main_cams_iasi_L2.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets comparison between CAMS and IASI (L2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and supporting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Related to the system\n",
    "import os \n",
    "\n",
    "# Related to the data retrieval\n",
    "from sentinelsat.sentinel import SentinelAPI, geojson_to_wkt\n",
    "import cdsapi\n",
    "import cfgrib\n",
    "import geojson\n",
    "import urllib3\n",
    "\n",
    "# Related to the data analysis\n",
    "import math\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from itertools import product\n",
    "import scipy.interpolate\n",
    "from scipy.spatial.distance import cdist\n",
    "from scipy.optimize import curve_fit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Related to the results\n",
    "from copy import copy\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.transforms as mtransforms\n",
    "import matplotlib.ticker as mticker\n",
    "import matplotlib.patches as mpatches\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import cartopy.feature as cfeature\n",
    "import geocoder\n",
    "import seaborn as sns\n",
    "from matplotlib import animation\n",
    "from IPython.display import HTML, display\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run functions/functions_general.ipynb\n",
    "%run functions/functions_cams.ipynb\n",
    "%run functions/functions_iasi.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hide pandas warning\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "# Hide API request warning\n",
    "urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n",
    "\n",
    "# Increase animation limit\n",
    "matplotlib.rcParams['animation.embed_limit'] = 25000000\n",
    "\n",
    "# Hide labels animation warning\n",
    "warnings.filterwarnings('ignore', category = UserWarning, module='cartopy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Define datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define component (CO, O3 or SO2)\n",
    "component_nom = 'CO'\n",
    "\n",
    "# Define model\n",
    "model = 'cams'\n",
    "model_full_name = 'cams-global-atmospheric-composition-forecasts'\n",
    "\n",
    "# Define sensor\n",
    "sensor = 'iasi'\n",
    "sensor_type = 'L2'\n",
    "satellites = ['A', 'B', 'C']\n",
    "apply_kernels = False\n",
    "\n",
    "# Define search period\n",
    "start_date = '2020-02-01'\n",
    "end_date = '2020-02-01'\n",
    "\"\"\"\n",
    "# Define extent\n",
    "area_name = 'Global'\n",
    "lon_min = -180\n",
    "lon_max = 180\n",
    "lat_min = -90\n",
    "lat_max = 90\n",
    "\"\"\"\n",
    "# Define extent\n",
    "area_name = 'Asia'\n",
    "lon_min = 110\n",
    "lon_max = 135\n",
    "lat_min = 30\n",
    "lat_max = 45\n",
    "\n",
    "# Define resolution (Minimum: 1x1)\n",
    "lat_res = 1\n",
    "lon_res = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The comparison is possible and will start now.\n"
     ]
    }
   ],
   "source": [
    "# Check if comparison is possible\n",
    "comparison_check(sensor, model, component_nom, model_full_name, sensor_type, apply_kernels)\n",
    "\n",
    "# Get component full name and molecular weight\n",
    "component, component_MW, product_type, sensor_column, column_type = components_table(sensor, component_nom, sensor_type)\n",
    "\n",
    "# Folders generation\n",
    "generate_folders(model, sensor, component_nom, sensor_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEARCH PERIOD\n",
      "- In days: ('2020-02-01',)\n",
      "SEARCH BOUNDING BOX\n",
      "Latitudes: from 30 to 45\n",
      "Longitudes: from 110 to 135\n"
     ]
    }
   ],
   "source": [
    "# Generate array with search dates\n",
    "dates = search_period(start_date, end_date, sensor, sensor_type)\n",
    "\n",
    "# Create bbox\n",
    "bbox = search_bbox(lon_min, lat_min, lon_max, lat_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Model data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download and read data as xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-30 11:06:04,449 INFO Welcome to the CDS\n",
      "2022-01-30 11:06:04,453 INFO Sending request to https://ads.atmosphere.copernicus.eu/api/v2/resources/cams-global-atmospheric-composition-forecasts\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file does not exist, it will be downloaded.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-30 11:06:04,538 INFO Request is completed\n",
      "2022-01-30 11:06:04,542 INFO Downloading https://download-0003.copernicus-atmosphere.eu/cache-compute-0003/cache/data6/adaptor.mars_constrained.external-1643494880.6687298-15925-15-f399ee7d-db32-4bd8-8199-e96a8b68f720.grib to /home/alba/adc-toolbox/data/cams/CO/CAMS_FORECAST_CO_TC_2020-02-01_2020-02-01_Asia.grib (57K)\n",
      "2022-01-30 11:06:04,657 INFO Download rate 499.4K/s\n"
     ]
    }
   ],
   "source": [
    "model_product_name, model_type = CAMS_download(dates, start_date, end_date, component, component_nom, \n",
    "                                               lat_min, lat_max, lon_min, lon_max, area_name, model_full_name, \n",
    "                                               model_level = 'single', CAMS_UID = None, CAMS_key = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  * latitude    (latitude) float64 30.0 30.4 30.8 31.2 ... 43.6 44.0 44.4 44.8\n",
       "  * longitude   (longitude) float64 110.0 110.4 110.8 ... 134.0 134.4 134.8\n",
       "    valid_time  (step) datetime64[ns] 2020-02-01 ... 2020-02-01T21:00:00\n",
       "Data variables:\n",
       "    component   (time, step, latitude, longitude) float32 0.001347 ... 0.0008058\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2022-01-30T11:06 GRIB to CDM+CF via cfgrib-0.9.9...</pre><div class='xr-wrap' hidden><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-8059ae37-70da-4e82-9fc4-27a0855b815d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8059ae37-70da-4e82-9fc4-27a0855b815d' 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'>latitude</span>: 38</li><li><span class='xr-has-index'>longitude</span>: 63</li><li><span class='xr-has-index'>step</span>: 8</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8453da70-fa0d-4c72-80bd-6446d951cdfb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8453da70-fa0d-4c72-80bd-6446d951cdfb' class='xr-section-summary' >Coordinates: <span>(7)</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>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d04d322-ab47-4c10-a79b-367694589fa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d04d322-ab47-4c10-a79b-367694589fa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4757dba6-9bc7-4be9-ac34-9d6aeab6e4ee' class='xr-var-data-in' type='checkbox'><label for='data-4757dba6-9bc7-4be9-ac34-9d6aeab6e4ee' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><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'>2020-02-01</div><input id='attrs-743d977f-b5dc-448e-a919-efc396e7138a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-743d977f-b5dc-448e-a919-efc396e7138a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b7881c0-c487-4eb6-ad05-191873e70ecd' class='xr-var-data-in' type='checkbox'><label for='data-1b7881c0-c487-4eb6-ad05-191873e70ecd' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 03:00:00 ... 21:00:00</div><input id='attrs-f944d2df-6b80-4c0b-a059-c13731a38aa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f944d2df-6b80-4c0b-a059-c13731a38aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4affb13-921b-41a0-b5fd-af66980cacbf' class='xr-var-data-in' type='checkbox'><label for='data-d4affb13-921b-41a0-b5fd-af66980cacbf' 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 since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([             0, 10800000000000, 21600000000000, 32400000000000,\n",
       "       43200000000000, 54000000000000, 64800000000000, 75600000000000],\n",
       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-39268225-4e98-46fa-81fe-821424b54428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39268225-4e98-46fa-81fe-821424b54428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccc4e3a6-c745-42e3-9d44-0b30ca63ead7' class='xr-var-data-in' type='checkbox'><label for='data-ccc4e3a6-c745-42e3-9d44-0b30ca63ead7' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.0 30.4 30.8 ... 44.0 44.4 44.8</div><input id='attrs-a13c7341-fee4-440e-b4fd-1c36747910ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a13c7341-fee4-440e-b4fd-1c36747910ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-964aa225-5d30-431c-8c32-0c0fd65b17c6' class='xr-var-data-in' type='checkbox'><label for='data-964aa225-5d30-431c-8c32-0c0fd65b17c6' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([30. , 30.4, 30.8, 31.2, 31.6, 32. , 32.4, 32.8, 33.2, 33.6, 34. , 34.4,\n",
