Part2-ulmo_nwis_and_cuahsi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Case 2 - Part 1: Using ulmo to explore USGS NWIS and CUAHSI HIS Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Emilio Mayorga](https://github.com/emiliom), University of Washington\n",
    "- [WaterHackWeek](https://waterhackweek.github.io/) Cyberseminar\n",
    "- 2019-2-7\n",
    "- **Re-ran successfully by Emilio on 2/26/2020 using his `whwtimeseries_cso.yml` conda env file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pytz\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import folium\n",
    "\n",
    "import ulmo\n",
    "from ulmo.util import convert_datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Request and explore NWIS sites in a HUC8 subbasin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upper Little Tennessee HUC8 Subbasin 06010202. See [an interactive map on our GitHub repository.](https://github.com/waterhackweek/tsdata_access/blob/master/UpperLittleTennessee_HUC-8Subbasin_ID06010202.geojson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "HUC8code = '06010202'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Issue the `ulmo nwis.get_sites` request, then cleanup and examine the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "making request for sites: http://waterservices.usgs.gov/nwis/dv/\n",
      "processing data from request: https://waterservices.usgs.gov/nwis/dv/?format=waterml&hucs=06010202\n",
      "making request for sites: http://waterservices.usgs.gov/nwis/iv/\n",
      "processing data from request: https://waterservices.usgs.gov/nwis/iv/?format=waterml&hucs=06010202\n"
     ]
    }
   ],
   "source": [
    "nwis_sites = ulmo.usgs.nwis.get_sites(huc=HUC8code, service=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the response look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dict, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(nwis_sites), len(nwis_sites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['03500000', '0350011450', '03500240', '03500500', '0350056050', '03501000', '03501500', '03501975', '03502500', '03503000', '03503500', '03504000', '03505500', '03505550', '03506500', '03507000', '03513500', '03514000', '350413083130045', '350557083325845', '351205083213545'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwis_sites.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a Python dictionary of sites. Let's examine one of the sites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "site_code = '03501500'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'code': '03501500',\n",
       " 'name': 'LITTLE TENNESSEE R AT FRANKLIN, NC',\n",
       " 'network': 'NWIS',\n",
       " 'agency': 'USGS',\n",
       " 'location': {'latitude': '35.18592846',\n",
       "  'longitude': '-83.3712647',\n",
       "  'srs': 'EPSG:4326'},\n",
       " 'timezone_info': {'uses_dst': True,\n",
       "  'dst_tz': {'abbreviation': 'EDT', 'offset': '-04:00'},\n",
       "  'default_tz': {'abbreviation': 'EST', 'offset': '-05:00'}},\n",
       " 'county': '37113',\n",
       " 'huc': '06010202',\n",
       " 'site_type': 'ST',\n",
       " 'state_code': '37'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwis_sites[site_code]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing it doesn't tell us is what parameters are measured at the site. Too bad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pull out lat & lon from the nested dictionary\n",
    "for k, v in nwis_sites.items():\n",
    "    v['longitude'] = v['location']['longitude']\n",
    "    v['latitude'] = v['location']['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>USGS</td>\n",
       "      <td>USGS</td>\n",
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       "      <td>USGS</td>\n",
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       "      <td>351205083213545</td>\n",
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       "      <th>county</th>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37173</td>\n",
       "      <td>...</td>\n",
       "      <td>37113</td>\n",
       "      <td>37173</td>\n",
       "      <td>37173</td>\n",
       "      <td>37173</td>\n",
       "      <td>37173</td>\n",
       "      <td>37173</td>\n",
       "      <td>37173</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "      <td>37113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huc</th>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>...</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
       "      <td>06010202</td>\n",
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       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>35.15</td>\n",
       "      <td>35.15776944</td>\n",
