third_party_libraries/ACT_QC.ipynb

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    "# Everyone's favorite topic: Quality Control!\n",
    "\n",
    "ARM uses three official types of Quality Control (QC).\n",
    "* Embedded\n",
    "* DQR\n",
    "* Instrument Handbook / VAP Technical Report"
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    "The embedded QC will come with the netCDF file and can be used immediately, well sort of. Since ARM is 30+ years old the standards have changed. Initially it was assumed a researcher would read the netCDF file header and manually integrate the embedded QC or read the DQR reports and manually apply the results. That is not even possible anymore. Every researcher is using multiple files from multiple datastreams over multiple years. We need an automated process."
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    "So after you have downloaded the data, you can read it and view the embedded quality control variables. ARM standard states an accompanying quality control variable will be the same variable name prepended with \"qc_\". Let's take a look at one."
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    "import act\n",
    "from pathlib import Path\n",
    "import warnings\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "warnings.filterwarnings('ignore')  # Turn off warnings for remainder of this tutorial"
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      "text/plain": [
       "'../data/sgpmetE13.b1/sgpmetE13.b1.20191101.000000.cdf'"
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   "source": [
    "filename = str(Path('..', 'data', 'sgpmetE13.b1', 'sgpmetE13.b1.20191101.000000.cdf'))\n",
    "filename"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 317kB\n",
       "Dimensions:                       (time: 1440)\n",
       "Coordinates:\n",
       "  * time                          (time) datetime64[ns] 12kB 2019-11-01 ... 2...\n",
       "Data variables: (12/50)\n",
       "    base_time                     datetime64[ns] 8B 2019-11-01\n",
       "    time_offset                   (time) datetime64[ns] 12kB 2019-11-01 ... 2...\n",
       "    atmos_pressure                (time) float32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
       "    qc_atmos_pressure             (time) int32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
       "    temp_mean                     (time) float32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
       "    qc_temp_mean                  (time) int32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
       "    ...                            ...\n",
       "    qc_logger_volt                (time) int32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
       "    logger_temp                   (time) float32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
       "    qc_logger_temp                (time) int32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
       "    lat                           float32 4B ...\n",
       "    lon                           float32 4B ...\n",
       "    alt                           float32 4B ...\n",
       "Attributes: (12/33)\n",
       "    command_line:                met_ingest -s sgp -f E13\n",
       "    process_version:             ingest-met-4.40-0.el7\n",
       "    dod_version:                 met-b1-7.3\n",
       "    input_source:                /data/collection/sgp/sgpmetE13.00/MET_Table1...\n",
       "    site_id:                     sgp\n",
       "    platform_id:                 met\n",
       "    ...                          ...\n",
       "    qc_bit_4_assessment:         Indeterminate\n",
       "    history:                     created by user dsmgr on machine garnet at 2...\n",
       "    _file_dates:                 [&#x27;20191101&#x27;]\n",
       "    _file_times:                 [&#x27;000000&#x27;]\n",
       "    _datastream:                 sgpmetE13.b1\n",
       "    _arm_standards_flag:         1</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-0124e69d-6286-4518-98b3-7725f4b51e44' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0124e69d-6286-4518-98b3-7725f4b51e44' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5c3d0a61-cc77-458b-86f6-66854629cb54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c3d0a61-cc77-458b-86f6-66854629cb54' class='xr-section-summary' >Coordinates: <span>(1)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-11-01 ... 2019-11-01T23:59:00</div><input id='attrs-3b92924f-e48a-44b6-9de7-5e17fedfce2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b92924f-e48a-44b6-9de7-5e17fedfce2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-237983f1-924a-4ec2-b3a6-ae0813fc6ce6' class='xr-var-data-in' type='checkbox'><label for='data-237983f1-924a-4ec2-b3a6-ae0813fc6ce6' 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>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:01:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:02:00.000000000&#x27;, ..., &#x27;2019-11-01T23:57:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T23:58:00.000000000&#x27;, &#x27;2019-11-01T23:59:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a82e422-c9b1-4ca1-a315-d960ceebb93b' class='xr-section-summary-in' type='checkbox'  ><label for='section-0a82e422-c9b1-4ca1-a315-d960ceebb93b' class='xr-section-summary' >Data variables: <span>(50)</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>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-11-01</div><input id='attrs-34bb0cb6-419b-4155-8b1f-8c07109a6bbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34bb0cb6-419b-4155-8b1f-8c07109a6bbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e9373af-5b42-4d04-abd4-4364873805db' class='xr-var-data-in' type='checkbox'><label for='data-3e9373af-5b42-4d04-abd4-4364873805db' 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>string :</span></dt><dd>2019-11-01 00:00:00 0:00</dd><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2019-11-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-11-01 ... 