py3.6_plot_netCDF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# netCDF plotting tool guided towards DEHM and its input data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Info and imported packages\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Author: Zhuyun Ye, Niels S. Hvidberg\n",
    "# copied to Niels 01/03 2024\n",
    "# Description: Python script usefull for working with NetCDF\n",
    "#              and contain tools to convert DEHM binary output \n",
    "#              to NetCDF files. The conversion requires a \n",
    "#              species file which is not present in the current\n",
    "#              SPA_DEHM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opening netCDF files for plotting\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Printing text in colors\n",
    "class bcolors:\n",
    "    HEADER = '\\033[95m'\n",
    "    OKBLUE = '\\033[94m'\n",
    "    OKCYAN = '\\033[96m'\n",
    "    OKGREEN = '\\033[92m'\n",
    "    WARNING = '\\033[93m'\n",
    "    FAIL = '\\033[91m'\n",
    "    ENDC = '\\033[0m'\n",
    "    BOLD = '\\033[1m'\n",
    "    UNDERLINE = '\\033[4m'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show(xr_dataset):\n",
    "    \"\"\"\n",
    "    This function gives you a way to display xarray datasets per command, instead of implicitly\n",
    "    by calling the dataset instance as the last thing in a cell.\n",
    "    \"\"\"\n",
    "    from IPython.display import display, HTML\n",
    "    from xarray.core.formatting_html import dataset_repr\n",
    "    display(HTML(dataset_repr(xr_dataset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def xr_open_old(data_dir, days=[1, 31], hours=[0, 23]):\n",
    "    infile = []\n",
    "    dataset = []\n",
    "    for i in range(days[0], days[1]+1):\n",
    "        if (i == 1):\n",
    "            z = 1\n",
    "        else:\n",
    "            z = 0\n",
    "    \n",
    "        for j in range(z+hours[0], hours[1]+1):\n",
    "            name = f'201101{i:02d}{j:02d}.nc'\n",
    "            infile.append(data_dir+'/'+name)\n",
    "            dataset.append(xr.open_dataset(infile[-1]))\n",
    "    \n",
    "    return dataset\n",
    "\n",
    "def xr_open(data_dir, days=[1, 31], hours=[0, 23]):\n",
    "    infile = []\n",
    "    dataset = []\n",
    "    for i in range(days[0], days[1]+1):\n",
    "        if (i == 1):\n",
    "            z = 1\n",
    "        else:\n",
    "            z = 0\n",
    "    \n",
    "        for j in range(z+hours[0], hours[1]+1):\n",
    "            name = f'1_201101{i:02d}{j:02d}.nc'\n",
    "            infile.append(data_dir+'/'+name)\n",
    "            dataset.append(xr.open_dataset(infile[-1]))\n",
    "    \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Opening dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:              (time: 8, level: 34, latitude: 40, longitude: 66,\n",
       "                          calendar_components: 6)\n",
       "Coordinates:\n",
       "  * calendar_components  (calendar_components) int32 1 2 3 4 5 6\n",
       "  * latitude             (latitude) float64 22.5 23.5 24.5 ... 59.5 60.5 61.5\n",
       "  * level                (level) int32 1 2 3 4 5 6 7 8 ... 28 29 30 31 32 33 34\n",
       "  * longitude            (longitude) float64 -128.5 -127.5 ... -64.5 -63.5\n",
       "  * time                 (time) datetime64[ns] 2023-10-12T01:30:00 ... 2023-1...\n",
       "Data variables:\n",
       "    bio                  (time, level, latitude, longitude) float32 ...\n",
       "    ff                   (time, level, latitude, longitude) float32 ...\n",
       "    fires                (time, level, latitude, longitude) float32 ...\n",
       "    ocean                (time, level, latitude, longitude) float32 ...\n",
       "    bg                   (time, level, latitude, longitude) float32 ...\n",
       "    decimal_date         (time) float64 ...\n",
       "    time_components      (time, calendar_components) int32 ...\n",
       "Attributes:\n",
       "    notes:        This file contains CarbonTracker mole fractions averaged ov...\n",
       "    disclaimer:   CarbonTracker is an open product of the NOAA Earth System R...\n",
       "    email:        carbontracker.team@noaa.gov\n",
       "    url:          http://carbontracker.noaa.gov\n",
       "    institution:  NOAA Earth System Research Laboratory\n",
       "    conventions:  CF-1.5\n",
       "    history:      Created 2024-03-25 21:01:45 UTC by ./NRT_v2024-1.eaf.ml137....\n",
