Western-Indian-Ocean-Workshop-ioos_qc-demo.ipynb

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    "<h1 style=\"text-align: center;\">\n",
    "    IOOS QARTOD software (ioos_qc)<br>\n",
    "</h1>\n",
    "\n",
    "---\n",
    "![image.png](https://cdn.ioos.noaa.gov/media/2019/05/qartodlogo2.jpg)\n",
    "\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "    Mathew Biddle<br>\n",
    "    mathew.biddle@noaa.gov<br>\n",
    "    <a href=\"https://orcid.org/0000-0003-4897-1669\">https://orcid.org/0000-0003-4897-1669</a><br>\n",
    "    NOAA/NOS/IOOS<br>\n",
    "    <br>\n",
    "    <br>\n",
    "    <sub>Materials available at <a href=\"https://github.com/MathewBiddle/WIO_workshop\">https://github.com/MathewBiddle/WIO_workshop</a>.\n",
    "    </sub><br>\n",
    "</p>"
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    "This presentation will demonstrate how to [run ``ioos_qc``](https://github.com/ioos/ioos_qc) on a time-series dataset. ``ioos_qc`` implements the [Quality Assurance / Quality Control of Real Time Oceanographic Data (QARTOD)](https://ioos.noaa.gov/project/qartod/)."
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   "source": [
    "### Key Objectives of QARTOD"
   ]
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   "source": [
    "* Establish authoritative QA/QC procedures for the [U.S. IOOS core variables](http://www.iooc.us/task-teams/core-ioos-variables/), as necessary, including detailed information about the sensors and procedures used to measure the variables."
   ]
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   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
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   "source": [
    "* Produce written manuals for these QA/QC procedures"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "source": [
    "* From the list of individual QA/QC procedures and guidelines developed, define a baseline set of QA/QC procedures that can be used for certification of RCOOS data providers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
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   "source": [
    "* Facilitate QA/QC integration with Global Ocean Observing System (GOOS) and other international ocean observation efforts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "source": [
    "* Engage the Federal Agencies and IOOS Regions that are part of, or contribute to, US IOOS who will use the established QA/QC procedure"
   ]
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   "source": [
    "* Work efficiently, without duplication of effort, to facilitate the implementation of common QA/QC procedures amongst US IOOS Partners."
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    "## Let's go get these data\n",
    "\n",
    "We will be using the water level data from a [fixed station in Kotzebue, AK](https://www.google.com/maps?q=66.895035,-162.566752).\n",
    "\n",
    "<img align=\"center\" src=\"http://erddap.aoos.org/erddap/tabledap/kotzebue-alaska-water-level.png?longitude,latitude,z&time%3E=2021-06-23T00%3A00%3A00Z&time%3C=2021-06-30T00%3A00%3A00Z&longitude%3E=-180&longitude%3C=-116&latitude%3E=26&latitude%3C=90&.draw=markers&.marker=5%7C5&.color=0x000000&.colorBar=%7C%7C%7C%7C%7C&.bgColor=0xffccccff\">\n",
    "\n",
    "We will get the data from the [AOOS ERDDAP server](http://erddap.aoos.org/erddap/). "
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   "execution_count": 1,
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   "source": [
    "from erddapy import ERDDAP\n",
    "\n",
    "e = ERDDAP(\n",
    "    server=\"http://erddap.aoos.org/erddap/\",\n",
    "    protocol=\"tabledap\"\n",
    ")\n",
    "\n",
    "e.dataset_id = \"kotzebue-alaska-water-level\"\n",
    "\n",
    "e.constraints = {\n",
    "    \"time>=\": \"2018-09-05T21:00:00Z\",\n",
    "    \"time<=\": \"2019-07-10T19:00:00Z\",\n",
    "}"
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   "source": [
    "## Return data and metadata"
   ]
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     "slide_type": "skip"
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   "source": [
    "import json\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "def nice_print(indict):\n",
