sjpfenninger · March 11, 2014 10:37
diff --git a/example_ephem.ipynb b/example_ephem.ipynb
 {
 "metadata": {
  "name": "example_ephem"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import ephem\n\nimport pandas as pd",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "date_times = pd.date_range('2013-01-01 00:00',\n                           '2013-12-31 23:00',\n                           freq='H')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = pd.DataFrame(index=date_times,\n                  columns=['sun_alt', 'sun_az'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sun_alt</th>\n      <th>sun_az</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-01-01 00:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 01:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 02:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 03:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 04:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 05:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 06:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 07:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 08:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 09:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 10:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 11:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 12:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 13:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 14:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 15:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 16:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 17:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 18:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 19:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 20:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 21:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 22:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 23:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 00:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 01:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 02:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 03:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 04:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 05:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 06:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 07:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 08:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 09:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 10:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 11:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 12:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 13:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 14:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 15:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 16:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 17:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 18:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 19:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 20:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 21:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 22:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 23:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 00:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 01:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 02:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 03:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 04:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 05:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 06:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 07:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 08:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 09:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 10:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 11:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th></th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n  </tbody>\n</table>\n<p>8760 rows \u00d7 2 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "                    sun_alt sun_az\n2013-01-01 00:00:00     NaN    NaN\n2013-01-01 01:00:00     NaN    NaN\n2013-01-01 02:00:00     NaN    NaN\n2013-01-01 03:00:00     NaN    NaN\n2013-01-01 04:00:00     NaN    NaN\n2013-01-01 05:00:00     NaN    NaN\n2013-01-01 06:00:00     NaN    NaN\n2013-01-01 07:00:00     NaN    NaN\n2013-01-01 08:00:00     NaN    NaN\n2013-01-01 09:00:00     NaN    NaN\n2013-01-01 10:00:00     NaN    NaN\n2013-01-01 11:00:00     NaN    NaN\n2013-01-01 12:00:00     NaN    NaN\n2013-01-01 13:00:00     NaN    NaN\n2013-01-01 14:00:00     NaN    NaN\n2013-01-01 15:00:00     NaN    NaN\n2013-01-01 16:00:00     NaN    NaN\n2013-01-01 17:00:00     NaN    NaN\n2013-01-01 18:00:00     NaN    NaN\n2013-01-01 19:00:00     NaN    NaN\n2013-01-01 20:00:00     NaN    NaN\n2013-01-01 21:00:00     NaN    NaN\n2013-01-01 22:00:00     NaN    NaN\n2013-01-01 23:00:00     NaN    NaN\n2013-01-02 00:00:00     NaN    NaN\n2013-01-02 01:00:00     NaN    NaN\n2013-01-02 02:00:00     NaN    NaN\n2013-01-02 03:00:00     NaN    NaN\n2013-01-02 04:00:00     NaN    NaN\n2013-01-02 05:00:00     NaN    NaN\n2013-01-02 06:00:00     NaN    NaN\n2013-01-02 07:00:00     NaN    NaN\n2013-01-02 08:00:00     NaN    NaN\n2013-01-02 09:00:00     NaN    NaN\n2013-01-02 10:00:00     NaN    NaN\n2013-01-02 11:00:00     NaN    