       "      <td>35.15888889</td>\n",
       "      <td>35.07064737</td>\n",
       "      <td>35.0761111</td>\n",
       "      <td>35.166484</td>\n",
       "      <td>35.18592846</td>\n",
       "      <td>35.2328611</td>\n",
       "      <td>35.2784285</td>\n",
       "      <td>35.33638889</td>\n",
       "      <td>...</td>\n",
       "      <td>35.1275</td>\n",
       "      <td>35.29870237</td>\n",
       "      <td>35.305</td>\n",
       "      <td>35.37564868</td>\n",
       "      <td>35.4084267</td>\n",
       "      <td>35.48481705</td>\n",
       "      <td>35.4773122</td>\n",
       "      <td>35.07027778</td>\n",
       "      <td>35.09916667</td>\n",
       "      <td>35.2</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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       "          03500000   0350011450     03500240     03500500  0350056050  \\\n",
       "agency        USGS         USGS         USGS         USGS        USGS   \n",
       "code      03500000   0350011450     03500240     03500500  0350056050   \n",
       "county       37113        37113        37113        37113       37113   \n",
       "huc       06010202     06010202     06010202     06010202    06010202   \n",
       "latitude     35.15  35.15776944  35.15888889  35.07064737  35.0761111   \n",
       "\n",
       "           03501000     03501500    03501975    03502500     03503000  ...  \\\n",
       "agency         USGS         USGS        USGS        USGS         USGS  ...   \n",
       "code       03501000     03501500    03501975    03502500     03503000  ...   \n",
       "county        37113        37113       37113       37113        37173  ...   \n",
       "huc        06010202     06010202    06010202    06010202     06010202  ...   \n",
       "latitude  35.166484  35.18592846  35.2328611  35.2784285  35.33638889  ...   \n",
       "\n",
       "          03504000     03505500  03505550     03506500    03507000  \\\n",
       "agency        USGS         USGS      USGS         USGS        USGS   \n",
       "code      03504000     03505500  03505550     03506500    03507000   \n",
       "county       37113        37173     37173        37173       37173   \n",
       "huc       06010202     06010202  06010202     06010202    06010202   \n",
       "latitude   35.1275  35.29870237    35.305  35.37564868  35.4084267   \n",
       "\n",
       "             03513500    03514000  350413083130045  350557083325845  \\\n",
       "agency           USGS        USGS             USGS             USGS   \n",
       "code         03513500    03514000  350413083130045  350557083325845   \n",
       "county          37173       37173            37113            37113   \n",
       "huc          06010202    06010202         06010202         06010202   \n",
       "latitude  35.48481705  35.4773122      35.07027778      35.09916667   \n",
       "\n",
       "          351205083213545  \n",
       "agency               USGS  \n",
       "code      351205083213545  \n",
       "county              37113  \n",
       "huc              06010202  \n",
       "latitude             35.2  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwis_sites_df = pd.DataFrame(nwis_sites)\n",
    "nwis_sites_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>03500000</th>\n",
       "      <td>USGS</td>\n",
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       "      <td>37113</td>\n",
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       "      <td>35.15</td>\n",
       "      <td>{'latitude': '35.15', 'longitude': '-83.379722...</td>\n",
       "      <td>-83.3797222</td>\n",
       "      <td>LITTLE TENNESSEE RIVER NEAR PRENTISS, NC</td>\n",
       "      <td>NWIS</td>\n",
       "      <td>ST</td>\n",
       "      <td>37</td>\n",
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       "      <td>37113</td>\n",
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       "      <td>{'latitude': '35.15776944', 'longitude': '-83....</td>\n",
       "      <td>-83.4243694</td>\n",
       "      <td>CARTOOGECHAYE CREEK AT SR 1148 NEAR FRANKLIN, NC</td>\n",
       "      <td>NWIS</td>\n",
       "      <td>ST</td>\n",
       "      <td>37</td>\n",
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      "text/plain": [
       "           agency        code county       huc     latitude  \\\n",
       "03500000     USGS    03500000  37113  06010202        35.15   \n",
       "0350011450   USGS  0350011450  37113  06010202  35.15776944   \n",
       "\n",
       "                                                     location    longitude  \\\n",
       "03500000    {'latitude': '35.15', 'longitude': '-83.379722...  -83.3797222   \n",
       "0350011450  {'latitude': '35.15776944', 'longitude': '-83....  -83.4243694   \n",