    "    return print(json.dumps(indict, indent=2, default=str))"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                                 (timeseries: 1,\n",
       "                                                             obs: 7241)\n",
       "Coordinates:\n",
       "    latitude                                                (timeseries) float64 ...\n",
       "    longitude                                               (timeseries) float64 ...\n",
       "    time                                                    (obs) datetime64[ns] ...\n",
       "Dimensions without coordinates: timeseries, obs\n",
       "Data variables:\n",
       "    station                                                 (timeseries) object ...\n",
       "    rowSize                                                 (timeseries) int32 ...\n",
       "    z                                                       (obs) float64 0.0...\n",
       "    sea_surface_height_above_sea_level_geoid_mhhw           (obs) float64 0.4...\n",
       "    sea_surface_height_above_sea_level_geoid_mhhw_qc_agg    (obs) float64 1.0...\n",
       "    sea_surface_height_above_sea_level_geoid_mhhw_qc_tests  (obs) float64 2.1...\n",
       "Attributes: (12/54)\n",
       "    cdm_data_type:                 TimeSeries\n",
       "    cdm_timeseries_variables:      station,longitude,latitude\n",
       "    contributor_email:             sales@stilltek.com,dugan@aoos.org,,feedbac...\n",
       "    contributor_name:              Stillwater Technologies LLC,Alaska Ocean O...\n",
       "    contributor_role:              collaborator,sponsor,contributor,processor\n",
       "    contributor_role_vocabulary:   NERC\n",
       "    ...                            ...\n",
       "    station_id:                    100053\n",
       "    summary:                       Timeseries data from &#x27;Kotzebue, Alaska, Wa...\n",
       "    time_coverage_end:             2019-07-10T19:00:00Z\n",
       "    time_coverage_start:           2018-09-05T21:00:00Z\n",
       "    title:                         Kotzebue, Alaska, Water Level\n",
       "    Westernmost_Easting:           -162.566752</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0988b5ce-5aea-4864-9b1d-735e3d2f5d4c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0988b5ce-5aea-4864-9b1d-735e3d2f5d4c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>timeseries</span>: 1</li><li><span>obs</span>: 7241</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f7df1731-7d0c-4ebf-b290-4eb1f5e21b4f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7df1731-7d0c-4ebf-b290-4eb1f5e21b4f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(timeseries)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e4d74b5-aae7-46cf-9c08-bae31a213555' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e4d74b5-aae7-46cf-9c08-bae31a213555' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea0144e1-7d1f-493c-9688-c2bd63277013' class='xr-var-data-in' type='checkbox'><label for='data-ea0144e1-7d1f-493c-9688-c2bd63277013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>actual_range :</span></dt><dd>[66.895035 66.895035]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([66.895035])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(timeseries)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3f8e9d71-a2c6-4fba-9142-cb5eda1d850c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f8e9d71-a2c6-4fba-9142-cb5eda1d850c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-921a248d-dc9a-4f0d-8a96-d3b75c6815bd' class='xr-var-data-in' type='checkbox'><label for='data-921a248d-dc9a-4f0d-8a96-d3b75c6815bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>actual_range :</span></dt><dd>[-162.566752 -162.566752]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-162.566752])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1436643d-e0a5-431e-a367-270cbecbf6dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1436643d-e0a5-431e-a367-270cbecbf6dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f56d34e-0d3e-4cea-98a8-89cb258f1d3a' class='xr-var-data-in' type='checkbox'><label for='data-9f56d34e-0d3e-4cea-98a8-89cb258f1d3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>512</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>actual_range :</span></dt><dd>[1.5361812e+09 1.5627852e+09]</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>ioos_category :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1970 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-09-05T21:00:00.000000000&#x27;, &#x27;2018-09-05T22:00:00.000000000&#x27;,\n",