       "       34.8, 35.2, 35.6, 36. , 36.4, 36.8, 37.2, 37.6, 38. , 38.4, 38.8, 39.2,\n",
       "       39.6, 40. , 40.4, 40.8, 41.2, 41.6, 42. , 42.4, 42.8, 43.2, 43.6, 44. ,\n",
       "       44.4, 44.8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>110.0 110.4 110.8 ... 134.4 134.8</div><input id='attrs-af1c55d8-7a71-4601-83ad-3738baa22aa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af1c55d8-7a71-4601-83ad-3738baa22aa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e9fa162-24f3-4584-b02d-d8ee92dd3b13' class='xr-var-data-in' type='checkbox'><label for='data-9e9fa162-24f3-4584-b02d-d8ee92dd3b13' 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'></dl></div><div class='xr-var-data'><pre>array([110. , 110.4, 110.8, 111.2, 111.6, 112. , 112.4, 112.8, 113.2, 113.6,\n",
       "       114. , 114.4, 114.8, 115.2, 115.6, 116. , 116.4, 116.8, 117.2, 117.6,\n",
       "       118. , 118.4, 118.8, 119.2, 119.6, 120. , 120.4, 120.8, 121.2, 121.6,\n",
       "       122. , 122.4, 122.8, 123.2, 123.6, 124. , 124.4, 124.8, 125.2, 125.6,\n",
       "       126. , 126.4, 126.8, 127.2, 127.6, 128. , 128.4, 128.8, 129.2, 129.6,\n",
       "       130. , 130.4, 130.8, 131.2, 131.6, 132. , 132.4, 132.8, 133.2, 133.6,\n",
       "       134. , 134.4, 134.8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02807b85-18ee-46bd-bf67-5ddca5af9d14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02807b85-18ee-46bd-bf67-5ddca5af9d14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e15c95fc-a17e-4690-b1f2-86843bc42bf6' class='xr-var-data-in' type='checkbox'><label for='data-e15c95fc-a17e-4690-b1f2-86843bc42bf6' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-02-01T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-01T06:00:00.000000000&#x27;, &#x27;2020-02-01T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-01T12:00:00.000000000&#x27;, &#x27;2020-02-01T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-01T18:00:00.000000000&#x27;, &#x27;2020-02-01T21:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7bc8a531-f379-45c0-8a70-fb61a25ee981' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7bc8a531-f379-45c0-8a70-fb61a25ee981' class='xr-section-summary' >Data variables: <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>component</span></div><div class='xr-var-dims'>(time, step, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.001347 0.001361 ... 0.0008058</div><input id='attrs-306dbf47-5bce-41c5-a6bc-a23fa4b3379f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-306dbf47-5bce-41c5-a6bc-a23fa4b3379f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfae2a85-57bf-41e3-b648-932532350286' class='xr-var-data-in' type='checkbox'><label for='data-dfae2a85-57bf-41e3-b648-932532350286' 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>GRIB_paramId :</span></dt><dd>210127</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>2394</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>63</dd><dt><span>GRIB_Ny :</span></dt><dd>38</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_mass_content_of_carbon_monoxide</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcco</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.4</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.4</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>44.8</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>110.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>134.8</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column Carbon monoxide</dd><dt><span>GRIB_shortName :</span></dt><dd>tcco</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column Carbon monoxide</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_carbon_monoxide</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.00134688, 0.00136141, 0.00146683, ..., 0.00095685,\n",
       "          0.0009453 , 0.00093365],\n",
       "         [0.00142438, 0.00137696, 0.00147397, ..., 0.00095057,\n",
       "          0.00094103, 0.00092983],\n",
       "         [0.00147495, 0.00148162, 0.00157885, ..., 0.00093509,\n",
       "          0.00092708, 0.00091744],\n",
       "         ...,\n",
       "         [0.00070086, 0.00069122, 0.00067561, ..., 0.00081037,\n",
       "          0.00079914, 0.00083579],\n",
       "         [0.00072319, 0.0007094 , 0.00068945, ..., 0.00083637,\n",
       "          0.00082168, 0.00081706],\n",
       "         [0.00070311, 0.00070157, 0.00069524, ..., 0.00083351,\n",
       "          0.00082572, 0.00080899]],\n",
       "\n",
       "        [[0.00132041, 0.00132985, 0.00141119, ..., 0.00095502,\n",
       "          0.00094998, 0.00094341],\n",
       "         [0.00136585, 0.00132796, 0.0014177 , ..., 0.00094494,\n",
       "          0.00094   , 0.00093332],\n",
       "         [0.0014107 , 0.00141646, 0.00153977, ..., 0.00093248,\n",
       "          0.00092544, 0.00091733],\n",
       "...\n",
       "         [0.00074302, 0.00073231, 0.0007154 , ..., 0.00081151,\n",
       "          0.00076667, 0.00078041],\n",
       "         [0.00075456, 0.00074322, 0.00072322, ..., 0.00087258,\n",
       "          0.00082627, 0.00078555],\n",
       "         [0.00073591, 0.00073358, 0.00072557, ..., 0.0008782 ,\n",
       "          0.00086172, 0.00081103]],\n",
       "\n",
       "        [[0.00138448, 0.00137518, 0.00147588, ..., 0.00095396,\n",
       "          0.00094317, 0.00093458],\n",
       "         [0.00133972, 0.0013434 , 0.00157556, ..., 0.00097146,\n",
       "          0.00095882, 0.00094495],\n",
       "         [0.00130836, 0.00135835, 0.00152022, ..., 0.0009883 ,\n",
       "          0.00097562, 0.0009601 ],\n",
       "         ...,\n",
       "         [0.0007648 , 0.00075301, 0.00073536, ..., 0.00077461,\n",
       "          0.00076033, 0.00078706],\n",
       "         [0.00077019, 0.00075719, 0.00073676, ..., 0.00083095,\n",
       "          0.00080944, 0.00079133],\n",
       "         [0.00074554, 0.00074234, 0.00073471, ..., 0.00085986,\n",
       "          0.00084365, 0.00080583]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2f147f3-eddf-4a25-93f2-66217294ab70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2f147f3-eddf-4a25-93f2-66217294ab70' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2022-01-30T11:06 GRIB to CDM+CF via cfgrib-0.9.9.0/ecCodes-2.22.1 with {&quot;source&quot;: &quot;/home/alba/adc-toolbox/data/cams/CO/CAMS_FORECAST_CO_TC_2020-02-01_2020-02-01_Asia.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (latitude: 38, longitude: 63, step: 8, time: 1)\n",
       "Coordinates:\n",
       "    number      int64 ...\n",
       "  * time        (time) datetime64[ns] 2020-02-01\n",
       "  * step        (step) timedelta64[ns] 00:00:00 03:00:00 ... 18:00:00 21:00:00\n",
       "    surface     float64 ...\n",
       "  * latitude    (latitude) float64 30.0 30.4 30.8 31.2 ... 43.6 44.0 44.4 44.8\n",
       "  * longitude   (longitude) float64 110.0 110.4 110.8 ... 134.0 134.4 134.8\n",
       "    valid_time  (step) datetime64[ns] ...\n",
       "Data variables:\n",
       "    component   (time, step, latitude, longitude) float32 0.001347 ... 0.0008058\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2022-01-30T11:06 GRIB to CDM+CF via cfgrib-0.9.9..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ds, dates, model_levels_df = CAMS_read(model_product_name, component, component_nom, dates)\n",
    "model_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect 137 levels model data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a [Pa]</th>\n",
       "      <th>b</th>\n",
       "      <th>ph [hPa]</th>\n",
       "      <th>pf [hPa]</th>\n",
       "      <th>Geopotential Altitude [m]</th>\n",
       "      <th>Geometric Altitude [m]</th>\n",
       "      <th>Temperature [K]</th>\n",
       "      <th>Density [kg/m^3]</th>\n",
       "      <th>ph [Pa]</th>\n",
       "      <th>Depth [m]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hybrid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.000365</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>79301.79</td>\n",