       "\n",
       "                                                        name network  \\\n",
       "03500000            LITTLE TENNESSEE RIVER NEAR PRENTISS, NC    NWIS   \n",
       "0350011450  CARTOOGECHAYE CREEK AT SR 1148 NEAR FRANKLIN, NC    NWIS   \n",
       "\n",
       "           site_type state_code  \\\n",
       "03500000          ST         37   \n",
       "0350011450        ST         37   \n",
       "\n",
       "                                                timezone_info  \n",
       "03500000    {'uses_dst': True, 'dst_tz': {'abbreviation': ...  \n",
       "0350011450  {'uses_dst': True, 'dst_tz': {'abbreviation': ...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's flip (transpose) the dataframe, so each row is a site\n",
    "nwis_sites_df = nwis_sites_df.T\n",
    "nwis_sites_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>county</th>\n",
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       "      <td>LITTLE TENNESSEE RIVER NEAR PRENTISS, NC</td>\n",
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       "      <td>CARTOOGECHAYE CREEK AT SR 1148 NEAR FRANKLIN, NC</td>\n",
       "      <td>NWIS</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           agency        code county       huc   latitude  longitude  \\\n",
       "03500000     USGS    03500000  37113  06010202  35.150000 -83.379722   \n",
       "0350011450   USGS  0350011450  37113  06010202  35.157769 -83.424369   \n",
       "\n",
       "                                                        name network  \\\n",
       "03500000            LITTLE TENNESSEE RIVER NEAR PRENTISS, NC    NWIS   \n",
       "0350011450  CARTOOGECHAYE CREEK AT SR 1148 NEAR FRANKLIN, NC    NWIS   \n",
       "\n",
       "           site_type state_code  \n",
       "03500000          ST         37  \n",
       "0350011450        ST         37  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final cleanups, improvements\n",
    "nwis_sites_df['latitude'] = nwis_sites_df['latitude'].astype(float)\n",
    "nwis_sites_df['longitude'] = nwis_sites_df['longitude'].astype(float)\n",
    "nwis_sites_df.drop(columns=['location', 'timezone_info'], inplace=True)\n",
    "nwis_sites_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What's that \"site_type\"?* Summarize all values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ST    18\n",
       "AT     3\n",
       "Name: site_type, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwis_sites_df['site_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Site type codes are defined at http://help.waterdata.usgs.gov/site_tp_cd. For our purposes:\n",
    " - ST: Stream\n",
    " - AT: Atmosphere\n",
    " \n",
    "We could also have issued the `nwis.get_sites` query by passing a `site_type` value or list, like this:\n",
    "```python\n",
    "nwis_sits = ulmo.usgs.nwis.get_sites(huc=HUC8code, site_type=['ST'], service=None)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's map the sites on an interactive map, with Folium\n",
    "Colored by site type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fe4a0389ef0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map(tiles='Stamen terrain')\n",
    "\n",
    "for idx, row in nwis_sites_df.iterrows():\n",
    "    folium.Marker(location=[row['latitude'], row['longitude']], \n",
    "                  icon=folium.Icon(color='blue' if row['site_type'] == 'ST' else 'gray'),\n",
    "                  popup=\"<b>{0}</b> {1}. Site type: {2}. {3}\".format(\n",
    "                      row['code'], row['name'], row['site_type'], row['network'])\n",
    "                 ).add_to(m)\n",
    "    \n",
    "# Set the map extent (bounds) to the extent of the sites\n",
    "m.fit_bounds(m.get_bounds())\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous use case notebook from Yifan illustrated how to pull the time series data from nwis using ulmo, by specifying the site code and parameter code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How does this NWIS view compare to what we can find on that HUC8 using the **ModelMyWatershed App?**\n",
    "Let's go there and do some querying and browsing. https://modelmywatershed.org. We can navigate to the specific HUC8, or upload a GeoJSON spatial file for the HUC8, generated in advance.\n",
    "\n",
    "Also, I wonder what data are available near the **Coweeta** LTER station ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use `ulmo.cuahsi.wof` to pull time series data from a nearby site on CUAHSI HIS\n",
    "\n",
