242_Sentinel-5P_TROPOMI_UVAI_L2_load_browse.ipynb 139 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<img src='../img/EU-Copernicus-EUM_3Logos.png' alt='Logo EU Copernicus EUMETSAT' align='right' width='50%'></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<a href=\"../00_index.ipynb\"><< Index</a><br>\n",
    "<a href=\"./241_Sentinel-5P_TROPOMI_CO_L2_load_browse.ipynb\"><< 241 - Sentinel-5P TROPOMI - Carbon Monoxide - Level 2</a><span style=\"float:right;\"><a href=\"./251_Sentinel-3_OLCI_radiance_L1_load_browse.ipynb\">251 - Sentinel-3 OLCI - Radiances - Level 1 >></a></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
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    "<b>20 - DATA EXPLORATION</b></div>"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>SEE ALSO</b>\n",
    "\n",
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    "**30 - CASE STUDIES**\n",
    " - [318 - Indonesian fires 2020](../30_case_studies/318_fire_indonesia_2020.ipynb)\n",
    " - [319 - Portugal fires 2020](../30_case_studies/319_fire_portugal_2020.ipynb)\n",
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    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.4.2 Copernicus Sentinel-5P TROPOMI UV Aerosol Index (UVAI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sentinel-5P carries the `TROPOMI` instrument, which is a spectrometer in the UV-VIS-NIR-SWIR spectral range. `TROPOMI` provides measurements on: `Ozone`, `NO`<sub>`2`</sub>, `SO`<sub>`2`</sub>, `Formaldehyde`, `Aerosol`, `Carbon monoxide`, `Methane` and `Clouds`.\n",
    "\n",
    "For fire monitoring , the `TROPOMI UV Aerosol Index (UVAI)` data can be used. \n",
    "\n",
    "Positive values of UVAI (typically > abt. 1.0) indicate the presence of absorbing-type aerosols: \n",
    "- `smoke from forest fires`, \n",
    "- `volcanic ash`, or \n",
    "- `desert dust`. \n",
    "\n",
    "The UVAI value depends on (i) the amount of aerosols, (ii) height of the aerosol plume, and (iii) aerosol type. \n",
    "\n",
    "Typically UVAI is more sensitive to the elevated aerosol layers and hence it can be used to track the (long-range) transport of smoke from fires. To asses the ambient air quality near the surface, other aerosol observations, such as AOD from satellites, in situ aerosol observations or modeled surface concentrations are recommended to be used together with UVAI. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This module has the following outline:\n",
    "* [1 - Load and browse Sentinel-5P TROPOMI UV Aerosol Index data](#load_browse_uvai)\n",
    "* [2 - Select an aerosol index variable](#select_uvai)\n",
    "* [3 - Read and apply the quality flag to the UVAI data](#quality_flag_uvai)\n",
    "* [4 - Visualize the Sentinel-5P TROPOMI UV Aerosol Index values](#visualize_uvai)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load required libraries"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 1,
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   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
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    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
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    "from matplotlib.axes import Axes\n",
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    "\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
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    "from cartopy.mpl.geoaxes import GeoAxes\n",
    "GeoAxes._pcolormesh_patched = Axes.pcolormesh\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter(action = \"ignore\", category = RuntimeWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load helper functions"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {},
   "outputs": [],
   "source": [
    "%run ../functions.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='load_browse_uvai'></a>Load and browse Sentinel-5P TROPOMI UV Aerosol Index data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Sentinel-5P TROPOMI Aerosol Index Level 2 file is organised in two groups: `PRODUCT` and `METADATA`. The `PRODUCT` group stores the main data fields of the product, including `latitude`, `longitude` and the variable itself. The `METADATA` group provides additional metadata items.\n",
    "\n",
    "Sentinel-5P TROPOMI variables have the following dimensions:\n",
    "* `scanline`: the number of measurements in the granule / along-track dimension index\n",
    "* `ground_pixel`: the number of spectra in a measurement / across-track dimension index\n",
    "* `time`: time reference for the data\n",
    "* `corner`: pixel corner index\n",