       "    version:      CarbonTracker release version CT-NRT.v2024-1 - mole fractio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6155708b-836a-4f51-9abd-42998e807af8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6155708b-836a-4f51-9abd-42998e807af8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 8</li><li><span class='xr-has-index'>level</span>: 34</li><li><span class='xr-has-index'>latitude</span>: 40</li><li><span class='xr-has-index'>longitude</span>: 66</li><li><span class='xr-has-index'>calendar_components</span>: 6</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-84332944-2594-4fca-a74a-618d7a490337' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84332944-2594-4fca-a74a-618d7a490337' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>calendar_components</span></div><div class='xr-var-dims'>(calendar_components)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6</div><input id='attrs-efd55a87-a075-49f5-aa53-54e7143ca1fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efd55a87-a075-49f5-aa53-54e7143ca1fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bd8f9eb-51f9-4521-ab82-34a6d971dfb6' class='xr-var-data-in' type='checkbox'><label for='data-5bd8f9eb-51f9-4521-ab82-34a6d971dfb6' 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>none</dd><dt><span>long_name :</span></dt><dd>calendar_components</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5, 6], dtype=int32)</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'>22.5 23.5 24.5 ... 59.5 60.5 61.5</div><input id='attrs-721b7667-646f-44b5-a182-abd8da628f0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-721b7667-646f-44b5-a182-abd8da628f0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6769a6c1-05e3-4786-a8e2-b49adfdb7ceb' class='xr-var-data-in' type='checkbox'><label for='data-6769a6c1-05e3-4786-a8e2-b49adfdb7ceb' 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>long_name :</span></dt><dd>latitude</dd><dt><span>actual_range :</span></dt><dd>[22. 62.]</dd></dl></div><div class='xr-var-data'><pre>array([22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5,\n",
       "       34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5,\n",
       "       46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5,\n",
       "       58.5, 59.5, 60.5, 61.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 29 30 31 32 33 34</div><input id='attrs-9dabfc02-0295-4881-8ed9-51bb3f1ce6bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dabfc02-0295-4881-8ed9-51bb3f1ce6bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ed0adb1-0ae8-4e4f-86e0-8fafad56fe26' class='xr-var-data-in' type='checkbox'><label for='data-1ed0adb1-0ae8-4e4f-86e0-8fafad56fe26' 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>none</dd><dt><span>long_name :</span></dt><dd>level</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],\n",
       "      dtype=int32)</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'>-128.5 -127.5 ... -64.5 -63.5</div><input id='attrs-efaf58db-271b-4d2e-876a-ca3f6dfdc7af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efaf58db-271b-4d2e-876a-ca3f6dfdc7af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb0691d7-4e04-4c72-bb6e-4429666df9c8' class='xr-var-data-in' type='checkbox'><label for='data-cb0691d7-4e04-4c72-bb6e-4429666df9c8' 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_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>actual_range :</span></dt><dd>[-129.  -63.]</dd></dl></div><div class='xr-var-data'><pre>array([-128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5, -121.5, -120.5,\n",
       "       -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5, -112.5, -111.5,\n",
       "       -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5, -103.5, -102.5,\n",
       "       -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,\n",
       "        -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,\n",
       "        -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,\n",
       "        -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5,\n",
       "        -65.5,  -64.5,  -63.5])</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'>2023-10-12T01:30:00 ... 2023-10-...</div><input id='attrs-16882952-e8ce-4118-bcf1-30d61a0b4032' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16882952-e8ce-4118-bcf1-30d61a0b4032' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08c5dad4-06aa-420e-95b8-1579fcbb9dc7' class='xr-var-data-in' type='checkbox'><label for='data-08c5dad4-06aa-420e-95b8-1579fcbb9dc7' 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</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-10-12T01:30:00.000000000&#x27;, &#x27;2023-10-12T04:30:00.000000000&#x27;,\n",
       "       &#x27;2023-10-12T07:30:00.000000000&#x27;, &#x27;2023-10-12T10:30:00.000000000&#x27;,\n",
       "       &#x27;2023-10-12T13:30:00.000000000&#x27;, &#x27;2023-10-12T16:30:00.000000000&#x27;,\n",
       "       &#x27;2023-10-12T19:30:00.000000000&#x27;, &#x27;2023-10-12T22:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba8e6b37-82ff-4491-9131-88548343a189' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba8e6b37-82ff-4491-9131-88548343a189' 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>bio</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43fab304-f9fe-4d3e-b9a0-7e1cd2efbeaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43fab304-f9fe-4d3e-b9a0-7e1cd2efbeaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bff3fba9-b731-4775-a37f-0f4c74617eb0' class='xr-var-data-in' type='checkbox'><label for='data-bff3fba9-b731-4775-a37f-0f4c74617eb0' 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>micromol mol-1</dd><dt><span>comment :</span></dt><dd>component due to terrestrial biosphere exchange (since 2000-01-01)</dd></dl></div><div class='xr-var-data'><pre>[718080 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ff</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ec6e33ab-4ccd-400b-a227-b03ccfbdad1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec6e33ab-4ccd-400b-a227-b03ccfbdad1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb138faa-2ef7-4b80-ae09-c314a5658b99' class='xr-var-data-in' type='checkbox'><label