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   "id": "94b7c663",
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   "source": [
    "## Introducing adi_runner 1: quick-start\n",
    "adi_runner is a versatile Python package designed to offer an intuitive API that simplifies interactions with PCM (Processing Configuration Manager) and ADI (ARM Data Integrator) technologies. This powerful tool empowers users to navigate ARM (Advanced RISC Machine) data, implement the VAPs (Value-added products) algorithm, and develop comprehensive VAPs workflows with ease."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e760aa",
   "metadata": {},
   "source": [
    "This Notebook demonstrate the quick-start to use AdiRunner, and get you familiar with the basic APIs. Here are the steps to develop a VAP workflow using AdiRunner.\n",
    "\n",
    "Steps:\n",
    "* Instantiate AdiRunner instance with essential process info (e.g., pcm-name, site, facility, date, etc.)\n",
    "* Run process method to retrieve input datasets, transformed datasets, and output datasets.\n",
    "* Get datasets in interests (e.g., adi_runner.input_datasets), and perform data analysis, visualization (quick-plot).\n",
    "* Implement algorithm on target variables."
   ]
  },
  {
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   "execution_count": 1,
   "id": "effabdbb-edb3-4024-bf58-54cb06b7602b",
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   "source": [
    "# import the essential packages\n",
    "# (use the `pip install adi_notebook==<desired-version>` command if adi-notebook is not installed in the environment)\n",
    "from adi_notebook.runner import AdiRunner\n",
    "from matplotlib import pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9087b6",
   "metadata": {},
   "source": [
    "Demo 1:\n",
    "A simple pcm (adi_demo_0) exploration and VAP development\n",
    "basic info:\n",
    "* 'input_datastreams': ['met.b1'], \n",
    "* 'output_datastreams': ['adiregulargrid.c1'], \n",
    "* 'coordinates': ['half_min_grid']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e5f8ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdiRunner(process_name='adi_demo_0', site='sgp', facility='C1', begin_date='20190119', end_date='20190122', pcm_link=None, DATASTREAM_DATA_IN='/data/archive', DATASTREAM_DATA_OUT='/data/home/kefeimo/data/datastream', QUICKLOOK_DATA='/data/home/kefeimo/data/quicklook', LOGS_DATA='/data/home/kefeimo/data/logs', CONF_DATA='/data/home/kefeimo/data/conf', ADI_PY_MODE='development', ADI_CACHE_DIR='/home/kefeimo/.adi_tmp')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# object instantiation, and quick review the pcm-info\n",
    "# Note: the pcm-name, site, facility, date info can be changed on-the-fly, \n",
    "# as long as they are valid.\n",
    "\n",
    "adi_runner = AdiRunner(process_name=\"adi_demo_0\", \n",
    "                       site=\"sgp\", facility=\"C1\", \n",
    "                       begin_date=\"20190119\", end_date=\"20190122\",\n",
    "                       DATASTREAM_DATA_IN=\"/data/archive\",\n",
    "                       DATASTREAM_DATA_OUT= f'/data/home/{os.environ[\"USER\"]}/data/datastream',\n",
    "                       QUICKLOOK_DATA= f'/data/home/{os.environ[\"USER\"]}/data/quicklook',\n",
    "                       LOGS_DATA= f'/data/home/{os.environ[\"USER\"]}/data/logs',\n",
    "                       CONF_DATA= f'/data/home/{os.environ[\"USER\"]}/data/conf',\n",
    "                       ADI_PY_MODE= \"development\",\n",
    "                      )  \n",
    "\n",
    "# When print the AdiRunner instance itself, it displays the basic information about the PCM.\n",
    "adi_runner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "781975fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: show_progressbar = True. \n",
      "\tYou can view logs later with the `print_logs` method of the returned `ProcessStatus` object.\n",
      "\te.g., status.print_logs().\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 20190121 --> 20190122: 100%|██████████ | 3/3 [00:00<00:00,  0.28s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ProcessStatus=Data Consolidate Mode: Successful."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# process the data. (underneath the hook, it runs through all the ADI hooks)\n",
    "# It prints out the log at the cell for ease of debug.\n",
    "status = adi_runner.run_data_consolidator()\n",
    "status"
   ]
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  {
   "cell_type": "markdown",
   "id": "b11d6fbb",
   "metadata": {},
   "source": [
    "### Retrieve data in interests\n",
    "* Property methods `.input_datasets`, `.output_datasets` to access datasets as indicated by their names, respectively.\n",
    "* The retrieved data is in the structure of AdiDatasetList[AdiDatasetDict[str, xarray.DataSet]]\n",
    "  * The most outer layer is a list-like data class AdiDatasetList, which stores the retrieved data in each processing intervals.\n",
    "  * The second layer is a dict-like data class AdiDatasetDict, with its key referencing the datastream name and value referencing retrieved datasets. \n",
    "  * The retrieved datasets are xarray.DataSet.\n",
    "* Tip: when printing AdiDatasetList and AdiDatasetDict object, it gives a summary of the encapsulated datasets.\n",
    "\n",
    "Note: we will cover `.transformed_datasets` method and the retrieved data in the remaining tutorial."
