Fig12_fish_variability_x01.ipynb

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    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import xarray as xr\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "from glob import glob\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import metpy.calc as mpcalc\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from scipy import stats\n",
    "import pop_tools \n",
    "import matplotlib as mpl\n",
    "import matplotlib.path as mpath\n",
    "import dask\n",
    "import esmlab\n",
    "import utils\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cmocean\n",
    "import matplotlib.colors as colors"
   ]
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    "def get_ClusterClient():\n",
    "    import dask\n",
    "    from dask_jobqueue import PBSCluster\n",
    "    from dask.distributed import Client\n",
    "    cluster = PBSCluster(\n",
    "        cores=1,\n",
    "        memory='25GB',\n",
    "        processes=1,\n",
    "        queue='casper',\n",
    "        resource_spec='select=1:ncpus=1:mem=25GB',\n",
    "        project='NCGD0011',\n",
    "        walltime='02:00:00',\n",
    "        interface='ext',)\n",
    "\n",
    "    dask.config.set({\n",
    "        'distributed.dashboard.link':\n",
    "        'https://jupyterhub.hpc.ucar.edu/stable/user/{USER}/proxy/{port}/status'\n",
    "    })\n",
    "    client = Client(cluster)\n",
    "    return cluster, client"
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    "cluster, client = get_ClusterClient()\n",
    "cluster.scale(5) "
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "acdebf59-c2c5-46cf-b451-5eedd1940d60",
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       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://128.117.208.119:36887</li>\n",
       "  <li><b>Dashboard: </b><a href='https://jupyterhub.hpc.ucar.edu/stable/user/eromashkova/proxy/8787/status' target='_blank'>https://jupyterhub.hpc.ucar.edu/stable/user/eromashkova/proxy/8787/status</a>\n",
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   "cell_type": "markdown",
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   "metadata": {},
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    "# Read in data"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (fish: 8, group: 9, nlat: 2400, nlon: 3600, time: 42)\n",
       "Coordinates:\n",
       "  * fish        (fish) object &#x27;Sf&#x27; &#x27;Sp&#x27; &#x27;Sd&#x27; &#x27;Mf&#x27; &#x27;Mp&#x27; &#x27;Md&#x27; &#x27;Lp&#x27; &#x27;Ld&#x27;\n",
       "  * group       (group) object &#x27;Sf&#x27; &#x27;Sp&#x27; &#x27;Sd&#x27; &#x27;Mf&#x27; ... &#x27;Lp&#x27; &#x27;Ld&#x27; &#x27;benthic_prey&#x27;\n",
       "  * nlat        (nlat) int64 0 1 2 3 4 5 6 ... 2394 2395 2396 2397 2398 2399\n",
       "  * nlon        (nlon) int64 0 1 2 3 4 5 6 ... 3594 3595 3596 3597 3598 3599\n",
       "  * time        (time) int64 1980 1981 1982 1983 1984 ... 2018 2019 2020 2021\n",
       "Data variables:\n",
       "    biomass     (time, group, nlat, nlon) float64 dask.array&lt;chunksize=(42, 9, 8, 8), meta=np.ndarray&gt;\n",
       "    fish_yield  (time, fish, nlat, nlon) float64 dask.array&lt;chunksize=(42, 8, 8, 8), meta=np.ndarray&gt;\n",
       "    production  (time, fish, nlat, nlon) float64 dask.array&lt;chunksize=(42, 8, 8, 8), meta=np.ndarray&gt;</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-c39d28f9-bbb5-4eac-9018-76f8653788a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c39d28f9-bbb5-4eac-9018-76f8653788a3' 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'>fish</span>: 8</li><li><span class='xr-has-index'>group</span>: 9</li><li><span class='xr-has-index'>nlat</span>: 2400</li><li><span class='xr-has-index'>nlon</span>: 3600</li><li><span class='xr-has-index'>time</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4f7f7344-95ef-4dfa-857e-bb8521464e43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f7f7344-95ef-4dfa-857e-bb8521464e43' 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'>fish</span></div><div class='xr-var-dims'>(fish)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Sf&#x27; &#x27;Sp&#x27; &#x27;Sd&#x27; ... &#x27;Md&#x27; &#x27;Lp&#x27; &#x27;Ld&#x27;</div><input id='attrs-3999adb0-1b59-4942-9bc6-94485e013ffb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3999adb0-1b59-4942-9bc6-94485e013ffb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22cb19a1-9d38-40a1-9b66-ead90b4005dc' class='xr-var-data-in' type='checkbox'><label