for='data-cb138faa-2ef7-4b80-ae09-c314a5658b99' 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>micromol mol-1</dd><dt><span>comment :</span></dt><dd>component due to fossil fuel burning (since 2000-01-01)</dd></dl></div><div class='xr-var-data'><pre>[718080 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fires</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-db3bb286-6ddd-43cb-ac93-30297f23f842' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db3bb286-6ddd-43cb-ac93-30297f23f842' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-213748d7-8989-474a-8125-c8a7728bcd89' class='xr-var-data-in' type='checkbox'><label for='data-213748d7-8989-474a-8125-c8a7728bcd89' 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>micromol mol-1</dd><dt><span>comment :</span></dt><dd>component due to direct fire emissions (since 2000-01-01)</dd></dl></div><div class='xr-var-data'><pre>[718080 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocean</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e88158fd-26cb-41e7-b623-7750ab4ad80b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e88158fd-26cb-41e7-b623-7750ab4ad80b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfe3af00-1f28-4ec8-8e2b-a0efd2456c00' class='xr-var-data-in' type='checkbox'><label for='data-cfe3af00-1f28-4ec8-8e2b-a0efd2456c00' 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>micromol mol-1</dd><dt><span>comment :</span></dt><dd>component due to air-sea exchange (since 2000-01-01)</dd></dl></div><div class='xr-var-data'><pre>[718080 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bg</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0138fccb-ebb2-4083-a956-31b1bdafd117' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0138fccb-ebb2-4083-a956-31b1bdafd117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad35718e-0560-44a4-b112-a99cad52f36f' class='xr-var-data-in' type='checkbox'><label for='data-ad35718e-0560-44a4-b112-a99cad52f36f' 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>micromol mol-1</dd><dt><span>comment :</span></dt><dd>background - component due to initial condition (at 2000-01-01)</dd></dl></div><div class='xr-var-data'><pre>[718080 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>decimal_date</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-009cbb6b-50d6-45f7-a51b-b6ce407cbf56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-009cbb6b-50d6-45f7-a51b-b6ce407cbf56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5217ea0a-bcad-47ae-a00d-83e075e13334' class='xr-var-data-in' type='checkbox'><label for='data-5217ea0a-bcad-47ae-a00d-83e075e13334' 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>years</dd></dl></div><div class='xr-var-data'><pre>[8 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_components</span></div><div class='xr-var-dims'>(time, calendar_components)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7504803f-cbb6-455e-84ea-a683cda98c7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7504803f-cbb6-455e-84ea-a683cda98c7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-186c4624-aa98-4e06-9246-b1332e6048e4' class='xr-var-data-in' type='checkbox'><label for='data-186c4624-aa98-4e06-9246-b1332e6048e4' 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>none</dd><dt><span>long_name :</span></dt><dd>Integer value calendar components of UTC time.</dd><dt><span>comment :</span></dt><dd>year, month, day, hour, minute, second</dd></dl></div><div class='xr-var-data'><pre>[48 values with dtype=int32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d538733f-6443-4876-a470-b2bc0ee32832' class='xr-section-summary-in' type='checkbox'  ><label for='section-d538733f-6443-4876-a470-b2bc0ee32832' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>calendar_components</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3cc1bc5b-76b1-4f84-b4e5-8f9d6d94fdcb' class='xr-index-data-in' type='checkbox'/><label for='index-3cc1bc5b-76b1-4f84-b4e5-8f9d6d94fdcb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6], dtype=&#x27;int32&#x27;, name=&#x27;calendar_components&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e288649-caca-417d-8a90-3ec209fd2dd4' class='xr-index-data-in' type='checkbox'/><label for='index-0e288649-caca-417d-8a90-3ec209fd2dd4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5,\n",
       "       34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5,\n",
       "       46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5,\n",
       "       58.5, 59.5, 60.5, 61.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-68c4d5af-d0a3-4ace-a0a1-35eb06ee669c' class='xr-index-data-in' type='checkbox'/><label for='index-68c4d5af-d0a3-4ace-a0a1-35eb06ee669c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e49dfe90-c9d5-412f-8098-53fedac8255d' class='xr-index-data-in' type='checkbox'/><label for='index-e49dfe90-c9d5-412f-8098-53fedac8255d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5, -121.5, -120.5,\n",
       "       -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5, -112.5, -111.5,\n",
       "       -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5, -103.5, -102.5,\n",
       "       -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,\n",
       "        -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,\n",
