{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multidimensional Coordinates example using CMIP6 Pangeo ocean model data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "xr.set_options(display_style='html')\n",
    "import intake\n",
    "import cftime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.path as mpath\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open CMIP6 online catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 7715 dataset(s) from 520279 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>520279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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   ],
   "source": [
    "cat_url = \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
    "col = intake.open_esm_datastore(cat_url)\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search corresponding data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>MPI-M</td>\n",
       "      <td>MPI-ESM1-2-HR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r9i1p1f1</td>\n",
       "      <td>Omon</td>\n",
       "      <td>chl</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/hist...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190710</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  activity_id institution_id      source_id experiment_id member_id table_id  \\\n",
       "0        CMIP          MPI-M  MPI-ESM1-2-HR    historical  r9i1p1f1     Omon   \n",
       "\n",
       "  variable_id grid_label                                             zstore  \\\n",
       "0         chl         gn  gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/hist...   \n",
       "\n",
       "   dcpp_init_year   version  \n",
       "0             NaN  20190710  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat = col.search(source_id=['MPI-ESM1-2-HR'], experiment_id=['historical'], table_id=['Omon'], variable_id=['chl'], member_id=['r9i1p1f1'])\n",
    "cat.df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dictionary from the list of datasets we found\n",
    "- This step may take several minutes so be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
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    "dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CMIP.MPI-M.MPI-ESM1-2-HR.historical.Omon.gn']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "list(dset_dict.keys())"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open dataset\n",
    "\n",
    "- Use `xarray` python package to analyze netCDF dataset\n",
    "- `open_dataset` allows to get all the metadata without loading data into memory. \n",
    "- with `xarray`, we only load into memory what is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (bnds: 2, i: 802, j: 404, lev: 40, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 795 796 797 798 799 800 801\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 397 398 399 400 401 402 403\n",
       "    latitude            (j, i) float64 dask.array&lt;chunksize=(404, 802), meta=np.ndarray&gt;\n",
       "  * lev                 (lev) float64 6.0 17.0 27.0 ... 5.17e+03 5.72e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array&lt;chunksize=(40, 2), meta=np.ndarray&gt;\n",
       "    longitude           (j, i) float64 dask.array&lt;chunksize=(404, 802), meta=np.ndarray&gt;\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "  * member_id           (member_id) &lt;U8 &#x27;r9i1p1f1&#x27;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    chl                 (member_id, time, lev, j, i) float32 dask.array&lt;chunksize=(1, 2, 40, 404, 802), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array&lt;chunksize=(404, 802, 4), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array&lt;chunksize=(404, 802, 4), meta=np.ndarray&gt;\n",
       "Attributes: (12/50)\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   146096.0\n",
       "    cmor_version:            3.5.0\n",
       "    ...                      ...\n",
       "    title:                   MPI-ESM1-2-HR output prepared for CMIP6\n",
       "    tracking_id:             hdl:21.14100/4e922d88-7711-4df6-9bdd-b849acd45a7...\n",
       "    variable_id:             chl\n",
       "    variant_label:           r9i1p1f1\n",
       "    intake_esm_varname:      [&#x27;chl&#x27;]\n",
