From 8d4771b7a9681fc78a265ee10105df79bdc91e31 Mon Sep 17 00:00:00 2001
From: Filipe Fernandes <ocefpaf@gmail.com>
Date: Mon, 11 Jul 2022 14:30:48 -0300
Subject: [PATCH 1/2] update ERDDAP_IOOS_Sensor_Map

---
 .../2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb   | 658 ++++++------------
 1 file changed, 214 insertions(+), 444 deletions(-)

diff --git a/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb b/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb
index a8665045..10e6456f 100644
--- a/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb
+++ b/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb
@@ -8,34 +8,20 @@
     "\n",
     "Created: 2017-03-21\n",
     "\n",
-    "<div class=\"alert alert-danger\">\n",
-    "  <strong>This notebook is stale and won't run due to missing data upstream and changes in the libraries used.</strong>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Web Map Services are a great way to find data you may be looking for in a particular geographic area.\n",
-    "\n",
-    "Suppose you were exploring the [IOOS Sensor Map](https://via.hypothes.is/https://sensors.ioos.us/#map),\n",
-    "and after selecting Significant Wave Height,\n",
-    "had selected buoy 44011 on George's Bank:\n",
-    "\n",
-    "![2017-03-27_16-21-08](https://cloud.githubusercontent.com/assets/1872600/24376518/213e6c4c-130a-11e7-9744-2f23e9660adf.png)\n",
+    "Updated: 2022-07-07\n",
     "\n",
-    "You click the `ERDDAP` link and generate a URL to download the data as `CSV` ![2017-03-27_16-24-35](https://cloud.githubusercontent.com/assets/1872600/24376521/2377afc8-130a-11e7-9a3b-c1c46e43d20d.png).\n",
+    "Web Map Services are a great way to find data you may be looking for in a particular geographic area.\n",
     "\n",
-    "You notice that the URL that is generated\n",
+    "Suppose you are exploring the [IOOS Sensor Map](https://sensors.ioos.us/#map),\n",
+    "you select Oxygen and click on the only returned value, the Moss Landing Marine Laboratories (MLML) station.\n",
     "\n",
-    "[https://erddap.axiomdatascience.com/erddap/tabledap/sensor_service.csvp?time,depth,station,parameter,unit,value&time>=2017-02-27T12:00:00Z&station=\"urn:ioos:station:wmo:44011\"&parameter=\"Significant Wave Height\"&unit=\"m\"](https://erddap.axiomdatascience.com/erddap/tabledap/sensor_service.csvp%3Ftime%2Cdepth%2Cstation%2Cparameter%2Cunit%2Cvalue%26time%3E%3D2017-02-27T12%3A00%3A00Z%26station%3D%22urn%3Aioos%3Astation%3Awmo%3A44011%22%26parameter%3D%22Significant%20Wave%20Height%22%26unit%3D%22m%22)\n",
+    "![sensor_map.png](https://user-images.githubusercontent.com/950575/178321765-74ed0562-b942-4d97-af8b-85158bc6488c.png)\n",
     "\n",
+    "One can download the data in multiple forms from the site, including generating an [ERDDAP URL](https://erddap.sensors.ioos.us/erddap/tabledap/mlml_mlml_sea.csv?time%2Cmole_concentration_of_dissolved_molecular_oxygen_in_sea_water%2Cmole_concentration_of_dissolved_molecular_oxygen_in_sea_water_qc_agg%2Cz&time%3E%3D2022-07-01T12%3A35%3A13Z&time%3C%3D2022-07-11T12%3A35%3A13Z) for the request.\n",
     "\n",
+    "These features makes Sensor map an extremely useful tool for quick data explorations but now imagine if you want automate that instead of exploring the Sensor Map interactively? Or if you want to make multiple small modification to your query? It would be very tedious and error prone to try that with the Sensor Map interface. The good news is that we cab automate that by querying the ERDDAP server directly.\n",
     "\n",
-    "is fairly easy to understand,\n",
-    "and that a program could construct that URL fairly easily.\n",
-    "Let's explore how that could work..."
+    "First we need to instantiate a server object."
