odm2api_sample_fromsqlite.ipynb

{
 "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",
    "Emilio Mayorga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mayorga/miniconda/envs/odm2api_odm2channel/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from odm2api.ODMconnection import dbconnection\n",
    "from odm2api.ODM2.services.readService import ReadODM2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a connection to the ODM2 database\n",
    "# ----------------------------------------\n",
    "odm2db_fpth = '/home/mayorga/Desktop/TylerYeats/ODM2-LittleBear1.sqlite'\n",
    "session_factory = dbconnection.createConnection('sqlite', odm2db_fpth, 2.0)\n",
    "read = ReadODM2(session_factory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USU36: Temperature\n"
     ]
    }
   ],
   "source": [
    "# Run some basic sample queries.\n",
    "# ------------------------------\n",
    "# Get all of the variables from the database and print their names to the console\n",
    "allVars = read.getVariables()\n",
    "\n",
    "for x in allVars:\n",
    "    print x.VariableCode + \": \" + x.VariableNameCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jeff Horsburgh\n",
      "\n",
      "-------- Information about an Affiliation ---------\n",
      "Unable to demo getAllAffiliations 'NoneType' object is not iterable\n"
     ]
    }
   ],
   "source": [
    "# Get all of the people from the database\n",
    "allPeople = read.getPeople()\n",
    "\n",
    "for x in allPeople:\n",
    "    print x.PersonFirstName + \" \" + x.PersonLastName\n",
    "\n",
    "try:\n",
    "    print \"\\n-------- Information about an Affiliation ---------\"\n",
    "    allaff = read.getAffiliations()\n",
    "    for x in allaff:\n",
    "        print x.PersonObj.PersonFirstName + \": \" + str(x.OrganizationID)\n",
    "except Exception as e:\n",
    "    print \"Unable to demo getAllAffiliations\", e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allaff = read.getAffiliations()\n",
    "type(allaff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SamplingFeatures tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from odm2api.ODM2.models import SamplingFeatures\n",
    "# read._session.query(SamplingFeatures).filter_by(SamplingFeatureTypeCV='Site').all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USU-LBR-Mendon: Little Bear River at Mendon Road near Mendon, Utah\n"
     ]
    }
   ],
   "source": [
    "# Get all of the SamplingFeatures from the database that are Sites\n",
    "try:\n",
    "    siteFeatures = read.getSamplingFeatures(type='Site')\n",
    "    numSites = len(siteFeatures)\n",
    "\n",
    "    for x in siteFeatures:\n",
    "        print x.SamplingFeatureCode + \": \" + x.SamplingFeatureName\n",
    "except Exception as e:\n",
    "    print \"Unable to demo getSamplingFeatures(type='Site')\", e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Sites('1', '1', 'Stream', '41.718473', '-111.946402', '<SpatialReferences('1', 'None', 'Unknown', 'The spatial reference is unknown', 'None')>', 'USU-LBR-Mendon')>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getSamplingFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Sites('1', '1', 'Stream', '41.718473', '-111.946402', '<SpatialReferences('1', 'None', 'Unknown', 'The spatial reference is unknown', 'None')>', 'USU-LBR-Mendon')>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getSamplingFeatures(codes=['USU-LBR-Mendon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ElevationDatumCV': u'NGVD29',\n",
       " 'Elevation_m': 1345.0,\n",
       " 'FeatureGeometry': <SQLitePersistentSpatialElement at 0x7f239e9d78d0; <WKBSpatialElement at 0x7f239e9d7310; u'POINT (41.718473 -111.946402)'>>,\n",
       " 'FeatureGeometryWKT': u'POINT (41.718473 -111.946402)',\n",
       " 'Latitude': 41.718473,\n",
       " 'Longitude': -111.946402,\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'6ae9eceb-989d-11e5-a9c7-6c4008bf018e',\n",
       " 'SiteTypeCV': u'Stream',\n",
       " 'SpatialReferenceID': 1,\n",
       " 'SpatialReferenceObj': <SpatialReferences('1', 'None', 'Unknown', 'The spatial reference is unknown', 'None')>,\n",
       " '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f239ea23a10>}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now get the SamplingFeature object for a SamplingFeature code\n",
    "sf_lst = read.getSamplingFeatures(codes=['USU-LBR-Mendon'])\n",
