odm2api_sample_fromsqlite-OLD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# odm2api sample\n",
    "Largely from https://github.com/ODM2/ODM2PythonAPI/blob/master/Examples/Sample.py   \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 sys\n",
    "import os\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 *"
   ]
  },
  {
   "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.getAllAffiliations()\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.getAllAffiliations()\n",
    "type(allaff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SamplingFeatures tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<SamplingFeatures('USU-LBR-Mendon', 'Little Bear River at Mendon Road near Mendon, Utah', 'None', '1345.0', 'POINT (41.718473 -111.946402)')>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [
    {
     "data": {
      "text/plain": [
       "[<SamplingFeatures('USU-LBR-Mendon', 'Little Bear River at Mendon Road near Mendon, Utah', 'None', '1345.0', 'POINT (41.718473 -111.946402)')>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getSamplingFeaturesByType('Site')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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.getSamplingFeaturesByType('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 getSamplingFeaturesByType\", e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<SamplingFeatures('USU-LBR-Mendon', 'Little Bear River at Mendon Road near Mendon, Utah', 'None', '1345.0', 'POINT (41.718473 -111.946402)')>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getSamplingFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SamplingFeatures('USU-LBR-Mendon', 'Little Bear River at Mendon Road near Mendon, Utah', 'None', '1345.0', 'POINT (41.718473 -111.946402)')>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read.getSamplingFeatureByCode('USU-LBR-Mendon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Now get the SamplingFeature object for a SamplingFeature code\n",
    "sf = read.getSamplingFeatureByCode('USU-LBR-Mendon')\n",
    "# vars(sf)\n",
    "# 1/31/2016: Leads to error with latest from odm2api master:\n",
    "# \"TypeError: vars() argument must have __dict__ attribute\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<SamplingFeatures('USU-LBR-Mendon', 'Little Bear River at Mendon Road near Mendon, Utah', 'None', '1345.0', 'POINT (41.718473 -111.946402)')> \n",
      "\n",
      "<class 'odm2api.ODM2.models.SamplingFeatures'>\n",
      "<class 'geoalchemy.spatialite.SQLitePersistentSpatialElement'>\n"
     ]
    }
   ],
   "source": [
    "print sf, \"\\n\"\n",
    "print type(sf)\n",
    "print type(sf.FeatureGeometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'desc': <WKBSpatialElement at 0x7f6cd3b82fd0; 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": [
    {
     "data": {
      "text/plain": [
       "(shapely.geometry.point.Point, 'POINT (41.718473 -111.946402)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------ Foreign Key Example --------- \n",
      "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": [
    "# Drill down and get objects linked by foreign keys\n",
    "print \"\\n------------ Foreign Key Example --------- \\n\",\n",
    "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": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------- Example of Retrieving Attributes of a Time Series Result -------\n",
      "Some of the attributes for the TimeSeriesResult retrieved using getTimeSeriesResultByResultID(): \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": [
    "# Now get a particular Result using a ResultID\n",
    "print \"\\n------- Example of Retrieving Attributes of a Time Series Result -------\"\n",
    "try:\n",
    "    tsResult = read.getTimeSeriesResultByResultId(1)\n",
    "    # Get the site information by drilling down\n",
    "    sf_tsResult = tsResult.ResultObj.FeatureActionObj.SamplingFeatureObj\n",
    "    print(\n",
    "        \"Some of the attributes for the TimeSeriesResult retrieved using getTimeSeriesResultByResultID(): \\n\" +\n",
    "        \"ResultTypeCV: \" + tsResult.ResultObj.ResultTypeCV + \"\\n\" +\n",
    "        # Get the ProcessingLevel from the TimeSeriesResult's ProcessingLevel object\n",
    "        \"ProcessingLevel: \" + tsResult.ResultObj.ProcessingLevelObj.Definition + \"\\n\" +\n",
    "        \"SampledMedium: \" + tsResult.ResultObj.SampledMediumCV + \"\\n\" +\n",
    "        # Get the variable information from the TimeSeriesResult's Variable object\n",