    ]
   },
   {
@@ -44,46 +30,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import requests\n",
-    "\n",
-    "try:\n",
-    "    from urllib.parse import urlencode\n",
-    "except ImportError:\n",
-    "    from urllib import urlencode\n",
+    "from erddapy import ERDDAP\n",
     "\n",
     "\n",
-    "def encode_erddap(urlbase, fname, columns, params):\n",
-    "    \"\"\"\n",
-    "    urlbase: the base string for the endpoint\n",
-    "             (e.g.: https://erddap.axiomdatascience.com/erddap/tabledap).\n",
-    "    fname: the data source (e.g.: `sensor_service`) and the response (e.g.: `.csvp` for CSV).\n",
-    "    columns: the columns of the return table.\n",
-    "    params: the parameters for the query.\n",
-    "\n",
-    "    Returns a valid ERDDAP endpoint.\n",
-    "    \"\"\"\n",
-    "    urlbase = urlbase.rstrip(\"/\")\n",
-    "    if not urlbase.lower().startswith((\"http:\", \"https:\")):\n",
-    "        msg = \"Expected valid URL but got {}\".format\n",
-    "        raise ValueError(msg(urlbase))\n",
-    "\n",
-    "    columns = \",\".join(columns)\n",
-    "    params = urlencode(params)\n",
-    "    endpoint = \"{urlbase}/{fname}?{columns}&{params}\".format\n",
-    "\n",
-    "    url = endpoint(urlbase=urlbase, fname=fname, columns=columns, params=params)\n",
-    "    r = requests.get(url)\n",
-    "    r.raise_for_status()\n",
-    "    return url"
+    "server = \"http://erddap.sensors.ioos.us/erddap\"\n",
+    "e = ERDDAP(server=server, protocol=\"tabledap\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the function we defined above, we can now bypass the forms and get the data by generating the URL \"by hand\". Below we have a query for `Significant Wave Height` from buoy `44011`, a buoy on George's Bank off the coast of Cape Cod, MA, starting at the beginning of the year 2017.\n",
-    "\n",
-    "\\* For more information on how to use tabledap, please check the [NOAA ERDDAP documentation](https://via.hypothes.is/http://coastwatch.pfeg.noaa.gov/erddap/tabledap/documentation.html) for more information on the various parameters and responses of ERDDAP."
+    "Now we can search for \"dissolved oxygen moss landing\" and inspect the dataset_ids."
    ]
   },
   {
@@ -95,60 +53,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "https://erddap.axiomdatascience.com/erddap/tabledap/sensor_service.csvp?time,value,station,longitude,latitude,parameter,unit,depth&time>=2017-01-00T00:00:00Z&station=\"urn:ioos:station:wmo:44011\"&parameter=\"Significant+Wave+Height\"&unit=\"m\"\n"
+      "http://erddap.sensors.ioos.us/erddap/search/advanced.csv?page=1&itemsPerPage=1000&protocol=tabledap&cdm_data_type=(ANY)&institution=(ANY)&ioos_category=(ANY)&keywords=(ANY)&long_name=(ANY)&standard_name=(ANY)&variableName=(ANY)&minLon=(ANY)&maxLon=(ANY)&minLat=(ANY)&maxLat=(ANY)&minTime=&maxTime=&searchFor=dissolved+oxygen+moss+landing\n"
      ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    from urllib.parse import unquote\n",
-    "except ImportError:\n",
-    "    from urllib2 import unquote\n",
-    "\n",
-    "\n",
-    "urlbase = \"https://erddap.axiomdatascience.com/erddap/tabledap\"\n",
-    "\n",
-    "fname = \"sensor_service.csvp\"\n",
-    "\n",
-    "columns = (\n",
-    "    \"time\",\n",
-    "    \"value\",\n",
-    "    \"station\",\n",
-    "    \"longitude\",\n",
-    "    \"latitude\",\n",
-    "    \"parameter\",\n",
-    "    \"unit\",\n",
-    "    \"depth\",\n",
-    ")\n",
-    "params = {\n",
-    "    # Inequalities do not exist in HTTP parameters,\n",
-    "    # so we need to hardcode the `>` in the time key to get a '>='.\n",
-    "    # Note that a '>' or '<' cannot be encoded with `urlencode`, only `>=` and `<=`.\n",
-    "    \"time>\": \"2017-01-00T00:00:00Z\",\n",
-    "    \"station\": '\"urn:ioos:station:wmo:44011\"',\n",
-    "    \"parameter\": '\"Significant Wave Height\"',\n",
-    "    \"unit\": '\"m\"',\n",
-    "}\n",
-    "\n",
-    "url = encode_erddap(urlbase, fname, columns, params)\n",
-    "\n",