       "        -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,\n",
       "        -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5,\n",
       "        -65.5,  -64.5,  -63.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fa3933a0-dcd8-4edf-b9ed-9ff48faf049e' class='xr-index-data-in' type='checkbox'/><label for='index-fa3933a0-dcd8-4edf-b9ed-9ff48faf049e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-10-12 01:30:00&#x27;, &#x27;2023-10-12 04:30:00&#x27;,\n",
       "               &#x27;2023-10-12 07:30:00&#x27;, &#x27;2023-10-12 10:30:00&#x27;,\n",
       "               &#x27;2023-10-12 13:30:00&#x27;, &#x27;2023-10-12 16:30:00&#x27;,\n",
       "               &#x27;2023-10-12 19:30:00&#x27;, &#x27;2023-10-12 22:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92bc5e5d-86ca-4ed0-b0af-cdf276ee7acc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92bc5e5d-86ca-4ed0-b0af-cdf276ee7acc' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>notes :</span></dt><dd>This file contains CarbonTracker mole fractions averaged over each time interval.  The times on the date axis are the centers of each averaging period.</dd><dt><span>disclaimer :</span></dt><dd>CarbonTracker is an open product of the NOAA Earth System Research Laboratory using data from the Global Monitoring Division greenhouse gas observational network and collaborating institutions.  Model results including figures and tabular material found on the CarbonTracker website may be used for non-commercial purposes without restriction, but we request that the following acknowledgement text be included in documents or publications made using CarbonTracker results: &quot;CarbonTracker results provided by NOAA/ESRL, Boulder, Colorado, USA, http://carbontracker.noaa.gov.&quot;  Since we expect to continuously update the CarbonTracker product, it is important to identify which version you are using.  To provide accurate citation, please include the version of the CarbonTracker release in any use of these results.  The CarbonTracker team welcomes special requests for data products not offered by default on this website, and encourages proposals for collaborative activities.  Contact us at carbontracker.team@noaa.gov.</dd><dt><span>email :</span></dt><dd>carbontracker.team@noaa.gov</dd><dt><span>url :</span></dt><dd>http://carbontracker.noaa.gov</dd><dt><span>institution :</span></dt><dd>NOAA Earth System Research Laboratory</dd><dt><span>conventions :</span></dt><dd>CF-1.5</dd><dt><span>history :</span></dt><dd>Created 2024-03-25 21:01:45 UTC by ./NRT_v2024-1.eaf.ml137.orion.x.</dd><dt><span>version :</span></dt><dd>CarbonTracker release version CT-NRT.v2024-1 - mole fraction output revision 1 of Tue Mar 26 20:28:01 2024</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose case and scroll down to the relevant section\n",
    "data = 'CT'\n",
    "match data:\n",
    "    # DEHM data\n",
    "    case 'dehm4':\n",
    "        dir1 = 'netcdf1'\n",
    "        dir2 = 'netcdf2'\n",
    "        dir3 = 'netcdf3'\n",
    "        dir4 = 'netcdf4'\n",
    "        \n",
    "        ds1 = xr_open_old(dir1)\n",
    "        ds2 = xr_open_old(dir2)\n",
    "        ds3 = xr_open_old(dir3)\n",
    "        ds4 = xr_open_old(dir4)\n",
    "        ds = ds1\n",
    "        show(ds[0])\n",
    "    \n",
    "    case 'dehm test':\n",
    "        ds1 = xr.open_dataset('4_2020010101.nc')\n",
    "        #ds2 = xr.open_dataset('nc/jena.nc')\n",
    "        ds=ds1\n",
    "        show(ds)\n",
    "        \n",
    "    case 'dehm input':\n",
    "        dir1 = 'only_cams_glob/2011/netcdf'\n",
    "        dir2 = 'only_jena_ocn/2011/netcdf'\n",
    "        \n",
    "        ds1 = xr_open(dir1)\n",
    "        ds2 = xr_open(dir1)\n",
    "        ds = ds1\n",
    "        show(ds[0])\n",
    "    \n",
    "    # CAMS-GLOB data\n",
    "    case 'cams-glob':\n",
    "        infile = '/home/niels/WinDoc/Data/CAMS/CAMS-GLOB/CAMS-GLOB-ANT_Glb_0.1x0.1_anthro_co2_excl_short-cycle_org_C_v5.3_monthly.nc'\n",
    "        ds  = xr.open_dataset(infile)\n",
    "        show(ds)\n",
    "    \n",
    "    case 'cams-reg':\n",
    "        infile = '/home/niels/WinDoc/Data/CAMS/CAMS-REG/CAMS-REG-GHG_v6_0_emissions_year2020.nc'\n",
    "        ds  = xr.open_dataset(infile)\n",
    "        show(ds)\n",
    "    \n",
    "    # Jena data\n",
    "    case 'jena': \n",
    "        infile = '/home/niels/WinDoc/Data/JENA/oc_v2023.pCO2.nc'\n",
    "        ds_jena = xr.open_dataset(infile)\n",
    "        ds = ds_jena\n",
    "        show(ds)\n",
    "\n",
    "    # Carbon Tracker data\n",
    "    case 'CT': \n",
    "        infile1 = '/home/niels/WinDoc/Data/CT/CT-NRT.v2024-1.flux1x1.20231012.nc'\n",
    "        infile2 = '/home/niels/WinDoc/Data/CT/CT-NRT.v2024-1.molefrac_components_nam1x1_2023-10-12.nc'\n",
    "        infile3 = '/home/niels/WinDoc/Data/CT/CT-NRT.v2024-1.molefrac_nam1x1_2023-10-12.nc'\n",
    "        ds1 = xr.open_dataset(infile1)\n",
    "        ds2 = xr.open_dataset(infile2)\n",
    "        ds3 = xr.open_dataset(infile3)\n",
    "        ds = ds2\n",
    "        show(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2020-06-01T00:00:00.000000000')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time.values[245]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1998, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_month(year,month,first_month):\n",