for='data-22cb19a1-9d38-40a1-9b66-ead90b4005dc' 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;Sf&#x27;, &#x27;Sp&#x27;, &#x27;Sd&#x27;, &#x27;Mf&#x27;, &#x27;Mp&#x27;, &#x27;Md&#x27;, &#x27;Lp&#x27;, &#x27;Ld&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>group</span></div><div class='xr-var-dims'>(group)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Sf&#x27; &#x27;Sp&#x27; ... &#x27;Ld&#x27; &#x27;benthic_prey&#x27;</div><input id='attrs-ed55bf13-850e-46ab-a8c0-3f9a16028ff0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed55bf13-850e-46ab-a8c0-3f9a16028ff0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b49782-330e-4364-99b2-889dd23ad8cc' class='xr-var-data-in' type='checkbox'><label for='data-60b49782-330e-4364-99b2-889dd23ad8cc' 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;Sf&#x27;, &#x27;Sp&#x27;, &#x27;Sd&#x27;, &#x27;Mf&#x27;, &#x27;Mp&#x27;, &#x27;Md&#x27;, &#x27;Lp&#x27;, &#x27;Ld&#x27;, &#x27;benthic_prey&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nlat</span></div><div class='xr-var-dims'>(nlat)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2396 2397 2398 2399</div><input id='attrs-e9149751-822c-41e8-950e-6d5ec8421991' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9149751-822c-41e8-950e-6d5ec8421991' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-712034fe-b868-412a-8660-f2bde085eb28' class='xr-var-data-in' type='checkbox'><label for='data-712034fe-b868-412a-8660-f2bde085eb28' 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([   0,    1,    2, ..., 2397, 2398, 2399])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nlon</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 3596 3597 3598 3599</div><input id='attrs-7b9bd0cf-d3f0-4de4-b128-e487747f725f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b9bd0cf-d3f0-4de4-b128-e487747f725f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e46ba00d-098e-4aab-b1f0-263cba83cd01' class='xr-var-data-in' type='checkbox'><label for='data-e46ba00d-098e-4aab-b1f0-263cba83cd01' 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([   0,    1,    2, ..., 3597, 3598, 3599])</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'>int64</div><div class='xr-var-preview xr-preview'>1980 1981 1982 ... 2019 2020 2021</div><input id='attrs-9bdc63b2-7225-432b-b867-4a94de19302b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bdc63b2-7225-432b-b867-4a94de19302b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b9b411c-f25b-47e4-97a2-94d56062e446' class='xr-var-data-in' type='checkbox'><label for='data-2b9b411c-f25b-47e4-97a2-94d56062e446' 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([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n",
       "       1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
       "       2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
       "       2016, 2017, 2018, 2019, 2020, 2021])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf2a4668-0306-481c-bea2-286964e6b741' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf2a4668-0306-481c-bea2-286964e6b741' 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>biomass</span></div><div class='xr-var-dims'>(time, group, nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(42, 9, 8, 8), meta=np.ndarray&gt;</div><input id='attrs-57acd6a3-db1d-48a4-bbd1-4e250e960be0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57acd6a3-db1d-48a4-bbd1-4e250e960be0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afc713ed-b980-45fe-8297-b1f0fa7a77aa' class='xr-var-data-in' type='checkbox'><label for='data-afc713ed-b980-45fe-8297-b1f0fa7a77aa' 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",
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       "    <tr><th> Bytes </th><td> 26.13 GB </td> <td> 193.54 kB </td></tr>\n",
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       "  <line x1=\"110\" y1=\"65\" x2=\"230\" y2=\"65\" />\n",
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       "  <line x1=\"110\" y1=\"71\" x2=\"230\" y2=\"71\" />\n",
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       "  <line x1=\"110\" y1=\"72\" x2=\"230\" y2=\"72\" />\n",
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       "  <line x1=\"110\" y1=\"73\" x2=\"230\" y2=\"73\" />\n",