2019-11-01T23:59:00</div><input id='attrs-7620793c-e168-47e9-a615-394c98986d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7620793c-e168-47e9-a615-394c98986d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c972b00-03c9-4257-abcc-a6ef9a3dda12' class='xr-var-data-in' type='checkbox'><label for='data-5c972b00-03c9-4257-abcc-a6ef9a3dda12' 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>long_name :</span></dt><dd>Time offset from base_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:01:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:02:00.000000000&#x27;, ...,\n",
       "       &#x27;2019-11-01T23:57:00.000000000&#x27;, &#x27;2019-11-01T23:58:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T23:59:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atmos_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-8d204f8f-dd40-4bab-b71e-76d16a3e68fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d204f8f-dd40-4bab-b71e-76d16a3e68fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d430164f-c81f-429b-8dc5-277d7ecc95d2' class='xr-var-data-in' type='checkbox'><label for='data-d430164f-c81f-429b-8dc5-277d7ecc95d2' 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>long_name :</span></dt><dd>Atmospheric pressure</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>80.0</dd><dt><span>valid_max :</span></dt><dd>110.0</dd><dt><span>valid_delta :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 5.62 kiB </td>\n",
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       "                        <th> Bytes </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_pw_code_1hr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-23576967-a91e-487b-b158-9191c0d002d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23576967-a91e-487b-b158-9191c0d002d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a88c01e-ff6f-464c-86c6-77c89645d65d' class='xr-var-data-in' type='checkbox'><label for='data-4a88c01e-ff6f-464c-86c6-77c89645d65d' 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>long_name :</span></dt><dd>Quality check results on field: PWD 1 hour present weather code</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>See global attributes for individual bit descriptions.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 5.62 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9bea554c-a155-43c7-bd1a-f7a7a3c4da7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bea554c-a155-43c7-bd1a-f7a7a3c4da7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1485de84-15c0-4b07-a3e6-c53ae702ba94' class='xr-var-data-in' type='checkbox'><label for='data-1485de84-15c0-4b07-a3e6-c53ae702ba94' 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>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa9b2bbc-4af4-4c89-8e67-043de79cdaec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa9b2bbc-4af4-4c89-8e67-043de79cdaec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad99006e-75ff-4279-8203-f79e4b963352' class='xr-var-data-in' type='checkbox'><label for='data-ad99006e-75ff-4279-8203-f79e4b963352' 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>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eabfe0ec-d0f9-4860-8091-5ec84afeb958' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eabfe0ec-d0f9-4860-8091-5ec84afeb958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0153251b-b64d-4a43-8b1a-35f9fe6223c9' class='xr-var-data-in' type='checkbox'><label for='data-0153251b-b64d-4a43-8b1a-35f9fe6223c9' 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>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee4a6576-7196-496e-aabc-3f59365f7985' class='xr-section-summary-in' type='checkbox'  ><label for='section-ee4a6576-7196-496e-aabc-3f59365f7985' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fdba1ad4-15e5-401a-bd93-6d051549fd2b' class='xr-index-data-in' type='checkbox'/><label for='index-fdba1ad4-15e5-401a-bd93-6d051549fd2b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-11-01 00:00:00&#x27;, &#x27;2019-11-01 00:01:00&#x27;,\n",
       "               &#x27;2019-11-01 00:02:00&#x27;, &#x27;2019-11-01 00:03:00&#x27;,\n",
       "               &#x27;2019-11-01 00:04:00&#x27;, &#x27;2019-11-01 00:05:00&#x27;,\n",
       "               &#x27;2019-11-01 00:06:00&#x27;, &#x27;2019-11-01 00:07:00&#x27;,\n",
       "               &#x27;2019-11-01 00:08:00&#x27;, &#x27;2019-11-01 00:09:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-11-01 23:50:00&#x27;, &#x27;2019-11-01 23:51:00&#x27;,\n",
       "               &#x27;2019-11-01 23:52:00&#x27;, &#x27;2019-11-01 23:53:00&#x27;,\n",
       "               &#x27;2019-11-01 23:54:00&#x27;, &#x27;2019-11-01 23:55:00&#x27;,\n",
       "               &#x27;2019-11-01 23:56:00&#x27;, &#x27;2019-11-01 23:57:00&#x27;,\n",
