DRB_vizer_json_services.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRB Vizer json services\n",
    "10/30/2015. Emilio Mayorga.   \n",
    "Run with Emilio's \"IOOS_test1\" conda envioronment (recent versions of most packages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://www.wikiwatershed-vs.org examples:\n",
    "- meta requests\n",
    "  - http://www.wikiwatershed-vs.org/services/get_asset_info.php?opt=meta&asset_type=siso\n",
    "  - http://www.wikiwatershed-vs.org/services/get_asset_info.php?opt=meta&asset_type=siso&asset_id=CRBCZO_WCC019\n",
    "- data & recent_values requests\n",
    "  - http://www.wikiwatershed-vs.org/services/get_asset_info.php?opt=data&asset_type=siso&asset_id=CRBCZO_WCC019&var_id=all&units_mode=v1\n",
    "  - http://www.wikiwatershed-vs.org/services/get_asset_info.php?opt=data&asset_type=siso&asset_id=CRBCZO_WCC019&var_id=H1_WaterTemp\n",
    "  - http://www.wikiwatershed-vs.org/services/get_asset_info.php?opt=recent_values&asset_type=siso&asset_id=CRBCZO_WCC019&var_id=H1_WaterTemp&units_mode=v2\n",
    "- plot request\n",
    "  - http://www.wikiwatershed-vs.org/services/get_asset_info.php?opt=plot&asset_type=siso&asset_id=USGS_01474500&var_id=H1_WaterTemp&range=30d&y_axis_mode=global"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages and set up utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import time\n",
    "import calendar\n",
    "import pytz\n",
    "#from matplotlib.dates import date2num, num2date\n",
    "\n",
    "utc_tz = pytz.utc\n",
    "\n",
    "def epochsec_to_dt(epochsec):\n",
    "    \"\"\" Return the datetime object for epoch seconds epochsec\n",
    "    \"\"\"\n",
    "    dtnaive_dt = datetime.datetime.utcfromtimestamp(epochsec)\n",
    "    dtutc_dt   = dtnaive_dt.replace(tzinfo=pytz.utc)\n",
    "    return dtutc_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_measurement_byvarid(metaresult, var_id):\n",
    "    return [e for e in metaresult['measurements'] if e['var_id'] == var_id][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Service end point\n",
    "For vizer instance that currently serves only the CRB and outlying areas, but will be expanded to the DRB to support the William Penn project needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vz_gai_url = \"http://www.wikiwatershed-vs.org/services/get_asset_info.php\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta info (metadata) requests\n",
    "siso = \"stationary in-situ observations\" (eg, a river gage, weather stationg, moored buoy, etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "meta_r = requests.get(vz_gai_url, params={'asset_type':'siso', 'opt':'meta'})\n",
    "meta = meta_r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([u'result', u'success'], True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta.keys(), meta['success']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(list, 59)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(meta['result']), len(meta['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# siso_id is the unique identifier (string type) for the station\n",
    "siso_id_lst = [e['siso_id'] for e in meta['result']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'CRBCZO_WCC019'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the response for the first station (index 0) in the returned list\n",
    "meta['result'][0]['siso_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'data_timezone': u'US/Eastern',\n",
       " u'lat': 39.8588,\n",
       " u'lon': -75.7833,\n",
       " u'measurements': [{u'depth': u'0.0m',\n",
       "   u'name': u'Conductivity',\n",
       "   u'units': u'mS/cm',\n",
       "   u'var_id': u'H1_Conductiv'},\n",
       "  {u'depth': u'0.0m',\n",
       "   u'name': u'Turbidity',\n",
       "   u'units': u'NTU',\n",
       "   u'var_id': u'H1_Turbidity'},\n",
       "  {u'depth': u'0.0m',\n",
       "   u'name': u'Water Level',\n",
       "   u'units': u'cm',\n",
       "   u'var_id': u'H2_WaterLevel'},\n",
       "  {u'depth': u'0.0m',\n",
       "   u'name': u'Water Temp.',\n",
       "   u'units': u'\\xb0C',\n",
       "   u'var_id': u'H1_WaterTemp'}],\n",
       " u'name': u'CRBCZO_WCC019',\n",
       " u'platform_type': u'River Gauge',\n",
       " u'provider': u'CRBCZO',\n",
       " u'provider_url': u'https://criticalzone.org/christina/',\n",
       " u'siso_id': u'CRBCZO_WCC019',\n",