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "2d02cad2-4a3d-40f2-816f-37190e249a5b",
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      "text/plain": [
       "DataStore\n",
       "[\n",
       "\t<0/2+1> \t2019-01-19--> 2019-01-20\t{\n",
       "\t\tmet_b1: xr.Dataset(time: 1440),\n",
       "\t}\n",
       "\t<1/2+1> \t2019-01-20--> 2019-01-21\t{\n",
       "\t\tmet_b1: xr.Dataset(time: 1440),\n",
       "\t}\n",
       "\t<2/2+1> \t2019-01-21--> 2019-01-22\t{\n",
       "\t\tmet_b1: xr.Dataset(time: 1440),\n",
       "\t}\n",
       "]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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    "# explore the input-dataset(s)\n",
    "# The adi_runner.input_datasets is in the structure of AdiDatasetList[AdiDatasetDict[str, xarray.DataSet]]\n",
    "adi_runner.input_datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a813303f-4872-4c9b-92ba-922be6c725cb",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataStore (interval=\t<1/ +1>\t2019-01-20--> 2019-01-21) \n",
       "{\n",
       "\tmet_b1: xr.Dataset(time: 1440),\n",
       "}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "adi_runner.input_datasets[1]"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ded640e2-3d86-4c71-adc4-53a72ea0c818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataStore (datastream=\tmet_b1) \n",
       "[\n",
       "\t<0/2+1>\t2019-01-19--> 2019-01-20\txr.Dataset(time: 1440),\n",
       "\t<1/2+1>\t2019-01-20--> 2019-01-21\txr.Dataset(time: 1440),\n",
       "\t<2/2+1>\t2019-01-21--> 2019-01-22\txr.Dataset(time: 1440),\n",
       "]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adi_runner.input_datasets[\"met_b1\"]"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3d63e8a-e421-4228-860d-991d8a1bb863",
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       "  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.Dataset&gt; Size: 23kB\n",
       "Dimensions:             (time: 1440)\n",
       "Coordinates:\n",
       "  * time                (time) datetime64[ns] 12kB 2019-01-19 ... 2019-01-19T...\n",
       "Data variables:\n",
       "    alt                 float32 4B 318.0\n",
       "    lat                 float32 4B 36.6\n",
       "    lon                 float32 4B -97.49\n",
       "    met_temperature     (time) float32 6kB 10.97 10.99 11.04 ... -3.621 -3.612\n",
       "    qc_met_temperature  (time) int32 6kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n",
       "Attributes: (12/32)\n",
       "    command_line:                met_ingest -s sgp -f E13\n",
       "    process_version:             ingest-met-4.39-0.el6\n",
       "    dod_version:                 met-b1-7.3\n",
       "    input_source:                /data/collection/sgp/sgpmetE13.00/Table1.201...\n",
       "    site_id:                     sgp\n",
       "    platform_id:                 met\n",
       "    ...                          ...\n",
       "    qc_bit_4_description:        Difference between current and previous valu...\n",
       "    qc_bit_4_assessment:         Indeterminate\n",
       "    history:                     created by user dsmgr on machine ruby at 201...\n",
       "    __dataset_type:              ADIDatasetType.RETRIEVED\n",
       "    __datastream_dsid:           1\n",
       "    __obs_index:                 0</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-c446d3c3-c2aa-4cd7-9113-fb6e3454fb6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c446d3c3-c2aa-4cd7-9113-fb6e3454fb6f' 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-d2b7039e-2eb9-4dc8-b18d-67f1a574384c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2b7039e-2eb9-4dc8-b18d-67f1a574384c' 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'>2019-01-19 ... 2019-01-19T23:59:00</div><input id='attrs-051aeb2a-f96b-49fb-be24-6b0aa3cf5262' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-051aeb2a-f96b-49fb-be24-6b0aa3cf5262' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80a90fc7-94c4-41c3-988f-0919cc2a3908' class='xr-var-data-in' type='checkbox'><label for='data-80a90fc7-94c4-41c3-988f-0919cc2a3908' 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>units :</span></dt><dd>seconds since 2019-01-19 00:00:00 0:00</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-19T00:00:00.000000000&#x27;, &#x27;2019-01-19T00:01:00.000000000&#x27;,\n",