       "      <td>80301.65</td>\n",
       "      <td>198.05</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5580.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.102241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>73721.58</td>\n",
       "      <td>74584.91</td>\n",
       "      <td>209.21</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2605.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.666084</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>71115.75</td>\n",
       "      <td>71918.79</td>\n",
       "      <td>214.42</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>4.67</td>\n",
       "      <td>2497.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.827977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0683</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>68618.43</td>\n",
       "      <td>69365.77</td>\n",
       "      <td>221.32</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>6.83</td>\n",
       "      <td>2407.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.746966</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0975</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>66210.99</td>\n",
       "      <td>66906.53</td>\n",
       "      <td>228.06</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2320.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>62.781250</td>\n",
       "      <td>0.988500</td>\n",
       "      <td>1002.2250</td>\n",
       "      <td>1000.5165</td>\n",
       "      <td>106.54</td>\n",
       "      <td>106.54</td>\n",
       "      <td>287.46</td>\n",
       "      <td>1.212498</td>\n",
       "      <td>100222.50</td>\n",
       "      <td>27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>22.835938</td>\n",
       "      <td>0.991984</td>\n",
       "      <td>1005.3562</td>\n",
       "      <td>1003.7906</td>\n",
       "      <td>79.04</td>\n",
       "      <td>79.04</td>\n",
       "      <td>287.64</td>\n",
       "      <td>1.215710</td>\n",
       "      <td>100535.62</td>\n",
       "      <td>25.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>3.757813</td>\n",
       "      <td>0.995003</td>\n",
       "      <td>1008.2239</td>\n",
       "      <td>1006.7900</td>\n",
       "      <td>53.92</td>\n",
       "      <td>53.92</td>\n",
       "      <td>287.80</td>\n",
       "      <td>1.218650</td>\n",
       "      <td>100822.39</td>\n",
       "      <td>22.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997630</td>\n",
       "      <td>1010.8487</td>\n",
       "      <td>1009.5363</td>\n",
       "      <td>30.96</td>\n",
       "      <td>30.96</td>\n",
       "      <td>287.95</td>\n",
       "      <td>1.221341</td>\n",
       "      <td>101084.87</td>\n",
       "      <td>20.96</td>\n",
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       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1013.2500</td>\n",
       "      <td>1012.0494</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>288.09</td>\n",
       "      <td>1.223803</td>\n",
       "      <td>101325.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           a [Pa]         b   ph [hPa]   pf [hPa]  Geopotential Altitude [m]  \\\n",
       "hybrid                                                                         \n",
       "1        2.000365  0.000000     0.0200     0.0100                   79301.79   \n",
       "2        3.102241  0.000000     0.0310     0.0255                   73721.58   \n",
       "3        4.666084  0.000000     0.0467     0.0388                   71115.75   \n",
       "4        6.827977  0.000000     0.0683     0.0575                   68618.43   \n",
       "5        9.746966  0.000000     0.0975     0.0829                   66210.99   \n",
       "...           ...       ...        ...        ...                        ...   \n",
       "133     62.781250  0.988500  1002.2250  1000.5165                     106.54   \n",
       "134     22.835938  0.991984  1005.3562  1003.7906                      79.04   \n",
       "135      3.757813  0.995003  1008.2239  1006.7900                      53.92   \n",
       "136      0.000000  0.997630  1010.8487  1009.5363                      30.96   \n",
       "137      0.000000  1.000000  1013.2500  1012.0494                      10.00   \n",
       "\n",
       "       Geometric Altitude [m] Temperature [K]  Density [kg/m^3]    ph [Pa]  \\\n",
       "hybrid                                                                       \n",
       "1                    80301.65          198.05          0.000018       2.00   \n",
       "2                    74584.91          209.21          0.000042       3.10   \n",
       "3                    71918.79          214.42          0.000063       4.67   \n",
       "4                    69365.77          221.32          0.000090       6.83   \n",
       "5                    66906.53          228.06          0.000127       9.75   \n",
       "...                       ...             ...               ...        ...   \n",
       "133                    106.54          287.46          1.212498  100222.50   \n",
       "134                     79.04          287.64          1.215710  100535.62   \n",
       "135                     53.92          287.80          1.218650  100822.39   \n",
       "136                     30.96          287.95          1.221341  101084.87   \n",
       "137                     10.00          288.09          1.223803  101325.00   \n",
       "\n",
       "        Depth [m]  \n",
       "hybrid             \n",
       "1         5580.21  \n",
       "2         2605.83  \n",
       "3         2497.32  \n",
       "4         2407.44  \n",
       "5         2320.96  \n",
       "...           ...  \n",
       "133         27.50  \n",
       "134         25.12  \n",
       "135         22.96  \n",
       "136         20.96  \n",
       "137         10.00  \n",
       "\n",
       "[137 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_levels_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get CAMS levels (total or tropospheric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model component units have been converted from kg m**-2 to molec cm-2.\n"
     ]
    }
   ],
   "source": [
    "model_ds, units = model_convert_units(model, model_ds, sensor, \n",
    "                                      component_MW, model_levels_df,\n",
    "                                      start_date, end_date, component_nom, apply_kernels,\n",
    "                                      CAMS_UID = None, CAMS_key = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert CAMS data units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model total columns will be compared to the total sensor columns.\n"
     ]
    },
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       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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;\n",
       "Dimensions:     (latitude: 38, longitude: 63, step: 8, time: 1)\n",
       "Coordinates:\n",
       "    number      int64 0\n",
       "  * time        (time) datetime64[ns] 2020-02-01\n",
       "  * step        (step) timedelta64[ns] 00:00:00 03:00:00 ... 18:00:00 21:00:00\n",
       "    surface     float64 0.0\n",
       "  * latitude    (latitude) float64 30.0 30.4 30.8 31.2 ... 43.6 44.0 44.4 44.8\n",
       "  * longitude   (longitude) float64 110.0 110.4 110.8 ... 134.0 134.4 134.8\n",
       "    valid_time  (step) datetime64[ns] 2020-02-01 ... 2020-02-01T21:00:00\n",
       "Data variables:\n",