    "    month = (month + (12-first_month)) % 12\n",
    "    year  = year - month//(11-first_month+1)\n",
    "\n",
    "    return year, month \n",
    "\n",
    "get_month(2000,3,11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with dehm data to create animations\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dehm_plotter(dataset, index=12, meandim='z', species=\"CO2.01\", close=False, central_longitude=-32, output_dir='frames'):\n",
    "    \"\"\"\n",
    "    Docstring for dehm_plotter:\n",
    "        This function is used to plot netCDF files (output from DEHM) on a polar\n",
    "        stereographic map.\n",
    "\n",
    "        dataset: A list of xarray-dataset objects with 3D field data.\n",
    "        index: The index for the specific dataset to workwith in dataset list.\n",
    "        meandim: Calculate the mean over this dimension. Most often 'time' or 'z'.\n",
    "        species: the kind to plot. For DEHM CO2 the tracers are called CO2.ntrac \n",
    "        (1 indexing!).\n",
    "\n",
    "        The function creates a plot, saves it and can also close the plot again. The\n",
    "        plot is saved under the name:\n",
    "        \n",
    "            f'{output_dir}/frame{index:03d}.png'\n",
    "    \"\"\"\n",
    "    if meandim:\n",
    "        da = dataset[index][species].mean(dim=meandim)\n",
    "        lat = dataset[index]['lat'].values\n",
    "        lon = dataset[index]['lon'].values\n",
    "    if meandim == 0:\n",
    "        da = dataset[species].isel(z=0)\n",
    "        lat = dataset['lat'].values\n",
    "        lon = dataset['lon'].values\n",
    "    else:\n",
    "        da = dataset[index][species].isel(z=0)\n",
    "        lat = dataset[index]['lat'].values\n",
    "        lon = dataset[index]['lon'].values\n",
    "        \n",
    "\n",
    "    \n",
    "    # Get values\n",
    "    pdata = da.values\n",
    "\n",
    "    # Set min/max values for plots:\n",
    "    vmin = da.min()\n",
    "    vmax = da.max()*0.9\n",
    "    \n",
    "    # This is the map projection we want to plot *onto*\n",
    "    map_proj = ccrs.NorthPolarStereo(central_longitude=central_longitude)\n",
    "    fig, ax = plt.subplots(subplot_kw={'projection': map_proj}, figsize=(7,7))\n",
    "\n",
    "    # Set coastline appearance\n",
    "    ax.coastlines(color='black',zorder=2,alpha=1,linewidth=1.5)\n",
    "\n",
    "    # Set gridlines appearance\n",
    "    gl = ax.gridlines(draw_labels=True, zorder=1, alpha=0.7, linewidth=1, crs=ccrs.PlateCarree())\n",
    "\n",
    "    # Set label style\n",
    "    gl.xlabel_style = {'size':7}\n",
    "    gl.ylabel_style = {'size':7}\n",
    "    \n",
    "    # Set colormap\n",
    "    cmap = plt.get_cmap('jet')\n",
    "    cmap.set_bad('w',1.0)\n",
    "\n",
    "    # Plot image\n",
    "    im = ax.pcolormesh(lon,\n",
    "                       lat,\n",
    "                       pdata,\n",
    "                       cmap=cmap,\n",
    "                       vmin=vmin, # Min value for colormap (especially used for videos)\n",
    "                       vmax=vmax, # Max value for colormap\n",
    "                       transform=ccrs.PlateCarree(),\n",
    "                       zorder=0,  # ?\n",
    "                       shading='auto')\n",
    "\n",
    "    # Colorbar\n",
    "    fig.colorbar(im, ax=ax)\n",
    "    \n",
    "    # Check if plot should be closed and saved instead of shown (Used for creating frames)\n",
    "    if close:\n",
    "        plt.savefig(f'{output_dir}/frame{index:03d}.png')\n",
    "        plt.close(fig)\n",
    "\n",
    "def make_frames_from_list(dataset, meandim, species, central_longitude=-32, output_dir='frames'):\n",
    "    index = 0\n",
    "    for i in range(1,32):\n",
    "        if (i == 1):\n",
    "            z = 1\n",
    "        else:\n",
    "            z = 0\n",
    "    \n",
    "        for j in range(z,24):\n",
    "            dehm_plotter(dataset=dataset, index=index, meandim=meandim, species=species, close=True, central_longitude=central_longitude, output_dir=output_dir)\n",
    "            index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Plotting single frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "species = f'CO2.{1:02d}'\n",
    "meandim = []\n",
    "central_longitude=-32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"No variable named 'CO2.01'. Variables on the dataset include ['longitude', 'latitude', 'time', 'decimal_time', 'time_components', 'bio_flux_opt', 'ocn_flux_opt', 'fossil_flux_imp', 'fire_flux_imp']\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1445\u001b[0m             \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_virtual_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'CO2.01'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1546\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_dataarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1547\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1548\u001b[0;31m                 raise KeyError(\n\u001b[0m\u001b[1;32m   1549\u001b[0m                     \u001b[0;34mf\"No variable named {key!r}. Variables on the dataset include {shorten_list_repr(list(self.variables.keys()), max_items=10)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1445\u001b[0m             \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_virtual_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(variables, key, dim_sizes)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mref_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msplit_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m     \u001b[0mref_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mref_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'CO2'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_574/2706808525.