDh5OczrP3b2vM4Lnpj1xOv3Y6VWKy2Zm24E347j/ehoHhOyFCj5wEuDZAD2NMNyAaZ4zqK0ApY0yEt1c1Dtia1c7W2vHAeIDmzZv7byxDhfp+O5SIiEhhdPBYCk2fSJ/jXvOjBqagG6MqEgj2HU2m1iPfpiep/1c+U5L6R98/spekpqXAqHPSk9ROj8NjB5SkBgFr7cPW2jhrbQ3gBuBna+1NwC/Add7N+gFf5nsw27xT0RQtne+nEpHCyRgzwRizyxizPMO6x4wxW40xS72vbhk+e9gYs9YYs8oY09mdqEVybtHGfb4kNTLcsO7pbkpSA5QSVZEcWrRxn2/AfcmikTx4/QYGz3QKHfWs05OEfglEhGXjYYUtC+HJcpBy1GnfmwBt782vsKXgPATcb4xZizNm9e18P+P3w5z3Ls/m+6lEpNB6F8hqrMpL1trzvK9vAYwxDXFu0DXy7jPOO1WXSEB79ac1XPfGbwD0blmVNaO6ER5WuGcSCWZ69FckB8b+spbnv18FQJ8Lq7Eg7X7eXLYDgFc7vkrHah2zd6AvBsKyT5zlGu2g39dQyKdcKsystTOBmd7l9UDLAg1g01znPb5XgZ5WRAoPa+1sY0yNbG5+FfCxtfYEsMF7Y64l8Fs+hSeSJ9ZaOo2exbrdTufA+L4XcHmjSmfZS9ymRFUkm658bQ4JW53pP0bfUIdH/7zO99mM62ZQsXjFsx/k6B54vnZ6+6bPoO5lp99eJCfC9JCMiPjdncaYm4FFwAPW2v041cznZ9im4Cqci+TQoaQUmjyWPh513tBLOKdUURcjkuzStxqRszienEaNod/4ktTXbonxJalFI4qytO/S7CWpf3yYOUl9ZJuSVMm7RGdaJEplc45eEZHsex2oDZwHbAdezOkBjDEDjTGLjDGLCvvUghJ4lm45kClJXTuqq5LUIJKrHlVjTAWcipfnAMeB5cAia63Hj7GJuG7trsN0Gj3b1/6/a/7mkfnvA9CnQR8eavnQ2Q/iSYNXmsLBLU673QNw6cj8CFdyIeivZwted94vGuxuHCJS6Fhrd55cNsa8BUzzNrcCVTNsWvAVzkXOYvzsdTz97UoArmlWhdHXn+dyRJJTOUpUjTEdgaFAGeAPYBfOtAxXA7WNMZ8BL1prD/k7UJGC9vniRB6Y/CcA7euVZU30/UxccRiANzu9Sesqrc9+kB0J8Ebb9Padi6Bc3fwIV3Ko0FzPEiY7701vcDcOESl0jDGVrbXbvc2eODfyAL4CJhljRuPc5KsLLHQhRJEs9Rw3lz82HwBgzI3NuKLJOS5HJLmR0x7VbsDt1trNp35gjIkArgAuAz73Q2wirhk8aQnfLHP+Nj985TmMWXszJDufzew1k7JFy579IN8OgYXjneVK8XDHryqYdIrkLVtIO3CAovHxbpy+cF3Poku6HYGIBDFjzEdAB6CcMSYReBToYIw5D7DARuAOAGvtX8aYT4G/gVRgsLU2zY24RTI6npxGg5HTfe1ZQzpQvWxxFyOSvMhRomqtHXKGj8taa6fmMR4RV6Wmeagz7Dtf+6ne4Ty39GYAyhUtx8//+hlztmQz6SA8Wy29/a93oVHPfIg2eB2c9g3bHnzQ166/PAETUeC13V601u7I6gNrbSoQ+Nczq6foRCSdMeYioA/QDqhM+nCGb4CJ1tqDp9vXWts7i9WnnV7LWjsKGJWngEX86NThWque6kJUhGZNCmZ5+mZojCkFXAvcCDTAefxDJCjtOJhEq2d+8rVvvnIRzy39DIDb4m/jnvPvOftB/poKk/ultx/aBEVL+TvUoORJTmb78OEc+urrTOurvPySG0kqwFLvxPYfAZ9baw+4EUSe7HbG3lCy6pm3E5FCzxjzHbAN+BIngTw5nOFcoCPwpTFmtLX2K/eiFMkfU/5I5L5PnOFaF59bnvcHFOwscZI/cvzt0BhTFGf+rBuBZkAszpiu2WfaTySQzVy1i/7v/A5AzfJF2V/+fqasdZ5ieqfzOzSv1PzMB7AW3mgHOxOcdovbofsL+Rly0EjetImN199A2oH0PDC8dGlqfDSJIjVquBeYM5VCJ5xJ6582xszHSVq/tNYedzOwbFvprWvS4Ep34xCRQNDXWrvnlHVHgCXe14vGmHIFH5ZI/rrvk6VM+cOp5fXkVY3oe1ENdwMSv8lpMaVJOI+T/AC8BvwMrPVOdi8SlJ75dgVvzl4PwID2pZi8a5AzGgeYc8McSkadZezfnrUw5oL09qA5zpjUEHfwm2/Y9sCDmdaVvKoHlZ58krAiRVyKKp13PNX3wPfGmCJAV5yk9WVjzE/W2ptcDTA7VnnH4dTr6m4cIhII+hhj5gJ/eIcv/EMWiaxI0ErzWOoO+xaP9zvb13e2JT5O9RoKk5z2qDYE9gMrgBXW2jRjjAZJSVCy1tLm2Z/ZdjAJgAd7nuDNlYMAqF6iOl9f/fXZx6P+/BTMft5ZLhEH9y6DsNAdD2FTUtj+2GMc/PyLTOvPef55Sl55hUtRnZ21NtkY8zfOte0CnKEMgW/rIue92kXuxiEigSAOeAWob4xJAOYC84B51tp9rkYm4md7jpyg+VMzfO0/H72ckkUjXYxI8kNOiymdZ4ypD/QGZhhj9gCxxpiKGefaEgl0h5JSMk0A3fPyWby50imidOd5d3JH0zvOfIDko/B0hiHZV42FZn3yI9SgkLJ1KxtvvInUnemXgbCSJanx8UdE1azpYmRnZoypitOL2hsojvPobw9r7UpXA8upcP1xFgl11toHAbxPiDQHWgO3AOONMQestQ3djE/EX+av38sN4+cDULFEFPMfvvTsHQsSlHI8RtX7Be5R4FFjzAU4X/B+N8YkWmuzMbGkiLuWJR6gx5i5AISZNIrXH8aMLc5nH3b7kCblm5z5AGtmwIfXpreHrIPioTns5/CMGSTeeVemdSW6daXyM88QFhXlUlTZY4yZhzNO9VOcaWoWuxySiIg/FAVKACW9r21AgqsRifjJmJ/X8MIPqwEY0KYmI6/U/ZfCLE+lNr1f7BYbY4bgjF0VCWgT5mzgiWl/A9ClaQRzk4f6Pvut92/EFIk5/c7WwntXwsZfnXZ8L7j2rfwMNyDZ1FR2Pv0M+ydNyrS+8lNPUuq661yKKleGAr9aG6RzvARp2CKSP4wx44FGwGFgAc5jv6OttftdDUzET7q/+it/bTsEwP9ubk6nhhVdjkjyW06LKQ0Hxp061sH7RW+2MeYSoJi1dpofYxTxi15v/MbCjc4/3QGd9zN583MANCzbkI+7f3zmx0YObIaXMxRIuvVHqBpapc9Tdu5k0803k7Jps2+diYqi5meTiapb18XIcu1inF6GLL/EBfz1bJ9TAIwSce7GISKBohoQBawBtgKJQPBNuyVyiuPJaTQYOd3XnvNQR+JKF3MxIikoOe1RTQC+NsYk4ZQ6340zR1dd4DxgBvC0XyMUyaOklDTqj0i/wF3W4Tsmb54FwJDmQ7i50c1nPsDcV+DHkc5yZHEYuimkxgQe+fVXttw+MNO6mEsvpcrz/yWsWFD/oUgApgXt9SzRW0ipagt34xCRgGCt7WKcO66NcManPgA0NsbsA36z1j7qaoAiubBu9xEufXGWr736qa4UiQhzMSIpSDktpvQlzoTRdYE2QGXgEDARGBg0cw9KyFi76widRnsvcCaV2PrDme+t9zP5ysnUL1P/9DunnoBRlcB6nHaX56DVoPwNOEBYj4ddL77IvrcnZFpfccRwytwU+LO2ZEfQX88SFzrvcaHVsy8ip+d9wm25MeYAcND7ugJoiVNfRCRofLl0K/d8vBSAdnXL8cGtF7ockRS0XI1RtdauwXm0JNuMMdHAbJzHUiKAz3R3T/LT54sTeWDynwC0qJvCyogRvs8W3LiAYpFn6A3cOBfe7Zbevn8FlDjn9NsXEmmHD7PljkEcX7Ik0/qaX3xOdMPCWbAgN9ezgJD4u/Mepx5VEQFjzN04PamtgRS8U9MAE1AxJQky93+6lC+WbAXg8R6N6Ne6hrsBiSvyVEwph04Al1hrjxhjIoE5xpjvrLXzCzAGCRH//nAx3ybsAOC6Dlv5fudrALSo1IIJnSecaVf4pA+s+NpZrnMZ9PksP0MNCCfWrGF9j6syFegp1qoVcWPGEB5T3MXI5LS2OzdhqHyWKtUiEipqAJOB+6y1212ORSRXPB5L/RHTSU5znmb76s42NIkr5XJU4pYCS1S9j6Mc8TYjvS+VrRS/SknzUHfYd772RW0+4/udzli+Ea1G0Kter9PvfHgnvHhuevvmL6FWh/wJNEAcmj6drffel2ld2f8bRPm779acZMEiIrCnARKRAjPSWnvkTBsYY2LOto2IW/YfTabZkz/62n8+ejkli4ZOTRD5p1wlqsaYNtbauWdbl8V+4cBioA4w1lq7IDfnF8nK1gPHafPsz07DJBNbfyTLvfWpp141ldqlap9+50UTYFqGhG3YDogsmn/Bush6POx6/gX2vfNOpvVxY8cQe+mlLkUlIiJ59KUxZinwJbDYWnsUwBhTC+gI9ALeAgr/Y0ISdP7YvJ+e4+YBULpYJEtGXKYb5pLrHtXXgPOzsS4Ta20acJ4xphQwxRjT2Fq7POM2xpiBwECAatWq5TI8CTU//LWDgR8sBqB2lUPsKpFerHVRn0VEhZ+m1ykt1elFPbbXaXccDu2H5He4rshq/KmJjqbmF18QVaumi5G5yxhzLvA6UNFa29gY0wToYa19yuXQ/iFYp3wVkfxnrb3UGNMNuANoY4wpDaQCq4BvgH7W2h1uxiiSlXfmbuDxr5057nu3rMYz18SfZQ8JFTmdR/UinEH65Y0x92f4qAQQnt3jWGsPGGN+AboAy0/5bDwwHqB58+b6ViZnNXxqAhPnO3N7dr5oDfMOvA1A+7j2jLl0zOl33LoE3uqY3r77DyhTKz9DdcWJtWtZf9XVkJbmW6fxp5m8BQwB3gSw1i4zxkwCAi5RFRE5E2vtt8C3bschkl3931nIzFW7ARhzYzOuaFL4C1dK9uW0R7UIEOPdLzbD+kPAdWfa0RhTHkjxJqlFgcuA53J4fhEfj8cS/9j3HE12ErCmLd9j3oEVAIxqO4oetXucfuev7oIl7zvLcS3h1h+gkD1icmj692y9995M68oOuoPy99yjx2kyK2atXXjKf5NUt4IREREp7FLTPNTJUFPkpwfaU7t8jIsRSSDK6Tyqs4BZxph3rbWbcniuysB73nGqYcCn1tppOTyGCAB7jpyg+VMznEbYCWLrPcr6w05zWs9pVC9RPesdj+2D/2Z4zPWGj6B+t6y3DUKnm/80bsxrxHbq5FJUAW+PMaY23uJuxpjrgLNWzDzdlFvGmJrAx0BZnDH5fa21yf4MOArv4YwmPRcRkeCS6Tsc8PcTnSlWpCAnIpFgkdt/FVHGmPE4pdB9x7DWXnK6Hay1y4BmuTyfiM+8dXu48S2nDleZMjtIqfiy77MlfZYQGX6aCnHLJsMXt6W3H06EqNistw0ynqNH2fLvwRxbkF6fzERFUXPKF0TVKnyPM/vZYJzhBvWNMVuBDUCfbOyX5ZRbwP3AS9baj40xbwC34oyB9ZsaxjvMrNy5Z95QREQkgCzetI9rX/8NgLjSRfn1Px31lJecVm4T1cnAG8D/gLS1oibnAAAgAElEQVSzbCviN6N/WMWrP68FoPUFf5Jw7CMAutboyn/b/zfrnTweGNsS9q5x2q3vgssLx/DDlO3b2XDtdaTt2+dbV+zCC4kbO1bjT7PJWrse6GSMKQ6EWWsPZ3O/0025dQlwo3f9e8Bj+DlRrWW8Hb5l6/jzsCIS5LxPrf1lra3vdiwip5owZwNPTHOKJvVvXYPHejRyOSIJdLlNVFOttX794iVyJtZaLnlxFhv2HAUsdZq9TsIxp4DSC+1foHONzlnvuHuVk6Se9O8FUCH4/34f//NPNl5/Q6Z1ZfrdTIWHHsKE6XHQnDilMNzJO7sHcaZ3WHqWfTNNuQWsAw5Ya0+OcU0Eqvg75qpml7NQuoa/Dy0iQcxam2aMWWWMqWat3ex2PCIn3fLOQn7xFk16/abz6Rpf2eWIJBjkNlH92hjzb2AKzuNvAFhr951+F5HcOZSUQpPHfnAaYceIrfcEO5Oc5vfXfs85MaepEDfjcZgz2lkuWwcG/w5BnsQdnPYN2x58MNO6Sk88TulevVyKqFBo7n197W1fASwDBhljJltrT9NV/88pt4Bs3wXJy1RcVY3zx55SpxmLLSKhrDTwlzFmIXD05Epr7RkqDIrkDxVNkrzIbaLaz/ueccJJC2gwnPjVssQD9BgzF4DwouspVmM8AAbDkr5LiAjL4p9w8jF4OsOdup5vQtMb/rldkLDWsue119gzLvNDDNXee4/iF7Y8zV6SA3HA+dbaIwDGmEdx5hy8GKe39LSJ6kkZpty6CChljInw9qrGAVtPs0+up+KqbLzz/paMy8luIhIaRrgdgAioaJLkXa7+tVhra559K5G8eXvOBp70jmWIbzyHjWlOkeh/nfsvRl40Muud1v4EE69Jbw9ZD8XL5neo+cKTnMy2Bx7k8I8/+taZ6GhqfTmVItXVk+ZHFcjwZAiQAlS01h43xpw4zT5nmnLrF5zpuj7Guan3pb8DrmT2Owsl9OiUiGRmrZ1ljKkO1LXWzjDGFCMHc92L+EPGoklVShVlzkMqmiQ5l6tE1XvRux+oZq0daIypC9TTdDPiL73e+I2FG/cBHirFP8fG1IMAjL10LBfHXZz1Th9cA+t+cpbj/wXX/q9ggvWz1H372HTjTSRv3OhbF924MdUmvE14iRLuBVZ4fQgsMMacTCivBCZ5iyv9fYb9spxyyxjzN/CxMeYp4A/gbX8HXMl4R1nEamJ0EcnMGHM7zrCCMkBtnHHybwCXuhmXhA4VTRJ/yW3/+zs4j8S19ra34lQCVqIqeZKUkkb9EdMBMOGHiTl3FEe9ZWl+6fUL5YqW++dOh7bB6Abp7QHfQ7VWBRCtfyWtXs2GHldlWleyZ08qP/kEJkKPyuQXa+2TxpjppF/PBllrF3mXbzrDfllOueWtIpyvz2SXM4echeLl8/M0IhKcBuNcgxYAWGvXGGMquBuShIoB7/7Ozyudgn/jbjqfbiqaJHmQ22+/ta211xtjegNYa48Z9edLHq3ddYROo2cBEF58FcWqvQNA6ajSzLx+JmEmi0JIC9+Cb73FhUw4DNsBEUUKKmS/ODJrFlvuGJRpXYUhQyh76wCXIgo91trfjTGbgGiAoKmYGeTFwUQkX5yw1iaf/FpmjInAqSMikm/SPJbaj3zra6tokvhDbhPVZO+YLAtgjKlN5jFeIjny2eJEHpz8JwA16n3H3jAnYR3QeAD3XXDfP3dIS4Xna0GS80gwnR6HtvcWVLh+sf+TT9nx6KOZ1sWNG0fsJR1diig0GWN6AC8C5wC7gGrASkDPKolIMJpljHkEKGqMuQz4N+lVzUX8TkWTJL/k9l/Ro8B0oKox5kOgDdDfX0FJaPn3h4v5NmEHkEZsg+Hs9d74ndB5Ai0qtfjnDtv+gPEd0tv3LIPSwVFcyFrL7ldeYe8bb2ZaX/PLqUTXq+dSVCHvSaAVMMNa28wY0xHo43JMIiK5NRS4FUgA7gC+BYKzaIMEvD8276fnuHmAiiaJ/+U4UTXGhOHM0XUNzpc7A9xjrd3j59ikkEtJ81DXO7eWidxHTJ30WUDm3DCHklEl/7nT1/fCYueRYOJawq0/QBBcEG1qKtuHDePgl1/51oWXLk3NKV8QWamSi5EJTuXevcaYMGNMmLX2F2PMy24HJSKSSx2Bidbat9wORAq3ifM3MXzqcgBuvqg6T1zV2OWIpLDJcaJqrfUYY/5jrf0UZ65BkRzbeuA4bZ79GYCIEkspWuVjAOqUqsMXPb7459244wfguQy9pjdMgvrdCyrcXPMcO8aWOwZx7PfffeuiGzWi2rvvEB4b62JkksEBY0wMMBv40BizCzjqckwiIrl1M/C6MWYf8CvOtW2OtXa/u2FJYXLXR3/w9Z/bAHi1dzN6NFUVevG/3D76O8MY8yDwCRm+0Flr9/klKinUfvhrBwM/WAxA+dofkVTEGZv6wAUP0L9x/3/u8PeX8OnN6e2hWyA6sKdpSd27l43X30BKYqJvXexlnTjnxRcJKxJcxZ5CwFXAceA+nCq/JYHHXY1IRCSXrLX9AIwx5+DM6TwWZwy+Bg1Knnk8loaPTicpxQPA9/deTL1KuvEu+SO3F63rve+DM6yzQK28hSOF3fCpCUycvxlMCrH1R5DkXf/pFZ/SoGyDzBtb64xF3b7UabccCN2eL8hwcyx50ybWdesOaWm+daVv7kvFoUMxqtAaqEZaax8CPMB7AMaY54CHXI1KRCQXjDF9gHZAPLAHGMP/t3ff8VVU6R/HP08SUkhCVYr0IkVFhUXsChaaKLpLERv2srorv11ULCgqKiqyoqjIioirgsja1gKCAoIiKAoGBZQSMPQmvQRyfn/McEkwBBJuz/f9et1X5px7Zua5w80hz5RzvCurIkdky85cmvX7LFD+sV9byqWWiWBEEu9K+oxqH+fc2yGIR+JUXp7jhH7j2b57LwnJq0lv8K/AezOumEHZMmULrrBhMTyXb4rKW6ZC9RPDFG3x7Zgzh+zulxeoq3L33VS+/roIRSTFcCF/TEo7FFInIhILngUWAUOBSc657MNZycxeBToBa5xzJ/h1lfDunqsLZAPdnHMb/SkJBwMdge3Atc6574P7MSSaLFyzhQsGfQlASlIC8x5pT0JC9I8RIrGt2Jd4nHN5wF0hiEXi1Lqtu6h/3yds372XMhWnB5LU06qfRlbPrD8mqV8+vT9JzagKD26I2iR1yxeTmNekaYEktcagZ2g6f56S1ChnZreZWRbQ2Mx+zPdaAvwY6fhERErCOXcUcD3evNCPmdlMM/vPYaz6GtD+gLo+wOfOuWOBz/0yeCfzjvVfNwMvBSF0iVLjf1oVSFLbH1+NBf07KEmVsNAzqhJS0xetp8e/vwGgXP0XcSnLAHjkjEe47NjLCjbO3QmPVd1f7vQvaHl9uEItlsLmQK39+kjSW7WKUERSAm8BnwJPsP+PL4At6stEJFaZWTm8+aDr4F0JLY/3aEORnHNfmlndA6o7A6395ZHAZLy7TToDrzvnHPCNmVUws+rOuZVH/gkkmjw1bj4vTl4EwIOdjuP6s+pFOCIpTfSMqoTMoM8W8NwXCyFhJ5mN+/mzo8L/Lv0fdcvXLdg4+yt4reP+8j8XQGb0TduybujLrH224Mwl9T78gNRGjSIUkRyBRGAzBfsxwLvdTcmqiMSoafleQ5xzOYdoX5Sq+ZLPVcC+s8k1gN/ytcvx65SoxpGLn59G1vJNALx982mcWr9yhCOS0qZEiapzTqdT5KCcc7QZOJns9dtJSF1Ger0XA+99f9X3lEk84MH7Mdd4I/sCNGoPV0TX48/OOdY8PZANr74aqEusUIF6H7xPmapVi1hTotwsCJw/OfAepqg98ZbIvoG6dNuViPyRc+5EAH/arWBu15mZO3TLgszsZrzbg6ldu3YwQ5IQyT/PPcA3955PtfKpEYxISqsSJapmdk1h9c65148sHIl1m3fmcqI/IlzyURNJOXoiAJ3qd+KJs58o2HjrWhjYcH/5mg+gfuvwBHoYXF4eKx98kE1j/xuoS65Th7pvjyaxQoUIRibBEKsn3Mrte9oiTd9BEfkjMzsB+A9QySvaWqCnc25uCTa3et8tvWZWHVjj1y8HauVrV9Ov+wPn3DBgGEDLli2LnehKeK3dsotTHpsYKC/o356UpMQIRiSlWUlv/T0l33IqcD7wPaBEtRTLytnExUOmAY70hk+SUOZ3AJ5t/Szn1zm/YOMf3oAP8t1xef8qKJMWvmCL4HJzWf6Pf7JlwoRAXeqJJ1L71VdJzEiPYGQSKmZ2CXCOX5zsnPsokvEUpbz5iWqqElURKdQw4B/OuUkAZtbarzujBNv6EOgJDPB/fpCv/g4zGw2cCmzS86mx7/tlG/nzi18DUP/odL74Z+vIBiSlXklv/f1b/rKZVQBGByUiiUmvTlvCIx/9jCVuJaNR/0D9xC4TqZqe7/bYvDwYfBJs8gZV4py74bz7wxxt4fJ27uS3W25l+4wZgbr0s86i5gtDSEhJiWBkEkpmNgDv5NubftWdZnaGc+6+CIZ1UBV0RVVEipa+L0kFcM5NNrNDnmU1s1F4AycdZWY5wEN4CeoYM7sBWAp085t/gjc1zUK86Wk0zH2Me2vGMu57LwuAa8+oS79Ljo9wRCIlv6J6oG1ATN5GJ0eu29DpzMzeQGL6L5St7T3HmV4mna97fE2C5ZsBafXP8NLp+8t3zIKjGhJpe7duZelVV7Nr/vxAXbmLLuKYJwdgScH6FZEo1hE42Z96CzMbCfwARF2i6lz+K6rlIxuMiESrxWbWF+/2X4CrgMWHWsk51+Mgb51/YIU/2u8fBqKT2PSPt2fz7g/endvP9WjOJScdE+GIRDwlfUb1f+wfhCQBOA4YE6ygJDbszN1Lk77jAEip+gHJlaYDcM1x13DXKQdMtTv+fpg+xFuuchzc9jVYZAeD2bNhA9ldupK7YkWgrkKPy6nWty+WUOwphiW2VQD2jfIb1RlgBju8hZRykQ1ERKLV9cDDwLt4f6tN9etECnDO0eLRCWzcngvAp3eeTdPq+r9FokdJLxcNzLe8B1h6hMOfS4xZuGYrFwyaAuSR0aQvZt5IpMPbDqdV9Xxzie7aCk/U2F/+y3Bo1iW8wR4gd9UqFl/UibxtgSmAqXzrLRx9551YhJNniYgngB/MbBLeULrnUHBe1aiSbkpUReSPzCwVuBVoCGQB/3TO5UY2KolW23fv4bgHxwfKcx5sS/myZYpYQyT8ipWomllDvDm1phxQf6aZpTjnFgU1OolK/52Vwz/fmYMl/U7GsQMC9VO7T6VC/gFefvkM3uq6v3z3EihbKYyRFrQ7O5tF7TsUqKty111UvkEnmksjM3sBeMs5N8rMJrN/kLh7nHOrIhdZ0TLY6S2kBHXmCRGJfSOBXLwrqB2ApkCviEYkUSl73TZaD5wcKC96vCOJCTpRL9GnuFdUnwXuLaR+s//exQdb0cxq4Y0KXBXvVpRhzrnBxdy/RNhtb8zi07mrSMr8kbSabwFQt1xdPrz0w/1XI52DkRdD9lSvfNIVcNlLEYoYdi1cyOJOBb+a1R59hIpdux5kDSklfgEG+lMujAFGOed+iHBMh5TKLm+hTNnIBiIi0eY451wzADMbDsyMcDwShSYtWMN1I74F4JxGR/P69a0OsYZI5BQ3Ua3qnMs6sNI5l2VmdQ+x7h6821C+N7NMYJaZTXDO/VzMGCQC8k/+nFrjTcqU874Gd7a4kxub3bi/4aYc+Fe+keJu/BxqtgxnqAE7F/zCks6dC9TV+NcgynXocJA1pDTxT5QNNrM6wOXAq2aWBozCS1p/iWiAB5Fmu70FJaoiUlDgNl/n3B49yiIHGvLFrwz8zPuv7e72jflr68gPaClSlOImqkXNh1DkJJj+/For/eUtZjYPqAEoUY1yy3/fwZkDvgDbQ2aTBwL1ozuN5vjK+ZLS6S/CeP+Ce1Ia9FkGSclhjhZ2zp/PkksvK1BX88UXyDzvvLDHItHPObcUeBJ40syaA68CDwJROcN52cAV1eiYd1hEosZJZrbZXzYgzS8b3kC9erC9FLt6+Aym/roOgNevb8U5jY6OcEQih1bcRPU7M7vJOffv/JVmdiMw63A34l99bQ7MKLqlRNpnP63i5v/MIiF5DekNBgXqZ1wxg7L7rujszYUBdSDXH5yo7WNwxh1hj3Xnzz+z5M9/KVBXc+hLZLZuHfZYJHaYWRLe81yX403DMBnoF8GQipSmRFVECuGci8qTaxJZeXmO+vd9Eih/eVcbalfWHTkSG4qbqPYC3jOzK9mfmLYEkoHLDrpWPmaWAfwX6OWc21zI+zcDNwPUrl27mOFJMD3wfhZvfLOMMhVmkFr9PQBaVWvF8HbD9zfKmQWv5LtS2WsuVKgV1jh3ZM0l+4DnTWv9exgZZ58d1jgktpjZhUAPvHlUZwKjgZudc9uKXDHCUnXrr4iIHIbNO3M5sd9ngfLPj7SjbLLmh5fYUaxvq3NuNXCGmbUBTvCrP3bOfXE465tZGbwk9U3n3LsH2ccwYBhAy5YtXWFtJLTy8hzN+o1n2+69pNUZSlLZbAAeOv0hujTKN7XM+7fD7De85bpnQ8//hXVu1B0//kh2t+4F6moNf4WMM88MWwwS0+4F3sJ7dn5jcVc+2ABxZlYJeBuoC2QD3Uqy/YPRFVURETmUhWu2cMGgLwHISEkiq19bTcEnMadEp1Wcc5OAScVZx7zfjuHAPOfcoEO1l8hYt3UXLftPBNtFZtOHAvUfXvoh9crX8wo7NsKTdfev1ONtaNw+bDFu/+EHlva4okBd7ddGkH7aaWGLQWKfc+5IH1oudIA44Frgc+fcADPrgzcn6z1HuK+AVHRFVUREDm7iz6u58fXvAOjYrBovXvmnCEckUjLhvP5/JnA1kGVms/26+5xznxSxjoTR14vWccW/Z5CQ+hvp9V4I1H9/1feUSfQngZ77Xxibb97Re3MgJTMs8W2fNYulV15VoK72yJGkn6qh1SX8ihggrjPQ2m82Eu+Z16AlqvtH/dUVVRERKej5z3/lmQneyL59Ox3HDWfVi3BEIiUXtkTVOTcNb+Q5iUKDPlvAc18sJLnyF6RU8Z5n6FCvA0+d85TXwDl46QxY4w/SfPod0O6xsMS2beZMll3Ts0BdnTf+Q9mWkZn2RuRABwwQV9VPYgFW4d0aHDRpmkdVREQK0fPVmUz5ZS0Ab954Kmc2PCrCEYkcGT1RXco552gzcDLZ67eR3mAgCcnrAXjm3GdoW7et12jdQhiS77aR276GqscXsrXg2jZjJst6HpCgvvUWZVs0D/m+RQ7XgQPE5X8GyDnnzKzQZ+1LOnCcnlEVEZH88vIcDe//hDz/fxuN7CvxQolqKbZvNDhL3EZm00cD9RO6TKBaejWvMOkJmDLAWy5XE3r9CAmhHQF/+/ffs/SKKwvU1R09irSTTw7pfkWK6yADxK02s+rOuZVmVh1YU9i6JR04Lg3d+isiIp6tu/ZwwkPjA+WfHm5Heor+vJf4oG9yKfVjzu9cMuQrEssupGydVwBIS0pjeo/pJCYkQu4OeKza/hUuGQItrg5pTIVNM1N3zNuknXhiSPcrUhJFDBD3IdATGOD//CCY+0013forIiKwdP02zn16MgDJiQks6N9eI/tKXFGiWgoNn7aERz/6mZSq/yO50lcAXNX0Ku5p5Y/3sngKvH7J/hV6L4SMo0MWz87581lyacFpeOu89SZlW7QI2T5FgqDQAeLwEtQxZnYDsBToFsydppLrLeiKqohIqTX117VcPXwmAG0aH82I6zSwpMQfJaqlTLeh05mZvY6Mxg9hCd4fvMMuHMbpx5zuNRjVAxb4AzE3vRi6vxGyWHYtWsTiizoVqNM0MxIrDjFA3Pmh2q+eURURKd1embqY/h/PA+Cudo25vU3DCEckEhpKVEuJnbl7adJ3HJa0icymTwTqv+z+JRVTK8KW1fBMo/0rXPsx1D0rJLHsXrqURe0Kzrtaa9jLZJxzTkj2JxJPkizPW9g3ZZSIiJQaf31zFp9krQLg1Wtbcl6ToA4sLxJVlKiWAgvXbOWCQVNIypxLWk3vCmmtzFp8fNnH3rMM342Aj3rtX+H+1VAmNehx5C5fzsLzLyhQV3PI82RecMFB1hARERER5xwnPfwZm3fuAWDiP86lYZWMCEclElpKVOPcf2fl8M935pB6zCjKlJ8DwN+a/42bT7wZ8vbCM01h62qvcZv74dy7gx5D7urVLLqwLW737kBdjUHPUK5jx6DvS0RERCSe7Ni9l6YPjguUf+zXlnKpuqtG4p8S1Th2639mMe6nHDKbPhCoG3XRKE446gRYlQVD893a+7fvoXKDoO5/z7p1LOp4EXmbNwfqqj/xBBUuuzSo+xERERGJR8t/38GZA74IlBc93pHEBI3sK6WDEtU4lLs3j2Pv/xRLXktm02cC9d9c8Q3pZdLh03tgxlCvsvpJcPMUCOJw5ns2bmRJ50vZs2b/9JHV+j1ExcsvD9o+REREROLZzCUb6PbydABa1a3EmFtPj3BEIuGlRDXO/LZhO2c/NYkyFWaSWv1dAFpUacHIDiNh52Z4rPz+xl1HwvHBu7q5d+tWsv/Shd1Llwbqqt7bh0o9ewZtHyIiIiLx7o1vlvLA+3MBuL1NA+5q1yTCEYmEnxLVOPJp1kpue/N70moPIyl9MQB9T+tLt8bdYP4nMLrH/sb3LIW0CkHZb96uXSzreS07Zs8O1B3dqxdH3XpLULYvIiIiUlr0fmcOY2flAPDilS3o2Kx6hCMSiQwlqnHi7rFzGDNrEZlNHwrUfdD5A+qXrwfD28JvM7zKFj3hkueCsk+3Zw85vXqxdeLngbrKN93E0f/4P280YRERERE5LM45znpyEst/3wHAp3eeTdPq5SIclUjkKFGNcXvzHA3v/wRLySGzyZBA/ayrZpG8eSU8nO+q6U2ToEaLI96nc45VD/Xj9zFjAnXl//Jnqj/6KJaQcMTbF5HCuUgHICIiIbFrz14aP7B/ZN8f+l5IxfTkCEYkEnlKVGPY6s07OfXxz0muPImUKuMBaFe3HQPPHQhfPQcT+noNU8rD3Ysg8ciHMl/73HOse/GlQDmjdWtqDnkeS9JXSURERKS41mzZSavH9t+dtvCxDiQl6sS/iLKLGDVp/hque20m6Q0GkpC8HoBnzn2GtrXaQP+qsGen17DDU3DqkT8ruuE/b7D6sccC5dRmzajzn9dJSE094m2LiIiIlEZzfvudzi98BUDT6uX49M6zIxyRSPRQohqD+n34EyNn/ERm00cDdRO6TKDaxuXw6FH7G/7fz1C+xhHta9NHH7Oid+9Aucwxx1Dv/fdILKdnJkRERERK6t3vc/jHmDkAXHtGXfpdcnyEIxKJLkpUY0henuPkRz5jW+I8MhoNByAtKY3pPaaT+FEv+P51r2Hds6Hn/45obtStU6fy2003B8qWnEyDiRMoU6XKEX0GERERkdLu4f/9xIivsgEY2PUkuvypZmQDEolCSlRjxLqtu2jZfyIpVT+gbCVv8udrjruGu064CR6ptL9hj9HQuEOJ97Nj9myyL+9RoK7B+HEk16lT4m2KiIiIiOei56by04rNALx/+5mcXCs40wWKxBslqjHg60XruOLfX5PRpC9meQAMbzucVr+vhifzJZB9foPUkt2Su+vXX1l88SUF6uq99y6pTZuWOG4RERER8ezZm0fD+z8NlGfedz5VymmsD5GDUaIa5Z4eP58Xp31LZtOnAnXTuk+l/BtdIOdbr+KUm+CigSXafu7KlSxsc16ButqvjyS9VasSxywiIiLxw8yygS3AXmCPc66lmVUC3gbqAtlAN+fcxkjFGO02bttN80cnBMoL+rcnJSkxghGJRD8lqlFq36TPq/Omk9FwNAANKzTk3XOexQbku4p68xQ45uRib3/vli0svvgS9qxaFair+cIQMs8//4hjFxERkbjTxjm3Ll+5D/C5c26AmfXxy/dEJrTotmDVFto9+yUA1cun8nWf87AjGEdEpLRQohqFNm3P5aRHPiOt5kjSMucB0Ltlb3pu2gKDT/QapZaHuxZDYvH+Cd3u3Sy78Sa2z5wZqKv28MNU7N4taPGLiIhI3OsMtPaXRwKTUaL6B+PmruLWN2YB8OfmNRjUvfgXF0RKKyWqUWbW0o38ZehkMps+GKh7p+Momgy7EHK3exXtB8BptxVru845Vj34IL+/MzZQV/mWW6jyf72CEreIiIjELQd8ZmYOeNk5Nwyo6pxb6b+/Cqgaseii1KAJv/Dc578C8Ejn47nm9LqRDUgkxihRjSIvTFrIM5MnkdnkuUDdt23+TeoLZ+5v1GsuVKhVrO2u+/e/WfvMoEA5s0N7ajzzDJaQcMQxi4iISNw7yzm33MyqABPMbH7+N51zzk9i/8DMbgZuBqhdu3boI40SV77yDV8tXA/AqJtO4/QGlSMckUjsUaIaBZxzdBg8lUW7PyK9vjcaXOtarXl+WyK82s5rVPsMuO6TYs2Nuunjj1nxz96Bcupxx1HnrTdJSNUIcyIiInJ4nHPL/Z9rzOw9oBWw2syqO+dWmll1YM1B1h0GDANo2bJloclsPMnLc9S/75NAeerdbahVqWwEIxKJXWFLVM3sVaATsMY5d0K49hvttu7awwkPjaNs/UGkVlwLwJOnPUTHUTfsb3T5W9DkosPe5vZvv2Xp1dcEygkZGTSY8BlJFSsGLW4RERGJf2aWDiQ457b4y22BR4APgZ7AAP/nB5GLMjps2ZlLs36fBco/P9KOssm6JiRSUuH87XkNGAK8HsZ9RrW5yzdx8YvjyWz6aKBu/Mn3cEz+JLXPMm/gpMOwa/FiFncsmNA2GD+O5Dp1DrKGiIiISJGqAu/5o9QmAW8558aZ2bfAGDO7AVgKlOpRGbPXbaP1wMkApJVJ5KeH25GQoJF9RY5E2AwMVf4AABqtSURBVBJV59yXZlY3XPuLdq9OW8JjX3xARqPhACQnJDMz9ygS37vda/Cn6+DiZw9rW3vWrWNhm/NwubmBujqj3qJs8+ZBj1tERERKD+fcYuCkQurXA5rTDvjyl7Vc86o3m8L5Taow/NpTIhyRSHzQ/QgR0OWlr8na+Rpl60wH4Kr6nbnn8+eBhV6DmyZBjRaH3E7e9u0s6daN3QsXBepqPDeYcm3bhiJsEcmnsMcZzKwS8DZQF8gGujnnNkYqRhERCa1Xpi6m/8feVIJ3tWvM7W0aRjgikfgRdYlqPI8Ot2P3Xpo++DEZTfqSXDYPgFdqXsKpnz/vNUjOgHuyIbFMkdtxe/eS8/c72fr554G6qvf2oVLPnqEKXUT+6DX++DhDH+Bz59wAM+vjlzWvoIhIHLr9ze/5OMuboWd4z5ac31Qz9IgEU9QlqvE6Otwvq7fQbsj7ZDZ9KlA3beUmyi8Z4hXaPgZn3HHI7ax59lnWD305UK541VVUvf8+rBijAYvIkTvI4wydgdb+8khgMkpURUTiinOOFo9OYON275Grif84l4ZVMiIclUj8ibpENR6NmrmMvhNfJ6Ph2wA0zKjJu1lfE0gte2VBhaKvHm/66GNW9N4/1Uz6WWdRa+hLWJL+CUWiSFXn3Ep/eRXeICQiIhIndubupUnfcYHynIfaUj6t6DvhRKRkwjk9zSi8Kw1HmVkO8JBzbni49h8p146YyYztT5FWYwEAvdMb0TNrovdmrVPh+vFFzo2648cfye7WPVBOPPooGnzyCYmZmSGNW0SOjHPOmdlB7wqJ58ccRETi0Yrfd3DGgC8C5YWPdSApMSGCEYnEt3CO+tsjXPuKBrv27KVx3w/JbPIgSf7dIGNzVtI4d5lX6P4GNL34oOvnrlrFwtZtCtRpqhmRqLfazKo751aaWXVgzcEaxutjDiIi8ei77A10GeoNgtmidgXe/euZEY5IJP7pvtEQWLJuG+cPeZPMJs8H6r7N/o1U5/8tes9SSKtQ6Lp527ez5C9d2L1kSaCu9muvkX7aqSGNWUSC4kO8ie8H+D8/iGw4IrElNzeXnJwcdu7cGelQwiY1NZWaNWtSpoxuH41Wo2Yu4953swC45Zz63NuxaYQjEikdlKgG2fs/LOfuic+SXs97fuE8l8bgbO+2X1r0hEueK3Q9l5fHirvuZvPHHwfqqj38MBW7l+r5s0WiVmGPM+AlqGPM7AZgKaBfYJFiyMnJITMzk7p165aKQQKdc6xfv56cnBzq1asX6XCkEPe+m8Womd7dcIMvP5nOJ9eIcEQipYcS1SC6/c1ZTN7Wm5Qq6wB4es062m/b7r154xdQ80+Frrf+lVdYM/CZQLniFVdQte8DpeI/aZFYVcTjDOeHNRCROLJz585Sk6QCmBmVK1dm7dq1kQ5FDuCc4/xBU1i8dhsAH/3tLE6oUT7CUYmULkpUgyB3bx6NHhxLRqNHSUjx6j5btpzqe/dCUhrc+1uhc6Nu+WISOX/9a6Cc1rw5dUa+hiUnhyt0ERGRqFJaktR9StvnjQW79+TR6IFPA+Vv77+AozNTIhiRSOmkRPUI5WzczrlDXiaj0asApDrHN9m/kQhw4aNw5t//sM7OBb+wpHPnQNlSUmg46QuSKlUKU9QiEouc05hLIiKhtG7rLlr2nxgoL+jfnpSkxAhGJFJ6KVE9AuPmruLOCX0pW/sbAK7etJm7N/zuvXnnHKhYt0D7PevX82vrNpCbG6ir9+EHpDZqFK6QRSSGKU0VEQmducs30en5aQDUrVyWSb1b64q3SAQpUS2hu8f+wKfbriHZvwg6fOVqWu3cBce0gJu+KDA3qtu9m+yrr2bnnB8DdTVfepHMNm0O3KyIiIiIhNn/5qzgb6N+AKB7y1o82eXECEckIkpUi2lvnqPJw2+QWv+pQN20pTmUz8uDbq/DcZ0LtF/95FNsGDEiUK5yV28q33BD2OIVERGJRQ//7yd+XrE5qNs87phyPHTx8Qd9v0+fPtSqVYvbb78dgH79+pGRkUHv3r0LtHPOcffdd/Ppp59iZjzwwAN0794dgCeffJI33niDhIQEOnTowIABA4L6GST4Bnw6n6FTFgHw2GUncOWpmrNeJBooUS2G1Zt3cuYLT5FW/x0AGu/azTsrVmEA92RDWsVA283jxrG81/8FyuU6duCYgQOxhITwBi0iIiKHpXv37vTq1SuQqI4ZM4bx48f/od27777L7NmzmTNnDuvWreOUU07hnHPOYfbs2XzwwQfMmDGDsmXLsmHDhnB/BCkG5xxdh07nu6UbARhzy+m0qqfxQkSihRLVwzR5wRpum3gbacf8AsDd6zdy9eYtcPJVcOkLgXa7fv2VxRdfEignValC/U8+ITEjPewxi4iIxKqirnyGSvPmzVmzZg0rVqxg7dq1VKxYkVq1av2h3bRp0+jRoweJiYlUrVqVc889l2+//ZYpU6Zw3XXXUbZsWQAqaZDEqLVnbx4N798/su+0e9pQs2LZCEYkIgdSonoYHvzwe97b2JOkDK/835yVNMrNhRsmQq1TANi7eTMLL2xL3qZNgfXqf/IJKfU1gbeIiEis6Nq1K2PHjmXVqlWB23klvmzanstJj3wWKP/8SDvKJutPYpFoo/tQi5CX5zhpwHDe29gzUPdd9jIa7QUeWAu1TsHl5fHb7XfwS6tTA0lqzReG0HT+PCWpIhJUmp1GJPS6d+/O6NGjGTt2LF27di20zdlnn83bb7/N3r17Wbt2LV9++SWtWrXiwgsvZMSIEWzfvh1At/5GoV9WbwkkqZmpSSx+vKOSVJEopd/Mg1i/dRenv3Q/KdW9Z1Mu2Ladf61ZBxf0g7O8Z0/Xj3iNNU8+GVin8q23UKVXrwhEKyIiIsFw/PHHs2XLFmrUqEH16tULbXPZZZcxffp0TjrpJMyMp556imrVqtG+fXtmz55Ny5YtSU5OpmPHjjz++ONh/gRyMOPmruLWN2YBcNGJ1XnhihYRjkhEiqJEtRBfL1zHTZP+QkoV70zo02vW0X7bdvj7bKhUj23fzGDZtdcG2qe1/BN1RozAypSJUMQiIiISLFlZWUW+b2Y8/fTTPP300394r0+fPvTp0ydUoUkJDRy/gCGTFgLw0MXHcd2ZuutNJNopUT3AY5/OZPSaG0hI9soTli2nWpUToPcUcleuZGGTpgXaHzttKklHHRWBSEVERETkULq89HVgZN9RN53G6Q0qRzgiETkcSlR9zjlOe/ZFtlcaCkBaXh7Tl+aQ2GUEecdeRPYlndn166+B9nXfHk3aSSdFKlwREREJsaysLK6++uoCdSkpKcyYMSNCEUlxaGRfkdimRBVv9LdWw24juZL3H0/PTZvpveF33N1LWDXwBTa+dX+gbbVHH6HiQQZXEBERkfjRrFkzZs+eHekwpAR+376bkx+ZECjPf7Q9qWUSIxiRiBRXqU9UZy5Zyw1fnkdyRa/86srVnNL4z2yq14kVLc4MtCt/2WVUf/wxzCxCkYpIaefQsL8iIocyb+VmOgyeCsDRmSnMvO98/f0mEoNKdaL6xISpvLXir4HytKW/kXreCObdeB8wBYAytWpR/4P3SSirW0VEREREotlHP67gjrd+AODPLWowqNvJEY5IREqqVCaqzjnOHfo0G8v+B4Cmu3YzatkaFk9rzp637gu0azB+HMl16kQqTBERERE5TP0+/InXvs4GoP+lJ3DVafobTiSWlbpEdeuuPbR6pSuJGd4Q5X3WbeC8X1rxy5QEYBUANQYPply7thGMUkREREQOh3OOP/WfyIZtuwEYe+vptKxbKcJRiciRKlWJ6ndLV3Ld5LYkZnjlsV+tJ+/L8vzOfAAqXnEFVfs+oOcYRERERGLAgYMmzXrgAipnpEQwIhEJllKTqA74/DPezPknAFU3Op4fupc8ygOQdEx1Gnz0kZ5DFREREYkRPyzbyGUvfg1AWplEfnq4HQkJutggEi9KRaJ64b/7sCr5Y8rscbzwSi4VNiYE3qv/8UekNGgQwehERETkDz7tA6uygrvNas2gw4CDvt2nTx9q1arF7bffDkC/fv3IyMigd+/eBdpNnjyZhx56iAoVKpCVlUW3bt1o1qwZgwcPZseOHbz//vs0aNCAa6+9ltTUVL777js2b97MoEGD6NSpU3A/Uyn1ytTF9P94HgCXn1KLAX85McIRiUiwxXWiumP3Hs4aeRa7k7dx1Rd7uWSGA7wk9Zinn6L8xRdHNkARkWJwmp1GJKS6d+9Or169AonqmDFjGD9+fKFt58yZw7x586hUqRL169fnxhtvZObMmQwePJjnn3+eZ599FoDs7GxmzpzJokWLaNOmDQsXLiQ1NTVsnyke/fnFr/h+2e8AvHBFCy46sXqEIxKRUIjbRPXbZUu5flInmi/L49538gL1mg9VREQkBhRx5TNUmjdvzpo1a1ixYgVr166lYsWK1KpVq9C2p5xyCtWrewlSgwYNaNvWG4SxWbNmTJo0KdCuW7duJCQkcOyxx1K/fn3mz5/PySdrypSS2LQ9l5Me+SxQnnJXa+pUTo9gRCISSmFNVM2sPTAYSARecc6F5H+hp8eP4OMFzzDmxb2BusQKFWgwcQKJGRmh2KWISEC4+joRCb6uXbsyduxYVq1aRffu3Q/aLiVl/4A9CQkJgXJCQgJ79uwJvHfgifF4OlEezr5u0vw1XPfat4HyL/07kJyUUMQaIhLrwpaomlki8AJwIZADfGtmHzrnfg7mfq4c2onLRy+i06r9dfXef4/UJk2CuRsRkUKFq68TkdDo3r07N910E+vWrWPKlClHvL133nmHnj17smTJEhYvXkzjxo2DEGXkhbOvu3Hkd0yctxqAnqfX4eHOJwR7FyIShcJ5RbUVsNA5txjAzEYDnYGgdGjbdm7jmTtO4YFp+x/iqvboI1Ts2jUYmxcROVyh6+v0kKpIyB1//PFs2bKFGjVqBG7tPRK1a9emVatWbN68maFDh8bT86kh/bsOYOGaLVww6MtA+Z1bT+cUzY8qUmqEM1GtAfyWr5wDnBqMDX/33jDS7/0X+1LS5HNOpf7LI+Lq9hoRiRkh6+vIyw3KZkSkaFlZRY823Lp1a1q3bh0oT548+aDvXXDBBQwdOjTIEUaFkPV1u/bspfED4wLlxATjp4fbkVomMRibF5EYEXWDKZnZzcDN4J2FPByrdm6gAbAnEY6b9jWJFSuGMEIRkSNXkr7O8AaGm5NwPCeFLDIRkeApSV+XkrQ/IX3xyhZ0bKZRfUVKo3AmqsuB/EPn1fTrCnDODQOGAbRs2fKw7nPr1KMPy1p1onYDPbMgIhEXsr4uJTUd+m1SkioSJllZWVx99dUF6lJSUpgxY8Zhrf/aa6+FIKqoEbK+DmDR4x1JTNCdcSKlWTgT1W+BY82sHl5HdjlwRbA2riRVRKJESPs6EQmfZs2aMXv27EiHEa1C2tcpSRWRsCWqzrk9ZnYHMB5vGPNXnXM/hWv/IiLhoL5O5Mg450rVGBMuRgdJU18nIqEW1mdUnXOfAJ+Ec58iIuGmvk6kZFJTU1m/fj2VK1cuFcmqc47169fH7EjA6utEJJSibjAlERERKZ1q1qxJTk4Oa9eujXQoYZOamkrNmjUjHYaISNRRoioiIiJRoUyZMtSrVy/SYYiISBRIiHQAIiIiIiIiIvkpURUREREREZGookRVREREREREoopF87DoZrYWWFqMVY4C1oUonFBQvKEVa/FC7MUciXjrOOeODvM+Q0p9XdRRvKEXazGrrwsC9XVRJ9bihdiLWfEe2kH7uqhOVIvLzL5zzrWMdByHS/GGVqzFC7EXc6zFGy9i7bgr3tCKtXgh9mKOtXjjRawdd8UberEWs+I9Mrr1V0RERERERKKKElURERERERGJKvGWqA6LdADFpHhDK9bihdiLOdbijRexdtwVb2jFWrwQezHHWrzxItaOu+INvViLWfEegbh6RlVERERERERiX7xdURUREREREZEYFxeJqpm1N7MFZrbQzPpEOh4AM6tlZpPM7Gcz+8nM7vTrK5nZBDP71f9Z0a83M3vO/ww/mlmLCMWdaGY/mNlHfrmemc3w43rbzJL9+hS/vNB/v26E4q1gZmPNbL6ZzTOz06P5GJvZ//nfh7lmNsrMUqPpGJvZq2a2xszm5qsr9vE0s55++1/NrGeo4y4t1NcFNW71daGNN6r7On+/6u+ilPq6oMatvi608aqvCyXnXEy/gERgEVAfSAbmAMdFQVzVgRb+cibwC3Ac8BTQx6/vAzzpL3cEPgUMOA2YEaG4/wG8BXzkl8cAl/vLQ4Hb/OW/AkP95cuBtyMU70jgRn85GagQrccYqAEsAdLyHdtro+kYA+cALYC5+eqKdTyBSsBi/2dFf7liJL4f8fRSXxf0uNXXhS7WqO/r/H2pv4vCl/q6oMetvi50saqvC3XskfgSBvngnw6Mz1e+F7g30nEVEucHwIXAAqC6X1cdWOAvvwz0yNc+0C6MMdYEPgfOAz7yv6TrgKQDjzUwHjjdX07y21mY4y3vdxB2QH1UHmO/Q/vN/yVP8o9xu2g7xkDdAzqzYh1PoAfwcr76Au30KvG/i/q64MWovi608cZEX+fvT/1dlL3U1wU1RvV1oY1XfV2I446HW3/3fUn2yfHrooZ/ab85MAOo6pxb6b+1CqjqL0fD53gWuBvI88uVgd+dc3sKiSkQr//+Jr99ONUD1gIj/NtaXjGzdKL0GDvnlgMDgWXASrxjNovoPsZQ/OMZDd/leBT1x1V9Xciorwsf9XeRF/XHVH1dyKivC5+Y6OviIVGNamaWAfwX6OWc25z/PeedknARCewAZtYJWOOcmxXpWIohCe9Whpecc82BbXi3LwRE2TGuCHTG64iPAdKB9hENqpii6XhKdFFfF1Lq6yIgmo6pRA/1dSGlvi4CoumYHigeEtXlQK185Zp+XcSZWRm8zuxN59y7fvVqM6vuv18dWOPXR/pznAlcYmbZwGi820QGAxXMLKmQmALx+u+XB9aHMV7wzubkOOdm+OWxeB1ctB7jC4Alzrm1zrlc4F284x7NxxiKfzwjfZzjVdQeV/V1Iae+LnzU30Ve1B5T9XUhp74ufGKir4uHRPVb4Fh/hK1kvIeTP4xwTJiZAcOBec65Qfne+hDo6S/3xHvGYV/9Nf5oW6cBm/Jdkg8559y9zrmazrm6eMfwC+fclcAkoMtB4t33Obr47cN6NsY5twr4zcwa+1XnAz8TpccY79aQ08ysrP/92Bdv1B7jQuI4nOM5HmhrZhX9s41t/To5MurrgkB9XVjEal93YCzq7yJDfV0QqK8LC/V1oRbKB2DD9cIboeoXvFHi7o90PH5MZ+FdRv8RmO2/OuLdi/458CswEajktzfgBf8zZAEtIxh7a/aPDlcfmAksBN4BUvz6VL+80H+/foRiPRn4zj/O7+ONRBa1xxh4GJgPzAX+A6RE0zEGRuE9Z5GLd2bzhpIcT+B6P+6FwHWR+i7H20t9XdBjV18Xunijuq/z96v+Lkpf6uuCHrv6utDFq74uhC/zdywiIiIiIiISFeLh1l8RERERERGJI0pURUREREREJKooURUREREREZGookRVREREREREoooSVREREREREYkqSlRLCTPba2az8736+PVnm9lPfl2amT3tl58uwT7uO6D8dZBi3xqM7eTb3rVmNsRfvtXMrgnm9kUkctTXFdie+jqROKW+rsD21NfFKU1PU0qY2VbnXEYh9UOBac65N/zyJry5lPYGax9HqrDtmlmSc27PwcqH2N61ePNC3RHcSEUk0tTXFVj3WtTXicQl9XUF1r0W9XVxSVdUSzEzuxHoBjxqZm+a2YdABjDLzLqb2dFm9l8z+9Z/nemvl2FmI8wsy8x+NLO/mNkAIM0/g/em326r/3O0mV2Ub7+vmVkXM0v0z/R962/nlkPE29rMpvpx/nxg2W/zvpnN8s8e3pxv3evM7Bczmwmcma++n5n19pdv8mOZ43/usvnifc7MvjazxWbWJd/69/jHYY5/DDCzBmY2zo9jqpk1Kfm/kogcKfV16utESgP1derr4o5zTq9S8AL2ArPzvbr79a8BXfK125pv+S3gLH+5NjDPX34SeDZfu4oHrpu/DFwGjPSXk4HfgDTgZuABvz4F+A6oV0js+7bTGti2r82BZb+ukv8zDZgLVAaqA8uAo/39fwUM8dv1A3r7y5Xzbac/8Ld8x+gdvBM7xwEL/foOwNdA2QP2/TlwrL98KvBFpP/99dKrtLzU16mv00uv0vBSX6e+rjS8kpDSYodz7uRirnMBcJyZ7SuXM7MMv/7yfZXOuY2H2M6nwGAzSwHaA18653aYWVvgxHxnssoDxwJLitjWTOfckiLKfzezy/zlWv72qgGTnXNrAczsbaBRIds+wcz6AxXwzkCOz/fe+865PLwzflX9uguAEc657QDOuQ3+8TkDeCffcUsp4vOISHCpr1NfJ1IaqK9TXxf3lKhKURKA05xzO/NX5vtFPSzOuZ1mNhloB3QHRu/bFN7ZrfEHW7cQ2w5WNrPWeJ3M6c657f4+U4ux7deAS51zc8x73qF1vvd25Vsu6gAkAL+X4D8PEYkc9XX7qa8TiV/q6/ZTXxcD9IyqFOUz4G/7Cma275d0AnB7vvqK/mKumZU5yLbeBq4DzgbG+XXjgdv2rWNmjcws/QjiLQ9s9DuzJsBpfv0M4Fwzq+zvq+tB1s8EVvptrjyM/U0Arsv3zEMl59xmYImZdfXrzMxOOoLPJCKhp76uaOrrROKD+rqiqa+LMkpUS499D8Tvew04jHX+DrQ074H4n4Fb/fr+QEUzm2tmc4A2fv0w4EfzH7o/wGfAucBE59xuv+4VvIflvzezucDLHNlV/nFAkpnNAwYA3wA451biPbMwHe85hnkHWb8vXuf3FTD/UDtzzo0DPgS+M7PZQG//rSuBG/xj8xPQuYSfR0SKT32d+jqR0kB9nfq6uKfpaURERERERCSq6IqqiIiIiIiIRBUlqiIiIiIiIhJVlKiKiIiIiIhIVFGiKiIiIiIiIlFFiaqIiIiIiIhEFSWqIiIiIiIiElWUqIqIiIiIiEhUUaIqIiIiIiIiUeX/Aew/T764PP/bAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "temps = pvlib.temperature.sapm_cell(total_irrad['poa_global'], 5, 10, **thermal_params)\n",
    "\n",
    "sapm_2 = pvlib.pvsystem.sapm(effective_irradiance, temps, module)\n",
    "\n",
    "plot_sapm(sapm_2, effective_irradiance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sapm_1['p_mp'].plot(label='30 C,  0 m/s')\n",
    "sapm_2['p_mp'].plot(label=' 5 C, 10 m/s')\n",
    "plt.legend()\n",
    "plt.ylabel('Pmp')\n",
    "plt.title('Comparison of a hot, calm day and a cold, windy day');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SAPM IV curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IV curve function only calculates the 5 points of the SAPM. We will add arbitrary points in a future release, but for now we just interpolate between the 5 SAPM points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore', np.RankWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sapm_to_ivframe(sapm_row):\n",
    "    pnt = sapm_row\n",
    "\n",
    "    ivframe = {'Isc': (pnt['i_sc'], 0),\n",
    "              'Pmp': (pnt['i_mp'], pnt['v_mp']),\n",
    "              'Ix': (pnt['i_x'], 0.5*pnt['v_oc']),\n",
    "              'Ixx': (pnt['i_xx'], 0.5*(pnt['v_oc']+pnt['v_mp'])),\n",
    "              'Voc': (0, pnt['v_oc'])}\n",
    "    ivframe = pd.DataFrame(ivframe, index=['current', 'voltage']).T\n",
    "    ivframe = ivframe.sort_values(by='voltage')\n",
    "    \n",
    "    return ivframe\n",
    "\n",
    "def ivframe_to_ivcurve(ivframe, points=100):\n",
    "    ivfit_coefs = np.polyfit(ivframe['voltage'], ivframe['current'], 30)\n",
    "    fit_voltages = np.linspace(0, ivframe.loc['Voc', 'voltage'], points)\n",
    "    fit_currents = np.polyval(ivfit_coefs, fit_voltages)\n",
    "    \n",
    "    return fit_voltages, fit_currents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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DwYDZbGbVqlVNPSQhhBBCCNGEbtoH8latWsWkSZMoLi4G4OjRo0yaNAmAMWPGNOXQhBBCCCFEE7lpg+OZM2e6AuMKxcXFPP7U8yT/2JZmfj408/Uh0M+HAF+3z27pzXydec7PrrPzc4BbuWZu7fj61L29ixBCCCGEuPpu2uD42LFjHtOt508zMq4DF8utXCyzUVxm42K5jZJyGycKy7lY5vh8sdyRV2q1X3LfPgblFlgbCDBWDZ4deQZXWpV0Px8CjAZXIB7gOgyVbTrP/kYDBoME4UIIIYQQ9XXTBscdO3bk6NGjNdIjOnbktaEx9W7HbteUWB2BcokziL5YZueiM4C+WGalpNz53S3QLnYLskvc8s4Wl3Gi3E6JtWr5ctvlPTjp7yWQDjA6z9XTfX1cec38HJ/93csZDR7L+/sa8DcaZEZcCCGEENe1mzY4njdvXpU1x+B4R/i8efMuqR2DQRHoZyTQr3FvZbnN7gy+7VWC6sqg3F4ZnLvy7ZS6la0I0kvKbZSW2zldVObWlqNsifXyA3FwBOOeAmf3ILuizKWcHcG387MzePd3tunnIzPkQgghhLgybtrguOKhu5kzZ3Ls2DE6duzIvHnzrtmH8Xx9DPj6GGgR0Ph9WW12SqyVwXZF4F1qdQ/CnWe3NEdw7QyynbPfpc5zSbmNolIrp4vKKC13LEepaL/Uasdqb9iWgn4+1QJot89+RoMrqK6Y4a4MtA1uZSqDbce5av2KMn7V6vkZHXWMPjf15i9CCHFTyMrKYty4ceTl5aGUYtKkSUydOhWAgoICHn30UTIzMzGbzaxdu5bg4GD279/PL3/5S7777jvmzZvH9OnTq7Rps9mIi4vDZDKxceNGj/2uWLGCuXPnAvDqq68yfvz4KvlJSUkcPnyY9PR0j/W3bt3K1KlTsdlsTJw4kRkzZgCwdOlSFi1axKFDhzh16hRt27atUTc/P58RI0bwr3/9i8cff5ylS5e68mbOnMkf/vAHzpw5Q1FRkdf7lpyczPLly/Hx8WHJkiXcf//9tY7LXWlpKePGjePbb7+lTZs2fPrpp5jN5lrbbYibNjgGR4B8rQbDTcnoYyDIx0CQ/9X79bDa7K6A2f1c6gy2XcG01U6Z1U6pW+BdcS5zlXfmO+uUWe2cL7Fy2lpGqbNcSbmdMmtlH1eCQeEKnisC5urBdUW647OP87NyS3ME5ZWfVZV03yrfHWdfH8/ffX0c7cpSFyGEuHKMRiPvvfcevXv35vz58/Tp04fExERiYmKYP38+AwcOZMaMGcyfP5/58+fz9ttvExISwpIlS1i/fr3HNhcvXkyXLl0oLCz0mF9QUMDs2bNJS0tDKUWfPn1ISkoiODgYgHXr1hEUFOR1zDabjaeffprPP/+c8PBw4uPjSUpKIiYmhjvvvJMhQ4aQkJDgtX5AQABvvvkm6enpNYLvoUOH8swzzxAdHe21fkZGBmvWrGHv3r0cP36ce++9lx9//BHA67jcLV++nODgYA4ePMiaNWt4+eWX+fTTT7226+Pj43Us9XFTB8fi2mF0zrw2v4oBeQWtNeU27QqoK4LsiiC8+vfqZcpsVctWpNUo5wzKCy9aq9VzBOrlNk2ZzY6tgbPo1VUEyb7OANo9mPY1Ksd3t8Da18dTmqOsn4/B1ZajjHLlVwbmlWm+PjX7cQ/eK9JkWYwQ4noRFhZGWFgYAC1atKBLly7k5OQQExNDSkoK27dvB2D8+PEkJCTw9ttvExoaSmhoKJs2barRXnZ2Nps2bWLmzJksXLjQY5/btm0jMTGRkJAQABITE9m6dSujR4+mqKiIhQsX8sEHHzBy5EiP9VNTU7FYLERFRQEwatQoUlJSiImJoVevXnVec/Pmzbnrrrs4ePBgjbx+/frVWT8lJYVRo0bh7+9PZGQkFouF1NRUAK/jql7/jTfeAGDEiBE888wzaK29ttu/f/86x1QbCY7FTU8p5Zi9NRpo0dSDAWx27QqmS23OoNlqp9xWNdgut1Wmlbmdy612V6DtXq/cZqfMrS33uuU2OxdKrZTbdI30cpum3Gqn1JneGHwMzgDeOTNeGVwr5wy7W8DtmiWvGpj7+bjPmPu4gvmKWfvq5arM1lfM9Pv6uNIrls401cz7qlWrrptlX0I0mS0z4MR/rmyb7bvBA/PrVTQzM5Pdu3fTt29fAPLy8lyBc/v27cnLy6uzjWnTprFgwQLOnz/vtUxOTg4dOnRwfQ8PDycnJweAWbNm8cILLxAYGHhJ9Xft2lXn2Bpiw4YNpKWlMWfOHHJycqoE0e7j9zau1157jbi4OJKSkqqM32g00qpVK/Lz82tttyEkOBbiGuNjUI4dRvx8AN+mHk4VWmtsdu0Kvl2BtNWO1W6nzKorA29nnrUi4HYF6Zoyqw2r3dmGW51St8C94o8CVzt2x7n4os35B0DlHwNV/jiw6Ss6++4eLNe2fr36w6PuD5VW7gxTsVNM5Y4vgX5G5zaNBteWjZ+uWS0vKRLiGldUVMTw4cNZtGgRLVu2rJGvVN3vNNi4cSOhoaH06dPHNeN8Kfbs2cOhQ4d4//33yczMvOT6jSkpKYmkpKTLrj9nzpwrOJpLI8GxEKLelFIYfRRGH2hGw9Z0NSZHAF91lr36Uhb3wL7M7bNr6YzNsX7dfdmMYz27vcra9eIyK2eKPa+Zv9yZ9pzf/Aqrh5cUTZ46nR3cTqCfkeZ+PgT6O89+RoICjAT5G2nu7zgH+VemBfkb8ZGlK+JGVM8Z3iutvLyc4cOHM2bMGIYNG+ZKb9euHbm5uYSFhZGbm0toaGit7ezYsYMNGzawefNmSkpKKCwsZOzYsTz77LNMnjwZcASJJpOpSvCcnZ1NQkICO3fuJC0tDbPZjNVq5eTJkyQkJLBy5UqGDh0KwJQpU+jRowdZWVlV6ptMpit4R2pnMpm89l+fcVXUDw8Px2q1cu7cOdq0aVNruw0hwbEQ4objY1D4GByzs03J7pwdr76VYkm1LRZdLxtynqcuOOWxvQv5eRzIK+JCqZULZTYulFrrvdNLcz8fWgT4EhRgpGWAkZbNfGkZ4EurZr60bGZ0fW4d6EurZn60DnR8bt3MjwBfebBTiApaayZMmECXLl341a9+VSUvKSmJFStWMGPGDFasWMFDDz1Ua1vJyckkJycDsH37dt59910++eQTwDErXKGgoIBXXnmFM2fOAPDZZ5+RnJxMSEgITz75JOBY4jFkyBBXEO1e32q1cuDAAY4cOYLJZGLNmjX88Y9/bNiNuARJSUn84he/4Fe/+hXHjx/nwIED3HHHHWit6zWuivvav39//vSnP3HPPfeglPLabkNJcCyEEI3EYFAEXEaQvtDbS4oiOvL5r+6uklZmdawXLyq1cqHMSlGJ83OpjaLScs6XWF1HxffCknIKLpRx5PQFCi+WU1hirXUpir/RQEhzP4ID/WgT5DiHNHccbYL8aBvkT1vnuU2QP839fCSYFjesHTt2sHLlSrp160bPnj0BeOuttxg8eDAzZsxg5MiRLF++nIiICNauXQvAiRMniIuLo7CwEIPBwKJFi8jIyPC4HMOTkJAQZs2aRXx8POBYj1vxcF59GI1Gli5dyv3334/NZuOJJ54gNjYWgCVLlrBgwQJOnDhB9+7dGTx4MB999FGNNsxmM4WFhZSVlbF+/Xo+++wzYmJieOmll/jjH/9IcXEx4eHhTJw4kTfeeKPKmuPY2FhGjhxJTEwMRqORZcuWuXaU8DYu9zXHEyZM4LHHHsNisRASEsKaNWsAam23IZTWV/bJ+IaIi4vTaWlpTT0MIYRoUqtWrfL4kqIPPvigUdYca60pLrNx9mI554rLOXuxzHku52xxOWeKyyi4UMaZC2UUFDvO+RfKOF9i9dheM18fbmnhT2gLf0Jb+hPaIoBbWvjTvmUAYa0CaN8qgLBWzZzr6oW4NPv27aNLly5NPQxxHfH0O6OU+lZrHeepvMwcCyHENeZqv6RIKUVz53plU+tm9a5XZrVTcKGM00WlzqOM/KJSTp0v5eT5Uk6eL2H/ifN8/eNpzpfWDKRbB/rSvmUAt7ZuRnhwM0ytmxEeHEh4sON7SHM/mYEWQlx1EhwLIcQ16Hp4SZGf0UB750xwXYrLrOQVlpJ77iInzpWQe67E9TnnbAlpmQUUVpuJDvTzoWNIIBFtAolo09xxDmmOuW0gt7ZqJvtjCyEahQTHQgghGl2gn5HItkYi2zb3WqawpJycMxfJPnORnDPFHCu4yLGCCxw6dYGvfjhVZfePAF8DkW2D6HRLc6JucZwtoUFYQoPwN8pyDSHE5ZPgWAghxDWhZYAvLcN86RJW8yElu11zorCEo/nFHDl9gcOnijh0qoh/Z59j839yqXie0MegiGzbnM7tWtC5fQtua9eCmLCWdAhpJks0hBD1IsGxEEKIa57BoLi1dTNubd2M/p3aVMkrKbeRmX+BA3lF/Jh3nv0nzpN+/Byb03OpeOa8ZYCR2Ftb0dXUkq6mVsTe2orIts1l/2chRA0SHAshhLiuBfj6cHv7ltzevuqMc3GZlR/zitiXW0h6zjnSjxeyYudR1/KMIH8jPTq0onfHYHp1bE3PDsGENPdriksQQlxDDE09ACGEEKIxBPoZ6dmhNaPv6Mi8h7uR8vSd7J19P1un/ZR3RnTn571u5WxxOb/efognfp9G7zc/Z8C723lh7fesTcviWH4x19J2p+LakJWVxYABA4iJiSE2NpbFixe78goKCkhMTCQ6OprExETXSzv2799P//798ff35913363Rps1mo1evXgwZMsRrvytWrCA6Opro6GhWrFhRIz8pKYmuXbt6rb9161Y6d+6MxWJh/vzKNwsuXboUi8WCUorTp097rJufn8+AAQMICgrimWeeqZL37bff0q1bNywWC88995zH/2a01jz33HNYLBa6d+/Od999V+/rAu/3tbZ2G0KCYyGEEDcNXx8Dt7dvySNxHZj7825seu6n/OeN+1gzqR8vD7odS2gQX/1wkpf+9G9+9s5X/GT+l0xbs5vVqcc4mn+hqYcvrgFGo5H33nuPjIwMvvnmG5YtW0ZGRgYA8+fPZ+DAgRw4cICBAwe6gtCQkBCWLFnC9OnTPba5ePHiWvduLigoYPbs2ezatYvU1FRmz57tChAB1q1bR1BQkNf6NpuNp59+mi1btpCRkcHq1atdY77zzjv54osviIiI8Fo/ICCAN99802Ng/+STT/Lhhx9y4MABDhw4wNatW2uU2bJliyv/gw8+cL3Vr67rquDtvnprt6EkOBZCCHFTC/Qz0i+qDU8mdOLDcXGkzbyXz57/GW8+FEvviGD+cTCf/173H+5+Zzt3v/MVr67/D5/tPUGRh72bxY0vLCyM3r17A9CiRQu6dOlCTk4OACkpKYwfPx6A8ePHs379egBCQ0OJj4/H19e3RnvZ2dls2rSJiRMneu1z27ZtJCYmEhISQnBwMImJia4gtKioiIULF/Lqq696rZ+amorFYiEqKgo/Pz9GjRpFSkoKAL169cJsNtd6zc2bN+euu+4iIKDqto25ubkUFhbSr18/lFKMGzfOdc3uUlJSGDduHEop+vXrx9mzZ8nNza31uqrX93RfvbXbULLmWAghhHBjMChua+fY6eKx/ma01hw6dYEdB0/z9x9Pse67HD755hhGg6J3RDAJnW/hvpj2WEK9z9yJxvF26tvsL9h/Rdu8PeR2Xr7j5XqVzczMZPfu3fTt2xeAvLw8wsLCAGjfvj15eXl1tjFt2jQWLFjA+fPnvZbJycmhQ4cOru/h4eGugHzWrFm88MILBAYGXlL9Xbt21Tm2uuTk5BAeHu5xXL/97W8BmDJlitfx13ZdEydOZMqUKcTFxXm9r97qV5S9XDd1cLxq1aqr