emiliom/pyoos_hads_pandas_demo1.ipynb

## pyoos_hads_pandas_demo1.ipynb
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Accessing a \"NERRS\" station with Pyoos via its [HADS Collector](https://github.com/ioos/pyoos/tree/master/pyoos/collectors/hads), not the CDMO\n",
      "Currently it only illustrates a request for a station (\"feature\") given its known station_code (nesdis_id); no other type of collection query is illustrated. Then, it shows access to data from the specified station; extraction of station metadata; and conversion of the returned multi-variable time series to a pandas DataFrame, followed by a time series plot from the DataFrame. 2014 June 18. Emilio Mayorga."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime, timedelta\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from pyoos.collectors.hads.hads import Hads"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/anaconda/anaconda/envs/ioos_em_13/lib/python2.7/site-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# FROM pyoos SOS handling\n",
      "# Convenience function to build record style time series representation\n",
      "# Test for string NaN added for HADS collector. Ideally the change should be\n",
      "# made in pyoos, on the HADS parser so 'values' returns floats or np.nan\n",
      "def flatten_element(p):\n",
      "    rd = {'time':p.time}\n",
      "    for m in p.members:\n",
      "        rd[m['standard']] = float(m['value']) if m['value'] != 'NaN' else np.nan\n",
      "        #rd[m['standard']] = m['value']\n",
      "    return rd\n",
      "\n",
      "# sta.get_unique_members() serves the same function as the pyoos SOS get_unique_members method\n",
      "# Convenience function to extract a dict of unique members (observed properties) by standard name\n",
      "obsprops_bystdname = lambda sta: {m['standard']:m for m in sta.get_unique_members()}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Not used, for now\n",
      "#states_url = \"http://amazon.nws.noaa.gov/hads/charts/OR.html\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hadsData = Hads()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Access South Slough North Spit station, for the last 2 days (roughly)\n",
      "#hadsData.filter(features=['346F229A'],\n",
      "#                  start=datetime.utcnow() - timedelta(days=2),\n",
      "#                  end=datetime.utcnow()  - timedelta(hours=12))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#hadsData.filter(features=['346F229A'], states_url=states_url)\n",
      "#hadsData.filter(states_url=states_url)\n",
      "# NOT CLEAR YET IF THIS FILTER IF ALSO NEEDED, \n",
      "# OR IF SETTING hadsData.station_codes IS ENOUGH\n",
      "hadsData.filter(features=['346F229A'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<pyoos.collectors.hads.hads.Hads at 0x7faa10051d10>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hadsData.station_codes = ['346F229A']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hadsData.metadata_url, hadsData.obs_retrieval_url, hadsData.states_url"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "('http://amazon.nws.noaa.gov/nexhads2/servlet/DCPInfo',\n",
        " 'http://amazon.nws.noaa.gov/nexhads2/servlet/DecodedData',\n",
        " 'http://amazon.nws.noaa.gov/hads/goog_earth/')"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "respCollect = hadsData.collect()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(respCollect.elements)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta = respCollect.elements[0]\n",
      "obsprops_bystdname_dict = obsprops_bystdname(sta)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sta.get_location()\n",
      "sta._properties"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "POINT Z (-123.7211111111111 43.41388888888889 0)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "{'channel': u'34  ',\n",
        " 'country': 'USA',\n",
        " 'horizontal_crs': 'EPSG:4326',\n",
        " 'hsa': u'MFR',\n",
        " 'init_transmit': u'000930',\n",
        " 'location_description': u'NERRS WATER QUALITY SITE AT SOUTH SLOUGH NORTH SPIT NEAR NORTH BEND 2WNW (CTCLUSI)',\n",
        " 'manufacturer': u'SU',\n",
        " 'owner': u'NOAERD',\n",
        " 'state': u'OR',\n",
        " 'vertical_crs': None,\n",
        " 'vertical_units': 'ft'}"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obsprops_bystdname_dict.keys(), obsprops_bystdname_dict['dissolved_oxygen']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(['sea_water_temperature',\n",