       "    intake_esm_dataset_key:  CMIP.MPI-M.MPI-ESM1-2-HR.historical.Omon.gn</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-61db209f-0db7-4e8f-b549-917507671265' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-61db209f-0db7-4e8f-b549-917507671265' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>i</span>: 802</li><li><span class='xr-has-index'>j</span>: 404</li><li><span class='xr-has-index'>lev</span>: 40</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-68719e80-6171-461a-b85a-e4547b2ee239' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68719e80-6171-461a-b85a-e4547b2ee239' class='xr-section-summary' >Coordinates: <span>(9)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 797 798 799 800 801</div><input id='attrs-5c249579-7033-4919-9446-ebca3d2d1f78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c249579-7033-4919-9446-ebca3d2d1f78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c18e14-d4e4-4cf7-b431-45bf7c3d774a' class='xr-var-data-in' type='checkbox'><label for='data-18c18e14-d4e4-4cf7-b431-45bf7c3d774a' 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>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 799, 800, 801], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 399 400 401 402 403</div><input id='attrs-9f624e97-4cf4-4080-9e84-33cfb4add7a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f624e97-4cf4-4080-9e84-33cfb4add7a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c464670-dfa3-4328-a4d0-6f2be152c559' class='xr-var-data-in' type='checkbox'><label for='data-9c464670-dfa3-4328-a4d0-6f2be152c559' 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>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 401, 402, 403], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(404, 802), meta=np.ndarray&gt;</div><input id='attrs-bf196352-8bc3-4d70-b73f-afbe548f36a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf196352-8bc3-4d70-b73f-afbe548f36a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adb18860-16cf-4547-a6a4-b746fd031f79' class='xr-var-data-in' type='checkbox'><label for='data-adb18860-16cf-4547-a6a4-b746fd031f79' 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>bounds :</span></dt><dd>vertices_latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.59 MB </td> <td> 2.59 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (404, 802) </td> <td> (404, 802) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.44887780548628 0.0,60.44887780548628\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"80.448878\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >802</text>\n",
       "  <text x=\"140.000000\" y=\"30.224439\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.224439)\">404</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.0 17.0 27.0 ... 5.17e+03 5.72e+03</div><input id='attrs-f41eed7e-187f-4c9c-a955-b49e344e6406' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f41eed7e-187f-4c9c-a955-b49e344e6406' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d463c007-0442-48b1-822b-d4d5e0afe5a4' class='xr-var-data-in' type='checkbox'><label for='data-d463c007-0442-48b1-822b-d4d5e0afe5a4' 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>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   6. ,   17. ,   27. ,   37. ,   47. ,   57. ,   68.5,   82.5,  100. ,\n",
       "        122.5,  150. ,  182.5,  220. ,  262.5,  310. ,  362.5,  420. ,  485. ,\n",
       "        560. ,  645. ,  740. ,  845. ,  960. , 1085. , 1220. , 1365. , 1525. ,\n",
       "       1700. , 1885. , 2080. , 2290. , 2525. , 2785. , 3070. , 3395. , 3770. ,\n",
       "       4195. , 4670. , 5170. , 5720. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bnds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(40, 2), meta=np.ndarray&gt;</div><input id='attrs-c61baf56-01d7-4e89-9a63-450c0d5f65f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c61baf56-01d7-4e89-9a63-450c0d5f65f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-919880e4-1d6a-437d-aa78-0671775c50b6' class='xr-var-data-in' type='checkbox'><label for='data-919880e4-1d6a-437d-aa78-0671775c50b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 640 B </td> <td> 640 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (40, 2) </td> <td> (40, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(404, 802), meta=np.ndarray&gt;</div><input id='attrs-03f0e53b-bf69-4d48-92aa-c57cf53c5754' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03f0e53b-bf69-4d48-92aa-c57cf53c5754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82890eb0-c74a-410c-85b4-d9c0376c20d3' class='xr-var-data-in' type='checkbox'><label for='data-82890eb0-c74a-410c-85b4-d9c0376c20d3' 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>bounds :</span></dt><dd>vertices_longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.59 MB </td> <td> 2.59 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (404, 802) </td> <td> (404, 802) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"80.448878\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >802</text>\n",