    "vars(sf_lst[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sf = sf_lst[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Sites('1', '1', 'Stream', '41.718473', '-111.946402', '<SpatialReferences('1', 'None', 'Unknown', 'The spatial reference is unknown', 'None')>', 'USU-LBR-Mendon')> \n",
      "\n",
      "<class 'odm2api.ODM2.models.Sites'>\n",
      "<type 'unicode'> POINT (41.718473 -111.946402)\n",
      "<class 'geoalchemy.spatialite.SQLitePersistentSpatialElement'>\n"
     ]
    }
   ],
   "source": [
    "print sf, \"\\n\"\n",
    "print type(sf)\n",
    "print type(sf.FeatureGeometryWKT), sf.FeatureGeometryWKT\n",
    "print type(sf.FeatureGeometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'desc': <WKBSpatialElement at 0x7f239e9d7310; u'POINT (41.718473 -111.946402)'>}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(sf.FeatureGeometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Represents a Geometry value as loaded from the database.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.FeatureGeometry.__doc__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(u'POINT (41.718473 -111.946402)', None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.FeatureGeometry.geom_wkb, sf.FeatureGeometry.geom_wkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (41.718473 -111.946402)\n",
      "POINT (41.718473 -111.946402)\n",
      "POINT (41.718473 -111.946402)\n",
      "POINT (41.718473 -111.946402)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(shapely.geometry.point.Point, 'POINT (41.718473 -111.946402)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4/25/2016: Don't know why the shape is listed 4 times ...\n",
    "type(sf.shape()), sf.shape().wkt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back to the rest of the demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<TimeSeriesResults('1', 'None', 'None', 'None', 'Time series coverage', 'None', 'None', '30.0', 'Average')>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getResults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Actions('1', 'Observation', '2007-08-16 16:30:00', 'An observation action that generated a time series result.')>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firstResult = read.getResults()[0]\n",
    "firstResult.FeatureActionObj.ActionObj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Foreign Key Example\n",
    "Drill down and get objects linked by foreign keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The FeatureAction object for the Result is:  <FeatureActions('1', '1', '1', )>\n",
      "The Action object for the Result is:  <Actions('1', 'Observation', '2007-08-16 16:30:00', 'An observation action that generated a time series result.')>\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: \", firstResult.FeatureActionObj\n",
    "    print \"The Action object for the Result is: \", action_firstResult\n",
    "    print (\"\\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",
    "except Exception as e:\n",
    "    print \"Unable to demo Foreign Key Example: \", e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of Retrieving Attributes of a Time Series Result using a ResultID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(odm2api.ODM2.models.TimeSeriesResults,\n",
       " {'AggregationStatisticCV': u'Average',\n",
       "  'FeatureActionID': 1,\n",
       "  'FeatureActionObj': <FeatureActions('1', '1', '1', )>,\n",
       "  'IntendedTimeSpacing': 30.0,\n",
       "  'IntendedTimeSpacingUnitsID': 102,\n",
       "  'ProcessingLevelID': 1,\n",
       "  'ResultDateTime': datetime.datetime(2015, 12, 1, 19, 35, 57, 609318),\n",
       "  'ResultDateTimeUTCOffset': -7,\n",
       "  'ResultID': 1,\n",
       "  'ResultTypeCV': u'Time series coverage',\n",
       "  'ResultUUID': u'6af05c61-989d-11e5-9d19-6c4008bf018e',\n",
       "  'SampledMediumCV': u'Surface Water',\n",
       "  'SpatialReferenceID': None,\n",
       "  'SpatialReferenceObj': None,\n",
       "  'StatusCV': u'Unknown',\n",
       "  'TaxonomicClassifierID': None,\n",
       "  'UnitsID': 96,\n",
       "  'ValidDateTime': None,\n",
       "  'ValidDateTimeUTCOffset': None,\n",
       "  'ValueCount': 24206,\n",
       "  'VariableID': 1,\n",
       "  'XLocation': None,\n",
       "  'XLocationUnitsID': None,\n",
       "  'XLocationUnitsObj': None,\n",
       "  'YLocation': None,\n",
       "  'YLocationUnitsID': None,\n",
       "  'ZLocation': None,\n",