       "       &#x27;2018-09-05T23:00:00.000000000&#x27;, ..., &#x27;2019-07-10T17:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-10T18:00:00.000000000&#x27;, &#x27;2019-07-10T19:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a84c5908-77a3-4f07-97f4-2d1aebd6c09c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a84c5908-77a3-4f07-97f4-2d1aebd6c09c' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>station</span></div><div class='xr-var-dims'>(timeseries)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5dea0ce-bf68-4fdc-83fa-b6f354909fc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5dea0ce-bf68-4fdc-83fa-b6f354909fc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc169a42-0c96-414b-8d5c-30b73669beab' class='xr-var-data-in' type='checkbox'><label for='data-cc169a42-0c96-414b-8d5c-30b73669beab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>ioos_category :</span></dt><dd>Identifier</dd><dt><span>ioos_code :</span></dt><dd>urn:ioos:station:com.axiomdatascience:100053</dd><dt><span>long_name :</span></dt><dd>Kotzebue, Alaska, Water Level</dd><dt><span>short_name :</span></dt><dd>kotzebue-alaska-water-level</dd><dt><span>type :</span></dt><dd>fixed</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rowSize</span></div><div class='xr-var-dims'>(timeseries)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a1b9bf3-cdfa-4d74-ab82-0bda5644f9ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a1b9bf3-cdfa-4d74-ab82-0bda5644f9ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb4ad7a4-4fb6-4fa8-9751-524d9f526f78' class='xr-var-data-in' type='checkbox'><label for='data-bb4ad7a4-4fb6-4fa8-9751-524d9f526f78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>ioos_category :</span></dt><dd>Identifier</dd><dt><span>long_name :</span></dt><dd>Number of Observations for this TimeSeries</dd><dt><span>sample_dimension :</span></dt><dd>obs</dd></dl></div><div class='xr-var-data'><pre>array([7241])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2f010f82-455a-4f39-ba84-643ba91ef697' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f010f82-455a-4f39-ba84-643ba91ef697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4037801d-0ad3-44f1-82fd-2a99a3fe091e' class='xr-var-data-in' type='checkbox'><label for='data-4037801d-0ad3-44f1-82fd-2a99a3fe091e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>495</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>actual_range :</span></dt><dd>[0. 0.]</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>Altitude</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>altitude</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_height_above_sea_level_geoid_mhhw</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1f107ba-4822-4ac5-bd2f-f5fe03438955' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1f107ba-4822-4ac5-bd2f-f5fe03438955' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0b90609-16d3-4ced-98ff-b3bc3ff190b9' class='xr-var-data-in' type='checkbox'><label for='data-e0b90609-16d3-4ced-98ff-b3bc3ff190b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>512</dd><dt><span>actual_range :</span></dt><dd>[-2.84988   4.163568]</dd><dt><span>ancillary_variables :</span></dt><dd>sea_surface_height_above_sea_level_geoid_mhhw_qc_agg sea_surface_height_above_sea_level_geoid_mhhw_qc_tests</dd><dt><span>id :</span></dt><dd>1000206</dd><dt><span>ioos_category :</span></dt><dd>Other</dd><dt><span>long_name :</span></dt><dd>Water Level</dd><dt><span>platform :</span></dt><dd>station</dd><dt><span>short_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>standard_name_url :</span></dt><dd>http://mmisw.org/ont/cf/parameter/sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>vertical_datum :</span></dt><dd>MHHW</dd></dl></div><div class='xr-var-data'><pre>array([0.4785  , 0.442   , 0.4968  , ..., 0.09144 , 0.082296, 