{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TMY tutorial\n",
    "\n",
    "This tutorial shows how to use the ``pvlib.tmy`` module to read data from TMY2 and TMY3 files.\n",
    "\n",
    "This tutorial has been tested against the following package versions:\n",
    "* pvlib 0.3.0\n",
    "* Python 3.5.1\n",
    "* IPython 4.1\n",
    "* pandas 0.18.0\n",
    "\n",
    "Authors:\n",
    "* Will Holmgren (@wholmgren), University of Arizona. July 2014, July 2015, March 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# built in python modules\n",
    "import datetime\n",
    "import os\n",
    "import inspect\n",
    "\n",
    "# python add-ons\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting libraries\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "try:\n",
    "    import seaborn as sns\n",
    "except ImportError:\n",
    "    pass\n",
    "\n",
    "import pvlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pvlib comes packaged with a TMY2 and a TMY3 data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the absolute file path to your pvlib installation\n",
    "pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(pvlib)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the TMY data using the functions in the ``pvlib.tmy`` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmy3_data, tmy3_metadata = pvlib.tmy.readtmy3(os.path.join(pvlib_abspath, 'data', '703165TY.csv'))\n",
    "tmy2_data, tmy2_metadata = pvlib.tmy.readtmy2(os.path.join(pvlib_abspath, 'data', '12839.tm2'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the TMY3 metadata and the first 5 lines of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'USAF': 703165, 'Name': '\"SAND POINT\"', 'State': 'AK', 'TZ': -9.0, 'latitude': 55.317, 'longitude': -160.517, 'altitude': 7.0}\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETR</th>\n",
       "      <th>ETRN</th>\n",
       "      <th>GHI</th>\n",
       "      <th>GHISource</th>\n",
       "      <th>GHIUncertainty</th>\n",
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      ],
      "text/plain": [
       "                           ETR  ETRN  GHI  GHISource  GHIUncertainty  DNI  \\\n",
       "datetime                                                                    \n",
       "1997-01-01 01:00:00-09:00    0     0    0          1               0    0   \n",
       "1997-01-01 02:00:00-09:00    0     0    0          1               0    0   \n",
       "1997-01-01 03:00:00-09:00    0     0    0          1               0    0   \n",
       "1997-01-01 04:00:00-09:00    0     0    0          1               0    0   \n",
       "1997-01-01 05:00:00-09:00    0     0    0          1               0    0   \n",
       "\n",
       "                           DNISource  DNIUncertainty  DHI  DHISource  \\\n",
       "datetime                                                               \n",
       "1997-01-01 01:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 02:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 03:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 04:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 05:00:00-09:00          1               0    0          1   \n",
       "\n",
       "                                  ...            AOD  AODSource  \\\n",
       "datetime                          ...                             \n",
       "1997-01-01 01:00:00-09:00         ...          0.051          F   \n",
       "1997-01-01 02:00:00-09:00         ...          0.051          F   \n",
       "1997-01-01 03:00:00-09:00         ...          0.051          F   \n",
       "1997-01-01 04:00:00-09:00         ...          0.051          F   \n",
       "1997-01-01 05:00:00-09:00         ...          0.051          F   \n",
       "\n",
       "                           AODUncertainty   Alb  AlbSource  AlbUncertainty  \\\n",
       "datetime                                                                     \n",
       "1997-01-01 01:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 02:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 03:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 04:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 05:00:00-09:00               8  0.24          F               8   \n",