9gYqIYQQ1yellGsP5fE/MVNmtfPt0TP8/cAp/vbDKRZs/YEFW38gqm1zEmPbcV9Me3p1aC0vKbnBFRUVMXz4cBYtWkTLljW3H1RK1bl94MaNGwkNDaVPnz5s3779ksewZ88eDh06xPvvv09mZuYl129MU6ZMaVD9jz76yGN6fe5rQ920wfGqVauYNGkSxcXFABw9epRJkyYBSIAshBDCKz+jgf6d2tC/UxteHm9jdOEAACAASURBVHQ7x89e5It9eXyekcfyr4/wP387TNsgf+6LbcdDPW4l3hwigXIjqe8M75VWXl7O8OHDGTNmDMOGDXOlt2vXjtzcXMLCwsjNzSU0NLTWdnbs2MGGDRvYvHkzJSUlFBYWMnbsWJ599lkmT54MwJw5czCZTFWC5+zsbBISEti5cydpaWmYzWasVisnT54kISGBlStXMnToUMARpPbo0YOsrKwq9U0mU4Pvg8lkIjs7u852TSaTx/69XVd13u6rt3YbTGt9zRx9+vTRV0tERIQGahwRt7bT+tgurXP/o/Xpg1qfO6518Rmty0u1ttuv2viEEEJcf84Wl+n1u7P1U598q29/dYuOeHmj7vfWF3repgz9n+yz2i7/P9JgGRkZTdq/3W7Xjz32mJ46dWqNvOnTp+vk5GSttdbJycn6xRdfrJL/+uuv63feecdju1999ZV+8MEHPebl5+drs9msCwoKdEFBgTabzTo/P79KmSNHjujY2FiP9cvLy3VkZKQ+fPiwLi0t1d27d9fp6elVykREROhTp055vminjz/+WD/99NNV0uLj4/XOnTu13W7XgwYN0ps2bapRb+PGjXrQoEHabrfrnTt36vj4+Hpfl9be76u3dqvz9DsDpGkv8ehNO3N87Ngxz+nH82B5oudKygd8A8EvEHybOT5XOVdLMwZ4Kede3nkY3er73LQ/FiGEuK61aubLQz1NPNTTRHGZlc8z8vjL98f5eMcRPvj7YaLaNufhXiZGxIUT1qpZUw9XXIYdO3awcuVKunXrRs+ePQF46623GDx4MDNmzGDkyJEsX76ciIgI1q5dC8CJEyeIi4ujsLAQg8HAokWLyMjI8Lgcw5OQkBBmzZpFfHw8AK+99hohISH1HrPRaGTp0qXcf//92Gw2nnjiCWJjYwFYsmQJCxYs4MSJE3Tv3p3Bgwd7XNJgNpspLCykrKyM9evX89lnnxETE8Ovf/1rHn/8cS5evMgDDzzAAw88AFRdczx48GA2b96MxWIhMDCQjz/+uM7rcl9z7O2+emu3oZS+hvZwjIuL02lpaVelL7PZzNGjR2ukR5jakfnVJ1Be7HZcdDtfhLILYC2pmlZeDGXFYL1YNU3bL31wBmNlAG0McH4OqBZwVw+q3b97KFvls7OMsRn4+IK8UlUIIRrV2eIytqSfIGVPDt8cLsCgIKFzKI/Gd+Ce20Px9ZHNo+pr3759tW57JkR1nn5nlFLfaq3jPJW/aaco582bV2XNMUBgYCDz3n4Pou+9Mp1oDbYyZxDtIZguv+gWTFfklUD5Bce5Sl6J43PJWTifW9lORRlb2eWNURmqBtjGgMrA2ehfNbB2P7uX81re3y3f7ZCAXAhxk2kd6MfoOzoy+o6OHMsvZm1aFv/7bRaTV56kbZA/I/qEM/qODkS0ad7UQxXipnfTBscVD9016m4VSjkDRH9o7H89s9ucwXJJ1WDcWuKWXv3sLOetTPlFKC2sDNStpZWfLzcYB2dAHlAtyA6oDKaN/h7SazvXp4w/+Dh/FgafK3ffhRDiEnVsE8j0+zsz7d5otv9wijX/yuLDrw/zP38/xL1d2jHhrkj6RoY0+hP5QgjPbtplFaKB7DZHEG0trRZYlzpns51Bt3vA7fpe4uV7ac10W6lbUO783lAG38o/WioCZk/fPeb5Oc8Bbp/9Ksv6+HnO8/GrWc7HT2bRhRAAnDhXwqpdR/nkm6OcKS4nJqwlT9wVydAeYfgb5Q96d7KsQlwqWVYhrg6DD/g1dxxXk91eGTC7gmn3oLrUw/cSx0x3lfzSymDbWlazTPEFt+9u6RV9c6X+qFTOwNktYDZWBM7+juDZ6DxXCbL9vKS5p/tWS/OrbMvgWzW9ts8GHwnghWhk7VsF8MJ9nXl6gIX1u3P43Y4jTP/f75m/ZT+P/ySCcT8x0zKg5tvVhBBXngTH4vpiMIDBuT66qWgNdqszWC5zC5rdg2y3NFsp2Mo9pJVVfnZPc5Utd/tDoAxKi5z5ZZV928qrpl2xoN2dqgyWDUYPAbSvW75vte/GWsq4pbuCdWPVz57arBHce2n3Blk+Iy8rurkE+Pow6o6OPBrfgX8cPM1HXx/h3c9+5IO/H+aJuyL55Z2RtGomQbIQjUmCYyEulVKVQdm1xmatDMZdgbNbcG4vrxpQu9KtNdNtZdXKu+dba5a1lTnaKSuq7N9eXrV89f4bkzJ4D8Y9Btm1zKAb/au14V/5uWLJTfVZfo/LbPyrlq9jRl5eVnTzUkrx0+hb+Gn0Lfwn+xxLvjzAoi8OsPzrI/zyTjNP3BVJ60C/ph6mEDckCY6FuJH4GK+ffbK1dqxdrx5AVwTZ3gJ0u9UtKK8WmNcn3e7+B4FbsF9+zvmHhHt5txn+inFeMaraevaKh1ArHzSdOf1LiosvVqlVXFzMzF89w5iIk1X3R/d131c90LHkqeK7X3NHoC7LY65L3cJb8eG4ODKOF7L0qwMs+fIgy/9xhCfuimTy3Z0I8r9O/pu/QWRlZTFu3Djy8vJQSjFp0iSmTp0KQEFBAY8++iiZmZmYzWbWrl1LcHAw+/fv55e//CXfffcd8+bNY/r06VXatNlsxMXFYTKZ2Lhxo8d+V6xYwdy5cwF49dVXGT9+fJX8pKQkDh8+THp6usf6W7duZerUqdhsNiZOnMiMGTMAWLp0KYsWLeLQoUOcOnWKtm3beqyfnJzM8uXL8fHxYcmSJdx///0ALF68mA8//BCtNf/1X//FtGnTatTVWjN16lQ2b95MYGAgv//97+ndu3e9rqu2+1pbuw0h/0UJIZqGUpXBfFMuk7kUWtdcyuIeSFdZDlNWdV27+1r56stwKrZjdF8nX17CsfyLHodx7ORZ+OKNSxu7wQi+zSufFfBrDn5BjrN/kOOzfwvnOQj8Wzq+B7QE/1aVnwNaOQJuCbSvuphbW/LrMX344cR5lnx5gP/35UFWpx5j6r23MSq+g+yVfJUYjUbee+89evfuzfnz5+nTpw+JiYnExMQwf/58Bg4cyIwZM5g/fz7z58/n7bffJiQkhCVLlrB+/XqPbS5evJguXbpQWFjoMb+goIDZs2eTlpaGUoo+ffqQlJREcHAwAOvWrSMoKMjrmG02G08//TSff/454eHhxMfHk5SURExMDHfeeSdDhgzx+NrmChkZGaxZs4a9e/dy/Phx7r33Xn788Uf27dvHhx9+SGpqKn5+fgwaNIghQ4ZgsViq1N+yZQsHDhzgwIED7Nq1iyeffJJdu3bVeV0VvN1Xb+02lATHQghRX0o5lkgYr84/Z3ec4/llRR07doSZ+932Ta/YD93txUVlxc49050vLiovdpzLihx5ZRccR9EJyC9ypJeed5Sri8HXESRXHM1aQ7PgakeI4xzYBgJDoHlbR8AtQXWDdW7fgmW/6M2kn55l3uZ9zFqfzsc7jjBj0O0kxrSTLeAaWVhYGGFhYQC0aNGCLl26kJOTQ0xMDCkpKWzfvh2A8ePHk5CQwNtvv01oaCihoaFs2rSpRnvZ2dls2rSJmTNnsnDhQo99btu2jcTERNfb4xITE9m6dSujR4+mqKiIhQsX8sEHHzBy5EiP9VNTU7FYLERFRQEwatQoUlJSiImJoVevXnVec0pKCqNGjcLf35/IyEgsFgupqalkZ2fTt29fAgMDAbj77rtZt24dL730Uo3648aNQylFv379OHv2LLm5uWzfvt3rdVWv7+m+emu34udzuSQ4FkKIa5TXlxW99Vbli3uuNLvNESSXFUFJoeNzaaHjKCmEknNux1nH+eJZOHMULp5xpHl7M6jB1xkst4GgW6D5LdA81Pk5FIJCoUV7aBHmCK4NMhNamx4dWvPppH58se8k87fsY9LKb7kjMoRZD8bQLbxVUw/vqjjx1luU7tt/Rdv073I77V95pV5lMzMz2b17N3379gUgLy/PFZi1b9+evLy8OtuYNm0aCxYs4Pz5817L5OTk0KFDB9f38PBwcnJyAJg1axYvvPCCK0Ctb/1LmWHNycmhX79+Nfrv2rUrM2fOJD8/n2bNmrF582bi4hy7o7m/Ptrb+Gu7LvfXR3u7r97qX9PBsVIqEzgP2ACrt/3khBBC1HRVXlZUncHHORPcGi4nvrLbofScI1AuPgPF+dWO03AhHy6cgjP/gqJTjhnuGuPwhaB2zmC5PbQ0QSuT8xzuOLcIu37W2DcSpRSJMe0Y0PkW1vwri0Vf/MhDy/7BuP5mfnXfbbL9WyMqKipi+PDhLFq0iJYtW9bIV0rVOYu/ceNGQkND6dOnj2tm9FLs2bOHQ4cO8f7775OZmXnJ9RuqS5cuvPzyy9x33300b96cnj174uPj2CloypQpDWr7o48+8phen/vaUFfjf1UGaK1PX4V+hBDihjNmzJjra2cKg6FyaUVIPeuUXYCik87jBJw/Aedz4Xye45x/EA7/DcqqzawpA7QMh9YdITgCWkdUnkMiHcH1TbLEwOhjYGy/CJJ63sp7235gxc5MNv8nl9eGxvBgt7AbdqlFfWd4r7Ty8nKGDx/OmDFjGDZsmCu9Xbt2rn/Wz83NJTQ0tNZ2duzYwYYNG9i8eTMlJSUUFhYyduxYnn32WSZPngzAnDlzMJlMVYLn7OxsEhIS2LlzJ2lpaZjNZqxWKydPniQhIYGVK1cydOhQwBGk9ujRg6ysrCr1TSZTva/XZDJ5rT9hwgQmTJgAwCuvvEJ4eHi963u7ruq83dfaxtUgWutGO4BMoG19y/fp00cLIYQQHl08q/WJvVr/+JnW//qd1l/M0fpPE7X+KFHrd27T+vWWVY95t2r9mzu1/nSc1l/M1vq7T7TO+pfWF8819ZU0uu+zzugHl/xdR7y8UT+2fJfOPF3U1EO6YjIyMpq0f7vdrh977DE9derUGnnTp0/XycnJWmutk5OT9Ysvvlgl//XXX9fvvPOOx3a/+uor/eCDD3rMy8/P12azWRcUFOiCggJtNpt1fn5+lTJHjhzRsbGxHuuXl5fryMhIffjwYV1aWqq7d++u09PTq5SJiIjQp06d8lg/PT1dd+/eXZeUlOjDhw/ryMhIbbVatdZa5+Xlaa21Pnr0qO7cubM+c+ZMjfobN27UgwYN0na7Xe/cuVPHx8fX+7q09n5fvbVbnaffGSBNe4lHG3vmWAOfKaU08D9a6w+qF1BKTQImgfMhEyGEEMKTigcA28V4zi8vgXNZcCYTCg5D/iEoOAS538O+v4C2VZZtaYK2t8Ett8MtnaFdLITGOHbquAF0D29NytN38Yedmbz32Y/c9/7fmXbvbUz6WRQ+hhtzFvlq2bFjBytXrqRbt2707NkTgLfeeovBgwczY8YMRo4cyfLly4mIiGDt2rUAnDhxgri4OAoLCzEYDCxatIiMjAyPyzE8CQkJYdasWcTHxwPw2muvuR5iqw+j0cjSpUu5//77sdlsPPHEE8TGxgKwZMkSFixYwIkTJ+jevTuDBw+usaQhNjaWkSNHEhMTg9FoZNmyZa7lE8OHDyc/Px9fX1+WLVtG69atgaprjgcPHszmzZuxWCwEBgby8ccf13ld7muOvd1Xb+02lHIEz41DKWXSWucopUKBz4FntdZ/91Y+Li5Op6WlNdp4hBBC3KRs5Y6g+fSPcOoH57EfTh9wW/OsHMsx2nWF9t0c51t7Qstbm3LkDXbiXAlvbNjL1r0niIsIZuHInnRs4/3hrWvdvn376NKlS1MPQ1xHPP3OKKW+1V6ehWvUmWOtdY7zfFIp9X/AHYDX4FgIIYRoFD6+0Dbacdz+YGW63e6Ybc7bC3npcOI/jvO+DZVlgtqDqTfc2htMvRznwPrP2jW19q0C+M3Y3qzfk8Nr6/cyaPHfmTUkhlHxHW7YtchCNESjBcdKqeaAQWt93vn5PmBOY/UnhBBCXDKDwfEQX3AE3D64Mr20yBEwH98Nx7+DnO/gh82V+W2ioWNf6NjfcYREXdMP/ymleLhXOHdEtmH62u/573X/4YuMPOYP784tLfybenhCXFMac+a4HfB/zr9KjcAftdZbG7E/IYQQ4srwD3IGv30r00rOwfE9kPMtZKXC/k2w+xNHXvNboGM/MP8UIu92rGO+BoNlU+tmrJrYl4//mcnbW/dz/6K/s2B4d+6NadfUQxPimtFowbHW+jDQo7HaF0IIIa6qgFYQdbfjAMeSjNM/QtY3cOwbOPpPx4N/4FiKEXW3I1COutuxN/M1wmBQTLgrkp9Ft2Xap3uY+Ic0+tkz2PnpUrKysq7OftpCXMNu7t3ThRBCiMtlMEDo7Y6jz+OOtDOZjj2Zj/wNDv4V/v2pI/2WLnDb/XDbIAiPvyZeXhLdrgV/fvInjHzxHdYuewNtLQXg6NGjTJo0CUACZHFTatTdKi6V7FYhhBDihmG3w8kMOLwdDnwGR3eA3QoBrSE60REoRyc6ZqSbkNls5ujRozXSIyIimuSta3WR3SrEpbrU3SrkxfVCCCFEYzAYoH1X+MkzMH4DvHQYHlkBnQfDoS/hzxPgHQv88VH4fo1jTXMTOHbs2CWl3+yysrIYMGAAMTExxMbGsnjxYldeQUEBiYmJREdHk5iYyJkzZwDYv38//fv3x9/fn3fffbdGmzabjV69ejFkyBCv/a5YsYLo6Giio6NZsWJFjfykpCS6du3qtf7WrVvp3LkzFouF+fPnu9KXLl2KxWJBKcXp095faJycnIzFYqFz585s27bNlf7+++8TGxtL165dGT16NCUlJTXqlpaW8uijj2KxWOjbt2+VP7q8tevuyJEj9O3bF4vFwqOPPkpZWVmd7TaEBMdCCCHE1RDQCmJ/Dg//BqYfgCc+g/j/cmwf93+TnYHyKEegXHq+7vauEG8v4Apq055Sq81j3s3MaDTy3nvvkZGRwTfffMOyZcvIyMgAYP78+QwcOJADBw4wcOBAVxAaEhLCkiVLmD59usc2Fy9eXOtseEFBAbNnz2bXrl2kpqYye/ZsV+ANsG7dOoKCvL/Axmaz8fTTT7NlyxYyMjJYvXq1a8x33nknX3zxBREREV7rZ2RksGbNGvbu3cvWrVt56qmnsNls5OTksGTJEtLS0khPT8dms7FmzZoa9ZcvX05wcDAHDx7k+eef5+WXX6613epefvllnn/+eQ4ePEhwcDDLly+vtd2GkuBYCCGEuNoMPo6dMAa9BdPSYcLnzkD5345A+d3bYN1kOPJ3x/KMRjRv3jwCA6u+FMTXPwC/fr9g3PJUzhWXN2r/15uwsDB69+4NQIsWLejSpQs5OTkApKSkMH78eADGjx/P+vXrAQgNDSU+Ph5fX98a7WVnZ7Np0yYmTpzotc9t27aRmJhISEgIwcHBJCYmsnWrYwOwoqIiFi5cyKuvvuq1fmpqKhaLhaioKPz8/Bg1ahQpKSkA9OrVC7PZXOs1p6SkMGrUKPz9/YmMjMRisZCamgqA1Wrl4sWLWK1WiouLufXWmi/Ncb8vI0aM4K9//Sta61rbraC15ssvv2TEiBFA1fvqrd2GavonAoQQQoibmcEAHe5wHPfNhexU+H41pK+Df6+BVh2h52joMdrxBr8rrOKhu5kzZ3Ls2DHXbhXNYxJ46U//5uHf7OD3j99xTb5V7+u1P3I6q+iKttm2QxA/HXlbvcpmZmaye/du+vZ1bPmXl5dHWFgYAO3btycvL6/ONqZNm8aCBQs4f977vxbk5OTQoUMH1/fw8HBXQD5r1ixeeOGFGn/g1FV/165ddY7NvX6/fv1q9N+/f3+mT59Ox44dadasGffddx/33Xcf4HgVdFxcHElJSVX6NxqNtGrVivz8fK/tAq7XWPv5+dG6dWuMRmONMt7abdu2bb2vzROZORZCCCGuFQaDY7/koYth+o8w7CNo0wn+tgCW9IQ/POTYX9l+ZZc7jBkzhszMTOx2O5mZmYwZM4af9zLxhwl3kF9UxsO/3sHuY2fqbugmUlRUxPDhw1m0aBEtW7aska+UqvMNhBs3biQ0NJQ+ffpc1hj27NnDoUOHePjhhy+rfkOdOXOGlJQUjhw5wvHjx7lw4QKffOLY+3vOnDkkJSVddtubN2/2OAt9NcjMsRBCCHEt8m0G3R9xHOeyYc9q+PZjWPMLx2xy/AToPa5RX2XdL6oNf37yJ/zy96mM+uAbFo/qyaCuYY3W36Wq7wzvlVZeXs7w4cMZM2YMw4YNc6W3a9eO3NxcwsLCyM3NJTQ0tNZ2duzYwYYNG9i8eTMlJSUUFhYyduxYnn32WSZPngw4gkyTycT27dtd9bKzs0lISGDnzp2kpaVhNpuxWq2cPHmShIQEVq5cydChQwGYMmUKPXr0ICsrq0p9k8lU7+s1mUwe63/xxRdERkZyyy23ADBs2DD++c9/MnbsWI/1w8PDsVqtnDt3jjZt2nht112bNm04e/YsVqsVo9FYpYy3dhtMa33NHH369NFCCCGE8MJarvXe9Vp//KDWr7fU+s1Qrdc/pfWJ9Ebt9tT5Ev3Q0n9o84yN+uN/HG7UvuqSkZHRpP3b7Xb92GOP6alTp9bImz59uk5OTtZaa52cnKxffPHFKvmvv/66fueddzy2+9VXX+kHH3zQY15+fr42m826oKBAFxQUaLPZrPPz86uUOXLkiI6NjfVYv7y8XEdGRurDhw/r0tJS3b17d52eXvV3JiIiQp86dcpj/fT0dN29e3ddUlKiDx8+rCMjI7XVatXffPONjomJ0RcuXNB2u12PGzdOL1mypEb9pUuX6smTJ2uttV69erV+5JFHam23uhEjRujVq1drrbWePHmyXrZsWa3tVufpdwZI017i0SYPiN0PCY6FEEKIejqRrvWGqVrPbe8IlFc9qvWx1EbrrrjUqieu+JeOeHmj/s32g43WT12aOjj++uuvNaC7deume/TooXv06KE3bdqktdb69OnT+p577tEWi0UPHDjQFcDm5uZqk8mkW7RooVu1aqVNJpM+d+5clXZrC4611nr58uW6U6dOulOnTvp3v/tdjfzagmOttd60aZOOjo7WUVFReu7cua70xYsXa5PJpH18fHRYWJieMGGCx/pz587VUVFR+rbbbtObN292pb/22mu6c+fOOjY2Vo8dO1aXlJRorbWeNWuWTklJ0VprffHiRT1ixAjdqVMnHR8frw8dOlRnuw888IDOycnRWmt96NAhHR8frzt16qRHjBjh6qO2dt1danAsLwERQgghrmfFBfCvj+CbX8PFM2D+Kfz0BYhKgDrWvF6qcpud5z/dw8Z/5/L8vbfx3EBLnetqrzR5CYi4VPISECGEEOJmEhgCd7/k2BLu/rcg/yCs/Dl8OAB+2ApXcBLM18fA4lG9GNbbxPtf/Mg72364IltnCXEtkeBYCCGEuBH4B0H/p2Hq947dLi6egdWPwscPwLH6b9tVFx+D4t0RPRh9R0d+vf0QczftkwBZ3FAkOBZCCCFuJEZ/6PM4PJMGDy6EgsPwu/tg9Wg4ue+KdGEwKN56uCuP/8TM8n8cYVZKOna7BMjixiDBsRBCCHEj8vF1bPf23G6451XI/Af85iew/inH1nANpJTi9aExTL47ik++OcaslHSZQRY3BAmOhRBCiBuZX3P42YuO5Rb9noL//C8sjYd/vA/WsgY1rZRixqDbmXJ3J1btOsa7n/1whQYtRNOR4FgIIYS4GQSGwP3zHMstOt0DX7zhmEk+vL1BzSqleHlQZ0bf0ZFlXx3io68PX5HhCtFUJDgWQgghbibBETBqFfzif8FudbyS+n8fh3M5l92kUoq5P+/Kg93CmLtpH2vTsuqudJ3KyspiwIABxMTEEBsby+LFi115BQUFJCYmEh0dTWJiImfOOF65vX//fvr374+/vz/vvvtujTZtNhu9evViyJAhXvtdsWIF0dHRREdHs2LFihr5SUlJdO3a1Wv9rVu30rlzZywWC/Pnz3elL126FIvFsSXf6dOnvdZPTk7GYrHQuXNntm3bBsAPP/xAz549XUfLli1ZtGhRjbpaa5577jksFgvdu3fnu+++q/d1gff7Wlu7DeJtA+SmOOQlIEIIIcRVVHZR6+1vO960NzdM638u1dpW8w1l9VVSbtVjP/pGR87YqLem517BgVZq6peAHD9+XH/77bdaa60LCwt1dHS03rt3r9Za6xdffLHKG/JeeuklrbXWeXl5OjU1Vb/yyise35D33nvv6dGjR9f6hrzIyEidn5+vCwoKdGRkpC4oKHDl//nPf9ajR4/2+hIQq9Wqo6Ki9KFDh1xvyKsY83fffaePHDlS6xvy9u7dW+VNdlFRUTXeZGe1WnW7du10ZmZmjfqbNm3SgwYN0na7Xe/cuVPfcccd9bquCt7uq7d2q7vUl4DIzLEQQghxs/INcOyR/NQ3YL4Ttr0Cv3/QscPFZfA3+vDbsX3o0aE1z/5xN/885H0m8noVFhZG7969AWjRogVdunQhJ8cx656SksL48eMBGD9+POvXrwcgNDSU+Ph4fH19a7SXnZ3Npk2bmDhxotc+t23bRmJiIiEhIQQHB5OYmMjWrVsBKCoqYuHChbz66qte66empmKxWIiKisLPz49Ro0aRkpICQK9evTCbzbVec0pKCqNGjcLf35/IyEgsFgupqalVyvz1r3+lU6dOREREeKw/btw4lFL069ePs2fPkpubW+t1Va/v6b56a7ehjA1uQQghhBDXt5BI+MVa+H41bJkBv7kTEudA3AQwXNo8WnN/Ix8/Hs/I/9nJf61IY82k/nQLb9Uow/7q9x9w8uiVXeMcGhHFgMcn1atsZmYmu3fvpm/fvgDk5eURFhYGQPv27cnLy6uzjWnTprFgwQLOnz/vtUxOTg4dOnRwfQ8PD3cF5LNmzeKFF14gMDDwkurv2lX/va9zcnLo16+fx/4rrFmzhtGjR7u+//a3vwVgypQpXsdf23VNnDiRKVOmEBcX5/W+eqtfUfZyycyxEEIIIRyvmu75C3hqYHjugwAAIABJREFUJ3TsB5unwycPs+rDJZjNZgwGA2azmVWrVtXZVOtAP/7wRF9aB/oxYcW/OHGu5CpcwNVVVFTE8OHDWbRoES1btqyRr5Sq89XaGzduJDQ0lD59+lzWGPbs2cOhQ4d4+OGHL6v+lVJWVsaGDRt45JFHXGlTpkxhypQpl93mRx99RFxczbc71+e+NpTMHAshhBCiUisTjF0H337Mqrd/xaSUv1Bc7ti/+OjRo0ya5JhVHTNmTK3NtG8VwPLH4xj+638yaWUaayf3J8DX54oOtb4zvFdaeXk5w4cPZ8yYMQwbNsyV3q5dO3JzcwkLCyM3N5fQ0NBa29mxYwcbNmxg8+bNlJSUUFhYyNixY3n22WeZPHkyAHPmzMFkMrF9+3ZXvezsbBISEti5cydpaWmYzWasVisnT54kISGBlStXMnToUMARpPbo0YOsrKwq9U0mU72v12Qy1Vp/y5Yt9O7dm3bt2l1SfW/XVZ23+1rXuC6bt8XITXHIA3lCCCHEtSMi3KSBGkdERES92/hs7wltnrFRP/PH77Tdbm/wmJr6gTy73a4fe+wxPXXq1Bp506dPr/Lg2Isvvlgl//XXX/f4QJ7WWn/11Ve1PpBnNpt1QUGBLigo0GazWefn51cpc+TIEa8P5JWXl+vIyEh9+PBh1wN56enpVcrU9kBeevr/Z+/O46Oq7j6Of85MJnuAhJ2QBcKmiIAKuAAiCqJY0bobtW6grbVaa+3TB+ueqm3VWq20KFr1iVarYlVwAwEBEcEFFUHWJKyBJCRkn+08f0wSkhAgQCaT5ft+ve5r7px77r2/WF/1e++ce+73dR7I69OnT50H8i677DL7/PPPN7ivtda+9957dR6cGzFiRKP/LmsP/M/1QMet73AfyAt5IK69KByLiIi0HMaYBsOxMeawjvPMgg025Xfv2b/NW3fUNYU6HC9evNgCdsiQIXbo0KF26NChds6cOdZaa/Py8uz48eNtv3797JlnnlkT9Hbs2GETExNtXFyc7dixo01MTLRFRUV1jnuwcGyttbNmzbJpaWk2LS2twSB6sHBsbWBmh/79+9u+ffvahx56qKb9ySeftImJidbpdNqePXvaG264ocH9H3roIdu3b187YMAAO3fu3Jr2kpISm5CQYAsLC+v0nzFjhp0xY4a1NnBB8Ytf/ML27dvXHnfccXbFihWH/LtuuOGGmn4H+ud6sOPWdrjh2AS2twwnnXSSXblyZajLEBERESA1NZXs7Oz92lN6diFr++5GH8dayx2vr2L219v4x1UnMOm4I39gas2aNRxzzDFHvL+0Pw39O2OM+dJau/+gZvRAnoiIiBxARkbGfrMgRIc7yDilBObcCd7KRh3HGMPDPx3C8ORO/Pq1VazeXhSMckWahMKxiIiINCg9PZ2ZM2eSkpKCMYaUlBRmPvcC6dPugBXPwgvnQsmuRh0r0uXkn1efSKdoF1NfXMnu4sYFa5HmpnAsIiIiB5Senk5WVhZ+v5+srCzSr74Gzs6AS1+C3NXw7JmQ+0OjjtUtLpJnrzmJgjI3t776FT5/yxnaKVJN4VhEREQO37FT4Lq54HPDrImwYV6jdjsusSMPXTCEzzcV8Lf564/o1C3peSlp2Y7k3xWFYxERETkyiSfA1PkQnwKZl8KK5xq128Un9uaiE3rzt0/W89mGw3vFdGRkJPn5+QrIckjWWvLz84mMjDys/TRbhYiIiBydymJ44wZY/yGc/AuY+BA4Dv7Cj9JKL+c/vYS9FV7m/moMXeMiGnUqj8fD1q1bqahoe2/dk6YXGRlJ7969cblcddoPNluFwrGIiIgcPb8PPpwOy2fAoPPgolngOvgdu7U79zLl6aWM7JPAi9eNxOEI7muBRappKjcREREJLocTznkEJj0Ka9+DVy8Hd9lBdxnUowP3nT+YxevzmLFoYzMVKnJwCsciIiLSdE6+GaY8A5sXwf9dBBV7D9r98hFJnD+0F49/vI4VWQXNVKTIgSkci4iISNMang4XPQdbv4CXpkDZgUOvMYaMC48jKT6KW1/5moJSdzMWKrK/dh2OMzMzSU1NxeFwkJqaSmZmZqhLEhERaRuOuwgu+z/I/R7+dd5BXxYSF+ni6StPoKDUzf++9Z1mopCQarfhODMzk2nTppGdnY21luzsbKZNm6aALCIi0lQGngNXvg57Ngfeple07YBdj0vsyB0TB/DB6p28++2OZixSpK52O1tFamoq2dnZ+7V36N6BX735KyKcEUQ6I4kIq/p0RhAZFklkWGSD2+r3C3eG43K4GjiziIhIO5O9DF65FKI7w/UfQFyPBrv5/JaLZnxGdn4pH/369EZP7yZyuDSVWwMcDkfDP9sYGP/aeCp9lVR6K6nwHfk8ik7jrAnLEc6I/dYb+l4dssOd4TUBvKZvdXv9far2i3BGEO4IxxhNhSMiIi3M1pXw4vmBF4ZcOweiExrstmFXCef+bTFnDOzKP646Uf9Nk6A4WDgOa+5iWork5OQG7xynJKcw/5L5Nd+ttbj9biq8FXUCc6WvsqatwldBpbeyznp1n/r71F4vdhfj9rn36+v2H93DCBHOiJpwHe4MP2CQrl7q921on4a21W8Ld4bjMO12pI6IiBxM75Pgilcg85LAcs1/ISJ2v279usXymwkDePj9tbyzajtThiWGoFhpz9ptOM7IyGDatGmUle2bgzE6OpqMjIw6/YwxNWGwufitH7fPXScwV6/XDtk12+svDWyrDuFun5u9ZXtrju/2uan079vHcnS/JLgcrv1CtMvpIsIRaGsoUIc7aq3X6lP7WNX9arY5XXX2czlcdfbTnQYRkRao7zi4+AV4/Rr495WB8cgNvCjkxjF9+WD1Tu59ZzWnpHWmW9zhvf5X5Gi022EVEHgob/r06eTk5JCcnExGRgbp6enNdv6WxlqL1+89aLCuE6prba/fVv3d7a+7T4WvAo/Ps184r/7ut/4m+Vuqw3J1oK69Xh2sq9tczn19q/er7lNznKoAXv979f4uh2u/fWr3dzlchDnCFNpFRAC+eRXevjnwJr1LXgTn/vfqqodXnD6gKzOv1vAKaVoacyythtfvrQnhbn8gMHt8nkCQrgratbcfcr3Wd4/fU6fN4/PU+WyoT1OrDszVgbpOuK5awhxhDbfV2q9OW6312vvWaWugf5gjjDAT6F+9Xr1/mAlToBeR4Fr+T3j/Lhh6ReClIY79h+XN/HQjf5y7lr9eNowLhmt4hTQdjTmWVqM6tEW7okNdSuBOuvXW3Ol2+/eFZ4/fc8BwXb2ter2h9jptfg9ev3fftqpjlbpL6/Sp6Vfv+MFWHZprL07jrBO6a4frmj4OJy7jqlmv3q/+PtXb6nyv+nQ5XDXnqm6vff46bbX22++z9j719m1N4V+/dkmbMuomqCiCBRkQ2Snw6ul6bhjdlw++DwyvODWtM906aHiFBJ/CscgBGGNwmcCd1pYQ1htSHeCrQ3N1yK4O9XXa6316/V481lPTr+Y4VfvX9Kndv2rdZ301bbWPVb1e7i3H5/fVOU71OXx+Hz7r23esqn5NNaTmcDmNM7DUC9rVIbo6UB8wnFeH+lptdUJ9rQuJ2v3rXHA0cAFS/47/R7M/4qHfPERFeWAGnezsbKZOnUqxu5jLr7i8zq8GejBWWo2xvw28PW/5DOicBiOn1tnsdBj+fMlQznlyMfe/+wN/Tz8hRIVKe6JwLNKK1Q7wUUSFupyj4rd+fP5AaK4dvhtcrxWy62+rDtu1Pz1+D37rr9uv3r71w3ztNp+/1nlrnafSW0mZLatTU/W+Hr+nzjFrH+NI/PiHH/GU1/2loLy8nF/99lf83fH3Ou3Vd+hrxsLXGyNffyy+y+k65Mw0ted0r26PCosi0hlZMwd89Xenw3nE/x5IO2MMnJ0BBZvg/d8FAnLa+Dpd0rrGcsu4fjwxbx1XrM9jdP8uISpW2guNORYRaUa17/bXDsz73Yn3172jP6rXqAbnZjfG8PLqlxscclN7CE/9z+rx/LUfmvX4PDUPyFZ4K4549ppwRzhRriiiwuou0WHRxLhiiHZF11mPdcUS44ohLjyOGFcMsa5YYsNjA5+uWIXt9qBiLzx/duANejfOg64D6m72+Jj4xKeEOQ0f3DaW8DD9OiJHRw/kiYi0cgd6q2dKSgpZWVlNfr7qEL/fVJL15nQv95UH5nyvmsO9zFtGhbeCcm85ZZ4yyr3lgXVvWc33Uk8ppZ5Syrxlhy4EiHXFEhceR4fwDjWfHSM61iydIjrVfHaK6ER8ZDydIjoR5tCPo63Knmx4djxEdoAb5+/3kpBP1uZy/b9W8rtJg/j5uLQQFSlthR7IExFp5Ro7N3tTqRmyE+4ilv1f1NAU/NZPhbeCEk8JpZ5SStwlNevF7mJKPCUUu4spdhez172Xve69FLuL2VKyhe/zv6eosohKX2XD9WPoENGB+Ih4EiIT6BLVhc5RnekS1aVm6RzVme7R3UmITNA47ZYgPgUuz4QXfxKYB/nq2eB01WweP6g7E47tzt/mr2fKsF706tS6h5JJy6U7xyIirYRmq9hfubecosoiiiqLKKwsZE/FHvZU7mFPxR4KKgpqPvMr8skrz6PYXbzfMcJMGF2iu9Atuhvdo7vTPbo7PWJ60DOmJ71ie9EzpicJkQmtamaTVq16DuQTr4Xz/hoYl1xlS0EZZz2+iDOP6cYz6SeGrkZp9TSsQkREBKj0VZJfHgjKu8t3s7tsN7vKdpFblktuWW5gvTR3vyEfkc5Iesb2JDE2kaS4JJLjkkmKSyIpLonEuMRmfYtquzDvPljyBEx6BE7+eZ1NT81fz2Mfr+PlG0Yypn/X0NQnrZ7CsYiISCNZa9nr3suO0h1sL9le87m9ZDtbS7aypXgLpZ7Smv4GQ8+YnvTp1Ic+HfrQp+O+pXNkZ91xPhJ+P7x+Nfz4Plz3PiSPqtlU4fEx6a+f4jCG928fQ0SYHtiUw6dwLCIi0kSsteyp3MOW4i3k7M1ha/FWsvZmsbloM1l7syj3ltf07RjRkQHxA2qW/p36k9YprcXOnd6iVBTBP8eCzwM3L6nzgN7CH3dx7QsruGvSQH4xrl8Ii5TWSuFYRESkGfitn9zSXDYXbWZT0SY2FG5g/Z71rC9cXxOaDYaUDikM7jKYwZ0Dy6CEQQrMDdn+NcyaCH3PgCv+XecV0ze9vJJP1+Ux7zenk6iH8+QwKRyLiIiEkN/62Va8jXV71rFuzzrWFqxldf5qcstyAXAYB3069OG4LscxrNswhncbTp+OfTSLBsDymfD+b2HCg3Dar2qat+4JPJw38dge/O2K4SEsUFojhWMREZEWKK88jx/yf2B13mq+z/+e73Z/x57KPUBgSMbQrkMZ3m04J3Q7gSFdhuCqNbVZu2FtYGq3tXP2G3/85w/X8vcFG3nv1tEcl9gxhEVKa6NwLCIi0gpYa8nem83Xu77mm93f8PWur9lctBmAqLAoTuh+AqN6jGJUz1EMShjUfu4s14w/9sLNi2vGH++t8HD6nxYwuFdH/u/GUYc4iMg+CsciIiKtVGFFIV/mfsnnOz5n+c7lNWG5Y0RHRvUYxdjeYxmdOJrOUZ1DXGmQVY8/ThsfGH9cNQvIrCWbefC9H3jp+pGMHaCp3aRxFI5FRETaiNzSXL7Y+QXLdyxn2fZl7CrfhcEwpMsQxvYey+lJpzMwfmDbnEKugfHHlV4fZz62iA6RLt67dTQORxv8u6XJKRyLiIi0QdZa1hasZdHWRXy69VO+y/sOgO7R3ZmQMoGzU8/m+K7Ht53hF9ZWzX/8Ady0CLoPBuC/32zjtn9/w18vG8YFwxNDXKS0BgrHIiIi7UBeeR5Lti1hfs58lm5bisfvoUdMDyamTOTs1LMZ0mVI67+jXJoPz4yCuJ4w9RNwuvD7LT95eglF5R7m/+Z0vRhEDknhWEREpJ0pdhezcMtCPsz6kKXbl+L1e0mMTeQnaT9hStoUesf1DnWJR27Nu/DaVTDuf2Hc7wBYvH43V8/6grsnH8ONY/qGuEBp6RSORURE2rG97r0syFnA3M1zWbZ9GRbLyB4juaDfBZyVchZRYa3wJRpvToXVb8HUBdDzeACunrWc77YV8eldZ9Ahsh1OeyeNpnAsIiIiAOwo2cE7G9/h7Q1vs7VkK7GuWCb1mcTlAy9nYMLAUJfXeGUF8MzJENM1EJDDwvl+WxHnPbWEX4xL465Jg0JdobRgBwvHQR+hb4xxGmO+Nsa8F+xziYiIyMH1jO3JTUNvYs5P5/D82c8zPnk8czbN4eJ3L+a6D65jXvY8vH5vqMs8tOgE+MmTkPs9fPpnAI5L7MgFw3rx/NLN7CyqCHGB0lo1x+OrtwFrmuE8IiIi0kgO42BEjxFkjM7g44s/5s6T7mRH6Q5+vfDXTH5rMi98/wJFlUWhLvPgBp4DQ6+ExY8F5kEGfjNxID6/5alP1oe4OGmtghqOjTG9gcnAc8E8j4iIiBy5jhEd+dngnzHnwjn89Yy/khiXyONfPs5Z/zmLR794lNzS3FCXeGCTHobYbjD75+CtJCkhmktOSuI/K7fq7rEckWDfOf4rcBfgD/J5RERE5Cg5HU7OTD6T589+njd+8gYTUyfy6tpXOeetc3jo84fYXrI91CXuL6oTnP8U7F4DCx8G4Oenp+G3ln8s2hji4qQ1Clo4NsacB+yy1n55iH7TjDErjTErd+/eHaxyRERE5DAMTBhIxugM3rvwPab0m8Kb699k8luTufeze9myd0uoy6ur/wQYfhUs/Rvk/kBSQjQXDk/k1S9y2FWsu8dyeII2W4Ux5mHgasALRAIdgLestVcdaB/NViEiItIy7SzdyfPfP8+b697EZ31c0O8Cbhl2C12ju4a6tICyAnjqROh2DFw7h6z8MsY/tpAbRvdh+uRjQ12dtDAhma3CWvt7a21va20qcDnwycGCsYiIiLRcPWJ68L+j/pcPLvqAKwZdwX83/pfJsycz45sZlHnKQl1eYPaKs+6D7KXw7eukdolhyrBE/u/zHPJLKkNdnbQibeRl6yIiItIcukZ35Xcjf8c7U95hTOIYnln1DJNnT+aNdW+Efgq44VdD4knw0d1QXsgtZ/Sjwutj1pLNoa1LWpVmCcfW2oXW2vOa41wiIiISfEkdknhs3GO8fM7L9I7tzf3L7ueSdy/hvqfvIzU1FYfDQWpqKpmZmc1XlMMBkx+D0t2w8GH6dYtl8pCevPhZFoVl7uarQ1o13TkWERGRIzas2zBeOuclHh/3OJsWbOKB3zxAdnY21lqys7OZNm1a8wbkXsNgxA3wxUzY8S2/HN+PUreP55dmNV8N0qopHIuIiMhRMcYwIWUChbMLse66D/qXlZUxffr05i1o/N0QlQBz72RQt1jOHtydF5ZuZm+Fp3nrkFZJ4VhERESaxJYtDU/xlpOT07yFRMXDhAdgy3JY9Qq3ju9PcYWXF3X3WBpB4VhERESaRHJycoPtYQlh/P2bv+PxN+Od26FXQNIo+Pgejkvwc+agbsxaupmSyhA/NCgtnsKxiIiINImMjAyio6PrtEVFRzHpF5P4x6p/8LP3f0b23uzmKab64bzyPTD/QW49sz+FZR5eWd5M55dWS+FYREREmkR6ejozZ84kJSUFYwwpKSk8O/NZ3nngHf5y+l/I3pvNJe9ewpvr3iRYLyGro8cQGDEVvnyBYRE7OaVvZ/61NAuPzx/8c0urFbQ35B0JvSFPRESk7dpZupO7l97N8h3LGZ80nvtOvY/4yPjgnrQ0H/42DFJOY96wJ7nxpZX87YrhnD+0V3DPKy1aSN6QJyIiIlJbj5gezJwwkztPupPF2xbz03d+ymfbPgvuSWM6w+jbYd37jI/aQJ8uMcxavKl57lxLq6RwLCIiIs3GYRz8bPDPeHXyq3SK6MTN827m2W+fDW5YHfVziOuFY/69XH9qCqu2FvFl9p7gnU9aNYVjERERaXYDEwbyyuRXmNRnEn/7+m/csfAOSj2lwTlZeDSc8XvYuoJLY7+mY5RLr5SWA1I4FhERkZCICovi0TGP8tuTfsuCLQu4cs6VbC4KUmgdeiV0HUTEwoe4akQvPly9ky0FZcE5l7RqCsciIiISMsYYrhl8DTMnzGRPxR6unHMlC7csbPoTOcPgrPugYCPTYpfgMIYX9FIQaYDCsYiIiITcyJ4jee2810jukMytn9zKjFUzmn4c8oBJkHwqHZc/xk+P68hrK3L0SmnZj8KxiIiItAg9Y3vy4qQXOT/tfJ755hnuW3YfXn8TvtHOmMBrpUt38ZvYjyl1+3h9RcOvvJb2S+FYREREWozIsEgeOu0hbjr+Jt5a/xa3L7idcm95050gaQQccz7dv5vJhOTA0AqvXgoitSgci4iISItijOGXw3/JH07+A4u3LebGj25kT0UTTr125r3greAPce+yrbCcD1fnNt2xpdVTOBYREZEW6dKBl/L4uMf5seBHrnn/GraVbGuaA3fpBydeS9Lm1xkVX8xzSzY1zXGlTVA4FhERkRbrzOQzeXbisxRUFHDV3KtYW7C2aQ485g4Mhgc6z+PrnEK9FERqKByLiIhIiza823BeOuclwhxhXPfBdXy3+7ujP2jH3jA8nQE7/ku/iCJeXpZ19MeUNkHhWERERFq8tE5pvHzOy8RHxnPTxzfxfd73R3/Q0XdgrJ+Hus1n7vc72VPqPvpjSquncCwiIiKtQo+YHjx/9vN0jOjItI+msTpv9dEdMD4Fjr+ckQXv0tFbwJtfbW2aQqVVUzgWERGRVqM6IHeI6MDUj6fyQ/4PR3fAMXfg8Hu4O34er3yR0/QvHpFWR+FYREREWpWesT2ZdfYs4lxxTP1oKmvy1xz5wTqnwZBLmOx+n8LdO/h8U0HTFSqtksKxiIiItDqJsYnMOnsWMa4Ypn489ehmsRhzJ05fBb+I/IBXvshpuiKlVVI4FhERkVapd1xvZp09i6iwKKZ+NJXNRZuP7EBdB2AGX8jVjo9Y9v168ksqm7ZQaVUUjkVERKTVSopLYtbEWTiMg5/P+zl55XlHdqCxdxLhL+NqM5c3vtSDee2ZwrGIiIi0askdkvn7mX+noKKAW+bfQpmn7PAP0n0wHPMTbnR9xLvLf8Dv14N57ZXCsYiIiLR6x3U5jj+P/TNrC9Zy56I78fq9h3+Qsb8lxpZyRtHbLNuU3/RFSqugcCwiIiJtwulJpzN91HQWb1vMQ58/dPjTsvUciq/f2dzg+oA3lv0YnCKlxVM4FhERkTbj0oGXMnXIVN5c/yYzv5152Ps7x95BJ0qI+/ENdhfrwbz2SOFYRERE2pRbh9/K+Wnn8/Q3T/P2hrcPb+ekUVR0G8a1jrn8Z2V2cAqUFk3hWERERNoUYwz3nXIfJ/c8mfs/u5+VO1cezs5EjrmVvo6dZH/+th7Ma4cUjkVERKTNcTldPD7ucXrH9ebORXeyq2xX43c+dgrlUT2YUjabJRuOcGo4abUUjkVERKRNiguP469n/JUybxl3LLwDj8/TuB2dLlyn3sypzh9YtnRBcIuUFkfhWERERNqstE5pPHjag6zavYpHVzza6P3CTrqOcuui1/s34XA4SE1NJTMzM4iVSkuhcCwiIiJt2tmpZ3Pt4Gt57cfXeGfjO43aJ/OtObzwVQWXDfTRPQays7OZNm2aAnI7oHAsIiIibd5tJ9zGyB4jeWDZA6zJX3PI/tOnT+expWWEOeCWEeEAlJWVMX369GCXKiGmcCwiIiJtXpgjjD+N/ROdIjrx64W/prCi8KD9c3Jy2LTH8t+1Xn5+kouosH3t0rYpHIuIiEi70DmqM0+Me4JdZbv4n8X/g8/vO2Df5ORkAJ743E3naAdXD3XVaZe2S+FYRERE2o0hXYfw+1G/Z+n2pfxr9b8O2C8jI4Po6GgW5/hYud3Hr08OJyY6moyMjOYrVkJC4VhERETalYv7X8zElIk8/c3T/JD/Q4N90tPTmTlzJikpKfz1czeDujh57v5ppKenN3O10twUjkVERKRdMcZwzyn3kBCRwP8s/h/KveUN9ktPTycrK4sZy/aw0yYwglXNXKmEgsKxiIiItDsdIzry0OiH2Fy0mSe+fOKgfeNiYljR7SLSSr7EvbPhO83Sdigci4iISLt0Sq9TuPrYq3l17ass3rr4oH07j74Bj3Wyfd4/mqk6CRWFYxEREWm3bjvhNvp16sc9n91DQUXBAfuNPG4gixwj6brpLfA0PAxD2gaFYxEREWm3IpwRPDLmEYoqi7j/s/ux1jbYL8zpYNeAK4nxF1Py9ZvNXKU0J4VjERERadcGJgzkthNu45MtnzB7w+wD9jth3Pls9nen9LNnm7E6aW4KxyIiItLuXX3s1YzsMZJHvniELXu3NNhnUM9OfBJzLt0Lv4Fdh34FtbROCsciIiLS7jmMg4zRGTiMgwc+f+CAwysiTrqKF7/1kTR4FA6Hg9TUVDIzM5u5WgkmhWMRERERoEdMD2474TY+3/E57216r8E+JVmruem9crbmFWOtJTs7m2nTpikgtyGNCsfGmG7GmAuNMbcYY643xow0xrT6YJ2ZmUlqaqqu/ERERASASwdcyvFdjufPK/5MYUXhftsfefBeKj3+Om1lZWVMnz69uUqUIDtowDXGnGGM+RCYA5wD9ASOBe4GvjPG3G+M6RD8MpteZmYm06ZNIzs7e9+V39SpvPzcc/grKrA+X6hLFBERkWbmdDi555R7KHYX89iXj+23PScnp8H9DtQurY850JgaAGPMn4GnrLX7/S9ujAkDzgOc1tommdPkpJNOsitXrmyKQx1Samoq2dnZ+7X3DAtjflq/wBenExMeXrW4cLjCMS5XrbZ632vWA5+O/bYfpL+r6hzh4eAKfNbpU/szLKxZ/hmJiIi0V098+QTPf/88z5/9PCN6jKhpP1B+SElJISsrqxkrlKNhjPnSWnvRMBJJAAAgAElEQVRSg9sOFo4PcdDu1trco6qsnuYMxw6Ho8HB9gbY9c+ZWLc7sHg8+9bdbqzHjb92u8eDdXsO0j/wvYmLbzg07/fpqvu93np1EK9eNy7XvmDeQP866wdpw+XCGNO0f7OIiEgzKveWc+F/L8TlcPHG+W8Q4YwA9v3yXFZWVtM3OjqamTNnkp6eHqpy5TAdLBwf1i1IY0wn4CLgSuAYoNfRlxcaycnJDV75Jaek0GXa1CY9l7UWPB78