    "\n",
    "Sentinel-5P TROPOMI data is disseminated in `netCDF`. You can load a `netCDF` file with the `open_dataset()` function of the xarray library. In order to load the variable as part of a Sentinel-5P data files, you have to specify the following keyword arguments: \n",
    "- `group='PRODUCT'`: to load the `PRODUCT` group\n",
    "\n",
    "Let us load a Sentinel-5P TROPOMI data file as `xarray.Dataset` from 15 August 2020 and inspect the data structure:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {},
   "outputs": [
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".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",
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       "Dimensions:                          (scanline: 4173, ground_pixel: 450, time: 1, corner: 4)\n",
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       "Coordinates:\n",
       "  * scanline                         (scanline) float64 0.0 1.0 ... 4.172e+03\n",
       "  * ground_pixel                     (ground_pixel) float64 0.0 1.0 ... 449.0\n",
       "  * time                             (time) datetime64[ns] 2020-09-19\n",
       "  * corner                           (corner) float64 0.0 1.0 2.0 3.0\n",
       "    latitude                         (time, scanline, ground_pixel) float32 ...\n",
       "    longitude                        (time, scanline, ground_pixel) float32 ...\n",
       "Data variables:\n",
       "    delta_time                       (time, scanline) datetime64[ns] 2020-09-...\n",
       "    time_utc                         (time, scanline) object &#x27;2020-09-19T06:1...\n",
       "    qa_value                         (time, scanline, ground_pixel) float32 ...\n",
       "    aerosol_index_354_388            (time, scanline, ground_pixel) float32 ...\n",
       "    aerosol_index_340_380            (time, scanline, ground_pixel) float32 ...\n",
       "    aerosol_index_354_388_precision  (time, scanline, ground_pixel) float32 ...\n",
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       "    aerosol_index_340_380_precision  (time, scanline, ground_pixel) float32 ...</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-80d24e70-70c5-4598-8300-67adc6c40bf4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-80d24e70-70c5-4598-8300-67adc6c40bf4' 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'>scanline</span>: 4173</li><li><span class='xr-has-index'>ground_pixel</span>: 450</li><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>corner</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-75c3e9a8-7163-4186-87da-a18662554342' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75c3e9a8-7163-4186-87da-a18662554342' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scanline</span></div><div class='xr-var-dims'>(scanline)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 4.171e+03 4.172e+03</div><input id='attrs-b001db25-4ba9-47da-b416-4d3732dc968f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b001db25-4ba9-47da-b416-4d3732dc968f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ad03d89-d6c6-47a2-931b-bbc3bf19cb25' class='xr-var-data-in' type='checkbox'><label for='data-0ad03d89-d6c6-47a2-931b-bbc3bf19cb25' 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>1</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>along-track dimension index</dd><dt><span>comment :</span></dt><dd>This coordinate variable defines the indices along track; index starts at 0</dd></dl></div><div class='xr-var-data'><pre>array([0.000e+00, 1.000e+00, 2.000e+00, ..., 4.170e+03, 4.171e+03, 4.172e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ground_pixel</span></div><div class='xr-var-dims'>(ground_pixel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 447.0 448.0 449.0</div><input id='attrs-a032c1f5-0f07-480b-87d2-73a8241fab78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a032c1f5-0f07-480b-87d2-73a8241fab78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60a50077-bb14-4ed1-9a9c-2a455adefcba' class='xr-var-data-in' type='checkbox'><label for='data-60a50077-bb14-4ed1-9a9c-2a455adefcba' 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>1</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>across-track dimension index</dd><dt><span>comment :</span></dt><dd>This coordinate variable defines the indices across track, from west to east; index starts at 0</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 447., 448., 449.])