notebooks/ODM2_Example3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ODM2 Example 3: Use ODM2 API to Retrieve Data for Manipulation or Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to connect to an ODM2 database using the ODM2 Python API, retrieve data, and use it for analysis or visualization. This example uses SQLite for the database because it doesn't require a server. However, the ODM2 Python API demonstrated here can alse be used with ODM2 databases implemented in MySQL, PostgresSQL or Microsoft SQL Server.\n",
    "\n",
    "More details of the ODM2 Python API and its source code and latest development can be found at: https://github.com/ODM2/ODM2PythonAPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "import os\n",
    "import sqlite3\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import folium\n",
    "from shapely.geometry import Point\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "from odm2api.ODMconnection import dbconnection\n",
    "import odm2api.ODM2.services.readService as odm2rs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a Connection to the ODM2 SQLite Database\n",
    "\n",
    "For this example, an ODM2 SQLite database has been created and loaded with water quality sample data from multiple monitoring sites in the iUTAH Gradients Along Mountain to Urban Transitions (GAMUT) water quality monitoring network. Water quality samples have been collected and analyzed for nitrogen, phosphorus, total coliform, E-coli, and some water isotopes.\n",
    "\n",
    "The example database is located in the `wshp2017_tutorial_content/data` directory on this JupyterHub server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign directory paths and SQLite file name\n",
    "dpth = os.getcwd()\n",
    "dbname_sqlite = \"ODM2_Example2.sqlite\"\n",
    "\n",
    "sqlite_pth = os.path.join(dpth, os.path.pardir, \"data\", dbname_sqlite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database connection successful!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    session_factory = dbconnection.createConnection('sqlite', sqlite_pth, 2.0)\n",
    "    read = odm2rs.ReadODM2(session_factory)\n",
    "    print(\"Database connection successful!\")\n",
    "except Exception as e:\n",
    "    print(\"Unable to establish connection to the database: \", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Some Basic Queries on the ODM2 Database\n",
    "\n",
    "This section shows some example of how to use the API to run both simple and more advanced queries on the ODM2 database, as well as how to examine the query output in convenient ways thanks to Python tools.\n",
    "\n",
    "Simple query functions like **getVariables( )** return objects similar to the entities in ODM2, and individual attributes can then be retrieved from the objects returned. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all Variables\n",
    "A simple query with simple output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all of the Variables from the ODM2 database then read the records\n",
    "# into a Pandas DataFrame to make it easy to view and manipulate\n",
    "allVars = read.getVariables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NoDataValue</th>\n",
       "      <th>SpeciationCV</th>\n",
       "      <th>VariableCode</th>\n",
       "      <th>VariableDefinition</th>\n",
       "      <th>VariableNameCV</th>\n",
       "      <th>VariableTypeCV</th>\n",
       "      <th>_sa_instance_state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VariableID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>N</td>\n",
       "      <td>TN</td>\n",
       "      <td>None</td>\n",
       "      <td>Nitrogen, total</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>P</td>\n",
       "      <td>TP</td>\n",
       "      <td>None</td>\n",
       "      <td>Phosphorus, total</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>N</td>\n",
       "      <td>Nitrate</td>\n",
       "      <td>None</td>\n",
       "      <td>Nitrogen, dissolved nitrite (NO2) + nitrate (NO3)</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>N</td>\n",
       "      <td>Ammonia</td>\n",
       "      <td>None</td>\n",
       "      <td>Nitrogen, NH4</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>P</td>\n",
       "      <td>Phosphate</td>\n",
       "      <td>None</td>\n",
       "      <td>Phosphorus, orthophosphate dissolved</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Tcoliform</td>\n",
       "      <td>None</td>\n",
       "      <td>Coliform, total</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>E-Coli</td>\n",
       "      <td>None</td>\n",
       "      <td>E-coli</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>C</td>\n",
       "      <td>DOC</td>\n",
       "      <td>None</td>\n",
       "      <td>Carbon, dissolved organic</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>N</td>\n",
       "      <td>TDN</td>\n",
       "      <td>None</td>\n",
       "      <td>Nitrogen, total dissolved</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-9999.0000000000</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Abs254</td>\n",
       "      <td>None</td>\n",
       "      <td>Absorbance</td>\n",
       "      <td>Water quality</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                 NoDataValue    SpeciationCV VariableCode VariableDefinition  \\\n",
       "VariableID                                                                     \n",
       "1           -9999.0000000000               N           TN               None   \n",
       "2           -9999.0000000000               P           TP               None   \n",
       "3           -9999.0000000000               N      Nitrate               None   \n",
       "4           -9999.0000000000               N      Ammonia               None   \n",
       "5           -9999.0000000000               P    Phosphate               None   \n",
       "6           -9999.0000000000  Not Applicable    Tcoliform               None   \n",
       "7           -9999.0000000000  Not Applicable       E-Coli               None   \n",
       "8           -9999.0000000000               C          DOC               None   \n",
       "9           -9999.0000000000               N          TDN               None   \n",
       "10          -9999.0000000000  Not Applicable       Abs254               None   \n",
       "\n",
       "                                               VariableNameCV VariableTypeCV  \\\n",
       "VariableID                                                                     \n",
       "1                                             Nitrogen, total  Water quality   \n",
       "2                                           Phosphorus, total  Water quality   \n",
       "3           Nitrogen, dissolved nitrite (NO2) + nitrate (NO3)  Water quality   \n",
       "4                                               Nitrogen, NH4  Water quality   \n",
       "5                        Phosphorus, orthophosphate dissolved  Water quality   \n",
       "6                                             Coliform, total  Water quality   \n",
       "7                                                      E-coli  Water quality   \n",
       "8                                   Carbon, dissolved organic  Water quality   \n",
       "9                                   Nitrogen, total dissolved  Water quality   \n",
       "10                                                 Absorbance  Water quality   \n",
       "\n",
       "                                           _sa_instance_state  \n",
       "VariableID                                                     \n",
       "1           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "2           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "3           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "4           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "5           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "6           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "7           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "8           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "9           <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "10          <sqlalchemy.orm.state.InstanceState object at ...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all of the Variables from the ODM2 database then read the records\n",
    "# into a Pandas DataFrame to make it easy to view and manipulate\n",
    "allVars = read.getVariables()\n",
    "\n",
    "variables_df = pd.DataFrame.from_records([vars(variable) for variable in allVars], index='VariableID')\n",
    "variables_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all People\n",
    "Another simple query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PersonFirstName</th>\n",
       "      <th>PersonID</th>\n",
       "      <th>PersonLastName</th>\n",
       "      <th>PersonMiddleName</th>\n",
       "      <th>_sa_instance_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>1</td>\n",
       "      <td>Mesner</td>\n",
       "      <td></td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dane</td>\n",
       "      <td>2</td>\n",
       "      <td>Brophy</td>\n",
       "      <td></td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ben</td>\n",
       "      <td>3</td>\n",
       "      <td>Rider</td>\n",
       "      <td></td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Michelle</td>\n",
       "      <td>4</td>\n",
       "      <td>Baker</td>\n",
       "      <td></td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Erin</td>\n",
       "      <td>5</td>\n",
       "      <td>Jones</td>\n",
       "      <td></td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  PersonFirstName  PersonID PersonLastName PersonMiddleName  \\\n",
       "0           Nancy         1         Mesner                    \n",
       "1            Dane         2         Brophy                    \n",
       "2             Ben         3          Rider                    \n",
       "3        Michelle         4          Baker                    \n",
       "4            Erin         5          Jones                    \n",
       "\n",
       "                                  _sa_instance_state  \n",
       "0  <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "1  <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "2  <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "3  <sqlalchemy.orm.state.InstanceState object at ...  \n",
       "4  <sqlalchemy.orm.state.InstanceState object at ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allPeople = read.getPeople()\n",
    "pd.DataFrame.from_records([vars(person) for person in allPeople]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Site Sampling Features: pass arguments to the API query\n",
    "\n",
    "Some of the API functions accept arguments that let you subset what is returned. For example, I can query the database using the **getSamplingFeatures( )** function and pass it a SamplingFeatureType of \"Site\" to return a list of those SamplingFeatures that are Sites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all of the SamplingFeatures from the ODM2 database that are Sites\n",
    "siteFeatures = read.getSamplingFeatures(type='Site')\n",
    "\n",
    "# Read Sites records into a Pandas DataFrame\n",
    "# ()\"if sf.Latitude\" is used only to instantiate/read Site attributes)\n",
    "df = pd.DataFrame.from_records([vars(sf) for sf in siteFeatures if sf.Latitude])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we know this is a *geospatial* dataset (Sites, which have latitude and longitude), we can use more specialized (spatialized?) Python tools like `GeoPandas` (geospatially enabled Pandas) and `Folium` interactive maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ElevationDatumCV</th>\n",
       "      <th>Elevation_m</th>\n",
       "      <th>FeatureGeometryWKT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>SamplingFeatureCode</th>\n",
       "      <th>SamplingFeatureDescription</th>\n",
       "      <th>SamplingFeatureGeotypeCV</th>\n",
       "      <th>SamplingFeatureID</th>\n",
       "      <th>SamplingFeatureName</th>\n",
       "      <th>SamplingFeatureTypeCV</th>\n",
       "      <th>SamplingFeatureUUID</th>\n",
       "      <th>SiteTypeCV</th>\n",
       "      <th>SpatialReferenceID</th>\n",
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       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EGM96</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>40.745078</td>\n",
       "      <td>-111.854449</td>\n",
       "      <td>RB_1300E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>Red Butte Creek at 1300E (downstream of spring)</td>\n",
       "      <td>Site</td>\n",
       "      <td>0DDE8EF6-EC2F-42C0-AB50-20C6C02E89B2</td>\n",
       "      <td>Stream</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "      <td>POINT (-111.854449 40.745078)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EGM96</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>40.745106</td>\n",
       "      <td>-111.854389</td>\n",
       "      <td>RB_1300ESpring</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>Spring that enters Red Butte Creek at 1300E</td>\n",
       "      <td>Site</td>\n",
       "      <td>9848BBFE-EA3F-4918-A324-13E8EDE5381C</td>\n",
       "      <td>Spring</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "      <td>POINT (-111.854389 40.745106)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EGM96</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>None</td>\n",
       "      <td>40.741583</td>\n",
       "      <td>-111.917667</td>\n",
       "      <td>RB_900W_BA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>Red Butte Creek terminus at Jordan River at 13...</td>\n",
       "      <td>Site</td>\n",
       "      <td>688017BC-9E02-4444-A21D-270366BE2348</td>\n",
       "      <td>Stream</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "      <td>POINT (-111.917667 40.741583)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EGM96</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>None</td>\n",
       "      <td>40.766134</td>\n",
       "      <td>-111.826530</td>\n",
       "      <td>RB_Amphitheater</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>Red Butte Creek below Red Butte Garden Amphith...</td>\n",
       "      <td>Site</td>\n",
       "      <td>9CFE685B-5CDA-4E38-98D9-406D645C7D21</td>\n",
       "      <td>Stream</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "      <td>POINT (-111.82653 40.766134)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EGM96</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>None</td>\n",
       "      <td>40.779602</td>\n",
       "      <td>-111.806669</td>\n",
       "      <td>RB_ARBR_AA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>Red Butte Creek above Red Butte Reservoir Adan...</td>\n",
       "      <td>Site</td>\n",
       "      <td>98C7F63A-FDFB-4898-87C6-5AA8EC34D1E4</td>\n",
       "      <td>Stream</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;sqlalchemy.orm.state.InstanceState object at ...</td>\n",
       "      <td>POINT (-111.806669 40.779602)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ElevationDatumCV  Elevation_m FeatureGeometryWKT   Latitude   Longitude  \\\n",
       "0            EGM96       1356.0               None  40.745078 -111.854449   \n",
       "1            EGM96       1356.0               None  40.745106 -111.854389   \n",
       "2            EGM96       1289.0               None  40.741583 -111.917667   \n",
       "3            EGM96       1519.0               None  40.766134 -111.826530   \n",
       "4            EGM96       1648.0               None  40.779602 -111.806669   \n",
       "\n",
       "  SamplingFeatureCode SamplingFeatureDescription SamplingFeatureGeotypeCV  \\\n",
       "0            RB_1300E                       None                     None   \n",
       "1      RB_1300ESpring                       None                     None   \n",
       "2          RB_900W_BA                       None                     None   \n",
       "3     RB_Amphitheater                       None                     None   \n",
       "4          RB_ARBR_AA                       None                     None   \n",
       "\n",
       "   SamplingFeatureID                                SamplingFeatureName  \\\n",
       "0                  1    Red Butte Creek at 1300E (downstream of spring)   \n",
       "1                  2        Spring that enters Red Butte Creek at 1300E   \n",
       "2                  3  Red Butte Creek terminus at Jordan River at 13...   \n",
       "3                  4  Red Butte Creek below Red Butte Garden Amphith...   \n",
       "4                  5  Red Butte Creek above Red Butte Reservoir Adan...   \n",
       "\n",
       "  SamplingFeatureTypeCV                   SamplingFeatureUUID SiteTypeCV  \\\n",
       "0                  Site  0DDE8EF6-EC2F-42C0-AB50-20C6C02E89B2     Stream   \n",
       "1                  Site  9848BBFE-EA3F-4918-A324-13E8EDE5381C     Spring   \n",
       "2                  Site  688017BC-9E02-4444-A21D-270366BE2348     Stream   \n",
       "3                  Site  9CFE685B-5CDA-4E38-98D9-406D645C7D21     Stream   \n",
       "4                  Site  98C7F63A-FDFB-4898-87C6-5AA8EC34D1E4     Stream   \n",
       "\n",
       "   SpatialReferenceID                                 _sa_instance_state  \\\n",
       "0                   1  <sqlalchemy.orm.state.InstanceState object at ...   \n",
       "1                   1  <sqlalchemy.orm.state.InstanceState object at ...   \n",
       "2                   1  <sqlalchemy.orm.state.InstanceState object at ...   \n",
       "3                   1  <sqlalchemy.orm.state.InstanceState object at ...   \n",
       "4                   1  <sqlalchemy.orm.state.InstanceState object at ...   \n",
       "\n",
       "                        geometry  \n",
       "0  POINT (-111.854449 40.745078)  \n",
       "1  POINT (-111.854389 40.745106)  \n",
       "2  POINT (-111.917667 40.741583)  \n",
       "3   POINT (-111.82653 40.766134)  \n",
       "4  POINT (-111.806669 40.779602)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a GeoPandas GeoDataFrame from Sites DataFrame\n",
    "ptgeom = [Point(xy) for xy in zip(df['Longitude'], df['Latitude'])]\n",
    "gdf = gpd.GeoDataFrame(df, geometry=ptgeom, crs={'init': 'epsg:4326'})\n",
    "gdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of records (features) in GeoDataFrame\n",
    "len(gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5p/HFVUtr7qfew6wq3D9i9i4nE5sRas2Ndf/oqzzx8puHtzsYwX/+0asANRNKI6PW3T9i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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4529fd1c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A trivial plot, easy to generate thanks to GeoPandas\n",
    "gdf.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A site has a `SiteTypeCV`. Let's examine the site type distribution, and use that information to create a new GeoDataFrame column to specify a map marker color by `SiteTypeCV`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stream    24\n",
       "Spring     1\n",
       "Name: SiteTypeCV, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['SiteTypeCV'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf[\"color\"] = gdf.apply(lambda feat: 'green' if feat['SiteTypeCV'] == 'Stream' else 'red', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: While the database holds a copy of the **ODM2 Controlled Vocabularies**, the complete description of each CV term is available from a web request to the CV API at http://vocabulary.odm2.org. Want to know more about how a \"spring\" is defined? Here's one simple way, using `Pandas` to access and parse the CSV web service response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>name</th>\n",
       "      <th>definition</th>\n",
       "      <th>category</th>\n",
       "      <th>provenance</th>\n",
       "      <th>provenance_uri</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spring</td>\n",
       "      <td>Spring</td>\n",
       "      <td>A location at which the water table intersects...</td>\n",
       "      <td>Spring Sites</td>\n",
       "      <td>Adapted from USGS Site Types.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://wdr.water.usgs.gov/nwisgmap/help/sitety...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     term    name                                         definition  \\\n",
       "0  spring  Spring  A location at which the water table intersects...   \n",
       "\n",
       "       category                     provenance  provenance_uri  \\\n",
       "0  Spring Sites  Adapted from USGS Site Types.             NaN   \n",
       "\n",
       "                                                note  \n",
       "0  http://wdr.water.usgs.gov/nwisgmap/help/sitety...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sitetype = 'spring'\n",
    "pd.read_csv(\"http://vocabulary.odm2.org/api/v1/sitetype/{}/?format=csv\".format(sitetype))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we'll create an interactive and helpful `Folium` map of the sites.** This map features:\n",
    "- Automatic panning to the location of the sites (no hard wiring, except for the zoom scale), based on GeoPandas functionality and information from the ODM2 Site Sampling Features\n",
    "- Color coding by `SiteTypeCV` \n",
    "- Marker clustering\n",
    "- Simple marker pop ups with content from the ODM2 Site Sampling Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://unpkg.com/leaflet@1.0.1/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.0.0/leaflet.markercluster-src.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.0.0/leaflet.markercluster.js"></script>
    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.0.1/dist/leaflet.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" />
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css" />
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.0.0/MarkerCluster.Default.css" />
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.0.0/MarkerCluster.css" />
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css" />
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <style> #map_1640035489744158b6f792b226647cc7 {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_1640035489744158b6f792b226647cc7" ></div>
        