       "\n",
       "                           Lprecipdepth  Lprecipquantity  LprecipSource  \\\n",
       "datetime                                                                  \n",
       "1997-01-01 01:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 02:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 03:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 04:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 05:00:00-09:00         -9900            -9900              ?   \n",
       "\n",
       "                           LprecipUncertainty  \n",
       "datetime                                       \n",
       "1997-01-01 01:00:00-09:00                   0  \n",
       "1997-01-01 02:00:00-09:00                   0  \n",
       "1997-01-01 03:00:00-09:00                   0  \n",
       "1997-01-01 04:00:00-09:00                   0  \n",
       "1997-01-01 05:00:00-09:00                   0  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(tmy3_metadata)\n",
    "tmy3_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1175fa6d8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmy3_data['GHI'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TMY readers have an optional argument to coerce the year to a single value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmy3_data, tmy3_metadata = pvlib.tmy.readtmy3(os.path.join(pvlib_abspath, 'data', '703165TY.csv'), coerce_year=1987)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116df3c88>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmy3_data['GHI'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the TMY2 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'WBAN': '12839', 'City': 'MIAMI', 'State': 'FL', 'TZ': -5, 'latitude': 25.8, 'longitude': -80.26666666666667, 'altitude': 2.0}\n",
      "                           year  month  day  hour  ETR  ETRN  GHI GHISource  \\\n",
      "1962-01-01 00:00:00-05:00  62.0    1.0  1.0   1.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 01:00:00-05:00  62.0    1.0  1.0   2.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 02:00:00-05:00  62.0    1.0  1.0   3.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 03:00:00-05:00  62.0    1.0  1.0   4.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 04:00:00-05:00  62.0    1.0  1.0   5.0  0.0   0.0  0.0         ?   \n",
      "\n",
      "                           GHIUncertainty  DNI          ...            \\\n",
      "1962-01-01 00:00:00-05:00             0.0  0.0          ...             \n",
      "1962-01-01 01:00:00-05:00             0.0  0.0          ...             \n",
      "1962-01-01 02:00:00-05:00             0.0  0.0          ...             \n",
      "1962-01-01 03:00:00-05:00             0.0  0.0          ...             \n",
      "1962-01-01 04:00:00-05:00             0.0  0.0          ...             \n",
      "\n",
      "                          PwatUncertainty   AOD  AODSource AODUncertainty  \\\n",
      "1962-01-01 00:00:00-05:00             8.0  62.0          F            8.0   \n",
      "1962-01-01 01:00:00-05:00             8.0  62.0          F            8.0   \n",
      "1962-01-01 02:00:00-05:00             8.0  62.0          F            8.0   \n",
      "1962-01-01 03:00:00-05:00             8.0  62.0          F            8.0   \n",
      "1962-01-01 04:00:00-05:00             8.0  62.0          F            8.0   \n",
      "\n",
      "                           SnowDepth  SnowDepthSource SnowDepthUncertainty  \\\n",
      "1962-01-01 00:00:00-05:00        0.0                A                  7.0   \n",
      "1962-01-01 01:00:00-05:00        0.0                A                  7.0   \n",
      "1962-01-01 02:00:00-05:00        0.0                A                  7.0   \n",
      "1962-01-01 03:00:00-05:00        0.0                A                  7.0   \n",
      "1962-01-01 04:00:00-05:00        0.0                A                  7.0   \n",
      "\n",
      "                           LastSnowfall  LastSnowfallSource  \\\n",