0.054864])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_height_above_sea_level_geoid_mhhw_qc_agg</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad623d02-f206-4712-905b-329e37757575' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad623d02-f206-4712-905b-329e37757575' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d708192-27df-41ff-9ca8-3776762d3634' class='xr-var-data-in' type='checkbox'><label for='data-0d708192-27df-41ff-9ca8-3776762d3634' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>4096</dd><dt><span>actual_range :</span></dt><dd>[1 4]</dd><dt><span>flag_meanings :</span></dt><dd>PASS NOT_EVALUATED SUSPECT FAIL MISSING</dd><dt><span>flag_values :</span></dt><dd>[1 2 3 4 9]</dd><dt><span>ioos_category :</span></dt><dd>Other</dd><dt><span>long_name :</span></dt><dd>Water Level QARTOD Aggregate Quality Flag</dd><dt><span>references :</span></dt><dd>https://github.com/ioos/ioos_qc</dd><dt><span>short_name :</span></dt><dd>sea_surface_height_above_sea_level_qc_agg</dd><dt><span>standard_name :</span></dt><dd>aggregate_quality_flag</dd></dl></div><div class='xr-var-data'><pre>array([1., 1., 1., ..., 1., 1., 1.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_height_above_sea_level_geoid_mhhw_qc_tests</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71f6df84-bc3b-418a-92e8-e1ef1a7f591b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71f6df84-bc3b-418a-92e8-e1ef1a7f591b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3947a24c-b49a-4cc5-8561-7047ebc55a4a' class='xr-var-data-in' type='checkbox'><label for='data-3947a24c-b49a-4cc5-8561-7047ebc55a4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>512</dd><dt><span>comment :</span></dt><dd>11-character string with results of individual QARTOD tests. 1: Gap Test, 2: Syntax Test, 3: Location Test, 4: Gross Range Test, 5: Climatology Test, 6: Spike Test, 7: Rate of Change Test, 8: Flat-line Test, 9: Multi-variate Test, 10: Attenuated Signal Test, 11: Neighbor Test</dd><dt><span>flag_meanings :</span></dt><dd>PASS NOT_EVALUATED SUSPECT FAIL MISSING</dd><dt><span>flag_values :</span></dt><dd>[1 2 3 4 9]</dd><dt><span>ioos_category :</span></dt><dd>Other</dd><dt><span>long_name :</span></dt><dd>Water Level QARTOD Individual Tests</dd><dt><span>references :</span></dt><dd>https://github.com/ioos/ioos_qc</dd><dt><span>short_name :</span></dt><dd>sea_surface_height_above_sea_level_qc_tests</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level quality_flag</dd></dl></div><div class='xr-var-data'><pre>array([2.147484e+09, 2.147484e+09, 2.147484e+09, ..., 2.147484e+09,\n",
       "       2.147484e+09, 2.147484e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6ea4a96-b3cb-412e-a9a9-24d6358afa76' class='xr-section-summary-in' type='checkbox'  ><label for='section-b6ea4a96-b3cb-412e-a9a9-24d6358afa76' class='xr-section-summary' >Attributes: <span>(54)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cdm_data_type :</span></dt><dd>TimeSeries</dd><dt><span>cdm_timeseries_variables :</span></dt><dd>station,longitude,latitude</dd><dt><span>contributor_email :</span></dt><dd>sales@stilltek.com,dugan@aoos.org,,feedback@axiomdatascience.com</dd><dt><span>contributor_name :</span></dt><dd>Stillwater Technologies LLC,Alaska Ocean Observing System (AOOS),NOAA Alaska-Pacific River Forecast Center (APRFC),Axiom Data Science</dd><dt><span>contributor_role :</span></dt><dd>collaborator,sponsor,contributor,processor</dd><dt><span>contributor_role_vocabulary :</span></dt><dd>NERC</dd><dt><span>contributor_url :</span></dt><dd>https://stilltek.com,http://www.aoos.org/,https://www.weather.gov/aprfc/,https://www.axiomdatascience.com</dd><dt><span>Conventions :</span></dt><dd>IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.0</dd><dt><span>creator_country :</span></dt><dd>USA</dd><dt><span>creator_email :</span></dt><dd>dggspubs@alaska.gov</dd><dt><span>creator_institution :</span></dt><dd>Alaska Division of Geological &amp; Geophysical Surveys (AK-DGGS)</dd><dt><span>creator_name :</span></dt><dd>Alaska Division of Geological &amp; Geophysical Surveys (AK-DGGS)</dd><dt><span>creator_sector :</span></dt><dd>gov_state</dd><dt><span>creator_type :</span></dt><dd>institution</dd><dt><span>creator_url :</span></dt><dd>http://dggs.alaska.gov/</dd><dt><span>defaultDataQuery :</span></dt><dd>sea_surface_height_above_sea_level_geoid_mhhw,z,time,sea_surface_height_above_sea_level_geoid_mhhw_qc_agg&amp;time&gt;=max(time)-3days</dd><dt><span>Easternmost_Easting :</span></dt><dd>-162.566752</dd><dt><span>featureType :</span></dt><dd>TimeSeries</dd><dt><span>geospatial_lat_max :</span></dt><dd>66.895035</dd><dt><span>geospatial_lat_min :</span></dt><dd>66.895035</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-162.566752</dd><dt><span>geospatial_lon_min :</span></dt><dd>-162.566752</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_units :</span></dt><dd>m</dd><dt><span>history :</span></dt><dd>Downloaded from Stillwater Technologies LLC at https://stilltek.com/cgi-bin/qrySBD.php?site=ktzb\n",
       "2022-06-06T13:16:01Z https://stilltek.com/cgi-bin/qrySBD.php?site=ktzb\n",
       "2022-06-06T13:16:01Z http://erddap.aoos.org/erddap/tabledap/kotzebue-alaska-water-level.ncCF?&amp;time%3E=1536181200.0&amp;time%3C=1562785200.0</dd><dt><span>id :</span></dt><dd>100053</dd><dt><span>infoUrl :</span></dt><dd>https://portal.aoos.org/#metadata/100053/station</dd><dt><span>institution :</span></dt><dd>Alaska Division of Geological &amp; Geophysical Surveys (AK-DGGS)</dd><dt><span>license :</span></dt><dd>The data may be used and redistributed for free but is not intended\n",
       "for legal use, since it may contain inaccuracies. Neither the data\n",
       "Contributor, ERD, NOAA, nor the United States Government, nor any\n",
       "of their employees or contractors, makes any warranty, express or\n",
       "implied, including warranties of merchantability and fitness for a\n",
       "particular purpose, or assumes any legal liability for the accuracy,\n",
       "completeness, or usefulness, of this information.</dd><dt><span>naming_authority :</span></dt><dd>com.axiomdatascience</dd><dt><span>Northernmost_Northing :</span></dt><dd>66.895035</dd><dt><span>platform :</span></dt><dd>fixed</dd><dt><span>platform_name :</span></dt><dd>Kotzebue, Alaska, Water Level</dd><dt><span>platform_vocabulary :</span></dt><dd>http://mmisw.org/ont/ioos/platform</dd><dt><span>processing_level :</span></dt><dd>Level 2</dd><dt><span>publisher_country :</span></dt><dd>USA</dd><dt><span>publisher_email :</span></dt><dd>sales@stilltek.com</dd><dt><span>publisher_institution :</span></dt><dd>Stillwater Technologies LLC</dd><dt><span>publisher_name :</span></dt><dd>Stillwater Technologies LLC</dd><dt><span>publisher_sector :</span></dt><dd>industry</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>https://stilltek.com</dd><dt><span>references :</span></dt><dd>http://dggs.alaska.gov/pubs/staff/jroverbeck,https://stilltek.com/cgi-bin/qrySBD.php?site=ktzb,https://stilltek.com/cgi-bin/qrySBD.php?site=ktzb,https://water.weather.gov/ahps2/hydrograph.php?wfo=pafg2&amp;gage=kzta2,https://github.com/ioos/ioos_qc</dd><dt><span>sourceUrl :</span></dt><dd>https://stilltek.com/cgi-bin/qrySBD.php?site=ktzb</dd><dt><span>Southernmost_Northing :</span></dt><dd>66.895035</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v72</dd><dt><span>station_id :</span></dt><dd>100053</dd><dt><span>summary :</span></dt><dd>Timeseries data from &#x27;Kotzebue, Alaska, Water Level&#x27; (kotzebue-alaska-water-level)</dd><dt><span>time_coverage_end :</span></dt><dd>2019-07-10T19:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2018-09-05T21:00:00Z</dd><dt><span>title :</span></dt><dd>Kotzebue, Alaska, Water Level</dd><dt><span>Westernmost_Easting :</span></dt><dd>-162.566752</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                                 (timeseries: 1,\n",
       "                                                             obs: 7241)\n",
       "Coordinates:\n",
       "    latitude                                                (timeseries) float64 ...\n",
       "    longitude                                               (timeseries) float64 ...\n",
       "    time                                                    (obs) datetime64[ns] ...\n",
       "Dimensions without coordinates: timeseries, obs\n",
       "Data variables:\n",
       "    station                                                 (timeseries) object ...\n",