       " u'url': u'http://swrcsensors.dreamhosters.com/charts_main_SL031.php'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta['result'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine all stations (siso assets) by first importing into a Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stations_rec = []\n",
    "for sta in meta['result']:\n",
    "    sta_rec = {key:sta[key] for key in ['siso_id', 'name', 'lat', 'lon', \n",
    "                                        'platform_type', 'provider']}\n",
    "    stations_rec.append(sta_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59\n"
     ]
    }
   ],
   "source": [
    "stations_df = pd.DataFrame.from_records(stations_rec)\n",
    "stations_df.set_index('siso_id', inplace=True, verify_integrity=True)\n",
    "stations_df.index.name = 'siso_id'\n",
    "print len(stations_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>platform_type</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>siso_id</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>CRBCZO_WCC019</th>\n",
       "      <td>39.8588</td>\n",
       "      <td>-75.7833</td>\n",
       "      <td>CRBCZO_WCC019</td>\n",
       "      <td>River Gauge</td>\n",
       "      <td>CRBCZO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DAGF</th>\n",
       "      <td>39.6695</td>\n",
       "      <td>-75.7503</td>\n",
       "      <td>DEOS DAGF</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DBKB</th>\n",
       "      <td>39.3953</td>\n",
       "      <td>-75.6317</td>\n",
       "      <td>DEOS DBKB</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DBUK1</th>\n",
       "      <td>39.8200</td>\n",
       "      <td>-75.7272</td>\n",
       "      <td>DEOS DBUK1</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DCHI</th>\n",
       "      <td>39.7328</td>\n",
       "      <td>-75.5184</td>\n",
       "      <td>DEOS DCHI</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DCLY</th>\n",
       "      <td>39.8092</td>\n",
       "      <td>-75.4556</td>\n",
       "      <td>DEOS DCLY</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DDMV</th>\n",
       "      <td>39.6758</td>\n",
       "      <td>-75.6303</td>\n",
       "      <td>DEOS DDMV</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DFHM</th>\n",
       "      <td>39.7099</td>\n",
       "      <td>-75.8386</td>\n",
       "      <td>DEOS DFHM</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DGLW</th>\n",
       "      <td>39.6059</td>\n",
       "      <td>-75.7269</td>\n",
       "      <td>DEOS DGLW</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOS_DGRN</th>\n",
       "      <td>39.8025</td>\n",
       "      <td>-75.6072</td>\n",
       "      <td>DEOS DGRN</td>\n",
       "      <td>Weather Station</td>\n",
       "      <td>DEOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   lat      lon           name    platform_type provider\n",
       "siso_id                                                                 \n",
       "CRBCZO_WCC019  39.8588 -75.7833  CRBCZO_WCC019      River Gauge   CRBCZO\n",
       "DEOS_DAGF      39.6695 -75.7503      DEOS DAGF  Weather Station     DEOS\n",
       "DEOS_DBKB      39.3953 -75.6317      DEOS DBKB  Weather Station     DEOS\n",
       "DEOS_DBUK1     39.8200 -75.7272     DEOS DBUK1  Weather Station     DEOS\n",
       "DEOS_DCHI      39.7328 -75.5184      DEOS DCHI  Weather Station     DEOS\n",
       "DEOS_DCLY      39.8092 -75.4556      DEOS DCLY  Weather Station     DEOS\n",
       "DEOS_DDMV      39.6758 -75.6303      DEOS DDMV  Weather Station     DEOS\n",
       "DEOS_DFHM      39.7099 -75.8386      DEOS DFHM  Weather Station     DEOS\n",
       "DEOS_DGLW      39.6059 -75.7269      DEOS DGLW  Weather Station     DEOS\n",
       "DEOS_DGRN      39.8025 -75.6072      DEOS DGRN  Weather Station     DEOS"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summaries (station counts) by *platform_type* and *provider*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "River Gauge             30\n",
       "Weather Station         19\n",
       "Fixed Shore Platform     6\n",
       "Well                     2\n",
       "Soil Pit                 2\n",
       "Name: platform_type, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_df.platform_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USGS          27\n",
       "DEOS          18\n",
       "CRBCZO         5\n",
       "NOS/CO-OPS     5\n",
       "SWRC           3\n",
       "USCRN          1\n",
