{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nutrient Cycle (Ncycle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Mike Levy\n",
      "\n",
      "Compiler    : GCC 9.3.0\n",
      "OS          : Linux\n",
      "Release     : 3.10.0-1127.18.2.el7.x86_64\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 72\n",
      "Architecture: 64bit\n",
      "\n",
      "Hostname: crhtc19\n",
      "\n",
      "Git hash: e13ef5bc0c05f1929d94119169b29c8bc48997aa\n",
      "\n",
      "numpy     : 1.20.2\n",
      "matplotlib: 3.4.1\n",
      "xarray    : 0.17.0\n",
      "cmocean   : 2.0\n",
      "cartopy   : 0.18.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "import utils\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import cmocean\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark -a \"Mike Levy\" -d -iv -m -g -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (nlat: 384, nlon: 320)\n",
       "Coordinates:\n",
       "    TLAT       (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "    TLONG      (nlat, nlon) float64 320.6 321.7 322.8 ... 318.9 319.4 319.8\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    AREA       (nlat, nlon) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    DENITRIF   (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    diaz_Nfix  (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan</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-a114a6ae-70ce-4f2d-b9d9-f972b61ab92d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a114a6ae-70ce-4f2d-b9d9-f972b61ab92d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-999d3fda-ce4b-4e2c-83d7-a7df7344f17e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-999d3fda-ce4b-4e2c-83d7-a7df7344f17e' class='xr-section-summary' >Coordinates: <span>(2)</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>TLAT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.22 -79.22 ... 72.19 72.19</div><input id='attrs-aa3bd3f9-ca86-4d3d-a79c-b6837228172d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa3bd3f9-ca86-4d3d-a79c-b6837228172d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7217830-f6b3-4c4e-9f31-c239b33e9f54' class='xr-var-data-in' type='checkbox'><label for='data-f7217830-f6b3-4c4e-9f31-c239b33e9f54' 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>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[-79.22052261, -79.22052261, -79.22052261, ..., -79.22052261,\n",
       "        -79.22052261, -79.22052261],\n",
       "       [-78.68630626, -78.68630626, -78.68630626, ..., -78.68630626,\n",
       "        -78.68630626, -78.68630626],\n",
       "       [-78.15208992, -78.15208992, -78.15208992, ..., -78.15208992,\n",
       "        -78.15208992, -78.15208992],\n",
       "       ...,\n",
       "       [ 71.29031715,  71.29408252,  71.30160692, ...,  71.30160692,\n",
       "         71.29408252,  71.29031716],\n",
       "       [ 71.73524335,  71.73881845,  71.74596231, ...,  71.74596231,\n",
       "         71.73881845,  71.73524335],\n",
       "       [ 72.18597561,  72.18933231,  72.19603941, ...,  72.19603941,\n",
       "         72.18933231,  72.18597562]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLONG</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>320.6 321.7 322.8 ... 319.4 319.8</div><input id='attrs-9371549a-1597-41e5-af23-fcba59e8cd9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9371549a-1597-41e5-af23-fcba59e8cd9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-562a4863-7307-48b6-b54d-6c1bd348e087' class='xr-var-data-in' type='checkbox'><label for='data-562a4863-7307-48b6-b54d-6c1bd348e087' 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>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       ...,\n",
       "       [320.25133086, 320.75380113, 321.25577325, ..., 318.74424456,\n",
       "        319.24621668, 319.74869143],\n",
       "       [320.23459477, 320.70358949, 321.17207442, ..., 318.82794339,\n",
       "        319.29642832, 319.76542721],\n",
       "       [320.21650899, 320.6493303 , 321.08163473, ..., 318.91838308,\n",
       "        319.3506875 , 319.78351267]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7e40a51-b03d-42e9-9b68-5ffb0a293535' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7e40a51-b03d-42e9-9b68-5ffb0a293535' 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>AREA</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f12db2d2-61a2-41e5-b990-d72f259ad42e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f12db2d2-61a2-41e5-b990-d72f259ad42e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88c09697-3609-4277-a29d-a39cbbe95562' class='xr-var-data-in' type='checkbox'><label for='data-88c09697-3609-4277-a29d-a39cbbe95562' 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>area of T cells</dd><dt><span>units :</span></dt><dd>m^2</dd></dl></div><div class='xr-var-data'><pre>array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.52530781e+09, 1.52530781e+09, 1.52530781e+09, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DENITRIF</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-bf5d65b0-c3da-4a8a-b5e9-9c7b8db3a34c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf5d65b0-c3da-4a8a-b5e9-9c7b8db3a34c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2019dbba-8518-4a2b-93c6-b46b51cd296c' class='xr-var-data-in' type='checkbox'><label