       "    component   (time, step, latitude, longitude) float32 2.896e+18 ... 1.733...\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2022-01-30T11:06 GRIB to CDM+CF via cfgrib-0.9.9...</pre><div class='xr-wrap' hidden><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-74bb34e2-2b02-4d5b-9257-a1d84c8e99ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74bb34e2-2b02-4d5b-9257-a1d84c8e99ff' 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'>latitude</span>: 38</li><li><span class='xr-has-index'>longitude</span>: 63</li><li><span class='xr-has-index'>step</span>: 8</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3d14edb7-4a5f-4782-b9b0-58057e321469' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3d14edb7-4a5f-4782-b9b0-58057e321469' class='xr-section-summary' >Coordinates: <span>(7)</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>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-b3bcace5-7e60-4f97-89e9-72871c7f128f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3bcace5-7e60-4f97-89e9-72871c7f128f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e998835e-3f46-4bfd-a549-f86253922f15' class='xr-var-data-in' type='checkbox'><label for='data-e998835e-3f46-4bfd-a549-f86253922f15' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><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'>2020-02-01</div><input id='attrs-bd49f657-783d-4f20-9285-82f0f6f9cdee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd49f657-783d-4f20-9285-82f0f6f9cdee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33c4e0bb-5471-4f2d-8164-42d5a5248863' class='xr-var-data-in' type='checkbox'><label for='data-33c4e0bb-5471-4f2d-8164-42d5a5248863' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 03:00:00 ... 21:00:00</div><input id='attrs-b85a9ae9-995c-430e-88ab-0915653f5945' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b85a9ae9-995c-430e-88ab-0915653f5945' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2bcc523-9da7-4c6c-9de4-9d8702492d11' class='xr-var-data-in' type='checkbox'><label for='data-b2bcc523-9da7-4c6c-9de4-9d8702492d11' 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 since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([             0, 10800000000000, 21600000000000, 32400000000000,\n",
       "       43200000000000, 54000000000000, 64800000000000, 75600000000000],\n",
       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-24958d7b-5513-4df0-9363-a5b0bcbf1a75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24958d7b-5513-4df0-9363-a5b0bcbf1a75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e723e57-1ab9-4ad7-a244-586cf03c3c2b' class='xr-var-data-in' type='checkbox'><label for='data-2e723e57-1ab9-4ad7-a244-586cf03c3c2b' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.0 30.4 30.8 ... 44.0 44.4 44.8</div><input id='attrs-6e81739b-6494-45a0-9ea7-f2378559d334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e81739b-6494-45a0-9ea7-f2378559d334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2e7cec0-a06b-4de8-baa0-20166bbf56a9' class='xr-var-data-in' type='checkbox'><label for='data-d2e7cec0-a06b-4de8-baa0-20166bbf56a9' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([30. , 30.4, 30.8, 31.2, 31.6, 32. , 32.4, 32.8, 33.2, 33.6, 34. , 34.4,\n",
       "       34.8, 35.2, 35.6, 36. , 36.4, 36.8, 37.2, 37.6, 38. , 38.4, 38.8, 39.2,\n",
       "       39.6, 40. , 40.4, 40.8, 41.2, 41.6, 42. , 42.4, 42.8, 43.2, 43.6, 44. ,\n",
       "       44.4, 44.8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>110.0 110.4 110.8 ... 134.4 134.8</div><input id='attrs-66c8ab31-a39f-452a-8548-032ce7cc03dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66c8ab31-a39f-452a-8548-032ce7cc03dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00982ae1-ad1e-4ca5-bf3b-10318080163e' class='xr-var-data-in' type='checkbox'><label for='data-00982ae1-ad1e-4ca5-bf3b-10318080163e' 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'></dl></div><div class='xr-var-data'><pre>array([110. , 110.4, 110.8, 111.2, 111.6, 112. , 112.4, 112.8, 113.2, 113.6,\n",
       "       114. , 114.4, 114.8, 115.2, 115.6, 116. , 116.4, 116.8, 117.2, 117.6,\n",
       "       118. , 118.4, 118.8, 119.2, 119.6, 120. , 120.4, 120.8, 121.2, 121.6,\n",
       "       122. , 122.4, 122.8, 123.2, 123.6, 124. , 124.4, 124.8, 125.2, 125.6,\n",
       "       126. , 126.4, 126.8, 127.2, 127.6, 128. , 128.4, 128.8, 129.2, 129.6,\n",
       "       130. , 130.4, 130.8, 131.2, 131.6, 132. , 132.4, 132.8, 133.2, 133.6,\n",
       "       134. , 134.4, 134.8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2020-02-01T21:00:00</div><input id='attrs-756c4a53-dbe0-494e-832e-991f7c085ce6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-756c4a53-dbe0-494e-832e-991f7c085ce6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ea52813-37e4-4f4e-bf3f-3afabd74b237' class='xr-var-data-in' type='checkbox'><label for='data-3ea52813-37e4-4f4e-bf3f-3afabd74b237' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-02-01T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-01T06:00:00.000000000&#x27;, &#x27;2020-02-01T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-01T12:00:00.000000000&#x27;, &#x27;2020-02-01T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-01T18:00:00.000000000&#x27;, &#x27;2020-02-01T21:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6e84e08-0ac9-4030-afc3-02aecac57911' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6e84e08-0ac9-4030-afc3-02aecac57911' class='xr-section-summary' >Data variables: <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>component</span></div><div class='xr-var-dims'>(time, step, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.896e+18 2.927e+18 ... 1.733e+18</div><input id='attrs-d49a1e74-840e-4929-9a58-0bab7554dfda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d49a1e74-840e-4929-9a58-0bab7554dfda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fe0740b-b8b6-4091-9c0b-468bf66a6c46' class='xr-var-data-in' type='checkbox'><label for='data-4fe0740b-b8b6-4091-9c0b-468bf66a6c46' 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>units :</span></dt><dd>molec cm-2</dd></dl></div><div class='xr-var-data'><pre>array([[[[2.8957123e+18, 2.9269588e+18, 3.1536116e+18, ...,\n",
       "          2.0571751e+18, 2.0323416e+18, 2.0072961e+18],\n",
       "         [3.0623452e+18, 2.9603971e+18, 3.1689508e+18, ...,\n",
       "          2.0436731e+18, 2.0231561e+18, 1.9990771e+18],\n",
       "         [3.1710726e+18, 3.1853982e+18, 3.3944425e+18, ...,\n",
       "          2.0103880e+18, 1.9931719e+18, 1.9724525e+18],\n",
       "         ...,\n",
       "         [1.5068040e+18, 1.4860764e+18, 1.4525320e+18, ...,\n",
       "          1.7422574e+18, 1.7181026e+18, 1.7969055e+18],\n",
       "         [1.5548100e+18, 1.5251781e+18, 1.4822740e+18, ...,\n",
       "          1.7981423e+18, 1.7665581e+18, 1.7566338e+18],\n",
       "         [1.5116541e+18, 1.5083388e+18, 1.4947373e+18, ...,\n",
       "          1.7920073e+18, 1.7752641e+18, 1.7392909e+18]],\n",
       "\n",
       "        [[2.8388112e+18, 2.8591112e+18, 3.0339905e+18, ...,\n",
       "          2.0532391e+18, 2.0424058e+18, 2.0282855e+18],\n",
       "         [2.9365130e+18, 2.8550361e+18, 3.0479724e+18, ...,\n",
       "          2.0315789e+18, 2.0209506e+18, 2.0065866e+18],\n",
       "         [3.0329393e+18, 3.0453055e+18, 3.3104272e+18, ...,\n",
       "          2.0047720e+18, 1.9896362e+18, 1.9721998e+18],\n",
       "...\n",
       "         [1.5974530e+18, 1.5744266e+18, 1.5380795e+18, ...,\n",
       "          1.7446927e+18, 1.6482945e+18, 1.6778391e+18],\n",
       "         [1.6222709e+18, 1.5978754e+18, 1.5548822e+18, ...,\n",
       "          1.8760031e+18, 1.7764440e+18, 1.6888817e+18],\n",
       "         [1.5821660e+18, 1.5771568e+18, 1.5599470e+18, ...,\n",
       "          1.8880730e+18, 1.8526477e+18, 1.7436729e+18]],\n",
       "\n",
       "        [[2.9765555e+18, 2.9565705e+18, 3.1730600e+18, ...,\n",
       "          2.0509559e+18, 2.0277574e+18, 2.0092917e+18],\n",
       "         [2.8803211e+18, 2.8882290e+18, 3.3873598e+18, ...,\n",
       "          2.0885876e+18, 2.0614175e+18, 2.0315922e+18],\n",
       "         [2.8129133e+18, 2.9203826e+18, 3.2683931e+18, ...,\n",
       "          2.1247851e+18, 2.0975348e+18, 2.0641671e+18],\n",
       "         ...,\n",
       "         [1.6442865e+18, 1.6189211e+18, 1.5809776e+18, ...,\n",
       "          1.6653688e+18, 1.6346746e+18, 1.6921359e+18],\n",
       "         [1.6558714e+18, 1.6279239e+18, 1.5839925e+18, ...,\n",
       "          1.7865030e+18, 1.7402541e+18, 1.7013084e+18],\n",
       "         [1.6028796e+18, 1.5959998e+18, 1.5795800e+18, ...,\n",
       "          1.8486497e+18, 1.8137929e+18, 1.7325006e+18]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e533582-5a93-49bb-bbe9-d930e9302684' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e533582-5a93-49bb-bbe9-d930e9302684' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2022-01-30T11:06 GRIB to CDM+CF via cfgrib-0.9.9.0/ecCodes-2.22.1 with {&quot;source&quot;: &quot;/home/alba/adc-toolbox/data/cams/CO/CAMS_FORECAST_CO_TC_2020-02-01_2020-02-01_Asia.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (latitude: 38, longitude: 63, step: 8, time: 1)\n",