       "Name: provider, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_df.provider.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Request and examine one station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# USGS Schuylkill River at Philadelphia\n",
    "siso_id = 'USGS_01474500'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meta info request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# (asset_id, a more generic descriptor for the unique id of any asset)\n",
    "meta_r = requests.get(vz_gai_url, params={'asset_type':'siso', 'opt':'meta', \n",
    "                                          'asset_id':siso_id})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use [0] to pull out the dict from the single-element list\n",
    "metaresult = meta_r.json()['result'][0]  # ideally, should first test for success"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'H1_Conductiv', u'-0.5m'),\n",
       " (u'H1_Discharge', u'0m'),\n",
       " (u'H1_Oxygen', u'-0.5m'),\n",
       " (u'H1_pH', u'-0.5m'),\n",
       " (u'A2_Rain', u'1m'),\n",
       " (u'H1_RiverStage', u'0m'),\n",
       " (u'H2_Turbidity', u'-0.5m'),\n",
       " (u'H1_WaterTemp', u'-0.5m')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# var_id is the unique identifier for a \"measurement\" (or variable)\n",
    "[(d['var_id'], d['depth']) for d in metaresult['measurements']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'USGS 01474500'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metaresult['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_r = requests.get(vz_gai_url, params={'asset_type':'siso', 'opt':'data', 'units_mode': 'v1',\n",
    "                                          'asset_id':siso_id, 'var_id':'all'})\n",
    "data = data_r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 8, [u'var_id', u'data'], 1825)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['success'], len(data['result']), data['result'][0].keys(), len(data['result'][0]['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'A2_Rain': 0,\n",
       " u'H1_Conductiv': 1,\n",
       " u'H1_Discharge': 2,\n",
       " u'H1_Oxygen': 3,\n",
       " u'H1_RiverStage': 5,\n",
       " u'H1_WaterTemp': 6,\n",
       " u'H1_pH': 4,\n",
       " u'H2_Turbidity': 7}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mapping of var_id string to 'result' list element index\n",
    "var_ids = {e['var_id']:i for i,e in enumerate(data['result'])}\n",
    "var_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "var_id = 'H1_Discharge'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'depth': u'0m',\n",
       " u'name': u'Discharge',\n",
       " u'units': u'm\\xb3/s',\n",
       " u'var_id': u'H1_Discharge'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_measurement_byvarid(metaresult, var_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{u'depth': u'0m', u'time': 1446183000, u'value': 265.6095},\n",
       " {u'depth': u'0m', u'time': 1446184800, u'value': 272.4055},\n",
       " {u'depth': u'0m', u'time': 1446186600, u'value': 274.6708},\n",
       " {u'depth': u'0m', u'time': 1446188400, u'value': 277.2193},\n",
       " {u'depth': u'0m', u'time': 1446190200, u'value': 277.2193},\n",
       " {u'depth': u'0m', u'time': 1446192000, u'value': 277.2193},\n",
       " {u'depth': u'0m', u'time': 1446193800, u'value': 277.2193},\n",
       " {u'depth': u'0m', u'time': 1446195600, u'value': 274.6708},\n",
       " {u'depth': u'0m', u'time': 1446197400, u'value': 272.4055},\n",
       " {u'depth': u'0m', u'time': 1446199200, u'value': 270.1402}]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['result'][var_ids[var_id]]['data'][-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Pull out data time series for one variable based on var_id\n",
    "# returns a list of dicts\n",
    "data_lst = data['result'][var_ids[var_id]]['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame.from_records(data_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440831600</td>\n",
       "      <td>18.3491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440833400</td>\n",
       "      <td>17.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440835200</td>\n",
       "      <td>17.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440837000</td>\n",
       "      <td>17.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440838800</td>\n",
       "      <td>16.8767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  depth        time    value\n",