-    "print(unquote(url))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here is a cool part about ERDDAP `tabledap` - The data `tabledap` `csvp` response can be easily read by Python's pandas `read_csv` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
+    },
     {
      "data": {
       "text/html": [
@@ -170,177 +77,226 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>value</th>\n",
-       "      <th>station</th>\n",
-       "      <th>longitude (degrees_east)</th>\n",
-       "      <th>latitude (degrees_north)</th>\n",
-       "      <th>parameter</th>\n",
-       "      <th>unit</th>\n",
-       "      <th>depth (m)</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time (UTC)</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
+       "      <th>griddap</th>\n",
+       "      <th>Subset</th>\n",
+       "      <th>tabledap</th>\n",
+       "      <th>Make A Graph</th>\n",
+       "      <th>wms</th>\n",
+       "      <th>files</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Summary</th>\n",
+       "      <th>FGDC</th>\n",
+       "      <th>ISO 19115</th>\n",
+       "      <th>Info</th>\n",
+       "      <th>Background Info</th>\n",
+       "      <th>RSS</th>\n",
+       "      <th>Email</th>\n",
+       "      <th>Institution</th>\n",
+       "      <th>Dataset ID</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2018-03-25 14:00:00+00:00</th>\n",
-       "      <td>5.6</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-03-25 13:50:00+00:00</th>\n",
-       "      <td>5.6</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-03-25 13:00:00+00:00</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-03-25 12:50:00+00:00</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/tabledap/...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/tabledap/...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CeNCOOS in situ water quality monitoring using...</td>\n",
+       "      <td>The Seawater intake station is maintained by M...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/metadata/...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/metadata/...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/info/mlml...</td>\n",
+       "      <td>https://sensors.ioos.us/#metadata/48035/station</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/rss/mlml_...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/subscript...</td>\n",
+       "      <td>Moss Landing Marine Laboratory</td>\n",
+       "      <td>mlml_mlml_sea</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2018-03-25 12:00:00+00:00</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/tabledap/...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/tabledap/...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CeNCOOS in situ water quality monitoring at Mo...</td>\n",
+       "      <td>The Monterey shore station is maintained by Mo...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/metadata/...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/metadata/...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/info/mlml...</td>\n",
+       "      <td>https://sensors.ioos.us/#metadata/20362/station</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/rss/mlml_...</td>\n",
+       "      <td>http://erddap.sensors.ioos.us/erddap/subscript...</td>\n",
+       "      <td>Moss Landing Marine Laboratory</td>\n",
+       "      <td>mlml_monterey</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                           value                     station  \\\n",
-       "time (UTC)                                                     \n",
-       "2018-03-25 14:00:00+00:00    5.6  urn_ioos_station_wmo_44011   \n",
-       "2018-03-25 13:50:00+00:00    5.6  urn_ioos_station_wmo_44011   \n",
-       "2018-03-25 13:00:00+00:00    4.6  urn_ioos_station_wmo_44011   \n",
-       "2018-03-25 12:50:00+00:00    4.6  urn_ioos_station_wmo_44011   \n",
-       "2018-03-25 12:00:00+00:00    5.1  urn_ioos_station_wmo_44011   \n",
+       "   griddap  Subset                                           tabledap  \\\n",