       "Coordinates:\n",
       "    number      int64 0\n",
       "  * time        (time) datetime64[ns] 2020-02-01\n",
       "  * step        (step) timedelta64[ns] 00:00:00 03:00:00 ... 18:00:00 21:00:00\n",
       "    surface     float64 0.0\n",
       "  * latitude    (latitude) float64 30.0 30.4 30.8 31.2 ... 43.6 44.0 44.4 44.8\n",
       "  * longitude   (longitude) float64 110.0 110.4 110.8 ... 134.0 134.4 134.8\n",
       "    valid_time  (step) datetime64[ns] 2020-02-01 ... 2020-02-01T21:00:00\n",
       "Data variables:\n",
       "    component   (time, step, latitude, longitude) float32 2.896e+18 ... 1.733...\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2022-01-30T11:06 GRIB to CDM+CF via cfgrib-0.9.9..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ds = CAMS_get_levels_data(model_ds, model_product_name, model_levels_df, \n",
    "                                column_type, lat_min, lat_max, lon_min, lon_max)\n",
    "model_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Sensor data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download and read data as xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULTS\n",
      "For 2020-02-01:\n",
      "IASI_METOPA_L2_CO_20200201_ULB-LATMOS_V6.5.0.nc was downloaded.\n",
      "IASI_METOPB_L2_CO_20200201_ULB-LATMOS_V6.5.0.nc was downloaded.\n",
      "IASI_METOPC_L2_CO_20200201_ULB-LATMOS_V6.5.0.nc was downloaded.\n"
     ]
    }
   ],
   "source": [
    "dates = sensor_download(sensor, sensor_type, component_nom, dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2669703/1362158709.py:86: RuntimeWarning: invalid value encountered in true_divide\n",
      "  zi = zi / counts\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sensor_column&#x27; (time: 1, latitude: 180, longitude: 360)&gt;\n",
       "array([[[0.02426029, 0.024177  , 0.0233791 , ..., 0.02614328,\n",
       "         0.02574695,        nan],\n",
       "        [0.0291054 , 0.02650232, 0.02995933, ..., 0.02811868,\n",
       "         0.02861892,        nan],\n",
       "        [0.01848878, 0.01850079, 0.01918354, ..., 0.01987049,\n",
       "         0.02048329,        nan],\n",
       "        ...,\n",
       "        [0.02357248, 0.0255702 , 0.0259032 , ..., 0.02480064,\n",
       "         0.02576851,        nan],\n",
       "        [0.02805027,        nan, 0.03028313, ..., 0.02423287,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * longitude   (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude    (latitude) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "    delta_time  datetime64[ns] 2020-02-01\n",
       "  * time        (time) datetime64[ns] 2020-02-01\n",
       "Attributes:\n",
       "    units:    mol m-2</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sensor_column'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 180</li><li><span class='xr-has-index'>longitude</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-36626579-25dd-431e-91ee-42a200af1f91' class='xr-array-in' type='checkbox' checked><label for='section-36626579-25dd-431e-91ee-42a200af1f91' 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>0.02426 0.02418 0.02338 0.01865 0.0211 0.0203 ... nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[0.02426029, 0.024177  , 0.0233791 , ..., 0.02614328,\n",
       "         0.02574695,        nan],\n",
       "        [0.0291054 , 0.02650232, 0.02995933, ..., 0.02811868,\n",
       "         0.02861892,        nan],\n",
       "        [0.01848878, 0.01850079, 0.01918354, ..., 0.01987049,\n",
       "         0.02048329,        nan],\n",
       "        ...,\n",
       "        [0.02357248, 0.0255702 , 0.0259032 , ..., 0.02480064,\n",
       "         0.02576851,        nan],\n",
       "        [0.02805027,        nan, 0.03028313, ..., 0.02423287,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-07ecb67c-c438-400c-8715-175c45e43b6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07ecb67c-c438-400c-8715-175c45e43b6f' class='xr-section-summary' >Coordinates: <span>(4)</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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-6861d7f1-9d1f-47c1-8f6a-6cfd509a8c14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6861d7f1-9d1f-47c1-8f6a-6cfd509a8c14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9e1ac34-2d70-4d0a-bad9-e8cd61112aca' class='xr-var-data-in' type='checkbox'><label for='data-c9e1ac34-2d70-4d0a-bad9-e8cd61112aca' 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'></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-d2d0cdd6-352a-413d-a0a4-ae248d5e047f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2d0cdd6-352a-413d-a0a4-ae248d5e047f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec706b7e-c200-4152-8402-18b8c3b57ca1' class='xr-var-data-in' type='checkbox'><label for='data-ec706b7e-c200-4152-8402-18b8c3b57ca1' 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'></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01</div><input id='attrs-bdcd8509-c22a-4831-be93-06b39418c734' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bdcd8509-c22a-4831-be93-06b39418c734' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2a9f4f9-550a-4229-b7d2-2904c0113d93' class='xr-var-data-in' type='checkbox'><label for='data-b2a9f4f9-550a-4229-b7d2-2904c0113d93' 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'></dl></div><div class='xr-var-data'><pre>array(&#x27;2020-02-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><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'>2020-02-01</div><input id='attrs-19260aa4-c874-4a35-b9e5-56de47699f64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19260aa4-c874-4a35-b9e5-56de47699f64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53740733-75d9-4309-8a1b-20e02a3e7885' class='xr-var-data-in' type='checkbox'><label for='data-53740733-75d9-4309-8a1b-20e02a3e7885' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7fa9bbe-97de-4053-ba84-a77f36d8ad7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7fa9bbe-97de-4053-ba84-a77f36d8ad7c' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mol m-2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sensor_column' (time: 1, latitude: 180, longitude: 360)>\n",
       "array([[[0.02426029, 0.024177  , 0.0233791 , ..., 0.02614328,\n",
       "         0.02574695,        nan],\n",
       "        [0.0291054 , 0.02650232, 0.02995933, ..., 0.02811868,\n",
       "         0.02861892,        nan],\n",
       "        [0.01848878, 0.01850079, 0.01918354, ..., 0.01987049,\n",
       "         0.02048329,        nan],\n",
       "        ...,\n",
       "        [0.02357248, 0.0255702 , 0.0259032 , ..., 0.02480064,\n",
       "         0.02576851,        nan],\n",
       "        [0.02805027,        nan, 0.03028313, ..., 0.02423287,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * longitude   (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude    (latitude) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "    delta_time  datetime64[ns] 2020-02-01\n",
       "  * time        (time) datetime64[ns] 2020-02-01\n",
       "Attributes:\n",
       "    units:    mol m-2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensor_ds, _, _ = sensor_read(sensor, sensor_type, sensor_column, component_nom, dates, lat_res, lon_res)\n",
    "sensor_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert IASI data units (From mol/m<sup>2</sup> to molecules/cm<sup>2</sup>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sensor component units have been converted from mol m-2 to molec cm-2.\n"
     ]
    },
    {
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       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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;sensor_column&#x27; (time: 1, latitude: 180, longitude: 360)&gt;\n",
       "array([[[1.46098860e+18, 1.45597278e+18, 1.40792209e+18, ...,\n",
       "         1.57438522e+18, 1.55051714e+18,            nan],\n",
       "        [1.75276778e+18, 1.59600696e+18, 1.80419294e+18, ...,\n",
       "         1.69334617e+18, 1.72347144e+18,            nan],\n",
       "        [1.11342032e+18, 1.11414328e+18, 1.15525957e+18, ...,\n",
       "         1.19662878e+18, 1.23353227e+18,            nan],\n",
       "        ...,\n",
       "        [1.41956749e+18, 1.53987310e+18, 1.55992720e+18, ...,\n",
       "         1.49352918e+18, 1.55181556e+18,            nan],\n",