       "0    0m  1440831600  18.3491\n",
       "1    0m  1440833400  17.6129\n",
       "2    0m  1440835200  17.6129\n",
       "3    0m  1440837000  17.6129\n",
       "4    0m  1440838800  16.8767"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create `dtutc` column with parsed datetime. Also, it's safer to rename the \"value\" column to something unlikely to conflict with pandas method names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_df['dtutc'] = data_df.time.map(lambda es: epochsec_to_dt(es))\n",
    "data_df.set_index('dtutc', inplace=True, verify_integrity=True)\n",
    "data_df.index.name = 'dtutc'\n",
    "data_df = data_df.rename(columns={'value':var_id})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1825 entries, 2015-08-29 07:00:00+00:00 to 2015-10-30 10:00:00+00:00\n",
      "Data columns (total 3 columns):\n",
      "depth           1825 non-null object\n",
      "time            1825 non-null int64\n",
      "H1_Discharge    1825 non-null float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 57.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>time</th>\n",
       "      <th>H1_Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dtutc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-29 07:00:00+00:00</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440831600</td>\n",
       "      <td>18.3491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-29 07:30:00+00:00</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440833400</td>\n",
       "      <td>17.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-29 08:00:00+00:00</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440835200</td>\n",
       "      <td>17.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-29 08:30:00+00:00</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440837000</td>\n",
       "      <td>17.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-29 09:00:00+00:00</th>\n",
       "      <td>0m</td>\n",
       "      <td>1440838800</td>\n",
       "      <td>16.8767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          depth        time  H1_Discharge\n",
       "dtutc                                                    \n",
       "2015-08-29 07:00:00+00:00    0m  1440831600       18.3491\n",
       "2015-08-29 07:30:00+00:00    0m  1440833400       17.6129\n",
       "2015-08-29 08:00:00+00:00    0m  1440835200       17.6129\n",
       "2015-08-29 08:30:00+00:00    0m  1440837000       17.6129\n",
       "2015-08-29 09:00:00+00:00    0m  1440838800       16.8767"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>H1_Discharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.825000e+03</td>\n",
       "      <td>1825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.443738e+09</td>\n",
       "      <td>37.350980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.815587e+06</td>\n",
       "      <td>35.249309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.440832e+09</td>\n",
       "      <td>11.921300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.441726e+09</td>\n",
       "      <td>17.612900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.444410e+09</td>\n",
       "      <td>25.088500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.445305e+09</td>\n",
       "      <td>37.944200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.446199e+09</td>\n",
       "      <td>277.219300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               time  H1_Discharge\n",
       "count  1.825000e+03   1825.000000\n",
       "mean   1.443738e+09     37.350980\n",
       "std    1.815587e+06     35.249309\n",
       "min    1.440832e+09     11.921300\n",
       "25%    1.441726e+09     17.612900\n",
       "50%    1.444410e+09     25.088500\n",
       "75%    1.445305e+09     37.944200\n",
       "max    1.446199e+09    277.219300"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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epgfAWnslcAfd9+XtbTXsiJR3EREREZHhFdNhemPMUqDSWnvKWrvHnVvWaJyt39pwdlYp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4dc64ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_info = get_measurement_byvarid(metaresult, var_id)\n",
    "title = \"%s (%s) at %s\" % (var_info['name'], var_id, metaresult['name'])\n",
    "data_df[var_id].plot(title=title, figsize=[11,5])\n",
    "plt.ylabel(var_info['units']);"
   ]
  }
 ],
 "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
}