       "               &#x27;2019-11-01 23:58:00&#x27;, &#x27;2019-11-01 23:59:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1440, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-edbeb260-0090-495d-a669-ba88c442dddf' class='xr-section-summary-in' type='checkbox'  ><label for='section-edbeb260-0090-495d-a669-ba88c442dddf' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>met_ingest -s sgp -f E13</dd><dt><span>process_version :</span></dt><dd>ingest-met-4.40-0.el7</dd><dt><span>dod_version :</span></dt><dd>met-b1-7.3</dd><dt><span>input_source :</span></dt><dd>/data/collection/sgp/sgpmetE13.00/MET_Table1.20191101000000.dat</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>platform_id :</span></dt><dd>met</dd><dt><span>facility_id :</span></dt><dd>E13</dd><dt><span>data_level :</span></dt><dd>b1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>datastream :</span></dt><dd>sgpmetE13.b1</dd><dt><span>serial_number :</span></dt><dd>118</dd><dt><span>sampling_interval :</span></dt><dd>variable, see instrument handbook</dd><dt><span>averaging_interval :</span></dt><dd>60 seconds</dd><dt><span>averaging_interval_comment :</span></dt><dd>The time assigned to each data point indicates the end of the averaging interval.</dd><dt><span>tbrg :</span></dt><dd>Tipping Bucket Rain Gauge</dd><dt><span>pwd :</span></dt><dd>Present Weather Detector</dd><dt><span>wind_speed_offset :</span></dt><dd>0.000000</dd><dt><span>wind_speed_slope :</span></dt><dd>0.098000</dd><dt><span>tbrg_precip_corr_info :</span></dt><dd>0.000000 * tbrg_precip_total^2 + 1.024000 * tbrg_precip_total</dd><dt><span>qc_bit_comment :</span></dt><dd>The QC field values are a bit packed representation of true/false values for the tests that may have been performed. A QC value of zero means that none of the tests performed on the value failed.</dd><dt><span>qc_bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>qc_bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>qc_bit_2_description :</span></dt><dd>Value is less than the valid_min.</dd><dt><span>qc_bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>qc_bit_3_description :</span></dt><dd>Value is greater than the valid_max.</dd><dt><span>qc_bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>qc_bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>qc_bit_4_assessment :</span></dt><dd>Indeterminate</dd><dt><span>history :</span></dt><dd>created by user dsmgr on machine garnet at 2019-11-01 02:40:01, using ingest-met-4.40-0.el7</dd><dt><span>_file_dates :</span></dt><dd>[&#x27;20191101&#x27;]</dd><dt><span>_file_times :</span></dt><dd>[&#x27;000000&#x27;]</dd><dt><span>_datastream :</span></dt><dd>sgpmetE13.b1</dd><dt><span>_arm_standards_flag :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 317kB\n",
       "Dimensions:                       (time: 1440)\n",
       "Coordinates:\n",
       "  * time                          (time) datetime64[ns] 12kB 2019-11-01 ... 2...\n",
       "Data variables: (12/50)\n",
       "    base_time                     datetime64[ns] 8B 2019-11-01\n",
       "    time_offset                   (time) datetime64[ns] 12kB 2019-11-01 ... 2...\n",
       "    atmos_pressure                (time) float32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
       "    qc_atmos_pressure             (time) int32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
       "    temp_mean                     (time) float32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
       "    qc_temp_mean                  (time) int32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
       "    ...                            ...\n",
       "    qc_logger_volt                (time) int32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
       "    logger_temp                   (time) float32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
       "    qc_logger_temp                (time) int32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
       "    lat                           float32 4B ...\n",
       "    lon                           float32 4B ...\n",
       "    alt                           float32 4B ...\n",
       "Attributes: (12/33)\n",
       "    command_line:                met_ingest -s sgp -f E13\n",
       "    process_version:             ingest-met-4.40-0.el7\n",
       "    dod_version:                 met-b1-7.3\n",
       "    input_source:                /data/collection/sgp/sgpmetE13.00/MET_Table1...\n",
       "    site_id:                     sgp\n",
       "    platform_id:                 met\n",
       "    ...                          ...\n",
       "    qc_bit_4_assessment:         Indeterminate\n",
       "    history:                     created by user dsmgr on machine garnet at 2...\n",
       "    _file_dates:                 ['20191101']\n",
       "    _file_times:                 ['000000']\n",
       "    _datastream:                 sgpmetE13.b1\n",
       "    _arm_standards_flag:         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = act.io.arm.read_arm_netcdf(filename)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3485af15-bb9a-4cdc-89dd-8e642272e00c",
   "metadata": {},
   "source": [
    "Let's set a variable to the variable name in the Dataset and a second variable to the quality control variable name. Then we will print the DataArray to see the strucure of the data and attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a99b555c-2b01-4ca9-bab5-32b8167bce0d",
   "metadata": {},