    "For examples of CUAHSI HIS catalog \"spatial querying\" (based on a bounding box) analogous to the NWIS spatial querying we've presented, go to [this CUAHSI resource on HydroShare](https://www.hydroshare.org/resource/c184323fc95c4c3e85c663f519b47084/). It includes a Jupyter notebook and a presentation on the CUAHI HIS Central API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore relevant sites on **HydroClient**\n",
    "\n",
    "I want to find a site \"nearby\", even if it's not in that HUC8, that meets these criteria:\n",
    "- Has dissolved oxygen\n",
    "- Is from a source other than the big federal agencies\n",
    "\n",
    "To do this, let's go to HydroClient. http://data.cuahsi.org. There, we can also visualize the time series using the HydroShare TimeSeries Viewer App, or download the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back to the code, to pull in the site's time series data\n",
    "I got `networkcd`, `sitecd` and `varcd` from the HydroClient results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# http://hiscentral.cuahsi.org/pub_network.aspx?n=5639\n",
    "networkcd = 'ORSANCO_HAB'\n",
    "\n",
    "# The \"WSDL URL\" (CUAHSI WaterOneFlow web service endpoint)\n",
    "wsdlurl = \"http://hydroportal.cuahsi.org/{}/cuahsi_1_1.asmx?WSDL\".format(networkcd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sitecd = 'RCByrd'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But first let's explore site information (metadata, data availability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "siteinfo = ulmo.cuahsi.wof.get_site_info(wsdlurl, \"{}:{}\".format(networkcd, sitecd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['code', 'name', 'network', 'location', 'series'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siteinfo.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The response is in **CUAHSI WaterML 1.1 format**, transformed by `ulmo` from the original XML into a Python dictionary for easier access."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'code': 'RCByrd',\n",
       " 'name': 'Robert C. Byrd Locks and Dam',\n",
       " 'network': 'ORSANCO_HAB',\n",
       " 'location': {'latitude': '38.685029',\n",
       "  'longitude': '-82.183691',\n",
       "  'srs': 'EPSG:4269'},\n",
       " 'series': {'ORSANCO_HAB:Bat_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Bat_30',\n",
       "    'id': '59',\n",
       "    'name': 'Battery voltage',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'V',\n",
       "     'code': '168',\n",
       "     'name': 'volts',\n",
       "     'type': 'Potential Difference'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Temp_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Temp_30',\n",
       "    'id': '60',\n",
       "    'name': 'Temperature, sensor',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'degC',\n",
       "     'code': '96',\n",
       "     'name': 'degree celsius',\n",
       "     'type': 'Temperature'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Sp_Cond_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Sp_Cond_30',\n",
       "    'id': '61',\n",
       "    'name': 'Specific Conductance',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'uS/cm',\n",
       "     'code': '192',\n",
       "     'name': 'microsiemens per centimeter',\n",
       "     'type': 'Electrical Conductivity'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:pH_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'pH_30',\n",
       "    'id': '62',\n",
       "    'name': 'pH',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'pH',\n",
       "     'code': '309',\n",
       "     'name': 'pH Unit',\n",
       "     'type': 'Dimensionless'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Turb_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Turb_30',\n",
       "    'id': '63',\n",
       "    'name': 'Turbidity',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'NTU',\n",
       "     'code': '221',\n",
       "     'name': 'nephelometric turbidity units',\n",
       "     'type': 'Turbidity'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:ODOSat_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'O2',\n",
       "    'code': 'ODOSat_30',\n",
       "    'id': '64',\n",
       "    'name': 'Oxygen, dissolved',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': '%',\n",
       "     'code': '1',\n",
       "     'name': 'percent',\n",
       "     'type': 'Dimensionless'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:ODO_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'O2',\n",
       "    'code': 'ODO_30',\n",
       "    'id': '65',\n",
       "    'name': 'Oxygen, dissolved',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'mg/L',\n",
       "     'code': '199',\n",
       "     'name': 'milligrams per liter',\n",