</body>
<script>    
    

            
                var southWest = L.latLng(-90, -180);
                var northEast = L.latLng(90, 180);
                var bounds = L.latLngBounds(southWest, northEast);
            

            var map_1640035489744158b6f792b226647cc7 = L.map(
                                  'map_1640035489744158b6f792b226647cc7',
                                  {center: [40.7628298932,-111.834746072],
                                  zoom: 11,
                                  maxBounds: bounds,
                                  layers: [],
                                  worldCopyJump: false,
                                  crs: L.CRS.EPSG3857
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            var tile_layer_e4963892cd7c46c6bd022d4b5ba8e531 = L.tileLayer(
                'https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png',
                {
                    maxZoom: 18,
                    minZoom: 1,
                    continuousWorld: false,
                    noWrap: false,
                    attribution: '(c) <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors (c) <a href="http://cartodb.com/attributions">CartoDB</a>, CartoDB <a href ="http://cartodb.com/attributions">attributions</a>',
                    detectRetina: false
                    }
                ).addTo(map_1640035489744158b6f792b226647cc7);

        
    
            var marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119 = L.markerClusterGroup();
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            var marker_3ad85380702b48d2bbc2cb824ad53285 = L.marker(
                [40.745078,-111.854449],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_e606d0b5b7cb4faa91fcc35e2f160201 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_3ad85380702b48d2bbc2cb824ad53285.setIcon(icon_e606d0b5b7cb4faa91fcc35e2f160201);
            
    
            var popup_c9cdf37c49be492c90d76ffa4392e4a4 = L.popup({maxWidth: '300'});

            
                var html_07e5feec54544caba6b471ea1f1d0308 = $('<div id="html_07e5feec54544caba6b471ea1f1d0308" style="width: 100.0%; height: 100.0%;">RB_1300E (Stream): Red Butte Creek at 1300E (downstream of spring)</div>')[0];
                popup_c9cdf37c49be492c90d76ffa4392e4a4.setContent(html_07e5feec54544caba6b471ea1f1d0308);
            

            marker_3ad85380702b48d2bbc2cb824ad53285.bindPopup(popup_c9cdf37c49be492c90d76ffa4392e4a4);

            
        
    

            var marker_694f54075a5d4faca301615fc28dae33 = L.marker(
                [40.745106,-111.854389],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_4409bfdcb66e4cd7added2aa17e9a130 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'red',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_694f54075a5d4faca301615fc28dae33.setIcon(icon_4409bfdcb66e4cd7added2aa17e9a130);
            
    
            var popup_01fd6cbc853b46459d155bd6eef1867a = L.popup({maxWidth: '300'});

            
                var html_ea214f2146d54255aab5d99bd99f522d = $('<div id="html_ea214f2146d54255aab5d99bd99f522d" style="width: 100.0%; height: 100.0%;">RB_1300ESpring (Spring): Spring that enters Red Butte Creek at 1300E</div>')[0];
                popup_01fd6cbc853b46459d155bd6eef1867a.setContent(html_ea214f2146d54255aab5d99bd99f522d);
            

            marker_694f54075a5d4faca301615fc28dae33.bindPopup(popup_01fd6cbc853b46459d155bd6eef1867a);

            
        
    

            var marker_a24dd5449be24fd9a2072665f3d6bcd9 = L.marker(
                [40.741583,-111.917667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_6b4b32f0b8a642ec88021dc69276328d = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_a24dd5449be24fd9a2072665f3d6bcd9.setIcon(icon_6b4b32f0b8a642ec88021dc69276328d);
            
    
            var popup_3f001a3f1e6743d6a25e67cb9d9dc6cb = L.popup({maxWidth: '300'});

            
                var html_6a6f41fa224944958857c53e0ad8bf85 = $('<div id="html_6a6f41fa224944958857c53e0ad8bf85" style="width: 100.0%; height: 100.0%;">RB_900W_BA (Stream): Red Butte Creek terminus at Jordan River at 1300 South (pipe entering river)</div>')[0];
                popup_3f001a3f1e6743d6a25e67cb9d9dc6cb.setContent(html_6a6f41fa224944958857c53e0ad8bf85);
            

            marker_a24dd5449be24fd9a2072665f3d6bcd9.bindPopup(popup_3f001a3f1e6743d6a25e67cb9d9dc6cb);

            
        
    

            var marker_3c0a6bfaa861454db85fc83f06095b7a = L.marker(
                [40.766134,-111.82653],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_2015d47362564030a791cc7c4b630a38 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_3c0a6bfaa861454db85fc83f06095b7a.setIcon(icon_2015d47362564030a791cc7c4b630a38);
            
    
            var popup_5736aee01e5c48759b20f56f4a10ef35 = L.popup({maxWidth: '300'});

            
                var html_8018409260714d129ad425ac721094a4 = $('<div id="html_8018409260714d129ad425ac721094a4" style="width: 100.0%; height: 100.0%;">RB_Amphitheater (Stream): Red Butte Creek below Red Butte Garden Amphitheater</div>')[0];
                popup_5736aee01e5c48759b20f56f4a10ef35.setContent(html_8018409260714d129ad425ac721094a4);
            

            marker_3c0a6bfaa861454db85fc83f06095b7a.bindPopup(popup_5736aee01e5c48759b20f56f4a10ef35);

            
        
    

            var marker_100c00d8a5774fc498d78320b710702f = L.marker(
                [40.779602,-111.806669],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_b14063de9c6a41aca0c228576602a0f9 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_100c00d8a5774fc498d78320b710702f.setIcon(icon_b14063de9c6a41aca0c228576602a0f9);
            
    
            var popup_a30260e14da54b56a093963fdd38bc08 = L.popup({maxWidth: '300'});

            
                var html_d72690aa648646449055fe91349fdec3 = $('<div id="html_d72690aa648646449055fe91349fdec3" style="width: 100.0%; height: 100.0%;">RB_ARBR_AA (Stream): Red Butte Creek above Red Butte Reservoir Adanced Aquatic</div>')[0];
                popup_a30260e14da54b56a093963fdd38bc08.setContent(html_d72690aa648646449055fe91349fdec3);
            

            marker_100c00d8a5774fc498d78320b710702f.bindPopup(popup_a30260e14da54b56a093963fdd38bc08);

            
        
    

            var marker_321ec528e2f841b8852cb8cb26ac03fc = L.marker(
                [40.763958,-111.828286],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_1e0aed80bad2460c886aa4dd79bf0312 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_321ec528e2f841b8852cb8cb26ac03fc.setIcon(icon_1e0aed80bad2460c886aa4dd79bf0312);
            
    
            var popup_4f7d63d17a104e15af533b33d53b842d = L.popup({maxWidth: '300'});

            
                var html_b2fb9f4b08c845dea9fbed03d46b055e = $('<div id="html_b2fb9f4b08c845dea9fbed03d46b055e" style="width: 100.0%; height: 100.0%;">RB_CG_BA (Stream): Red Butte Creek at Cottams Grove Basic Aquatic</div>')[0];
                popup_4f7d63d17a104e15af533b33d53b842d.setContent(html_b2fb9f4b08c845dea9fbed03d46b055e);
            

            marker_321ec528e2f841b8852cb8cb26ac03fc.bindPopup(popup_4f7d63d17a104e15af533b33d53b842d);

            
        
    

            var marker_c9f3efce30ae4c74b04d96ece4abe242 = L.marker(
                [40.757225,-111.833722],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_018407d06b6046bca016707f32ea76ba = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_c9f3efce30ae4c74b04d96ece4abe242.setIcon(icon_018407d06b6046bca016707f32ea76ba);
            
    
            var popup_9537c54e2dea4befb6c9fb5a8e1bb28c = L.popup({maxWidth: '300'});

            
                var html_a2a071a8fa6c47d1b2b6cde2a5db17cf = $('<div id="html_a2a071a8fa6c47d1b2b6cde2a5db17cf" style="width: 100.0%; height: 100.0%;">RB_FD_AA (Stream): Red Butte Creek at Foothill Drive Advanced Aquatic</div>')[0];
                popup_9537c54e2dea4befb6c9fb5a8e1bb28c.setContent(html_a2a071a8fa6c47d1b2b6cde2a5db17cf);
            

            marker_c9f3efce30ae4c74b04d96ece4abe242.bindPopup(popup_9537c54e2dea4befb6c9fb5a8e1bb28c);

            
        
    

            var marker_02d4f111c9f94e65aea7dc8d8725863a = L.marker(
                [40.760139,-111.830222],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_3104a9cda0274e04a3d984a48535e2be = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_02d4f111c9f94e65aea7dc8d8725863a.setIcon(icon_3104a9cda0274e04a3d984a48535e2be);
            
    
            var popup_fa9599c6fc8a4042b29a281af35e0123 = L.popup({maxWidth: '300'});