        "  'dissolved_oxygen_saturation',\n",
        "  'water_level',\n",
        "  'dissolved_oxygen',\n",
        "  'battery_voltage',\n",
        "  'salinity',\n",
        "  'sea_water_ph_reported_on_total_scale',\n",
        "  'sea_water_electrical_conductivity',\n",
        "  'turbidity'],\n",
        " {'description': '',\n",
        "  'name': 'Dissolved Oxygen',\n",
        "  'standard': 'dissolved_oxygen',\n",
        "  'unit': 'ppm'})"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flattenedsta_0 = map(flatten_element, sta.elements)\n",
      "sta_0df = pd.DataFrame.from_records(flattenedsta_0, index=['time'])\n",
      "sta_0df.head()"
     ],
     "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>battery_voltage</th>\n",
        "      <th>dissolved_oxygen</th>\n",
        "      <th>dissolved_oxygen_saturation</th>\n",
        "      <th>salinity</th>\n",
        "      <th>sea_water_electrical_conductivity</th>\n",
        "      <th>sea_water_ph_reported_on_total_scale</th>\n",
        "      <th>sea_water_temperature</th>\n",
        "      <th>turbidity</th>\n",
        "      <th>water_level</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-06-11 01:15:00</th>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 13.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-11 01:30:00</th>\n",
        "      <td> 13.9</td>\n",
        "      <td> 8.33</td>\n",
        "      <td> 97.1</td>\n",
        "      <td> 29.63</td>\n",
        "      <td> 45.72</td>\n",
        "      <td> 7.66</td>\n",
        "      <td> 57.18</td>\n",
        "      <td> 2.2</td>\n",
        "      <td> 13.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-11 01:45:00</th>\n",
        "      <td> 13.9</td>\n",
        "      <td> 8.45</td>\n",
        "      <td> 98.8</td>\n",
        "      <td> 29.43</td>\n",
        "      <td> 45.43</td>\n",
        "      <td> 7.71</td>\n",
        "      <td> 57.61</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 13.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-11 02:00:00</th>\n",
        "      <td> 13.9</td>\n",
        "      <td> 8.31</td>\n",
        "      <td> 96.4</td>\n",
        "      <td> 29.83</td>\n",
        "      <td> 46.01</td>\n",
        "      <td> 7.70</td>\n",
        "      <td> 56.71</td>\n",
        "      <td> 3.0</td>\n",
        "      <td> 14.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-11 02:15:00</th>\n",
        "      <td> 13.8</td>\n",
        "      <td> 8.48</td>\n",
        "      <td> 98.1</td>\n",
        "      <td> 30.07</td>\n",
        "      <td> 46.35</td>\n",
        "      <td> 7.73</td>\n",
        "      <td> 56.30</td>\n",
        "      <td> 3.4</td>\n",
        "      <td> 14.53</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                     battery_voltage  dissolved_oxygen  \\\n",
        "time                                                     \n",
        "2014-06-11 01:15:00              NaN               NaN   \n",
        "2014-06-11 01:30:00             13.9              8.33   \n",
        "2014-06-11 01:45:00             13.9              8.45   \n",
        "2014-06-11 02:00:00             13.9              8.31   \n",
        "2014-06-11 02:15:00             13.8              8.48   \n",
        "\n",
        "                     dissolved_oxygen_saturation  salinity  \\\n",
        "time                                                         \n",
        "2014-06-11 01:15:00                          NaN       NaN   \n",
        "2014-06-11 01:30:00                         97.1     29.63   \n",
        "2014-06-11 01:45:00                         98.8     29.43   \n",
        "2014-06-11 02:00:00                         96.4     29.83   \n",
        "2014-06-11 02:15:00                         98.1     30.07   \n",
        "\n",
        "                     sea_water_electrical_conductivity  \\\n",
        "time                                                     \n",
        "2014-06-11 01:15:00                                NaN   \n",
        "2014-06-11 01:30:00                              45.72   \n",
        "2014-06-11 01:45:00                              45.43   \n",
        "2014-06-11 02:00:00                              46.01   \n",
        "2014-06-11 02:15:00                              46.35   \n",
        "\n",
        "                     sea_water_ph_reported_on_total_scale  \\\n",
        "time                                                        \n",
