ARMDataObservations.ipynb

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    "import act\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt"
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     "text": [
      "[DOWNLOADING] sgpaosmetE13.a1.20190404.000000.nc\n",
      "\n",
      "If you use these data to prepare a publication, please cite:\n",
      "\n",
      "Kyrouac, J., Springston, S., & Tuftedal, M. Meteorological Measurements\n",
      "associated with the Aerosol Observing System (AOSMET). Atmospheric Radiation\n",
      "Measurement (ARM) User Facility. https://doi.org/10.5439/1984920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set your username and token here!\n",
    "username = 'xmansoura'\n",
    "token = '77da82486bcd2f4b'\n",
    "\n",
    "# Set the datastream and start/enddates\n",
    "datastream = 'sgpaosmetE13.a1'\n",
    "startdate = '2019-04-04'\n",
    "enddate = '2019-04-05'\n",
    "\n",
    "# Use ACT to easily download the data.  Watch for the data citation!  Show some support\n",
    "# for ARM's instrument experts and cite their data if you use it in a publication\n",
    "result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)"
   ]
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     "name": "stderr",
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     "text": [
      "ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/share/proj failed\n"
     ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 6MB\n",
       "Dimensions:              (time: 86399)\n",
       "Coordinates:\n",
       "  * time                 (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
       "Data variables: (12/17)\n",
       "    base_time            datetime64[ns] 8B 2019-04-04\n",
       "    time_offset          (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
       "    rh_ambient           (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
       "    temperature_ambient  (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
       "    pressure_ambient     (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
       "    wind_speed           (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
       "    ...                   ...\n",
       "    heater_volts         (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
       "    supply_volts         (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
       "    ref_volts            (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
       "    lat                  float32 4B ...\n",
       "    lon                  float32 4B ...\n",
       "    alt                  float32 4B ...\n",
       "Attributes: (12/23)\n",
       "    command_line:                aosmet_ingest -s sgp -f E13\n",
       "    Conventions:                 ARM-1.2\n",
       "    process_version:             ingest-aosmet-1.0-0.el6\n",
       "    dod_version:                 aosmet-a1-2.2\n",
       "    input_source:                /data/collection/sgp/sgpaosmetE13.00/sgpaosE...\n",
       "    site_id:                     sgp\n",
       "    ...                          ...\n",
       "    doi:                         10.5439/1325987\n",
       "    history:                     created by user dsmgr on machine ruby at 201...\n",
       "    _file_dates:                 [&#x27;20190404&#x27;]\n",
       "    _file_times:                 [&#x27;000000&#x27;]\n",
       "    _datastream:                 sgpaosmetE13.a1\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-0a470994-ff6f-4330-8b6a-765743ad95cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a470994-ff6f-4330-8b6a-765743ad95cf' 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>: 86399</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-611bbc7b-31a0-4524-bc81-d55c286c900b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-611bbc7b-31a0-4524-bc81-d55c286c900b' 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'>2019-04-04T00:00:00.220000 ... 2...</div><input id='attrs-7c06bbfe-fdce-42a0-9fa2-e95db568a94d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c06bbfe-fdce-42a0-9fa2-e95db568a94d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ab43e05-cfb9-42e7-9542-b099db673869' class='xr-var-data-in' type='checkbox'><label for='data-1ab43e05-cfb9-42e7-9542-b099db673869' 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;2019-04-04T00:00:00.220000000&#x27;, &#x27;2019-04-04T00:00:01.220000000&#x27;,\n",
       "       &#x27;2019-04-04T00:00:02.220000000&#x27;, ..., &#x27;2019-04-04T23:59:57.160000000&#x27;,\n",