for='data-2019dbba-8518-4a2b-93c6-b46b51cd296c' 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>Denitrification</dd><dt><span>title_str :</span></dt><dd>Denitrification (191.9 Tg N yr$^{-1}$)</dd><dt><span>units :</span></dt><dd>mmol m$^{-2}$ yr$^{-1}$</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diaz_Nfix</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-2a49b14c-b274-42df-9470-407410c90f8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a49b14c-b274-42df-9470-407410c90f8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2723a9ba-e2b7-4191-8f60-7e06b2c9d36f' class='xr-var-data-in' type='checkbox'><label for='data-2723a9ba-e2b7-4191-8f60-7e06b2c9d36f' 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_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Nitrogen fixation</dd><dt><span>title_str :</span></dt><dd>Nitrogen fixation (243.2 Tg N yr$^{-1}$)</dd><dt><span>units :</span></dt><dd>mmol m$^{-2}$ yr$^{-1}$</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbd44949-496b-47ef-bf38-bf1943798071' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bbd44949-496b-47ef-bf38-bf1943798071' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (nlat: 384, nlon: 320)\n",
       "Coordinates:\n",
       "    TLAT       (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "    TLONG      (nlat, nlon) float64 320.6 321.7 322.8 ... 318.9 319.4 319.8\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    AREA       (nlat, nlon) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    DENITRIF   (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    diaz_Nfix  (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_zarr('data/Ncycle.zarr').compute()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (nlat: 384, nlon: 321)\n",
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       "    TLONG      (nlat, nlon) float64 -220.6 -219.4 -218.3 ... -39.57 -39.86\n",
       "    AREA       (nlat, nlon) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    DENITRIF   (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    diaz_Nfix  (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan</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-8fdf8e00-89a3-458f-9d6d-fcfb2a16f228' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8fdf8e00-89a3-458f-9d6d-fcfb2a16f228' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nlat</span>: 384</li><li><span>nlon</span>: 321</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3a868a80-fdcb-4a22-aa17-e0484c9fb070' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a868a80-fdcb-4a22-aa17-e0484c9fb070' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-48f2d913-7e78-494d-b4d6-3369bfb63c93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48f2d913-7e78-494d-b4d6-3369bfb63c93' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.22 -79.22 ... 80.31 80.31</div><input id='attrs-31879a6a-e7fc-496e-818d-99da2460f8df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31879a6a-e7fc-496e-818d-99da2460f8df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3491e679-cd6a-41d5-8744-02fbe22ffbee' class='xr-var-data-in' type='checkbox'><label for='data-3491e679-cd6a-41d5-8744-02fbe22ffbee' 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([[-79.22052261, -79.22052261, -79.22052261, ..., -79.22052261,\n",
       "        -79.22052261, -79.22052261],\n",
       "       [-78.68630626, -78.68630626, -78.68630626, ..., -78.68630626,\n",
       "        -78.68630626, -78.68630626],\n",
       "       [-78.15208992, -78.15208992, -78.15208992, ..., -78.15208992,\n",
       "        -78.15208992, -78.15208992],\n",
       "       ...,\n",
       "       [ 81.44584238,  81.44584238,  81.44466079, ...,  81.44229771,\n",
       "         81.44466079,  81.44584238],\n",
       "       [ 80.87803543,  80.87803543,  80.87705778, ...,  80.87510244,\n",
       "         80.87705778,  80.87803543],\n",
       "       [ 80.31321311,  80.31321311,  80.31241206, ...,  80.31080987,\n",
       "         80.31241206,  80.31321311]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLONG</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-220.6 -219.4 ... -39.57 -39.86</div><input id='attrs-de98a755-430c-45da-8cf0-eeab16d0abdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de98a755-430c-45da-8cf0-eeab16d0abdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2123c6a9-df00-4fcb-b499-8f7f45822934' class='xr-var-data-in' type='checkbox'><label for='data-2123c6a9-df00-4fcb-b499-8f7f45822934' 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([[-220.56249613, -219.43749609, -218.31249606, ...,  137.18750382,\n",
       "         138.31250385,  139.43750388],\n",
       "       [-220.56249613, -219.43749609, -218.31249606, ...,  137.18750382,\n",
       "         138.31250385,  139.43750388],\n",
       "       [-220.56249613, -219.43749609, -218.31249606, ...,  137.18750382,\n",
       "         138.31250385,  139.43750388],\n",
       "       ...,\n",
       "       [ -39.7932723 ,  -40.20670806,  -40.62006565, ...,  -38.96679888,\n",
       "         -39.37991654,  -39.79327229],\n",
       "       [ -39.82753619,  -40.17244447,  -40.51730173, ...,  -39.13798089,\n",