       "  <text x=\"140.000000\" y=\"30.224439\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.224439)\">404</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-e9abcf0a-aca8-46e9-82f1-ac445de68b57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9abcf0a-aca8-46e9-82f1-ac445de68b57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b52e4326-4826-4fcb-ae3b-257fda505e53' class='xr-var-data-in' type='checkbox'><label for='data-b52e4326-4826-4fcb-ae3b-257fda505e53' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-19ee4bd4-2559-4824-9b8b-55d5ab59fb93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19ee4bd4-2559-4824-9b8b-55d5ab59fb93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75914076-7686-4a11-8022-20c4dc2e767b' class='xr-var-data-in' type='checkbox'><label for='data-75914076-7686-4a11-8022-20c4dc2e767b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 31.68 kB </td> <td> 31.68 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (1980, 2) </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r9i1p1f1&#x27;</div><input id='attrs-fd58411e-ccd9-4ae8-83d4-a1a050b0f295' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd58411e-ccd9-4ae8-83d4-a1a050b0f295' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a501d02-c48b-4114-b9c7-6bc4aeaddc9a' class='xr-var-data-in' type='checkbox'><label for='data-8a501d02-c48b-4114-b9c7-6bc4aeaddc9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r9i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7ceca07-e32e-446b-9fe0-8ecbb24a994e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7ceca07-e32e-446b-9fe0-8ecbb24a994e' class='xr-section-summary' >Data variables: <span>(3)</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>chl</span></div><div class='xr-var-dims'>(member_id, time, lev, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2, 40, 404, 802), meta=np.ndarray&gt;</div><input id='attrs-dd23d591-e557-4a59-8947-6d0bbae14ec7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd23d591-e557-4a59-8947-6d0bbae14ec7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8bddfc7-a528-47c4-9566-1445633245ed' class='xr-var-data-in' type='checkbox'><label for='data-d8bddfc7-a528-47c4-9566-1445633245ed' 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>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Sum of chlorophyll from all phytoplankton group concentrations.  In most models this is equal to chldiat+chlmisc, that is the sum of Diatom Chlorophyll Mass Concentration and Other Phytoplankton Chlorophyll Mass Concentration</dd><dt><span>history :</span></dt><dd>2019-08-30T07:10:19Z altered by CMOR: replaced missing value flag (-9e+33) and corresponding data with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Mass Concentration of Total Phytoplankton Expressed as Chlorophyll in Sea Water</dd><dt><span>original_name :</span></dt><dd>chl</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_phytoplankton_expressed_as_chlorophyll_in_sea_water</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <text x=\"17.779082\" y=\"157.779082\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,17.779082,157.779082)\">404</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-46abe833-dedf-490d-9bce-593a37255f3a' class='xr-section-summary-in' type='checkbox'  ><label for='section-46abe833-dedf-490d-9bce-593a37255f3a' class='xr-section-summary' >Attributes: <span>(50)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>146096.0</dd><dt><span>cmor_version :</span></dt><dd>3.5.0</dd><dt><span>contact :</span></dt><dd>cmip6-mpi-esm@dkrz.de</dd><dt><span>creation_date :</span></dt><dd>2019-08-30T07:10:20Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacello volcello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.MPI-M.MPI-ESM1-2-HR.historical.none.r9i1p1f1</dd><dt><span>grid :</span></dt><dd>gn</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-08-30T07:10:20Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology, Hamburg 20146, Germany</dd><dt><span>institution_id :</span></dt><dd>MPI-M</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by MPI-M is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>50 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r9i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>project_id :</span></dt><dd>CMIP6</dd><dt><span>realization_index :</span></dt><dd>9</dd><dt><span>realm :</span></dt><dd>ocnBgchem</dd><dt><span>references :</span></dt><dd>MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400,\n",