       "       &#x27;2019-04-04T23:59:58.160000000&#x27;, &#x27;2019-04-04T23:59:59.160000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8548cc6e-66c4-48b6-9e16-4ed55deb9530' class='xr-section-summary-in' type='checkbox'  ><label for='section-8548cc6e-66c4-48b6-9e16-4ed55deb9530' class='xr-section-summary' >Data variables: <span>(17)</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'>2019-04-04</div><input id='attrs-ae8d56cf-be1a-43ca-ac52-f1cb9710e7e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae8d56cf-be1a-43ca-ac52-f1cb9710e7e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-871890a8-6132-4884-a7c8-d7c6d629b3d6' class='xr-var-data-in' type='checkbox'><label for='data-871890a8-6132-4884-a7c8-d7c6d629b3d6' 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>2019-04-04 00:00:00 0:00</dd><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2019-04-04T00:00:00.000000000&#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'>2019-04-04T00:00:00.220000 ... 2...</div><input id='attrs-bb90a8cd-9fa5-4020-bfef-7ee75dd6e345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb90a8cd-9fa5-4020-bfef-7ee75dd6e345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8e9bb2e-5f00-41f1-a715-a849ff51c7a8' class='xr-var-data-in' type='checkbox'><label for='data-d8e9bb2e-5f00-41f1-a715-a849ff51c7a8' 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><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-04-04T00:00:00.220000000&#x27;, &#x27;2019-04-04T00:00:01.220000000&#x27;,\n",
       "       &#x27;2019-04-04T00:00:02.220000000&#x27;, ...,\n",
       "       &#x27;2019-04-04T23:59:57.160000000&#x27;, &#x27;2019-04-04T23:59:58.160000000&#x27;,\n",
       "       &#x27;2019-04-04T23:59:59.160000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_ambient</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=(86399,), meta=np.ndarray&gt;</div><input id='attrs-8c60e8cd-0003-483b-9cde-b1e15dcb3ddf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c60e8cd-0003-483b-9cde-b1e15dcb3ddf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfae8cf4-0a07-4fad-a510-7c9867d92034' class='xr-var-data-in' type='checkbox'><label for='data-bfae8cf4-0a07-4fad-a510-7c9867d92034' 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>Ambient air relative humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_ambient</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=(86399,), meta=np.ndarray&gt;</div><input id='attrs-074a28fd-b7d3-42e0-971b-651858a4f66d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-074a28fd-b7d3-42e0-971b-651858a4f66d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c019d87c-495e-40a0-ab1b-3e00c573a83b' class='xr-var-data-in' type='checkbox'><label for='data-c019d87c-495e-40a0-ab1b-3e00c573a83b' 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>Ambient air temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_ambient</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=(86399,), meta=np.ndarray&gt;</div><input id='attrs-908d7023-12fb-4e56-badf-8f5a5c0a6b9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-908d7023-12fb-4e56-badf-8f5a5c0a6b9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a59ffce8-c345-4432-8a4c-8dffe719ffb7' class='xr-var-data-in' type='checkbox'><label for='data-a59ffce8-c345-4432-8a4c-8dffe719ffb7' 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>Ambient pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</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=(86399,), meta=np.ndarray&gt;</div><input id='attrs-77ba6433-bdc7-4af7-8557-f5e89ad2b3a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77ba6433-bdc7-4af7-8557-f5e89ad2b3a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dec1c1aa-1842-41bc-95a6-7b6129844a3b' class='xr-var-data-in' type='checkbox'><label for='data-dec1c1aa-1842-41bc-95a6-7b6129844a3b' 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>Wind speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_direction</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=(86399,), meta=np.ndarray&gt;</div><input id='attrs-ec491a09-8328-4be8-a59c-8c698d7a6a9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec491a09-8328-4be8-a59c-8c698d7a6a9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8cab819-96fb-4ded-bda2-d893525316ea' class='xr-var-data-in' type='checkbox'><label for='data-c8cab819-96fb-4ded-bda2-d893525316ea' 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>Wind direction, relative to true North</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                        <td> 337.50 kiB </td>\n",