       "         -39.48268046,  -39.82753618],\n",
       "       [ -39.85740526,  -40.14257567,  -40.42771349, ...,  -39.28723255,\n",
       "         -39.5722687 ,  -39.85740525]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AREA</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-9afc534b-e557-47b4-9dee-6470def30cfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9afc534b-e557-47b4-9dee-6470def30cfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-623d02e4-4755-4cd7-93d8-e159502612dc' class='xr-var-data-in' type='checkbox'><label for='data-623d02e4-4755-4cd7-93d8-e159502612dc' 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>area of T cells</dd><dt><span>units :</span></dt><dd>m^2</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DENITRIF</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-36b0c21a-7936-4f35-bd46-d2f23596db3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36b0c21a-7936-4f35-bd46-d2f23596db3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fdf052a-03e0-4c0a-9bc2-fe53c51cd849' class='xr-var-data-in' type='checkbox'><label for='data-9fdf052a-03e0-4c0a-9bc2-fe53c51cd849' 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>Denitrification</dd><dt><span>title_str :</span></dt><dd>Denitrification (191.9 Tg N yr$^{-1}$)</dd><dt><span>units :</span></dt><dd>mmol m$^{-2}$ yr$^{-1}$</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diaz_Nfix</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-9d41e537-3a9e-4636-8893-05f8fd205908' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d41e537-3a9e-4636-8893-05f8fd205908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74bb7f91-50bb-4ce8-af1f-55a631e548a5' class='xr-var-data-in' type='checkbox'><label for='data-74bb7f91-50bb-4ce8-af1f-55a631e548a5' 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_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Nitrogen fixation</dd><dt><span>title_str :</span></dt><dd>Nitrogen fixation (243.2 Tg N yr$^{-1}$)</dd><dt><span>units :</span></dt><dd>mmol m$^{-2}$ yr$^{-1}$</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b609f14-d2bd-465a-99cc-bf039e235eea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b609f14-d2bd-465a-99cc-bf039e235eea' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (nlat: 384, nlon: 321)\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    TLAT       (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 80.31 80.31 80.31\n",
       "    TLONG      (nlat, nlon) float64 -220.6 -219.4 -218.3 ... -39.57 -39.86\n",
       "    AREA       (nlat, nlon) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    DENITRIF   (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    diaz_Nfix  (nlat, nlon) float32 nan nan nan nan nan ... nan nan nan nan nan"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsp = utils.pop_add_cyclic(ds)\n",
    "dsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "gs = gridspec.GridSpec(nrows=2, ncols=2, width_ratios=(1, 0.02,))\n",
    "\n",
    "prj = ccrs.Robinson(central_longitude=305.0)\n",
    "\n",
    "axs = []\n",
    "maps = []\n",
    "\n",
    "levels = dict(\n",
    "    diaz_Nfix=[0., 5., 10., 15., 20., 30., 40., 50., 60., 80., 100., 120., 140., 200., ],\n",
    "    DENITRIF=[0., 10., 25., 50., 100., 150., 200., 400., 600., 800., 1000., 1500., ]\n",
    ")\n",
    "\n",
    "cmap_field = cmocean.cm.dense\n",
    "\n",
    "contour_spec = dict(\n",
    "    diaz_Nfix=dict(\n",
    "        levels=levels['diaz_Nfix'],\n",
    "        extend='max',\n",
    "        cmap=cmap_field,\n",
    "        norm=colors.BoundaryNorm(levels['diaz_Nfix'], ncolors=cmap_field.N),\n",
    "    ),\n",
    "    DENITRIF=dict(\n",
    "        levels=levels['DENITRIF'],\n",
    "        extend='max',\n",
    "        cmap=cmap_field,\n",
    "        norm=colors.BoundaryNorm(levels['DENITRIF'], ncolors=cmap_field.N),\n",
    "    ),\n",
    ")\n",
    "\n",
    "for i, var in enumerate(['diaz_Nfix', 'DENITRIF']):\n",
    "    \n",
    "    ax = plt.subplot(gs[i, 0], projection=prj)                         \n",
    "    maps.append(ax)\n",
    "    \n",
    "    cf = ax.contourf(dsp.TLONG,\n",
    "                     dsp.TLAT,\n",
    "                     dsp[var],\n",
    "                     **contour_spec[var],\n",
    "                     transform=ccrs.PlateCarree())\n",
    "\n",
    "    cl = ax.contour(dsp.TLONG,\n",
    "                     dsp.TLAT,\n",
    "                     dsp[var],\n",
    "                     levels=levels[var],\n",
    "                     colors='k', linewidths=0.2,\n",
    "                     transform=ccrs.PlateCarree())\n",
    "    \n",
    "    land = ax.add_feature(\n",
    "        cartopy.feature.NaturalEarthFeature(\n",
    "            'physical','land','110m',\n",
    "            edgecolor='face',\n",
    "            facecolor='gray'\n",
    "        )\n",
    "    )  \n",
    "                             \n",
    "    ax.set_title(dsp[var].attrs['title_str'])\n",
    "\n",
    "    cb = plt.colorbar(cf, shrink=0.9)\n",
    "    cb.set_label(dsp[var].units)\n",
    "    \n",
    "utils.label_plots(fig, maps, xoff=0.02, yoff=0)       \n",
    "utils.savefig('Ncycle.pdf')"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}