{
 "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 0.2.1\n",
    "* Python 2.7.10\n",
    "* IPython 3.2\n",
    "* pandas 0.16.2\n",
    "\n",
    "Authors:\n",
    "* Will Holmgren (@wholmgren), University of Arizona. July 2014, July 2015."
   ]
  },
  {
   "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": [
      "{'TZ': -9.0, 'Name': '\"SAND POINT\"', 'altitude': 7.0, 'USAF': 703165, 'longitude': -160.517, 'State': 'AK', 'latitude': 55.317}\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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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",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>-9900</td>\n",
       "      <td>?</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01 04:00:00-09:00</th>\n",
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       "      <td>0.24</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>-9900</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<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 0x7f8056b33850>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uzpqOLjQ4eYifPvUDe8qaP34rNtQgP3yppmj0+rTFLrfEgl780Yg4C7gSOBD4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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f808d537f90>"
      ]
     },
     "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 0x7f8056513190>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SSbLE+CLfeGgb4BzEFLQDsrW+BfDetEepHhJzqBORw0AXMAoMG2MuEZGFwIPA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f807c9547d0>"
      ]
     },
     "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": [
      "{'City': 'MIAMI', 'TZ': -5, 'altitude': 2.0, 'longitude': -80.26666666666667, 'State': 'FL', 'WBAN': '12839', 'latitude': 25.8}\n",
      "                           year  month  day  hour  ETR  ETRN  GHI GHISource  \\\n",
      "1962-01-01 00:00:00-05:00    62      1    1     1    0     0    0         ?   \n",
      "1962-01-01 01:00:00-05:00    62      1    1     2    0     0    0         ?   \n",
      "1962-01-01 02:00:00-05:00    62      1    1     3    0     0    0         ?   \n",
      "1962-01-01 03:00:00-05:00    62      1    1     4    0     0    0         ?   \n",
      "1962-01-01 04:00:00-05:00    62      1    1     5    0     0    0         ?   \n",
      "\n",
      "                           GHIUncertainty  DNI          ...            \\\n",
      "1962-01-01 00:00:00-05:00               0    0          ...             \n",
      "1962-01-01 01:00:00-05:00               0    0          ...             \n",
      "1962-01-01 02:00:00-05:00               0    0          ...             \n",
      "1962-01-01 03:00:00-05:00               0    0          ...             \n",
      "1962-01-01 04:00:00-05:00               0    0          ...             \n",
      "\n",
      "                          PwatUncertainty  AOD  AODSource AODUncertainty  \\\n",
      "1962-01-01 00:00:00-05:00               8   62          F              8   \n",
      "1962-01-01 01:00:00-05:00               8   62          F              8   \n",
      "1962-01-01 02:00:00-05:00               8   62          F              8   \n",
      "1962-01-01 03:00:00-05:00               8   62          F              8   \n",
      "1962-01-01 04:00:00-05:00               8   62          F              8   \n",
      "\n",
      "                           SnowDepth  SnowDepthSource SnowDepthUncertainty  \\\n",
      "1962-01-01 00:00:00-05:00          0                A                    7   \n",
      "1962-01-01 01:00:00-05:00          0                A                    7   \n",
      "1962-01-01 02:00:00-05:00          0                A                    7   \n",
      "1962-01-01 03:00:00-05:00          0                A                    7   \n",
      "1962-01-01 04:00:00-05:00          0                A                    7   \n",
      "\n",
      "                           LastSnowfall  LastSnowfallSource  \\\n",
      "1962-01-01 00:00:00-05:00            88                   E   \n",
      "1962-01-01 01:00:00-05:00            88                   E   \n",
      "1962-01-01 02:00:00-05:00            88                   E   \n",
      "1962-01-01 03:00:00-05:00            88                   E   \n",
      "1962-01-01 04:00:00-05:00            88                   E   \n",
      "\n",
      "                          LastSnowfallUncertaint  \n",
      "1962-01-01 00:00:00-05:00                      7  \n",
      "1962-01-01 01:00:00-05:00                      7  \n",
      "1962-01-01 02:00:00-05:00                      7  \n",
      "1962-01-01 03:00:00-05:00                      7  \n",
      "1962-01-01 04:00:00-05:00                      7  \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 0x7f80564e8790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JFfvZsq+94PUErQRe3OTGmRtZR8/AKKu2H3Y9hFLWwc+14e/O2g46UjR2LpZe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\nyWQymUwmMw3IgnUmk8lkMplMJjMN+H8oZpUKoqRliQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80567bfad0>"
      ]
     },
     "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 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}