            
                var html_52272893d27044bda34d5603a76857a3 = $('<div id="html_52272893d27044bda34d5603a76857a3" style="width: 100.0%; height: 100.0%;">RB_GIRF (Stream): Red Butte Creek below Green Infrastructure Research Facility</div>')[0];
                popup_fa9599c6fc8a4042b29a281af35e0123.setContent(html_52272893d27044bda34d5603a76857a3);
            

            marker_02d4f111c9f94e65aea7dc8d8725863a.bindPopup(popup_fa9599c6fc8a4042b29a281af35e0123);

            
        
    

            var marker_30dcdb3574604eaca8d948b3cfb538a4 = L.marker(
                [40.74125,-111.918028],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_f191544a075441fcac8652da985fd5ef = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_30dcdb3574604eaca8d948b3cfb538a4.setIcon(icon_f191544a075441fcac8652da985fd5ef);
            
    
            var popup_83262970b5a547f896bbb33f20d5aae6 = L.popup({maxWidth: '300'});

            
                var html_ca603f57bdac4577a7b4d79876511718 = $('<div id="html_ca603f57bdac4577a7b4d79876511718" style="width: 100.0%; height: 100.0%;">RB_Jordan (Stream): Jordan River at 1300 South 900 West</div>')[0];
                popup_83262970b5a547f896bbb33f20d5aae6.setContent(html_ca603f57bdac4577a7b4d79876511718);
            

            marker_30dcdb3574604eaca8d948b3cfb538a4.bindPopup(popup_83262970b5a547f896bbb33f20d5aae6);

            
        
    

            var marker_1687223bd30846ca961fd86e1405703c = L.marker(
                [40.809522,-111.765472],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_42647a07d7a148ab8d68841e384bc8a1 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_1687223bd30846ca961fd86e1405703c.setIcon(icon_42647a07d7a148ab8d68841e384bc8a1);
            
    
            var popup_11be04e926d1417799a28b8fdd434af9 = L.popup({maxWidth: '300'});

            
                var html_ff50db7a00e94d529d75577e7411da47 = $('<div id="html_ff50db7a00e94d529d75577e7411da47" style="width: 100.0%; height: 100.0%;">RB_KF_BA (Stream): Knowlton Fork at Knowlton Fork Basic Aquatic</div>')[0];
                popup_11be04e926d1417799a28b8fdd434af9.setContent(html_ff50db7a00e94d529d75577e7411da47);
            

            marker_1687223bd30846ca961fd86e1405703c.bindPopup(popup_11be04e926d1417799a28b8fdd434af9);

            
        
    

            var marker_177de6bf5be14cd28229409231b0e43a = L.marker(
                [40.802361,-111.766944],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_7a3d008451ad46de810a8f851460e33a = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_177de6bf5be14cd28229409231b0e43a.setIcon(icon_7a3d008451ad46de810a8f851460e33a);
            
    
            var popup_dcc1b924ef0d475c8456f71d37f488a3 = L.popup({maxWidth: '300'});

            
                var html_363482afa7a5456290daea91a549a8f0 = $('<div id="html_363482afa7a5456290daea91a549a8f0" style="width: 100.0%; height: 100.0%;">RB_KFJunction (Stream): Red Butte Creek at Knowltons Fork junction</div>')[0];
                popup_dcc1b924ef0d475c8456f71d37f488a3.setContent(html_363482afa7a5456290daea91a549a8f0);
            

            marker_177de6bf5be14cd28229409231b0e43a.bindPopup(popup_dcc1b924ef0d475c8456f71d37f488a3);

            
        
    

            var marker_419d10d930724dc08c2a73094b351a99 = L.marker(
                [40.744936,-111.8575],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_10b775bbb5254efbb8e33af330ec96a8 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_419d10d930724dc08c2a73094b351a99.setIcon(icon_10b775bbb5254efbb8e33af330ec96a8);
            
    
            var popup_bf67b37b2a574ec4abd7c4c4358184c0 = L.popup({maxWidth: '300'});

            
                var html_ea8e95683c514f05b1d96b657bea4bcb = $('<div id="html_ea8e95683c514f05b1d96b657bea4bcb" style="width: 100.0%; height: 100.0%;">RB_LDSWard (Stream): Red Butte Creek at LDS ward house at 1150 E Yale Ave</div>')[0];
                popup_bf67b37b2a574ec4abd7c4c4358184c0.setContent(html_ea8e95683c514f05b1d96b657bea4bcb);
            

            marker_419d10d930724dc08c2a73094b351a99.bindPopup(popup_bf67b37b2a574ec4abd7c4c4358184c0);

            
        
    

            var marker_af3f3d87dd8a4adf8bc97d4780121a94 = L.marker(
                [40.743794,-111.873406],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_8dab73a34d24488fa80879eb8424dc61 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_af3f3d87dd8a4adf8bc97d4780121a94.setIcon(icon_8dab73a34d24488fa80879eb8424dc61);
            
    
            var popup_babbe620fdf742ee901e2857a0687513 = L.popup({maxWidth: '300'});

            
                var html_6fdbdf1535b24447844ac309a1bc10ef = $('<div id="html_6fdbdf1535b24447844ac309a1bc10ef" style="width: 100.0%; height: 100.0%;">RB_LibLakeDock (Stream): Liberty Lake at Liberty Lake Dock</div>')[0];
                popup_babbe620fdf742ee901e2857a0687513.setContent(html_6fdbdf1535b24447844ac309a1bc10ef);
            

            marker_af3f3d87dd8a4adf8bc97d4780121a94.bindPopup(popup_babbe620fdf742ee901e2857a0687513);

            
        
    

            var marker_4f30696cfba94f1396c0aae094ccfcdb = L.marker(
                [40.742346,-111.8721],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_da2e35745d6e4701b8727c120849b5f7 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_4f30696cfba94f1396c0aae094ccfcdb.setIcon(icon_da2e35745d6e4701b8727c120849b5f7);
            
    
            var popup_c7dd3ff3c23c4b69821f1a857ef39308 = L.popup({maxWidth: '300'});

            
                var html_21795f00bb49406ea316a01748636749 = $('<div id="html_21795f00bb49406ea316a01748636749" style="width: 100.0%; height: 100.0%;">RB_LibLakeEmigration (Stream): Emigration Creek terminus at Liberty Lake (pipe entering lake)</div>')[0];
                popup_c7dd3ff3c23c4b69821f1a857ef39308.setContent(html_21795f00bb49406ea316a01748636749);
            

            marker_4f30696cfba94f1396c0aae094ccfcdb.bindPopup(popup_c7dd3ff3c23c4b69821f1a857ef39308);

            
        
    

            var marker_33270c605d574b5b80e244b0abd17702 = L.marker(
                [40.743881,-111.872147],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_2c613c0e3aae476fb9c8c9135cb0c0b4 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_33270c605d574b5b80e244b0abd17702.setIcon(icon_2c613c0e3aae476fb9c8c9135cb0c0b4);
            
    
            var popup_95c7ca047340426f8fb4c763ba8580f3 = L.popup({maxWidth: '300'});

            
                var html_78d1c573fb9348b5bded65065a03e2e2 = $('<div id="html_78d1c573fb9348b5bded65065a03e2e2" style="width: 100.0%; height: 100.0%;">RB_LibLakeRedButte (Stream): Red Butte Creek terminus at Liberty Lake (pipe entering lake)</div>')[0];
                popup_95c7ca047340426f8fb4c763ba8580f3.setContent(html_78d1c573fb9348b5bded65065a03e2e2);
            

            marker_33270c605d574b5b80e244b0abd17702.bindPopup(popup_95c7ca047340426f8fb4c763ba8580f3);

            
        
    

            var marker_8adbb5d451674175b00aa2ce7d10f168 = L.marker(
                [40.749658,-111.841814],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_e3e9e959114542db8694f30486932064 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_8adbb5d451674175b00aa2ce7d10f168.setIcon(icon_e3e9e959114542db8694f30486932064);
            
    
            var popup_7a41d47ebb154f8996306e2bef969e3a = L.popup({maxWidth: '300'});

            
                var html_81ea5cd37cd943368265bcb8293eb763 = $('<div id="html_81ea5cd37cd943368265bcb8293eb763" style="width: 100.0%; height: 100.0%;">RB_MillerEntrance (Stream): Red Butte Creek at Miller Park Enterance at 900 South</div>')[0];
                popup_7a41d47ebb154f8996306e2bef969e3a.setContent(html_81ea5cd37cd943368265bcb8293eb763);
            

            marker_8adbb5d451674175b00aa2ce7d10f168.bindPopup(popup_7a41d47ebb154f8996306e2bef969e3a);

            
        
    

            var marker_20e4a28cbea045aaaca7fd3eefa7e9cf = L.marker(
                [40.787611,-111.796389],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_8dbbc302f30f470e88fc81f8f07a287f = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_20e4a28cbea045aaaca7fd3eefa7e9cf.setIcon(icon_8dbbc302f30f470e88fc81f8f07a287f);
            
    
            var popup_58038f4513bd40f58fdb3f3e1c32a153 = L.popup({maxWidth: '300'});

            
                var html_e785ab291f1b4dd4944adbb8846a5253 = $('<div id="html_e785ab291f1b4dd4944adbb8846a5253" style="width: 100.0%; height: 100.0%;">RB_PFJunction (Stream): Parleys Fork at Parleys Fork junction</div>')[0];
                popup_58038f4513bd40f58fdb3f3e1c32a153.setContent(html_e785ab291f1b4dd4944adbb8846a5253);
            

            marker_20e4a28cbea045aaaca7fd3eefa7e9cf.bindPopup(popup_58038f4513bd40f58fdb3f3e1c32a153);

            
        
    

            var marker_9813a70160b3438cb0067d017ad83541 = L.marker(
                [40.80289722,-111.7643389],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_683d2bc794f14cef84aea6021262c937 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_9813a70160b3438cb0067d017ad83541.setIcon(icon_683d2bc794f14cef84aea6021262c937);
            
    
            var popup_4a72b95370d441c3915df4167aa4f2eb = L.popup({maxWidth: '300'});

            
                var html_ba54cc0d5c54478f9f360c227946b103 = $('<div id="html_ba54cc0d5c54478f9f360c227946b103" style="width: 100.0%; height: 100.0%;">RB_RBFork (Stream): Red Butte Creek at Red Butte Fork</div>')[0];
                popup_4a72b95370d441c3915df4167aa4f2eb.setContent(html_ba54cc0d5c54478f9f360c227946b103);
            

            marker_9813a70160b3438cb0067d017ad83541.bindPopup(popup_4a72b95370d441c3915df4167aa4f2eb);

            
        
    

            var marker_912e27255940403aa9d599810a478a9a = L.marker(
                [40.774228,-111.817025],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_d836c5c8901f438ea069259a5b09c60b = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_912e27255940403aa9d599810a478a9a.setIcon(icon_d836c5c8901f438ea069259a5b09c60b);
            
    
            var popup_42dc68d9e030402984b50176cf8269aa = L.popup({maxWidth: '300'});

            
                var html_4b3a94e6a400414980cab8d831440731 = $('<div id="html_4b3a94e6a400414980cab8d831440731" style="width: 100.0%; height: 100.0%;">RB_RBG_BA (Stream): Red Butte Creek at Red Butte Gate Basic Aquatic</div>')[0];
                popup_42dc68d9e030402984b50176cf8269aa.setContent(html_4b3a94e6a400414980cab8d831440731);
            

            marker_912e27255940403aa9d599810a478a9a.bindPopup(popup_42dc68d9e030402984b50176cf8269aa);

            
        
    

            var marker_9ffdcc6c6861452fa8f7a0077cf0a48f = L.marker(
                [40.779379,-111.808632],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_273d2f7a58e447efbfcc6c43415d5c1e = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_9ffdcc6c6861452fa8f7a0077cf0a48f.setIcon(icon_273d2f7a58e447efbfcc6c43415d5c1e);
            
    
            var popup_0c0f3d7979ed41939bd863e1c1fae995 = L.popup({maxWidth: '300'});

            
                var html_d4079972e6a1448190fe581f4387b4f0 = $('<div id="html_d4079972e6a1448190fe581f4387b4f0" style="width: 100.0%; height: 100.0%;">RB_RBReservoir (Stream): Red Butte Reservoir at boat ramp</div>')[0];
                popup_0c0f3d7979ed41939bd863e1c1fae995.setContent(html_d4079972e6a1448190fe581f4387b4f0);
            

            marker_9ffdcc6c6861452fa8f7a0077cf0a48f.bindPopup(popup_0c0f3d7979ed41939bd863e1c1fae995);

            
        
    

            var marker_29f11e8def4a4065b301168475304aa3 = L.marker(
                [40.74711111,-111.8447389],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_3519f3736b2b4d6298ad9abc456d6374 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_29f11e8def4a4065b301168475304aa3.setIcon(icon_3519f3736b2b4d6298ad9abc456d6374);
            
    
            var popup_150164f56e604640af08a13c7acb6bec = L.popup({maxWidth: '300'});