</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'>2020-09-19</div><input id='attrs-6b3bf815-fe91-4179-a543-2703c294bcc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b3bf815-fe91-4179-a543-2703c294bcc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9977a813-9679-41b9-acf9-2c2b2f71f67e' class='xr-var-data-in' type='checkbox'><label for='data-9977a813-9679-41b9-acf9-2c2b2f71f67e' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>reference time for the measurements</dd><dt><span>comment :</span></dt><dd>The time in this variable corresponds to the time in the time_reference global attribute</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-09-19T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>corner</span></div><div class='xr-var-dims'>(corner)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0</div><input id='attrs-3d9f6f41-2a14-4912-9105-d1682fda1e43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d9f6f41-2a14-4912-9105-d1682fda1e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a4d98e8-e28c-4deb-a90d-8810963209a8' class='xr-var-data-in' type='checkbox'><label for='data-4a4d98e8-e28c-4deb-a90d-8810963209a8' 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>1</dd><dt><span>long_name :</span></dt><dd>pixel corner index</dd><dt><span>comment :</span></dt><dd>This coordinate variable defines the indices for the pixel corners; index starts at 0 (counter-clockwise, starting from south-western corner of the pixel in ascending part of the orbit)</dd></dl></div><div class='xr-var-data'><pre>array([0., 1., 2., 3.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time, scanline, ground_pixel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-435dc863-1034-4bde-8b34-e426711bfd90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-435dc863-1034-4bde-8b34-e426711bfd90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6111e46a-1883-43ab-bd6c-6c494bb2fa92' class='xr-var-data-in' type='checkbox'><label for='data-6111e46a-1883-43ab-bd6c-6c494bb2fa92' 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>pixel center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>bounds :</span></dt><dd>/PRODUCT/SUPPORT_DATA/GEOLOCATIONS/latitude_bounds</dd></dl></div><div class='xr-var-data'><pre>[1877850 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time, scanline, ground_pixel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7532af76-7bc4-4e16-86fd-195dad659d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7532af76-7bc4-4e16-86fd-195dad659d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f46024e-b4e1-4066-b395-345ad830b24c' class='xr-var-data-in' type='checkbox'><label for='data-0f46024e-b4e1-4066-b395-345ad830b24c' 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>pixel center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>bounds :</span></dt><dd>/PRODUCT/SUPPORT_DATA/GEOLOCATIONS/longitude_bounds</dd></dl></div><div class='xr-var-data'><pre>[1877850 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42d3b7ee-2bf2-4715-9d57-754027777b00' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42d3b7ee-2bf2-4715-9d57-754027777b00' class='xr-section-summary' >Data variables: <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>delta_time</span></div><div class='xr-var-dims'>(time, scanline)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-30f2eb26-6cc2-4c21-8604-2867b9c41de5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30f2eb26-6cc2-4c21-8604-2867b9c41de5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9767f945-d2ec-47b0-b9d1-bc6591e17b00' class='xr-var-data-in' type='checkbox'><label for='data-9767f945-d2ec-47b0-b9d1-bc6591e17b00' 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>offset of start time of measurement relative to time_reference</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2020-09-19T06:12:59.441000000&#x27;, &#x27;2020-09-19T06:13:00.281000000&#x27;,\n",
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       "        &#x27;2020-09-19T06:13:01.121000000&#x27;, ..., &#x27;2020-09-19T07:11:22.161000000&#x27;,\n",
       "        &#x27;2020-09-19T07:11:23.001000000&#x27;, &#x27;2020-09-19T07:11:23.841000000&#x27;]],\n",
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       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_utc</span></div><div class='xr-var-dims'>(time, scanline)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-725361d2-e964-4002-b95a-c38e7f9a9074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-725361d2-e964-4002-b95a-c38e7f9a9074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e543c91-87b8-407c-b1e1-66cc48e3b110' class='xr-var-data-in' type='checkbox'><label for='data-2e543c91-87b8-407c-b1e1-66cc48e3b110' 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 of observation as ISO 8601 date-time string</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2020-09-19T06:12:59.441000Z&#x27;, &#x27;2020-09-19T06:13:00.281000Z&#x27;,\n",
