{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# odm2api demo with Little Bear SQLite sample DB\n",
    "Largely from https://github.com/ODM2/ODM2PythonAPI/blob/master/Examples/Sample.py   \n",
    "- 4/25/2016. Started testing with the new `odm2` conda channel, based on the new `0.5.0-alpha` odm2api release. See my `odm2api_odm2channel` env. Ran into problems b/c the SQLite database needed to be updated to have a `SamplingFeature.FeatureGeometryWKT` field; so I added and populated it manually with `SQLite Manager`.\n",
    "- 2/7/2016. Tested successfully with `sfgeometry_em_1` branch, with my overhauls. Using `odm2api_dev` env.\n",
    "- 2/1 - 1/31. Errors with SamplingFeatures code, with latest odm2api from master (on env `odm2api_jan31test`). *The code also fails the same way with the `odm2api` env, but it does still run fine with the `odm2api_jan21` env! I'm investigating the differences between those two envs.*\n",
    "- 1/22-20,9/2016.\n",
    "\n",
    "Author: Emilio Mayorga"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a connection to the ODM2 database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from odm2api.ODMconnection import dbconnection\n",
    "\n",
    "\n",
    "odm2db_fpth = os.path.join('data', 'ODM2.sqlite')\n",
    "session_factory = dbconnection.createConnection('sqlite', odm2db_fpth, 2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from odm2api.ODM2.services.readService import ReadODM2\n",
    "\n",
    "\n",
    "read = ReadODM2(session_factory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run some basic sample queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USU36: Temperature\n"
     ]
    }
   ],
   "source": [
    "allVars = read.getVariables()\n",
    "\n",
    "for x in allVars:\n",
    "    print('{}: {}'.format(x.VariableCode, x.VariableNameCV))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read some metadata from the database: people and affiliation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jeff Horsburgh\n",
      "Jeff: 1\n"
     ]
    }
   ],
   "source": [
    "allPeople = read.getPeople()\n",
    "\n",
    "if allPeople:\n",
    "    for x in allPeople:\n",
    "        print('{} {}'.format(x.PersonFirstName, x.PersonLastName))\n",
    "\n",
    "allaff = read.getAffiliations()\n",
    "\n",
    "if allaff:\n",
    "    for x in allaff:\n",
    "        print('{}: {}'.format(x.PersonObj.PersonFirstName, x.OrganizationID))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SamplingFeatures tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all of the SamplingFeatures from the database that are Sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    siteFeatures = read.getSamplingFeatures(type='Site')\n",
    "    numSites = len(siteFeatures)\n",
    "    for x in siteFeatures:\n",
    "        print(': '.format(x.SamplingFeatureCode, x.SamplingFeatureName))\n",
    "except Exception as e:\n",
    "    print('Unable to demo getSamplingFeatures(type=\"Site\")\\n{}'.format(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Sites({'SamplingFeatureDescription': None, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746f86990>, 'SamplingFeatureGeotypeCV': u'Point', 'SamplingFeatureName': u'Little Bear River at Mendon Road near Mendon, Utah', 'ElevationDatumCV': u'NGVD29', 'Elevation_m': 1345.0, 'SamplingFeatureUUID': u'938e328c-0e38-11e6-819c-f45c8999816f', 'SamplingFeatureTypeCV': u'Site', 'SamplingFeatureCode': u'USU-LBR-Mendon', 'SamplingFeatureID': 1, 'FeatureGeometryWKT': u'POINT (-111.946402 41.718473)'})>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getSamplingFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Sites({'SamplingFeatureDescription': None, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746f86990>, 'SamplingFeatureGeotypeCV': u'Point', 'SamplingFeatureName': u'Little Bear River at Mendon Road near Mendon, Utah', 'ElevationDatumCV': u'NGVD29', 'Elevation_m': 1345.0, 'SamplingFeatureUUID': u'938e328c-0e38-11e6-819c-f45c8999816f', 'SamplingFeatureTypeCV': u'Site', 'SamplingFeatureCode': u'USU-LBR-Mendon', 'SamplingFeatureID': 1, 'FeatureGeometryWKT': u'POINT (-111.946402 41.718473)'})>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getSamplingFeatures(codes=['USU-LBR-Mendon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now get the SamplingFeature object for a SamplingFeature code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ElevationDatumCV': u'NGVD29',\n",