       "  <line x1=\"110\" y1=\"73\" x2=\"230\" y2=\"73\" />\n",
       "  <line x1=\"110\" y1=\"73\" x2=\"230\" y2=\"73\" />\n",
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       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
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       "  <line x1=\"110\" y1=\"76\" x2=\"230\" y2=\"76\" />\n",
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       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
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       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
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       "  <line x1=\"110\" y1=\"83\" x2=\"230\" y2=\"83\" />\n",
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       "  <line x1=\"110\" y1=\"84\" x2=\"230\" y2=\"84\" />\n",
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       "  <line x1=\"110\" y1=\"85\" x2=\"230\" y2=\"85\" />\n",
       "  <line x1=\"110\" y1=\"85\" x2=\"230\" y2=\"85\" />\n",
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       "  <line x1=\"110\" y1=\"86\" x2=\"230\" y2=\"86\" />\n",
       "  <line x1=\"110\" y1=\"86\" x2=\"230\" y2=\"86\" />\n",
       "  <line x1=\"110\" y1=\"87\" x2=\"230\" y2=\"87\" />\n",
       "  <line x1=\"110\" y1=\"87\" x2=\"230\" y2=\"87\" />\n",
       "  <line x1=\"110\" y1=\"87\" x2=\"230\" y2=\"87\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
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       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
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       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
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       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
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       "  <line x1=\"120\" y1=\"14\" x2=\"120\" y2=\"94\" />\n",
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       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"94\" />\n",
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       "  <line x1=\"136\" y1=\"14\" x2=\"136\" y2=\"94\" />\n",
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       "  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"94\" />\n",
       "  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"94\" />\n",
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       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
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       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
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       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
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       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
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       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
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       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
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       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
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       "  <line x1=\"110\" y1=\"86\" x2=\"230\" y2=\"86\" />\n",
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       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
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       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
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       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
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       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
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       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
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       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
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       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
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       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"94\" />\n",
       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"94\" />\n",
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       "  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"94\" />\n",