       "     'type': 'Concentration'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Chlorophyll_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Chlorophyll_30',\n",
       "    'id': '66',\n",
       "    'name': 'Chlorophyll (a+b+c)',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'ug/L',\n",
       "     'code': '204',\n",
       "     'name': 'micrograms per liter',\n",
       "     'type': 'Concentration'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Chl_RFU_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Cl',\n",
       "    'code': 'Chl_RFU_30',\n",
       "    'id': '67',\n",
       "    'name': 'Chlorine, dissolved',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'RFU',\n",
       "     'code': '332',\n",
       "     'name': 'relative fluorescence units',\n",
       "     'type': 'Fluorescence'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:BGA-PC_RAW_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'BGA-PC_RAW_30',\n",
       "    'id': '68',\n",
       "    'name': 'Blue-green algae (cyanobacteria), phycocyanin',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': '-',\n",
       "     'code': '137',\n",
       "     'name': 'dimensionless',\n",
       "     'type': 'Dimensionless'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:BGA-PC_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'BGA-PC_30',\n",
       "    'id': '69',\n",
       "    'name': 'Blue-green algae (cyanobacteria), phycocyanin',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'ug/L',\n",
       "     'code': '204',\n",
       "     'name': 'micrograms per liter',\n",
       "     'type': 'Concentration'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Depth_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Depth_30',\n",
       "    'id': '70',\n",
       "    'name': 'Water depth',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'm',\n",
       "     'code': '52',\n",
       "     'name': 'meter',\n",
       "     'type': 'Length'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Wiper_Pos_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Wiper_Pos_30',\n",
       "    'id': '71',\n",
       "    'name': 'Position',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': '-',\n",
       "     'code': '1379',\n",
       "     'name': 'Boolean',\n",
       "     'type': 'Dimensionless'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}},\n",
       "  'ORSANCO_HAB:Cable_Pwr_30': {'variable': {'value_type': 'Field Observation',\n",
       "    'data_type': 'Regular Sampling',\n",
       "    'general_category': 'Water Quality',\n",
       "    'sample_medium': 'Surface water',\n",
       "    'no_data_value': '-9999',\n",
       "    'speciation': 'Not Applicable',\n",
       "    'code': 'Cable_Pwr_30',\n",
       "    'id': '72',\n",
       "    'name': 'Electric Power',\n",
       "    'vocabulary': 'ORSANCO_HAB',\n",
       "    'time': {'is_regular': True,\n",
       "     'interval': '30',\n",
       "     'units': {'abbreviation': 'min',\n",
       "      'code': '102',\n",
       "      'name': 'minute',\n",
       "      'type': 'Time'}},\n",
       "    'units': {'abbreviation': 'V',\n",
       "     'code': '168',\n",
       "     'name': 'volts',\n",
       "     'type': 'Potential Difference'}},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "    'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "    'organization': 'Marshall College ',\n",
       "    'source_description': 'Marshall College',\n",
       "    'citation': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "    'quality_control_level_code': '-9999',\n",
       "    'definition': 'Unknown'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "    'begin_date_time': '2017-07-27T12:00:00',\n",
       "    'end_date_time': '2017-09-05T12:00:00',\n",
       "    'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "    'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       "   '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}}}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siteinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Request, parse and plot the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "varcd = \"ORSANCO_HAB:ODO_30\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'variable': {'value_type': 'Field Observation',\n",
       "  'data_type': 'Regular Sampling',\n",
       "  'general_category': 'Water Quality',\n",
       "  'sample_medium': 'Surface water',\n",
       "  'no_data_value': '-9999',\n",
       "  'speciation': 'O2',\n",
       "  'code': 'ODO_30',\n",
       "  'id': '65',\n",
       "  'name': 'Oxygen, dissolved',\n",
       "  'vocabulary': 'ORSANCO_HAB',\n",
       "  'time': {'is_regular': True,\n",
       "   'interval': '30',\n",