       "        [1.68922641e+18,            nan, 1.82369242e+18, ...,\n",
       "         1.45933758e+18,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]])\n",
       "Coordinates:\n",
       "  * longitude   (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude    (latitude) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "    delta_time  datetime64[ns] 2020-02-01\n",
       "  * time        (time) datetime64[ns] 2020-02-01\n",
       "Attributes:\n",
       "    units:    molec cm-2</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sensor_column'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 180</li><li><span class='xr-has-index'>longitude</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1e27cc81-5058-4ba3-8fdb-cdb3422c50fd' class='xr-array-in' type='checkbox' checked><label for='section-1e27cc81-5058-4ba3-8fdb-cdb3422c50fd' 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>1.461e+18 1.456e+18 1.408e+18 1.123e+18 1.27e+18 ... nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[1.46098860e+18, 1.45597278e+18, 1.40792209e+18, ...,\n",
       "         1.57438522e+18, 1.55051714e+18,            nan],\n",
       "        [1.75276778e+18, 1.59600696e+18, 1.80419294e+18, ...,\n",
       "         1.69334617e+18, 1.72347144e+18,            nan],\n",
       "        [1.11342032e+18, 1.11414328e+18, 1.15525957e+18, ...,\n",
       "         1.19662878e+18, 1.23353227e+18,            nan],\n",
       "        ...,\n",
       "        [1.41956749e+18, 1.53987310e+18, 1.55992720e+18, ...,\n",
       "         1.49352918e+18, 1.55181556e+18,            nan],\n",
       "        [1.68922641e+18,            nan, 1.82369242e+18, ...,\n",
       "         1.45933758e+18,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-4b8288ba-9dac-4c9d-8180-062cee804582' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4b8288ba-9dac-4c9d-8180-062cee804582' class='xr-section-summary' >Coordinates: <span>(4)</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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-c5374060-967e-41b6-8165-e530d12f8d0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5374060-967e-41b6-8165-e530d12f8d0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd222a76-8e69-42b5-adf0-7cd1bfe241a8' class='xr-var-data-in' type='checkbox'><label for='data-fd222a76-8e69-42b5-adf0-7cd1bfe241a8' 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'></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-e861d9d4-cae5-491a-bba7-817c89b9397f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e861d9d4-cae5-491a-bba7-817c89b9397f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4beee3cc-a050-4c0b-92f1-0b2e9ad1a563' class='xr-var-data-in' type='checkbox'><label for='data-4beee3cc-a050-4c0b-92f1-0b2e9ad1a563' 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'></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01</div><input id='attrs-5ea91bce-e78f-413d-956b-47de89813125' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ea91bce-e78f-413d-956b-47de89813125' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa8dc0b-e2c1-40dc-b2d8-307c7d7609b8' class='xr-var-data-in' type='checkbox'><label for='data-caa8dc0b-e2c1-40dc-b2d8-307c7d7609b8' 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'></dl></div><div class='xr-var-data'><pre>array(&#x27;2020-02-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><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'>2020-02-01</div><input id='attrs-7115fcb7-d1a0-4ae2-82eb-cc882e655e12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7115fcb7-d1a0-4ae2-82eb-cc882e655e12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7139977c-4233-4aa5-b3f1-780847e804b5' class='xr-var-data-in' type='checkbox'><label for='data-7139977c-4233-4aa5-b3f1-780847e804b5' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0d4475b-8ed7-4254-b96f-a0ce5dee5aef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e0d4475b-8ed7-4254-b96f-a0ce5dee5aef' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>molec cm-2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sensor_column' (time: 1, latitude: 180, longitude: 360)>\n",
       "array([[[1.46098860e+18, 1.45597278e+18, 1.40792209e+18, ...,\n",
       "         1.57438522e+18, 1.55051714e+18,            nan],\n",
       "        [1.75276778e+18, 1.59600696e+18, 1.80419294e+18, ...,\n",
       "         1.69334617e+18, 1.72347144e+18,            nan],\n",
       "        [1.11342032e+18, 1.11414328e+18, 1.15525957e+18, ...,\n",
       "         1.19662878e+18, 1.23353227e+18,            nan],\n",
       "        ...,\n",
       "        [1.41956749e+18, 1.53987310e+18, 1.55992720e+18, ...,\n",
       "         1.49352918e+18, 1.55181556e+18,            nan],\n",
       "        [1.68922641e+18,            nan, 1.82369242e+18, ...,\n",
       "         1.45933758e+18,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]])\n",
       "Coordinates:\n",
       "  * longitude   (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude    (latitude) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "    delta_time  datetime64[ns] 2020-02-01\n",
       "  * time        (time) datetime64[ns] 2020-02-01\n",
       "Attributes:\n",
       "    units:    molec cm-2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensor_ds = sensor_convert_units(sensor_ds, sensor, component_nom)\n",
    "sensor_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Match data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOR DATE: 2020-02-01\n",
      "The model dataset does not contain levels data.\n",
      "The model dataset will be merged with the sensor dataset by nearest neighbours.\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>delta_time</th>\n",
       "      <th>time</th>\n",
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       "      <td>29.5</td>\n",
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       "      <td>29.5</td>\n",
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       "      <td>2020-02-01</td>\n",
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       "      <td>29.5</td>\n",
       "      <td>113.5</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2.929941e+18</td>\n",
       "      <td>0</td>\n",
       "      <td>4.200092e+18</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.871950e+18</td>\n",
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       "      <th>412</th>\n",
       "      <td>44.5</td>\n",
       "      <td>131.5</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>1.553249e+18</td>\n",
       "      <td>0</td>\n",
       "      <td>1.772683e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>44.5</td>\n",
       "      <td>132.5</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>1.726649e+18</td>\n",
       "      <td>0</td>\n",
       "      <td>1.826557e+18</td>\n",
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       "      <th>414</th>\n",
       "      <td>44.5</td>\n",
       "      <td>133.5</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020-02-01</td>\n",
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       "      <td>1.786769e+18</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     latitude  longitude delta_time       time  sensor_column  step_index  \\\n",
       "0        29.5      109.5 2020-02-01 2020-02-01            NaN           0   \n",
       "1        29.5      110.5 2020-02-01 2020-02-01            NaN           0   \n",
       "2        29.5      111.5 2020-02-01 2020-02-01   4.003890e+18           0   \n",
       "3        29.5      112.5 2020-02-01 2020-02-01   3.273320e+18           0   \n",
       "4        29.5      113.5 2020-02-01 2020-02-01   2.929941e+18           0   \n",
       "..        ...        ...        ...        ...            ...         ...   \n",
       "411      44.5      130.5 2020-02-01 2020-02-01   1.665525e+18           0   \n",
       "412      44.5      131.5 2020-02-01 2020-02-01   1.553249e+18           0   \n",
       "413      44.5      132.5 2020-02-01 2020-02-01   1.726649e+18           0   \n",
       "414      44.5      133.5 2020-02-01 2020-02-01   1.764015e+18           0   \n",
       "415      44.5      134.5 2020-02-01 2020-02-01   1.796754e+18           0   \n",
       "\n",
       "     model_column  \n",
       "0    2.895712e+18  \n",
       "1    2.926959e+18  \n",
       "2    3.968372e+18  \n",
       "3    4.214759e+18  \n",
       "4    4.200092e+18  \n",