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       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
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       "  <line x1=\"139\" y1=\"14\" x2=\"139\" y2=\"94\" />\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>production</span></div><div class='xr-var-dims'>(time, fish, nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(42, 8, 8, 8), meta=np.ndarray&gt;</div><input id='attrs-02c6f59b-4ddb-44ee-8d2b-e90fb5643574' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02c6f59b-4ddb-44ee-8d2b-e90fb5643574' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3098657d-190a-4115-ba63-9ddd245ba861' class='xr-var-data-in' type='checkbox'><label for='data-3098657d-190a-4115-ba63-9ddd245ba861' 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",
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       "  <line x1=\"110\" y1=\"56\" x2=\"230\" y2=\"56\" />\n",
       "  <line x1=\"110\" y1=\"56\" x2=\"230\" y2=\"56\" />\n",
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       "  <line x1=\"110\" y1=\"57\" x2=\"230\" y2=\"57\" />\n",
       "  <line x1=\"110\" y1=\"57\" x2=\"230\" y2=\"57\" />\n",
       "  <line x1=\"110\" y1=\"57\" x2=\"230\" y2=\"57\" />\n",
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       "  <line x1=\"110\" y1=\"58\" x2=\"230\" y2=\"58\" />\n",
       "  <line x1=\"110\" y1=\"58\" x2=\"230\" y2=\"58\" />\n",
       "  <line x1=\"110\" y1=\"58\" x2=\"230\" y2=\"58\" />\n",
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       "  <line x1=\"110\" y1=\"60\" x2=\"230\" y2=\"60\" />\n",
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       "  <line x1=\"110\" y1=\"62\" x2=\"230\" y2=\"62\" />\n",
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       "  <line x1=\"110\" y1=\"65\" x2=\"230\" y2=\"65\" />\n",
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       "  <line x1=\"110\" y1=\"66\" x2=\"230\" y2=\"66\" />\n",
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       "  <line x1=\"110\" y1=\"71\" x2=\"230\" y2=\"71\" />\n",
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       "  <line x1=\"110\" y1=\"72\" x2=\"230\" y2=\"72\" />\n",
       "  <line x1=\"110\" y1=\"72\" x2=\"230\" y2=\"72\" />\n",
       "  <line x1=\"110\" y1=\"72\" x2=\"230\" y2=\"72\" />\n",
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       "  <line x1=\"110\" y1=\"73\" x2=\"230\" y2=\"73\" />\n",
       "  <line x1=\"110\" y1=\"73\" x2=\"230\" y2=\"73\" />\n",
       "  <line x1=\"110\" y1=\"73\" x2=\"230\" y2=\"73\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
       "  <line x1=\"110\" y1=\"74\" x2=\"230\" y2=\"74\" />\n",
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       "  <line x1=\"110\" y1=\"76\" x2=\"230\" y2=\"76\" />\n",
       "  <line x1=\"110\" y1=\"76\" x2=\"230\" y2=\"76\" />\n",
       "  <line x1=\"110\" y1=\"76\" x2=\"230\" y2=\"76\" />\n",
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       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"77\" x2=\"230\" y2=\"77\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"78\" x2=\"230\" y2=\"78\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
       "  <line x1=\"110\" y1=\"79\" x2=\"230\" y2=\"79\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"80\" x2=\"230\" y2=\"80\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"81\" x2=\"230\" y2=\"81\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
       "  <line x1=\"110\" y1=\"82\" x2=\"230\" y2=\"82\" />\n",
       "  <line x1=\"110\" y1=\"83\" x2=\"230\" y2=\"83\" />\n",
       "  <line x1=\"110\" y1=\"83\" x2=\"230\" y2=\"83\" />\n",
       "  <line x1=\"110\" y1=\"83\" x2=\"230\" y2=\"83\" />\n",
       "  <line x1=\"110\" y1=\"84\" x2=\"230\" y2=\"84\" />\n",
       "  <line x1=\"110\" y1=\"84\" x2=\"230\" y2=\"84\" />\n",
       "  <line x1=\"110\" y1=\"84\" x2=\"230\" y2=\"84\" />\n",
       "  <line x1=\"110\" y1=\"84\" x2=\"230\" y2=\"84\" />\n",
       "  <line x1=\"110\" y1=\"85\" x2=\"230\" y2=\"85\" />\n",
       "  <line x1=\"110\" y1=\"85\" x2=\"230\" y2=\"85\" />\n",
       "  <line x1=\"110\" y1=\"85\" x2=\"230\" y2=\"85\" />\n",
       "  <line x1=\"110\" y1=\"85\" x2=\"230\" y2=\"85\" />\n",
       "  <line x1=\"110\" y1=\"86\" x2=\"230\" y2=\"86\" />\n",
       "  <line x1=\"110\" y1=\"86\" x2=\"230\" y2=\"86\" />\n",
       "  <line x1=\"110\" y1=\"86\" x2=\"230\" y2=\"86\" />\n",