       "   'units': {'abbreviation': 'min',\n",
       "    'code': '102',\n",
       "    'name': 'minute',\n",
       "    'type': 'Time'}},\n",
       "  'units': {'abbreviation': 'mg/L',\n",
       "   'code': '199',\n",
       "   'name': 'milligrams per liter',\n",
       "   'type': 'Concentration'}},\n",
       " '{http://www.cuahsi.org/water_ml/1.1/}method': {'method_id': '81',\n",
       "  'method_description': 'Data was collected in-situ at regular intervals by datasonde'},\n",
       " '{http://www.cuahsi.org/water_ml/1.1/}source': {'source_id': '53',\n",
       "  'organization': 'Marshall College ',\n",
       "  'source_description': 'Marshall College',\n",
       "  'citation': 'Unknown'},\n",
       " '{http://www.cuahsi.org/water_ml/1.1/}quality_control_level': {'quality_control_level_id': '-9999',\n",
       "  'quality_control_level_code': '-9999',\n",
       "  'definition': 'Unknown'},\n",
       " '{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval': {'variable_time_interval_type': 'TimeIntervalType',\n",
       "  'begin_date_time': '2017-07-27T12:00:00',\n",
       "  'end_date_time': '2017-09-05T12:00:00',\n",
       "  'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       "  'end_date_time_utc': '2017-09-05T12:00:00'},\n",
       " '{http://www.cuahsi.org/water_ml/1.1/}value_count': {'value_count': '1927'}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siteinfo['series'][varcd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'variable_time_interval_type': 'TimeIntervalType',\n",
       " 'begin_date_time': '2017-07-27T12:00:00',\n",
       " 'end_date_time': '2017-09-05T12:00:00',\n",
       " 'begin_date_time_utc': '2017-07-27T12:00:00',\n",
       " 'end_date_time_utc': '2017-09-05T12:00:00'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_interval = siteinfo['series'][varcd]['{http://www.cuahsi.org/water_ml/1.1/}variable_time_interval']\n",
    "time_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "site_tsval = ulmo.cuahsi.wof.get_values(wsdlurl, \"{}:{}\".format(networkcd, sitecd), varcd,\n",
    "                                        start=time_interval['begin_date_time_utc'], \n",
    "                                        end=time_interval['end_date_time_utc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['site', 'variable', 'values', 'censor_codes', 'methods', 'quality_control_levels', 'sources'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_tsval.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def site_series_values_to_df(series_values, variable_name):\n",
    "    # Create a clean timeseries list of (dt,val) tuples\n",
    "    tsdt_tuplst = [(convert_datetime(valdict['datetime']).replace(tzinfo=pytz.utc), \n",
    "                    float(valdict['value']))\n",
    "                   for valdict in series_values['values']]\n",
    "    \n",
    "    dt, val = zip(*tsdt_tuplst)\n",
    "    ts_df = pd.DataFrame({'time':dt, variable_name:val})\n",
    "    ts_df.set_index('time', inplace=True)\n",
    "    ts_df.sort_index(ascending=True, inplace=True)\n",
    "    return ts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_df = site_series_values_to_df(site_tsval, varcd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>ORSANCO_HAB:ODO_30</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-09-05 10:00:00+00:00</th>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-05 10:30:00+00:00</th>\n",
       "      <td>5.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-05 11:00:00+00:00</th>\n",
       "      <td>5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-05 11:30:00+00:00</th>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-05 12:00:00+00:00</th>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ORSANCO_HAB:ODO_30\n",
       "time                                         \n",
       "2017-09-05 10:00:00+00:00                5.23\n",
       "2017-09-05 10:30:00+00:00                5.27\n",
       "2017-09-05 11:00:00+00:00                5.22\n",
       "2017-09-05 11:30:00+00:00                5.29\n",
       "2017-09-05 12:00:00+00:00                5.40"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 6))\n",
    "varlabel = ts_df.columns[0]\n",
    "ts_df[varlabel].plot(style='-', ax=ax)\n",
    "\n",
    "sitevariable = siteinfo['series'][varcd]['variable']\n",
    "ax.set_ylabel(\"{} ({}), {}\".format(sitevariable['name'], varlabel, sitevariable['units']['abbreviation']))\n",
    "ax.set_title(\"{}:{} | {}\".format(networkcd, sitecd, siteinfo['name']));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:whwtimeseries]",
   "language": "python",
   "name": "conda-env-whwtimeseries-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}