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dehm_plotter(dataset=ds1,\n\u001b[0m\u001b[1;32m      2\u001b[0m              \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#            meandim=meandim,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m              \u001b[0mmeandim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_574/1016040199.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(dataset, index, meandim, species, close, central_longitude, output_dir)\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mspecies\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmeandim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mlat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mlon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lon'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmeandim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0mda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mspecies\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0mlat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mlon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lon'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1545\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1546\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_dataarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1547\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1548\u001b[0;31m                 raise KeyError(\n\u001b[0m\u001b[1;32m   1549\u001b[0m                     \u001b[0;34mf\"No variable named {key!r}. Variables on the dataset include {shorten_list_repr(list(self.variables.keys()), max_items=10)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1550\u001b[0m                 ) from e\n\u001b[1;32m   1551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"No variable named 'CO2.01'. Variables on the dataset include ['longitude', 'latitude', 'time', 'decimal_time', 'time_components', 'bio_flux_opt', 'ocn_flux_opt', 'fossil_flux_imp', 'fire_flux_imp']\""
     ]
    }
   ],
   "source": [
    "dehm_plotter(dataset=ds1,\n",
    "             index=100,  \n",
    "#            meandim=meandim, \n",
    "             meandim=0, \n",
    "             species=species, \n",
    "             close=False, \n",
    "             central_longitude=central_longitude, \n",
    "            )\n",
    "plt.savefig('jena_ocean.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if True:\n",
    "    make_frames_from_list(dataset=ds1, meandim=meandim, species=species, central_longitude=central_longitude, output_dir='frames1')\n",
    "    make_frames_from_list(dataset=ds2, meandim=meandim, species=species, central_longitude=central_longitude, output_dir='frames2')\n",
    "    #make_frames_from_list(dataset=ds3, meandim=meandim, species=species, central_longitude=central_longitude, output_dir='frames3')\n",
    "    #make_frames_from_list(dataset=ds4, meandim=meandim, species=species, central_longitude=central_longitude, output_dir='frames4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make a video from the frames, run this function with the desired directory as input:\n",
    "\n",
    "> ./make_video n\n",
    "> \n",
    "> ffmpeg -y -i frames\\\\$1/frame%03d.png -c:v libx264 -pix_fmt yuv420p video/video\\\\$1.mp4\n",
    "\n",
    "n is the first argument and thus becomes $1. n is the label for the directory and output so the function reads in from frames{n} and writes to videos/video{n}.mp4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!./make_video 1\n",
    "!./make_video 2\n",
    "# !./make_video 3\n",
    "# !./make_video 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with JENA netCDF data\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def g(y,m,d):\n",
    "    \"\"\"\n",
    "    Returns the day number in the Gregorian calender. Check: \n",
    "    https://stackoverflow.com/a/12863278/23730517\n",
    "    for explanation.\n",
    "    \"\"\"\n",
    "    m = (m + 9) % 12\n",
    "    y = y - m//10\n",
    "    return 365*y + y//4 - y//100 + y//400 + (m*306 + 5)//10 + (d - 1)\n",
    "\n",
    "def jena_day_count(y,m,d):\n",
    "    return g(y,m,d) - g(1957,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d:1 is kept\n",
      "m:1 transformed to 10\n",
      "y:1957 transformed to 1956\n",
      "days before this year:\n",
      "365*yyyy + yyyy//4 - yyyy//100 + yyyy//400 =\n",
      "365*1956 + 1956//4 - 1956//100 + 1956//400 = 714414\n",
      "days from this year:\n",
      "(mm*306 + 5)//10 + (dd - 1) =\n",
      "(10*306 + 5)//10 + ( 1 - 1) =\n",
      "(   306          +     0  ) = 306\n",
      "g(1956,10, 1) = 714628\n",
      "g(1957, 1, 1) = 714720\n",
      "jena_day_count is: 0\n"