       " 'Elevation_m': 1345.0,\n",
       " 'FeatureGeometryWKT': u'POINT (-111.946402 41.718473)',\n",
       " 'SamplingFeatureCode': u'USU-LBR-Mendon',\n",
       " 'SamplingFeatureDescription': None,\n",
       " 'SamplingFeatureGeotypeCV': u'Point',\n",
       " 'SamplingFeatureID': 1,\n",
       " 'SamplingFeatureName': u'Little Bear River at Mendon Road near Mendon, Utah',\n",
       " 'SamplingFeatureTypeCV': u'Site',\n",
       " 'SamplingFeatureUUID': u'938e328c-0e38-11e6-819c-f45c8999816f',\n",
       " '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f9746f86990>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_lst = read.getSamplingFeatures(codes=['USU-LBR-Mendon'])\n",
    "\n",
    "vars(sf_lst[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Sites({'SamplingFeatureDescription': None, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746f86990>, 'SamplingFeatureGeotypeCV': u'Point', 'SamplingFeatureName': u'Little Bear River at Mendon Road near Mendon, Utah', 'ElevationDatumCV': u'NGVD29', 'Elevation_m': 1345.0, 'SamplingFeatureUUID': u'938e328c-0e38-11e6-819c-f45c8999816f', 'SamplingFeatureTypeCV': u'Site', 'SamplingFeatureCode': u'USU-LBR-Mendon', 'SamplingFeatureID': 1, 'FeatureGeometryWKT': u'POINT (-111.946402 41.718473)'})>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf = sf_lst[0]\n",
    "\n",
    "sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'odm2api.ODM2.models.Sites'>\n",
      "(<type 'unicode'>, u'POINT (-111.946402 41.718473)')\n"
     ]
    }
   ],
   "source": [
    "print(type(sf))\n",
    "\n",
    "print(type(sf.FeatureGeometryWKT), sf.FeatureGeometryWKT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = \"\"\"\n",
    "<!DOCTYPE html>\n",
    "<html>\n",
    "<head>\n",
    "<style>\n",
    "table {{\n",
    "    width:100%;\n",
    "}}\n",
    "table, th, td {{\n",
    "    border: 1px solid black;\n",
    "    border-collapse: collapse;\n",
    "}}\n",
    "th, td {{\n",
    "    padding: 5px;\n",
    "    text-align: left;\n",
    "}}\n",
    "table#t01 tr:nth-child(odd) {{\n",
    "    background-color: #eee;\n",
    "}}\n",
    "table#t01 tr:nth-child(even) {{\n",
    "   background-color:#fff;\n",
    "}}\n",
    "</style>\n",
    "</head>\n",
    "<body>\n",
    "\n",
    "<table id=\"t01\">\n",
    "  <tr>\n",
    "    <td>Code</td>\n",
    "    <td>{}</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>TypeCV</td>\n",
    "    <td>{}</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>Name</td>\n",
    "    <td>{}</td>\n",
    "  </tr>\n",
    "</table>\n",
    "</body>\n",
    "</html>\n",
    "\"\"\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f9746f26fd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "lon, lat = sf.Longitude, sf.Latitude\n",
    "\n",
    "m = folium.Map(location=[lat, lon], zoom_start=16)\n",
    "\n",
    "icon = folium.Icon(color='orange', icon='info-sign', prefix='glyphicon')\n",
    "\n",
    "width, height = 310, 130\n",
    "html = table(sf.SamplingFeatureCode, sf.SamplingFeatureTypeCV, sf.SamplingFeatureName)\n",
    "iframe = folium.IFrame(html, width=width, height=height)\n",
    "popup = folium.Popup(iframe)\n",
    "\n",