      "1962-01-01 00:00:00-05:00          88.0                   E   \n",
      "1962-01-01 01:00:00-05:00          88.0                   E   \n",
      "1962-01-01 02:00:00-05:00          88.0                   E   \n",
      "1962-01-01 03:00:00-05:00          88.0                   E   \n",
      "1962-01-01 04:00:00-05:00          88.0                   E   \n",
      "\n",
      "                          LastSnowfallUncertaint  \n",
      "1962-01-01 00:00:00-05:00                    7.0  \n",
      "1962-01-01 01:00:00-05:00                    7.0  \n",
      "1962-01-01 02:00:00-05:00                    7.0  \n",
      "1962-01-01 03:00:00-05:00                    7.0  \n",
      "1962-01-01 04:00:00-05:00                    7.0  \n",
      "\n",
      "[5 rows x 70 columns]\n"
     ]
    }
   ],
   "source": [
    "print(tmy2_metadata)\n",
    "print(tmy2_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the TMY readers can access TMY files directly from the NREL website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmy3_data, tmy3_metadata = pvlib.tmy.readtmy3('http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/data/tmy3/722740TYA.CSV', coerce_year=2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Tucson TMY GHI')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LLPdQ6QTTpxc5PJVQVTy6dD/zpYl8wkpTdbMArAZwvojUish3ANwF4DIRqQRwmfE3AMwHcABAFYAnAPwAAFS1BcDvAaw3/rvNGEZU1DJ9aW2oeCKZtpFdcSzNm4/UBreyJ/LRvnQ+9d+WF/9xSXbLyeAm6WBTFx7KoefAmavjteIRF4Pn/sel2V5xpLUHNS1D3wgC8fStuxfuwf8aXb0TUXEbMm1DVb86yKhLTKZVAD8cZDlPAngyo9IRFbBdde3ozDR/coggRpItYWQfeGyrbcOoYYO/aJcsShbLzvWR99oDzfjwORMcaU7PD82SVVsM9gbn/M1FIAAgirSt1Dhfhn4vk6Z8PtLag2dWHcJvjDavB3sZOFWiJ8+uUBTRmPbpip6Iig+75yZyyJXTV2DulqGD1FRWa56Hcri5C5+8d6npuFnrqtM28SYpTdVlHN5YnWGQ7/Hlx9fglS1HMl2rZcX2wuDOuja8sjm33+vvm2ux00LzeX3kEPeWBuKXnUjMvbSa/rtB6n1VTUtPMnC2KnHTuO5gC95903wca2eHMETFjN1zE3nIkB1/pPTMl86TFQcz6mEvVS4Bph11iYebux2plZy7pc6TLT/kYt+xTvz4xS05LeMnL55INcjHzUViH45EXcx57v9FcyxK/8OxuqUbp40dnttCicizWPNM5CFD1zxb68DEDqqa8Xqspkakyz1+8O1KvPum+Zmt2IKf/W0rXsnwSQANYoggO10QnkiZcDOJZmDsnM1jlpT5+80rAHpCUVz+4PK0PUQSUWFi8EyUoRfWVVtqv9nM3C1H0tZ+7qlvT9t7YOKa72K6aFo+SCsmwJbI180c9HRpG9no/yRIJJ5Ss6e+A7fP2w0AuO21Xbj/zezb4iYi72DaBlGGps3ZjuFlAez5/RWm42dvqMFbu46Zjrvxhfgj9sFeQpq9oRazN5wIntcfcrdRGrZ5W7x6QlFUNfS9CUzenMUUItk1KfirOduw62jHoOO9eIN1oKkTTV2hrOcfmEYlSNxhJG4SnjS6Af/pp8/Pej1E5A0MnomykOgm28wvXtpm23qufWy1bcuyynInKSZBkOX3BR3Kre3oDaOmJX1b2RT3kxe3DNoByrtumo8PnDUOr/zwYxkvd9Y6a92KW91XbnllO84YNwI/+MS5GZfFzOI9x1B5rO9Nw388mttxNiB0Fm/eJBCRPRg8Ew2hMxjBM6sO4Xv/9m5fNUGVVVN1LgcM33pqPTYcZo6pFZtrBv5OXaEIdh+Nt7yxpSZ91/P5kug90a7g+dtPb7BlOX2Y5DwTUfFizjPREO5esAf3vLEX87cfdbsoSUPV3GbbTnL8cX1KU3UOBcNOBBexmDJwHkRFZRO+8MhKRKLpm4dbWdWMK/60Ii9lyveN1pK9Dbj2sVV5WVdApOiaRSSiE1jzTJQiGlNsqTmOD75zQnJYVyje0UlvOFr0HW28K8dWLtzMkc617MXsJ7O3oLEjiJauEKKq+MaMdTjWXhxdSSdahenf8Ul/N/x1Mzoy7bTIqv49FjJwJipqrHkmSvHn5fvxH4+uxur9zclhgTzUxHqRU2kbC3fUY1ttcbW37HWp2+WFdTUDXhTM1Ju76vGNGWszni+W0kyMXTdaT608hHfdNB/Hh3jhL5+HbrF1A09EfTF4JjJ8Y8ZaPLy4CkC8i97EsETLGTFl2xN2+N5zG1FR1eR2MfxJgEPN2XWekyocVayojG/D3nAUn32oAhsPD90yzLtumo+2nnDW6+0KRrDL6A2xMxjBFX9agdte3wUAONqWvle/XJ4adYciGXWwk1rzbHWtq/Y34fOPrER4iNQaInIfg2ciw4rKJnSFogCAqNF18IrKpuTFPqZD9+xXTIo9RcWvWnJoks3MvmMd2H6kDb+em1mX1m/varAUcCdsPNyC/565EVdOX4FQJIa1B5qTLzcCQ9dk57I33zBrC65+qAIdvdkH/kP5+d+2YXN1K+qHuAkgIvcxeCbf21R9HG/2a7LLrPInlkWPe4O5/MHlQz5mTidfcS3j52Lh3Iacvz1+7Oysa8d1T6yxPN9Nf9+eURNx//Ho6uQTi2yOxVz25USQH4pYqxVOrXk+1NSF2eutNd8HxG/YV+0/Uatvpcb7WHsvnjbakSYi5zF4Jt/7wiOrMHXmxj7DEjXPqVTVtjzNPfUdeGOQNnaJnOJELm51y4k0kFUp7woUk8RRP1grNv2Hpv7Ox7vD+MXLfdt+f2PnwJzxgHE1vunv23HdE/FxP39pG65+qAJNnQNf7lxR2YhVxs3E1Jkb8dvXdqG6udviNyKiXDB4JsqAnTWxMc0sNWJXXTu6jbSSeFmcrRZWZQ+DlJ6bqT2ZrtmOfdksdF62rxHrDvZNPxmqtY3/nrkxmTOeEDCZaXN1vOnFnpTjPuEbM9bhur/Eg+y27vhTrEgshuNdIUyeNg9PVrAmmsgpDJ6JzOShralM86evnL4CM9ccBgDcvXAPzvmV95pmYxNd3uRkjOtGyxKNHUEctuHFR6vS/X7XP7kOjyzd32fY0bYeS00Bpt585PIrptaIJ16cnL3BeqoIEWWG7TwTZcDOIERtzKF2gsLb5aPMOXJz48IN08f/uCTjeezYl63+flZ7MTznV/Mx5Z3j8dL3/zVtx0ZWy644UcZgJIYdR9rwT5NOsjYzEVnGmmeiQZg9kraztY0YA1PKM1WgrMTm075H9mMnb/QS54I75u/Gw0uqbF12olfMnGqeTYYdbOrC1Q9VoKHdWusdq6qasJJNSBJZwppn8pXGjiB6QlGcffLIrOa38/pcjO1Gs3MIb/vQ7W/bujxVuFLznI1cjrXEvLM31AIAfvjJc3MuT3/ZPhX47EMVONAUT2Exu4HoCEZwqoXlJPKnX//RRTj31NEYXlaSXYGIfIA1z+QrH7r9bVx8zxL851Prkp2fDMbsQmTnC1K/e20X3u3hLqXjLwxSMXByO3oldr574R7MXH3I7WJYYtaTuFnahpWAensGnbdYcfVDFbj57ztsXSZRsWHwTL60dG8j/uvZ9HmJDByJCseKyibcmqajlpyCfJtPBiUm0bNZQJ0NO/LaN9ccz30hREWMaRtEZErBXgaLhd29ClJu4ulN9h9bv3ttJ04/aXifYZfctwyTxo3Aymmfsn19RH7F4JnIhMA8cGQoSXRCvHUHryRuOOPqh1agIxixbXnXPrYKIbMuTG3Qv+3ohCOtPYPO82TFQdz2+q6+A3miI0qLaRtEZCrTSueZaw6jocPam/1ECc+uPoTGjqHbRHbLjiPtti5v/SFrKRGTp81DTcvAoPfO+bsxedo828oza121bcsi8gsGz1RUZm+oQU2LPV3U9o8dKxs6k93h+kGmnZHf+soOvL7tqGPlIW/Ktd7513N34od/3WRLWdIp5Ary1CPxLzb3HFjIvwuRW5i2QUVDVfGLl7bh5FHl2HjrZX3GHW3rwfdmbsxweX3/fnb1YTy7+nCuxSwYbHaOrLAj+GrrDue+kEF0hyL42l/WojfsTKpEvpWVCKJsJJ7IVax5pqLTbPJy1FMrD2Frrb1NOhU79jBIxWD9oePYXN3qdjFsUxZw/rLNw54oPQbPVDTSBXrBcDSjZYkgw6QFIv/xYmssz605bLlXvYLkwQdCDy2qxP1v7nW7GER5w+CZfCHCx5wZa+4Mofa4PfnjVLzsiOW6wxHsO9Zhw5KAW17ZgakpKVoejDWzEosprn9yHTp6s2v54wuPrESlyW9slp6V6U3RfW/tw/TF9nZbTuRlDJ6pYFUe60B36MSFxO7wONNKtdbuEJ5aae/LPNmyo0ZwRsVBXDW9wobSEKVX09KDTz+wHBGbmnArtnatF+9pwMr9TVi2rzHrZWyqbsXdC63VDveEo6aBNhHF8YVBKkixmOKyB5bjX999Mv76Xxe6XRwAwP/+bRve3p2+y2+iYmNnO8923QDnI+VqzYFmPLJ0v+PrAYDfvbZr6IksiMYG3pyYbb5j7UFc9sBy7PvDFSgvDfQb1wtV4B0nDUdzZxDzt7OFHfIfBs9U0Fbtb05+djv/sq3HO7Vdxd5xBXmDV5Oh8nEq+J+/bkZTp3fbpzYTzfB3iZn8kB+5YxEA4NBdV+HGF7agIqX5zj317Zh88igMLyvJqZxEXse0DSpITl8bBZLxBZhNuxHlxq4jKPXYdeo+sqQAr54xG9/9mDxtXp/AGQAuf3AFbpqz3bZ1EHkVa56p4GypaUWzSY2PV2vBiIqZl28Z71qwB3vq7e0hMKGkAJ/uREzSNuy24fDgPShedv8ydPRGsOamSxwvB5GTGDxTwfncwyvzsp6M8yY9dC11O4WFfMTm/b6isglfn7EWC278ON57+tislpHY/x9b5lxOciDgoQPeojzEzkkN7b2IKdDWE8ak8SNQ09KNyobO/BWAyEEMnskzmjqDCEZimDRuRFbz2x0vMv4kyi8F8MbOegDAuoMt2QfPNpZpMAHWPKf1YSM3mqgYFWDWFhWrKX94Gx+7azEAIBpT7DjCHgGJvGzT4eOoqGwaesIMJCp0c3l6ko8b3wKMnU279bb75eLjXSHMXH3I1mUSeQ2DZ/Kkh5dU4eqHKrCt1nq3unY2TxXvYZCI0vny42vQ0GFfixNHW3vR0h0GkNvxl4+m6uwO0PPxwnE40+Y2stARjODWuTstT7/hUAtW7bf3BozIaUzbIM+ZPG1e8nNdaw/++cxxLpbGOi9VRLGpOipEF9+zJPk5l+DU6Q5FU89RhcTsZ3H7TPHFx1YDiDd9R1QoWPNMrmjqDKKutQcAMG/b0UEvRplcBO3Peba+QK+9oOe18hBlyqyNYcqN24EyUbFgzTPlVTSm2FPfbrnbZzcvoJmsWbUwcyCJihEPRXvwnEZkLqeaZxH5iYjsFJEdIjJLRIaLyDkislZEKkXkRREpN6YdZvxdZYyfbMcXoMIyfVGl5cAZcP7xKxF5Eyue3efkNnh4SVXBpr8QZV3zLCKTANwA4H2q2iMiswF8BcCVAB5Q1RdE5DEA3wHwqPHvcVU9V0S+AuBuAF/O+RtQQYhEYzj35gUZz+dW+oEgswsHr/NE9srHS3/kTu3yd55ej0V7GvK/YiKb5JrzXApghIiUAhgJ4CiATwF4yRj/DIDPGZ+vMf6GMf4S4VtNvhHJsgo5k7SNdJPmY0/zUvfcPLSo0Pmp5vnJioPoDkfcLkbeMHCmQpd18KyqRwDcC6Aa8aC5DcBGAK2qmjgL1AKYZHyeBKDGmDdiTH9ytuunwpJtLJfPHrEGyOhlRR9d6YnywE9H1G2v70JNS4/j6zFtbWOIc3Ndaw8mT5uH17fVOVImokKUdfAsIuMRr00+B8AZAEYBuMJk0sTxanaIDjiWRWSqiGwQkQ2NjY3ZFo98yO7HvJksz08XeqJ84P2oN+ypbwcAvLSx1uWSEHlHLmkblwI4qKqNqhoGMAfAvwIYZ6RxAMCZABK3q7UAzgIAY/xJAFr6L1RVH1fVKao6ZeLEiTkUj7yEF8L8Yk04FTrmPNvPrAbLS+lmRIUil+C5GsCFIjLSyF2+BMAuAEsAfNGY5noAc43Prxp/wxi/WHmFpyFY3UF+NGsz3vfrN+xdd4ZtTLuRZsxDiIpVLrs2U/6JyElZt7ahqmtF5CUAmwBEAGwG8DiAeQBeEJE/GMNmGLPMADBTRKoQr3H+Si4FJ0r12lZ78/Gyufi6EzwzUCAiZ0z5w9voDPrnRUYiq3LqJEVVfwPgN/0GHwDwYZNpewFcm8v6iPIpo05SXHrEnLrWT923FOUlASz88cWulIXITnyq4r7UwNkLmyMWU3z/+Y34r4+/C1MmT3C7OORj7J6bfCGbE39m3XO7kzuYWsYDjV3YU98BgE3VUeFjB0n5UUiniqbOIN7YeQzff36T20Uhn2PwTHnhhVoLP2GtHRH15+WzwvdmbsSsddX49tPrEYxE3S4OUVoMnskxqordR9tdWfd3nl6PcDS3RqK9fKFJUAALdxxlN7dUdHj/lx9eqXheuLMev5qzHYv3NGDHEXeuG0RWMXimjBxo7ERv2FqtwIvra3DFn1Zg6d6GvOcEL9rTgL1GCkM2Cqn5pjmbjrhdBCLbZdK7KDnPi1ujuTOIY+29bheDfCinFwbJP5o7QY4tAAAgAElEQVQ7g+gMRvCp+5bh8n98Bx77xgeHnGeXUet8qKkLHz7H3Zc7ssnrK4Sm6gBgWFmJOysmclAuwVoh3fy6zpNJz+m3fmNHsM/TtkN3XeV0gYj6YPBMab26tQ43zNrcZ1hFVVNGyxCRrB/B7jjShneMHY6LzjsluwXkILMeBl1qbUO989iVyFasebYdzxVE9mDaBiVFY4rKY31THV5cXz1gOquPUxOT5VKx8fSqQ/j6jLXZL4CIChJDZyLyKgbPlHT/W3tx2QPLUdVwIoAOmES+UYttSCVqYwXuXAhzrrjKMG2DiOzDY8pblu9r9PSLyU9WHMTkafPQE2JLHeQ8Bs+UtLm6FQBQ3xZMDjMLnnlR8w630kWInMYXBu1n9ot6MZUj003/9821uH3+bgDA8e6QAyUi6ovBMyWVBOKn0WjKmSsxLFU0zZntf/66Ce+5eQGAvifAQmx3OLMeBv3ZMYmXa6LIv3x4KFoSicawv7HT7WLY7icvbk0+EeVNF+UDg2dKStQyx1LSMkxi57RpG69vO4pQv/aV23sjONpWYM0JSaatbbj3wiARkRWVDZ245L5laOg4cT724o2GWZlmrj6ER5ftH3JenhMpH9jaBiUlaplT79zN0jasnGxTayTveWMv7nljb+4FpAH+4daFbheBiApMR28Ep44B9h3r8GSwaVamW+futDTvYDXPsZjiJ7O34JsfnYwPvnN8LsUjYs1zsTvY1GW5q9NELXNqzbIXayWsSuQDVzd3oyeUeW+DmTVVR0RUGEpEsLOuDZ9+YDm21LS6XRxbDfZg9Hh3CHO31OG7z6zPb4GoKLHmuYh19IbxyXuX4nMfOAMPfuVfhpw+kbNrsTENzxgq7/bie5bkqSRERN4XECnanvkGS6FLXNfM3uMhyhRrnotYosmeiqpml0tSmDLuYdC5ohD