       "Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217</dd><dt><span>source :</span></dt><dd>MPI-ESM1.2-HR (2017): \n",
       "aerosol: none, prescribed MACv2-SP\n",
       "atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa)\n",
       "atmosChem: none\n",
       "land: JSBACH3.20\n",
       "landIce: none/prescribed\n",
       "ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m)\n",
       "ocnBgchem: HAMOCC6\n",
       "seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)</dd><dt><span>source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>status :</span></dt><dd>2020-04-18;created; by gcs.cmip6.ldeo@gmail.com</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Omon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc</dd><dt><span>title :</span></dt><dd>MPI-ESM1-2-HR output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/4e922d88-7711-4df6-9bdd-b849acd45a77\n",
       "hdl:21.14100/c80d8b77-125c-4d40-84d8-5d96d26c8871\n",
       "hdl:21.14100/256ed3bd-05a0-4e98-bbbb-d45570361013\n",
       "hdl:21.14100/985630f9-9922-41ed-8cfe-2e5dbe49a5ee\n",
       "hdl:21.14100/07ae37ed-16a0-4412-96b0-d2625894c68f\n",
       "hdl:21.14100/9c36da05-c73c-4259-baf5-76d3877b5cfc\n",
       "hdl:21.14100/20715c6d-d9f8-483e-922c-05374cac92ae\n",
       "hdl:21.14100/33ca1735-dfbb-4d5c-8457-164d34583d2d\n",
       "hdl:21.14100/39619a6e-af06-4fe8-99ec-bb442024599a\n",
       "hdl:21.14100/cbad2b59-fba5-49b8-aa09-106afb600812\n",
       "hdl:21.14100/162c662d-839d-430a-8261-0ac73f4643d0\n",
       "hdl:21.14100/1c87d835-d9c9-4458-bd52-a972498fcfb8\n",
       "hdl:21.14100/ea60206c-889c-4f00-b295-daf4a4f02d96\n",
       "hdl:21.14100/589eea2f-deef-439e-a2e1-afa4a506fcc7\n",
       "hdl:21.14100/eba1472c-3029-4be3-acdf-288c38c86336\n",
       "hdl:21.14100/48b01132-09f5-4f45-b05b-e10a41eb29d8\n",
       "hdl:21.14100/c63ce8ba-967f-4827-afa9-3339d22d9d44\n",
       "hdl:21.14100/dd2dbac5-8c70-4902-8f52-b8668684da80\n",
       "hdl:21.14100/434b1bcc-de97-437e-b1f8-b468c054e819\n",
       "hdl:21.14100/d1fc1095-0263-4231-884b-67b6b3aa3248\n",
       "hdl:21.14100/a1ce2b6c-0787-4cf2-adc3-9c53f5577d05\n",
       "hdl:21.14100/60bff6a6-ffde-4604-a53a-89442b8f0fc7\n",
       "hdl:21.14100/b57d4410-a2c2-41a5-bdc8-ceeb9ae553c3\n",
       "hdl:21.14100/b96a67c6-c1ce-4603-b528-665880ca4288\n",
       "hdl:21.14100/63f99a97-4708-4063-ae9d-13dc81a6b8d4\n",
       "hdl:21.14100/4a3f98bd-a2ff-4fbb-99a8-9ad632810b75\n",
       "hdl:21.14100/b9c209c3-29e3-47b2-889c-104de3f08297\n",
       "hdl:21.14100/33b509f0-f0c6-48b3-a342-62b727a50bae\n",
       "hdl:21.14100/4c9dbc22-a1fc-4a8f-aa74-6cf062b232c7\n",
       "hdl:21.14100/fb0ac57b-b06b-4d0c-b322-4b90434a4b6a\n",
       "hdl:21.14100/00ba5a3f-281e-4bea-a673-4b65e9d60c93\n",
       "hdl:21.14100/7e50067b-045e-4dda-b221-b68f57c831c0\n",
       "hdl:21.14100/ca3ddf73-a346-488f-a862-1f1db4a9448b</dd><dt><span>variable_id :</span></dt><dd>chl</dd><dt><span>variant_label :</span></dt><dd>r9i1p1f1</dd><dt><span>intake_esm_varname :</span></dt><dd>[&#x27;chl&#x27;]</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.MPI-M.MPI-ESM1-2-HR.historical.Omon.gn</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (bnds: 2, i: 802, j: 404, lev: 40, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 795 796 797 798 799 800 801\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 397 398 399 400 401 402 403\n",
       "    latitude            (j, i) float64 dask.array<chunksize=(404, 802), meta=np.ndarray>\n",
       "  * lev                 (lev) float64 6.0 17.0 27.0 ... 5.17e+03 5.72e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(40, 2), meta=np.ndarray>\n",
       "    longitude           (j, i) float64 dask.array<chunksize=(404, 802), meta=np.ndarray>\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "  * member_id           (member_id) <U8 'r9i1p1f1'\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    chl                 (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 2, 40, 404, 802), meta=np.ndarray>\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(404, 802, 4), meta=np.ndarray>\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(404, 802, 4), meta=np.ndarray>\n",
       "Attributes: (12/50)\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   146096.0\n",
       "    cmor_version:            3.5.0\n",