+       "0      NaN     NaN  http://erddap.sensors.ioos.us/erddap/tabledap/...   \n",
+       "1      NaN     NaN  http://erddap.sensors.ioos.us/erddap/tabledap/...   \n",
        "\n",
-       "                           longitude (degrees_east)  latitude (degrees_north)  \\\n",
-       "time (UTC)                                                                      \n",
-       "2018-03-25 14:00:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-25 13:50:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-25 13:00:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-25 12:50:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-25 12:00:00+00:00                   -66.619                    41.098   \n",
+       "                                        Make A Graph  wms  files  \\\n",
+       "0  http://erddap.sensors.ioos.us/erddap/tabledap/...  NaN    NaN   \n",
+       "1  http://erddap.sensors.ioos.us/erddap/tabledap/...  NaN    NaN   \n",
        "\n",
-       "                                         parameter unit  depth (m)  \n",
-       "time (UTC)                                                          \n",
-       "2018-03-25 14:00:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-25 13:50:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-25 13:00:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-25 12:50:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-25 12:00:00+00:00  Significant Wave Height    m        0.0  "
+       "                                               Title  \\\n",
+       "0  CeNCOOS in situ water quality monitoring using...   \n",
+       "1  CeNCOOS in situ water quality monitoring at Mo...   \n",
+       "\n",
+       "                                             Summary  \\\n",
+       "0  The Seawater intake station is maintained by M...   \n",
+       "1  The Monterey shore station is maintained by Mo...   \n",
+       "\n",
+       "                                                FGDC  \\\n",
+       "0  http://erddap.sensors.ioos.us/erddap/metadata/...   \n",
+       "1  http://erddap.sensors.ioos.us/erddap/metadata/...   \n",
+       "\n",
+       "                                           ISO 19115  \\\n",
+       "0  http://erddap.sensors.ioos.us/erddap/metadata/...   \n",
+       "1  http://erddap.sensors.ioos.us/erddap/metadata/...   \n",
+       "\n",
+       "                                                Info  \\\n",
+       "0  http://erddap.sensors.ioos.us/erddap/info/mlml...   \n",
+       "1  http://erddap.sensors.ioos.us/erddap/info/mlml...   \n",
+       "\n",
+       "                                   Background Info  \\\n",
+       "0  https://sensors.ioos.us/#metadata/48035/station   \n",
+       "1  https://sensors.ioos.us/#metadata/20362/station   \n",
+       "\n",
+       "                                                 RSS  \\\n",
+       "0  http://erddap.sensors.ioos.us/erddap/rss/mlml_...   \n",
+       "1  http://erddap.sensors.ioos.us/erddap/rss/mlml_...   \n",
+       "\n",
+       "                                               Email  \\\n",
+       "0  http://erddap.sensors.ioos.us/erddap/subscript...   \n",
+       "1  http://erddap.sensors.ioos.us/erddap/subscript...   \n",
+       "\n",
+       "                      Institution     Dataset ID  \n",
+       "0  Moss Landing Marine Laboratory  mlml_mlml_sea  \n",
+       "1  Moss Landing Marine Laboratory  mlml_monterey  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from pandas import read_csv\n",
+    "import pandas as pd\n",
     "\n",
-    "df = read_csv(url, index_col=0, parse_dates=True)\n",
     "\n",
-    "# Prevent :station: from turning into an emoji in the webpage.\n",
-    "df[\"station\"] = df.station.str.split(\":\").str.join(\"_\")\n",
+    "url = e.get_search_url(search_for=\"dissolved oxygen moss landing\", response=\"csv\")\n",
     "\n",
-    "df.head()"
+    "print(url)\n",
+    "df = pd.read_csv(url)\n",
+    "df"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With the `DataFrame` we can easily plot the data."