5Tm49DFI6gQBQEijOy/9glT+JoNqPL3aT/Yrz6KE4niOyfpHPrbapiYicFhBBmYdrYL/42Gp8/7mNWc27+2g7lu9rHDA8EVSbfe2Zaw5j8rR56ApGLK8nEo1h8rR5eMzCS4xUfBg8+4I/w8C8vgjjz5+YiAqQiPdrYBfsqM9qvh/N2oxvPrluwPDEi4RmL8E/VXEQAFBvMZVl9voa3PvmPgDAo0sZPPsRc56pqOUSQGdSa83OSoioUAREiv6cNXnaPFx07ik4eXQ57v/SB5LB89G2Xvx09hbc/R//jLKSeP1heWn832DY2ovlv3h5W/JzYl7yF251IiIiKjoVVU2Yu6UOR9t6+lSkzNl0BHvrO5J/mzXTaqYzGMGsddUDhlc1FF/HM5Qeg+ciJg4nPbd2h/o0tu9FudStZPrCIBHZh8eUw3z0+8ZMKpSz2b9ufWUHfjVne59hjR1BXHr/sj7BOBU/Bs8+kDhJbKttxbqDLbYt9wO3vYUP377ItuV5STb5gB5PISTyDa/n81J+RU0i5adWHsTMNYf7DBsqoK5r7Rl0XGNHMKuyUWFi8Fxgtta0YsMhawFw/+vHv//fSnzpz6sHnz6XgnmQIn/dZvuoEocoL9p6QpizqdbtYhQtP52zojEdcD2cs/kIbn1lBwDrFR/h6OA50cx99he+MFhgrnl4JQDg0F1XWZ7HTydJO2WWtsFfmchOs9bVYNa6GreLUZT8drqKWuz5K5eXKNt7wmjo6MWpY4ZnvQwqHLxVKmLFVpOcDZ9dI4hs9bcNDF6LlZ8CaLtaFkmXDvTdZzcMmsY4b9tR1Ld5+/0gygyD5wIwd8sRNHUOzKeKxhQHGvmW72ByqQ3ed6wDK/c3WV9X1msi8q6fv7Rt6Imo4BR7M3VOyaZCqjccxQ//uglfn7E2OUxV8as527HxsH3vIFF+MXj2uIb2Xtz4whZMfXbDgHH3vLEXn7pvGaqbu10oWWHINn5+fPmBAW9VExFRYUp3LbDaMlU276Em2o4+ltIBSzASw6x11bjuibWDzUYex+DZg17ZfASTp81Da3cI4diJht37W3uwGQDQ2Jn+cVD/GthfvLQV22pb082RWYEJqkyTISLyIquVKHansszZVItHllUBADp6I5g8bR46esPJFw8TnbRQ4eGW86CnVsa7Cj3Y1DVoQFbd3J18CWKwA36w/KzZG2rxn0+tt1QWp9uKdpLC2ceTk6fNS1mX4kiaZoyIiLxClakbqcwulaFIDIebu3Ja7k9nb8Wflx3oM+xoWy/C0fhvXxIQ7GfqZUFi8OyySDSGQ019D9BE0LunvgNrDsRrl/sHyBffswTbatvi44ZYh9n4oXpSosyxnU8iIu9RTV+rbDbu96/vwr/ds7TP+0Z2VSYlnga39YRxyX3LsKuu3ZblUv4weHbZH9/Yi0/cu7RPraXRUyh+NWc7fjp7K4D0tQQvrq9J1lanSvfCnNWmewpd3u4RlMkuRFQY4m3gu10Kb0t0KNbnZX0LsfPkafOGbKWm/09f3x6//tce70ZvOJpJMcklDJ5dlqhZbkqptQyYPENKd6J7aWMtfvfarkHHm80bK9Dgma2LEBFRJtT432DM0jYSnZ4kXvgDrL/X8sSKA4OOM7sez99ej0eX7sdFdy/BjS9sBhBvZeuJ5Qfw67k7sPHwcQDA1Gc34KK7F1ssBTmJnaS4LHEwpqZRmAbPWSw73Txm3ZVaXq5iQKpJPqze34yvPrEm7+u1gjU55FWpuflEADt1GozZryISb27uH25daH05Q/y8/ce/tPFET5pL9jYCAG58YUty2Kx11ai8/Uq8ueuY5TKQsxg8uyyR36x9hg2czu5zXcy4mW7pCiEgwLiR5RnN/4l7l9pbIAuqMqx1zuf1IZ5TxwsSkRdk06QYFa+rH6rAO8YO3vPfULuLWT8L6eTyTlFZYGBp2CqH93CLuCxxktchap6zqXu2cvxe8Pu38IHb3upTFq/Kpnj5fImPoTMRFQItkHc0Xttah+YMA1czqubNvVqdN59KTILn7lAUP5q1Ob8FobQYPDtsa00rNlfH85Ui0diADk0SgXLqAWp3EFssNaLZ/C478/QWs/KNQSIiW/1o1mZ855mBHYR53dAtYKXLvza/0L22tc50+N6UVrkS2nvDbP3JYQyeM9TaHcLcLUcsT3/Nwyvx+UdWAQDuXrgHF9+zBEfbTrSscSLnOf1yrDfyrjho5COnbcfT47XM9tC81qYzdiYislfq9dJpaw80Y299B4AcK7HSXAy+99xGfOKepTksvK/PPLgcX3m877tAH7tzMT50+9u2rYMGYvCcoRte2IIbX9iS8Qtz017ehqdWHgIANHeGksPN0jbMWA3MnltbjU/euxQbD7ekn7cAIz0vd9hSJJX7RESekpdzq3EhvnPBHnzmweU5Ly5dkQ82daE7NHhzdHY8Ke4IRgYd9+bOehxq6sLs9TVF81TaDXxhMEPHjLyp3sjgO/+rW+vw8XNPwfhRJ17Ce2G9ebuPZi8MmrG6kydSRA42deOs8SMtzUO5i7e2wRMRkRd4/f0Nt6n65OGjS7zcCdnUmRuTnyeMKsel7zvN9nW8vesY3nvGWEwaN8L2ZXsFa54zlDgpx2Lm44+09uCGWZvxg+c3DbqMPvnNxr9DHWzePRTzJ5sLYj4vEAXadDYREaWRaSzs4di5j67Q4DXUufjusxtw1fQVjizbKxg8Z2mwfOKo0Wf96gPNGbWvOlRKguWDUU0/FgUv15Sw1pmICkXa92E8pnBKeoLTv+/kafPwyNKqnJfTFYyiqTOIo209tvds2NodtnV5XsPgOUNmrWOkKikZOsTLpgY10+CszyoK8exjg3zHs4V0QSIiorh0l+TdR9uxyUiHtL687Kt6rF5F/rhwb9brSLjp79sx5Q9v46N3Lsb3nts49Awm7py/O9mVeX+Hm/PfmVq+5BQ8i8g4EXlJRPaIyG4R+aiITBCRt0Sk0vh3vDGtiMh0EakSkW0icoE9XyF/Xt1ah/be+N3UYIFZqUkbjf1lE9Rl1cNgkcVyWaVt5Cn5sdh+ayIqXoV0vnK7rNPmbO/T21+xWmr0bFh5bGDTd+n8efkBfOnPq03H/ds9S7E2g2UVklxrnv8EYKGq/gOA9wPYDWAagEWqeh6ARcbfAHAFgPOM/6YCeDTHdedVdXM3bpi1GbXH483mDFbLaN7BiQ0yzbnKflbP8nJrG4D3y0fkFzwWi4nzVzC/vGBq5Qn2ZQ8MbPouF48t24/Hlu23bXlekXXwLCJjAVwMYAYAqGpIVVsBXAPgGWOyZwB8zvh8DYBnNW4NgHEicnrWJXdQR28Yk6fNw5xNJ/qbD/ZrXSNmdMf8+9d3Yfm+RrxqNGBuoeI5q8f7WdU8F03Y7JyfvGhfjQJ/byJv6A1Hcaw9ux7l/ODpVYcwb9tRt4vhCb99dSd2HslPZ1pOsjtvefK0eXhoUeWA4Y0dQfzkxS14dWsdHl8+dFC8ZG8j7lqwx7ZyeUUuTdW9C0AjgKdE5P0ANgK4EcBpqnoUAFT1qIicakw/CUBqe221xjDPHcF1rfGT7qNL9+MLF5xpOs3x7hAaO4KYUXEQMyoOAgDed/oYjB9Zbjp9rnLJeS6al9kyrB1QC7P8fbP1Dm/SrqtIfmKiYtDQEcRH7ljkdjE86+lVh9wugmc48Vs41cFKOh+9czFGlpcM0YZ0Zsu87619eMdJw/HFD54JEUF9Wy9un78br22ty/jaOXnaPNxwyXn46WXvyawQHpVL8FwK4AIAP1LVtSLyJ5xI0TBjtjsN2JQiMhXxtA6cffbZORQve4na49Tm4/ofDN96av2A+a78UwXOnDB0u4ZmO/Bzaw6jsTOIf3//GebzDLlUa+vJdD6/PM4iIiLv8VvFRC5fN13gnO2yf/7SNowZXobL/+kduPDO3G5Ipy+qLJrgOZec51oAtaq61vj7JcSD6WOJdAzj34aU6c9Kmf9MAAM6a1fVx1V1iqpOmThxYg7Fy555xyVDR5GhaAwHGrN7u3Te9qO4YdbmQcdb7p47q7UXBi/H8UzZICKifEqkizqtvae4m53LRtbBs6rWA6gRkfONQZcA2AXgVQDXG8OuBzDX+PwqgG8arW5cCKAtkd7hNYma50KugRVJH0gXYrCXacsZfquxICIi/0hX4WZmqBTOv20w7wm5vTeM5s5gRuvK1JoDzXjgrX2OrsNOuXbP/SMAz4tIOYADAL6FeEA+W0S+A6AawLXGtPMBXAmgCkC3Ma0nJYK0PmkbbhXGkGmwm3qMMIZ0ntcCdY8Vh4goKzyX5c/PX9pmOvwP83bjD/N2O7bempbuZAsfPymQtI6cgmdV3QJgismoS0ymVQA/zGV9+WKe85y/8NlsVVaDs6J8UdCQzRY4bexw28thRuG9AJqIiPIvp/cFHbyQePEStaKyEd+Ysc7tYmQs15rnopRoqzkWc2b52TU7l/101gNvt+vX7Te8LH+daHrpxFR8W5KIKH/cOod2haK45ZXtLq09vx5bth8rq5rcLkZW2D23RYUWjIiwJjSfVLXg9hEiIq8rtieoVjy3ptqR5Tr5U06eNg/1bZm1rX7Xgj1YUcnguWiYpU3YmbXhx5OBG/g7ExERxTndUMCWmuO+ue4yeDZhtu0LJaXBLzuuVfn6NTSP67LCS2UhIqLiF+952e1S5AeDZ4tsrXkearzNO18hNktXaPxywiAiyieeWu3j9HUqGvPP1mLwbCIRKB9p7cGv5+5ArIB2iNRWQfwe0Pn86xMREeVNTP1TVcfgeQjPrj6M/Y2deV2naS13DnukWRDtl8A6f9/TJz8ouWLytHluF4GIKC2/xBUAg2fLCqWHwdSc52zvAX20/xetAtldiYjS8lNAVuhiqr5574rBs4n+274Qd4XUYN+s/IVyM1AovHS+CEdjBbnPEhGRc5jzbB8GzxbZudPlK9BKtx4vBXtOylcGlib/z31+OoEREXmNV8/A+bge5rqGxo4gesNRW8riJAbPHrNkTwMa2oNuF6Mo+OUGgYioWPklDaBY5Lq5PnT72/j20+vtKYyD2D23BfYfu4Mv8Fs27jR2FNsr6R0zKg5ia02r28UY1Otb6xCKOtSfOxERUY4K5T5k1f5mt4swJAbPJoqudQqLZfdKoGzm96/vymq+fG236Yur8rMiizy8KYmIqEj5pbE6pm24IH85z/7YiQfjl4PYjH+/OREVE57L7MPf0j4Mnk30D7rU+F8hMCtloZTdCX68f/DjdyYiIvf55frDtA0TA5qqK8CdQSBpQ2a7v1IoEkNbT9jmpRIREZEd/P402k4Mni3iPpfej1/cjPnb690uRl/cZkREhY3ncdvwp7QPg2cT/Xcw1cLc6dIF/Ha/UOa5wNngx5QVP35nIiJbefkNeg+zUtG4cMfRgn9SzeDZBfkObfxaa+7Tr01ERC463NyNydPmuV2MAZyOBawu/nvPbXK0HPnAFwZN9M8LUrjfX3t2tYnZlblYgu1i+R6ZUmVTdURUHHx6Gi9YfnnyyZpnE/03/VXTK/DJ8yfat3wH9y3TNqrNpnOuCJ7ixwBa4Z/tS0REFvHCYBvWPFu0ZG+j20XIiIg/A8dUfrkDJiIqVm4/9S10R1p7MHnaPKw/1JKX9fllczF4NlFsG9+vJx+ffm3fbm8iKj48m+VmtdHV9ax11axQshGD5wJS39ZredpcWgjJ5gDji8newdMjERH1l496Fb9cfxg8m/Le5g9HFRfeucjRdSQC4GKpuCySr0FERJSVfD+J9MuTTwbPJhxvziUPO1dqznM+dmUvVjz75SDuz6dfm4iKEM9nuWntPtGeMn9K+zB4pgGK6QDz5YnXj9+ZiMhGXqwQysbt83cDADp6I3lZn18uPwyeTTi98Z1cvvb57Jfd2Jy/vz0REVFcJBrz7dNYJzB4pgGyOcCEbwx6ht9vmoioePB8Vlj8Ep+zkxQTzuc8O7t8K+vxxR2o+vPE64dNS0RE1omI41fD3766Ew++tc/htXgDa55NOB1wObn8nXVtAOJtOibXp8BDiyodWyfgzfwwPwbORERE/amq4xUr3aEo6jJoUreQMXh2g4M78IHGLgDAmgMtfQ6U+/rdDaZLs2DNZWHj5iOiYuHG9ejWV3aguSuY/xVTwWDahgk/BI9+SNtQ9ce2JCIi+8xcc9jtIjiCT2Ptw5pnF+Rr9832QCmWA8yvgbMfboyIiMg6XhXsxeDZRLG8MJhPXm1sowh/6iH58TsTUXHi+cweAvDHtBGDZxfkq2Y3kyD9yYqDWHugJeP5EsSDrwzyPEFEVOB4IrdFJKY4ntLbIOWGOc8mHG9tw4Mng9te35X87MXyZcOv6Qs+/dpEVIy8Vy9TkFZUNuEzDy53uxhFgzXPJhxP23B28e7w6AnOjwF0seSsExEReRGDZxd4PaDLpnRejJ29/SsTERFRIWLw7IK8tbaRx+gx4ME3Bj1+j+Icv35vIiKiPGDwTANkUzMe8F7sDMCfcWRHMIKeUNTtYhARERWlnF8YFJESABsAHFHVq0XkHAAvAJgAYBOAb6hqSESGAXgWwAcBNAP4sqoeynX9TnC8xjJPEZ0dua9Wfwsv1jz7M3QGLrlvmdtFICIiKlp21DzfCGB3yt93A3hAVc8DcBzAd4zh3wFwXFXPBfCAMZ0nOd7ahseDumxK1xGM2F6OXPk2bYOIiIgck1PwLCJnArgKwF+MvwXApwC8ZEzyDIDPGZ+vMf6GMf4SY3rfyVdQl249aYtgjGzrDiMcLewIlAE0ERER2SnXtI0HAfwCwBjj75MBtKpqohqyFsAk4/MkADUAoKoREWkzpm/KsQy2K9QeBlUVIvYt//23vWnPglzCuJmIqLBFojEs3HHU7WIQ9ZF1zbOIXA2gQVU3pg42mVQtjEtd7lQR2SAiGxobG7MtnqflrbWNrOcrjrCTtc5ERIUtpsD3ntvkdjGI+sglbeNjAP5dRA4h/oLgpxCviR4nIoka7TMB1BmfawGcBQDG+JMAtPRfqKo+rqpTVHXKxIkTcyhe9hx/X5BRXR7xtyYiIiL7ZB08q+qvVPVMVZ0M4CsAFqvq1wAsAfBFY7LrAcw1Pr9q/A1j/GL1aBTp0WINqX+xs/0eBfr1ByiWGnQiIiLyDifaef4lgJ+KSBXiOc0zjOEzAJxsDP8pgGkOrLsgOBnS2fEGZrGEnMVyE0BERETekXM7zwCgqksBLDU+HwDwYZNpegFca8f6nOZ82obDK0isJz+r8TQG0ERERGQn9jBYROyKE4sl4CySr0FEREQewuDZhPPBY37CumIJgrOlyqxnIiIisheDZ1MO9zDogYgufQcqWrAvTRIRERE5icGzC5wKS+OdpKS+Mpj9mo61B3MvEBEREVGRYfBswu+VrqrAqv2e6/gxY6rclkRERF5xvCvkdhFsYUtrG5QZJwO6PvXOWa5n8Z4Gpm0QERGRrf7l92+5XQRbMHg24XhTdQ6twa6lLt7TYNOS3MXXBYmIiMhuTNtwAdt5zh/WoBMREZGdGDybKOR4y42iezU/upC3IxEREXkTg2cTTtdWOtfahj05z5m67om1+VlRhhg8ExERkd0YPLvAyeC8T0t1xNQVIiIishWDZxPFEnD5Pd/X39+eiIiInMDg2UVf/vNqrD3YYtvy2LpEX36/eSAiIv/iNdA5DJ5NOL2/JZZvZ+Bsuh5Hl14YeO4gIiIiOzF4NuF0Da6Tyxcw6TmBcTMREfkVK4+cw+C5iKj2Dcx9f+D4/fsTERGR7Rg8m8lT2gY5j3ngREREZCcGzy5wMnhm2sYJDJyJiMiveAV0DoNnEwW9w6XEzn4PHlnDT0RERHZj8GzC8dY2nF180pr9zXlak4cxgCYiIiIbMXh2gaNtL6YsevriKufWUwAYNxMRkV+xnWfnMHg24XxTdQ5iynNSvPURIiIiIvsweC4iqoydiYiIiJzE4NmE4086WB2aF35/YZKIiPyLV0DnMHg24XzszF06H1TZ4gYRERHZi8GzC5wK6BiUExEREcDKIycxeDZRyG+oCpOekwp3KxIREZFXMXh2gZNBXQHH/fZTZW08ERER2YrBswnHc56dSttQ1jwTERERUzmdxOC5yAgbq0viaYOIiIjsxuDZjOPdc3sjrLv0/mVuF8FRbG2DiIj87Kr/d7rbRShKDJ5NON7DoGOtbWSmqqHTkXIQERGRu1SBkeUlbhejKDF4dgErQ/OjkFtNISIiIm8qdbsAXuR0zLVkTwNOGzPM2ZUQFLxRISIiInsxeHbB4j0NWLynwfblsqaViIiIyFlM2zBRyDEom6o7oZC3IxERUa4YEziDwbMJxlzFQcHaeCIi8ide/pzD4LmI8DghIiIichaDZxOsrSwOql