   "outputs": [
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       "dask.array&lt;open_dataset-qc_temp_mean, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray&gt;\n",
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       "    long_name:    Quality check results on field: Temperature mean\n",
       "    units:        unitless\n",
       "    description:  See global attributes for individual bit descriptions.</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'qc_temp_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-47b564b3-92f2-40e4-97bf-535c0c049bb3' class='xr-array-in' type='checkbox' checked><label for='section-47b564b3-92f2-40e4-97bf-535c0c049bb3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "       &#x27;2019-11-01T00:02:00.000000000&#x27;, ..., &#x27;2019-11-01T23:57:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T23:58:00.000000000&#x27;, &#x27;2019-11-01T23:59:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e8dc403-4653-432f-a4ef-baacec32b192' class='xr-section-summary-in' type='checkbox'  ><label for='section-2e8dc403-4653-432f-a4ef-baacec32b192' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db1b5fcb-6ae7-449a-8dc7-8a60386f31e2' class='xr-index-data-in' type='checkbox'/><label for='index-db1b5fcb-6ae7-449a-8dc7-8a60386f31e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-11-01 00:00:00&#x27;, &#x27;2019-11-01 00:01:00&#x27;,\n",
       "               &#x27;2019-11-01 00:02:00&#x27;, &#x27;2019-11-01 00:03:00&#x27;,\n",
       "               &#x27;2019-11-01 00:04:00&#x27;, &#x27;2019-11-01 00:05:00&#x27;,\n",
       "               &#x27;2019-11-01 00:06:00&#x27;, &#x27;2019-11-01 00:07:00&#x27;,\n",
       "               &#x27;2019-11-01 00:08:00&#x27;, &#x27;2019-11-01 00:09:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-11-01 23:50:00&#x27;, &#x27;2019-11-01 23:51:00&#x27;,\n",
       "               &#x27;2019-11-01 23:52:00&#x27;, &#x27;2019-11-01 23:53:00&#x27;,\n",
       "               &#x27;2019-11-01 23:54:00&#x27;, &#x27;2019-11-01 23:55:00&#x27;,\n",
       "               &#x27;2019-11-01 23:56:00&#x27;, &#x27;2019-11-01 23:57:00&#x27;,\n",
       "               &#x27;2019-11-01 23:58:00&#x27;, &#x27;2019-11-01 23:59:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1440, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8707609b-b992-44f5-8908-7de1a306c5e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8707609b-b992-44f5-8908-7de1a306c5e7' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on field: Temperature mean</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>See global attributes for individual bit descriptions.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'qc_temp_mean' (time: 1440)> Size: 6kB\n",
       "dask.array<open_dataset-qc_temp_mean, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 12kB 2019-11-01 ... 2019-11-01T23:59:00\n",
       "Attributes:\n",
       "    long_name:    Quality check results on field: Temperature mean\n",
       "    units:        unitless\n",
       "    description:  See global attributes for individual bit descriptions."
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "var_name = 'temp_mean'\n",
    "qc_var_name = 'qc_' + var_name\n",
    "ds[qc_var_name]"
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    "Notice that there are three variable attributes: _long_name, units, description_\n",
    "\n",
    "_descripton_ just says go look at global attributes to decode? How does the ACT code know how to apply those? Well it does not right away. If we look at the global attributes, you can see they are a bit of a mess."
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       " 'qc_bit_1_description': 'Value is equal to missing_value.',\n",
       " 'qc_bit_1_assessment': 'Bad',\n",
       " 'qc_bit_2_description': 'Value is less than the valid_min.',\n",
       " 'qc_bit_2_assessment': 'Bad',\n",
       " 'qc_bit_3_description': 'Value is greater than the valid_max.',\n",
       " 'qc_bit_3_assessment': 'Bad',\n",
       " 'qc_bit_4_description': 'Difference between current and previous values exceeds valid_delta.',\n",
       " 'qc_bit_4_assessment': 'Indeterminate',\n",
       " 'history': 'created by user dsmgr on machine garnet at 2019-11-01 02:40:01, using ingest-met-4.40-0.el7',\n",
       " '_file_dates': ['20191101'],\n",
       " '_file_times': ['000000'],\n",
       " '_datastream': 'sgpmetE13.b1',\n",
       " '_arm_standards_flag': 1}"
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    "While there is a standard for the ARM QC it's not the one used by CF or ACT. So we need to update it. Luckily ACT can do this for you. We just need to call the cleanup() method to convert all the declaration of QC values from the global attributes to declarations uder the QC variable."
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   "execution_count": 6,
   "id": "61538288-2752-435f-b516-b417a5545943",
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   "execution_count": 7,
   "id": "d711c699-2977-4796-b222-050d1f127c9a",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;qc_temp_mean&#x27; (time: 1440)&gt; Size: 6kB\n",
       "dask.array&lt;open_dataset-qc_temp_mean, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray&gt;\n",
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       "  * time     (time) datetime64[ns] 12kB 2019-11-01 ... 2019-11-01T23:59:00\n",