     ]
    }
   ],
   "source": [
    "y = 1957\n",
    "m = 1\n",
    "d = 1\n",
    "m_org = m\n",
    "y_org = y\n",
    "print(f\"d:{d} is kept\")\n",
    "print(f\"m:{m} transformed to {(m + 9) % 12}\")\n",
    "m = (m + 9) % 12\n",
    "print(f\"y:{y} transformed to {y - m//10}\")\n",
    "y = y - m//10\n",
    "print(f\"days before this year:\")\n",
    "print(f\"365*yyyy + yyyy//4 - yyyy//100 + yyyy//400 =\") \n",
    "print(f\"365*{y} + {y}//4 - {y}//100 + {y}//400 = {365*y + y//4 - y//100 + y//400}\") \n",
    "\n",
    "print(f\"days from this year:\")\n",
    "print(f\"(mm*306 + 5)//10 + (dd - 1) =\")\n",
    "print(f\"({m:2d}*306 + 5)//10 + ({d:2d} - 1) =\")\n",
    "print(f\"(   {(m*306 + 5)//10}          +     {(d - 1)}  ) = {(m*306 + 5)//10 + (d - 1)}\")\n",
    "\n",
    "print(f\"g({y:4d},{m:2d},{d:2d}) = {g(y,m,d)}\")\n",
    "print(\"g({0},{1:2d},{2:2d}) = {3}\".format(y_org,m_org,d,g(1957,1,1)))\n",
    "\n",
    "print(f\"jena_day_count is: {jena_day_count(y_org,m_org,d)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def JENA_plotter(dataset, species, isel={'mtime':0}, sel={}, close=False, central_longitude=-32, output_dir='plots', figspec='pCO2'):\n",
    "    \"\"\"\n",
    "    Docstring for \"JENA_plotter\":\n",
    "        This function is used to plot netCDF files on a polar\n",
    "        stereographic map.\n",
    "\n",
    "        dataset  : A list of xarray-dataset objects with 3D field data.\n",
    "        species  : The kind to plot. For DEHM CO2 the tracers are called CO2.ntrac \n",
    "        isel/sel : A dictionary containing the variable and value or slices to choose\n",
    "                   in the form {var:val/slice/index}. \"isel\" goes\n",
    "                   by index and \"sel\" goes by value.\n",
    "        meandim  : Calculate the mean over this dimension. Most often 'time' or 'z'.\n",
    "\n",
    "        The function creates a plot and optionally saves and closes the plot again. The\n",
    "        plot is saved under the name:\n",
    "        \n",
    "            f'{output_dir}/{figname}.png'\n",
    "    \"\"\"\n",
    "    # Select values by index/slice/value.\n",
    "    if isel:\n",
    "        da = dataset[species].isel(isel)\n",
    "    if sel:\n",
    "        da = dataset[species].sel(sel)\n",
    "    else:\n",
    "        da = dataset[species].isel(mtime=0)\n",
    "\n",
    "    # Set latitude and longitude values\n",
    "    lat, lon = da['lat'].values, da['lon'].values\n",
    "    \n",
    "    # plot the first layer\n",
    "    pdata = da.values\n",
    "    \n",
    "    # This is the map projection we want to plot *onto*\n",
    "    map_proj = ccrs.NorthPolarStereo(central_longitude=central_longitude)\n",
    "    fig, ax = plt.subplots(subplot_kw={'projection': map_proj}, figsize=(7,7))\n",
    "\n",
    "    # Set appearance for coastlines\n",
    "    ax.coastlines(color='black',zorder=2,alpha=1,linewidth=1.5)\n",
    "\n",
    "    # Set appearance for gridlines\n",
    "    gl = ax.gridlines(draw_labels=True, zorder=1, alpha=0.7, linewidth=1, crs=ccrs.PlateCarree())\n",
    "\n",
    "    # Set label size\n",
    "    gl.xlabel_style = {'size':7}\n",
    "    gl.ylabel_style = {'size':7}\n",
    "\n",
    "    # Set colormap\n",
    "    cmap = plt.get_cmap('jet')\n",
    "    cmap.set_bad('w',1.0)\n",
    "    \n",
    "    # Plot image\n",
    "    im = ax.pcolormesh(lon,\n",
    "                       lat,\n",
    "                       pdata,\n",
    "                       cmap=cmap,\n",
    "#                      vmin=-0.1, # Min value for colormap (especially used for videos)\n",
    "#                      vmax=14,   # Max value for colormap\n",
    "                       transform=ccrs.PlateCarree(),\n",
    "                       zorder=0,  # ?\n",
    "                       shading='auto')\n",
    "\n",
    "    # Set Colorbar\n",
    "    fig.colorbar(im, ax=ax)\n",
    "\n",
    "    # Check if plot should be closed and saved instead of shown (Used for creating frames)\n",
    "    if close:\n",
    "        figname = f'plot_{figspec}.png'\n",
    "        plt.savefig(f'{output_dir}/{figname}.png')\n",
    "        plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the index for a specific time:\n",
    "# 2011 = 19723\n",
    "ymd = [2020, 6, 1]\n",
    "sel_time = f'{ymd[0]:4d}-{ymd[1]:2d}-{ymd[2]:2d}'\n",
    "sel_time_year = np.argwhere(ds.mtime.values==ds.mtime.sel(mtime=sel_time,method='nearest').values)[0]\n",
    "\n",
    "# Example of using slice to select slices of the data. \n",
    "# This can both be done with isel (using index) or sel (using values)\n",
    "_ = ds['mtime'].isel({'mtime':slice(sel_time_year[0],sel_time_year[0]+30)})\n",
    "del _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "species = 'pCO2'\n",
    "# domain 4\n",
    "sel={'mtime':ds['mtime'].values[0], 'lat':slice(55.0,57.0), 'lon':slice(8.0,16.0)}\n",
    "# Domain 1\n",
    "sel={'mtime':ds['mtime'].values[0], 'lat':slice(20.0,90.0), 'lon':slice(-180.0,180.0)}\n",
    "central_longitude=-32\n",
    "# print(ds[species].sel(sel)) # Testing settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "JENA_plotter(dataset=ds,\n",
    "             species=species,\n",
    "             sel=sel,\n",
    "             central_longitude=central_longitude,\n",