            
                var html_b6ef36c8578d4c1aa3ef42597d64992d = $('<div id="html_b6ef36c8578d4c1aa3ef42597d64992d" style="width: 100.0%; height: 100.0%;">RB_SLCO (Stream): Red Butte Creek at 1600 East SLCO Bonneview aquatic monitoring station</div>')[0];
                popup_150164f56e604640af08a13c7acb6bec.setContent(html_b6ef36c8578d4c1aa3ef42597d64992d);
            

            marker_29f11e8def4a4065b301168475304aa3.bindPopup(popup_150164f56e604640af08a13c7acb6bec);

            
        
    

            var marker_12eb697702944658a9b1ca1d650ffa40 = L.marker(
                [40.751677,-111.839529],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_21c68340bd4b463aaa18d18dd84dab37 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_12eb697702944658a9b1ca1d650ffa40.setIcon(icon_21c68340bd4b463aaa18d18dd84dab37);
            
    
            var popup_30ed2f1785994e1f95f334259b7c7b81 = L.popup({maxWidth: '300'});

            
                var html_d12742897ad24455a2aa52e8deb70a33 = $('<div id="html_d12742897ad24455a2aa52e8deb70a33" style="width: 100.0%; height: 100.0%;">RB_SunnysidePark (Stream): Red Butte Creek at Sunnyside Park</div>')[0];
                popup_30ed2f1785994e1f95f334259b7c7b81.setContent(html_d12742897ad24455a2aa52e8deb70a33);
            

            marker_12eb697702944658a9b1ca1d650ffa40.bindPopup(popup_30ed2f1785994e1f95f334259b7c7b81);

            
        
    

            var marker_2d30968d57464f66b977d97cc4079771 = L.marker(
                [40.789056,-111.796667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_451f106f102545c7b18d9c45ae74d14f = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_2d30968d57464f66b977d97cc4079771.setIcon(icon_451f106f102545c7b18d9c45ae74d14f);
            
    
            var popup_3974f463b4fe48f9a3bdee3f0f4eb456 = L.popup({maxWidth: '300'});

            
                var html_6afb0cc47d19475cae379422d3298cd4 = $('<div id="html_6afb0cc47d19475cae379422d3298cd4" style="width: 100.0%; height: 100.0%;">RB_ToddMeadow (Stream): Parleys Fork at Todds Meadow</div>')[0];
                popup_3974f463b4fe48f9a3bdee3f0f4eb456.setContent(html_6afb0cc47d19475cae379422d3298cd4);
            

            marker_2d30968d57464f66b977d97cc4079771.bindPopup(popup_3974f463b4fe48f9a3bdee3f0f4eb456);

            
        
    

            var marker_27c7c9264dbc43d1afd7d4ead4eb30ec = L.marker(
                [40.756048,-111.835348],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_d06caad220ab4fd3910781d91aa38e99 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_27c7c9264dbc43d1afd7d4ead4eb30ec.setIcon(icon_d06caad220ab4fd3910781d91aa38e99);
            
    
            var popup_40ca1db325594f14a8516e77ed0e03ca = L.popup({maxWidth: '300'});

            
                var html_fdbbd807c28742c883d04fb4d2a03668 = $('<div id="html_fdbbd807c28742c883d04fb4d2a03668" style="width: 100.0%; height: 100.0%;">RB_Walkway (Stream): Red Butte Creek at walkway near Veterens Administration buildings</div>')[0];
                popup_40ca1db325594f14a8516e77ed0e03ca.setContent(html_fdbbd807c28742c883d04fb4d2a03668);
            

            marker_27c7c9264dbc43d1afd7d4ead4eb30ec.bindPopup(popup_40ca1db325594f14a8516e77ed0e03ca);

            
        
    

            var marker_ba07aa43017a4a67a614f815cdf559b1 = L.marker(
                [40.746167,-111.846639],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_2e145fe4701a4811ae3eb13a0f4b7119);
            
    

                var icon_80717d8a265f432aa07a19e35cfafe36 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'green',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_ba07aa43017a4a67a614f815cdf559b1.setIcon(icon_80717d8a265f432aa07a19e35cfafe36);
            
    
            var popup_bf7570276df140c6b884319afe735ba3 = L.popup({maxWidth: '300'});

            
                var html_df204abc6e7247e0ab21190e4e8dba6c = $('<div id="html_df204abc6e7247e0ab21190e4e8dba6c" style="width: 100.0%; height: 100.0%;">RB_WestMiller (Stream): Red Butte Creek at West Miller Park</div>')[0];
                popup_bf7570276df140c6b884319afe735ba3.setContent(html_df204abc6e7247e0ab21190e4e8dba6c);
            

            marker_ba07aa43017a4a67a614f815cdf559b1.bindPopup(popup_bf7570276df140c6b884319afe735ba3);

            
        