bg/W4w6EaY+7brhusL1e2PZ4GgjmtY9Rr73Sjb+4JPC99rZ6/TjCC6SDqh+ij2gJqxu4w8L23X0PC6sV0Ot+p/a2sFoXCdXtVZ+E1TqHo9UPoRcRkSYUFRbFPSffw03zbuK5757jlmG3ANQE4Nvv/B35O7eR1NHJH598SsG4DTlkODbGRAFTCATi4UAccAHwaXBLC66MjIwGr/wyMjKa/FzGGAgPxxkeDsQ0+fGPlvV664bw6u/1w/aB2j0NtDe0T532fd/9FeX7grqngf5VS1BCfDWHo054JrwqWNcO1NWh2hUW2FanPWxfKA9roL3mWGGB4Tq1vpuwqj7Og3yvtV7nu9O5r39Y4DthYbpz3w5kZmYyffp0cnJySE5OJiMjQ/9xFmlipyaeyrl9zuW5757jnNRz6NupLxAIyOdecAk3/fFpXnfdD0M03LEtOej/msaYV4AxwEfAU8AnwAZr7cLglxZc1f8R0X9c2Be4oqJCXcpBWZ8vEJRrwrwHaoXsOiHf660b2r1Vd9e9Hqi9zeutCerUBHnvvmPV3l57H7cbf1lZ4Hi196laqNm36ntzP+BZFZprwnL1uqsqdDudVUG9uo+zpr3Oeq3+Ne1hTnA4929zOjGOQ/RzOgLnDHMGLkicVftVLTictfZz1Gyr+azap6bNUa9PQ9+rlrZ0wVD/Z93qqaSAdvn/YSLBdNeIu1iybQn3L7ufFya9gKNqsq74mHAi+57G5q1JpK58AXPCNSGuVJrKoR7I+4bAjBYvAf+21m41xmyy1vYNRjHNOeZYpDlZvz8QvmsvHm8g3Ne0+faFd5+vKnAf4LvXh/VVBe+afb3ga3jd+rx19qu5QKiuy1fdXt3f13C73xdYr97u9wfWPZ7AsVryLC/Vwbn+p9MJTkcg2DsdGFO/nwFHvT61Px2O/Y6Jwxy4j9MBxtHwOZ2OfRcG9fs49m0bescdbM3P3+9PTOrale//9a999VZfkDhqHbv2xUft72H1Lj6qL6hqX7zUbmtDFxsihzJ7/Wzu+eweHjj1AS7sf2FN++srtrDm7Ue51/Uy/Pwz6D44hFXK4TiqB/KMMYOAK4DLgDxgIHBcUz+MBwrHIq2dtRZ8vjqhG5+vKmTvW68O1jXbvD7wN/Dp8wfCefV+1WHc76/Vp/53f+DCwOcHW9Xf5w8E+4Y+fd593/123zH8Pqy/1t/j8zV8jP36+AP1VPUJrNff5q+zT0N9DjaMaPCPa2loqwFWDxwUtP9966gKyg3+OhHm2jfkp8FhSPs/U1D7QeA632vP9hMRgYmomgkoIgITXvU9IgITGVnnUw8GS1Oy1nLV+1exvWQ7cy6cQ7QrGoDCMjcTH3qTZRG34DzlFpj4YIgrlcY6qgfyrLVrgXuBe40xJxIIyiuMMVuttac2baki0poZYwJBCSA8PNTltGp1LjTqhemkIUPI2bp1v32SevWi73vv7gv3NZ+1Lix89S4g6lyM1LuIqbpw2XeB4q/69aJ6e9UvEL6qXz1qfuGo9WtF7SFHtZ4xqBmO1NAzCU3xjIHDEQjKUVE4IiMxUZE4IqNwREUF1qNjcERH11qiAp8xsThiY3HExOCMjalZd8R1wBETrcDdThljuGvEXVw19yqe//55fjn8lwB0ig7n2P5pLNtyAqd99x/MWfcFfrmRVu2wRpBba78EvjTG/JbAWGQREQmCOhca9fzxkUcafKD4j3/6ExH9+jVfkUFirQ0E69qz9NRMmVmJrazEX1mJraz1vaISW1lR67MCW16Bv7ICW16Ov7wiEMzLK/DlF+DZug1/WVnNgtd76MIcDpxxcTji4nB0iMMZ1wFnhw44O3XE2bEjjo6BT2enTjg7dSIsIQFnfDzOTp0Cd9WlVRvadSjnpJ7Di6tf5OIBF9MjpgcAk4f0JHP9qYz2PwmbF0Ha+BBXKkfrUA/k3Q08Y60tqN1uA2MxPjXGjAeirbUNv4BcRESaXFt/oNgYUzMdJECwY6W1NhDCS0vxl5bhLy0JrJcEPn3FxfiLS/CVFOPfW4yveG/VZzHurM34CovwFRYG7ng3/AcFQnNCQiAwd+1CWNeuhHXpSliXLoRVf+/ePRCkdXe6xbr9xNuZnzOfJ796kofHPAzAxGN7cN/sEyh3xhG16t8Kx23Aoe4cfwe8a4ypAL4CdgORQH9gGDAP+GNQKxQRkf2kp6e3mTAcasaYmreaEh9/RMew1mIrKvAVFtYs3oICfAV78O0pwLtnD76CPXjz86j8YQ2l+Uvwl5TsX0t4OGHdu+Pq3j3w2bMHYb16EZ6YiCsxEVevXjiio4/2T5Yj1Cu2F9cMvobnvnuOKwddyZCuQ+gY7WJU/0Q+yDmFC9a8i6kshoi4UJcqR6FRb8gzxvQHTgN6AuXAGuBTa215UxajB/JERKS98JeX483Px7t7N97cXXh35eLJzcW7MxdvbvX6zv3uSDvj43ElJhKenIwrJZnwlBTCk1MIT03BGR+vO89BVuIuYfLsyaR0SOHFSS9ijOHNL7eS+cZ/eCviPrhgBgy7MtRlyiEc9RvyrLXrgfVNWpWIiEg75oiKIrx3b8J79z5gH+v3483Lw7NtG55t2wOf27fj2bKF8u++Y+8HHwQe2Kw+Zlwc4X37EJHWj4i0NCL6pRGe1g9Xr556E2gTiQ2P5dbht3L/svv5KPsjzk49m7OO7c7vHQMpiEgkYdWrCsetXKPuHDcX3TkWERFpPOt24962DU9ODu7sbNxZWVRu2kzlxg34dufV9DNRUUQM6E/koGOIPGYQkYMGETFggIZoHCGf38cl711CmaeM/17wXyKcEdz44gpG5TzHjb7XMLd/B52SQl2mHMRR3zk+wpNGEnjFdETVed6w1t4brPOJiIi0NyY8nIg+fYjo02e/bb7CQio3baJyw4bA8uM69n7wAYWvvVa1syE8NZXIY48laujxRB1/PBHHHhsYey0H5XQ4ufOkO7np45t4Zc0rXHfcdUw+viePrx3F1Ih/w3evw5jfhLpMOUKNHXN8mrV26aHa6m03QIy1tsQY4wKWALdZaz8/0D66cywiIhI81lq8O3ZQsXYtFWvWBJbvvsebW/VeL5eLyEGDiDr+eKKGDyd6xAhc3buFtugW7Jb5t/BV7le8d+F7hJsOnPDgx8yPf5TkyDK45QvQ+O8W66jekFd1gK+stSccqu0g+0cTCMc/t9YuP1A/hWMREZHm58nNpXzVKiq+/ZbyVd9Svno1tmoebVdKMtEjRhAzYkQgLPfqFeJqW45NhZv46Ts/5bKBl/H7Ub/nmue/4Lidb3OX++8w9RNIPDHUJcoBHPGwCmPMKcCpQFdjzB21NnWgEVNPGmOcwJdAP+DvBwvGIiIiEhqu7t1xTZxIh4kTAbBeLxVrf6RsxQrKVqyg+ON5FL3xZqBvUhKxY0YTM3oMMaNG4oiJCWXpIdW3U1+m9JvCG+ve4LrjrmPCsd3507rh3BkTgWPVvxWOW6mD3jk2xpwOjANuBv5Ra1Mx8G7VLBaHPokxnYDZwK3W2u/rbZsGTANITk4+MTs7+3DqFxERkSCzfj+V69ZR9sUKSj//nNLPPw/cWXa5iD7xxEBYHjOGiP79291UcttKtnHeW+dx0YCLuPGYOzn54fl8nPQv+pd+CXeshTCN4W6JmmJYRYq19qhSqzHmHqDMWvuXA/XRsAoREZGWz+92U/7VV5QsXkzp4iVUrlsHBIZgdJgwgbiJE4kcMqTdBOUHlj3A7A2zmXvhXG7+10ZOdK/knr33wuWvwqBzQ12eNKApwvEA4E4glVpDMay1B3xHojGmK+Cx1hYaY6KAj4BHD/aqaYVjERGR1sezcyclCxdR/PHHlC5fDl4vYT16EDdhAnETziL6pJPa9DzLO0p2MHn2ZKb0m0Ln8it54qM1rEv4Nc6UU+Gyl0NdnjSgKaZy+w+BYRXPAb5G7tMTeLFq3LEDeP1gwVhERERaJ1ePHsRffhnxl1+Gr6iI4gULKP54HoWvv86el18mrGdPOp5/Ph2nnE9E376hLrfJ9YztyUX9L+KNdW/w5OjL8eFkXddJHLPudSgvhKhOoS5RDkNj7xx/aa0N+qhy3TkWERFpO/ylpRQvWEjRO/+ldMlS8PuJPP54Ok45nw7nnktYfHyoS2wyuaW5nPvWuUzuO5lFn53BGbFbuS/3l3qddAt1sDvHjf2N411jzC+MMT2NMQnVSxPWKCIiIm2MIyaGjudNJnnmTPovWki33/0O63aT++BDrB97Otvu+A1lX35JS3pb75HqHtOdSwdeyjsb3+HkAZZXtnbG3zEZVs8OdWlymBobjn8G/Bb4jMDUbF8CusUrIiIijRLWtSudr7uWvm/Pps/bs0m48gpKliwhO/0qNl9wIXtef53/e+EFUlNTcTgcpKamkpmZGeqyD8v1x11PmCOMPeFzcfssm7tPhI2fQFlBqEuTw9CocGyt7dPA0vYGDYmIiEjQRQ4aRPff/57+CxfQ44H7wRieu+12pt54I9nZ2Vhryc7OZtq0aa0qIHeN7splAy9j+e6P6dShkNnuEeD3wto5oS5NDkOjwrExJtoYc7cxZmbV9/7GmPOCW5qIiIi0ZY7oaOIvvZQ+s9/iKSwVfn+d7WVlZUyfPj1E1R2Z6467jghnBN2SP+WlrE7Y+FQNrWhlGjus4gXATeBteQDbgIeCUpGIiIi0K8YYtubmNrgtJzub8u++b3BbS9QlqguXD7qcnb5llPh3sK3XJNi0UEMrWpHGhuM0a+2fAA+AtbYMaB8ze4uIiEjQJScnN9jeMzycrEsuIeeGGyn76utmrurIXDf4OkqXl7Ll6VtJuvTPpD5RSObjvw91WdJIjQ3H7qoXeVgAY0waUBm0qkRERKRdycjIIDo6uk5bdHQ0j8yYQdff3EHF2rVkX3klW375Syo3bQpRlY0z9825bHlhC949pYAlu8gy7cFZrWr8dHvW2HmOJwB3A8cSeNPdacC11tqFTVmM5jkWERFpvzIzM5k+fTo5OTkkJyeTkZFBeno6AP6yMgpeeon8Z5/DX1FBp4suossvb8HVrVuIq95famoq2dnZ+7WnJPUmK2dLCCqS+o7q9dHGGAdwMTAfOJnAcIrPrbV5TV2owrGIiIgcjLeggLxnZrDn3//GuFx0vu5aEq6/AWdsTKhLq+FwOBqcu9kY8Ptb/5zObcFRvQTEWusH7rLW5ltr51hr3wtGMBYRERE5lLCEBHrcPZ20uXOIO2Mcec/MYOM5k9g7d26LeZnIgcZPJydENXMlciQaO+Z4njHmTmNMkt6QJyIiIqEWnpxM4uOPk/r6a7i6dWfbHb9hy41TcefkhLq0BsdPR0WEkXE6ULI7NEVJozU2HF8G3AJ8it6QJyIiIi1E1PHHk/r6a3S/+27Kv/mGTT85n7wZM/C73SGrKT09nZkzZ5KSkoIxBldnFxN/fj7pQ1yw5p2Q1SWN09gxx5dYa18LdjEacywiIiJHypO7i9xHHqb4/Q8I79uXHvfdS8zIkSGtyVrL8Fnn4grzsrykEEdsd7j2vZDWJE0z5vi3TV6ViIiISBNyde9G7yeeIOnZmViPh5xrfkbuw4/grwzd7LPGGE7s9FMqzE7mp54E2UuhuOEXnkjLoDHHIiIi0qbEjhlD33f+S3x6OgUvvkjWxZdQ8eOPIavnyuPOw++O55my7WD9GlrRwmnMsYiIiLQ5jqgoevzhbpJm/hPvnj1kXXwJ+S/8C+v3N3stp6V1w184lg3lm/im+wBYPbvZa5DGa1Q4ttb2aWDpG+ziRERERI5G7Nix9H3nv8SMHcuuRx8l5/ob8Ozc2aw1RLqcjOhyNsYfzQudu0L2Zxpa0YI1KhwbY65paAl2cSIiIiJHKywhgd5PP0XPhx6k/Ntv2TTlAkoWL27WGs4cmERF/sksKN/KZpcT1r3frOeXxmvssIoRtZYxwH3A+UGqSURERKRJGWPodPHF9J39Fq6ePdky7Sby/jmz2V4ccsbAbnj2nIrDuHipay9YO6dZziuHr7HDKm6ttUwFTgBig1uaiIiISNMKT0kh9dVX6HDuuex+4gm23XY7/tLSoJ83uXM0feK708k3ijkRTvZu/hQqS4J+Xjl8jb1zXF8p0KcpCxERERFpDo6oKHr95c90+93vKJ43j6zLL8edlRX0854+sCs7tw6nHB/vRLtg4/ygn1MOX2PHHL9rjHmnankP+BHQo5YiIiLSKhlj6HzdtSQ/Pwvv7jw2X3IpJYsWBfWcZwzsRkVpL1JjjuG1jh2xa/QykJaosXeO/wI8VrU8DIy11v5P0KoSERERaQYxJ59MnzffwJXUmy03/5yCl14O2rlG9kkgyuWkiz2DrDAHy7Png88TtPPJkTloODbG9DPGnGatXVRrWQqkGGPSmqlGERERkaBxJSaS+sorxJ11Frl//CO7Hns8KA/qRbqcnJrWmQ2b04gPi+bfkQZyljX5eeToHOrO8V+BvQ20763aJiIiItLqOSIjSfzrE3S6/DLyn32WHf87Hetp+ru64wZ2ZUuBh/G9z2dBdBQ7V7/R5OeQo3OocNzdWvtd/caqttSgVCQiIiISAsbppMe999Ll1l9SNHs2W395K/7y8iY9x7iB3QDo6B+PNYY3ti6AZppOThrnUOG400G2RTVlISIiIiKhZoyh6y230OO++yhZvJica6/Du2dPkx0/KSGatK4xfLXJwdi4NN5w+fDs+KbJji9H71DheKUxZmr9RmPMjcCXwSlJREREJLTiL7+MxCf/SsWaNWSnX4Vnx44mO/a4gd1YvrmAC465jvwwJ/O//meTHVuO3qHC8e3AdcaYhcaYx6qWRcANwG3BL09EREQkNDpMmEDyrOfw7tpF9s+uxZOb2yTHPWNgN9xeP9aOpLd18mru501yXGkaBw3H1tpca+2pwP1AVtVyv7X2FGvtzuCXJyIiIhI60SNGkPzcs/jy88n52bV4d+8+6mOO6BNPdLiTT9flc1mXE/nK6WNdzqdNUK00hca+PnqBtfapquWTYBclIiIi0lJEDRtG0sx/4tm1i+zrrsObn39Ux4sIc3JqWhcW/LiLKSfcQoTfz+tfz2iiauVoHenro0VERETajegTTyTpHzPwbN1GznXXH/VDeuMGdmXrnnLywwYwyefi3cLVlLhLmqhaORoKxyIiIiKNEDNyJEkznsGdnU3O9TfgKyw84mONG9gVgEXrdnN5r7GUYXl37WtNVaocBYVjERERkUaKOeUUej/9NO4NG8i5cSq+vQ29K+3QesdH06dLDEs35HHckKsZXFnJ62syg/JmPjk8CsciIiIihyF2zGgSn/obFT/+yNZf3ILf7T6i44zu14XPN+Xj7jGcn7odbKjYzQ8FPzRxtXK4FI5FREREDlPcuHH0evhhylauZMf0u4/oju/o/l0oc/v4eksRk5LGE2Etb697MwjVyuFQOBYRERE5Ah3Pm0zX229n77vvkvfUU4e9/8l9O+MwsHRDHh2OmcL40jLmbppDpa8yCNVKYykci4iIiByhzjdNo+PFF5H3zAwK35p9WPt2jHIxNKkTizfkQeoYLihzs9dbxoItC4JUrTSGwrGIiIjIETLG0PPee4k59RR23HMPpcuWHdb+Y/p1YdWWQvb6XYzqPoIefnh7w9tBqlYaQ+FYRERE5CgYl4vEJ58kok8ftt76KyrWrWv0vqf164LfwrKN+TgHnM35RUUs27aM3NKmeVW1HD6FYxEREZGj5IyLI+mf/8BERbLl5pvx7NrVqP2GJwdeJb1kfR70n8AFJaX48fPupneDXLEciMKxiIiISBNw9epF0j/+ga+wiK233optxBRv4WEOTu7bmaUb8qBzGkkdUjiRKN7e8LbmPA4RhWMRERGRJhI1eDC9Hn6YilXfkvvonxq1z2n9urApr5RtheWBu8f5uWTvzeab3d8EuVppiMKxiIiISBPqcPZEEn72M/ZkZlI0Z84h+4/p3wWAJet3Q/8JTCwuIsoRrgfzQkThWERERKSJdbvzN0SdcAI7/nAPlRs2HLRv/26xdIuLYMmGfEgZTbQzkomurnyw+QPKPGXNVLFUUzgWERERaWLG5SLxicdxREWx9bbb8ZeWHrivMYzu14WlG/LwOyOg7+lckLedMm8Z83LmNWPVAgrHIiIiIkHh6t6dxMf+gnvzZnb84Z6DPmA3un8XCkrd/LBjL/Q7ixPzskmK7qGhFSGgcCwiIiISJDEnn0zXX/2KvXPnsifzlQP2O61fYNzx0g2BKd0MMCUqmRU7V7CleEszVSugcCwiIiISVJ2nTSV23DhyH32U8m8anoGie4dIBnSPZcmGPIhPhS4DmZKfi8HwzsZ3mrfgdk7hWERERCSIjMNBr0cfwdWtG1vvuANfcXGD/Ub368oXmwuo8Pig/wR65Czn5O4jeHfju5rzuBkpHIuIiIgEmbNjRxIf+wvenbnkPvxIg31G9+9MpdfPl9l7oP8E8LmZHJvKtpJtrNq9qpkrbr8UjkVERESaQdSwYXS+8UaK3nqL4k8W7Ld9VJ/OuJyGxevzIPkUCI/lzIJdhDvC+SDrgxBU3D4pHIuIiIg0ky6/vIWIgQPZcc89ePfsqbMtJiKM4cnxgYfywiKg7zhiNy5gbO8xfJj1IT6/LzRFtzMKxyIiIiLNxBEeTq8/PYqvqIid992/31ji0f268P32IvaUugNDK4q2cE7CUPLK81iRuyJEVbcvCsciIiIizShy4EC63norxR9+yN736r5eenT/LlgLSzfmQb8JAIzdu4cYVwzvb34/FOW2OwrHIiIiIs2s8w3XEzVsGDsffBBPbm5N+/GJHYmNCOPzTfnQMRG6DSZy4yeMTxrPx9kf4/a5Q1h1+6BwLCIiItLMjNNJr0cexno8zLjsclJSUnA4HPRL60vCjuUs25gf6Nh/AuQsY1LiWIrdxSzdtjS0hbcDCsciIiIiIRCemsqnp5zM7z5dRE5ODtZasrOzWf7Sw6xa+B679lZAv7PA7+WUCg+dIjrxfpaGVgSbwrGIiIhIiGTMmUNFvYfy3JUVFH76Ess25UPSSHBF48r6lAkpE1i4ZSFlnrIQVds+KByLiIiIhMiWLVsabPftzQuMOw6LgJTTYOMnnNPnHMq95SzauqiZq2xfFI5FREREQiQ5ObnB9pjO3feNO047A/I3cGJEN7pFd2Pu5rnNWGH7o3AsIiIiEiIZGRlER0fXaYuOjubyX9xFVn4ZO4rKoe8ZADg2L2JS6iSWbFtCUWVRKMptFxSORUREREIkPT2dmTNnkpKSgjGGnmFhPHbRRfz65usBAkMruh0DsT1g4wLO7XMuXr+XT3I+CXHlbZfCsYiIiEgIpaenk5WVhd/vZ8VttzPu629IqyigY5QrMLTCGOg7DjYv4tj4QSTFJWloRRApHIuIiIi0EN3u+i0mIoJdDz3IqNT4wIwVAGnjoSwfk/sd5/Q5hy92fkFeeV5oi22jFI5FREREWghXt250ve02Sj9bxuQ9P7CloJyte8oCd44BNgWGVvitnw+zPgxlqW1W0MKxMSbJGLPAGPODMWa1Mea2YJ1LREREpK2Iv/IKIo49hgH/eY5oT0VgaEVcd+g2GDYuIK1TGgPiB/D+Zr0QJBiCeefYC/zGWnsscDJwizHm2CCeT0RERKTVM04nPe+9FwryuWHDPD7fVBDYkHYG5CwDdxmTUiexavcqdpbuDG2xbVDQwrG1doe19quq9WJgDZAYrPOJiIiItBVRQ4fS6dJLmbTuU3K+/A5rbWBKN58bcj7jrJSzADRrRRA0y5hjY0wqMBxY3sC2acaYlcaYlbt3726OckRERERavK6334Y/MorJn7/FloJySDkVnOGwcQF9OvYhrWMa83Pmh7rMNifo4dgYEwu8Cdxurd1bf7u1dqa19iRr7Uldu3YNdjkiIiIirUJYfDzh11zLqNw1fDtnPoRHQ/LJsGkhAGemnMnK3JXsqdgT2kLbmKCGY2OMi0AwzrTWvhXMc4mIiIi0Nf1uvoH86E7E/esf+4ZW5H4PJbs4K/ks/NbPwi0LQ11mmxLM2SoMMAtYY619PFjnEREREWmrnFFRfD3hMrpt38Te998PPJQHsGkhgxIGkRibyLyceaEtso0J5p3j04CrgfHGmG+qlnODeD4RERGRNid+yvls7tCTHX95HJtwDEQlwMYFGGM4M/lMlm1fRom7JNRlthnBnK1iibXWWGuPt9YOq1r0rkMRERGRw3BK/27MGjwZu30be17/D/Q9HTZ+AtZyVspZePweFm9bHOoy2wy9IU9ERESkBevbJYYtacezpc9g8p55Bl+PU6BkJ+xey9CuQ+kS1YV52Rpa0VQUjkVERERaMGMMp/Trwj8HTcZXWEj+kqoXf2xcgMM4GJ80nsXbFlPhrQhtoW2EwrGIiIhIC3dy3858GdENc9bZFPx7Np6IvrBpARCY0q3cW86y7ctCXGXboHAsIiIi0sKd0rczAKvOvgL8fnav6QJZS8HrZkSPEcSFx2nWiiaicCwiIiLSwqV0jqZ7hwiWloQTn55O0cptVOZVwtYVuBwuzkg6g4VbFuLxe0JdaquncCwiIiLSwhljGNmnM19sLqDztGk4IqPIWx0HWYFZKs5MPpO97r2s3LkyxJW2fgrHIiIiIq3AyD4J7NxbwXYbQXx6OntzoqhcGRhKcWqvU4kKi2J+zvwQV9n6KZTU8EoAACAASURBVByLiIiItAKj+iQAsHxzPgnXXYsJd5I3byO4y4gMi2R04mjm58zHb/0hrrR1UzgWERERaQX6dY0lPtrFF5sLCEtIIP7c09mbHYF7+bsAnJV8FnnleazavSrElbZuCsciIiIirYDDYRiRmsAXWQUAdL71txgH5M16EYCxvcficrj0QpCjpHAsIiIi0kqM7JNAdn4ZO4sqCEtMpdPQOIqWb8a9dSux4bGc3PNk5ufMx1ob6lJbrXYdjjMzM0lNTcXhcJCamkpmZmaoSxIRERE5oFF9AvMd19w9vngCxljyZ/wdgPHJ49lWso31hetDVmNr127DcWZmJtOmTSM7OxtrLdnZ2UydOo3nn3uR8hI3FaUe3BVePG4fPq8fv9/qKkxERERC6pieccRGhPHF5nwAXMPOpmPfMgr/+y6e7ds5vffpACzasiiUZbZqYaEuIFSmT59OWVlZnbby8jLu/PVdlK9MOuB+DofBOAzGaXAYAp9VbTXbHPXb9u3nOGh/6m5zNnSs/fs22MdpMIZa6/U+HYHtjkOco2a91n77zttwDcYE5mMUERGRphXmdHBiSjxfbA7cOSZpJF2Oc1O42U/+c7Pocc8fGNx5MIu2LmLq8VNDW2wr1W7DcU5OToPthaW7GXPZAKzf4vcF7hb7/Tbw3W+xvnrf/VS1++t+r9PH4veD9furPi0+t39fP1t1rlr9q49T51jVfWygjpZ8I9tUXziY/S8ADnwhQVW43hfk97tgaOBYxtT7XvuYpoH2ehcNdY5nagX8ehcC+85T6+KiTk2199//4mK/v73231e/TmPAQeBTFxtST2ZmJtOnTycnJ4fk5GQyMjJIT08PdVki0kxG9kngzx/+SEGpm4SYqP9v787jo7ruu49/zmwajfYNIQmkEavBbAYhCQQGG+9LnKRps9DaaZLHaZo2TRM3SeOmaZuSp3kad2+SulmchbjxkthO6iU22GxmE5sNxiwGSewCJKF9mZn7/HFnRhotZjHSMNL3/XrpdWfOPffe39UV4nevfnMO7uvKyDx8nKYnnyTn0w+ybMIyvrvnu5zvOE9Ock68w004YzY5Li4upra2dtD2OTdNiENEl8+yYpP02GTciknw+ybt/ZN9+waAgYl/dLvQwP0H+yb1DLyJGORGIWZ7q8/xrD43HRaD3FhYWIE+x4ue98C+Md8PKzaWyI0F1/BNxVAiyXr/JBvTN7k2ffr17WMn7pjem4NIW8w++yf2xgw87lDr+r8erL+jN9GPxBPtO8T+Mf3Oqd+x6H+syI1EpA/h9/3WR/YBsdtjGPQcgCH79H3f90YmEnPf14Nuf5kiJWGRv3zV1tby4IMPAihBFhkjIuMdb69p4Pbrx0PpjeT4v0XT/gLO/+AHLPv0+/nOnu+w8cRG7ptyX5yjTTxjNjletWpVzH8wAD6fj1WrVsUxqstjTLi8wxnvSBJLJLkecMNg9SbV/W86ehPtId5Hnuj3S/Cjx7B6k3fL6nPTYEH/G4rIvrEG79u3X/RGo+8xYm4wiNlP5HXvORPzV4mYffT7flh9jxN93a8tfPMRiRkL+4Yksp8EvDEZdoMk1zE3Bn3XYfjy9784oCSsvb2dP/vjLxJ8q3SIpL1/cj7EccKfQhl4Y9A/lsFvWhhwozXEjVTfv5T02ybmpm3Qv7jEvu9fita37MwuBXP0eW2iryNfxmFwOh29bS67v8i1bPaEDJJcDrYe6U2OPamryLhxHk2/eIKpn/oU45LHse74OiXHV2DMJseRJyz60+TYE7mpwAm6rxh5/ZPp/ok2fduHSNaJJvsMmpj3baf/viAmWR+8X6Q9sq++6+nTPsi2FoPGNbBv+HwZZF0kzkH22fBP9YN+Xxua6ymckomFHT+WvY/o9zT6ve97HeyD9L0G0ZupYORmyuoX68Abp2h7/5vMQa5h35unAdf+WmHoTZhd4eQ5unTgdBkcTnvpdDlwuh32MvzaFWlzO3C5+7S7Hbg8TlweBy53n2WSA7fHicvjxJ3kxOV2RP+yITKYJJeTG4oz2VZjfyiPwvngTiG3Mo0L63po/OnPWLpoKS8cfYGeYA9upzu+ASeYMZscg50gKxkWGVl2uYP+479SxX85RElYSTG3/OHMOER09URLn0L0/iVmkL/AvGspWd9ysXC5VvTzGn3e97aFCAYjbaHoulAwRDBgvw4GQ4QCkffhZSBEMBAi0B2kqz1AMBAi2GO3BQMhAj32+1DwypJ+l8eBO8lOlt1eF54kJ26v/d7jddlfyU48yZHXLpKSXXh8LpJ8Lrw+N55kJw7nmB2UatQrL83hP9Yeormzh3SvB4or8TTvJG3FChqfeILl9/4tTx96muoz1SwqXBTvcBPKmE6ORUQSzWgoCRuKcRicjK6/6oRClp0wd4cI9AQJ9F1228ue7t73PV3B6DLy1d1pLztbe2g530l3Z5DujgA9XcGLHt/tdeL1ufGmuvGmuPCmevCm2K+T0zzhLzfJaR58aR6SfC49tU4QlaXZ/JsFO2obuWn6OCi9EV75OtkfXkXLyy8zY9tpkpxJrDu+TsnxZVJyLCKSQFQSllgcDoPD48TtcQJX90/boZBlJ88dAbo7AnR1BOhuD9DV3kNne7itLUBnW0/0q/lcM51tPXS1B4aMNznNjS8jiZTMJFIyPPbrDA8pmUmkZnlJy07Ck+zSKDpxdkNxFi6HYdvRht7kGEhOb8Q7ezatP32ciofKee3Ya3x54Zd1vS6DkmMRkQSjkjABO5FNCpdTXK5QMERnW4COlm7aW7rpaOmmo7nHft/cTduFbloaOjlz9AIdLT0DtncnOUnN9pKWlURqtpf0XC/puclk5CWTnpuMN0U1rsMt2eNkzoSM3vGOC+ZCUgamZgPZDzzAyYce4p4zVXwpuIEjF44wOXNyfANOIEqORURExhiH04Ev3YMv3cPFRsENBkJ2wtzURUtDJ62NXbQ2dtLaYC/r61robI1NoJN8LtJzk8kcl0xmvo/M8T6y8lPIzPfhThotRTPxV16aww82HqGjO0iyxwn+KqjZQPpnHqH+2+OZ8uJ+uBXWHV+n5PgyKDkWERGRITldDtKyvaRlexk/KWPQPt2dAZrPddJ8rsP+OtvBhbMdnKlp5tCO+pjx5VOzksguTCGnMJWcohRyJqSSlZ+C060PD16uitJsvrfuHXYda2Tx5Fy7tOLA85i202T//krqv/0Iy5dMZt2xdXxi1ifiHW7CUHIsIiIi74nH6yJ3Qiq5E1IHrAt0B7lwtoPG0+00nWmn8Uwb50+0cfzAMUIBO2s2DkNmvo+84lTGFaczriSN3OK0cK22TTNDDrTAn4UxsO1og50c+5faK2o2kPm7v8vZ//wO79/p5gu+3TR1NpHpzYxvwAlCybGIiIgMG5fHSU5RKjlFsYlzMBii6Uw7DSfaOH+ilfMnWjn+diMHt54B7MlqsgtTyCtJp/rwGv7221+io6MD0MyQEeleNzML0nvrjsfNBF8OHN2Ac97HyPzABwg98QvS58KGExu4d/K98Q04QSg5FhERkRHndDrs0orCVKYuzI+2tzV1UV/bTH1tC/W1LdS8cY5v/dc3oolxRHt7O1/96lfHdHIMUF6azePb6ugOhPC4HPbT46PrwbLIvv8PaHz8cd6/x8f6WeuVHF8iFfiIiIjINSMlM4nSuXlUvG8S9/7pXD7xj0toajs7aN+6umP8+t93s/uVOs6faLVnYRxjKkqz6ewJ8eaJJruhdCk0H4eGI3j8flJvuokVO3vYVrORntDAkUdkID05FhERkWuWMYbi4sFnhszPLaD5XCebnjoMgC/dw4QZWRTPzKFkVs6YGFJuoT8bgO01jSwoye6tO67dBDmTyX7gAVrXruWGXQF23baL8oLyOEabGJQci4iIyDVtqJkhH/mX/8fKlZW0NHRybH8Dx99u5NhbDRzcegbjMBROzaB0bh6lc3NJz0mO4xkMn5zUJCblpbD9aAN/tGwy5E6DlDyo2Qjz78dXvhDPjOncU32Q1469quT4Eig5FhERkWvaxWaGTMv2MrOqkJlVhVghi/raFo7uOcuRPefY+MQhNj5xiJwJqUyal8e0hflk5vvieTpX3cKSbF7cd5pQyMLhMFBSBTWbwLIwxpD78T+k+8tf4YW1L0H5l+Md7jXPXEv1OWVlZVZ1dXW8wxAREZFRoulMO0f3nOPoG2c59c4FsGBcSRrTysczdWE+vnRPvEN8z57acZyHntzDS5+/kenj02Dbf8PzD8Gf7YEsP1Z3N28uq+LN7Daqfv48/gx/vEOOO2PMDsuyygZbpw/kiYiIyKiVme/jhtuK+eBDC3jgm1Us/p0phEIWG588xGNf3shz/7abA1tOEegOxjvUK7bQnwXA9prwkG7+JfayZiMAxuPB94H3Me8di+27/zceISYUJcciIiIyJqRmJXHDrcV8+OFyPvrXFcy/vYSmM+288th+HvvKJjY+eYjG023xDvOyFWf7GJeW1Jsc511nj3dcsynap2TlJ8BA+69+HacoE4dqjkVERGTMyS5MofL9k6m4bxInDjaxb/0J3nz1OHvWHKNoehazbiyidF4uTue1/xzRGMNCfzbVNY2RBihZDLUbo33cRUXUzyli6sY6Ojtb8XoHzmYotmv/iouIiIgME2MME6Zncfv/mcX9/3cxFfdN4sLZdl7677385Kuvs+PFGro6AvEO86IW+rM40dTBiabwZCn+pdBUZ3+FJX/wfWS1Wrz53GPxCTJBKDkWERERAVIykii7088f/P1i7v7sHHKKUtnyzBF+8peb2Pyrw7Rd6Ip3iENaWGqPd1wdKa0oqbKXfUorZr/v4zSkGdqfemakw0soSo5FRERE+nA4DP7Zubzvc/P4va8upHhWDrt+W8dPH97Ma6vfpqm+/eI7GWHXjU8nLcnFtqPh5HjcTEjOiimtSElO52DVRHLfPEH38RNxivTap+RYREREZAh5xWnc/qlZfOxvK7lu0Xj2bz7Fz7++hTU/fouWhs54hxfldBjml2T1fijP4QiPd7wxpl/y++8B4PjqH450iAlDybGIiIjIRWSO87F85XXcv2oxc26eyMHtZ1j911vY9PRhOtt64h0eYNcdHzzTSlN7t91QUgWNNXCh9ylx+by72TXJ0PrMc1g910bc1xolxyIiIiKXKCUjiSW/O5WVf1vJ1LJx7H6ljp/+1WZ2vFhDT5zHSl7oj9Qdh0et8Ifrjmt7645LM0rZsSgHd2MrLa++OtIhJgQlxyIiIiKXKT0nmRUfn8lH/qqcwikZbHnmCKu/tpkDW04Rr9mH507MxO00bK8Nl1bkzwJvBtRsiPYxxpC1/GYa0g0Nv/hFXOK81ik5FhEREblCOUWp3P3ZuXzgi/NJyfLyymP7efafd8VlMhGv28mcCZlsj3woz+GE4sUxI1YAVE28kVfmGDo2vU73sWMjHue1TsmxiIiIyHtUODWTD31pActXTufc8Vb+5xvb2PLsOyM+LXWZP4s3T1ygsyd8XH8VNLwDzaeifcoLylk/z41lDE1PPjWi8SUCJcciIiIiV4FxGK5fWsTH/qaSqQvz2fFCLY//3VZq954fsRjK/dn0BC12H2uyG/xL7GWfuuM0TxoTp8zjwIxUmn75S30wrx8lxyIiIiJXkS/dwy0fn8l9f34DTpeD3/zHHn77g310tQ9/ErqgJAvoMxnI+DmQlD5gSLclRUv41fVtBM+do2XN2mGPK5EoORYREREZBhOmZ/Hhvyqn/N5S3tlRz/98YxsnDjYO6zEzfR6m56exLTJihcMJxZUDkuOqwip2TzL05GXS9MQTwxpTolFyLCIiIjJMnC4HC+8u5YNfWoDT7eCZf97F5l8dJhgIDdsxy/xZ7KxtJBgKj5rhXwLnD0HLmWif6dnTyfblsq88j7bNm+k5c2aIvY09So5FREREhlm+P50PP1zOzCWF7Hypjqe+VU3DqeEZ0aK8NJvWrgD7TzXbDSUD644dxkFVURVPTToHlkXzb34zLLEkIiXHIiIiIiPAneTkppXXcddnZtPa2MUT39zO3vUnrvq4yGXRyUDCdccFc8GTOmjd8cHUFkKzpnHhmWfiNj7ztUbJsYiIiMgIKp2bx0e+Vk7RtEzW/fwAr/3sbYI9V6/MoigzmaLMZLZH6o6dLrvuuDZ2vONFBYswGA5XFNF16DBd+/dftRgSmZJjERERkRGWkpHEPZ+dy4I7S3hr0yme+eed/PD7P8bv9+NwOPD7/axevfqK91/mz2JbTUPv0+CSKjj7NrSejfbJ9GYyO3c2z/nPY9xuLjz77Hs9rVFBybGIiIhIHBiHofK+ydz+f2bxv2uf4TOf+TS1tbVYlkVtbS0PPvjgFSfIC/3ZnG3poq6h3W6IjHdc93pMv6qiKqo73iZp2RIu/OZ/NeYxSo5FRERE4mrKgnG8vPcndAe6Ytrb29t5+OGHr2ifC8N1x9siU0kXzAO3b8BU0osLFxOyQhyrmkzw/HlaN23qv6sxR8mxiIiISJydOHl80Pa6uror2t/UcalkJLupjtQduzwwYSHUxj45npU7izR3Gq8VNeHMylJpBUqORUREROKuuLh40PaJEyde0f4cDkNZSRbbIyNWgF1acWYvdPROROJyuFg4fiGvn91O+t130bpmLcHm5is65mih5FhEREQkzlatWoXP54tpc7uS+NCyT9PdGbiifS4szebIuTbOtYbLNUoWAxbUbYnpt6hwESdaT9BxSyVWdzfNL754RccbLZQci4iIiMTZypUrefTRRykpKcEYQ0lJCasefoTJqYt47l9309l6+R+UW+jPAugtrSgqA6dn4JBuhYsA2JZxDs/kyVx47rn3djIJTsmxiIiIyDVg5cqV1NTUEAqFqKmp4S/+5rPc8eAszh1r5Zff3kFrY+dl7W9WUQZJLkdvaYXbayfI/T6UV5xWTGFKIZtPbyHjfe+jo3oH3ceOXa3TSjhKjkVERESuUZPm5XHv5+bS2tTF0/+4g8bTlz7ldJLLydyJmb0z5YFdWnFqD3S1RJuMMSwqXMS2U9tIuftOMGZMPz1WciwiIiJyDSualsUHvjCfYE+IXz2yk4ZTl54gl/uz2XuymbaucN2yvwqsIBzbGtOvsrCSlp4WDiY14Kuo4MKzz43Z6aSVHIuIiIhc4/KK0/jgQwvAGJ77l11cONtxSduV+bMIhix2H2uyGyaUg3EOGNKtcnwlBsPmk5vJuO8+eurq6Ni1+2qfRkJQciwiIiKSADLzfdz3Z/MIBEI896+7LqkGeX5JFsb0mQwkKRUKbxhQd5zpzWRGzgw2n9xM2q23YpKTx+yYx0qORURERBJETlEq7/vcPDpae3j2X3bT3tz9rv3TvW5mjE+nurZf3fGJHdAT+/R5UcEi3jj7Bp1JkHbrLTS/8AJW97vvfzRSciwiIiKSQMaVpHPPn8yltaHTHuat7d2HeVvoz2JnbRM9wZDd4F8CoR44vj2m36LCRQSsANWnq0m/6y5Czc20bd48XKdxzRq25NgY80NjTL0xZu9wHUNERERkLCqcksldn5lD45k2fv3ve951opCFpdl09AR562R45ruJFYAZUHd8w7gb8Dq9bD61mdTFi3Gkp9P8/AvDeBbXpuF8cvwYcMcw7l9ERERkzJo4M5vbPzWLs3Ut/O9/vkGgJzhov4X+bIDe8Y6TM2H8bKjZGNPP4/SwIH8Bm09uxng8pN1yCy1r1hAaY6UVw5YcW5a1Hmi4aEcRERERuSKT5uVxy8dncPJQE6/+7O1Bh1/LT/dSnO3rTY4BSqrssopAbOK7qHARRy4c4XTbadLvvINQayttGzcxlqjmWERERCSBTSsfT8X7Sjm49Qw7X6odtE+ZP4vqmsbe5NlfBYFOOLkzpl9lQSUAW05tIaWyEmdGBs0vjK3Sirgnx8aYB40x1caY6rNnz8Y7HBEREZGEs+BOP1MX5rPlmSO8s6t+wPpyfzbn27o5ci48gUjxYntZG/tUeFrWNHK8OXZphdtN2m230rpmDaHOy5u6OpHFPTm2LOtRy7LKLMsqy8vLi3c4IiIiIgnHGMPN919Hfmk6r/zoLc7WtcSsL4vUHUfGO07JgbwZA8Y7NsZQWVjJllNbCFkh0u64g1B7O60bNozIeVwL4p4ci4iIiMh753I7ufOPZuNNcfP8d9+g7UJXdN3kvBSyUzxsr2ns3aBksT2NdDB2pItFBYto6GzgUOMhUioqcGZl0fLCiyN1GnE3nEO5PQ5sBqYbY44bYz45XMcSEREREUjJSOLuz86hsz3A8999k0C3PYKFMYaykqzYD+X5q6C7FU6/EbOPSN3x5pObMS4XabfdRstrrxHquLQpqxPdcI5W8VHLsgosy3JbljXBsqwfDNexRERERMSWOyGNW/9wJvW1zaz9yf7oh/DKS7Opa2jnTHO4frikyl72qzvOT8lncsZkNp+yJwBJv/MOrPZ2WtetH7FziCeVVYiIiIiMMpPm5bHo/ZM5VF3PnjXHgD51x5Gnx2njIXvygMlAwB7SbceZHXQFu/AtXIgzJ4fmF8dGaYWSYxEREZFR6Ibbiimdm8vmX71DfW0z1xemk+x2Ut2/7rj2dQiFYrZdVLiIrmAXu+p3YZxO0m+/jdbXXiPU1jbCZzHylByLiIiIjEL2CBYz8KV7eOn7+7C6Q9xQnMm2o33rjpdAZxPU74vZtiy/DJdxseXkFgDS77wTq7OT1nXrRvIU4kLJsYiIiMgo5U1xc9snr6flfCevrX6bspIs3j7dTHNnj92hJDzecb8h3XxuH7PzZrP11FYAkufPx5WXR/MYGLVCybGIiIjIKFYwJZPye0s5VF3P5FZDyIKdteHSisxi+6t244DtKgsqeavhLS50XcA4naTdfjut69cTbB3dpRVKjkVERERGufm3lzDhuixOvXqScZajX2nFUvvJcb+644qCCkJWiOrT1QCk33UnVlcXra++OpKhjzglxyIiIiKjnMNhuOUPZ+LxOvlgp5ft75zvXVlSBR0NcPbtmG3m5M4h2ZXMllN23XHyvHm48vNpfuGFkQx9xCk5FhERERkDUjKSuOXjM0nrssg+2EZHeIIQ/EvsZU1saYXb6WZB/oJocmwcDtLvuIO2DRsItraOZOgjSsmxiIiIyBhRfH0OOQtymdPl4tU1NXZjVglkTByy7rimuYYzbWcASLvtVqyeHtrWj94JQZQci4iIiIwhd3xsOvXOEEdeOkZnW3jUCv8Su+44PJteRGQq6a2nw6NWzJuHMyeHllfWjGjMI0nJsYiIiMgYkpmSxKESD3SGeP3pw3ZjSRW0n4OzB2L6Ts2aSlZSVnS8Y+N0knbzTbSuW0eou3ukQx8RSo5FRERExpgZM3OpTg6w//VTHNvfAP4qe0XNhph+DuOgoqCCrae2YoWfKqeuWEGorY32rVtHOuwRoeRYREREZIypKM1mo6cHb3YSr/7sbXp8xZBeBLWbBvYtqKC+o56jF44CkLJoEQ6fj5aXXxnpsEeEkmMRERGRMWahP5uAgfY5GbSc72Trr4/apRU1G4esO46MWuFISiLlxhtpWbsWq9/YyKOBkmMRERGRMSYrxcN149PY2trGrGVF7Fl7jNPJN0PbWTh3KKbvhLQJFKUWRaeSBkhbsYLguXN07Nkz0qEPOyXHIiIiImNQRWk2O2obKbu3lNTMJNZumUDQcg05pNv209sJhAIApC5fBm43La+MvtIKJcciIiIiY1B5aQ4dPUEONLSx7GPTaazvYUfPxwdMBgJ2ctzS08L+8/sBcKalkVJeTssrr0Q/qDdaKDkWERERGYPKS7MB2HqkAf/sXKaV57Oj8Q7OHzg6oO64vKAc6K07Bki7ZQU9tXV0Hz48ckGPACXHIiIiImNQXloSk/NS2Hb0PABLfm8qHg+sP/0BrHOxCW+2N5vpWdNj6o5Tb14BQMua0TUhiJJjERERkTGqvDSH6ppGgiGL5FQPFbflcrJnFkfW7RzQt6Kggl31u+gMdALgzh+Hd+6cUTdbnpJjERERkTGqclI2LV0B3jrZDMDM2+eQ7TnB65uSCfQEY/pWFFTQHepmV/2uaFvailvo3LuXnlOnRjTu4aTkWERERGSMqijNAWBruLTC4XKy5Pq3aO5IZc+aYzF9y/LLcBlX7JBut9wCQMuatSMU8fBTciwiIiIyRo3P8FKS42Pr0YZo28T5U/AnbWPH80dpu9AVbfe5fczJmxPzobykSaV4Jk2iZc3oGdJNybGIiIjIGFbuz2Z7TQOhUHiECv8SqtIeIxgIsfXZIzF9Kwsqeev8W1zouhBtS1uxgvZt2wk2NY1k2MNGybGIiIjIGFYxKYem9h4O1rfYDbnTyMwIMGfiAfZvPsXZupbevgUVWFhsP7092pZ26y0QDNK6bt1Ihz4slByLiIiIjGEVfcY7BsAYKKmizPUDklPcbHjiYHSij9l5s/G5fDGlFd5Zs3CNGzdqZstTciwiIiIyhk3ISqYwwxv9UB4A/iUktR2m4pZ0Th2+wDs7zwLgdrhZkL8g5kN5xuEgdcXNtG7YSKijY6TDv+qUHIuIiIiMYcYYKiblsO1oQ+9U0P6lAMzI2U1OUSqvP304OrRbZUElNc01nG47Hd1H2opbsDo7adu8ZcD+E42SYxEREZExrqI0m3Ot3bxzts1uyJsOqeNx1K5jye9NpaWhMzq0W2VhJRA7lbSvfCHG5xsVdcdKjkVERETGuMpJ9njHm985ZzcYA5OWwZF1TJiagX92Drt+W0dXR4CpmVPJ9mbHJMcOj4fUqsW0rlvX+/Q5QSk5FhERERnjSnJ8FGUms+HQud7GScuh/RzUv0X5vZPoag+wZ80xuwyjoIKtp7bGJMKpy5cTOH2argMHRjz+q0nJsYiIiMgYZ4xh6dRcNr9znkAwZDeWLrOXR14jrziNSfPy2PNKHZ1tPSwqrRhM2gAAGjNJREFUWMS5jnO80/ROdB+pN94IQOtrr41w9FeXkmMRERERYcnUXFq6ArxxIjzBR0YR5EyFo3Yd8cJ7SunuDLJnzTEqCwbWHbvy8vDOmkXra4ldd6zkWERERERYPDkXY2BjTGnFMqjZBIFuciekMnn+OPasPUYWuRSnFcckx2CXVnTs2UOgoYFEpeRYRERERMhO8XB9YXq/5Hg59LTBiR0ALLzHT09XkF2v1FFZUMn209vpCfVEu6cuWwaWRduGDSMb/FWk5FhEREREAFgyJY+ddY20dQXsBv8SMA448hoAOYWpTC3L541Xj1OWWUl7oJ195/ZFt/dePxNnXi4tCVx3rORYRERERABYOjWXQMjqnS0vOQsK5kXrjgEW3u0n2B3E+2YhBsPmU5uj64zDQeqyZbRt2IjV09N/9wlBybGIiIiIALCgJIskl6PfkG7L4Ph26GoFIGt8ClPL8zm44SxzUuez5WRs3XHa8uWEWltp37lrJEO/apQci4iIiAgAXreT8tLsgXXHoQDUvh5tWnhXKcGgRfmpO3nj7Bu097RH16UsWoRxuxN2SDclxyIiIiIStWRKLofqWznT3Gk3TKwAZ1K07hggM9/H9Ip8XG/n4enysePMjug6R0oKvvLyhJ1KWsmxiIiIiEQtmZoL9BnSzZ0MxZUxyTFA2V2lEIIFJ28fdEi37iNH6K6tHYmQryolxyIiIiISNWN8OjkpHjYe7ld3XL8PWuujTRl5yUyvGM+M+kqqa2Pri1OX27PrJeLTYyXHIiIiIhLlcBgWT8ll4+FzWJZlN05abi+Pro/pO++WYhxBF94D4znfcT7a7pk4Ec/kyQk5W56SYxERERGJsXRKLmdbujh4xh6hgoJ54M0YUFqRU5RK1lQ3s07fyJbj22LWpS5fRtv27QRb20Yo6qtjTCfHq1evxu/343A48Pv9rF69Ot4hiYiIiMRdVbjueMOhs3aDwwn+pXBkHUSeJkf63jkDX086b7xeE9OeumwZ9PTQ9vqmkQj5qhmzyfHq1at58MEHqa2txbIsamtrefDBB5Ugi8h7phtvEUl0RZnJTMpN6Vd3vBwu1EHj0Zi+xTNy6Mq4AG/kEAqFou2+G27AkZ6ecHXHrngHEC8PP/ww7e3tMW3t7e188fOfZ1JS+J7BRBaRFya8MLE7M2ZgW3RVv237bDOgDyZ21UXa+8cV+7JfrDHh9Tv2JZ5nzPuhzqd/+4D4es9p6PMZZDtij32x8xz0e9M/piGOe7HrYTCxOxssTtNv275xXurPVf99D/Z6iOvQ/xxiz+3Sfp7eLfZL/b4PiPldz8FcNM7Bfkbs7fr2HeJ7/S7/Tq+myI135PdL5MYbYOXKlcN+fEl8q1ev5uGHH6auro7i4mJWrVqlnx2JiyVTc3my+jjdgRAel6O37vjIa5A9KdrPGENOpaH1pVx2Vh+krPw6u93tJnXJElrXrccKhTCOxHgmO2aT47q6ukHbz5w7x+tP6imPyKjXLykf6satb/Jtd40k2pG+prcd+NoTvx70xvtzf/RpWja8FNlN9AYt9vjh/YbbjSN8k2BMdBsTSfL7tA26H0e4zRG7r5jt+yzf7bUxjtg2h6NfP4d9vD6vCW9jtzsGbGcczt5tHI7YPg4HDocjdp0jfAyHA4fDObCvw4FxOHE4HThM72vjcOBwOnu36/ve6cThcNrLcJvT5bL7OcPbR859hOjmSq4lS6bk8pPNteysa6RyUg7kTIG0Qru0ouwTsX1vnMPTa3ez8+XuaHIMdt1x8/PP07lvH8mzZ4/0KVyRMZscFxcXUzvI2HvFxcV84fHnsAjX00QW0foaK/w+8tbq7Ruzos+2vTsZ0KV3f9ZFtomt7xmyPWZfVuy++uy/t9+ln2fvLoY6n0FiGupco7Fdxvn0j2Oo8xxwLrGB2Kfdf5uLff979zPU+Q3c18BzvOSfq/7ninVZMcbsu28c7xJbbDz94ruE2AdrH/DvZIhYsazBv8eDxN43xr4/S+8Wb9+fdbtL/+MO8bNpWUMcz4r+HNnn1bu+6Ue/YDCNrW1Mmr8w5pj2/vq+tgYc1wqFes+/z/EsK9RvP7HnYPXbB1hYIXt7y7KwQsHwLkPhfffu015avceMfIVC0e+lFbKi/SLrBvQNhezjhSLtIQhZhEKh2GNc4xxOVziBdvS+drlwOp04nC47oXa6cLic0ddOl8vu43LhdLlxOO11Tre7t83lxhV573bjdLv5iy9+YdCbq698+UvcedNyXB43LrcHl8eDy5OEw+mM03dFxoLKyTk4HYaNh87ZybEx9tPjgy9CKAR9ngRPyi6lpuRHXH/4Js4eayFvYhoAKUuXgjG0rl+v5Phat2rVqpi7cwCfz8c3v/lN+wlEHGMTkcRV/Hf/MPiNd0kJtz34p3GI6NoWSZqtUCT5DkWT8FAkwQ6/730dilkX8zoYDL8Pht+HCEVeh0JY4fWhULD3dTDY5yvQZz/B2HWBgN0WCBIMBggFgwQDAbu97/tggEBXF8FAgGCgh1AwYL/u6SEYDNrLHru9v1Nn6gf5LsHxEyf50Z9/ekC7w+mMJsouTxLupD7LJHvpTvLaX16v/d6bjMfrxeNNtl8nJ+P2evF4fXh8ySQl+3AneRPmT+AyfNK9buZOyGDj4XM8dPt0u3HSMtjzczj9BhTOi/Y1xpBf5qHnaBc7X67l9k/MAsCVlYV3zmzaNmwk77OfjcdpXLYxmxxH/jylui4RuZqGuvFetWpVHKO6dtllFs4x+fFwy7J6k+aAnTD/y+vzOH78xIC+hePzuetzf0Ggu4tAd3f4q8/rri4C3V30dHcR6LKXHc3NtHR30dPVSXdnJ4HOTgI93ZcWnDF2Ap3sI8mXgsfnw+tLweNLIcnnIyklFW/4K/o6NRVvahrJaWm4vckjWo4iw2fJ1Dz+Y+0hLrT3kOFzw+SbAQOHfhuTHAMs8lfwdN5GPNVJtH6gk9QsLwCpS5Zy7rvfJdjUhDMzMw5ncXnMYH/GjpeysjKruro63mGIiLwn+kCVXKn+Ncdg31w9+uijV+VnKBQK0tMZTpg7Oujp7KCns5Puzg77q6Od7vZ2ujrs113tbfayrY2u9na62lvtZVvboE++I5wuF960dJJT00hOSyc5PYPk9Ax8ka+MDHzpmfgys0jJzMKTPHQyrX9P8VVd08CHvreZf//oDdw7t9Bu/O8VYIXgwVdj+l7ousBdP76Pj+76GvNvKWHx70wBoGP3bmo+8lGK/ukR0u+6a6RPYVDGmB2WZZUNtm7MPjkWERkuK1eu1H/eckWG+6+aDofTfvLr80HWle/HsiwCXV10tLbQ1dZKZ1srna0tdLa20tHSTGdrCx0tLXS2NtPR0szZuho6LjTR2dY66P5cniRSMjNJycwmJSuL1OwcUrNyWLdjF1//x3+io7MT0AcU4+GG4ixyUz28uPd0b3I8/U5Y+w1oPgXpBdG+GUkZlE6YwPnTR9m3wUXZXX48yS68s2fjzMigdf2GayY5fjdKjkVERK4hiXBzZYyxa5i9XsjNu+TtgoEAHS3NdDRfoO1CE+1NjbT1+zp3rI6aPbvo6ezgm79ZG02MI9rb2/nzP/0TioIdpOflk5k/noxx40kfNw63J+lqn+qY53QYbr9+PL/adYLOniBetxOm32Unx4deggUfj+lfVVjFU9nP8MGTX+CtTSeZd0sxxukkpaqK1o0bE2JINyXHIiIiMiKcLhepWdmkZmVzsZS6q72dv0hNHXTd2cYm9rz8AoHurpj2lKxsMsaNJ2t8IVkFhWQVFpFVUETm+AIlzu/BnbMKWL21jnUHz3L79eNh3AzILIYDLwxIjhcVLuI7ad/BOyHEG2uPM+fmiTgchpQbl9L8/PN0HTiAd8aM+JzIJVJyLCIiItecJJ9vyGFXS0pK+NxPnqKj+QJNZ05zof40F86c5sLZMzSdOUXtGzvZt+6VmG3S88aRUzSR7AnF5E4oJmdiMTlFE/Ek+0bqlBJWxaRsMn1uXnjzlJ0cG2M/Pd7xGHS3g6f3ezgrdxZpnjROl+4nc8P1HHurgZJZOaRWVQHQun6DkmMRERGRK/Fuo78YY/BlZOLLyKRw2nUDtu3u7KDx1EkaT52g8dQJGk4c5/yJY9Tte4NgT0+0X3pePuP8peSVTGKc3/5Ky83TaBt9uJ0Obp2Rz4t7T9MVCJLkcsK0O2Dr9+zZ8q7rrSN2OVxUFlTy6unn+FjqPPZtOEHJrBxceXkkzZxB24YN5H76wfidzCVQciwiIiLXpPfyAUWPN5n80snkl06OaQ+Fglw4c5rzx49x/ngdZ2uPUl97lMPVW6OT0nhTUhk3aQoFU6YxfvI0xk+eSmp2ztU/wQRy1+wCntxxnNcPn+em68ZBSRUkpcOB52OSY7Drjl+ufZn8+V5qNp6ntbGL1KwkUpfeyPnvf59gSwvOtLQ4ncnFKTkWERGRa9bV/oCiw+Ekq8CuRZ6ysDLa3tPZydm6Gs7WHqH+6BFOv3OI7c89TSgYBCA1O4eCKdMpnD6DCdddT55/Ek7X2EmjFk/JIS3JxQt7T9nJscsDU1bAwZcGzJZXVWSXUNSXHMRaX8D+10+y8O5SUpcu4fx//RdtmzeTfttt8TqVixo7V1VERERkCG6vl8Jp18WUaPR0d3G25ginDx/k9DuHOHnobQ5tex0AV1IShVOnUzj9eibOnEXhtBm4PJ54hT/sklxOVswYx2/fOsOqYAi302HXHe/7FZzcCRN6hwwenzKeSRmT2NK+nruu+yPe2niSBXf6SZ47F0dqKm0bNio5FhEREUk0bk8ShdNmUDit9wNkrQ3nOXFgPycO7OPE/rfY+stfsOXpx3G5PRTNuJ7iWXMpmT2PPH8pDoczjtFffXfMKuCZ3SfZeqSBJVNzYcotYJz2qBUTYufTWFy4mCcPPsnnq/JY+4OD1O07j392LimLF9O6YQOWZV2zdd1KjkVEREQuUWp2DtMXLWH6oiWAPeTc8f17qXtzN3V797Dh54+xAfCmpuGfO59J8xfin7eA5NRrt8b2Ui2fnofP4+SFvafs5NiXDcWL7OR4xddi+lYVVfGz/T/jfH4Nyeke9m04aSfHS5fQ8tvf0n34MElTp8bpTN6dkmMRERGRK5Tk8zF5QTmTF5QD0NbUSN3ePdS+sYsju6p5e9M6jHFQOP06Js23++VMKI5z1FfG63Zy0/RxvLTvDH933yycDmPPlvfbh6GxFrJKon0X5C/A4/Dw+pnXWbr4d9j1Ui0tDZ2kLl0K2EO6XavJ8bU9RYmIiIhIAknJzGLGkuXc8cd/zmf+66d87O8foeIDv0tPZxcbfv4Yj33xj/nRFz7DpidWc66uBis8QkaiuGPWeM61dlFd02A3TL/TXh58MaZfsiuZ+fnzef3E61y/pBAL2L/pJO7x40maOpXWjRtGNvDLoCfHIiIiIsPAOBwUTJ1OwdTpVH34D2g5f47D1Vs4uGUjW375P2x5+nGyCycwbdESrlu8jJwJE+Md8kXddN04PC4HL+w9TcWkHMiZDLnT7NKKik/H9K0qrOKRHY/QnnyB4hnZvLXpFGV3+UlZupTGn/6UUFsbjpSUOJ3J0PTkWERERGQEpOXkcsPt9/Dhr/8Df/S9n7Dik39MSlY2W3/5BI998TOs/uqfs+vFX9PR0hzvUIeUmuRi2bQ8Xtp3mlAo/NR72h1QsxE6Y+NeXLQYgM0nN3P90iLamrqo3Xue1BuXYvX00LZ120iHf0mGNTk2xtxhjDlgjDlsjPnKcB5LREREJFGkZGYx77a7+L2//iaf/t6PWX7/pwgGAqz90X/xvU/fz7PfXsXh7Vui4yxfS+6cNZ5TFzrZc7zJbph+F4R64J01Mf2mZk5lXPI4Np3cRMmcHHwZ9gfzkufPx/h8tF2jpRXDlhwbY5zAfwJ3AjOBjxpjZg7X8UREREQSUUpmFgvufj/3/79/5w++9W/ccMc9nDy4n2e//ff89598gs1PPU5rYwOrV6/G7/fjcDjw+/2sXr06LvGumJFPx/7XuLnsehwOB5OWf4TH3jT4l98fE5sxhrS9afzHh/4Dt9vFf/9mFff/5W24vF4W73+LGd/4RtzPZTBmuArBjTGLgL+xLOv28Pu/BLAs6/8OtU1ZWZlVXV09LPGIiIiIJIpQMMiRndvZ8/Lz1OzZya5jp3iq+k26enqifXw+H48++uhVnUHwUqxevZoHPvEpgt2d0TaPE7r7POT2+Xw88MAD/PCxH9LV0XXRfY70uRhjdliWVTboumFMjj8E3GFZ1qfC7/8AqLAs60+G2kbJsYiIiEisxtMnmTFrNmfONwxYV1JSQk1NzYjG4/f7qa2tvWg/p9NJ8DLKQkbyXN4tOY77aBXGmAeBB8NvW40xB+IQRi5wLg7HleGl6zr66JqOTrquo5Ou69W1YLDG2tpajDE7RiiGyDUdNJb+LicxhhE/l5KhVgxncnwC6DsmyYRwWwzLsh4FHh3GOC7KGFM91N2DJC5d19FH13R00nUdnXRdR5+xck2Hc7SK7cBUY0ypMcYDfAR4bhiPJyIiIiLyngzbk2PLsgLGmD8BXgKcwA8ty9o3XMcTEREREXmvhrXm2LKs54Hnh/MYV0lcyzpk2Oi6jj66pqOTruvopOs6+oyJazpso1WIiIiIiCQaTR8tIiIiIhI25pNjTXGd+IwxPzTG1Btj9vZpyzbGvGyMORReZsUzRrl8xpiJxphXjTFvGWP2GWP+LNyua5ugjDFeY8w2Y8ye8DX923B7qTFma/j38C/CH+KWBGOMcRpjdhljfhN+r+ua4IwxNcaYN40xu40x1eG2Uf87eEwnx5rietR4DLijX9tXgDWWZU0F1oTfS2IJAF+0LGsmUAl8NvzvU9c2cXUBN1uWNReYB9xhjKkEvgX8s2VZU4BG4JNxjFGu3J8B+/u813UdHW6yLGtenyHcRv3v4DGdHAPlwGHLso5YltUN/A9wX5xjkstkWdZ6oP+0QfcBPw6//jHw/hENSt4zy7JOWZa1M/y6Bfs/3SJ0bROWZWsNv3WHvyzgZuCpcLuuaQIyxkwA7ga+H35v0HUdrUb97+CxnhwXAcf6vD8ebpPEl29Z1qnw69NAfjyDkffGGOMHbgC2omub0MJ/et8N1AMvA+8ATZZlBcJd9Hs4Mf0L8CUgFH6fg67raGABvzXG7AjPaAxj4Hdw3KePFhlulmVZxhgNy5KgjDGpwNPA5y3LarYfSNl0bROPZVlBYJ4xJhP4FXBdnEOS98gYcw9Qb1nWDmPM8njHI1fVEsuyThhjxgEvG2Pe7rtytP4OHutPji9pimtJSGeMMQUA4WV9nOORK2CMcWMnxqsty/pluFnXdhSwLKsJeBVYBGQaYyIPa/R7OPFUAe8zxtRglyfeDPwruq4Jz7KsE+FlPfbNbDlj4HfwWE+ONcX16PUc8ED49QPAs3GMRa5AuGbxB8B+y7L+qc8qXdsEZYzJCz8xxhiTDNyKXUv+KvChcDdd0wRjWdZfWpY1wbIsP/b/o2sty1qJrmtCM8akGGPSIq+B24C9jIHfwWN+EhBjzF3YtVKRKa5XxTkkuUzGmMeB5UAucAb4OvAM8ARQDNQCv2dZVv8P7ck1zBizBNgAvElvHeNXseuOdW0TkDFmDvYHeJzYD2eesCzr74wxk7CfOGYDu4DftyyrK36RypUKl1U8ZFnWPbquiS18/X4VfusCfm5Z1ipjTA6j/HfwmE+ORUREREQixnpZhYiIiIhIlJJjEREREZEwJcciIiIiImFKjkVEREREwpQci4iIiIiEKTkWEbnKjDGvGmNu79f2eWPMd99lmxpjTK4xJtMY88fDHyUYY95vjPlrY8wyY8zmfutcxpgzxphCY8y3jTE3j0RMIiLxpuRYROTqexx7MoS+PhJuv5hMYESSY+BLwHewx5OeYIwp6bPuFmCfZVkngX8HvjJCMYmIxJWSYxGRq+8p4O7wzJsYY/xAIbDBGPNRY8ybxpi9xphvDbLtPwCTjTG7jTH/aIxJNcasMcbsDG93X6SjMeZrxpgDxpiNxpjHjTEPhdsnG2NeNMbsMMZsMMZc1/8gxphpQJdlWecsywphD+rfN6GPJvOWZdUCOcaY8VfheyMick1TciwicpWFZ4vaBtwZbvoIdvJZAHwLuBmYByw0xry/3+ZfAd6xLGueZVl/AXQCH7Asaz5wE/CIsS0EfgeYGz5OWZ99PAr8qWVZC4CHsJ8O91cF7OzzPvq02xiTBNwFPN1n/c7wNiIio5or3gGIiIxSkWTz2fDyk8BC4DXLss4CGGNWAzdiT3c+FAN80xhzI/Y02kVAPnai+qxlWZ1ApzHm1+F9pgKLgSeNMZF9JA2y3wLgbOSNZVnV4afU04EZwNZ+U8LWYz/9FhEZ1ZQci4gMj2eBfzbGzAd8lmXtMMZMuIL9rATygAWWZfUYY2oA77v0dwBNlmXNu8h+O4CMfm2RhH4GA+ujveFtRERGNZVViIgMA8uyWoFXgR/Sm2huA5aFR6VwAh8F1vXbtAVI6/M+A6gPJ8Y3AZEPzW0C7jXGeMNPi+8JH7cZOGqM+V2AcAnG3EFC3A9M6df2OPD72GUfz/ZbNw3Ye/EzFxFJbEqORUSGz+PYNcGRD7adwq4pfhXYA+ywLCsmCbUs6zywKfyBvX8EVgNlxpg3gfuBt8P9tgPPAW8ALwBvAhfCu1kJfNIYswfYB9zHQOuBG0yf2gvLsvYDbcBay7LaIu3GGDd2Il195d8KEZHEYCzLincMIiJyBYwxqZZltRpjfNjJ7oOWZe282HZ9tv9X4NeWZb1ykX4fAOZblvW19xaxiMi1T0+ORUQS16PGmN3YI0k8fTmJcdg3Ad8l9HMBj1xucCIiiUhPjkVEREREwvTkWEREREQkTMmxiIiIiEiYkmMRERERkTAlxyIiIiIiYUqORURERETClByLiIiIiIT9f1iG/D9BVqduAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "times = ['2014-04-01 07:00:00', '2014-04-01 08:00:00', '2014-04-01 09:00:00', \n",
    "         '2014-04-01 10:00:00', '2014-04-01 11:00:00', '2014-04-01 12:00:00']\n",
    "times.reverse()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12,8))\n",
    "\n",
    "for time in times:\n",
    "    ivframe = sapm_to_ivframe(sapm_1.loc[time])\n",
    "\n",
    "    fit_voltages, fit_currents = ivframe_to_ivcurve(ivframe)\n",
    "\n",
    "    ax.plot(fit_voltages, fit_currents, label=time)\n",
    "    ax.plot(ivframe['voltage'], ivframe['current'], 'ko')\n",
    "    \n",
    "ax.set_xlabel('Voltage (V)')\n",
    "ax.set_ylabel('Current (A)')\n",
    "ax.set_ylim(0, None)\n",
    "ax.set_title('IV curves at multiple times')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### desoto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same weather data run through the Desoto model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "photocurrent, saturation_current, resistance_series, resistance_shunt, nNsVth = (\n",
    "    pvsystem.calcparams_desoto(total_irrad['poa_global'],\n",
    "                               temp_cell=temps,\n",
    "                               alpha_sc=cecmodule['alpha_sc'],\n",
    "                               a_ref=cecmodule['a_ref'],\n",
    "                               I_L_ref=cecmodule['I_L_ref'],\n",
    "                               I_o_ref=cecmodule['I_o_ref'],\n",
    "                               R_sh_ref=cecmodule['R_sh_ref'],\n",
    "                               R_s=cecmodule['R_s']) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "photocurrent.plot()\n",
    "plt.ylabel('Light current I_L (A)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "saturation_current.plot()\n",
    "plt.ylabel('Saturation current I_0 (A)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1.066023"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "resistance_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "resistance_shunt.plot()\n",
    "plt.ylabel('Shunt resistance (ohms)')\n",
    "plt.ylim(0, 5000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "nNsVth.plot()\n",
    "plt.ylabel('nNsVth');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single diode model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_diode_out = pvsystem.singlediode(photocurrent, saturation_current,\n",
    "                                        resistance_series, resistance_shunt, nNsVth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "single_diode_out['i_sc'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "single_diode_out['v_oc'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "single_diode_out['p_mp'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.2 64-bit ('pvlib38': conda)",
   "language": "python",
   "name": "python_defaultSpec_1595300091462"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}