        "2014-06-11 01:15:00                                   NaN   \n",
        "2014-06-11 01:30:00                                  7.66   \n",
        "2014-06-11 01:45:00                                  7.71   \n",
        "2014-06-11 02:00:00                                  7.70   \n",
        "2014-06-11 02:15:00                                  7.73   \n",
        "\n",
        "                     sea_water_temperature  turbidity  water_level  \n",
        "time                                                                \n",
        "2014-06-11 01:15:00                    NaN        NaN        13.39  \n",
        "2014-06-11 01:30:00                  57.18        2.2        13.62  \n",
        "2014-06-11 01:45:00                  57.61        2.5        13.91  \n",
        "2014-06-11 02:00:00                  56.71        3.0        14.24  \n",
        "2014-06-11 02:15:00                  56.30        3.4        14.53  "
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#hadsData._get_metadata(['346F229A'])\n",
      "# ---------------------------------\n",
      "#respMeta, respRawdata = hadsData.raw()\n",
      "#respMeta\n",
      "#Out[]: u'|346F229A|SSNO3|NERRS WATER QUALITY SITE AT SOUTH SLOUGH NORTH SPIT NEAR NORTH BEND 2WNW (CTCLUSI)|43 24 50|-124 16 44|MFR|OR|NOAERD|SU|34  |000930|60|TW|15,-9|0.018,-9|32.0|9|0.0|0.0|WC|15,-9|0.01,-9|0.0|9|0.0|0.0|WS|15,-9|0.01,-9|0.0|9|0.0|0.0|WX|15,-9|0.01,-9|0.0|9|0.0|0.0|WO|15,-9|0.01,-9|0.0|9|0.0|0.0|HM|15,-9|0.03281,-9|0.0|9|0.0|0.0|WP|15,-9|0.01,-9|0.0|9|0.0|0.0|WT|15,-9|0.01,-9|0.0|9|0.0|0.0|VB|15,-9|0.01,-9|0.0|9|0.0|0.0|\\r\\n'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Time series plot.\n",
      "obsprop_name = 'sea_water_temperature'\n",
      "obsprop = obsprops_bystdname_dict[obsprop_name]\n",
      "#sta_0df[obsprop_name].plot()\n",
      "#ylabel(obsprop_name + ' ('+obsprop['unit']+')');\n",
      "# THE VARIABLE COLUMNS ARE NOT NUMERIC (PROBABLY DUE TO THE 'NaN' values),\n",
      "# SO .plot() RETURNS AN ERROR.\n",
      "# 6/18/2014. I've fixed flatten_elements() to handle this, and it now works!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta_0df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "(732, 9)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta_0df.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "battery_voltage                         float64\n",
        "dissolved_oxygen                        float64\n",
        "dissolved_oxygen_saturation             float64\n",
        "salinity                                float64\n",
        "sea_water_electrical_conductivity       float64\n",
        "sea_water_ph_reported_on_total_scale    float64\n",
        "sea_water_temperature                   float64\n",
        "turbidity                               float64\n",
        "water_level                             float64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta_0df.tail()"
     ],
     "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>battery_voltage</th>\n",
        "      <th>dissolved_oxygen</th>\n",
        "      <th>dissolved_oxygen_saturation</th>\n",
        "      <th>salinity</th>\n",
        "      <th>sea_water_electrical_conductivity</th>\n",
        "      <th>sea_water_ph_reported_on_total_scale</th>\n",
        "      <th>sea_water_temperature</th>\n",
        "      <th>turbidity</th>\n",
        "      <th>water_level</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-06-18 15:00:00</th>\n",
        "      <td> 13.8</td>\n",
        "      <td> 9.41</td>\n",
        "      <td> 108.7</td>\n",
        "      <td> 30.92</td>\n",
        "      <td> 47.53</td>\n",
        "      <td> 7.82</td>\n",
        "      <td> 55.76</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 14.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-18 15:15:00</th>\n",
        "      <td> 14.4</td>\n",
        "      <td> 8.92</td>\n",
        "      <td> 104.1</td>\n",
        "      <td> 30.16</td>\n",
        "      <td> 46.46</td>\n",
        "      <td> 7.77</td>\n",
        "      <td> 57.02</td>\n",
        "      <td> 3.2</td>\n",
        "      <td> 13.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-18 15:30:00</th>\n",
        "      <td> 14.5</td>\n",
        "      <td> 8.83</td>\n",
        "      <td> 103.2</td>\n",
        "      <td> 30.03</td>\n",
        "      <td> 46.27</td>\n",
        "      <td> 7.74</td>\n",
        "      <td> 57.27</td>\n",
        "      <td> 3.6</td>\n",