       "       &#x27;2019-01-19T00:02:00.000000000&#x27;, ..., &#x27;2019-01-19T23:57:00.000000000&#x27;,\n",
       "       &#x27;2019-01-19T23:58:00.000000000&#x27;, &#x27;2019-01-19T23:59:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5021d660-5ec1-4ca5-85e3-264a45d396d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5021d660-5ec1-4ca5-85e3-264a45d396d8' class='xr-section-summary' >Data variables: <span>(5)</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>alt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>318.0</div><input id='attrs-e0e7619c-b182-49c2-97f0-130f9223b938' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0e7619c-b182-49c2-97f0-130f9223b938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abaeb3e2-a402-4f3a-a8db-6f3783932bd1' class='xr-var-data-in' type='checkbox'><label for='data-abaeb3e2-a402-4f3a-a8db-6f3783932bd1' 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><dt><span>__source_ds_name :</span></dt><dd>sgpmetE13.b1</dd><dt><span>__source_var_name :</span></dt><dd>alt</dd></dl></div><div class='xr-var-data'><pre>array(318., dtype=float32)</pre></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'>36.6</div><input id='attrs-9036a816-8b62-4271-9d4e-972794e204ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9036a816-8b62-4271-9d4e-972794e204ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5787444b-e86e-48de-8fc3-775f780bf49c' class='xr-var-data-in' type='checkbox'><label for='data-5787444b-e86e-48de-8fc3-775f780bf49c' 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.]</dd><dt><span>valid_max :</span></dt><dd>[90.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>__source_ds_name :</span></dt><dd>sgpmetE13.b1</dd><dt><span>__source_var_name :</span></dt><dd>lat</dd></dl></div><div class='xr-var-data'><pre>array(36.605, 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'>-97.49</div><input id='attrs-69ea3bc1-ee65-4276-b4a1-0ea98b6674df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69ea3bc1-ee65-4276-b4a1-0ea98b6674df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53893ad8-ece1-4c05-a4bf-74c4f1b864d1' class='xr-var-data-in' type='checkbox'><label for='data-53893ad8-ece1-4c05-a4bf-74c4f1b864d1' 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.]</dd><dt><span>valid_max :</span></dt><dd>[180.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>__source_ds_name :</span></dt><dd>sgpmetE13.b1</dd><dt><span>__source_var_name :</span></dt><dd>lon</dd></dl></div><div class='xr-var-data'><pre>array(-97.485, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>met_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.97 10.99 11.04 ... -3.621 -3.612</div><input id='attrs-c712c099-289a-4445-8032-97ddb26fe18b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c712c099-289a-4445-8032-97ddb26fe18b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9891ed13-800f-4cba-b761-4e5a5b1c4f41' class='xr-var-data-in' type='checkbox'><label for='data-9891ed13-800f-4cba-b761-4e5a5b1c4f41' 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>Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>[-40.]</dd><dt><span>valid_max :</span></dt><dd>[50.]</dd><dt><span>valid_delta :</span></dt><dd>[20.]</dd><dt><span>missing_value :</span></dt><dd>[-9999.]