       "..            ...  \n",
       "411  1.871950e+18  \n",
       "412  1.772683e+18  \n",
       "413  1.826557e+18  \n",
       "414  1.786769e+18  \n",
       "415  1.766558e+18  \n",
       "\n",
       "[416 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df = generate_match_df(sensor_ds, model_ds, bbox, sensor, component_nom, sensor_type, apply_kernels)\n",
    "match_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Merge data and calculate difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>model_column</th>\n",
       "      <th>sensor_column</th>\n",
       "      <th>difference</th>\n",
       "      <th>relative_difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">29.5</th>\n",
       "      <th>109.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>2.895712e+18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>2.926959e+18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>3.968372e+18</td>\n",
       "      <td>4.003890e+18</td>\n",
       "      <td>-3.551772e+16</td>\n",
       "      <td>-0.008871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>4.214759e+18</td>\n",
       "      <td>3.273320e+18</td>\n",
       "      <td>9.414384e+17</td>\n",
       "      <td>0.287610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>4.200092e+18</td>\n",
       "      <td>2.929941e+18</td>\n",
       "      <td>1.270151e+18</td>\n",
       "      <td>0.433507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">44.5</th>\n",
       "      <th>130.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1.871950e+18</td>\n",
       "      <td>1.665525e+18</td>\n",
       "      <td>2.064246e+17</td>\n",
       "      <td>0.123940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1.772683e+18</td>\n",
       "      <td>1.553249e+18</td>\n",
       "      <td>2.194346e+17</td>\n",
       "      <td>0.141275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1.826557e+18</td>\n",
       "      <td>1.726649e+18</td>\n",
       "      <td>9.990804e+16</td>\n",
       "      <td>0.057862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1.786769e+18</td>\n",
       "      <td>1.764015e+18</td>\n",
       "      <td>2.275382e+16</td>\n",
       "      <td>0.012899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1.766558e+18</td>\n",
       "      <td>1.796754e+18</td>\n",
       "      <td>-3.019548e+16</td>\n",
       "      <td>-0.016806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               model_column  sensor_column    difference  \\\n",
       "latitude longitude time                                                    \n",
       "29.5     109.5     2020-02-01  2.895712e+18            NaN           NaN   \n",
       "         110.5     2020-02-01  2.926959e+18            NaN           NaN   \n",
       "         111.5     2020-02-01  3.968372e+18   4.003890e+18 -3.551772e+16   \n",
       "         112.5     2020-02-01  4.214759e+18   3.273320e+18  9.414384e+17   \n",
       "         113.5     2020-02-01  4.200092e+18   2.929941e+18  1.270151e+18   \n",
       "...                                     ...            ...           ...   \n",
       "44.5     130.5     2020-02-01  1.871950e+18   1.665525e+18  2.064246e+17   \n",
       "         131.5     2020-02-01  1.772683e+18   1.553249e+18  2.194346e+17   \n",
       "         132.5     2020-02-01  1.826557e+18   1.726649e+18  9.990804e+16   \n",
       "         133.5     2020-02-01  1.786769e+18   1.764015e+18  2.275382e+16   \n",
       "         134.5     2020-02-01  1.766558e+18   1.796754e+18 -3.019548e+16   \n",
       "\n",
       "                               relative_difference  \n",
       "latitude longitude time                             \n",
       "29.5     109.5     2020-02-01                  NaN  \n",
       "         110.5     2020-02-01                  NaN  \n",
       "         111.5     2020-02-01            -0.008871  \n",
       "         112.5     2020-02-01             0.287610  \n",
       "         113.5     2020-02-01             0.433507  \n",
       "...                                            ...  \n",
       "44.5     130.5     2020-02-01             0.123940  \n",
       "         131.5     2020-02-01             0.141275  \n",
       "         132.5     2020-02-01             0.057862  \n",
       "         133.5     2020-02-01             0.012899  \n",
       "         134.5     2020-02-01            -0.016806  \n",
       "\n",
       "[416 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df = generate_merge_df(match_df, sensor_ds, model_ds, sensor, apply_kernels)\n",
    "merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_column</th>\n",
       "      <th>sensor_column</th>\n",
       "      <th>difference</th>\n",
       "      <th>relative_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.160000e+02</td>\n",
       "      <td>4.060000e+02</td>\n",
       "      <td>4.060000e+02</td>\n",
       "      <td>406.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.501781e+18</td>\n",
       "      <td>2.316503e+18</td>\n",
       "      <td>1.704205e+17</td>\n",
       "      <td>0.069540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.220988e+17</td>\n",
       "      <td>5.629447e+17</td>\n",
       "      <td>5.191168e+17</td>\n",
       "      <td>0.202334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.328358e+18</td>\n",
       "      <td>1.221350e+18</td>\n",
       "      <td>-1.181936e+18</td>\n",
       "      <td>-0.322603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.921252e+18</td>\n",
       "      <td>1.811077e+18</td>\n",
       "      <td>-1.508957e+17</td>\n",
       "      <td>-0.070411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.195477e+18</td>\n",
       "      <td>2.271134e+18</td>\n",
       "      <td>5.001495e+16</td>\n",
       "      <td>0.025423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.014831e+18</td>\n",
       "      <td>2.710987e+18</td>\n",
       "      <td>4.352479e+17</td>\n",
       "      <td>0.171600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.351103e+18</td>\n",
       "      <td>4.286582e+18</td>\n",
       "      <td>2.153078e+18</td>\n",
       "      <td>0.769820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model_column  sensor_column    difference  relative_difference\n",
       "count  4.160000e+02   4.060000e+02  4.060000e+02           406.000000\n",
       "mean   2.501781e+18   2.316503e+18  1.704205e+17             0.069540\n",
       "std    8.220988e+17   5.629447e+17  5.191168e+17             0.202334\n",
       "min    1.328358e+18   1.221350e+18 -1.181936e+18            -0.322603\n",
       "25%    1.921252e+18   1.811077e+18 -1.508957e+17            -0.070411\n",
       "50%    2.195477e+18   2.271134e+18  5.001495e+16             0.025423\n",
       "75%    3.014831e+18   2.710987e+18  4.352479e+17             0.171600\n",
       "max    5.351103e+18   4.286582e+18  2.153078e+18             0.769820"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descr_stats_table = merge_df.describe()\n",
    "descr_stats_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Show comparison plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select plot dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The plots will be shown for the following dates:\n",
      "['2020-02-01']\n"
     ]
    }
   ],
   "source": [
    "plot_dates = plot_period(sensor_ds, sensor_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select plot extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The plots will be shown for the following spatial extent: \n",
      "((110, 30), (135, 45))\n"
     ]
    }
   ],
   "source": [
    "plot_bbox = plot_extent(bbox)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare CAMS and IASI total columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame length between 110 and 135 should be one of these options: [1, 5, 25]\n",
      "Frame length between 30 and 45 should be one of these options: [1, 3, 5, 15]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose distribution (aggregated, individual or animated)\n",
    "plot_type = 'individual'\n",
    "\n",
    "# Define range (original, equal, centered or manual)\n",
    "range_type = 'manual'\n",
    "vmin_manual, vmax_manual = 0, 1*10**19\n",
    "vmin_manual_diff, vmax_manual_diff = None, None\n",
    "\n",
    "# Define projection and colors\n",
    "projection = ccrs.PlateCarree()\n",