       "    rowSize                                                 (timeseries) int32 ...\n",
       "    z                                                       (obs) float64 ...\n",
       "    sea_surface_height_above_sea_level_geoid_mhhw           (obs) float64 ...\n",
       "    sea_surface_height_above_sea_level_geoid_mhhw_qc_agg    (obs) float64 ...\n",
       "    sea_surface_height_above_sea_level_geoid_mhhw_qc_tests  (obs) float64 ...\n",
       "Attributes: (12/54)\n",
       "    cdm_data_type:                 TimeSeries\n",
       "    cdm_timeseries_variables:      station,longitude,latitude\n",
       "    contributor_email:             sales@stilltek.com,dugan@aoos.org,,feedbac...\n",
       "    contributor_name:              Stillwater Technologies LLC,Alaska Ocean O...\n",
       "    contributor_role:              collaborator,sponsor,contributor,processor\n",
       "    contributor_role_vocabulary:   NERC\n",
       "    ...                            ...\n",
       "    station_id:                    100053\n",
       "    summary:                       Timeseries data from 'Kotzebue, Alaska, Wa...\n",
       "    time_coverage_end:             2019-07-10T19:00:00Z\n",
       "    time_coverage_start:           2018-09-05T21:00:00Z\n",
       "    title:                         Kotzebue, Alaska, Water Level\n",
       "    Westernmost_Easting:           -162.566752"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cf_xarray\n",
    "\n",
    "data = e.to_xarray()\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes:   X: ['longitude']\n",
       "             Y: ['latitude']\n",
       "             T: ['time']\n",
       "             Z: n/a\n",
       "\n",
       "- CF Coordinates:   longitude: ['longitude']\n",
       "                    latitude: ['latitude']\n",
       "                    time: ['time']\n",
       "                    vertical: n/a\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   latitude: ['latitude']\n",
       "                    longitude: ['longitude']\n",
       "                    time: ['time']\n",
       "\n",
       "- Bounds:   n/a\n",
       "\n",
       "Data Variables:\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   aggregate_quality_flag: ['sea_surface_height_above_sea_level_geoid_mhhw_qc_agg']\n",
       "                    altitude: ['z']\n",
       "                    sea_surface_height_above_sea_level: ['sea_surface_height_above_sea_level_geoid_mhhw']\n",
       "                    sea_surface_height_above_sea_level quality_flag: ['sea_surface_height_above_sea_level_geoid_mhhw_qc_tests']\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X': ['longitude'], 'Y': ['latitude'], 'T': ['time']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cf.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'longitude': ['longitude'], 'latitude': ['latitude'], 'time': ['time']}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cf.coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latitude': ['latitude'],\n",
       " 'longitude': ['longitude'],\n",
       " 'time': ['time'],\n",
       " 'altitude': ['z'],\n",
       " 'sea_surface_height_above_sea_level': ['sea_surface_height_above_sea_level_geoid_mhhw'],\n",
       " 'aggregate_quality_flag': ['sea_surface_height_above_sea_level_geoid_mhhw_qc_agg'],\n",
       " 'sea_surface_height_above_sea_level quality_flag': ['sea_surface_height_above_sea_level_geoid_mhhw_qc_tests']}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cf.standard_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's plot the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 3.75))\n",
    "\n",
    "data.cf.plot.scatter('time','sea_surface_height_above_sea_level', ax=ax, s=5)\n",
    "\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Build the QC configuration\n",
    "\n",
    "Below we create a simple Quality Assurance/Quality Control (QA/QC) configuration that will be used as input for ``ioos_qc``. All the interval values are in the same units as the data.\n",
    "\n",
    "For more information on the tests and recommended values for QA/QC check the documentation of each test and its inputs: \n",
    "https://ioos.github.io/ioos_qc/api/ioos_qc.html#module-ioos_qc.qartod\n",