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       "        &#x27;2020-09-19T06:13:01.121000Z&#x27;, ..., &#x27;2020-09-19T07:11:22.161000Z&#x27;,\n",
       "        &#x27;2020-09-19T07:11:23.001000Z&#x27;, &#x27;2020-09-19T07:11:23.841000Z&#x27;]],\n",
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       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qa_value</span></div><div class='xr-var-dims'>(time, scanline, ground_pixel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92d9ac3f-86de-4695-a0d8-3b175f07bb87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92d9ac3f-86de-4695-a0d8-3b175f07bb87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4062650b-4580-4aa9-a790-8c3cb53e7d29' class='xr-var-data-in' type='checkbox'><label for='data-4062650b-4580-4aa9-a790-8c3cb53e7d29' 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>1</dd><dt><span>valid_min_ :</span></dt><dd>0</dd><dt><span>valid_max_ :</span></dt><dd>100</dd><dt><span>long_name :</span></dt><dd>data quality value</dd><dt><span>comment :</span></dt><dd>A continuous quality descriptor, varying between 0 (no data) and 1 (full quality data). Recommend to ignore data with qa_value &lt; 0.5</dd></dl></div><div class='xr-var-data'><pre>[1877850 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_index_354_388</span></div><div class='xr-var-dims'>(time, scanline, ground_pixel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94f6d77a-efa5-4d37-a2e0-1a085011836b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94f6d77a-efa5-4d37-a2e0-1a085011836b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4165ec5-402f-4a94-b37d-a0929c63ea58' class='xr-var-data-in' type='checkbox'><label for='data-a4165ec5-402f-4a94-b37d-a0929c63ea58' 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>1</dd><dt><span>proposed_standard_name :</span></dt><dd>ultraviolet_aerosol_index</dd><dt><span>comment :</span></dt><dd>Aerosol index from 388 and 354 nm</dd><dt><span>long_name :</span></dt><dd>Aerosol index from 388 and 354 nm</dd><dt><span>radiation_wavelength :</span></dt><dd>[354. 388.]</dd><dt><span>ancillary_variables :</span></dt><dd>aerosol_index_354_388_precision</dd></dl></div><div class='xr-var-data'><pre>[1877850 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_index_340_380</span></div><div class='xr-var-dims'>(time, scanline, ground_pixel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7079521-ed43-4d9e-a82f-de91b04419ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7079521-ed43-4d9e-a82f-de91b04419ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b74c56d-d6af-44b2-81c7-3e52caa48ead' class='xr-var-data-in' type='checkbox'><label for='data-5b74c56d-d6af-44b2-81c7-3e52caa48ead' 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>1</dd><dt><span>proposed_standard_name :</span></dt><dd>ultraviolet_aerosol_index</dd><dt><span>comment :</span></dt><dd>Aerosol index from 380 and 340 nm</dd><dt><span>long_name :</span></dt><dd>Aerosol index from 380 and 340 nm</dd><dt><span>radiation_wavelength :</span></dt><dd>[340. 380.]</dd><dt><span>ancillary_variables :</span></dt><dd>aerosol_index_340_380_precision</dd></dl></div><div class='xr-var-data'><pre>[1877850 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_index_354_388_precision</span></div><div class='xr-var-dims'>(time, scanline, ground_pixel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-786b2926-fc72-4d02-9e08-16011a54505c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-786b2926-fc72-4d02-9e08-16011a54505c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70fa0c1d-e881-4fb0-b73c-7000864f57ce' class='xr-var-data-in' type='checkbox'><label for='data-70fa0c1d-e881-4fb0-b73c-7000864f57ce' 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>1</dd><dt><span>proposed_standard_name :</span></dt><dd>ultraviolet_aerosol_index standard_error</dd><dt><span>comment :</span></dt><dd>Precision of aerosol index from 388 and 354 nm</dd><dt><span>long_name :</span></dt><dd>Precision of aerosol index from 388 and 354 nm</dd><dt><span>radiation_wavelength :</span></dt><dd>[354. 388.]