    "             output_dir='plots',\n",
    "             figspec='pCO2',\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Working with CAMS-GLOB netCDF data\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Key names/descriptions\n",
    "*get by calling:* list(ds.data_vars)\n",
    " 1. ags - Agricultural soil\n",
    " 2. ene - Power generation\n",
    " 3. fef - Fugitive emissions from fuels\n",
    " 4. ind - Industrial processes\n",
    " 5. ref - Refineries and fuel industries\n",
    " 6. res - Residential\n",
    " 7. shp - Ships\n",
    " 8. slv - Solvents application and production\n",
    " 9. sum - Sum of sectors\n",
    "10. swd - Solid waste and wastewater handeling\n",
    "11. tnr - Non-road transport\n",
    "12. tro - Road transport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species = 'ind'\n",
    "#species = list(ds.data_vars)[5]\n",
    "\n",
    "print('species:', species)\n",
    "\n",
    "sel_eu = {'time':'2024-06-01', 'lat':slice(40.0,60.0), 'lon':slice(-10.0,40.0)}\n",
    "sel_dk = {'time':'2024-06-01', 'lat':slice(54.0,58.0), 'lon':slice(7.0,15.5)}\n",
    "sel = sel_eu\n",
    "\n",
    "# Select values by index/slice/value.\n",
    "da = ds[species].sel(sel)\n",
    "\n",
    "# Set latitude and longitude values\n",
    "lat, lon = da['lat'].values, da['lon'].values\n",
    "\n",
    "# plot the first layer\n",
    "pdata = da.values\n",
    "\n",
    "# This is the map projection we want to plot *onto*\n",
    "map_proj = ccrs.NorthPolarStereo(central_longitude=32)\n",
    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.NorthPolarStereo(central_longitude=-32)}, figsize=(15,7))\n",
    "ax.coastlines(color='black',zorder=2,alpha=1,linewidth=1.5)\n",
    "gl = ax.gridlines(draw_labels=True,zorder=1,alpha=0.5,linewidth=0.5,crs=ccrs.PlateCarree())\n",
    "gl.xlabel_style = {'size':7}\n",
    "gl.ylabel_style = {'size':7}\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "cmap.set_bad('w',1.0)\n",
    "\n",
    "im = ax.pcolormesh(lon, lat, pdata, cmap=cmap,\n",
    "     vmin=0, vmax=1e-8,\n",
    "    transform=ccrs.PlateCarree(),\n",
    "    zorder=0, shading='auto')\n",
    "\n",
    "plt.colorbar(im, ax=ax, orientation='vertical',label=species)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Check sum - OK  <span style=\"color:green\">&check;</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking if the key 'sum' is equal to the actual sum.\n",
    "from decimal import *\n",
    "\n",
    "getcontext().prec = 12\n",
    "\n",
    "ds_no_sum = ds.drop_vars('sum')\n",
    "sum_sample = 0\n",
    "\n",
    "ds_no_sum = ds_no_sum.sel(time='2023-01-01', lat=0, lon=0, method='nearest')\n",
    "\n",
    "for key in ds_no_sum:\n",
    "    sum_sample =+ ds_no_sum[key].values\n",
    "\n",
    "print('Manual sum:', f'{sum_sample:+.8e}')\n",
    "print('Automatic sum:', ds['sum'].sel(time='2023-01-01', lat=0, lon=0, method='nearest').values)\n",
    "if np.isclose(sum_sample,ds['sum'].sel(time='2023-01-01', lat=0, lon=0, method='nearest').values):\n",
    "    print(\"'sum' is correct!\", bcolors.OKGREEN + u'\\u2713' + bcolors.ENDC)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Working with CAMS-REG netCDF data\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Working with CarbonTracker data\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Emission types\n",
    "1. Biosphere flux\n",
    "2. ocean flux\n",
    "3. fossil fuel flux\n",
    "4. forest fire flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#species = []\n",
    "species = list(ds.data_vars)[1:5]\n",
    "\n",
    "print('species:', species[:])\n",
    "print('--------------------------')\n",
    "\n",
    "sel_eu = {'latitude_source':slice(40.0,60.0), 'longitude_source':slice(-10.0,40.0)}\n",
    "sel_dk = {'latitude_source':slice(54.0,58.0), 'longitude_source':slice(7.0,15.5)}\n",
    "sel_area = sel_eu\n",
    "\n",
    "sel_category = None\n",
    "if False:\n",
    "    sel_category = {emis_category_index}\n",
    "\n",
    "# Select values by index/slice/value.\n",
    "if sel_category:\n",
    "    da = ds[species].sel(sel_area,sel_category)\n",
    "else:\n",
    "    da = ds[species].sel(sel_area)\n",
    "\n",
    "# Set latitude and longitude values\n",
    "lat, lon = da['lat'].values, da['lon'].values\n",
    "\n",
    "# plot the first layer\n",
    "pdata = da.values\n",
    "\n",
    "# This is the map projection we want to plot *onto*\n",
    "map_proj = ccrs.NorthPolarStereo(central_longitude=32)\n",
    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.NorthPolarStereo(central_longitude=-32)}, figsize=(15,7))\n",
    "ax.coastlines(color='black',zorder=2,alpha=1,linewidth=1.5)\n",
    "gl = ax.gridlines(draw_labels=True,zorder=1,alpha=0.5,linewidth=0.5,crs=ccrs.PlateCarree())\n",
    "gl.xlabel_style = {'size':7}\n",
    "gl.ylabel_style = {'size':7}\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "cmap.set_bad('w',1.0)\n",
    "\n",
    "im = ax.pcolormesh(lon, lat, pdata, cmap=cmap,\n",
    "     vmin=0, vmax=1e-8,\n",
    "    transform=ccrs.PlateCarree(),\n",
    "    zorder=0, shading='auto')\n",
    "\n",
    "plt.colorbar(im, ax=ax, orientation='vertical',label=species)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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