       "                        <td> 337.50 kiB </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ref_volts</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=(86399,), meta=np.ndarray&gt;</div><input id='attrs-3227dac0-0d4a-4e68-9879-3ab068ab770e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3227dac0-0d4a-4e68-9879-3ab068ab770e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-068f017c-6418-4c50-bee1-3aec58c6d78f' class='xr-var-data-in' type='checkbox'><label for='data-068f017c-6418-4c50-bee1-3aec58c6d78f' 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>Reference voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 337.50 kiB </td>\n",
       "                        <td> 337.50 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (86399,) </td>\n",
       "                        <td> (86399,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\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-4c266e0e-015c-4ae1-8098-0e7c31a6b552' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c266e0e-015c-4ae1-8098-0e7c31a6b552' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f5cd666-9f7f-4866-8cc2-c6d9cfc1a54c' class='xr-var-data-in' type='checkbox'><label for='data-9f5cd666-9f7f-4866-8cc2-c6d9cfc1a54c' 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-208ce641-6f48-47f5-addb-1bb98bb42af5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-208ce641-6f48-47f5-addb-1bb98bb42af5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8da0af0-2f4b-448f-8fbe-82dd4b3e5c5b' class='xr-var-data-in' type='checkbox'><label for='data-b8da0af0-2f4b-448f-8fbe-82dd4b3e5c5b' 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-1568b773-1127-4f39-8f6e-f20909935d31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1568b773-1127-4f39-8f6e-f20909935d31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f482e181-e8c1-4546-a96e-521a07cb54d0' class='xr-var-data-in' type='checkbox'><label for='data-f482e181-e8c1-4546-a96e-521a07cb54d0' 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-5b066f8e-1442-458c-ab42-b94f5e7452f1' class='xr-section-summary-in' type='checkbox'  ><label for='section-5b066f8e-1442-458c-ab42-b94f5e7452f1' 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-d026679e-12cc-41ac-9129-06dd997a0691' class='xr-index-data-in' type='checkbox'/><label for='index-d026679e-12cc-41ac-9129-06dd997a0691' 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;2019-04-04 00:00:00.220000&#x27;, &#x27;2019-04-04 00:00:01.220000&#x27;,\n",
       "               &#x27;2019-04-04 00:00:02.220000&#x27;, &#x27;2019-04-04 00:00:03.220000&#x27;,\n",
       "               &#x27;2019-04-04 00:00:04.220000&#x27;, &#x27;2019-04-04 00:00:05.220000&#x27;,\n",
       "               &#x27;2019-04-04 00:00:06.220000&#x27;, &#x27;2019-04-04 00:00:07.220000&#x27;,\n",
       "               &#x27;2019-04-04 00:00:08.220000&#x27;, &#x27;2019-04-04 00:00:09.220000&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-04-04 23:59:50.160000&#x27;, &#x27;2019-04-04 23:59:51.160000&#x27;,\n",
       "               &#x27;2019-04-04 23:59:52.160000&#x27;, &#x27;2019-04-04 23:59:53.160000&#x27;,\n",
       "               &#x27;2019-04-04 23:59:54.160000&#x27;, &#x27;2019-04-04 23:59:55.160000&#x27;,\n",
       "               &#x27;2019-04-04 23:59:56.160000&#x27;, &#x27;2019-04-04 23:59:57.160000&#x27;,\n",
       "               &#x27;2019-04-04 23:59:58.160000&#x27;, &#x27;2019-04-04 23:59:59.160000&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=86399, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32415979-3723-4c19-946d-d939a79a9170' class='xr-section-summary-in' type='checkbox'  ><label for='section-32415979-3723-4c19-946d-d939a79a9170' class='xr-section-summary' >Attributes: <span>(23)</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>aosmet_ingest -s sgp -f E13</dd><dt><span>Conventions :</span></dt><dd>ARM-1.2</dd><dt><span>process_version :</span></dt><dd>ingest-aosmet-1.0-0.el6</dd><dt><span>dod_version :</span></dt><dd>aosmet-a1-2.2</dd><dt><span>input_source :</span></dt><dd>/data/collection/sgp/sgpaosmetE13.00/sgpaosE13.met.01s.00.20190404.000000.raw.tsv</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>platform_id :</span></dt><dd>aosmet</dd><dt><span>facility_id :</span></dt><dd>E13</dd><dt><span>data_level :</span></dt><dd>a1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>datastream :</span></dt><dd>sgpaosmetE13.a1</dd><dt><span>serial_number :</span></dt><dd>K/WD</dd><dt><span>sampling_interval :</span></dt><dd>1 second</dd><dt><span>met_instrument_description :</span></dt><dd>WXT520</dd><dt><span>instrument_manufacturer :</span></dt><dd>N/A</dd><dt><span>mentor_name :</span></dt><dd>Kyrouac</dd><dt><span>mentor_affiliation :</span></dt><dd>ANL</dd><dt><span>doi :</span></dt><dd>10.5439/1325987</dd><dt><span>history :</span></dt><dd>created by user dsmgr on machine ruby at 2019-04-04 01:22:00, using ingest-aosmet-1.0-0.el6</dd><dt><span>_file_dates :</span></dt><dd>[&#x27;20190404&#x27;]</dd><dt><span>_file_times :</span></dt><dd>[&#x27;000000&#x27;]</dd><dt><span>_datastream :</span></dt><dd>sgpaosmetE13.a1</dd><dt><span>_arm_standards_flag :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 6MB\n",