       "Attributes:\n",
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       "    flag_assessments:  [&#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Indeterminate&#x27;]\n",
       "    fail_min:          -40.0\n",
       "    fail_max:          50.0\n",
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       "    standard_name:     quality_flag</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'qc_temp_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b8bc4111-7977-4186-91d6-b368f8c1c2d6' class='xr-array-in' type='checkbox' checked><label for='section-b8bc4111-7977-4186-91d6-b368f8c1c2d6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                        <td> 5.62 kiB </td>\n",
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       "       &#x27;2019-11-01T00:02:00.000000000&#x27;, ..., &#x27;2019-11-01T23:57:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T23:58:00.000000000&#x27;, &#x27;2019-11-01T23:59:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7715cf94-e6e7-4e01-a17e-88ae647501c9' class='xr-section-summary-in' type='checkbox'  ><label for='section-7715cf94-e6e7-4e01-a17e-88ae647501c9' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe3c9e59-b92b-4a62-94e6-4b902d6f80c2' class='xr-index-data-in' type='checkbox'/><label for='index-fe3c9e59-b92b-4a62-94e6-4b902d6f80c2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-11-01 00:00:00&#x27;, &#x27;2019-11-01 00:01:00&#x27;,\n",
       "               &#x27;2019-11-01 00:02:00&#x27;, &#x27;2019-11-01 00:03:00&#x27;,\n",
       "               &#x27;2019-11-01 00:04:00&#x27;, &#x27;2019-11-01 00:05:00&#x27;,\n",
       "               &#x27;2019-11-01 00:06:00&#x27;, &#x27;2019-11-01 00:07:00&#x27;,\n",
       "               &#x27;2019-11-01 00:08:00&#x27;, &#x27;2019-11-01 00:09:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-11-01 23:50:00&#x27;, &#x27;2019-11-01 23:51:00&#x27;,\n",
       "               &#x27;2019-11-01 23:52:00&#x27;, &#x27;2019-11-01 23:53:00&#x27;,\n",
       "               &#x27;2019-11-01 23:54:00&#x27;, &#x27;2019-11-01 23:55:00&#x27;,\n",
       "               &#x27;2019-11-01 23:56:00&#x27;, &#x27;2019-11-01 23:57:00&#x27;,\n",
       "               &#x27;2019-11-01 23:58:00&#x27;, &#x27;2019-11-01 23:59:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1440, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-800cf4a6-7186-489f-8f2d-4dcce2f08591' class='xr-section-summary-in' type='checkbox'  checked><label for='section-800cf4a6-7186-489f-8f2d-4dcce2f08591' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on field: Temperature mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;Value is equal to missing_value.&#x27;, &#x27;Value is less than the fail_min.&#x27;, &#x27;Value is greater than the fail_max.&#x27;, &#x27;Difference between current and previous values exceeds fail_delta.&#x27;]</dd><dt><span>flag_assessments :</span></dt><dd>[&#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Indeterminate&#x27;]</dd><dt><span>fail_min :</span></dt><dd>-40.0</dd><dt><span>fail_max :</span></dt><dd>50.0</dd><dt><span>fail_delta :</span></dt><dd>20.0</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'qc_temp_mean' (time: 1440)> Size: 6kB\n",
       "dask.array<open_dataset-qc_temp_mean, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 12kB 2019-11-01 ... 2019-11-01T23:59:00\n",
       "Attributes:\n",
       "    long_name:         Quality check results on field: Temperature mean\n",
       "    units:             1\n",
       "    flag_masks:        [1, 2, 4, 8]\n",
       "    flag_meanings:     ['Value is equal to missing_value.', 'Value is less th...\n",
       "    flag_assessments:  ['Bad', 'Bad', 'Bad', 'Indeterminate']\n",
       "    fail_min:          -40.0\n",
       "    fail_max:          50.0\n",
       "    fail_delta:        20.0\n",
       "    standard_name:     quality_flag"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[qc_var_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6ae209-e827-471f-8281-1b436f79a1c6",
   "metadata": {},
   "source": [
    "Notice how we have additional variable attributes listed under the quality control variable. Specifically there are new attributes: _flag_meanings, flag_masks, flag_assessments standard_name_ and the limit variables _fail_min, fail_max, fail_delta_. These are the required variable attributes defined under the CF standard. ACT uses that standard (mostly, a little better in fact) to make applying QC trivial. Almost too trivial in fact."
   ]
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  {
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   "source": [
    "But there is an even easier way to do all this. Instead of calling the cleanup() method after reading the data it can be applied during the reading call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "876c428c-0536-4342-8e82-01b04e26fa71",
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;qc_temp_mean&#x27; (time: 1440)&gt; Size: 6kB\n",
       "dask.array&lt;open_dataset-qc_temp_mean, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 12kB 2019-11-01 ... 2019-11-01T23:59:00\n",
       "Attributes:\n",
       "    long_name:         Quality check results on field: Temperature mean\n",
       "    units:             1\n",
       "    flag_masks:        [1, 2, 4, 8]\n",
       "    flag_meanings:     [&#x27;Value is equal to missing_value.&#x27;, &#x27;Value is less th...\n",