       "    ...                      ...\n",
       "    title:                   MPI-ESM1-2-HR output prepared for CMIP6\n",
       "    tracking_id:             hdl:21.14100/4e922d88-7711-4df6-9bdd-b849acd45a7...\n",
       "    variable_id:             chl\n",
       "    variant_label:           r9i1p1f1\n",
       "    intake_esm_varname:      ['chl']\n",
       "    intake_esm_dataset_key:  CMIP.MPI-M.MPI-ESM1-2-HR.historical.Omon.gn"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset = dset_dict['CMIP.MPI-M.MPI-ESM1-2-HR.historical.Omon.gn']\n",
    "dset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get metadata corresponding to Chl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'chl' (member_id: 1, time: 1980, lev: 40, j: 404, i: 802)>\n",
      "dask.array<broadcast_to, shape=(1, 1980, 40, 404, 802), dtype=float32, chunksize=(1, 2, 40, 404, 802), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "  * i          (i) int32 0 1 2 3 4 5 6 7 8 ... 794 795 796 797 798 799 800 801\n",
      "  * j          (j) int32 0 1 2 3 4 5 6 7 8 ... 396 397 398 399 400 401 402 403\n",
      "    latitude   (j, i) float64 dask.array<chunksize=(404, 802), meta=np.ndarray>\n",
      "  * lev        (lev) float64 6.0 17.0 27.0 37.0 ... 4.67e+03 5.17e+03 5.72e+03\n",
      "    longitude  (j, i) float64 dask.array<chunksize=(404, 802), meta=np.ndarray>\n",
      "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
      "  * member_id  (member_id) <U8 'r9i1p1f1'\n",
      "Attributes:\n",
      "    cell_measures:  area: areacello volume: volcello\n",
      "    cell_methods:   area: mean where sea time: mean\n",
      "    comment:        Sum of chlorophyll from all phytoplankton group concentra...\n",
      "    history:        2019-08-30T07:10:19Z altered by CMOR: replaced missing va...\n",
      "    long_name:      Mass Concentration of Total Phytoplankton Expressed as Ch...\n",
      "    original_name:  chl\n",
      "    standard_name:  mass_concentration_of_phytoplankton_expressed_as_chloroph...\n",
      "    units:          kg m-3\n"
     ]
    }
   ],
   "source": [
    "print(dset['chl'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0)],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset.time.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## shift longitude from 0,360 to -180,180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset.coords['longitude'] = (dset['longitude'] + 180) % 360 - 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-78.71779338013427, 89.76311914726304)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset['chl'].latitude.values.min(), dset['chl'].latitude.values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-179.99408443971072, 179.99893486577713)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset['chl'].longitude.values.min(), dset['chl'].longitude.values.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select geographical area, time and level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_selection = dset.isel(time=0, lev=0).where( (dset.latitude > 50) & (dset.longitude > -30) & (dset.longitude < 30) ).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polarCentral_set_latlim(lat_lims, ax):\n",
    "    ax.set_extent([-180, 180, lat_lims[0], lat_lims[1]], ccrs.PlateCarree())\n",
    "    # Compute a circle in axes coordinates, which we can use as a boundary\n",
    "    # for the map. We can pan/zoom as much as we like - the boundary will be\n",
    "    # permanently circular.\n",
    "    theta = np.linspace(0, 2*np.pi, 100)\n",
    "    center, radius = [0.5, 0.5], 0.5\n",
    "    verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n",
    "    circle = mpath.Path(verts * radius + center)\n",
    "\n",
    "    ax.set_boundary(circle, transform=ax.transAxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1597: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n",
      "  X, Y, C, shading = self._pcolorargs('pcolormesh', *args,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f109bda9b80>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=[10,10])\n",
    "\n",
    "# Fix extent\n",
    "minval = 0\n",
    "maxval = 100.\n",
    "\n",
    "ax = plt.subplot(1, 1, 1, projection=ccrs.NorthPolarStereo())\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "polarCentral_set_latlim([50,90], ax)\n",
    "dset_selection.chl.plot.pcolormesh(\n",
    "    ax=ax, transform=ccrs.PlateCarree(), x=\"longitude\", y=\"latitude\", add_colorbar=False, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}