</script>\" 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 0x7f4527e600d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = gdf.unary_union.centroid # GeoPandas heavy lifting\n",
    "m = folium.Map(location=[c.y, c.x], tiles='CartoDB positron', zoom_start=11)\n",
    "\n",
    "marker_cluster = folium.MarkerCluster().add_to(m)\n",
    "for idx, feature in gdf.iterrows():\n",
    "    folium.Marker(location=[feature.geometry.y, feature.geometry.x], \n",
    "                  icon=folium.Icon(color=feature['color']),\n",
    "                  popup=\"{0} ({1}): {2}\".format(\n",
    "                      feature['SamplingFeatureCode'], feature['SiteTypeCV'], feature['SamplingFeatureName'])\n",
    "                 ).add_to(marker_cluster)\n",
    "\n",
    "    \n",
    "# Done with setup. Time to render the map\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Objects and Related Objects from the Database (SamplingFeatures example)\n",
    "\n",
    "This code shows some examples of how objects and related objects can be retrieved using the API. In the following, we use the **getSamplingFeatures( )** function to return a particular sampling feature by passing in its SamplingFeatureCode. This function returns a list of SamplingFeature objects, so just get the first one in the returned list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odm2api.ODM2.models.Sites"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the SamplingFeature object for a particular SamplingFeature by passing its SamplingFeatureCode\n",
    "sf = read.getSamplingFeatures(codes=['RB_1300E'])[0]\n",
    "type(sf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ElevationDatumCV': u'EGM96',\n",
       " 'Elevation_m': 1356.0,\n",
       " 'FeatureGeometryWKT': None,\n",
       " 'Latitude': 40.745078,\n",
       " 'Longitude': -111.854449,\n",
       " 'SamplingFeatureCode': u'RB_1300E',\n",
       " 'SamplingFeatureDescription': None,\n",
       " 'SamplingFeatureGeotypeCV': None,\n",
       " 'SamplingFeatureID': 1,\n",
       " 'SamplingFeatureName': u'Red Butte Creek at 1300E (downstream of spring)',\n",
       " 'SamplingFeatureTypeCV': u'Site',\n",
       " 'SamplingFeatureUUID': u'0DDE8EF6-EC2F-42C0-AB50-20C6C02E89B2',\n",
       " 'SiteTypeCV': u'Stream',\n",
       " 'SpatialReferenceID': 1,\n",
       " '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f4529f85d90>}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple way to examine the content (properties) of a Python object, as if it were a dictionary\n",
    "vars(sf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also drill down and get objects linked by foreign keys. The API returns related objects in a nested hierarchy so they can be interrogated in an object oriented way. So, if I use the **getResults( )** function to return a Result from the database (e.g., a \"Measurement\" Result), I also get the associated Action that created that Result (e.g., a \"Specimen analysis\" Action)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------ Foreign Key Example --------- \n",
      "\n",
      "('The FeatureAction object for the Result is: ', <FeatureActions({'SamplingFeatureID': 26, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f452aa57e10>, 'FeatureActionID': 1, 'ActionID': 1})>)\n",
      "('The Action object for the Result is: ', <Actions({'_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f45275a4750>, 'MethodID': 2, 'ActionDescription': None, 'ActionFileLink': None, 'EndDateTime': None, 'BeginDateTimeUTCOffset': -7, 'BeginDateTime': datetime.datetime(2014, 10, 30, 0, 0), 'ActionTypeCV': u'Specimen analysis', 'ActionID': 1, 'EndDateTimeUTCOffset': None})>)\n",
      "\n",
      "The following are some of the attributes for the Action that created the Result: \n",
      "ActionTypeCV: Specimen analysis\n",
      "ActionDescription: None\n",
      "BeginDateTime: 2014-10-30 00:00:00\n",
      "EndDateTime: None\n",
      "MethodName: Astoria Total Phosphorus\n",
      "MethodDescription: Determination of total phosphorus by persulphate oxidation digestion and ascorbic acid method\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n------------ Foreign Key Example --------- \\n\")\n",
    "try:\n",
    "    # Call getResults, but return only the first Result\n",
    "    firstResult = read.getResults()[0]\n",
    "    print(\"The FeatureAction object for the Result is: \", firstResult.FeatureActionObj)\n",
    "    print(\"The Action object for the Result is: \", firstResult.FeatureActionObj.ActionObj)\n",
    "    \n",
    "    # Or, print some of those attributes in a more human readable form:\n",
    "    print(\"\\nThe following are some of the attributes for the Action that created the Result: \" +\n",
    "          \"\\nActionTypeCV: \" + firstResult.FeatureActionObj.ActionObj.ActionTypeCV + \n",
    "          \"\\nActionDescription: \" + str(firstResult.FeatureActionObj.ActionObj.ActionDescription) + \n",
    "          \"\\nBeginDateTime: \" + str(firstResult.FeatureActionObj.ActionObj.BeginDateTime) + \n",
    "          \"\\nEndDateTime: \" + str(firstResult.FeatureActionObj.ActionObj.EndDateTime) + \n",
    "          \"\\nMethodName: \" + firstResult.FeatureActionObj.ActionObj.MethodObj.MethodName + \n",
    "          \"\\nMethodDescription: \" + firstResult.FeatureActionObj.ActionObj.MethodObj.MethodDescription)\n",
    "except Exception as e:\n",
    "    print(\"Unable to demo Foreign Key Example: \", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a Result and its Attributes\n",
    "\n",
    "Because all of the objects are returned in a nested form, if you retrieve a result, you can interrogate it to get all of its related attributes. When a Result object is returned, it includes objects that contain information about Variable, Units, ProcessingLevel, and the related Action that created that Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------- Example of Retrieving Attributes of a Result -------\n",
      "The following are some of the attributes for the Result retrieved: \n",
      "ResultID: 1\n",
      "ResultTypeCV: Measurement\n",
      "ValueCount: 1\n",
      "ProcessingLevel: Raw Data\n",
      "SampledMedium: Liquid aqueous\n",
      "Variable: TP: Phosphorus, total\n",
      "Units: milligrams per liter\n",
      "SamplingFeatureID: 26\n",
      "SamplingFeatureCode: 3\n"
     ]
    }
   ],
   "source": [
    "# Get a particular Result\n",
    "print(\"\\n------- Example of Retrieving Attributes of a Result -------\")\n",
    "try:\n",
    "    firstResult = read.getResults()[0]\n",
    "    print(\n",
    "        \"The following are some of the attributes for the Result retrieved: \\n\" +\n",
    "        \"ResultID: \" + str(firstResult.ResultID) + \"\\n\" +\n",
    "        \"ResultTypeCV: \" + firstResult.ResultTypeCV + \"\\n\" +\n",
    "        \"ValueCount: \" + str(firstResult.ValueCount) + \"\\n\" +\n",
    "        # Get the ProcessingLevel from the Result's ProcessingLevel object\n",
    "        \"ProcessingLevel: \" + firstResult.ProcessingLevelObj.Definition + \"\\n\" +\n",
    "        \"SampledMedium: \" + firstResult.SampledMediumCV + \"\\n\" +\n",
    "        # Get the Variable information from the Result's Variable object\n",
    "        \"Variable: \" + firstResult.VariableObj.VariableCode + \": \" + firstResult.VariableObj.VariableNameCV + \"\\n\" +\n",
    "        # Get the Units information from the Result's Units object\n",
    "        \"Units: \" + firstResult.UnitsObj.UnitsName + \"\\n\" +\n",
    "        # Get the Specimen information by drilling down into the result object\n",
    "        \"SamplingFeatureID: \" + str(firstResult.FeatureActionObj.SamplingFeatureObj.SamplingFeatureID) + \"\\n\" +\n",
    "        \"SamplingFeatureCode: \" + firstResult.FeatureActionObj.SamplingFeatureObj.SamplingFeatureCode)\n",
    "except Exception as e:\n",
    "    print(\"Unable to demo example of retrieving Attributes of a Result: \", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last block of code returns a particular Measurement Result. From that I can get the SamplingFeaureID (in this case 26) for the Specimen from which the Result was generated. But, if I want to figure out which Site the Specimen was collected at, I need to query the database to get the related Site SamplingFeature.  I can use **getRelatedSamplingFeatures( )** for this.  Once I've got the SamplingFeature for the Site, I could get the rest of the SamplingFeature attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Site at which a Specimen was collected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pass the Sampling Feature ID of the specimen, and the relationship type\n",
    "relatedSite = read.getRelatedSamplingFeatures(sfid=26, relationshiptype='Was Collected at')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ElevationDatumCV': u'EGM96',\n",
       " 'Elevation_m': 1356.0,\n",
       " 'FeatureGeometryWKT': None,\n",
       " 'Latitude': 40.745078,\n",
       " 'Longitude': -111.854449,\n",
       " 'SamplingFeatureCode': u'RB_1300E',\n",
       " 'SamplingFeatureDescription': None,\n",
       " 'SamplingFeatureGeotypeCV': None,\n",
       " 'SamplingFeatureID': 1,\n",
       " 'SamplingFeatureName': u'Red Butte Creek at 1300E (downstream of spring)',\n",