    "folium.Marker(location=[lat, lon], icon=icon, popup=popup).add_to(m)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back to the rest of the demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<TimeSeriesResults({'ResultUUID': u'938fec1c-0e38-11e6-98c2-f45c8999816f', 'ValidDateTimeUTCOffset': None, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746c42d10>, 'VariableID': 1, 'ProcessingLevelID': 1, 'StatusCV': u'Unknown', 'ValueCount': 24206, 'ValidDateTime': None, 'UnitsID': 96, 'FeatureActionID': 1, 'ResultDateTimeUTCOffset': -7, 'TaxonomicClassifierID': None, 'ResultTypeCV': u'Time series coverage', 'SampledMediumCV': u'Surface Water', 'ResultDateTime': datetime.datetime(2016, 4, 29, 12, 31, 23, 801947), 'ResultID': 1})>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getResults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Actions({'_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746bc9490>, 'MethodID': 1, 'ActionDescription': u'An observation action that generated a time series result.', 'ActionFileLink': None, 'EndDateTime': datetime.datetime(2009, 1, 16, 12, 30), 'BeginDateTimeUTCOffset': -7, 'EndDateTimeUTCOffset': -7, 'ActionTypeCV': u'Observation', 'ActionID': 1, 'BeginDateTime': datetime.datetime(2007, 8, 16, 16, 30)})>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firstResult = read.getResults()[0]\n",
    "firstResult.FeatureActionObj.ActionObj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Foreign Key Example\n",
    "\n",
    "Drill down and get objects linked by foreign keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The FeatureAction object for the Result is: <FeatureActions({'FeatureActionID': 1, 'SamplingFeatureID': 1, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746bc6190>, 'ActionID': 1})>\n",
      "The Action object for the Result is: <Actions({'_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746bc9490>, 'MethodID': 1, 'ActionDescription': u'An observation action that generated a time series result.', 'ActionFileLink': None, 'EndDateTime': datetime.datetime(2009, 1, 16, 12, 30), 'BeginDateTimeUTCOffset': -7, 'EndDateTimeUTCOffset': -7, 'ActionTypeCV': u'Observation', 'ActionID': 1, 'BeginDateTime': datetime.datetime(2007, 8, 16, 16, 30)})>\n",
      "\n",
      "The following are some of the attributes for the Action that created the Result: \n",
      "ActionTypeCV: Observation\n",
      "ActionDescription: An observation action that generated a time series result.\n",
      "BeginDateTime: 2007-08-16 16:30:00\n",
      "EndDateTime: 2009-01-16 12:30:00\n",
      "MethodName: Quality Control Level 1 Data Series created from raw QC Level 0 data using ODM Tools.\n",
      "MethodDescription: Quality Control Level 1 Data Series created from raw QC Level 0 data using ODM Tools.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    # Call getResults, but return only the first result.\n",
    "    firstResult = read.getResults()[0]\n",
    "    action_firstResult = firstResult.FeatureActionObj.ActionObj\n",
    "    print('The FeatureAction object for the Result is: {}'.format(firstResult.FeatureActionObj))\n",
    "    print('The Action object for the Result is: {}'.format(action_firstResult))\n",
    "    print(\n",
    "        '\\nThe following are some of the attributes for the Action that created the Result: \\n' +\n",
    "        'ActionTypeCV: ' + action_firstResult.ActionTypeCV + '\\n' +\n",
    "        'ActionDescription: ' + action_firstResult.ActionDescription + '\\n' +\n",
    "        'BeginDateTime: ' + str(action_firstResult.BeginDateTime) + '\\n' +\n",
    "        'EndDateTime: ' + str(action_firstResult.EndDateTime) + '\\n' +\n",
    "        'MethodName: ' + action_firstResult.MethodObj.MethodName + '\\n' +\n",
    "        'MethodDescription: ' + action_firstResult.MethodObj.MethodDescription\n",
    "    )\n",
    "except Exception as e:\n",
    "    print('Unable to demo Foreign Key Example: {}'.format(e))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of Retrieving Attributes of a Time Series Result using a ResultID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(odm2api.ODM2.models.TimeSeriesResults,\n",
       " {'FeatureActionID': 1,\n",
       "  'FeatureActionObj': <FeatureActions({'FeatureActionID': 1, 'SamplingFeatureID': 1, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9746bc6190>, 'ActionID': 1})>,\n",