5pUZuIiIiKBYNnIiIioiLD6iPnMHg2Uai7myrYOTcRERGRgxg8myjkrA3hq7VJyoaeiYiIyGZZB88icpaILBGR3SKyU0RuNIZPEJG3RKTS+He8MVxEZLqIVInINhG5wK4vQWSGj6yIiMiv4k+jWaHmhFxqniMAfqaq7wVwIYAfisj7AEwDsEhVzwOwyPgbAK4AcJ7x31QAj+awbocVaNClfNkx1dK9jXh79zG3i0FERERFJOvgWVWPquom43MHgN0AJgG4BsAzxmTPAPic8fkaAM9q3BoA40Tk9KxLTjSEVfub8fzaareLQUREREXElpxnEZkM4F8ArAVwmqoeBeIBNoBTjckmAahJma3WGNZ/WVNFZIOIbGhsbLSjeBkr5Mpb5jwTERFRAYcynpdz8CwiowG8DODHqtqeblKTYQO2rao+rqpTVHXKxIkTcy1eVgp1h1MoJp8y0u1iEBERERWtnIJnESlDPHB+XlXnGIOPJdIxjH8bjOG1AM5Kmf1MAHW5rJ8G+uw/n+F2EYiIiMhlfAfKObm0tiEAZgDYrar3p4x6FcD1xufrAcxNGf5No9WNCwG0JdI7vKaQ97f23rDbRSAiIiIqWqU5zPsxAN8AsF1EthjDbgJwF4DZIvIdANUArjXGzQdwJYAqAN0AvpXDusmEKvDwkv1uF4OIiIioaGUdPKtqBQbv0O4Sk+kVwA+zXV8+sX1gIiIiKmSMZJzDHgaJiIiIiCxi8GyiUHOeqxo73S4CERERecDbu46hoqrJ7WIUpVxynotWgcbOuPax1W4XgYiIiDzgp7O3ul2EosWaZyIiIiIiixg8m2DbiERERERkhsEzEREREZFFDJ6JiIiIiCxi8GyCWRtEREREZIbBMxERERGRRQyeTbCHQSIiIiIyw+CZiIiIiMgiBs8mmPNMRERERGYYPJtg8ExEREREZhg8ExERERFZxODZBCueiYiIiMgMg2ciIiIiIosYPJtQJj0TERERkQkGzyYYOhMRERGRGQbPREREREQWMXg2w6pnIiIiIjLB4JmIiIiIyCIGzyaUVc9EREREZILBMxERERGRRQyeTbClOiIiIiIyw+DZBGNnIiIiIjLD4JmIiIiIyCIGzyaYtkFEREREZhg8ExERERFZxODZBJuqIyIiIiIzDJ5NMG2DiIiIiMwweCYiIiIisojBswlWPBMRERGRGQbPREREREQWMXg2w6RnIiIiIjLB4NkEQ2ciIiIiMsPgmYiIiIjIIgbPJpi1QURERERmGDwTEREREVnE4NmEsuqZiIiIiEwweDbB0JmIiIiIzDB4JiIiIiKyKO/Bs4hcLiJ7RaRKRKble/1WMGuDiIiIiMzkNXgWkRIADwO4AsD7AHxVRN6XzzIQEREREWUr3zXPHwZQpaoHVDUE4AUA1+S5DEO67fVdbheBiIiIyJe21ba6XYS0SvO8vkkAalL+rgXwkdQJRGQqgKkAcPbZZ+evZCmu/ufTUdPSjTMnjERtSzfGjigDAISjMZwyehh6w1GICEKRGFq6Qjj75JGoPd6DiaPLEYzE0N4bwZnjR+B4VwilJQGo6oBh0VgMzZ0hvHviaOxv7MTZE0aiNxJDW3cIZ04YidbuEEoDAURjimhMcfqonVsAABMpSURBVPLoclS3dOP0k4ajNxxDW08Yk8aPwJHjPTht7DD0hGNo7Q7hrAkjcbwrhGGlAYSjipgqJowqx/HuEMpLTgw7efQwtHQFMWZYGbrDUYQjMZwyZhiOd4VQViIIRWOIxYBTxgxD7fFuTBo3At2hKHrDUZw8ehh6QhEMKytBdzCC3nB83q5gBMNKA+gMRowyx5c3ZngpOoMRRKKKU8bEf7+yEkFHb9/pxo4oRUdvBGUlAYweVoruUBTDSgPoCUdRXhLAsLIAukNRjCovQW84hvbeME4ePQwCIKqKnlAUMVWMHV6GqCqgQFtPGCUBwUkjytAdimBEeSl6QhHEFBg9LL77K4DuYASK+LCecHy9XcEIAiIYZQwbPawUwUgUAkFZiaAzGMXY4aUQEfSEIygvCaCxM4iTR8V/CwDJeUeUlQw6bHhZCcqN363/dL3hKEYNK0UoEkNvOIrRw0vREzoxDADKS+P7mIgky1deGsDwshL0hqMIRqIYUVYKVcWwshIEI1EAQDASQ2lAMLK8NDlMIFBVDC8vQU8oPu/I8lLEYvFhveG+08VS1ptYR3x/C2N4WQC94VhyWEt3CGOGlaIzGIVCMWFkfNiIshIAQCgSw6hhpWjvDWNUeXz7KzT+PULxY06N13kT8wpODEtMN2pYKaKqiERjKC0JoDcURSAgKCsJJIcFwyeGRWN9p4upQiAYP7IsWeaOYOTEMGOfDkU1OW9PKIpQJIaJY4YhGIlhZHlJct83m25YaQAigmgsBhFBSUAQi8V/y/JSQW84vm2iyWEB9IajOGlEGYKRKGIxIBAQ9IajOP2k4ahv64WIYNzIMuMck1jmielKA4LSkviwUcNK4+eYEkEkqsnfIrGO9p4wAgHB2OFlaO0OYWzKsJNGlBnHa3xY6nrHjihDbzgK1fh6e0IRlJcGUtYh6AlF+w0LJKcLRxUB4zcYOF182JjhZQhHY1AFRg4rQVt3GGUlAYSjseTvFwzH+hwjI8pL0N4bxmhjWLx8QE8oinEjy9HWE4YAGDuiDG09YeP4jh/TY0eUoaM3fi5InLsS8ySOm9JAACPKStATjkIkvtyykgBC0RiGl5VAAAQjUZQGTgwLCNAbjqIkICgJBCAp16CYxn+HhFAkBhEgEosfD/FzQAmisRPTdYfi21gECEcVI4xjODGstCS+jp5wFCUycLqyEoFqfB0jjGN9RFl8HQpFaSB+/o2poiQg6A5FMH5kOXrDMXSHIsZ3Eowoj3+3UCSGmMavXyPKS9HcGUx+7/i2i5+LSwOB5PE6algpjrT24LQxw9ETjiIcjWHCqPI+w9p7wzhj3AjU9Zvu5NHxa9W4EWUIRxWdwQgmjY9PN3H0MPSEo+joPTFs/MhyBCPxfXXCqHKUBuLn9PjxHy9fc2cQp40djmAkitbucPJaOqKsFDFVBCNRjB8ZHzayvBQRYx8cNawUnb1hlAQCiMRiCEViyXlHlpciHI1fwxPf4+RR5egKRtETjmLS+BHGPqp9hsWnG4bOYBi94Vhy2BknjUBbTxiNHUGce+potPaEEI4oIrEYuoJRnHvqaNS19cSvbaEoOoMRvHtifNgZ40agrTuMrlAk+fmkkfFjXhXJOGFkeSnaesIIR2PJMk8cPQytPWGUiODk0eWoPd6DU8cMw/HuEMpKApgwKj5s0vgRyeUlhp06ZhiaOoOIKTBp3AjUHu/GO04ajpauECIxxRknjcDI8pLcAjmH5Tt4FpNhfTKMVfVxAI8DwJQpU1zJPv6/6y5wY7VERERE5HH5TtuoBXBWyt9nAqjLcxmIiIiIiLKS7+B5PYDzROQcESkH8BUAr+a5DEREREREWclr2oaqRkTkfwC8AaAEwJOqujOfZSAiIiIiyla+c56hqvMBzM/3eomIiIiIcsUeBomIiIiILGLwTERERERkEYNnIiIiIiKLGDwTEREREVnE4JmIiIiIyCIGz0REREREFjF4JiIiIiKySFTV7TIMSkQaARx2afWnAGhyad10AreDN3G7eBO3i3dx23gTt4s3ubVd3qmqE4eayNPBs5tEZIOqTnG7HH7H7eBN3C7exO3iXdw23sTt4k1e3y5M2yAiIiIisojBMxERERGRRQyeB/e42wUgANwOXsXt4k3cLt7FbeNN3C7e5OntwpxnIiIiIiKLWPNMRERERGQRg2ciIiIiIosYPJPrRETcLgMRUa54LiPyBwbP5AWliQ+8+HiHiJwvIjxHeIiIXCci7zc+81jxHh4vRD7guwNdRD4nIr93uxwEiMjlIvIGgHtF5PMAoHyD1XUicpmIrAXwXfjwHOFFInKpiKwA8CCAfwF4rHiJiFwlIq8D+L2IfMzt8lDyWv+QiExwuyx0QrHEYKVDT1L4jBqaAIBvAZgG4J0i8qaqrnC3ZP5jbIsyAHcA+CiAuwGcCeBaEdmhqpVuls+vjO1SCuBWAF8F8EtVnZM6nsFafhnbZDiAZwCcCuAPAK4BMNIYX6KqUfdKSAAgIh8E8BsAvwUwFsD1InKeqj4tIgFVjblaQJ8xjpvPA7gdwBgAS