+    "Interesting, we found two dataset_ids instead of the one showed in the sensor map!\n",
+    "Let's investigate what do we have in there and try to figure out what is going on."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/filipe/miniconda3/envs/IOOS/lib/python3.7/site-packages/pandas/core/sorting.py:257: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.\n",
-      "\tTo accept the future behavior, pass 'dtype=object'.\n",
-      "\tTo keep the old behavior, pass 'dtype=\"datetime64[ns]\"'.\n",
-      "  items = np.asanyarray(items)\n"
+      "mlml_mlml_sea: CeNCOOS in situ water quality monitoring using the seawater input for Moss Landing Marine Laboratory.\n",
+      "\n",
+      "The Seawater intake station is maintained by Moss Landing Marine Laboratories who share the data with CeNCOOS. Data collected includes temperature, conductivity, salinity, fluorescence, beam attenuation, transmission, dissolved oxygen, dissolved organic saturation, pH, and tide height. Seawater data observations are collected from raw seawater drawn through an intake pipe. The pipe intake opening is located at 36.8025N and 121.7915W and is ~16.6m (54.4ft) below MLLW. The seawater sensors are cleaned of biofouling agents on a weekly/twice-weekly interval, though some data drift can be observed in transmission, beam attenution, and fluorescence. These nearshore sensors are part of the Central and Norther California Ocean Observing System (CeNCOOS). They measure various water quality parameters at fixed points along the California coast.\n",
+      "\n",
+      "cdm_data_type = TimeSeries\n",
+      "VARIABLES:\n",
+      "time (seconds since 1970-01-01T00:00:00Z)\n",
+      "latitude (degrees_north)\n",
+      "longitude (degrees_east)\n",
+      "z (Altitude, m)\n",
+      "pco2 (microatm)\n",
+      "pco2_qc_agg (pCO2 QARTOD Aggregate Quality Flag)\n",
+      "pco2_qc_tests (pCO2 QARTOD Individual Tests)\n",
+      "sea_water_electrical_conductivity (Conductivity, mS.cm-1)\n",
+      "sea_water_electrical_conductivity_qc_agg (Conductivity QARTOD Aggregate Quality Flag)\n",
+      "sea_water_electrical_conductivity_qc_tests (Conductivity QARTOD Individual Tests)\n",
+      "fluorescence (microg.L-1)\n",
+      "fluorescence_qc_agg (Fluorescence QARTOD Aggregate Quality Flag)\n",
+      "fluorescence_qc_tests (Fluorescence QARTOD Individual Tests)\n",
+      "mole_concentration_of_nitrate_in_sea_water (Nitrate Concentration, micromol.L-1)\n",
+      "mole_concentration_of_nitrate_in_sea_water_qc_agg (Nitrate Concentration QARTOD Aggregate Quality Flag)\n",
+      "mole_concentration_of_nitrate_in_sea_water_qc_tests (Nitrate Concentration QARTOD Individual Tests)\n",
+      "mole_concentration_of_dissolved_molecular_oxygen_in_sea_water (Dissolved Oxygen Molecular Concentration, micromol.L-1)\n",
+      "mole_concentration_of_dissolved_molecular_oxygen_in_sea_water_qc_agg (Dissolved Oxygen Molecular Concentration QARTOD Aggregate Quality Flag)\n",
+      "mole_concentration_of_dissolved_molecular_oxygen_in_sea_water_qc_tests (Dissolved Oxygen Molecular Concentration QARTOD Individual Tests)\n",
+      "... (22 more variables)\n",
+      "\n",
+      "\n",
+      "mlml_monterey: CeNCOOS in situ water quality monitoring at Monterey Bay Commercial Wharf.\n",
+      "\n",
+      "The Monterey shore station is maintained by Moss Landing Marine Labs. The station has been operational since 2012 and consists of in water sensors that are fixed to a pier sampling every 15 minutes. These sensors provide near-real time observations of ocean water salinity, temperature, dissolved oxygen, chlorophyll fluorescence, turbidity and pH. These sensors provide near-real time observations of ocean water conductivity, salinity, temperature, depth, dissolved oxygen, chlorophyll fluorescence, turbidity and nitrate. These nearshore sensors are part of the Central and Norther California Ocean Observing System (CeNCOOS). They measure various water quality parameters at fixed points along the California coast.\n",
+      "\n",
+      "cdm_data_type = TimeSeries\n",
+      "VARIABLES:\n",
+      "time (seconds since 1970-01-01T00:00:00Z)\n",
+      "latitude (degrees_north)\n",
+      "longitude (degrees_east)\n",
+      "z (Altitude, m)\n",
+      "mass_concentration_of_chlorophyll_in_sea_water (Chlorophyll, microg.L-1)\n",