       "  'ProcessingLevelID': 1,\n",
       "  'ResultDateTime': datetime.datetime(2016, 4, 29, 12, 31, 23, 801947),\n",
       "  'ResultDateTimeUTCOffset': -7,\n",
       "  'ResultID': 1,\n",
       "  'ResultTypeCV': u'Time series coverage',\n",
       "  'ResultUUID': u'938fec1c-0e38-11e6-98c2-f45c8999816f',\n",
       "  'SampledMediumCV': u'Surface Water',\n",
       "  'StatusCV': u'Unknown',\n",
       "  'TaxonomicClassifierID': None,\n",
       "  'UnitsID': 96,\n",
       "  'ValidDateTime': None,\n",
       "  'ValidDateTimeUTCOffset': None,\n",
       "  'ValueCount': 24206,\n",
       "  'VariableID': 1,\n",
       "  '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f9746c42d10>})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsResult = read.getResults(ids=[1])[0]\n",
    "\n",
    "type(tsResult), vars(tsResult)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Why are `ProcessingLevelObj`, `VariableObj` and `UnitsObj` objects not shown in the above `vars()` listing!?** They **are** actually available, as demonstrated in much of the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some of the attributes for the TimeSeriesResult retrieved using getResults(ids=[]): \n",
      "ResultTypeCV: Time series coverage\n",
      "ProcessingLevel: Quality controlled data\n",
      "SampledMedium: Surface Water\n",
      "Variable: USU36: Temperature\n",
      "AggregationStatistic: Average\n",
      "Elevation_m: 1345.0\n",
      "SamplingFeature: USU-LBR-Mendon - Little Bear River at Mendon Road near Mendon, Utah\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    tsResult = read.getResults(ids=[1])[0]\n",
    "    # Get the site information by drilling down.\n",
    "    sf_tsResult = tsResult.FeatureActionObj.SamplingFeatureObj\n",
    "    print('Some of the attributes for the TimeSeriesResult retrieved using getResults(ids=[]): \\n' +\n",
    "          'ResultTypeCV: ' + tsResult.ResultTypeCV + '\\n' +\n",
    "          # Get the ProcessingLevel from the TimeSeriesResult's ProcessingLevel object.\n",
    "          'ProcessingLevel: ' + tsResult.ProcessingLevelObj.Definition + '\\n' +\n",
    "          'SampledMedium: ' + tsResult.SampledMediumCV + '\\n' +\n",
    "          # Get the variable information from the TimeSeriesResult's Variable object.\n",
    "          'Variable: ' + tsResult.VariableObj.VariableCode + ': ' + tsResult.VariableObj.VariableNameCV + '\\n' +\n",
    "          'AggregationStatistic: ' + tsResult.AggregationStatisticCV + '\\n' +\n",
    "          # Get the site information by drilling down.\n",
    "          'Elevation_m: ' + str(sf_tsResult.Elevation_m) + '\\n' +\n",
    "          'SamplingFeature: ' + sf_tsResult.SamplingFeatureCode + ' - ' +\n",
    "          sf_tsResult.SamplingFeatureName)\n",
    "except Exception as e:\n",
    "    print('Unable to demo Example of retrieving Attributes of a time Series Result: {}'.format(e))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of Retrieving Time Series Result Values, then plotting them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valueid</th>\n",
       "      <th>resultid</th>\n",
       "      <th>datavalue</th>\n",
       "      <th>valuedatetimeutcoffset</th>\n",
       "      <th>censorcodecv</th>\n",
       "      <th>qualitycodecv</th>\n",
       "      <th>timeaggregationinterval</th>\n",
       "      <th>timeaggregationintervalunitsid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valuedatetime</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>2009-01-04 18:00:00</th>\n",
       "      <td>23641</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.268333</td>\n",
       "      <td>-7</td>\n",
       "      <td>nc</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>30.0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-04 06:00:00</th>\n",
       "      <td>23617</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.266667</td>\n",
