notebooks/HADS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://gist.github.com/emiliom/35db17392c5d652faeb2"
   ]
  },
  {
   "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",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pyoos.collectors.hads.hads import Hads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 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()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Not used, for now\n",
    "#states_url = \"http://amazon.nws.noaa.gov/hads/charts/OR.html\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hadsData = Hads()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyoos.collectors.hads.hads.Hads at 0x7faa10051d10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hadsData.station_codes = ['346F229A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('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/')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hadsData.metadata_url, hadsData.obs_retrieval_url, hadsData.states_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "respCollect = hadsData.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(respCollect.elements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sta = respCollect.elements[0]\n",
    "obsprops_bystdname_dict = obsprops_bystdname(sta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT Z (-123.7211111111111 43.41388888888889 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'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'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print sta.get_location()\n",
    "sta._properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['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'})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obsprops_bystdname_dict.keys(), obsprops_bystdname_dict['dissolved_oxygen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>"
      ],
      "text/plain": [
       "                     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  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flattenedsta_0 = map(flatten_element, sta.elements)\n",
    "sta_0df = pd.DataFrame.from_records(flattenedsta_0, index=['time'])\n",
    "sta_0df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#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'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 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!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(732, 9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta_0df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta_0df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>"
      ],
      "text/plain": [
       "                     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  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta_0df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(732, paegan.cdm.dsg.features.base.point.Point)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sta.elements), type(sta.elements[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'description': '',\n",
       "  'name': 'Water Level',\n",
       "  'standard': 'water_level',\n",
       "  'unit': 'ft',\n",
       "  'value': u'13.39'}]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stael0 = sta.elements[0]\n",
    "stael0.members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uhsM/7jj3Dt82pOPS4f8vxN2vBC5Eqmc6WHI4naIdPmSL40c5/Pr6yiSdLBeq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\n4/F4PMXx/wCElJ/k+i3RowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa09530a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sta_0df[obsprop_name].plot()\n",
    "ylabel(obsprop_name + ' ('+obsprop['unit']+')');"
   ]
  }
 ],
 "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
}