0Xk79wO7inGGMwXtUoaFwVQhXitzQ8AFPydTyEytkUIwEIA/6aqrwJYBSAM4KCrhfMxY7uEAcQAvJQInEXk4yJS5m7p/MnYJj0AnlfVT6jqG4gfK98wxjNw9oZLAaxQ1fkA5gKoB/AjETlJVWNMr8kv4yb/AICLANwI4OuIV9CQS4oxBivq4FlEbhCRJ0Tku8agZaraoapPABglIt8xpivq38EL+m8LVX1bVSMiciWAOQDeA+AOEfmyMT0vOHmQsl2mGoMeA3C6iDwlItsB/ALADADfNqbndnFYyjb5LwBQ1bnG8BLEbzB3ishZbpbRz/pvHwBLAFwtIuONm50wgHbEjx2m1+SBiFwvIpelDNqhqs2q+jLi2+MLIlLuUvF8q5hjsIIrsFUi8p8ArgPwMoBviMivALwrZZJfA/ipccLj4xwHmWyLm0TkXGN0E4DLVfWjAJYC+LaITOYFx3n9tsvXROQWAEEArwAoB3AtgH83xn9BRM7mdnFWv23ydeNYeReQrGluB/B+AK2uFdLHTLbPzQAOAXgTwEwjL/1dAO4CMEFERrlUVF8QkfEi8hLiv/d9xg0mAKTW+P8JwGcB/FO/eVkR4KBij8GKNngGcAmAu1V1IYCfIZ47+LXESFVdAGA3gKkiMkZErnWnmL7Qf1uUw9gWqrpOVfcZ0+0C0Agg4kop/af/dhkG4L9V9RUAU1V1jxEsb0M8WAu7V1TfMDtWvp4YqarbAfQA+Io7xfM9s+vKN1X1R4g/ir5NVb8FoBfAcFXtcq+oxU9VjyN+4/JeABsRD8gS49T4dyWALQCuEJF/SDxlY0WA44o6Biu64Dml+n8zgKsBQFU3AFgN4Ix+b0L/EsCdACoBvCOf5fSDNNtiDeKpAf3fSv9PxF+Gas5XGf0ozXZZCeAcEflYv4v+9QBGADie14L6yBDHSvK8ZdSWvQlgOGvO8meIY+Y8EblIVatV9S1juqsA7M9/Sf0jZf9/VlVbATyC+BOydxq55iUp2+1BAL8CsAzxl29Z8+wQv8RgBR88i8g7jH8DAJBS/b8SQEBELjb+3gHgKIAzjOnPRfxgewXABar6UD7LXYxy2BbfFJEdAM4B8H0jb5BskuF2qcOJ7fIfIrIV8Udt31fV3rwWvIhle6wYtWWnAuhizZlzsjhmTjemv1hElgE4D/H3B8gmJtskUbPca/y7HsACxFvZgKpGjSD6NAD/B2AxgA+o6h9S56fciMg/isjwxN9+icEKNngWkX8RkUUw3thMbLCUu55KADsBfFniTTrVIn5nM9kY3wbgf1T1C6r6/9u79xg5qzqM49+nLcHKthZFDULQphrQAIo1UEuUi0D/MGCrYECiqEDSaPoHCf1HpYGmURMbMN7wCgUjKoKNDRorNcYWqYbeG4yXGhoIkoYGDds0VUMf/zjvtmvd1nF3Ou/ZmeeTbLIz77s7Z/fJO/nNzDm/89eeDr7PTCCL2c3xHZRpAjfY3tPb0fevLuTyJ2Cx7Y8kl+7owvMWwK227+ndqAdHF66Z3cAnbC+yvbeng+9Tx8hEYyw0+wrwxqage7Wk2ZR1NUtsX2X7uZ4Ovo9JOlfSY5QWmq8adf9A1GCTrnhuLpi7gPuB+2zfPOrY6J6aw8AGypzBlSrttk6mmRJg+3mnp/CEdCGLvQC2t9l+vLej719dzGWn7Y29HX1/6tbzFoBLq8fooi5eM0/bfrK3o+9PHWTi5p3l6ZKGoPz/gdXATkpOJzfvQD/dxt/Q5z5DaWu6yPazcKj3/EDUYJOueG4+apkBbLV9P4CkOaOf4FR2r3mA8spmGSWwDc3t+1oZeB9KFnVKLvVJJnVLPvXpMJM7gO/RdHGQdB1l4eZK4BzbW1oZfB+TNEXSHGCf7S82910uaRag5vYK+vxa0WSY9iNpHvCCm64MkmYCT1AumgXAHmAfZVHAPspOT8ts72rOnwKcZHu496PvL8miTsmlPsmkbsmnPl3IZB6wx3Y23OqiMXKZAWwBbgFuoiwo30PpnvF9yg7CfX2tVP3Os6RZkn4KPAp8UE3PTNsvAl8F3k9ZQXsdZSL6NcDfbH/I9i6NWuzRT6G1IVnUKbnUJ5nULfnUpwuZTG3O/20K5+45Ri7DwL3AcuAe2wuAbwPzgFMH4VqpungGTgLWAkua70dWbWL7S8AlttfbHtnY4R3AfviveWoxccmiTsmlPsmkbsmnPhPNJFvVHx9HzQV4hLL475XN7U2UrekPQP9fK9UVzyptyy6SNLOZhP5N4EFKIBdIet3IuS4N0kfMBZ4BXmqO9W1ovZIs6pRc6pNM6pZ86pNM6tRBLqcB2N4BLAU+KekUymZO53B4QWBf51JF8dysqj1V0q8oGzJcD9wt6RTbB2zvB9ZRJp1fOurnTpR0saRNlPlQn3d60U5IsqhTcqlPMqlb8qlPMqnTeHOx/R3KHOfbgQ8AN3lAOpu0XjyrtDYZWVX7rO33UFbLvkB5xQMc2mJzN3CWpFdImt58hPNPYIXtK314m+cYh2RRp+RSn2RSt+RTn2RSp3HkcmaTy4zm/juBW2wvsP37nv8BLWmt24akaZTJ5lOBnwEzgatt39AcF2Xnpmtt/7q5b4jSkPtC4AzgPE/C5tq1SRZ1Si71SSZ1Sz71SSZ1mmAu84HXM8C5tPLOs6SLgM2UjwB2UXYO+hdwiaTz4VCPx+WUjwNGvJfyimgbpYfjQIbWTcmiTsmlPsmkbsmnPsmkTl3IZTsDnsu0lh73ILDS9nehbL8JzKY0074bmKvS4mQ1Jcw32N5NmbB+me317Qy7LyWLOiWX+iSTuiWf+iSTOiWXCWprzvNm4EE1vRmB3wBn2F4FTJW0xGWl5unAS01o2P5JQuu6ZFGn5FKfZFK35FOfZFKn5DJBrRTPtvfb/ocP92a8HHi++f5jwJslPUJZxbkFDs2/iS5LFnVKLvVJJnVLPvVJJnVKLhPX1rQNoKzyBAy8FljT3D0MfAo4G3jKpc/gyPybOE6SRZ2SS32SSd2ST32SSZ2Sy/i13aruIHACsBc4t3mlcxtw0PZjI6FFTySLOiWX+iSTuiWf+iSTOiWXcWqtVd2hAUjzgMebr3tdmm5HC5JFnZJLfZJJ3ZJPfZJJnZLL+NRQPJ8OfBi406URerQkWdQpudQnmdQt+dQnmdQpuYxP68VzRERERMRk0fac54iIiIiISSPFc0REREREh1I8R0RERER0KMVzRERERESHUjxHRFRC0u2Sbj3G8YWS3tLB7/mP8yQtl3RZt8YZETHIUjxHREweC4H/WTwfeZ7tZbbXHbdRRUQMkBTPEREtkvRpSX+UtA44s7nvZklPSNou6WFJL5c0H7gK+IKkbZLmNF8/l7RZ0gZJZx3lvFWSrm5+925Jn5W0UdImSW+XtFbSXyQtHjWupc0Ydki6o4V/TURElaa1PYCIiEElaS5wLXAe5fl4C7AZ+LHtbzXnrAButP1lSWuAR2w/1Bz7JbDY9p8lXQB8zfalY5x35EM/Y/udku4CVgEXAi8DngS+LukK4E3A+YCANZLebXv9cftnRERMEimeIyLa8y5gte39AE3RC3B2UzTPAoaAtUf+oKQhYD7wo1HF8YkdPu7I4+wEhmwPA8OSDkiaBVzRfG1tzhuiFNMpniNi4KV4joho11jbvK4CFtreLumjwMVjnDMF+Lvtt43jMUe24T046vuR29Mo7zZ/zvY3xvG7IyL6WuY8R0S0Zz2wSNJ0STOAK5v7ZwDPSToBuH7U+cPNMWy/CDwl6RoAFW898rxxWgt8vHl3G0mnSXrNBH5fRETfSPEcEdES21uAHwLbgIeBDc2h24DfAY8Cfxj1Iz8AlkraKmkOpbC+UdJ2ynzl9x3lvP93XL8AHgA2StoJPMTEivGIiL4he6xPDCMiIiIi4kh55zkiIiIiokMpniMiIiIiOpTiOSIiIiKiQymeIyIiIiI6lOI5IiIiIqJDKZ4jIiIiIjqU4jkiIiIiokMpniMiIiIiOvRvg69DZ5WgerIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmy3_data['GHI'].plot(figsize=(12,6))\n",
    "plt.title('Tucson TMY GHI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}