+      "mass_concentration_of_chlorophyll_in_sea_water_qc_agg (Chlorophyll QARTOD Aggregate Quality Flag)\n",
+      "mass_concentration_of_chlorophyll_in_sea_water_qc_tests (Chlorophyll QARTOD Individual Tests)\n",
+      "sea_water_electrical_conductivity (Conductivity, mS.cm-1)\n",
+      "sea_water_electrical_conductivity_qc_agg (Conductivity QARTOD Aggregate Quality Flag)\n",
+      "sea_water_electrical_conductivity_qc_tests (Conductivity QARTOD Individual Tests)\n",
+      "mass_concentration_of_oxygen_in_sea_water (Dissolved Oxygen Concentration, mg.L-1)\n",
+      "mass_concentration_of_oxygen_in_sea_water_qc_agg (Dissolved Oxygen Concentration QARTOD Aggregate Quality Flag)\n",
+      "mass_concentration_of_oxygen_in_sea_water_qc_tests (Dissolved Oxygen Concentration QARTOD Individual Tests)\n",
+      "sea_water_practical_salinity (Salinity, 1e-3)\n",
+      "sea_water_practical_salinity_qc_agg (Salinity QARTOD Aggregate Quality Flag)\n",
+      "sea_water_practical_salinity_qc_tests (Salinity QARTOD Individual Tests)\n",
+      "sea_water_pressure (decibars)\n",
+      "sea_water_pressure_qc_agg (Sea Water Pressure QARTOD Aggregate Quality Flag)\n",
+      "sea_water_pressure_qc_tests (Sea Water Pressure QARTOD Individual Tests)\n",
+      "sea_water_temperature (Water Temperature, degree_Celsius)\n",
+      "... (9 more variables)\n",
+      "\n",
+      "\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x198 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "ax = df[\"value\"].plot(figsize=(11, 2.75), title=df[\"parameter\"][0])"
+    "for k, v in df[[\"Dataset ID\", \"Title\", \"Summary\"]].T.items():\n",
+    "    dataset_id = v[\"Dataset ID\"]\n",
+    "    title = v[\"Title\"]\n",
+    "    summary = v[\"Summary\"].replace(\"\\\\n\", \"\\n\")\n",
+    "    print(f\"{dataset_id}: {title}\\n\\n{summary}\\n\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You may notice that slicing the time dimension on the sever side is very fast when compared with an OPeNDAP request. The downloading of the time dimension data, slice, and subsequent downloading of the actual data are all much faster.\n",
-    "\n",
-    "ERDDAP also allows for filtering of the variable's values. For example, let's get Wave Heights that are bigger than 6 meters starting from 2016.\n",
+    "The Sensor map returned only the dataset_id `mlml_mlml_sea` which corresponds to the CeNCOOS in situ water quality monitoring using the seawater input for Moss Landing Marine Laboratory. However, we also got the dataset_id for `mlml_monterey`, or the commercial wharf water quality monitoring.\n",
     "\n",
-    "\\*\\* Note how we can lazily build on top of the previous query using Python's dictionaries."
+    "Let's request the ERDDAP info on the latter to try to understand why it is available on the server but did not make into the Sensor Map. We usually suspect some sort of cut off due to the time coverage."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -364,143 +320,61 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>value</th>\n",
-       "      <th>station</th>\n",
-       "      <th>longitude (degrees_east)</th>\n",
-       "      <th>latitude (degrees_north)</th>\n",
-       "      <th>parameter</th>\n",
-       "      <th>unit</th>\n",
-       "      <th>depth (m)</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time (UTC)</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
+       "      <th>Row Type</th>\n",
+       "      <th>Variable Name</th>\n",
+       "      <th>Attribute Name</th>\n",
+       "      <th>Data Type</th>\n",
+       "      <th>Value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2018-03-17 06:00:00+00:00</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-03-17 05:50:00+00:00</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-03-17 05:00:00+00:00</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-03-17 04:50:00+00:00</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-03-17 02:00:00+00:00</th>\n",
-       "      <td>6.1</td>\n",
-       "      <td>urn_ioos_station_wmo_44011</td>\n",
-       "      <td>-66.619</td>\n",
-       "      <td>41.098</td>\n",
-       "      <td>Significant Wave Height</td>\n",
-       "      <td>m</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>69</th>\n",
+       "      <td>attribute</td>\n",
+       "      <td>NC_GLOBAL</td>\n",
+       "      <td>time_coverage_end</td>\n",