</dd><dt><span>__source_ds_name :</span></dt><dd>sgpmetE13.b1</dd><dt><span>__source_var_name :</span></dt><dd>temp_mean</dd></dl></div><div class='xr-var-data'><pre>array([10.97 , 10.99 , 11.04 , ..., -3.611, -3.621, -3.612], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_met_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-42fb8229-a9db-4060-a8e2-e84f73c0f47c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42fb8229-a9db-4060-a8e2-e84f73c0f47c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96b042d6-1327-418a-807d-363c70628ffb' class='xr-var-data-in' type='checkbox'><label for='data-96b042d6-1327-418a-807d-363c70628ffb' 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: 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><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63bb87b4-4b99-42b6-8d58-1486f2316a0e' class='xr-section-summary-in' type='checkbox'  ><label for='section-63bb87b4-4b99-42b6-8d58-1486f2316a0e' 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-964a86ce-c021-4cf0-a566-76b7b3e1f495' class='xr-index-data-in' type='checkbox'/><label for='index-964a86ce-c021-4cf0-a566-76b7b3e1f495' 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;2019-01-19 00:00:00&#x27;, &#x27;2019-01-19 00:01:00&#x27;,\n",
       "               &#x27;2019-01-19 00:02:00&#x27;, &#x27;2019-01-19 00:03:00&#x27;,\n",
       "               &#x27;2019-01-19 00:04:00&#x27;, &#x27;2019-01-19 00:05:00&#x27;,\n",
       "               &#x27;2019-01-19 00:06:00&#x27;, &#x27;2019-01-19 00:07:00&#x27;,\n",
       "               &#x27;2019-01-19 00:08:00&#x27;, &#x27;2019-01-19 00:09:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-01-19 23:50:00&#x27;, &#x27;2019-01-19 23:51:00&#x27;,\n",
       "               &#x27;2019-01-19 23:52:00&#x27;, &#x27;2019-01-19 23:53:00&#x27;,\n",
       "               &#x27;2019-01-19 23:54:00&#x27;, &#x27;2019-01-19 23:55:00&#x27;,\n",
       "               &#x27;2019-01-19 23:56:00&#x27;, &#x27;2019-01-19 23:57:00&#x27;,\n",
       "               &#x27;2019-01-19 23:58:00&#x27;, &#x27;2019-01-19 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-b302e8fd-deac-4ba9-861d-e1f0ed112824' class='xr-section-summary-in' type='checkbox'  ><label for='section-b302e8fd-deac-4ba9-861d-e1f0ed112824' class='xr-section-summary' >Attributes: <span>(32)</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-4.39-0.el6</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/Table1.20190119_000000.raw</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>188</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 + 1.038000 * 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 ruby at 2019-01-19 01:21:00, using ingest-met-4.39-0.el6\n",
       "reprocessed for DQR ID D191106.1 more info at https://task.arm.gov/report/dqr/#s/_r::_</dd><dt><span>__dataset_type :</span></dt><dd>ADIDatasetType.RETRIEVED</dd><dt><span>__datastream_dsid :</span></dt><dd>1</dd><dt><span>__obs_index :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 23kB\n",
       "Dimensions:             (time: 1440)\n",
       "Coordinates:\n",
       "  * time                (time) datetime64[ns] 12kB 2019-01-19 ... 2019-01-19T...\n",
       "Data variables:\n",
       "    alt                 float32 4B 318.0\n",
       "    lat                 float32 4B 36.6\n",
       "    lon                 float32 4B -97.49\n",
       "    met_temperature     (time) float32 6kB 10.97 10.99 11.04 ... -3.621 -3.612\n",
       "    qc_met_temperature  (time) int32 6kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n",
       "Attributes: (12/32)\n",
       "    command_line:                met_ingest -s sgp -f E13\n",
       "    process_version:             ingest-met-4.39-0.el6\n",
       "    dod_version:                 met-b1-7.3\n",
       "    input_source:                /data/collection/sgp/sgpmetE13.00/Table1.201...\n",
       "    site_id:                     sgp\n",
       "    platform_id:                 met\n",
       "    ...                          ...\n",
       "    qc_bit_4_description:        Difference between current and previous valu...\n",