    "color_scale = ['turbo', 'turbo', 'coolwarm']\n",
    "\n",
    "# Get and decide the best width and height of the B/W frame lines\n",
    "options_height_lon = get_frame_possible_lengths(lon_min, lon_max)\n",
    "options_width_lat = get_frame_possible_lengths(lat_min, lat_max)\n",
    "width_lon = 5\n",
    "height_lat = 3\n",
    "\n",
    "visualize_model_vs_sensor(model, sensor, component_nom, units, merge_df, plot_dates, plot_bbox, 20, 1.05, \n",
    "                          model_type, sensor_type, range_type, plot_type, projection,\n",
    "                          color_scale, width_lon, height_lat, vmin_manual, vmax_manual,\n",
    "                          vmin_manual_diff, vmax_manual_diff,\n",
    "                          bbox_list = None, coords_list = None, regions_names = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Retrieve nearest values to specific coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>model_column</th>\n",
       "      <th>sensor_column</th>\n",
       "      <th>difference</th>\n",
       "      <th>relative_difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>lat_search</th>\n",
       "      <th>lon_search</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prague</th>\n",
       "      <th>50.07</th>\n",
       "      <th>14.43</th>\n",
       "      <th>44.5</th>\n",
       "      <th>109.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1.554810e+18</td>\n",
       "      <td>1.500351e+18</td>\n",
       "      <td>5.445865e+16</td>\n",
       "      <td>0.036297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seoul</th>\n",
       "      <th>37.54</th>\n",
       "      <th>126.95</th>\n",
       "      <th>37.5</th>\n",
       "      <th>126.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>2.991355e+18</td>\n",
       "      <td>1.906956e+18</td>\n",
       "      <td>1.084399e+18</td>\n",
       "      <td>0.568654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lagos</th>\n",
       "      <th>6.45</th>\n",
       "      <th>3.38</th>\n",
       "      <th>31.5</th>\n",
       "      <th>109.5</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>2.938934e+18</td>\n",
       "      <td>2.425194e+18</td>\n",
       "      <td>5.137402e+17</td>\n",
       "      <td>0.211835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            model_column  \\\n",
       "region lat_search lon_search latitude longitude time                       \n",
       "Prague 50.07      14.43      44.5     109.5     2020-02-01  1.554810e+18   \n",
       "Seoul  37.54      126.95     37.5     126.5     2020-02-01  2.991355e+18   \n",
       "Lagos  6.45       3.38       31.5     109.5     2020-02-01  2.938934e+18   \n",
       "\n",
       "                                                            sensor_column  \\\n",
       "region lat_search lon_search latitude longitude time                        \n",
       "Prague 50.07      14.43      44.5     109.5     2020-02-01   1.500351e+18   \n",
       "Seoul  37.54      126.95     37.5     126.5     2020-02-01   1.906956e+18   \n",
       "Lagos  6.45       3.38       31.5     109.5     2020-02-01   2.425194e+18   \n",
       "\n",
       "                                                              difference  \\\n",
       "region lat_search lon_search latitude longitude time                       \n",
       "Prague 50.07      14.43      44.5     109.5     2020-02-01  5.445865e+16   \n",
       "Seoul  37.54      126.95     37.5     126.5     2020-02-01  1.084399e+18   \n",
       "Lagos  6.45       3.38       31.5     109.5     2020-02-01  5.137402e+17   \n",
       "\n",
       "                                                            relative_difference  \n",
       "region lat_search lon_search latitude longitude time                             \n",
       "Prague 50.07      14.43      44.5     109.5     2020-02-01             0.036297  \n",
       "Seoul  37.54      126.95     37.5     126.5     2020-02-01             0.568654  \n",
       "Lagos  6.45       3.38       31.5     109.5     2020-02-01             0.211835  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions_names = ('Prague', 'Seoul', 'Lagos')\n",
    "coords_list = (50.07, 14.43,\n",
    "               37.54, 126.95,\n",
    "               6.45, 3.38)\n",
    "ymin = 0\n",
    "ymax = 0.5*10**19\n",
    "xticks = plot_dates\n",
    "\n",
    "timeseries_table = timeseries(merge_df, component_nom, sensor, sensor_type, model, \n",
    "                              plot_dates, units, ymin, ymax, xticks, regions_names, coords_list)\n",
    "timeseries_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Scatter plots by bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2016x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>Location</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>((110, 30), (135, 45))</td>\n",
       "      <td>1.181902</td>\n",
       "      <td>-2.439761e+17</td>\n",
       "      <td>0.638828</td>\n",
       "      <td>4.967571e+17</td>\n",
       "      <td>2.467676e+35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Period                Location         A             B        R2  \\\n",
       "0 2020-02-01  ((110, 30), (135, 45))  1.181902 -2.439761e+17  0.638828   \n",
       "\n",
       "           RMSE           MSE  \n",
       "0  4.967571e+17  2.467676e+35  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_seasons = False\n",
    "extent_definition = 'bbox' # bbox or country\n",
    "scatter_plot_type = 'individual' # aggregated or individual\n",
    "\n",
    "lim_min = 0\n",
    "lim_max = 0.6*10**19\n",
    "\n",
    "summary = scatter_plot(merge_df, component_nom, units, sensor, plot_dates, 1, \n",
    "                       extent_definition, show_seasons, scatter_plot_type, \n",
    "                       lim_min, lim_max, plot_bbox)\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Scatter plots by season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2016x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>Location</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Winter]</td>\n",
       "      <td>((110, 30), (135, 45))</td>\n",
       "      <td>1.181902</td>\n",
       "      <td>-2.439761e+17</td>\n",
       "      <td>0.638828</td>\n",
       "      <td>4.967571e+17</td>\n",
       "      <td>2.467676e+35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Period                Location         A             B        R2  \\\n",
       "0  [Winter]  ((110, 30), (135, 45))  1.181902 -2.439761e+17  0.638828   \n",
       "\n",
       "           RMSE           MSE  \n",
       "0  4.967571e+17  2.467676e+35  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_seasons = True\n",
    "extent_definition = 'bbox' # bbox or country\n",
    "scatter_plot_type = 'aggregated' # aggregated or individual\n",
    "\n",
    "lim_min = 0\n",
    "lim_max = 0.6*10**19\n",
    "\n",
    "summary = scatter_plot(merge_df, component_nom, units, sensor, plot_dates, 1, \n",
    "                       extent_definition, show_seasons, scatter_plot_type, \n",
    "                       lim_min, lim_max, plot_bbox)\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Scatter plots by country (Google API required!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nshow_seasons = False\\nextent_definition = 'country' # bbox or country\\nscatter_plot_type = 'aggregated' # aggregated or individual\\nplot_countries = ['China', 'South Korea']\\n\\nlim_min = 0\\nlim_max = 0.6*10**19\\n\\nsummary = scatter_plot(merge_df, component_nom, units, sensor, plot_dates, 1, \\n                       extent_definition, show_seasons, scatter_plot_type, \\n                       lim_min, lim_max, plot_countries)\\nsummary\\n\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "show_seasons = False\n",
    "extent_definition = 'country' # bbox or country\n",
    "scatter_plot_type = 'aggregated' # aggregated or individual\n",
    "plot_countries = ['China', 'South Korea']\n",
    "\n",
    "lim_min = 0\n",
    "lim_max = 0.6*10**19\n",
    "\n",
    "summary = scatter_plot(merge_df, component_nom, units, sensor, plot_dates, 1, \n",
    "                       extent_definition, show_seasons, scatter_plot_type, \n",
    "                       lim_min, lim_max, plot_countries)\n",
    "summary\n",
    "\"\"\""
   ]
  }
 ],
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