+       "      <td>String</td>\n",
+       "      <td>2019-11-29T15:45:00Z</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                           value                     station  \\\n",
-       "time (UTC)                                                     \n",
-       "2018-03-17 06:00:00+00:00    6.0  urn_ioos_station_wmo_44011   \n",
-       "2018-03-17 05:50:00+00:00    6.0  urn_ioos_station_wmo_44011   \n",
-       "2018-03-17 05:00:00+00:00    6.0  urn_ioos_station_wmo_44011   \n",
-       "2018-03-17 04:50:00+00:00    6.0  urn_ioos_station_wmo_44011   \n",
-       "2018-03-17 02:00:00+00:00    6.1  urn_ioos_station_wmo_44011   \n",
-       "\n",
-       "                           longitude (degrees_east)  latitude (degrees_north)  \\\n",
-       "time (UTC)                                                                      \n",
-       "2018-03-17 06:00:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-17 05:50:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-17 05:00:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-17 04:50:00+00:00                   -66.619                    41.098   \n",
-       "2018-03-17 02:00:00+00:00                   -66.619                    41.098   \n",
-       "\n",
-       "                                         parameter unit  depth (m)  \n",
-       "time (UTC)                                                          \n",
-       "2018-03-17 06:00:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-17 05:50:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-17 05:00:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-17 04:50:00+00:00  Significant Wave Height    m        0.0  \n",
-       "2018-03-17 02:00:00+00:00  Significant Wave Height    m        0.0  "
+       "     Row Type Variable Name     Attribute Name Data Type                 Value\n",
+       "69  attribute     NC_GLOBAL  time_coverage_end    String  2019-11-29T15:45:00Z"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "params.update(\n",
-    "    {\"value>\": 6, \"time>\": \"2016-01-00T00:00:00Z\",}\n",
-    ")\n",
-    "\n",
-    "url = encode_erddap(urlbase, fname, columns, params)\n",
-    "\n",
-    "df = read_csv(url, index_col=0, parse_dates=True)\n",
-    "\n",
-    "# Prevent :station: from turning into an emoji in the webpage.\n",
-    "df[\"station\"] = df.station.str.split(\":\").str.join(\"_\")\n",
+    "df = pd.read_csv(e.get_info_url(dataset_id=\"mlml_monterey\", response=\"csv\"))\n",
     "\n",
-    "df.head()"
+    "df.loc[df[\"Attribute Name\"] == \"time_coverage_end\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And now we can visualize the frequency of `Significant Wave Height` greater than 6 meters by month."
+    "Bingo! Looks like station is no longer collecting data and that is probably the reason why the Sensor Map is not showing it.\n",
+    "\n",
+    "For the sake of simplicity let's use only the `mlml_mlml_sea` and download the last month of data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1224x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -510,128 +384,24 @@
     }
    ],
    "source": [
-    "def key(x):\n",
-    "    return x.month\n",
-    "\n",
-    "\n",
-    "grouped = df[\"value\"].groupby(key)\n",
-    "\n",
-    "ax = grouped.count().plot.bar()\n",
-    "ax.set_ylabel(\"Significant Wave Height events > 6 meters\")\n",
-    "m = ax.set_xticklabels(\n",
-    "    [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Ago\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wow! Wintertime is pretty rough out on George's Bank!\n",
-    "\n",
-    "There is also a built-in relative time functionality so you can specify a specific time frame you look at. Here we demonstrate this part of the tool by getting the last 2 hours and displaying that with the `HTML` response in an `IFrame`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/filipe/miniconda3/envs/IOOS/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning: Consider using IPython.display.IFrame instead\n",
-      "  warnings.warn(\"Consider using IPython.display.IFrame instead\")\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<iframe src=\"https://erddap.axiomdatascience.com/erddap/tabledap/sensor_service.htmlTable?time,value,station,longitude,latitude,parameter,unit,depth&time%3E=now-2hours&time%3C=now&station=%22urn%3Aioos%3Astation%3Anerrs%3Awqbchmet%22&parameter=%22Wind+Speed%22&unit=%22m.s-1%22\" width=\"650\" height=\"370\"></iframe>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML\n",