</dd></dl></div><div class='xr-var-data'><pre>[1877850 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_index_340_380_precision</span></div><div class='xr-var-dims'>(time, scanline, ground_pixel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa317f0d-9a6d-41a7-adc7-f501a02434b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa317f0d-9a6d-41a7-adc7-f501a02434b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-332452a9-edb3-477e-815d-06d9e449ac36' class='xr-var-data-in' type='checkbox'><label for='data-332452a9-edb3-477e-815d-06d9e449ac36' 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>1</dd><dt><span>proposed_standard_name :</span></dt><dd>ultraviolet_aerosol_index standard_error</dd><dt><span>comment :</span></dt><dd>Precision of aerosol index from 380 and 340 nm</dd><dt><span>long_name :</span></dt><dd>Precision of aerosol index from 380 and 340 nm</dd><dt><span>radiation_wavelength :</span></dt><dd>[340. 380.]</dd></dl></div><div class='xr-var-data'><pre>[1877850 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7db7490-f04e-46f6-bf7f-9cadbad9ec59' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c7db7490-f04e-46f6-bf7f-9cadbad9ec59' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
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       "Dimensions:                          (scanline: 4173, ground_pixel: 450, time: 1, corner: 4)\n",
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       "Coordinates:\n",
       "  * scanline                         (scanline) float64 0.0 1.0 ... 4.172e+03\n",
       "  * ground_pixel                     (ground_pixel) float64 0.0 1.0 ... 449.0\n",
       "  * time                             (time) datetime64[ns] 2020-09-19\n",
       "  * corner                           (corner) float64 0.0 1.0 2.0 3.0\n",
       "    latitude                         (time, scanline, ground_pixel) float32 ...\n",
       "    longitude                        (time, scanline, ground_pixel) float32 ...\n",
       "Data variables:\n",
       "    delta_time                       (time, scanline) datetime64[ns] ...\n",
       "    time_utc                         (time, scanline) object ...\n",
       "    qa_value                         (time, scanline, ground_pixel) float32 ...\n",
       "    aerosol_index_354_388            (time, scanline, ground_pixel) float32 ...\n",
       "    aerosol_index_340_380            (time, scanline, ground_pixel) float32 ...\n",
       "    aerosol_index_354_388_precision  (time, scanline, ground_pixel) float32 ...\n",
       "    aerosol_index_340_380_precision  (time, scanline, ground_pixel) float32 ..."
      ]
     },
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     "execution_count": 3,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s5P_mf = xr.open_dataset('../eodata/sentinel5p/UVAI/2020/09/19/S5P_OFFL_L2__AER_AI_20200919T055125_20200919T073254_15207_01_010302_20200920T194235.nc', group='PRODUCT')\n",
    "s5P_mf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='select_uvai'></a>Select an `aerosol index` variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Sentinel-5P TROPOMI aerosol index data provide the Aerosol Index for two different wavelength pairs: \n",
    "- `340_380 nm`\n",
    "- `354_388 nm` \n",
    "\n",
    "For both parameters the interpretation of the index value is the same. \n",
    "\n",
    "Let us use the aerosol index for the wavelength pair 340_380 nm. The variable name is `aerosol_index_340_380`. You can select the variable from the data set, together with the geo-coordinates as follows:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
   "outputs": [],
   "source": [
    "uvai = s5P_mf.aerosol_index_340_380[0,:,:]\n",
    "lat = uvai.latitude\n",
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    "lon = uvai.longitude"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='quality_flag_uvai'></a>Read and apply the `Quality Flag` to the UVAI data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another important parameter to read is the general quality flag called `qa_value`. The quality flag removes the sun glint pixels, where \"falsely\" high positive UVAI values over sea can be detected. In order to exclude sun glint, it is recommended to use only values where the `qa_value` is higher than 0.8. "
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
   "outputs": [],
   "source": [
    "qa=s5P_mf.qa_value[0,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step before plotting is to mask the aerosol index data based on the qa mask. You filter data with the `xarray` function `where`."
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
   "outputs": [],
   "source": [
    "uvai_masked=uvai.where(qa > 0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='visualize_uvai'></a>Visualize the Sentinel-5P TROPOMI `UV Aerosol Index` values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you plot the data, you can define the geographical extent you wish to plot. Let us define a bounding box for  the southern part of Borneo, Indonesia."