    "\n",
    "[Manual for Real-Time Quality Control of Water Level Data](https://cdn.ioos.noaa.gov/media/2021/04/QARTOD_WaterLevelManual_Update_V2.1_Final.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "qc_config = {\n",
    "    \"qartod\": {\n",
    "        \n",
    "      \"gross_range_test\": {\n",
    "            \"suspect_span\": [ -2,  3],\n",
    "            \"fail_span\":    [-10, 10]\n",
    "      },\n",
    "        \n",
    "      \"flat_line_test\": {\n",
    "            \"tolerance\":         0.001,\n",
    "            \"suspect_threshold\": 10800,\n",
    "            \"fail_threshold\":    21600\n",
    "      },\n",
    "        \n",
    "      \"spike_test\": {\n",
    "            \"suspect_threshold\": 0.8,\n",
    "            \"fail_threshold\":      3,\n",
    "      }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"qartod\": {\n",
      "    \"gross_range_test\": {\n",
      "      \"suspect_span\": [\n",
      "        -2,\n",
      "        3\n",
      "      ],\n",
      "      \"fail_span\": [\n",
      "        -10,\n",
      "        10\n",
      "      ]\n",
      "    },\n",
      "    \"flat_line_test\": {\n",
      "      \"tolerance\": 0.001,\n",
      "      \"suspect_threshold\": 10800,\n",
      "      \"fail_threshold\": 21600\n",
      "    },\n",
      "    \"spike_test\": {\n",
      "      \"suspect_threshold\": 0.8,\n",
      "      \"fail_threshold\": 3\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "nice_print(qc_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "For `flat_line_test`:\n",
    "\n",
    "* 10800 seconds = 3 hours\n",
    "* 21600 seconds = 6 hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Run the QC tests with the supplied configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"gross_range_test\": \"[1 1 1 ... 1 1 1]\",\n",
      "  \"flat_line_test\": \"[1 1 1 ... 1 1 1]\",\n",
      "  \"spike_test\": \"[2 1 1 ... 1 1 2]\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from ioos_qc.config import QcConfig\n",
    "\n",
    "qc = QcConfig(qc_config)\n",
    "\n",
    "variable_name = data.cf.standard_names[\"sea_surface_height_above_sea_level\"][0]\n",
    "\n",
    "qc_results =  qc.run(\n",
    "    inp=data[variable_name],\n",
    "    tinp=data.cf[\"time\"],\n",
    ")\n",
    "\n",
    "nice_print(qc_results['qartod'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The results are returned in a dictionary format, similar to the input configuration, with a mask for each test. The results range from 1 to 4 meaning:\n",
    "\n",
    "**flag** | **meaning** \n",
    "---------:|:------------\n",
    " 1 | data passed the QA/QC \n",
    " 2 | did not run on this data point \n",
    " 3 | flag as suspect \n",
    " 4 | flag as failed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def plot_results(data, variable_name, results, title, test_name):\n",
    "    time = data.cf[\"time\"]\n",
    "    obs = data[variable_name]\n",
    "    qc_test = results[\"qartod\"][test_name]\n",
    "\n",
    "    qc_pass = np.ma.masked_where(qc_test != 1, obs)\n",
    "    qc_notrun = np.ma.masked_where(qc_test != 2, obs)\n",
    "    qc_suspect = np.ma.masked_where(qc_test != 3, obs)\n",
    "    qc_fail = np.ma.masked_where(qc_test != 4, obs)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(15, 3.75))\n",
    "    fig.set_title = f\"{test_name}: {title}\"\n",
    "    \n",
    "    ax.set_xlabel(f\"{time.long_name}\")\n",
    "    ax.set_ylabel(f\"{data[variable_name].long_name} [{data[variable_name].units}]\")\n",
    "\n",
    "    kw = {\"marker\": \"o\", \"linestyle\": \"none\"}\n",
    "    ax.plot(time, obs,  label=\"obs\", color=\"#A6CEE3\")\n",
    "    ax.plot(time, qc_notrun, markersize=2, label=\"qc not run\", color=\"gray\", alpha=0.2, **kw)\n",
    "    ax.plot(time, qc_pass, markersize=4, label=\"qc pass\", color=\"green\", alpha=0.5, **kw)\n",
    "    ax.plot(time, qc_suspect, markersize=4, label=\"qc suspect\", color=\"orange\", alpha=0.7, **kw)\n",
    "    ax.plot(time, qc_fail, markersize=6, label=\"qc fail\", color=\"red\", alpha=1.0, **kw)\n",
    "    ax.legend(loc='best',bbox_to_anchor=(1.12,.75))\n",
    "    ax.grid(True)\n",
    "\n",