       "    flag_assessments:  [&#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Indeterminate&#x27;]\n",
       "    fail_min:          -40.0\n",
       "    fail_max:          50.0\n",
       "    fail_delta:        20.0\n",
       "    standard_name:     quality_flag</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'qc_temp_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6c120de4-9d73-457e-89f5-6caa87d3e6b0' class='xr-array-in' type='checkbox' checked><label for='section-6c120de4-9d73-457e-89f5-6caa87d3e6b0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.62 kiB </td>\n",
       "                        <td> 5.62 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1440,) </td>\n",
       "                        <td> (1440,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-fa71e3b9-d0e9-4f24-ab2a-c33cd380493d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa71e3b9-d0e9-4f24-ab2a-c33cd380493d' class='xr-section-summary' >Coordinates: <span>(1)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-11-01 ... 2019-11-01T23:59:00</div><input id='attrs-12d3ebda-62a4-419b-8161-c8df73a181d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12d3ebda-62a4-419b-8161-c8df73a181d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-866a2e3c-1ac6-4968-bdee-810c31042b42' class='xr-var-data-in' type='checkbox'><label for='data-866a2e3c-1ac6-4968-bdee-810c31042b42' 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>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:01:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:02:00.000000000&#x27;, ..., &#x27;2019-11-01T23:57:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T23:58:00.000000000&#x27;, &#x27;2019-11-01T23:59:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cdbc159e-d6a9-4c36-a59c-9f60217255e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-cdbc159e-d6a9-4c36-a59c-9f60217255e2' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-930ab1ed-1ee2-4ab0-b52f-46b48001120b' class='xr-index-data-in' type='checkbox'/><label for='index-930ab1ed-1ee2-4ab0-b52f-46b48001120b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-11-01 00:00:00&#x27;, &#x27;2019-11-01 00:01:00&#x27;,\n",
       "               &#x27;2019-11-01 00:02:00&#x27;, &#x27;2019-11-01 00:03:00&#x27;,\n",
       "               &#x27;2019-11-01 00:04:00&#x27;, &#x27;2019-11-01 00:05:00&#x27;,\n",
       "               &#x27;2019-11-01 00:06:00&#x27;, &#x27;2019-11-01 00:07:00&#x27;,\n",
       "               &#x27;2019-11-01 00:08:00&#x27;, &#x27;2019-11-01 00:09:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-11-01 23:50:00&#x27;, &#x27;2019-11-01 23:51:00&#x27;,\n",
       "               &#x27;2019-11-01 23:52:00&#x27;, &#x27;2019-11-01 23:53:00&#x27;,\n",
       "               &#x27;2019-11-01 23:54:00&#x27;, &#x27;2019-11-01 23:55:00&#x27;,\n",
       "               &#x27;2019-11-01 23:56:00&#x27;, &#x27;2019-11-01 23:57:00&#x27;,\n",
       "               &#x27;2019-11-01 23:58:00&#x27;, &#x27;2019-11-01 23:59:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1440, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-218ca86c-1c80-45a9-9826-501e65dc9078' class='xr-section-summary-in' type='checkbox'  checked><label for='section-218ca86c-1c80-45a9-9826-501e65dc9078' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on field: Temperature mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;Value is equal to missing_value.&#x27;, &#x27;Value is less than the fail_min.&#x27;, &#x27;Value is greater than the fail_max.&#x27;, &#x27;Difference between current and previous values exceeds fail_delta.&#x27;]</dd><dt><span>flag_assessments :</span></dt><dd>[&#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Indeterminate&#x27;]</dd><dt><span>fail_min :</span></dt><dd>-40.0</dd><dt><span>fail_max :</span></dt><dd>50.0</dd><dt><span>fail_delta :</span></dt><dd>20.0</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd></dl></div></li></ul></div></div>"
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       "<xarray.DataArray 'qc_temp_mean' (time: 1440)> Size: 6kB\n",
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "ds = act.io.arm.read_arm_netcdf(filename, cleanup_qc=True)\n",
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    "And once we have the QC variable cleaned up we don't even need to know the name of the QC variable. The use of the standard_name attribute along with _ancillary_variables_ under the data variable means the code just knows what to do."
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    "So in fact we can ask ACT what is the name of the QC variable?"
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     "data": {
      "text/plain": [
       "'qc_temp_mean'"
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     "execution_count": 9,
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   "source": [
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    "Now that we have the data read and QC variable updated, we can use the QC. The question is how do you want to use the QC? The next logical step is to check the other location for QC - DQRs. We can use a function in ACT to query the ARM program for the DQR information and apply it to the Dataset we have. It will use a web-service to query the DQR database and if it finds a DQR that applies to the datastream we have read over the data dates it will add additional tests to the QC variable."