    "        \"Variable: \" + tsResult.ResultObj.VariableObj.VariableCode + \": \" + tsResult.ResultObj.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": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tsResult = read.getTimeSeriesResultByResultId(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(odm2api.ODM2.models.TimeSeriesResults,\n",
       " {'AggregationStatisticCV': u'Average',\n",
       "  'IntendedTimeSpacing': 30.0,\n",
       "  'IntendedTimeSpacingUnitsID': 102,\n",
       "  'ResultID': 1,\n",
       "  'ResultObj': <Results('1', '6af05c61-989d-11e5-9d19-6c4008bf018e', 'Time series coverage', '1', '24206')>,\n",
       "  'SpatialReferenceID': None,\n",
       "  'XLocation': None,\n",
       "  'XLocationUnitsID': None,\n",
       "  'YLocation': None,\n",
       "  'YLocationUnitsID': None,\n",
       "  'ZLocation': None,\n",
       "  'ZLocationUnitsID': None,\n",
       "  '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f6cd3afdf50>})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tsResult), vars(tsResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FeatureActionID': 1,\n",
       " 'FeatureActionObj': <FeatureActions('1', '1', '1', )>,\n",
       " 'ProcessingLevelID': 1,\n",
       " 'ProcessingLevelObj': <ProcessingLevels('1', '1', 'Quality controlled data', 'Quality controlled data that have passed quality assurance procedures such as routine estimation of timing and sensor calibration or visual inspection and removal of obvious errors. An example is USGS published streamflow records following parsing through USGS quality control procedures.')>,\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",
       " 'StatusCV': u'Unknown',\n",
       " 'TaxonomicClassifierID': None,\n",
       " 'UnitsID': 96,\n",
       " 'ValidDateTime': None,\n",
       " 'ValidDateTimeUTCOffset': None,\n",
       " 'ValueCount': 24206,\n",
       " 'VariableID': 1,\n",
       " 'VariableObj': <Variables('1', 'USU36', 'Temperature')>,\n",
       " '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f6cd4505f10>}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(tsResult.ResultObj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------- Example of Retrieving Time Series Result Values ---------\n",
      "<class 'odm2api.ODM2.models.TimeSeriesResultValues'> <TimeSeriesResultValues('-0.2683333', '2009-01-04 18:00:00', '30.0')>\n"
     ]
    },
    {
     "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>23641</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.268333</td>\n",
       "      <td>2009-01-04 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>1</th>\n",
       "      <td>23617</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.266667</td>\n",
       "      <td>2009-01-04 06:00:00</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>22922</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.263333</td>\n",
       "      <td>2008-12-18 01:00:00</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>3</th>\n",
       "      <td>23616</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.263333</td>\n",
       "      <td>2009-01-04 05:30:00</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>4</th>\n",
       "      <td>23618</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.263333</td>\n",
       "      <td>2009-01-04 06:30:00</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    23641         1  -0.268333 2009-01-04 18:00:00                      -7   \n",
       "1    23617         1  -0.266667 2009-01-04 06:00:00                      -7   \n",
       "2    22922         1  -0.263333 2008-12-18 01:00:00                      -7   \n",
       "3    23616         1  -0.263333 2009-01-04 05:30:00                      -7   \n",
       "4    23618         1  -0.263333 2009-01-04 06:30:00                      -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",
    "print \"\\n-------- Example of Retrieving Time Series Result Values ---------\"\n",
    "\n",
    "tsValues = read.getTimeSeriesResultValuesByResultId(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 0x7f6cd3b19490>"
      ]
     },
     "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.ResultObj.VariableObj.VariableNameCV + \" at \" + \n",
    "                        tsResult.ResultObj.FeatureActionObj.SamplingFeatureObj.SamplingFeatureName,\n",
    "                  ax=ax)\n",
    "    ax.set_ylabel(tsResult.ResultObj.VariableObj.VariableNameCV + \" (\" + \n",
    "                  tsResult.ResultObj.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}