{
 "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'longitude': -160.517, 'State': 'AK', 'latitude': 55.317, 'altitude': 7.0, 'Name': '\"SAND POINT\"', 'USAF': 703165, 'TZ': -9.0}\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <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",
       "      <th>DNI</th>\n",
       "      <th>DNISource</th>\n",
       "      <th>DNIUncertainty</th>\n",
       "      <th>DHI</th>\n",
       "      <th>DHISource</th>\n",
       "      <th>...</th>\n",
       "      <th>AOD</th>\n",
       "      <th>AODSource</th>\n",
       "      <th>AODUncertainty</th>\n",
       "      <th>Alb</th>\n",
       "      <th>AlbSource</th>\n",
       "      <th>AlbUncertainty</th>\n",
       "      <th>Lprecipdepth</th>\n",
       "      <th>Lprecipquantity</th>\n",
       "      <th>LprecipSource</th>\n",
       "      <th>LprecipUncertainty</th>\n",
       "    </tr>\n",
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       "      <th>datetime</th>\n",
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       "      <td>-9900</td>\n",
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       "      <td>8</td>\n",
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       "      <td>-9900</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01 05:00:00-09:00</th>\n",
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       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1112e1550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YD/btW1ycOqLzKOhAvuGGG7Bnzx5MmDABVVVVSE9PR3JyMoqLi+F0OuFwOFBX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\nIhFgIBMREYkAA5mIiEgEGMhEREQi8P8BRJFFEy33UnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1046b26a0>"
      ]
     },
     "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": {
    "collapsed": true
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f056eb8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P76GtbpMI8ihHSIbdOVZxDNrcSd0/6vYlDsQHHjptgAEMGOyqacNVl2Ull0yc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108a3cf8>"
      ]
     },
     "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'State': 'FL', 'altitude': 2.0, 'latitude': 25.8, 'City': 'MIAMI', 'WBAN': '12839', 'longitude': -80.26666666666667, 'TZ': -5}\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": {
    "collapsed": false
   },
   "source": [
    "Finally, the TMY readers can access TMY files directly from the NREL website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10fdef630>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6+h/o7YdahgtvNM2L13x7Cv/v5R2ev9/dVOxVs0ckK5kGCImHcUbcguA30qco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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a30ba8>"
      ]
     },
     "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": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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