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   "execution_count": 10,
   "id": "8e3c4d3d-1c8b-4fe3-99a7-ff28b42067b2",
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    "ds = act.qc.arm.add_dqr_to_qc(ds)"
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   "execution_count": 11,
   "id": "9a9d0de1-8db6-48ef-8577-0b61a49b7a0e",
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       "dask.array&lt;open_dataset-qc_temp_mean, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray&gt;\n",
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       "  * time     (time) datetime64[ns] 12kB 2019-11-01 ... 2019-11-01T23:59:00\n",
       "Attributes:\n",
       "    long_name:         Quality check results on field: Temperature mean\n",
       "    units:             1\n",
       "    flag_masks:        [1, 2, 4, 8]\n",
       "    flag_meanings:     [&#x27;Value is equal to missing_value.&#x27;, &#x27;Value is less th...\n",
       "    flag_assessments:  [&#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Indeterminate&#x27;]\n",
       "    fail_min:          -40.0\n",
       "    fail_max:          50.0\n",
       "    fail_delta:        20.0\n",
       "    standard_name:     quality_flag</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'qc_temp_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3401ada4-4c9d-4453-95cd-805939f71fa1' class='xr-array-in' type='checkbox' checked><label for='section-3401ada4-4c9d-4453-95cd-805939f71fa1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "       &#x27;2019-11-01T00:02:00.000000000&#x27;, ..., &#x27;2019-11-01T23:57:00.000000000&#x27;,\n",
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       "               &#x27;2019-11-01 00:06:00&#x27;, &#x27;2019-11-01 00:07:00&#x27;,\n",
       "               &#x27;2019-11-01 00:08:00&#x27;, &#x27;2019-11-01 00:09:00&#x27;,\n",
       "               ...\n",
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       "               &#x27;2019-11-01 23:52:00&#x27;, &#x27;2019-11-01 23:53:00&#x27;,\n",
       "               &#x27;2019-11-01 23:54:00&#x27;, &#x27;2019-11-01 23:55:00&#x27;,\n",
       "               &#x27;2019-11-01 23:56:00&#x27;, &#x27;2019-11-01 23:57:00&#x27;,\n",
       "               &#x27;2019-11-01 23:58:00&#x27;, &#x27;2019-11-01 23:59:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1440, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4bfdc841-0a71-4fac-ab52-601446356252' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4bfdc841-0a71-4fac-ab52-601446356252' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on field: Temperature mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;Value is equal to missing_value.&#x27;, &#x27;Value is less than the fail_min.&#x27;, &#x27;Value is greater than the fail_max.&#x27;, &#x27;Difference between current and previous values exceeds fail_delta.&#x27;]</dd><dt><span>flag_assessments :</span></dt><dd>[&#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Bad&#x27;, &#x27;Indeterminate&#x27;]</dd><dt><span>fail_min :</span></dt><dd>-40.0</dd><dt><span>fail_max :</span></dt><dd>50.0</dd><dt><span>fail_delta :</span></dt><dd>20.0</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'qc_temp_mean' (time: 1440)> Size: 6kB\n",
       "dask.array<open_dataset-qc_temp_mean, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 12kB 2019-11-01 ... 2019-11-01T23:59:00\n",
       "Attributes:\n",
       "    long_name:         Quality check results on field: Temperature mean\n",
       "    units:             1\n",
       "    flag_masks:        [1, 2, 4, 8]\n",
       "    flag_meanings:     ['Value is equal to missing_value.', 'Value is less th...\n",
       "    flag_assessments:  ['Bad', 'Bad', 'Bad', 'Indeterminate']\n",
       "    fail_min:          -40.0\n",
       "    fail_max:          50.0\n",
       "    fail_delta:        20.0\n",
       "    standard_name:     quality_flag"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['qc_temp_mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07fd7308-0884-49aa-a48e-f8a6f9e3a6bb",
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   "source": [
    "In this case there are no DQRs to apply so nothing is changed with the QC variable."
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  },
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   "cell_type": "markdown",
   "id": "3d64f363-3db0-466a-8615-d6dc288d357c",
   "metadata": {},
   "source": [
    "But lets read in some data from the ACT examples that does have a DQR to apply so we can see what is going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4900a1fe-3d1e-43b5-8236-c844837ce7a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in sample AOSMET data\n",
    "files = act.tests.sample_files.EXAMPLE_AOSMET\n",
    "ds_aosmet = act.io.arm.read_arm_netcdf(files)"
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  {
   "cell_type": "markdown",
   "id": "675ca476-9ed6-49a4-bc9f-276ad7a636c6",
   "metadata": {},
   "source": [
    "And this time we are going to ask that only one variable is updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "621f4be8-066c-4026-a05b-4762ff470f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "variable = 'temperature_ambient'\n",
    "ds_aosmet = act.qc.arm.add_dqr_to_qc(ds_aosmet, variable=variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59e9eabe-55ec-40e9-a3ad-9296d7e1967a",
   "metadata": {},
   "outputs": [
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       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;qc_temperature_ambient&#x27; (time: 1440)&gt; Size: 6kB\n",
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 12kB 2018-02-01 ... 2018-02-01T23:59:00\n",
       "Attributes:\n",
       "    long_name:         Quality check results on field: Ambient air temperature\n",
       "    units:             1\n",
       "    flag_masks:        [1]\n",
       "    flag_meanings:     [&#x27;D180212.1 : Site operations turned the blowers off t...\n",