       " 'SamplingFeatureTypeCV': u'Site',\n",
       " 'SamplingFeatureUUID': u'0DDE8EF6-EC2F-42C0-AB50-20C6C02E89B2',\n",
       " 'SiteTypeCV': u'Stream',\n",
       " 'SpatialReferenceID': 1,\n",
       " '_sa_instance_state': <sqlalchemy.orm.state.InstanceState at 0x7f4529f85d90>}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(relatedSite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------------\n",
    "\n",
    "## Return Results and Data Values for a Particular Site/Variable\n",
    "\n",
    "From the list of Variables returned above and the information about the SamplingFeature I queried above, I know that VariableID = 2 for Total Phosphorus and SiteID = 1 for the Red Butte Creek site at 1300E. I can use the **getResults( )** function to get all of the Total Phosphorus results for this site by passing in the VariableID and the SiteID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "siteID = 1  # Red Butte Creek at 1300 E (obtained from the getRelatedSamplingFeatures query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a list of Results at a particular Site, for a particular Variable, and of type \"Measurement\"\n",
    "v = variables_df[variables_df['VariableCode'] == 'TP']\n",
    "variableID = v.index[0]\n",
    "\n",
    "results = read.getResults(siteid=siteID, variableid=variableID, type=\"Measurement\")\n",
    "# Get the list of ResultIDs so I can retrieve the data values associated with all of the results\n",
    "resultIDList = [x.ResultID for x in results]\n",
    "len(resultIDList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Retrieve the Result (Data) Values, Then Create a Quick Time Series Plot of the Data\n",
    "\n",
    "Now I can retrieve all of the data values associated with the list of Results I just retrieved. In ODM2, water chemistry measurements are stored as \"Measurement\" results. Each \"Measurement\" Result has a single data value associated with it. So, for convenience, the **getResultValues( )** function allows you to pass in a list of ResultIDs so you can get the data values for all of them back in a Pandas data frame object, which is easier to work with. Once I've got the data in a Pandas data frame object, I can use the **plot( )** function directly on the data frame to create a quick visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>valuedatetime</th>\n",
       "      <th>valuedatetimeutcoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>2015-11-17 13:55:12</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0574</td>\n",
       "      <td>2015-05-12 14:24:00</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>2015-10-27 13:55:12</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>2015-12-01 12:14:24</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>312</td>\n",
       "      <td>312</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>2014-09-16 12:00:00</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   valueid  resultid  datavalue       valuedatetime  valuedatetimeutcoffset\n",
       "0        1         1     0.0100 2015-11-17 13:55:12                      -7\n",
       "1       10        10     0.0574 2015-05-12 14:24:00                      -7\n",
       "2       99        99     0.0424 2015-10-27 13:55:12                      -7\n",
       "3      196       196     0.0167 2015-12-01 12:14:24                      -7\n",
       "4      312       312     0.0102 2014-09-16 12:00:00                      -7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all of the data values for the Results in the list created above\n",
    "# Call getResultValues, which returns a Pandas Data Frame with the data\n",
    "resultValues = read.getResultValues(resultids=resultIDList)\n",
    "resultValues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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BfsBq3/YaXEmhvcc2G3hFRC4DLgPo06cPFRUV7RJaVVXV7mMThWnsGKmsLUSqa0x1fSES\nqXP1qn0G4o23ZlJZltlK7H0EJS/9xNNYSAv7tDOPVdX7gfsBxo8fr5MmTYpZnJ+Kigrae2yiMI0d\nI5W1hUh1jamuL0Qidb5TvRiWfwrAwOGjmXRI35iOC0pe+olnNdQaYIBvuz+wNgHHGoZhJJytu63N\nor3MAoaJyGARyQHOA16M8dhpwOdFpNRr2P68t88wDCOlyPDqQdK9F3fcjIWqNgBX4F7yi4GnVXWh\niNwkImcAiMgEEVkDnAv8RUQWesduBW7GGZxZwE3ePsMwjJQiOzODwpzMtO9rEdeZ8lT1ZeDlsH2/\n9K3PwlUxtXTsQ8BD8dRnGIbRGZQW5qS9N5T14DYMw+ggZYU5bDFjYRiGYbRGaUGOtVkYhmEYrVNW\nmJP2bRZmLAzDMDpImbVZGIZhGNEoK8xhd10jNfWNyZYSN8xYGIZhtBdvXInSghwAtqfxvBZmLAzD\nMDpIWWE2kN7zWpixMAzD6CChkkU6e0SZsTAMw+ggZYXOWKRzySJqD24RyQAOB8qBamChqm6ItzDD\nMIygUFqY/iWLiMZCRA4CrgFOAj4GNgF5wHAR2QP8BXhEVdN7LkHDMIxWEIGS/PRvs2itZHELcB/w\nHVXdby4JEekNnA98E3gkfvIMwzBSn6zMDIrzs9O6r0VEY6GqX2slbCPwh7goMgzDCCBlhTlsTWPX\n2daqob7U2oGq+lznyzEMwwgmpQVdtGQBnN5KmAJmLAzDMDzKCnNYu70m2TLiRmvG4n7g3fD2CsMw\nDKM5pQU5LFy7M9ky4kZr/SwuBGaLyJMicpGIHJAoUYZhGEEjNKdFun5ft9bAfTmAiIwEvgA8LCLF\nwHTgFeAtVU3fUbMMwzDaQFlhDnUNTeypa6QwN66TkCaFqD24VXWJqt6pqpOBE4D/4ebMfi/e4gzD\nMIJCaZr34o6lB3dZ2K53gddUNX19xAzDMGLAX+FU5hsfakBZQXIExZFYxob6ANd7+yP29eReLiIf\niMi4eIozDMMICulesojFWLwCnKqqPVW1B6794mnge8C98RRnGIYRFMrSfHyoWIzFeFWdFtpQ1VeB\nY1X1XSA3bsoMwzACgCDAvmqorbvTs4Y+lib7rSJyDfCkt/1VYJuIZAI2iKBhGAZQlJdFZoakbS/u\nWEoW5wP9ganAC8CB3r5M4Cvxk2YYhhEcMjKE0oJstqZpNVTUkoWqbga+HyF4WefKMQzDCC6lBTlp\nW7KIxXV2PPAzYJA/vqoeFj9ZhmEYwaO0MKdLe0P9HXgY+DJucMHQEhURmSwiS0VkmYhc20J4rog8\n5YW/JyKDvP3ZIvKIiMwXkcUiMiXG/2MYhpE0ygpy0tYbKpYG7k2q+mJbT+w1gN8DnAysAWaJyIuq\nusgX7RJgm6oOFZHzgN/gGtDPBXJV9VARKQAWicgTqrqirToMwzASRWlhDltXdl1vqOtF5AHgdaA2\ntDOG+SwmAstU9VMAEXkSOBPwG4szgRu89WeAu0VEcB0jC0UkC8gH6oD0Hc7RMIy0oKwwm2173GCC\n7lWWPsRiLL4FjASy2ecqG8t8Fv2A1b7tNcCRkeKoaoOI7AB64AzHmcA6oAD4kapuDU9ARC4DLgPo\n06cPFRUVMfyd5lRVVbX72ERhGjtGKmsLkeoaU11fiETqXLWqjsamxr3pbV1XT2OT8vJ/KijMjmws\ngpKXfmIxFoer6qHtOHdLORU+dm+kOBOBRqAcKAXeFJH/hEopeyOq3o+bd4Px48frpEmT2iETKioq\naO+xicI0doxU1hYi1TWmur4QidT59p7FZFau3Jve1g/W8MSSDxk9diKDexamhMbOIpYG7ndFZHQ7\nzr0GGODb7g+sjRTHq3IqBrbi+nG8oqr13nzfbwHj26HBMAwjboTPXVGWxuNDxWIsjgHmel5N8zwP\npXkxHDcLGCYig0UkBzgPCG8ofxE3yRLAOcAb3sx8q4ATxFEIHAUsieUPGYZhJIu940OlobGIpRpq\ncntO7LVBXAFMw/X2fkhVF4rITcD7nofVg8BjIrIMV6I4zzv8HuBvwAJcVdXfVDUWA2UYhpE0SkPj\nQ6Wh+2xEYyEi3VS1SlVXRosTKVxVXwZeDtv3S996Dc5NNvy4qpb2G4ZhpBp+p6d0Llm0Vg31goj8\nXkSO9aqCABCRISJyiYhMo52lDsMwjHSkICeTnKyMrlWyUNUTReRU4DvAZ0WkFGgAlgIvAReq6vrE\nyDQMw0h9RMT14k7DkkWrbRYtVSMZhmEYkXHjQ6VfL+5YvKEMwzCMGAn14k43zFgYhmF0Iuk6TLkZ\nC8MwjE6krDCnazVwi0hZawe2NFaTYRhGV6e0IIcd1fU0NDaRlZk+3+OtNXDPxo3TFGn8piFxUWQY\nhhFgygpzUIUd1fX06JabbDmdRmuus4MTKcQwDCMdKA11zNtT1zWMhR+vj8UwIC+0T1X/Gy9RhmEY\nQaUsNORHmrnPxjIH96XAD3Cjxs7FDer3DnBCfKUZhmGkNho+6QLpO/JsLK0vPwAmACtV9XhgLLAp\nrqoMwzACQnijblc2FjXegH+ISK6qLgFGxFeWYRhGMCkpyAZIu455sbRZrBGREmAq8JqIbKP5JEaG\nYRgGkJedSWFOZtqVLKIaC1U921u9QUSm42az+3dcVRmGYQSY0sL068UdtRpKRB4LravqDG/Soofi\nqsowDCPApGMv7ljaLA72b4hIJjAuPnIMwzCCTzqODxXRWIjIFBHZBRwmIjtFZJe3vRF4IWEKDcMw\nAkaXKlmo6q2qWgTcrqrdVbXIW3qo6pQEajQMwwgUrmTRxTrlqeoUETkDONbbVaGq/4qvLMMwjOBS\nVphNVW0DtQ2N5GZlJltOpxBLA/etuI55i7zlB94+wzAMowVC40Nt35M+pYtY+lmcBoxR1SYAEXkE\nmANYVZRhGEYL7Bsfqo4+3fOixA4GsQ62XuJbL46HEMMwjHRh78izaeQRFUvJ4lZgjtchT3BtFz+L\nqyrDMIyAINJ8yp+940OlkUdULA3cT4hIBW4wQQGuUdX18RZmGIaR6rQw6CzgvKEgvUoWsTRwv66q\n61T1RVV9QVXXi8jriRBnGIYRREq9wQTTaU6L1ubgzgMKgJ7e5EehslZ3oDwB2gzDMAJJVmYGxfnZ\naTXybGsli+/g5uEe6f2GlheAe2I5uYhMFpGlIrJMRK5tITxXRJ7ywt8TkUG+sMNE5B0RWSgi8z3j\nZRiGEQjKCnPYkkbVUK3NwX0XcJeIfF9V/9TWE3tjSN0DnAysAWaJyIuqusgX7RJgm6oOFZHzgN8A\nXxWRLOBx4Juq+qGI9ADSpzxnGEbaU1qQ3bXaLNpjKDwmAstU9VNVrQOeBM4Mi3Mm8Ii3/gxwojjX\ngs8D81T1Q0/DFlVtbKcOwzCMhFNWmJNWc1rE4jrbXvoBq33ba4AjI8VR1QYR2QH0AIYDKiLTgF7A\nk6r62/AEROQy4DKAPn36UFFR0S6hVVVV7T42UZjGjpHK2kKkusZU1xcikTrXrK6loaGhxfRqd9ay\nfltji2FByUs/8TQWzZ2Pm3uaRYqTBRyDc9fdA7wuIrNVdT8vLFW9H7gfYPz48Tpp0qR2Ca2oqKC9\nxyYK09gxUllbiFTXmOr6QiRS55tVi8hav7rF9N7Zs5iZG1Zw3HHHNeuLEZS89NNmYyEifYGtqlob\nJeoaYIBvuz/Np2MNxVnjtVMUA1u9/TNUdbOX5svAEYC57BpdiqlzKrl92lLWbq+mvCSfq08ZwVlj\n+yVblhEDpYU51DY0UV3fSEFOPL/LE0Osw334eQxYIiK/ixJvFjBMRAaLSA5wHvBiWJwXgQu99XOA\nN1RVgWm4eTQKPCNyHG4QQ8PoMkydU8mU5+ZTub0aBSq3VzPluflMnVOZbGlGDPjHh0oH2mwsVPUk\nYAjwtyjxGoArcC/+xcDTqrpQRG7yhjwHeBDoISLLgKuAa71jtwF34AzOXOADVX2prVoNI8jcPm0p\n1fX7+3VU1zdy+7SlSVJktIV940OlhyNn1LKRiBwErFHVWhGZBBwGPKqqC6Mdq6ovAy+H7fulb70G\nODfCsY/j3GcNo0uydnt1m/YbyaGlhldwc1pA+owPFUvJ4lmgUUSG4koCg4F/xFWVYRiUl+S3ab+R\nWqTb+FCxGIsmr0rpbOAPqvojoG98ZRmG8cOThjX7as3PzuTqU0YkRY/RHI00kiC+kWe7kLGoF5Gv\n4RqiQ9OpZsdPkmEYAJuqalGgZzf30snLzuDWLx1q3lABoXteNpkZkjbjQ8ViLL4FHA38SlWXi8hg\nrC3BMOLK5qpa7p3+CSeN6s37153M54b1ZMQB3c1QBIiMDKG0IDttShaxzGexCLjSt70cuC2eogyj\nq/OH/3xETX0jU04dBUDf4jyWrt+UZFVGWyktyEmbkkUs3lDLaWGOD1UdEhdFhtHF+XjDLv7x3iq+\nedRADurVDXCN2ht31VLb0EhuVmaSFRqxUppG40PF0q1wvG89D+fqWhYfOYZh/PrlxRTmZvGDk4bv\n3RfygNqwo5YDexQkS5rRRsoKcli+eXeyZXQKsYw6u8W3VKrqH4ATEqDNMLocb368ielLN/H9E4bu\n9aYB6OcZi7U7rI9FkChNozktYqmGOsK3mYEraRTFTZFhdFGaVLntpcUMKMvnws8M2i+sb7Gb+8s6\n5AWLskI3W56qNhtMMGjEUg31e996A7Ac+Ep85BhG1+XNNQ0sWb+He84/olm7RKgayoxFsCgtyKGx\nSdlZ00BxfrB7HLRqLEQkA/izqj6VID2G0SWpqm3guWX1jBtYyqmHHtAsPC87kx6FOazdUZMEdUZ7\nKSvc14s76Mai1TYLVW0C/i9BWgyjy/KXGZ+wo1a57rRREasr+pbkWckiYIQGE0yH8aFi6ZT3moj8\nREQGiEhZaIm7MsPoIqzdXs1f3/yUo/pmMvbA0ojxyovzzVikIq00RZSl0fhQsbRZXOz9+ksYihum\n3DCMDvK7aUtpUjhneE6r8cpL8nn7ky0JUmV0Buk0PlQsPbgHJ0KIYXRF5q3ZznNzKvnupIPombe+\n1bjlJXlU1Taws6ae7nnBrv/uKuyd06IrVEOJSLaIXCkiz3jLFSJid6phdBBV5ZaXFtOjMIfvTToo\nanzziEo9tPngFvtRmJNJTmYGW9NgAqRY2izuA8YB93rLOG+fYRgd4NVFG5i5fCs/PHk4RTGUFMxY\nBA8RobQwu8u0WUxQ1cN922+IyIfxEmQYXYG6hiZu+/cShvbuxtcmDIjpmL29uLeb