       "    qc_bit_4_assessment:         Indeterminate\n",
       "    history:                     created by user dsmgr on machine ruby at 201...\n",
       "    __dataset_type:              ADIDatasetType.RETRIEVED\n",
       "    __datastream_dsid:           1\n",
       "    __obs_index:                 0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adi_runner.input_datasets[0][\"met_b1\"]\n",
    "# or adi_runner.input_datasets[\"met_b1\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9aebcdf-09f7-44c7-bd11-28f461aaa24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataStore\n",
       "[\n",
       "\t<0/2+1> \t2019-01-19--> 2019-01-20\t{\n",
       "\t\tadiregulargrid.c1: xr.Dataset(time: 1440),\n",
       "\t}\n",
       "\t<1/2+1> \t2019-01-20--> 2019-01-21\t{\n",
       "\t\tadiregulargrid.c1: xr.Dataset(time: 1440),\n",
       "\t}\n",
       "\t<2/2+1> \t2019-01-21--> 2019-01-22\t{\n",
       "\t\tadiregulargrid.c1: xr.Dataset(time: 1440),\n",
       "\t}\n",
       "]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adi_runner.output_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1cb8eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataStore\n",
       "[\n",
       "\t<0/2+1> \t2019-01-19--> 2019-01-20\t{\n",
       "\t\tmet_b1+=>half_min_grid: xr.Dataset(time: 1441),\n",
       "\t}\n",
       "\t<1/2+1> \t2019-01-20--> 2019-01-21\t{\n",
       "\t\tmet_b1+=>half_min_grid: xr.Dataset(time: 1441),\n",
       "\t}\n",
       "\t<2/2+1> \t2019-01-21--> 2019-01-22\t{\n",
       "\t\tmet_b1+=>half_min_grid: xr.Dataset(time: 1441),\n",
       "\t}\n",
       "]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adi_runner.transformed_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07c930cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f671dc50fd0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quicklook output-dataset \"met_temperature\" variable\n",
    "ds_out = adi_runner.output_datasets[0]['adiregulargrid.c1']\n",
    "ds_out.met_temperature.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "703ea295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17915.   , 17915.125, 17915.25 , 17915.375, 17915.5  , 17915.625,\n",
       "        17915.75 , 17915.875, 17916.   ]),\n",
       " [Text(17915.0, 0, '01-19 00'),\n",
       "  Text(17915.125, 0, '01-19 03'),\n",
       "  Text(17915.25, 0, '01-19 06'),\n",
       "  Text(17915.375, 0, '01-19 09'),\n",
       "  Text(17915.5, 0, '01-19 12'),\n",
       "  Text(17915.625, 0, '01-19 15'),\n",
       "  Text(17915.75, 0, '01-19 18'),\n",
       "  Text(17915.875, 0, '01-19 21'),\n",
       "  Text(17916.0, 0, '01-20 00')])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Implement a simple VAP algorithm:\n",
    "# \"increase\" 20% of the temp-mean variable\n",
    "temp_mean_original = ds_out.met_temperature.data\n",
    "temp_mean_increased = ds_out.met_temperature.data * (1 + 0.2)\n",
    "\n",
    "plt.plot(ds_out.time, temp_mean_original, label='temp_mean_original')\n",
    "plt.plot(ds_out.time, temp_mean_increased, label='temp_mean_increased')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=30, ha='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442af42e",
   "metadata": {},
   "source": [
    "## Summary:\n",
    "* We introduce the basic usage of the AdiRunner API\n",
    "* It can easily retrieve input-datasets, transform-datasets, and output-datasets defined in PCM.\n",
    "* We introduce the data structure of the retrieved datasets, which is wrapped in a notebook-friendly data class AdiDatasetList.\n",
    "\n",
    "## What's next:\n",
    "* Introduce the transformation configuration.\n",
    "* Introduce the caching workflow.\n",
    "* Introduce the set_datastream_flag method.\n",
    "* Introduce the custom adi-hook (i.e., custom-pre-transform-hook, custom-process-data-hook)\n"
   ]
  }
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