       "    flag_assessments:  [&#x27;Bad&#x27;]\n",
       "    standard_name:     quality_flag</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'qc_temperature_ambient'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1165075b-7067-4154-bd1a-297b3242ca61' class='xr-array-in' type='checkbox' checked><label for='section-1165075b-7067-4154-bd1a-297b3242ca61' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1</span></div><div class='xr-array-data'><pre>array([0, 0, 0, ..., 1, 1, 1], dtype=int32)</pre></div></div></li><li class='xr-section-item'><input id='section-0baf6152-309c-45a1-9326-854e9d7a5e31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0baf6152-309c-45a1-9326-854e9d7a5e31' class='xr-section-summary' >Coordinates: <span>(1)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-02-01 ... 2018-02-01T23:59:00</div><input id='attrs-c39b1228-a4d3-43da-a716-75a319d235ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c39b1228-a4d3-43da-a716-75a319d235ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69f8fda3-494a-4c4a-bb5a-60f7db42df28' class='xr-var-data-in' type='checkbox'><label for='data-69f8fda3-494a-4c4a-bb5a-60f7db42df28' 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>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-02-01T00:01:00.000000000&#x27;,\n",
       "       &#x27;2018-02-01T00:02:00.000000000&#x27;, ..., &#x27;2018-02-01T23:57:00.000000000&#x27;,\n",
       "       &#x27;2018-02-01T23:58:00.000000000&#x27;, &#x27;2018-02-01T23:59:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96d5db69-2c83-461b-9690-944e1b7f12f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-96d5db69-2c83-461b-9690-944e1b7f12f4' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ee188f0-ca53-4303-9db2-9533d9f8e7ad' class='xr-index-data-in' type='checkbox'/><label for='index-3ee188f0-ca53-4303-9db2-9533d9f8e7ad' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2018-02-01 00:00:00&#x27;, &#x27;2018-02-01 00:01:00&#x27;,\n",
       "               &#x27;2018-02-01 00:02:00&#x27;, &#x27;2018-02-01 00:03:00&#x27;,\n",
       "               &#x27;2018-02-01 00:04:00&#x27;, &#x27;2018-02-01 00:05:00&#x27;,\n",
       "               &#x27;2018-02-01 00:06:00&#x27;, &#x27;2018-02-01 00:07:00&#x27;,\n",
       "               &#x27;2018-02-01 00:08:00&#x27;, &#x27;2018-02-01 00:09:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2018-02-01 23:50:00&#x27;, &#x27;2018-02-01 23:51:00&#x27;,\n",
       "               &#x27;2018-02-01 23:52:00&#x27;, &#x27;2018-02-01 23:53:00&#x27;,\n",
       "               &#x27;2018-02-01 23:54:00&#x27;, &#x27;2018-02-01 23:55:00&#x27;,\n",
       "               &#x27;2018-02-01 23:56:00&#x27;, &#x27;2018-02-01 23:57:00&#x27;,\n",
       "               &#x27;2018-02-01 23:58:00&#x27;, &#x27;2018-02-01 23:59:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1440, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48d7a719-eb65-4efb-b31d-135c11777da3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48d7a719-eb65-4efb-b31d-135c11777da3' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on field: Ambient air temperature</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_masks :</span></dt><dd>[1]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;D180212.1 : Site operations turned the blowers off to prevent instrument contamination from the ships exhaust. Instruments not sampling ambient air.&#x27;]</dd><dt><span>flag_assessments :</span></dt><dd>[&#x27;Bad&#x27;]</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'qc_temperature_ambient' (time: 1440)> Size: 6kB\n",
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 12kB 2018-02-01 ... 2018-02-01T23:59:00\n",
       "Attributes:\n",
       "    long_name:         Quality check results on field: Ambient air temperature\n",
       "    units:             1\n",
       "    flag_masks:        [1]\n",
       "    flag_meanings:     ['D180212.1 : Site operations turned the blowers off t...\n",
       "    flag_assessments:  ['Bad']\n",
       "    standard_name:     quality_flag"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc_variable = ds_aosmet.qcfilter.check_for_ancillary_qc(variable)\n",
    "ds_aosmet[qc_variable]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1839553-e2bf-433d-a22f-c1df765bb859",
   "metadata": {},
   "source": [
    "In this example, a QC variable does not exist in the netCDF file. When we tell ACT to apply the DQR it notices this missing QC variable and makes it before adding the results from the DQR web-service call. Notice we now have one test and the description indicates the DQR ID number so we can review it for further detail."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3d3164f-ea01-4391-bb4b-c20e14b02f30",
   "metadata": {},
   "source": [
    "Now that we have the QC updated we can look at the data and the QC information in one plot. The first axes is a plot of the data. The second axes is a plot of the quality control information. Since the QC information is a simple true false time series it is plotted like a shading plot. It also indicates the test number and the description of the test on the axes.\n",
    "\n",
    "Notice how we used the _assessment_overplot=True_ keyword in the .plot() call to ask that the QC is set to overlay red points. There are a lot of options/features in ACT to handle QC. Chances are if you need to do something, it's already in there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "beb60752-5d0b-4e92-87c7-2aa7ddea5b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data\n",
    "# Creat Plot Display\n",
    "display = act.plotting.TimeSeriesDisplay(ds_aosmet, figsize=(10, 8), subplot_shape=(2,))\n",
    "\n",
    "# Plot temperature data in top plot\n",
    "display.plot(variable, subplot_index=(0,), assessment_overplot=True)\n",
    "\n",
    "# Plot QC data\n",
    "display.qc_flag_block_plot(variable, subplot_index=(1,))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}