       "  <line x1=\"110\" y1=\"86\" x2=\"230\" y2=\"86\" />\n",
       "  <line x1=\"110\" y1=\"87\" x2=\"230\" y2=\"87\" />\n",
       "  <line x1=\"110\" y1=\"87\" x2=\"230\" y2=\"87\" />\n",
       "  <line x1=\"110\" y1=\"87\" x2=\"230\" y2=\"87\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"88\" x2=\"230\" y2=\"88\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"89\" x2=\"230\" y2=\"89\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"90\" x2=\"230\" y2=\"90\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"91\" x2=\"230\" y2=\"91\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"92\" x2=\"230\" y2=\"92\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"93\" x2=\"230\" y2=\"93\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" />\n",
       "  <line x1=\"110\" y1=\"94\" x2=\"230\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
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       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"113\" y1=\"14\" x2=\"113\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"115\" y1=\"14\" x2=\"115\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"116\" y1=\"14\" x2=\"116\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"117\" y1=\"14\" x2=\"117\" y2=\"94\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"94\" />\n",
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       "  <line x1=\"119\" y1=\"14\" x2=\"119\" y2=\"94\" />\n",
       "  <line x1=\"119\" y1=\"14\" x2=\"119\" y2=\"94\" />\n",
       "  <line x1=\"120\" y1=\"14\" x2=\"120\" y2=\"94\" />\n",
       "  <line x1=\"120\" y1=\"14\" x2=\"120\" y2=\"94\" />\n",
       "  <line x1=\"120\" y1=\"14\" x2=\"120\" y2=\"94\" />\n",
       "  <line x1=\"120\" y1=\"14\" x2=\"120\" y2=\"94\" />\n",
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       "  <line x1=\"121\" y1=\"14\" x2=\"121\" y2=\"94\" />\n",
       "  <line x1=\"121\" y1=\"14\" x2=\"121\" y2=\"94\" />\n",
       "  <line x1=\"121\" y1=\"14\" x2=\"121\" y2=\"94\" />\n",
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       "  <line x1=\"122\" y1=\"14\" x2=\"122\" y2=\"94\" />\n",
       "  <line x1=\"122\" y1=\"14\" x2=\"122\" y2=\"94\" />\n",
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       "  <line x1=\"123\" y1=\"14\" x2=\"123\" y2=\"94\" />\n",
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       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"94\" />\n",
       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"94\" />\n",
       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"94\" />\n",
       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"94\" />\n",
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       "  <line x1=\"125\" y1=\"14\" x2=\"125\" y2=\"94\" />\n",
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       "  <line x1=\"129\" y1=\"14\" x2=\"129\" y2=\"94\" />\n",
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       "  <line x1=\"130\" y1=\"14\" x2=\"130\" y2=\"94\" />\n",
       "  <line x1=\"130\" y1=\"14\" x2=\"130\" y2=\"94\" />\n",
       "  <line x1=\"130\" y1=\"14\" x2=\"130\" y2=\"94\" />\n",
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       "  <line x1=\"133\" y1=\"14\" x2=\"133\" y2=\"94\" />\n",
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       "  <line x1=\"134\" y1=\"14\" x2=\"134\" y2=\"94\" />\n",
       "  <line x1=\"134\" y1=\"14\" x2=\"134\" y2=\"94\" />\n",
       "  <line x1=\"134\" y1=\"14\" x2=\"134\" y2=\"94\" />\n",
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       "  <line x1=\"135\" y1=\"14\" x2=\"135\" y2=\"94\" />\n",
       "  <line x1=\"135\" y1=\"14\" x2=\"135\" y2=\"94\" />\n",
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       "  <line x1=\"136\" y1=\"14\" x2=\"136\" y2=\"94\" />\n",
       "  <line x1=\"136\" y1=\"14\" x2=\"136\" y2=\"94\" />\n",
       "  <line x1=\"136\" y1=\"14\" x2=\"136\" y2=\"94\" />\n",
       "  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"94\" />\n",
       "  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"94\" />\n",
       "  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"94\" />\n",
       "  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"94\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"94\" />\n",
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       "  <line x1=\"139\" y1=\"14\" x2=\"139\" y2=\"94\" />\n",