    "title = \"Water Level [MHHW] [m] : Kotzebue, AK\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's look at the `gross_range` test results\n",
    "The gross range test test should fail data outside the $\\pm$ 10 range and suspect data below -2, and greater than 3. As one can easily see all the major spikes are flagged as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(\n",
    "    data,\n",
    "    variable_name,\n",
    "    qc_results,\n",
    "    title,\n",
    "    \"gross_range_test\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'suspect_span': [-2, 3], 'fail_span': [-10, 10]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc_config['qartod']['gross_range_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's look at the `spike` test results\n",
    "\n",
    "An actual spike test, based on a data increase threshold, flags similar spikes to the gross range test but also indetifies other suspect unusual increases in the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(\n",
    "    data,\n",
    "    variable_name,\n",
    "    qc_results,\n",
    "    title,\n",
    "    \"spike_test\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'suspect_threshold': 0.8, 'fail_threshold': 3}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc_config['qartod']['spike_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's look at the `flat_line` test results\n",
    "\n",
    "The flat line test identifies issues with the data where values are \"stuck.\"\n",
    "\n",
    "`ioos_qc` succefully identified a huge portion of the data where that happens and flagged a smaller one as suspect. (Zoom in the red point to the left to see this one.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(\n",
    "    data,\n",
    "    variable_name,\n",
    "    qc_results,\n",
    "    title,\n",
    "    \"flat_line_test\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tolerance': 0.001, 'suspect_threshold': 10800, 'fail_threshold': 21600}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc_config['qartod']['flat_line_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What tests are currently available in `ioos_qc` for QARTOD?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attenuated_signal_test\n",
      "climatology_test\n",
      "density_inversion_test\n",
      "flat_line_test\n",
      "gross_range_test\n",
      "location_test\n",
      "rate_of_change_test\n",
      "spike_test\n"
     ]
    }
   ],
   "source": [
    "import ioos_qc\n",
    "\n",
    "for func in dir(ioos_qc.qartod):\n",
    "    if \"test\" in func: \n",
    "        print(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Where can you find more information?\n",
    "\n",
    "* IOOS CodeLab example: <https://ioos.github.io/ioos_code_lab/content/code_gallery/data_analysis_and_visualization_notebooks/2020-02-14-QARTOD_ioos_qc_Water-Level-Example.html>\n",
    "* Example notebooks: <https://github.com/ioos/ioos_qc/tree/master/docs/source/examples>\n",
    "* Source documentation: <https://ioos.github.io/ioos_qc/>\n",
    "* QARTOD manuals: <https://ioos.noaa.gov/project/qartod/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 style=\"text-align: center;\">\n",
    "    Thank you!<br>\n",
    "</h1>\n",
    "\n",
    "![image.png](https://cdn.ioos.noaa.gov/media/2019/05/qartodlogo2.jpg)\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "    Mathew Biddle<br>\n",
    "    <a href=\"mailto:mathew.biddle@noaa.gov\">Mathew.Biddle@noaa.gov</a><br>\n",
    "    <a href=\"https://orcid.org/0000-0003-4897-1669\">https://orcid.org/0000-0003-4897-1669</a><br>\n",
    "    NOAA/NOS/IOOS<br>\n",
    "    <br>\n",
    "    <br>\n",
    "    <sub>Materials available at <a href=\"https://github.com/MathewBiddle/WIO_workshop\">https://github.com/MathewBiddle/WIO_workshop</a>.\n",
    "    </sub><br>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "This notebook was adapt from Jessica Austin and Kyle Wilcox's [original ioos_qc examples](https://github.com/ioos/ioos_qc/blob/b34b3762d659362fb3af11f52d8905d18cd6ec7b/docs/source/examples/QartodTestExample_WaterLevel.ipynb). Please [see the ``ioos_qc`` documentation](https://ioos.github.io/ioos_qc/) for more examples."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}