NaN\n2013-01-02 12:00:00     NaN    NaN\n2013-01-02 13:00:00     NaN    NaN\n2013-01-02 14:00:00     NaN    NaN\n2013-01-02 15:00:00     NaN    NaN\n2013-01-02 16:00:00     NaN    NaN\n2013-01-02 17:00:00     NaN    NaN\n2013-01-02 18:00:00     NaN    NaN\n2013-01-02 19:00:00     NaN    NaN\n2013-01-02 20:00:00     NaN    NaN\n2013-01-02 21:00:00     NaN    NaN\n2013-01-02 22:00:00     NaN    NaN\n2013-01-02 23:00:00     NaN    NaN\n2013-01-03 00:00:00     NaN    NaN\n2013-01-03 01:00:00     NaN    NaN\n2013-01-03 02:00:00     NaN    NaN\n2013-01-03 03:00:00     NaN    NaN\n2013-01-03 04:00:00     NaN    NaN\n2013-01-03 05:00:00     NaN    NaN\n2013-01-03 06:00:00     NaN    NaN\n2013-01-03 07:00:00     NaN    NaN\n2013-01-03 08:00:00     NaN    NaN\n2013-01-03 09:00:00     NaN    NaN\n2013-01-03 10:00:00     NaN    NaN\n2013-01-03 11:00:00     NaN    NaN\n                        ...    ...\n\n[8760 rows x 2 columns]"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.loc['2013-01-10']",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sun_alt</th>\n      <th>sun_az</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-01-10 00:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 01:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 02:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 03:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 04:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 05:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 06:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 07:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 08:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 09:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 10:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 11:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 12:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 13:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 14:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 15:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 16:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 17:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 18:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 19:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 20:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 21:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 22:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013-01-10 23:00:00</th>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>24 rows \u00d7 2 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "                    sun_alt sun_az\n2013-01-10 00:00:00     NaN    NaN\n2013-01-10 01:00:00     NaN    NaN\n2013-01-10 02:00:00     NaN    NaN\n2013-01-10 03:00:00     NaN    NaN\n2013-01-10 04:00:00     NaN    NaN\n2013-01-10 05:00:00     NaN    NaN\n2013-01-10 06:00:00     NaN    NaN\n2013-01-10 07:00:00     NaN    NaN\n2013-01-10 08:00:00     NaN    NaN\n2013-01-10 09:00:00     NaN    NaN\n2013-01-10 10:00:00     NaN    NaN\n2013-01-10 11:00:00     NaN    NaN\n2013-01-10 12:00:00     NaN    NaN\n2013-01-10 13:00:00     NaN    NaN\n2013-01-10 14:00:00     NaN    NaN\n2013-01-10 15:00:00     NaN    NaN\n2013-01-10 16:00:00     NaN    NaN\n2013-01-10 17:00:00     NaN    NaN\n2013-01-10 18:00:00     NaN    NaN\n2013-01-10 19:00:00     NaN    NaN\n2013-01-10 20:00:00     NaN    NaN\n2013-01-10 21:00:00     NaN    NaN\n2013-01-10 22:00:00     NaN    NaN\n2013-01-10 23:00:00     NaN    NaN\n\n[24 rows x 2 columns]"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sun = ephem.Sun()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "observer = ephem.Observer()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "observer.lat = '-33.92'\nobserver.lon = '18.86'",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sun.compute(observer)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sun.alt",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "0.9007017612457275"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sun.az",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "0.7614235877990723"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for i in df.index:\n    observer.date = i\n    sun.compute(observer)\n    df.at[i, 'sun_az'] = sun.az\n    df.at[i, 'sun_alt'] = sun.alt",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sun_alt</th>\n      <th>sun_az</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-01-01 00:00:00</th>\n      <td>  -0.5333424</td>\n      <td>  2.804925</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 01:00:00</th>\n      <td>  -0.4363686</td>\n      <td>  2.555485</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 02:00:00</th>\n      <td>  -0.2976271</td>\n      <td>  2.344329</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 03:00:00</th>\n      <td>  -0.1288767</td>\n      <td>  2.167779</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 04:00:00</th>\n      <td>  0.06248335</td>\n      <td>  2.017285</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 05:00:00</th>\n      <td>   0.2615042</td>\n      <td>  1.883616</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 06:00:00</th>\n      <td>   0.4713036</td>\n      <td>  1.757556</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 07:00:00</th>\n      <td>   0.6864626</td>\n      <td>   1.62819</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 08:00:00</th>\n      <td>   0.9032696</td>\n      <td>  1.477507</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 09:00:00</th>\n      <td>    1.115926</td>\n      <td>  1.261511</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 10:00:00</th>\n      <td>    1.305346</td>\n      <td> 0.8217843</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 11:00:00</th>\n      <td>    1.374379</td>\n      <td>  6.038182</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 12:00:00</th>\n      <td>    1.236459</td>\n      <td>  5.239558</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 13:00:00</th>\n      <td>    1.033319</td>\n      <td>  4.924048</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 14:00:00</th>\n      <td>   0.8179364</td>\n      <td>  4.742611</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 15:00:00</th>\n      <td>   0.6012309</td>\n      <td>  4.603424</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 16:00:00</th>\n      <td>   0.3876636</td>\n      <td>  4.476964</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 17:00:00</th>\n      <td>   0.1809971</td>\n      <td>  4.349103</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 18:00:00</th>\n      <td>-0.006342059</td>\n      <td>  4.209952</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 19:00:00</th>\n      <td>  -0.1990943</td>\n      <td>  4.050354</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 20:00:00</th>\n      <td>   -0.357241</td>\n      <td>  3.861073</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 21:00:00</th>\n      <td>  -0.4813613</td>\n      <td>  3.634683</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 22:00:00</th>\n      <td>  -0.5585454</td>\n      <td>  3.371375</td>\n    </tr>\n    <tr>\n      <th>2013-01-01 23:00:00</th>\n      <td>  -0.5776199</td>\n      <td>  3.086149</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 00:00:00</th>\n      <td>  -0.5353309</td>\n      <td>  2.806496</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 01:00:00</th>\n      <td>  -0.4386084</td>\n      <td>  2.556459</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 02:00:00</th>\n      <td>   -0.299992</td>\n      <td>  2.344816</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 03:00:00</th>\n      <td>  -0.1314006</td>\n      <td>  2.167905</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 04:00:00</th>\n      <td>  0.06016944</td>\n      <td>  2.017144</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 05:00:00</th>\n      <td>   0.2591169</td>\n      <td>  1.883263</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 06:00:00</th>\n      <td>   0.4689339</td>\n      <td>  1.757014</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 07:00:00</th>\n      <td>   0.6841059</td>\n      <td>  1.627464</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 08:00:00</th>\n      <td>   0.9009076</td>\n      <td>  1.476613</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 09:00:00</th>\n      <td>    1.113524</td>\n      <td>  1.260682</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 10:00:00</th>\n      <td>    1.302944</td>\n      <td> 0.8232319</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 11:00:00</th>\n      <td>    1.373257</td>\n      <td>  6.049227</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 12:00:00</th>\n      <td>    1.236928</td>\n      <td>  5.246854</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 13:00:00</th>\n      <td>    1.034221</td>\n      <td>  4.928482</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 14:00:00</th>\n      <td>   0.8189343</td>\n      <td>  4.745876</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 15:00:00</th>\n      <td>   0.6022008</td>\n      <td>  4.606148</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 16:00:00</th>\n      <td>   0.3885344</td>\n      <td>  4.479437</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 17:00:00</th>\n      <td>   0.1817067</td>\n      <td>  4.351488</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 18:00:00</th>\n      <td>-0.005949598</td>\n      <td>  4.212358</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 19:00:00</th>\n      <td>  -0.1988945</td>\n      <td>  4.052855</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 20:00:00</th>\n      <td>  -0.3574258</td>\n      <td>  3.863694</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 21:00:00</th>\n      <td>  -0.4820236</td>\n      <td>  3.637362</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 22:00:00</th>\n      <td>  -0.5597456</td>\n      <td>  3.373919</td>\n    </tr>\n    <tr>\n      <th>2013-01-02 23:00:00</th>\n      <td>  -0.5793337</td>\n      <td>  3.088275</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 00:00:00</th>\n      <td>  -0.5374381</td>\n      <td>  2.808004</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 01:00:00</th>\n      <td>  -0.4409522</td>\n      <td>  2.557344</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 02:00:00</th>\n      <td>  -0.3024446</td>\n      <td>  2.345198</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 03:00:00</th>\n      <td>  -0.1339885</td>\n      <td>  2.167918</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 04:00:00</th>\n      <td>  0.05780236</td>\n      <td>  2.016884</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 05:00:00</th>\n      <td>   0.2566792</td>\n      <td>  1.882783</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 06:00:00</th>\n      <td>   0.4665208</td>\n      <td>  1.756333</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 07:00:00</th>\n      <td>   0.6817097</td>\n      <td>  1.626577</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 08:00:00</th>\n      <td>    0.898505</td>\n      <td>  1.475521</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 09:00:00</th>\n      <td>    1.111072</td>\n      <td>  1.259583</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 10:00:00</th>\n      <td>    1.300456</td>\n      <td> 0.8242722</td>\n    </tr>\n    <tr>\n      <th>2013-01-03 11:00:00</th>\n      <td>    1.371979</td>\n      <td>  6.060177</td>\n    </tr>\n    <tr>\n      <th></th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n  </tbody>\n</table>\n<p>8760 rows \u00d7 2 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "                         sun_alt     sun_az\n2013-01-01 00:00:00   -0.5333424   2.804925\n2013-01-01 01:00:00   -0.4363686   2.555485\n2013-01-01 02:00:00   -0.2976271   2.344329\n2013-01-01 03:00:00   -0.1288767   2.167779\n2013-01-01 04:00:00   0.06248335   2.017285\n2013-01-01 05:00:00    0.2615042   1.883616\n2013-01-01 06:00:00    0.4713036   1.757556\n2013-01-01 07:00:00    0.6864626    1.62819\n2013-01-01 08:00:00    0.9032696   1.477507\n2013-01-01 09:00:00     1.115926   1.261511\n2013-01-01 10:00:00     1.305346  0.8217843\n2013-01-01 11:00:00     1.374379   6.038182\n2013-01-01 12:00:00     1.236459   5.239558\n2013-01-01 13:00:00     1.033319   4.924048\n2013-01-01 14:00:00    0.8179364   4.742611\n2013-01-01 15:00:00    0.6012309   4.603424\n2013-01-01 16:00:00    0.3876636   4.476964\n2013-01-01 17:00:00    0.1809971   4.349103\n2013-01-01 18:00:00 -0.006342059   4.209952\n2013-01-01 19:00:00   -0.1990943   4.050354\n2013-01-01 20:00:00    -0.357241   3.861073\n2013-01-01 21:00:00   -0.4813613   3.634683\n2013-01-01 22:00:00   -0.5585454   3.371375\n2013-01-01 23:00:00   -0.5776199   3.086149\n2013-01-02 00:00:00   -0.5353309   2.806496\n2013-01-02 01:00:00   -0.4386084   2.556459\n2013-01-02 02:00:00    -0.299992   2.344816\n2013-01-02 03:00:00   -0.1314006   2.167905\n2013-01-02 04:00:00   0.06016944   2.017144\n2013-01-02 05:00:00    0.2591169   1.883263\n2013-01-02 06:00:00    0.4689339   1.757014\n2013-01-02 07:00:00    0.6841059   1.627464\n2013-01-02 08:00:00    0.9009076   1.476613\n2013-01-02 09:00:00     1.113524   1.260682\n2013-01-02 10:00:00     1.302944  0.8232319\n2013-01-02 11:00:00     1.373257   6.049227\n2013-01-02 12:00:00     1.236928   5.246854\n2013-01-02 13:00:00     1.034221   4.928482\n2013-01-02 14:00:00    0.8189343   4.745876\n2013-01-02 15:00:00    0.6022008   4.606148\n2013-01-02 16:00:00    0.3885344   4.479437\n2013-01-02 17:00:00    0.1817067   4.351488\n2013-01-02 18:00:00 -0.005949598   4.212358\n2013-01-02 19:00:00   -0.1988945   4.052855\n2013-01-02 20:00:00   -0.3574258   3.863694\n2013-01-02 21:00:00   -0.4820236   3.637362\n2013-01-02 22:00:00   -0.5597456   3.373919\n2013-01-02 23:00:00   -0.5793337   3.088275\n2013-01-03 00:00:00   -0.5374381   2.808004\n2013-01-03 01:00:00   -0.4409522   2.557344\n2013-01-03 02:00:00   -0.3024446   2.345198\n2013-01-03 03:00:00   -0.1339885   2.167918\n2013-01-03 04:00:00   0.05780236   2.016884\n2013-01-03 05:00:00    0.2566792   1.882783\n2013-01-03 06:00:00    0.4665208   1.756333\n2013-01-03 07:00:00    0.6817097   1.626577\n2013-01-03 08:00:00     0.898505   1.475521\n2013-01-03 09:00:00     1.111072   1.259583\n2013-01-03 10:00:00     1.300456  0.8242722\n2013-01-03 11:00:00     1.371979   6.060177\n                             ...        ...\n\n[8760 rows x 2 columns]"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.sun_alt.plot(figsize=(10, 4))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": "<matplotlib.axes.AxesSubplot at 0x1106ed9d0>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hlyaL9zxV5OMy2fGpz+VdjxPKUpPFG+JRb8h/H9SsE1CryfLmAPHqnH496C6G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\n2LFbx1B2/UWn+jkRrZTIu8y7Fzl4f2fz1n356rnQALx3fh0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+fRw6dEh68b937x527tyJ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\nQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x10e78b710>"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.sun_alt.loc['2013-07-01'].plot(figsize=(15, 5))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "<matplotlib.axes.AxesSubplot at 0x11073b390>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MqK9LBIAegxk4APhUdWOblm2t0Fs7D2lMZrQuGxSv4amRCmK7GPA1FfWteqWwSv8qqtag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\nAFyCAg4AAAAAXOI/a5DdHADLq1MAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x11073a810>"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.sun_alt.loc['2013-07-01'].plot(figsize=(15, 5))\ndf.sun_az.loc['2013-07-01'].plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": "<matplotlib.axes.AxesSubplot at 0x110873a90>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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H333o58ikiy1/TgYyWqHb9wF3I9ZMTN1Yt2iku7crdmPNuh1rJh7WTDy9VjNJ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\nYII=\n",
       "text": "<matplotlib.figure.Figure at 0x110496810>"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.sun_alt[df.index.hour == 12].plot(figsize=(15, 5))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": "<matplotlib.axes.AxesSubplot at 0x1107f2550>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4WAmZHiQPitrvjd4wE9uYjxgzkcZspDEbsTPNpMlowurdJXj/aA2evDge150d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\n5b0C6XA4+OEPf8iVK1fYsWMH8+fPx2g00tbWRmtrKykpKQG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       "text": "<matplotlib.figure.Figure at 0x110757f90>"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
 }