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {},
   "outputs": [],
   "source": [
    "latmin = -5\n",
    "latmax = 0.\n",
    "lonmin = 105.\n",
    "lonmax = 120."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the function [visualize_pcolormesh](./function.ipynb#visualize_pcolormesh) to plot the `Aerosol Index for the wavelength pair 340 to 380 nm`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following keyword arguments have to be defined:\n",
    "* `data_array`\n",
    "* `longitude`\n",
    "* `latitude`\n",
    "* `projection`\n",
    "* `color palette`\n",
    "* `unit`\n",
    "* `long_name`\n",
    "* `vmin`, \n",
    "* `vmax`\n",
    "* `extent (lonmin, lonmax, latmin, latmax)`\n",
    "* `set_global`"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1440x720 with 2 Axes>,\n",
       " <GeoAxesSubplot:title={'center':'Aerosol index from 380 and 340 nm'}>)"
      ]
     },
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     "execution_count": 8,
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     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_pcolormesh(data_array=uvai_masked,\n",
    "                     longitude=lon,\n",
    "                     latitude=lat,\n",
    "                     projection=ccrs.PlateCarree(),\n",
    "                     color_scale='afmhot_r',\n",
    "                     unit= 'UV Aerosol Index (340/380)',\n",
    "                     long_name=uvai.long_name,\n",
    "                     vmin=0, \n",
    "                     vmax=2,\n",
    "                     lonmin=lonmin,\n",
    "                     lonmax=lonmax,\n",
    "                     latmin=latmin,\n",
    "                     latmax=latmax,\n",
    "                     set_global=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<a href=\"../00_index.ipynb\"><< Index</a><br>\n",
    "<a href=\"./241_Sentinel-5P_TROPOMI_CO_L2_load_browse.ipynb\"><< 241 - Sentinel-5P TROPOMI - Carbon Monoxide - Level 2</a><span style=\"float:right;\"><a href=\"./251_Sentinel-3_OLCI_radiance_L1_load_browse.ipynb\">251 - Sentinel-3 OLCI - Radiances - Level 1 >></a></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<p><img src='../img/copernicus_logo.png' align='left' alt='Logo EU Copernicus' width='25%'></img></p>\n",
    "<br clear=left>\n",
    "<p style=\"text-align:left;\">This project is licensed under the <a href=\"../LICENSE\">MIT License</a> <span style=\"float:right;\"><a href=\"https://gitlab.eumetsat.int/eumetlab/atmosphere/atmosphere\">View on GitLab</a> | <a href=\"https://training.eumetsat.int/\">EUMETSAT Training</a>"
   ]
  }
 ],
 "metadata": {
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  "author": "Julia Wagemann",
  "title": "Explore Copernicus Sentinel-5P TROPOMI - Ultraviolet Aerosol Index L2",
  "description": "This notebook introduces you to Copernicus Sentinel-5P TROPOMI Level 2 data. You will learn how the Ultraviolet Aerosol Index (UVAI) product can be loaded and visualised.",
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  "image": "../img/242_img.png",
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  "services": {
    "eumetsat": {
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        "jupyter": {
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            "link":"https://ltpy.adamplatform.eu/hub/user-redirect/lab/tree/20_data_exploration/242_Sentinel-5P_TROPOMI_UVAI_L2_load_browse.ipynb",
            "service_contact": "ltpy@meeo.it",
            "service_provider": "MEEO s.r.l"
        },
        "git": {
            "link": "https://gitlab.eumetsat.int/eumetlab/atmosphere/atmosphere/-/blob/master/20_data_exploration/242_Sentinel-5P_TROPOMI_UVAI_L2_load_browse.ipynb",
            "service_contact": "training@eumetsat.int",
            "service_provider": "EUMETSAT"
        }
    }
  },
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  "tags": {
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   "domain": "Atmosphere",
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   "subtheme": "Atmospheric Composition",
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   "platform": "Sentinel-5P",
   "sensor": "TROPOMI",
   "tags": "Ultraviolet Aerosol Index"
   },
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  "kernelspec": {
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   "display_name": "Python 3 (ipykernel)",
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   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "version": "3.10.4"
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  }
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}