       "Dimensions:              (time: 86399)\n",
       "Coordinates:\n",
       "  * time                 (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
       "Data variables: (12/17)\n",
       "    base_time            datetime64[ns] 8B 2019-04-04\n",
       "    time_offset          (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
       "    rh_ambient           (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
       "    temperature_ambient  (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
       "    pressure_ambient     (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
       "    wind_speed           (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
       "    ...                   ...\n",
       "    heater_volts         (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
       "    supply_volts         (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
       "    ref_volts            (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
       "    lat                  float32 4B ...\n",
       "    lon                  float32 4B ...\n",
       "    alt                  float32 4B ...\n",
       "Attributes: (12/23)\n",
       "    command_line:                aosmet_ingest -s sgp -f E13\n",
       "    Conventions:                 ARM-1.2\n",
       "    process_version:             ingest-aosmet-1.0-0.el6\n",
       "    dod_version:                 aosmet-a1-2.2\n",
       "    input_source:                /data/collection/sgp/sgpaosmetE13.00/sgpaosE...\n",
       "    site_id:                     sgp\n",
       "    ...                          ...\n",
       "    doi:                         10.5439/1325987\n",
       "    history:                     created by user dsmgr on machine ruby at 201...\n",
       "    _file_dates:                 ['20190404']\n",
       "    _file_times:                 ['000000']\n",
       "    _datastream:                 sgpaosmetE13.a1\n",
       "    _arm_standards_flag:         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's read in the data using ACT and check out the data\n",
    "ds_mpl = act.io.read_arm_netcdf(result)\n",
    "\n",
    "ds_mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2eac54f-3d65-4db4-8ba4-13b9748fb46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'sgpaosmetE13.a1 rh_ambient on 20190404'}, xlabel='Time [UTC]', ylabel='(%)'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's take a look at the quality control information associated with a variable from the MPL\n",
    "variable = 'rh_ambient'\n",
    "\n",
    "# First, for many of the ACT QC features, we need to get the dataset more to CF standard and that\n",
    "# involves cleaning up some of the attributes and ways that ARM has historically handled QC\n",
    "ds_mpl.clean.cleanup()\n",
    "\n",
    "# Apply corrections for the ceilometer, correcting for the vertical height\n",
    "#ds_mpl = act.corrections.ceil.correct_ceil(ds_mpl,-999.0)\n",
    "\n",
    "# Next, let's take a look at visualizing the quality control information\n",
    "# Create a plotting display object with 2 plots\n",
    "display = act.plotting.TimeSeriesDisplay(ds_mpl, figsize=(10, 5), subplot_shape=(1,))\n",
    "\n",
    "# # Plot up the variable in the first plot\n",
    "display.plot(variable, subplot_index=(0,))\n",
    "\n",
    "# # Plot up the QC variable in the second plot\n",
    "# #display.qc_flag_block_plot(variable, subplot_index=(1,))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91caa296-a414-4c2b-9663-b727894f8c31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faabbf19-5e10-49fa-a2dc-59e55015e1a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "variable = ds_mpl['backscatter']\n",
    "variable.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bda48279-f56e-4985-887d-77cc93f32fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_mpl['backscatter'].compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2532a648-ec1b-453a-b9f4-7fb02e0fe89f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33305c9b-86fe-4ff8-b219-a0c03b889801",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}