       "  <line x1=\"139\" y1=\"14\" x2=\"139\" y2=\"94\" />\n",
       "  <line x1=\"140\" y1=\"14\" x2=\"140\" y2=\"94\" />\n",
       "  <line x1=\"140\" y1=\"14\" x2=\"140\" y2=\"94\" />\n",
       "  <line x1=\"140\" y1=\"14\" x2=\"140\" y2=\"94\" />\n",
       "  <line x1=\"140\" y1=\"14\" x2=\"140\" y2=\"94\" />\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (fish: 8, group: 9, nlat: 2400, nlon: 3600, time: 42)\n",
       "Coordinates:\n",
       "  * fish        (fish) object 'Sf' 'Sp' 'Sd' 'Mf' 'Mp' 'Md' 'Lp' 'Ld'\n",
       "  * group       (group) object 'Sf' 'Sp' 'Sd' 'Mf' ... 'Lp' 'Ld' 'benthic_prey'\n",
       "  * nlat        (nlat) int64 0 1 2 3 4 5 6 ... 2394 2395 2396 2397 2398 2399\n",
       "  * nlon        (nlon) int64 0 1 2 3 4 5 6 ... 3594 3595 3596 3597 3598 3599\n",
       "  * time        (time) int64 1980 1981 1982 1983 1984 ... 2018 2019 2020 2021\n",
       "Data variables:\n",
       "    biomass     (time, group, nlat, nlon) float64 dask.array<chunksize=(42, 9, 8, 8), meta=np.ndarray>\n",
       "    fish_yield  (time, fish, nlat, nlon) float64 dask.array<chunksize=(42, 8, 8, 8), meta=np.ndarray>\n",
       "    production  (time, fish, nlat, nlon) float64 dask.array<chunksize=(42, 8, 8, 8), meta=np.ndarray>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '/glade/scratch/kristenk/feisty_hires_proc/'\n",
    "files = sorted(glob(f'{path}/FEISTY-hi-res-x0.1-????.nc'))\n",
    "\n",
    "ds = xr.open_mfdataset(files,concat_dim=\"time\", coords='minimal', compat=\"override\", combine='nested')\n",
    "\n",
    "ds['time'] = np.arange(1980,2022,1)\n",
    "    \n",
    "chunks = {'nlat': 8, 'nlon': 8, 'time': 42} \n",
    "\n",
    "ds.chunk(chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e1d2c6e-f0de-4dd8-b122-f9a9a8e8f726",
   "metadata": {},
   "outputs": [],
   "source": [
    "# masking\n",
    "\n",
    "ds[\"biomass\"] = ds.biomass.where(ds.biomass<10)\n",
    "ds[\"biomass\"] = ds.biomass.where(ds.biomass>-0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cebe3939-c7e4-400e-af0c-3735f4e02e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid = pop_tools.get_grid('POP_tx0.1v3')\n",
    "lons = ds_grid.TLONG\n",
    "lats = ds_grid.TLAT\n",
    "area = ds_grid.TAREA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a765cc2a-518a-49a1-9e7a-37f8ee4d9e69",
   "metadata": {},
   "source": [
    "# Loading in all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33eeca11-3ffa-4de8-b779-b26bcc51b34d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_year_categories = xr.Dataset()\n",
    "\n",
    "tot_forage = ds.biomass.sel(group='Sf') + ds.biomass.sel(group='Mf')\n",
    "tot_pelagic = ds.biomass.sel(group='Sp') + ds.biomass.sel(group='Mp') + ds.biomass.sel(group='Lp')\n",
    "tot_demersal = ds.biomass.sel(group='Sd') + ds.biomass.sel(group='Md')+ ds.biomass.sel(group='Ld')\n",
    "tot_fish = tot_forage + tot_pelagic + tot_demersal\n",
    "\n",
    "all_year_categories[\"tot_forage\"] = tot_forage\n",
    "all_year_categories[\"tot_pelagic\"] = tot_pelagic\n",
    "all_year_categories[\"tot_demersal\"] = tot_demersal\n",
    "all_year_categories[\"tot_fish\"] = tot_fish\n",
    "\n",
    "all_year_categories = all_year_categories.drop_vars(\"group\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2c4ea8f-544c-4945-b75a-d0ed82a24acb",
   "metadata": {},
   "outputs": [
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       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (nlat: 2400, nlon: 3600, time: 42)\n",
       "Coordinates:\n",
       "  * nlat          (nlat) int64 0 1 2 3 4 5 6 ... 2394 2395 2396 2397 2398 2399\n",
       "  * nlon          (nlon) int64 0 1 2 3 4 5 6 ... 3594 3595 3596 3597 3598 3599\n",
       "  * time          (time) int64 1980 1981 1982 1983 1984 ... 2018 2019 2020 2021\n",
       "Data variables:\n",
       "    tot_forage    (time, nlat, nlon) float64 nan nan nan nan ... nan nan nan nan\n",
       "    tot_pelagic   (time, nlat, nlon) float64 nan nan nan nan ... nan nan nan nan\n",
       "    tot_demersal  (time, nlat, nlon) float64 nan nan nan nan ... nan nan nan nan\n",