        "      <td> 13.22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-18 15:45:00</th>\n",
        "      <td> 13.8</td>\n",
        "      <td> 8.74</td>\n",
        "      <td> 102.4</td>\n",
        "      <td> 29.95</td>\n",
        "      <td> 46.16</td>\n",
        "      <td> 7.75</td>\n",
        "      <td> 57.47</td>\n",
        "      <td> 2.4</td>\n",
        "      <td> 12.80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-06-18 16:00:00</th>\n",
        "      <td> 13.8</td>\n",
        "      <td> 8.76</td>\n",
        "      <td> 102.7</td>\n",
        "      <td> 29.93</td>\n",
        "      <td> 46.13</td>\n",
        "      <td> 7.74</td>\n",
        "      <td> 57.56</td>\n",
        "      <td> 3.0</td>\n",
        "      <td> 12.30</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                     battery_voltage  dissolved_oxygen  \\\n",
        "time                                                     \n",
        "2014-06-18 15:00:00             13.8              9.41   \n",
        "2014-06-18 15:15:00             14.4              8.92   \n",
        "2014-06-18 15:30:00             14.5              8.83   \n",
        "2014-06-18 15:45:00             13.8              8.74   \n",
        "2014-06-18 16:00:00             13.8              8.76   \n",
        "\n",
        "                     dissolved_oxygen_saturation  salinity  \\\n",
        "time                                                         \n",
        "2014-06-18 15:00:00                        108.7     30.92   \n",
        "2014-06-18 15:15:00                        104.1     30.16   \n",
        "2014-06-18 15:30:00                        103.2     30.03   \n",
        "2014-06-18 15:45:00                        102.4     29.95   \n",
        "2014-06-18 16:00:00                        102.7     29.93   \n",
        "\n",
        "                     sea_water_electrical_conductivity  \\\n",
        "time                                                     \n",
        "2014-06-18 15:00:00                              47.53   \n",
        "2014-06-18 15:15:00                              46.46   \n",
        "2014-06-18 15:30:00                              46.27   \n",
        "2014-06-18 15:45:00                              46.16   \n",
        "2014-06-18 16:00:00                              46.13   \n",
        "\n",
        "                     sea_water_ph_reported_on_total_scale  \\\n",
        "time                                                        \n",
        "2014-06-18 15:00:00                                  7.82   \n",
        "2014-06-18 15:15:00                                  7.77   \n",
        "2014-06-18 15:30:00                                  7.74   \n",
        "2014-06-18 15:45:00                                  7.75   \n",
        "2014-06-18 16:00:00                                  7.74   \n",
        "\n",
        "                     sea_water_temperature  turbidity  water_level  \n",
        "time                                                                \n",
        "2014-06-18 15:00:00                  55.76        4.0        14.08  \n",
        "2014-06-18 15:15:00                  57.02        3.2        13.62  \n",
        "2014-06-18 15:30:00                  57.27        3.6        13.22  \n",
        "2014-06-18 15:45:00                  57.47        2.4        12.80  \n",
        "2014-06-18 16:00:00                  57.56        3.0        12.30  "
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(sta.elements), type(sta.elements[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(732, paegan.cdm.dsg.features.base.point.Point)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stael0 = sta.elements[0]\n",
      "stael0.members"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[{'description': '',\n",
        "  'name': 'Water Level',\n",
        "  'standard': 'water_level',\n",
        "  'unit': 'ft',\n",
        "  'value': u'13.39'}]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta_0df[obsprop_name].plot()\n",
      "ylabel(obsprop_name + ' ('+obsprop['unit']+')');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uhsM/7jj3Dt82pOPS4f8vxN2vBC5Eqmc6WHI4naIdPmSL40c5/Pr6yiSdLBeq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\nVwZusx7TrjGVghHIAuZaBtuAlOocxRDl8PVB60I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       "text": [
        "<matplotlib.figure.Figure at 0x7faa09530a50>"
       ]
      }
     ],
     "prompt_number": 22
    }
   ],
   "metadata": {}
  }
 ]
}