-    "\n",
-    "fname = \"sensor_service.htmlTable\"\n",
-    "\n",
-    "params = {\n",
-    "    \"time>\": \"now-2hours\",\n",
-    "    \"time<\": \"now\",\n",
-    "    \"station\": '\"urn:ioos:station:nerrs:wqbchmet\"',\n",
-    "    \"parameter\": '\"Wind Speed\"',\n",
-    "    \"unit\": '\"m.s-1\"',\n",
-    "}\n",
-    "\n",
-    "url = encode_erddap(urlbase, fname, columns, params)\n",
-    "\n",
-    "iframe = '<iframe src=\"{src}\" width=\"650\" height=\"370\"></iframe>'.format\n",
-    "HTML(iframe(src=url))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`ERDDAP` responses are very rich. There are even multiple image formats in the automate graph responses.\n",
-    "Here is how to get a `.png` file for the temperature time-series. While you can specify the width and height, we chose just an arbitrary size."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe src=\"https://erddap.axiomdatascience.com/erddap/tabledap/sensor_service.png?time,value,station,longitude,latitude,parameter,unit,depth&time%3E=now-7days&station=%22urn%3Aioos%3Astation%3Anerrs%3Awqbchmet%22&parameter=%22Wind+Speed%22&unit=%22m.s-1%22&.size=450%7C500\" width=\"455\" height=\"505\"></iframe>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fname = \"sensor_service.png\"\n",
-    "\n",
-    "params = {\n",
-    "    \"time>\": \"now-7days\",\n",
-    "    \"station\": '\"urn:ioos:station:nerrs:wqbchmet\"',\n",
-    "    \"parameter\": '\"Wind Speed\"',\n",
-    "    \"unit\": '\"m.s-1\"',\n",
-    "}\n",
-    "\n",
-    "\n",
-    "width, height = 450, 500\n",
-    "params.update({\".size\": \"{}|{}\".format(width, height)})\n",
-    "\n",
-    "url = encode_erddap(urlbase, fname, columns, params)\n",
-    "\n",
-    "iframe = '<iframe src=\"{src}\" width=\"{width}\" height=\"{height}\"></iframe>'.format\n",
-    "HTML(iframe(src=url, width=width + 5, height=height + 5))"
+    "e.dataset_id = \"mlml_mlml_sea\"\n",
+    "e.variables = [\"time\", \"mole_concentration_of_dissolved_molecular_oxygen_in_sea_water\"]\n",
+    "e.constraints = {\"time>=\": \"now-30days\"}\n",
+    "    \n",
+    "df = e.to_pandas(index_col=\"time (UTC)\")\n",
+    "ax = df.plot(figsize=(17, 5));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This example tells us it is rough and cold out on George's Bank!\n",
+    "We hope this example demonstrate the flexibility of direct requests to the ERDDAP server used in the Sensor Map.\n",
+    "In this notebook we:\n",
     "\n",
-    "To explore more datasets, use the IOOS sensor map [website](https://sensors.ioos.us/#map)!"
+    "- Search the server with keywords.\n",
+    "- Found dataset_ids and checked their metadata.\n",
+    "- Identified the dataset_id of interested and request data at a specific time period."
    ]
   }
  ],
@@ -651,7 +421,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

From daaeda8cea10e999c2642389a83444707188689d Mon Sep 17 00:00:00 2001
From: Filipe Fernandes <ocefpaf@gmail.com>
Date: Tue, 12 Jul 2022 12:27:14 -0300
Subject: [PATCH 2/2] review actions

---
 .../2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb                  | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb b/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb
index 10e6456f..81f516e9 100644
--- a/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb
+++ b/jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb
@@ -205,7 +205,7 @@
    "metadata": {},
    "source": [
     "Interesting, we found two dataset_ids instead of the one showed in the sensor map!\n",
-    "Let's investigate what do we have in there and try to figure out what is going on."
+    "Let's investigate the datasets we found and try to figure out why there is a discrepancy."
    ]
   },
   {
@@ -360,7 +360,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bingo! Looks like station is no longer collecting data and that is probably the reason why the Sensor Map is not showing it.\n",
+    "Bingo! Looks like station is no longer collecting data.\n",
+    "The sensor map only shows real-time observations (past 4-hours).\n",
     "\n",
     "For the sake of simplicity let's use only the `mlml_mlml_sea` and download the last month of data."
    ]