       "  'ZLocationUnitsID': None,\n",
       "  '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f239e9564d0>})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsResult = read.getResults(ids=[1])[0]\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": 22,
   "metadata": {
    "collapsed": false
   },
   "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(\n",
    "        \"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: \", e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of Retrieving Time Series Result Values, then plotting them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ValueID</th>\n",
       "      <th>ResultID</th>\n",
       "      <th>DataValue</th>\n",
       "      <th>ValueDateTime</th>\n",
       "      <th>ValueDateTimeUTCOffset</th>\n",
       "      <th>CensorCodeCV</th>\n",
       "      <th>QualityCodeCV</th>\n",
       "      <th>TimeAggregationInterval</th>\n",
       "      <th>TimeAggregationIntervalUnitsID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.20000</td>\n",
       "      <td>2007-08-16 16:30:00</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>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22.19833</td>\n",
       "      <td>2007-08-16 17:00:00</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>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.18500</td>\n",
       "      <td>2007-08-16 17:30:00</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>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>22.03833</td>\n",
       "      <td>2007-08-16 18:00:00</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>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>21.88167</td>\n",
       "      <td>2007-08-16 18:30:00</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       ValueDateTime  ValueDateTimeUTCOffset  \\\n",
       "0        1         1   22.20000 2007-08-16 16:30:00                      -7   \n",
       "1        2         1   22.19833 2007-08-16 17:00:00                      -7   \n",
       "2        3         1   22.18500 2007-08-16 17:30:00                      -7   \n",
       "3        4         1   22.03833 2007-08-16 18:00:00                      -7   \n",
       "4        5         1   21.88167 2007-08-16 18:30:00                      -7   \n",
       "\n",
       "  CensorCodeCV QualityCodeCV  TimeAggregationInterval  \\\n",
       "0           nc       Unknown                     30.0   \n",
       "1           nc       Unknown                     30.0   \n",
       "2           nc       Unknown                     30.0   \n",
       "3           nc       Unknown                     30.0   \n",
       "4           nc       Unknown                     30.0   \n",
       "\n",
       "   TimeAggregationIntervalUnitsID  \n",
       "0                             102  \n",
       "1                             102  \n",
       "2                             102  \n",
       "3                             102  \n",
       "4                             102  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the values for a particular TimeSeriesResult\n",
    "\n",
    "tsValues = read.getResultValues(resultid=1)  # Return type is a pandas dataframe\n",
    "# Print a few Time Series Values to the console\n",
    "# tsValues.set_index('ValueDateTime', inplace=True)\n",
    "tsValues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6desGwLJlNUBP3CAfbhXafaC1tbVccglcfnlf5+haZ55YHz8eqqrM+jff1FJb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\nJHPnzuXLL79k1qxZ602FLVq0YNCgQYwZM2b9uR5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f239f536c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the time series\n",
    "try:\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    tsValues.plot(x='ValueDateTime', y='DataValue', kind='line',\n",
    "                  title=tsResult.VariableObj.VariableNameCV + \" at \" + \n",
    "                        tsResult.FeatureActionObj.SamplingFeatureObj.SamplingFeatureName,\n",
    "                  ax=ax)\n",
    "    \n",
    "    ax.set_ylabel(tsResult.VariableObj.VariableNameCV + \" (\" + \n",
    "                  tsResult.UnitsObj.UnitsAbbreviation + \")\")\n",
    "    ax.set_xlabel(\"Date/Time\")\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(True)\n",
    "except Exception as e:\n",
    "    print \"Unable to demo plotting of tsValues: \", e"
   ]
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}