+2yQKCvM7TLe\nUI3e1KoAiMgQoLGV+IZhROHxd1eyfPNufn7qKLIyY3kMoWe3XLIyxEoWAaOsC5Usrgami8inOCex\ngbg5LgzDaAfb99Rx1+sf87lhPZk0olfMx2VmCAcUW1+LoFFakMOidTuTLaPDxOIN9bqIDANG4IzF\nElWtjbsyw0hT/vTGMnbW1POzUyN3wItEeUm+9eIOGGWFOV2mZAGuUXuQF/9wEUFVH42bKsNIU1Zs\n3s2j76zgq+MHMKpv9zYfX16cx/srt3W+MCNulBbksL26nsYmJTMjuIMJxjLq7GPAQcBc9rVVKGDG\nwjDayG3/XkJ2ZgZXfX549MgtUF6Sz/p56wL/4ulKlBXmoAo7quv3G3Y+aMQ6+dFoVW3dodgwjFaZ\nuXwrryxcz49PHk7vorx2naO8JJ+GJmXTrloOKG7fOYzEsmxTFQDjbn6N8pJ8rj5lBCVJ1tQeYnHD\nWAA0HwbTMIyYaWpSbnlpEQd0z+PSz7V/pBybBCn1aK18N3VOJU/NXA246pjK7dVMeW4+b68NXie9\niCULEfkn7v8VAYtEZCawt2FbVc+IvzzDSA9e/HAt89bs4I6vHE5+Tvvn0O5bsm8SpCNaGXTQSA1u\nn7aUusam/fZV1zfy7EdN/CxJmtpLa9VQv0uYCsNIY2rqG/ntK0s4tF8xZ43p16FzWS/uYBHpOm2p\nCV6tfsRqKFWdEVqApUAx0B1Y6u2LiohMFpGlIrJMRK5tITxXRJ7ywt8TkUFh4QeKSJWI/KQtf8ow\nUokH/7ectTtq+Plpo8joYKN097xsinKzrBd3QAgZ93B65AXPOSGWgQQvBWYCXwLOAd4VkYtbPwpE\nJBO4B/gCMBr4moiMDot2CbBNVYcCdwK/CQu/E/h3tLQMI1XZuKuGe6cv4/Oj+3DUkB6dck6bBCk4\nXH3KCPKz9692zM/O5MvDgze2GiiYAAAgAElEQVQWa6w9uMeq6hYAEekBvA08FOW4icAyVf3UO+5J\n4ExgkS/OmcAN3vozwN0iIqqqInIW8CmwO8b/Yhgpx52vfUxtQxNTTh3Vaed0HfPMWKQC0XxEzxrr\nqh1v+tcitu6uo2e3HK47bTQlOz5OgLrOJRZjsQbY5dveBayO4bh+YfHWAEdGiqOqDSKyA+ghItXA\nNcDJQMQqKBG5DLgMoE+fPlRUVMQgqzlVVVXtPjZRmMaOkQxta3Y18eTMak4amMXKBbNYGSV+zBr3\n1LJyU0PC/08qX18/idRZWVlLQ0Pr16IEuOnILK54o47j+iolOz4OTF76icVYVALvicgLOO+oM4GZ\nInIVgKreEeG4lirlwu1wpDg3AneqalVrwyGo6v3A/QDjx4/XSZMmtfI3IlNRUUF7j00UprFjJEPb\nBQ/NpCivntsvPJ6SguidsWLVuKDpYypWf8RRn/0cednt96xqK6l8ff0kUmfFzoVkbVgTU3p3LZjB\nZsln0qSJgclLP7EYi0+8JcQL3m9RlOPWAP6xl/sDayPEWSMiWbhG9K24Esg5IvJbnGFuEpEaVb07\nBr2GkXQqlm7kvx9t4rrTRsVkKNqC3yNqSK9unXpuI35MHFzGC3PX0tgUPE8oiG0gwRtD6yKSAXRT\n1ViGUJwFDBORwbjSyXnA+WFxXgQuBN7BNZ6/4fUU/5wvzRuAKjMURlBoaGzi1y8vZmCPAi44elCn\nn7/cN6+FGYvgMHFwGX9/bxWLAzoCbSzeUP8Qke4iUohrnF4qIldHO05VG4ArgGnAYuBpVV0oIjeJ\nSKhD34O4NoplwFVAM/dawwgaT7+/ho82VDHlCyPJyYptroq2YL24g8mEQWWAG/YliMRSDTVaVXeK\nyNeBl3ENz7OB26MdqKove8f49/3St14DnBvlHDfEoNEwUoJdNfXc8dpSJg4q45SD4zNKTp/ueYhY\nx7ygUV6ST//SfGat2MqQ/slW03Zi+ezJFpFs4CzgBVWtp3lDtWEYwJ9nfMLmqjp+flrb56qIlZys\nDHp1yzVjkSK05TpPHFTGzOVbCeK4rLEYi78AK4BC4L8iMhAIZqWbYcSRyu3VPPDmcs4aU87hA+I7\nrmh5ST7rbBKkwDFhcBlbdtexfncaGgtV/aOq9lPVU9WxEjg+AdoMI1Dc/soSAK6ePDLuaZWX5FFp\nJYvAMXGwa7f4aFtjlJipRywN3MUicoeIvO8tv8eVMgzD8Ji7ejtT567l0s8N3tsAHU/Ki/NZu706\nkNUZXZkhPQvp2S2HpduaokdOMWKphnoI12v7K96yE/hbPEUZRpBQVX710iJ6dsvhu5OGJiTN8pJ8\nauqb2L4nePMidGVEhPEDy9KzZAEcpKrXq+qn3nIj0P7ZWwwjzXhlwXpmrdjGVSePoFturNPad4xQ\nXwurigoeEweXsblaA+egEIuxqBaRY0IbIvJZIFj/0jDiRF1DE7e9soQRfYr4yvjE+UOW+yZBMoJF\nqN1i1opg9beIxVhcDtwjIitEZCVwt7fPMLo8j76zgpVb9vCz00aRldn5HfAiESpZmEdU8BjVtzt5\nmcHrnBfLcB8fAoeLSHdv29xmDQPYtruOP77+MccN78Vxw3slNO0ehTnkZGVYySKAZGYIw0ozA1ey\niGosRCQX+DIwCMgKdUBR1ZviqswwUpSpcyq5fdrSve0FRw0pS7gGEaG82Nxng8rw0gye/biKbbvr\nKC3s3IEm40Us5eYXcMOSN+AmIgothtHlmDqnkinPzd/vJf3H15cxdU5lwrWUl+RbySKgDC91Q8sH\nqXQRi+tGf1WdHHclhhEAbp+2lOr6/d0eq+sbuX3a0r2zoiWK8pJ83lq2OaFpGp3D4OIMcrIymLVi\nK5+P0xhinU0sJYu3ReTQuCsxjAAQ6Us+GV/45cV5bNhZQ31j8Dp4pRPtGQIsJ1MY078kUI3cEY2F\niMwXkXnAMcAHIrJUROb59htGl6KpScnPaXlmuvIE9NpuKc0mhQ07zSMqiEwYXMqCtTvZXduQbCkx\n0Vo11BcTpsIwUpzGJuWaZ+exp66RrAyhwTfbWX52JlefMiLhmvzus/1LCxKevtExJg7uwT3TP2HO\nqu0cM6xnsuVEpbVqqA3A2cDVwGSgUlVXhpaEqDOMFKChsYkfPz2XZ2av4YcnDeP2cw6jX0k+gpuI\n6NYvHZrw9grYf3pVI3gccWAJGQIzl29JtpSYaK1k8QhQD7wJfAEYDfwgEaIMI1Wob2zih0/N5aV5\n67j6lBH83/Fu7Kezj0j+7DWhXtzmPhtMivKyGV3enZkB8YhqzViMVtVDAUTkQWBmYiQZRmpQ19DE\n95/4gGkLN/DzU0fx7WNTa0i0gpwsSgqyWbfd2iyCysRBPfj7eyupa2iKyxS8nUlr6vYOZ+nNp20Y\nXYaa+kYuf3w20xZu4IbTR6ecoQgRGqrcCCYTB5dS29DE/MrtyZYSldaMxeEistNbdgGHhdZFxIb8\nMNKWmvpGLntsNm8s2civzj6Eiz47ONmSImKTIAWb8YNc7/+Zy7clWUl0IhoLVc1U1e7eUqSqWb71\n7okUaRiJYk9dAxc/PIs3P97Eb798GF8/cmCyJbWK9eJOLh2dfKpnt1wO6lUYiJ7cqV1JZhgJpKq2\ngYsemsW7n27hjq8czlcmDEi2pKiUl+Szs6aBqoD46hvNmTi4jFkrttLYlNqzHpqxMAxgZ009Fzz4\nHrNXbeOu88Zy9tjkezvFwt6+Fla6CCwTBpWxq6aBpet3JVtKq5ixMLo8O/bU880H3mN+5Q7uOX8s\npx9enmxJMVNebO6zQScokyGZsTC6NFt313H+A++yeN0u7vv6OCYf0jfZktqETYKUfNoxNNR+9C8t\noLw4L+XHiUrMhMGGkYJsrqrlGw+8x6ebd3P/BeOYNKJ3siW1md5FuWRmiDVyB5wJg8t4+5MtqCrS\nnpEJE4CVLIwuycadNZx3/7us2LKbv100IZCGAiArM4M+RblWDRVwJg4uY9OuWlZu2ZNsKRGJq7EQ\nkcneaLXLROTaFsJzReQpL/w9ERnk7T9ZRGZ7I9zOFpET4qnT6Fqs21HNV+9/l7Xbq3n4WxP57NDU\nH8StNcpL8q0Xd8CZuLe/RepWRcXNWIhIJnAP+8aV+pqIjA6LdgmwTVWHAncCv/H2bwZO94YbuRB4\nLF46ja7Fmm17+Opf3mXTrloevXgiRw3pkWxJHaa8JJ+1O6xkEWSG9u5GaUF2So8TFc82i4nAMlX9\nFEBEnsRNz7rIF+dM4AZv/RngbhERVZ3ji7MQyBORXFWtjaNeI81ZtWUPX/vru+ysqefxS49kzICS\nZEvqFPqW5PHKghqampSMjNSs745EaD7ztdurKS/J5+pTRiRlBN9kIyJMGFSW0h5R0tEeiBFPLHIO\nMFlVL/W2vwkcqapX+OIs8OKs8bY/8eJsDjvP5ap6UgtpXAZcBtCnT59xTz75ZLu0VlVV0a1bt3Yd\nmyhMY8f4dFMVdy/MoLZRuXp8HoOKW57EKJm0N//+s7KexxfX8Yfj8ynJjV/Ncmdf37fX1vPwgjrq\nfBP95WTARYfk8Jny7HafN5H34WOLanlvXQN3n1jYpuNa0vjK8nqeXFrHnZPyKc1LXHPy8ccfP1tV\nx0eLF8+SRUufOOGWqdU4InIwrmrq8y0loKr3A/cDjB8/XidNmtQuoRUVFbT32ERhGtvPso27+OE9\nb5KRlc0z3zmSUX1Tc7Sa9uZfw6INPL74fQaPPoLD41ha6uzr+/PbXt/PUADUNcFLqzL52fntTyeR\n9+H0HQv4YPPaNqfXksbSg7bz5NK3yOo7kkkp2NcnnuZrDeAfL6E/sDZSHBHJAoqBrd52f+B54AJV\n/SSOOo00Zun6XZx3/7s0KTx52VEpayg6QtAmQfpkUxV3vLqUygiN8kH5H53NweXdKcjJTNmqqHiW\nLGYBw0RkMFAJnAecHxbnRVwD9jvAOcAbqqoiUgK8BExR1bfiqNFIYxau3cE3HniPnKwMpkzMY3if\nomRLigtBmARp065a/vnhWqbOrWTemh2IQG5WBrUNTc3i5udksnV3HWWFOUlQ2jY6sxI/KzODcQNL\n2+QRlcg2n7iVLLw5MK4ApgGLgadVdaGI3CQiZ3jRHgR6iMgy4Cog5F57BTAU+IWIzPWWYDrCG0lh\n3prtnP/X98jPzuSpy46mb7f07VJUnJ9NQU5myvXi3l3bwPNz1nDBQzM56tbXuelfi2hsUq47bRTv\nTjmR33z5MPKz9287yswQqusamXT7dB55ewUNjc2NSTozYVAZSzfsYsee+qhxp86pZMpz86ncXo3i\nPhamPDefqXMq46Itrj24VfVl4OWwfb/0rdcA57Zw3C3ALfHUZqQvH6zaxoUPzqS4IJsnvn0UA8oK\nWJFsUXFERFJmqPKGxibeXLaZqXMqeXXhBqrrG+lXks/lxw3hrDH9GOYr3YW+gMO/jEeXd+fGfy7k\n+hcX8o/3VnH9GaP5zEHB7gsTKxMGlaEK76/cyomj+rQa9/ZpS6mub9xvX3V9I7dPWxqX0oUN92Gk\nFbNWbOWih2bSsyiXJ7591N76/HSnb3Fe0oyFqvLhmh1MnVPJv+atZXNVHcX52Zx9RD/OGtOP8QNL\nI7r0njW2X4svtscvOZJpC9dzy0uLOf+v73HqoQcwfmApD/5vRVq72Y49sITsTGHm8ujGItL1jtd9\nYMbCSBve/mQzlzz8Pn1L8vjHpUdxgDcia1egX0k+SxI8xPXKLbuZOse1QyzfvJucrAxOGtWbM8f0\nY9KIXuRmtd89WUSYfEhfJo3ozf3//ZQ/vv4RL89fvzc8VOUCJN1gdOZYTnnZmRzWvySmznl9uuex\nfmfzqsd4fSCZsTDSgv9+tIlvP/o+B5YV8PdvH0nvoq5jKMC9IDbtqqW2obFDL+lobKmq5aX563h+\nTiVzVm1HBI4a3IPvHncQpxxyAMX57e8f0RJ52ZlceeIw/v7eSjbs3L9PbjyrXJLJhEFlPPDmp1TX\nNZKf0/K1bGhsojC3eVh+diZXnzIiLrrMWBiBZ/qSjXzn8dkM6VnI3y89kh7dcpMtKeGEvibX76hh\nYI+2dRCLRnVdI68t3sCDs2tY+OrrNDQpIw8oYsoXRnLGmHL6Fse/qm/jzpYHb0iFdprO5sjBZfx5\nxifMWbWNz0QYt+z3r33EJ5t2c/7EAcz4aHNCqubMWBjtJtxt77QDG5mUYA2vLlzP//3jA0YcUMRj\nFx9JaQDcLeOBfxKkzjAWjU3K259s5vk5lUxbsJ7ddY2U5QmXfm4IZ40tZ+QBie2vUl6S36JrcDq2\nSR0xsBQRmLlia4vG4rVFG7iv4hO+NvFAfv2lQxOmy4yF0S5Cbnshb4zK7dU8vBNGz6lMWLXAy/PX\nceUTczikXzGPXDyx06tAgsS+6VXb7z6rqixcu5Pn51Ty4odr2bSrlqK8LE4/vJwzx/SjetU8Tjh+\nZGdJbhNXnzJiv/stxJfHpVcVFDhX6JEHdG+xc96qLXu46um5HNKvO9efHj4ua3wxY2G0i9++sqTZ\ng1vXRMLqkF+YW8lVT3/I2AEl/O1bEyjK67qGAtjbmN+eapnVW/fwwtxKnp9TySebdpOdKZwwsjdn\njenH8SN7k+f1hahYnbxBCsPdbA8ozqO+oZGnZ63hwqMHpV3V45GDy3hq1mrqG5vIznR9hGrqG/nu\n32cjwH1fH7f3uiQKMxbGflTVNrBxZw2bdtWyce9Sw6ad+9Y37qple4ROQ4moQ35m9hp++syHTBhU\nxkMXTaAw127jvOxMenbLiXmo8m2763hp/jqmzqnk/ZXbADcBzyXHDOHUQw+gpCD1qvPC3WwXrt3B\n2fe+zQ+fmsvD35pIZsBG3G2NCYPKePjtFSyo3MHYA0sBuPGfC1m4dicPXjieAWUFCddkT1kXQFXZ\ntqfeMwA1bAx78TtD4Nb31DU2Oz4nM4NeRbn0KsplUI9CJg4u44W5a9lV09Asblam8MLcSk49tO/e\nL6LO5MmZq5jy/Hw+e1BP/nrB+IjeIl0R1zEvcjVUTX0jry/eyPNzKpnx0UbqG5Vhvbtx9SkjOHNM\nOf1LE/8C6ggHlxdz4xkHM+W5+dwzfRlXnjgs2ZI6jQmDnYGYtWIrYw8s5ZnZa3hi5mq+N+mgqP0v\n4kWXNxZT51Ryc8Uetr7yUqveBJ05BktnnauhsYktu+u8l7972e+3vquWTTtr2FRVS31j81FsCnMy\n6d09j15FuRzSr5jeRXn07p5L76Lc/daL87Ob+ZKPH1jWrA45U6CkIJsfPDmX2/69hIs+M4jzJh5I\ncX52u/+z/7ju+dnsqK7nuOG9+Ms3E18MT2Wmzqnkow27qKlv4rO3vbE3fxublPc+3cLzcyp5ZcF6\ndtU20Lsol4s+M4izxvZjdN/uKTvncyycN2EAM5dv5Y7XPuLRd1awpaqOsjzhF8Wtt511xjM4dU4l\nz85ew+66xv3yvDPoXZRHz2453PHqR9z68hIUGNqrkKtOHt4p528PXdpY7GukdS/SSB19WmrMbW+H\noFjOVVPf2KwUMOujOl7a9OE+I7Crhi2762hpOpLSguy9L/uDevVw60W53svfrfcqyu1Q9U1LQzWc\ndmAj1553EhUfbeSBN5dz67+XcNfrH+8dHC00aFys+ReeVzuq68kQOP2wvmYofITyqaZ+X/5e8+w8\nXphbyaJ1O9mws5ZuuVlMPuQAzh7bj6OG9EibKhsR4eghZUydU8nmqjoAttRoq/dXZzzPnflOiHT+\nbXvqaWza94Cv2VbNv+atS1q/ki5tLCKNrXLd1AXMXb2dmvpGqusbmbZw/d4H0R/v2ufm8driDWRn\nCJkZGWRnClmZQtbe9QyyMtx2VqaQnSncM31Zi2n+9Nl5/OmNj9m4q7bF6p0MgV6bN9G7KI/y4jzG\nDCimV8gIFOXSu7tb79ktl5ysxAyaF16HXFFRQUaGcMLIPpwwsg8L1+7gwf8t57kPmg9sVl3fyA0v\nLqS2oXm1V4hbX27eiN6kcOd/Puac8QMiHNX1aOk+rm1oYvrSTZw0qje/+GI/ThrVJ20N7F2vL2s2\n+mtr91dL91Us92Ms5+gsB4/bpy3dz1AA1DQ0JbUTYpc2FpEaY6tqG3j2gzXkZ2eSl53ZzFCEqKlv\nYvG6nTQ0Kg2NTdQ3KY1NSn1jk9vX1NRi9U9L1DU0MbxPEccM7bm3aihUHdSrKJf577/NCccf3+7/\nmgwOLi/mjq+M4fkPKlscynl7dT3XPDu/zedNx45YHSFSfgjwwIUTEismCUT6/229v9p7P8aipbPO\nk8x7v0sbi0gdffqV5PPWtSfs3f7sbW9EjPfGjydFTWevAWlSTr5jRotDSfcryee+b4yLeI6MANcr\nR8rnPt1zef57n4143Nn3vtVsiIfQ+Yx9dKUOay3R1vsr0n0V7X6M5RydleepeE3Td5D/GLj6lBHN\nxtNvaWyVWONFIjNDyMvOpFtuFtdMHtmhcwWRSPk35QujKC/Jj7hM+cKoLpdX7aGj92fQaev9Fem+\ninY/JvLeTMVr2qVLFqG6v5tf+JCtNRrRKyLSuPvtqTvszHMFhfb+566YV+2hq+dT+P8vyxN+ceah\nEf9/Z+RXvPM8Ja+pqqbFMm7cOG0v06dPb/exicI0doxU1hYi1TWmur4QQdCZShqB9zWGd2yXroYy\nDMMwYsOMhWEYhhEVMxaGYRhGVMxYGIZhGFERbWm8iAAiIpuAle08vCewuRPlxAPT2DFSWVuIVNeY\n6vpCBEFnKmkcqKq9okVKG2PREUTkfVUdn2wdrWEaO0YqawuR6hpTXV+IIOgMgsZwrBrKMAzDiIoZ\nC8MwDCMqZiwc9ydbQAyYxo6RytpCpLrGVNcXIgg6g6BxP6zNwjAMw4iKlSwMwzCMqJixMAzDMKLS\nZYyFBHmiYSMm7Bp3HexaJ54uYyyMLsHeIfdT8WUiIiNEJKWfORE5X0QO99ZTLg99pHQ+piNpn+Ei\ncrqIPAlcKyIDk62nJUTkLBG5Odk6IhEAfZNFZBrwOxE5G0BTyHNDRE4WkfeAS0nRZ05EThKRN4E/\nAGMhtfIwhIicJiL/Am4WkdimtUsg3rPyJxEpS7aWziatJz8SkZOAXwC/BCYA3xeR6ar6kohkqGrL\nk2snTl8GcDFwLTBQRF5V1TeTqSmE91WZAXyL1NWXDfwaOBr4DdAfOFdEFqjqxymgLwt3/30NuEZV\nn/OHJ/tl7GnMAx4BegO3AGcCBV54pqo2Jk/h/ojIOOB64AagO3ChiAxT1YeT/Tx7eXk28CugCKgQ\nkeeT/Y7pTFLyK6cTOQn4l6q+AvwFdxEvFpHCVLiInoaPcV9y3wNS5uvdmxelEVhG6uqrA14BjlPV\nF4G3gXpgeVLFsVdfPdAEPBMyFCLyORHJTq46h6exGvi7qk5S1Wm4PPymF54yhsLjJOBNVX0ZeAFY\nj/sALFbVpmRWm3mG/1PgGOAHwDdwHy9pQ1oZCxG5UkRuE5GveLveBj4jInmquhGoATJxX8vJ0niO\niBzp2/W2qu5S1b8ChSJyiRcvKdfGy8O/isil3q4ZqaxPVf+jqg0icirwHDAc+LWIfNWLn9AXiE/f\nZd6uPwN9ReRvIjIf+CnwIK5EmZR2AZ/GbwOo6gve/kycoV0oIgMSrSuccJ3AdOCLIlLqGbl6YCcu\nTxNebSYiF4rIyb5dC1R1i6o+62n7kojkJFJTPEkLYyGOHwFfBd4HbhSRC4ElwDrgaRGZjiu6vgB0\nT/TLTkR6i8gM4I/AFF/6Db71XwJXeQ9Dwks+InIRcD7wLPBNEZkCDPFFSTV9PxORoV7wZmCyqh4N\nVOBKkIMS+QIJ0/d1EbkOqAWmAjnAucAZXviXROTAJLzg/Bq/4eXhENhbktgJHA5sT6SucFrQ+XNg\nBfAq8JjXvjIEuA0oE5HCBGorFZFnvLR/7xlZAH/p5i7gdOCQsGNT2WmgVdLCWHgP3PHAdar6DHAV\nMAZ301+Kq+f8nap+C6gDBif6ZeeVbF4AJuMM2He8IAkVoVX138Bi4DIRKRKRcxOpETgR+I1Xbfdj\nXH3210OBKagvJ6RPVWeq6kdevEXAJqAhyfpyge+o6lTgMlVd4t2r83Av4/oE62tJYw6uygQAVZ0P\nVAPnJUGbn5buxQtU9fu4KtGbvOe5BshT1d2JEqaq23BGaxQwG/cRFQpT7/ctYC7wBREZGSppJrud\nqiME3lj4vsrfBz4He19qHwHjgYNUdY6qvuTFGwe8lySNf8K9yF4FThORvp6hyGDftbgGuBXXlnFA\ngvXNAb4IoKrvA+8A5WFeJ6mk711cFU+4V8xFuEbaLUnW9xYwWEQ+G/YyuxDIB7YlQl8Uje/iu8be\nl++rQF6Sqshay8thInKMqq5S1de8eKcBnyRQXyhPHlXV7cC9uFLiQO9ZzvT9hz8AU4AZOAcCK1kk\nEhEp9n4zYW8jMbiG2CIROdTbnoGrduruxT9VRGYCA3FF24RrVNV6VW3AtaUsAa4Mhatqo4gcBNyH\nq7Y4QlX/FCd9B3i/GX59uAcyQ0SO9bYX4EpB5V78obiHI1X1XSAiC4DBwHe9eu1k61vr0/dlEfkQ\nV33yXVWtiYe+dmjcm4fel29vYHcivoLbkZd9vfjHetW6w3DtQonSFyo51Hi/s4B/47ygUNVGz2j0\nAe4G3gDGqOot/uODSCCMhYhkiEh3cf7Vf4R9nhq++sKZQCNwsohkqeoioB/OZRbcl/DlqvplrxiZ\nSI0S9kWxGXgRGCEi/UWkp4h09/ZfoapfUtW1cdA4VkRex/NqCj2Yvi+hj4GFwFfFuU2uwZUeBnnh\nO1JU32AvfB6uuudCVd2Qgvo+wt2DF8RDXwc1DvKd5ieq+lA89HWCzlBergC+p6pnq2qnzzjXij6R\n5u2ddwNDReRgEeklIoNxz/L3VfUMVV3X2fqSQSCMhXehduHqV/vJPk+XrNALWVWXAbOAobh+AeAa\nF1d44R+r6gdJ0qiqqiKSKyK53tfHf3EPwwLgTaCPqu7w1bt3Gt4NfifwKPCIqn7bF+b3T9/lacnB\ndXDLBkrxqnNUdZPGof9CJ+jb7Ombq6pvp7C++ar6Tmfr6ySNe6vs1Lkkx4VOzMtVqrowCfrUKznk\ni0i3kBbgeWC+p7nUe8ZXdba+ZBIIY+ExEtdoeRfO06TIq9JBRG4WkQdxjU1/BCaKyGxgKzAtRTTe\nCDzAvmL05bhG7r8Ah8XjJRzCK/oWAXNU9VEv/YP8D6e4Htr/wJUefol7MN/0th+JlzbT13U0BkFn\njPpuBP6O5ykoIl/DNbr/Djg0nh+lSUVVU24BjgKGe+uhOTeygb8BB+Next/HtT8cg7uxhvqO7waU\npKDGg3zHn+TXHE993nZ3YCnu4XsL1yfhUeAIXN+E8DzMAIpMX2rqC4rGIOjsBH1H4Tws45qPyV6S\nLiDsopUAL+GKoNcBhb6wo4G7vPXLcF/w/wS6+W+qAGjMTKK+K3HufMfi3Dp/g/Ns6pWoPDR9XUNj\nEHR2gr64PsuptqTUTHki0g/4Mq5j0Ajgv+rcYENhNwOFOPfXT4BFqvojLzwhY8OkusbW9Hnhpeo1\n8IvI0cBNwFmqutv0pb6+oGgMgs5U15dqJL3NQpy743Ei0l1VK3Fz0z6N62xzpIiUe1FLgV648WDG\nApfjvIlGwX4ud11OYxv0oft7go0DVuO8yExfiuoLisYg6Ex1falMUkoWIiI4N7h/4AZa+wT3Nf4D\n9dzgxHUS+grwvqo+5u3r6QvvBuSo6tauqLGN+map6uPevlxcddnvcP71P9Y4eWCZvvTXGASdqa4v\nKCS8ZCHOZzrkcVCpqifiPAm24qw8sLe7/Arcl3mxuJFiN4vXQ1JVq+JoKFJaYzv0jfT05atqLW7I\nk1tU9fQ4PZymrwtoDILOVNcXJBJWshCRLFydXybwMs7j4BxVvdALF1wPzfNUdYa3rxtujP3P4LyK\nxmocOoMFRWMH9X0WOMt7moYAAAPjSURBVND0pa6+oGgMgs5U1xdEElKyEJHjcH0gSnHDctyMG0Tt\neBGZCHv9m2/CTWwS4jTcV8CHOP/leD6kKa2xE/TNNX2pqy8oGoOgM9X1BRZNgMsVboC/b/q27wW+\nixvwbba3LwNXr/g0MMjbdyZwrGk0femuLygag6Az1fUFdUnUzVWA81XO9La/Dtzqrc/FjaECbpTY\nJ5KSESmu0fSlt76gaAyCzlTXF9QlIdVQqrpHVWt13zSNJ+M6rIGbtW6UuAH4ngA+gL11igkj1TWa\nvvTWFxSNQdCZ6vqCSlYiExM3QqwCfXCjroLrPfkz3IxSy9X5PqOe6U80qa7R9KW3vqBohNTXmer6\ngkaiXWebcOMnbQYO86z7L4AmVf1f6MIlmVTXaPo6Rqrrg2BohNTXmer6gkWi671wg241Af8DLkl0\n+umg0fSlt76gaAyCzlTXF6Ql4T24RaQ/8E3gDnWdXlKOVNdo+jpGquuDYGiE1NeZ6vqCREoNJGgY\nhmGkJkkfSNAwDMNIfcxYGIZhGFExY2EYhmFExYyFYRiGERUzFoZhGEZUzFgYXR4RaRSRuSKyUEQ+\nFJGrRKTVZ0NEBonI+W1I4xURme6ls0xEdnjrc0XkMyLygIiM7vi/MYz4kNDhPgwjRalW1TEAItIb\nN6NaMXB9K8cMAs734raKiOQDZao60dueBPxEVb/oi/Z2u5QbRoKwkoVh+FDVjcBlwBXiGCQib4rI\nB97yGS/qbcDnvJLBj8TNjni7iMwSkXki8h3faScBFa2lKyIVIjLeW68Skd+IyGwR+Y+ITPTCPxWR\nM7w4raVnGJ2OGQvDCENVP8U9G72BjcDJqnoE8FXgj160a4E3VXWMqt4JXALsUNUJwATg2yIy2Iv7\nBeCVNkgoBCpUdRxu4LtbcCOnno2bsIco6RlGp2PVUIbRMqEhq7OBu0VkDNAIDI8Q//O4werO8baL\ngWHActw0nT9pQ9p17DMu84FaVa0Xkfm46q9o6RlGp2PGwjDCEJEhOMOwEddusQE4HFfaqIl0GG5S\nnWktnGu1qta1QUK97huHpwmoBVDVJnFzS0dMzzDihVVDGYYPEekF/Bm423thFwPrVLUJNyBdphd1\nF1DkO3Qa8F0RyfbOM1xECml7FVSsRErPMOKClSwMA/JFZC6uyqkBeAy4wwu7F3hWRM4FpgO7vf3z\ngAYR+RB4GLgLV0X0gTfr2ibgLGAy8P04aH4gQnqGERds1FnDiBMikgu8parjk63FMDqKGQvDMAwj\nKtZmYRiGYUTFjIVhGIYRFTMWhmEYRlTMWBiGYRhRMWNhGIZhRMWMhWEYhhGV/w/bSdhSIi2llAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4527463dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the time sequence of Measurement Result Values \n",
    "resultValues.plot(x='valuedatetime', y='datavalue', title=relatedSite.SamplingFeatureName,\n",
    "                  kind='line', use_index=True, linestyle='solid', style='o')\n",
    "ax = plt.gca()\n",
    "ax.set_ylabel(\"{0} ({1})\".format(results[0].VariableObj.VariableNameCV, \n",
    "                                 results[0].UnitsObj.UnitsAbbreviation))\n",
    "ax.set_xlabel('Date/Time')\n",
    "ax.grid(True)\n",
    "ax.legend().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Use the Force!* (We already used YODA ...)\n",
    "\n",
    "If I'm going to reuse a series of steps, it's always helpful to write little generic functions that can be called to quickly and consistently get what we need. To conclude this demo, here's one such function that encapsulates the `VariableID`, `getResults` and `getResultValues` queries we showed above. Then we leverage it to create a nice 2-variable (2-axis) plot of TP and TN vs time, and conclude with a reminder that we have ready access to related metadata about analytical lab methods and such."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_results_and_values(siteid, variablecode):\n",
    "    v = variables_df[variables_df['VariableCode'] == variablecode]\n",
    "    variableID = v.index[0]\n",
    "    \n",
    "    results = read.getResults(siteid=siteid, variableid=variableID, type=\"Measurement\")\n",
    "    resultIDList = [x.ResultID for x in results]\n",
    "    resultValues = read.getResultValues(resultids=resultIDList)\n",
    "    \n",
    "    return resultValues, results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fancy plotting, leveraging the `Pandas` plot method and `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GmNgV7/dr8R4/JHgzM7/l5eUxevRoAHJyckhOTqZbt24ALF68mAMOOIDKykr23ntvXnzx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POQvauMiYCqUra3qlahUVOc1O01qf3zjj2V6H5mQaan+sjLSKOVW4R0yGtJ3/Y55GWm1RQqGWm0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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4529e969d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot figure and axis set up (just *one* subplot, actually)\n",
    "f, ax = plt.subplots(1, figsize=(13, 6))\n",
    "\n",
    "# First plot (left axis)\n",
    "VariableCode = 'TP'\n",
    "resultValues_TP, results_TP = get_results_and_values(siteID, VariableCode)\n",
    "resultValues_TP.plot(x='valuedatetime', y='datavalue', label=VariableCode, \n",
    "                     style='o-', kind='line', ax=ax)\n",
    "ax.set_ylabel(\"{0}: {1} ({2})\".format(VariableCode, results_TP[0].VariableObj.VariableNameCV, \n",
    "                                      results_TP[0].UnitsObj.UnitsAbbreviation))\n",
    "\n",
    "# Second plot (right axis)\n",
    "VariableCode = 'TN'\n",
    "resultValues_TN, results_TN = get_results_and_values(siteID, VariableCode)\n",
    "resultValues_TN.plot(x='valuedatetime', y='datavalue', label=VariableCode, \n",
    "                     style='^-', kind='line', ax=ax,\n",
    "                     secondary_y=True)\n",
    "ax.right_ax.set_ylabel(\"{0}: {1} ({2})\".format(VariableCode, results_TN[0].VariableObj.VariableNameCV, \n",
    "                                               results_TN[0].UnitsObj.UnitsAbbreviation))\n",
    "\n",
    "# Tweak the figure\n",
    "ax.legend(loc=2)\n",
    "ax.right_ax.legend(loc=1)\n",
    "\n",
    "ax.grid(True)\n",
    "ax.set_xlabel('')\n",
    "ax.set_title(relatedSite.SamplingFeatureName);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's show some useful metadata. Use the `Results` records and their relationship to `Actions` (via `FeatureActions`) to **extract and print out the Specimen Analysis methods used for TN and TP**. Or at least for the *first* result for each of the two variables; methods may have varied over time, but the specific method associated with each result is stored in ODM2 and available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP METHOD:  Astoria Total Phosphorus (Determination of total phosphorus by persulphate oxidation digestion and ascorbic acid method)\n",
      "TN METHOD:  Astoria Total Nitrogen (Determination of total Nitrogen by persulphate oxidation digestion and cadmium reduction method)\n"
     ]
    }
   ],
   "source": [
    "print(\"TP METHOD:  {0} ({1})\".format(results_TP[0].FeatureActionObj.ActionObj.MethodObj.MethodName,\n",
    "                                     results_TP[0].FeatureActionObj.ActionObj.MethodObj.MethodDescription))\n",
    "\n",
    "print(\"TN METHOD:  {0} ({1})\".format(results_TN[0].FeatureActionObj.ActionObj.MethodObj.MethodName,\n",
    "                                     results_TN[0].FeatureActionObj.ActionObj.MethodObj.MethodDescription))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:odm2client]",
   "language": "python",
   "name": "conda-env-odm2client-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}