       "      <td>-7</td>\n",
       "      <td>nc</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>30.0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-18 01:00:00</th>\n",
       "      <td>22922</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.263333</td>\n",
       "      <td>-7</td>\n",
       "      <td>nc</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>30.0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-04 05:30:00</th>\n",
       "      <td>23616</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.263333</td>\n",
       "      <td>-7</td>\n",
       "      <td>nc</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>30.0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-04 06:30:00</th>\n",
       "      <td>23618</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.263333</td>\n",
       "      <td>-7</td>\n",
       "      <td>nc</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>30.0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     valueid  resultid  datavalue  valuedatetimeutcoffset  \\\n",
       "valuedatetime                                                               \n",
       "2009-01-04 18:00:00    23641         1  -0.268333                      -7   \n",
       "2009-01-04 06:00:00    23617         1  -0.266667                      -7   \n",
       "2008-12-18 01:00:00    22922         1  -0.263333                      -7   \n",
       "2009-01-04 05:30:00    23616         1  -0.263333                      -7   \n",
       "2009-01-04 06:30:00    23618         1  -0.263333                      -7   \n",
       "\n",
       "                    censorcodecv qualitycodecv  timeaggregationinterval  \\\n",
       "valuedatetime                                                             \n",
       "2009-01-04 18:00:00           nc       Unknown                     30.0   \n",
       "2009-01-04 06:00:00           nc       Unknown                     30.0   \n",
       "2008-12-18 01:00:00           nc       Unknown                     30.0   \n",
       "2009-01-04 05:30:00           nc       Unknown                     30.0   \n",
       "2009-01-04 06:30:00           nc       Unknown                     30.0   \n",
       "\n",
       "                     timeaggregationintervalunitsid  \n",
       "valuedatetime                                        \n",
       "2009-01-04 18:00:00                             102  \n",
       "2009-01-04 06:00:00                             102  \n",
       "2008-12-18 01:00:00                             102  \n",
       "2009-01-04 05:30:00                             102  \n",
       "2009-01-04 06:30:00                             102  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsValues = read.getResultValues(resultids=[1])  # Get the values for a particular TimeSeriesResult.\n",
    "\n",
    "tsValues.set_index('valuedatetime', inplace=True)\n",
    "\n",
    "tsValues.head()  # Return type is a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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tvt1xCSon3cc9eTOpbX3JVmFcN/OojlEI9fdyarOtc8Koe8y/s36xIU6DDrNH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\n6acyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9746bc9210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import dates\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 2.25))\n",
    "tsValues['datavalue'].plot(ax=ax)\n",
    "\n",
    "ax.set_ylabel('{} ({})'.format(\n",
    "    tsResult.VariableObj.VariableNameCV,\n",
    "    tsResult.UnitsObj.UnitsAbbreviation))\n",
    "ax.set_xlabel('')\n",
    "ax.xaxis.set_minor_locator(dates.MonthLocator())\n",
    "ax.xaxis.set_minor_formatter(dates.DateFormatter('%b'))\n",
    "ax.xaxis.set_major_locator(dates.YearLocator())\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%Y'))\n",
    "ax.grid(which='major', axis='y')\n",
    "ax.grid(which='minor', axis='x')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}