       "    tot_fish      (time, nlat, nlon) float64 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-9c7b4d03-dd80-4b4b-8520-540c4c34d38c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c7b4d03-dd80-4b4b-8520-540c4c34d38c' 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'>nlat</span>: 2400</li><li><span class='xr-has-index'>nlon</span>: 3600</li><li><span class='xr-has-index'>time</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-18ddff9a-cd80-40e6-bf73-3f825dfc2a73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18ddff9a-cd80-40e6-bf73-3f825dfc2a73' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>nlat</span></div><div class='xr-var-dims'>(nlat)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2396 2397 2398 2399</div><input id='attrs-ce5fc83f-6ce2-40d7-b9c2-495470562059' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce5fc83f-6ce2-40d7-b9c2-495470562059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc5eb1f7-dc33-4897-98b1-cb1d45ae3456' class='xr-var-data-in' type='checkbox'><label for='data-fc5eb1f7-dc33-4897-98b1-cb1d45ae3456' 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([   0,    1,    2, ..., 2397, 2398, 2399])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nlon</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 3596 3597 3598 3599</div><input id='attrs-6125a6f0-9a43-4aac-93d9-2ab783ca5633' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6125a6f0-9a43-4aac-93d9-2ab783ca5633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc8fa7b3-c896-4ac9-b40b-60294e42dc8f' class='xr-var-data-in' type='checkbox'><label for='data-dc8fa7b3-c896-4ac9-b40b-60294e42dc8f' 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([   0,    1,    2, ..., 3597, 3598, 3599])</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'>int64</div><div class='xr-var-preview xr-preview'>1980 1981 1982 ... 2019 2020 2021</div><input id='attrs-09c00300-3247-4a8f-8eb9-7452e2b06927' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09c00300-3247-4a8f-8eb9-7452e2b06927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab87afa9-b999-4816-9166-18182433709a' class='xr-var-data-in' type='checkbox'><label for='data-ab87afa9-b999-4816-9166-18182433709a' 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([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n",
       "       1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
       "       2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
       "       2016, 2017, 2018, 2019, 2020, 2021])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-926b9647-eddc-44ed-a6ed-c6084c903cf2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-926b9647-eddc-44ed-a6ed-c6084c903cf2' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tot_forage</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-fcc72ff3-18b4-42a4-ba13-451af4cb72a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fcc72ff3-18b4-42a4-ba13-451af4cb72a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddb7de94-f8b0-4616-a2b1-b63664b88bb5' class='xr-var-data-in' type='checkbox'><label for='data-ddb7de94-f8b0-4616-a2b1-b63664b88bb5' 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([[[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]],\n",
       "\n",
       "       [[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]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_pelagic</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-63ccf918-95a5-4fa5-b52c-033fda9093ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63ccf918-95a5-4fa5-b52c-033fda9093ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d5781df-0dc0-43b9-8184-3d819a838523' class='xr-var-data-in' type='checkbox'><label for='data-8d5781df-0dc0-43b9-8184-3d819a838523' 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([[[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]],\n",
       "\n",
       "       [[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]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_demersal</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-b5e684c4-cc4f-4034-b3ce-f19364b4e4e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5e684c4-cc4f-4034-b3ce-f19364b4e4e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef44208-adbd-4d9b-8668-b9cf0fb303ea' class='xr-var-data-in' type='checkbox'><label for='data-1ef44208-adbd-4d9b-8668-b9cf0fb303ea' 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([[[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]],\n",
       "\n",
       "       [[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]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_fish</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-93505198-da4e-4f11-9c3d-7d30f0ba474a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93505198-da4e-4f11-9c3d-7d30f0ba474a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3720c42a-2277-42b4-9789-70f586d65f52' class='xr-var-data-in' type='checkbox'><label for='data-3720c42a-2277-42b4-9789-70f586d65f52' 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([[[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]],\n",
       "\n",
       "       [[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]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [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],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72f06630-2596-4b10-8bc9-66b828e6bfca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72f06630-2596-4b10-8bc9-66b828e6bfca' 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: 2400, nlon: 3600, time: 42)\n",
       "Coordinates:\n",
       "  * nlat          (nlat) int64 0 1 2 3 4 5 6 ... 2394 2395 2396 2397 2398 2399\n",
       "  * nlon          (nlon) int64 0 1 2 3 4 5 6 ... 3594 3595 3596 3597 3598 3599\n",
       "  * time          (time) int64 1980 1981 1982 1983 1984 ... 2018 2019 2020 2021\n",
       "Data variables:\n",
       "    tot_forage    (time, nlat, nlon) float64 nan nan nan nan ... nan nan nan nan\n",
       "    tot_pelagic   (time, nlat, nlon) float64 nan nan nan nan ... nan nan nan nan\n",
       "    tot_demersal  (time, nlat, nlon) float64 nan nan nan nan ... nan nan nan nan\n",
       "    tot_fish      (time, nlat, nlon) float64 nan nan nan nan ... nan nan nan nan"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_year_categories.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df164426-4d9f-4d30-87a7-f90e316a6ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_year_categories[\"mixed\"] = xr.where(np.logical_and(\n",
    "    all_year_categories.tot_forage/all_year_categories.tot_fish<0.5,\n",
    "    np.logical_and(\n",
    "    all_year_categories.tot_pelagic/all_year_categories.tot_fish<0.5,\n",
    "    all_year_categories.tot_demersal/all_year_categories.tot_fish<0.5)), 1, 0)\n",
    "\n",
    "all_year_categories[\"f_dom\"] = xr.where(np.logical_and(\n",
    "    all_year_categories.mixed == 0, \n",
    "    all_year_categories.tot_forage/all_year_categories.tot_fish>=0.5), 2, 0)    \n",
    "\n",
    "all_year_categories[\"d_dom\"] = xr.where(np.logical_and(\n",
    "    all_year_categories.mixed == 0, \n",
    "    all_year_categories.tot_demersal/all_year_categories.tot_fish>=0.5), 3, 0)  \n",
    "\n",
    "all_year_categories[\"p_dom\"] = xr.where(np.logical_and(\n",
    "    all_year_categories.mixed == 0, \n",
    "    all_year_categories.tot_pelagic/all_year_categories.tot_fish>=0.5), 4, 0) \n",
    "\n",
    "all_year_categories[\"pf_dom\"] = xr.where(np.logical_and(\n",
    "    all_year_categories.mixed == 1,\n",
    "    all_year_categories.tot_demersal/all_year_categories.tot_fish<0.2), 1, 0)\n",
    "\n",
    "all_year_categories[\"df_dom\"] = xr.where(np.logical_and(\n",
    "    all_year_categories.mixed == 1, \n",
    "    all_year_categories.tot_pelagic/all_year_categories.tot_fish<0.2), 1, 0)\n",
    "\n",
    "all_year_categories[\"dom_categories\"] = (all_year_categories.f_dom + all_year_categories.mixed + \n",
    "                                         all_year_categories.d_dom + all_year_categories.p_dom)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b1ccce5-dbbe-4d0c-b2d1-2feee7084946",
   "metadata": {},
   "outputs": [],
   "source": [
    "box_info = {\n",
    "    'Southwest Pacific': {\n",
    "        'latmin': -55,\n",
    "        'latmax': -30,\n",
    "        'lonmin':  150.,\n",
    "        'lonmax':  210.,\n",
    "        'plot_loc': 'upper_left_special',\n",
    "        'fish_to_show': ['tot_forage', 'tot_pelagic']\n",
    "        },\n",
    "    'Equatorial Pacific': {\n",
    "        'latmin': -5,\n",
    "        'latmax':  5,\n",
    "        'lonmin': -140.,\n",
    "        'lonmax': -90.,\n",
    "        'plot_loc': 'upper_left',\n",
    "        'fish_to_show': ['tot_forage', 'tot_pelagic']\n",
    "        },\n",
    "    'Bering Strait': {\n",
    "        'latmin':  55,\n",
    "        'latmax':  68,\n",
    "        'lonmin': -170.,\n",
    "        'lonmax': -160,\n",
    "        'plot_loc': 'upper_right',\n",
    "        'fish_to_show': ['tot_forage', 'tot_demersal']\n",
    "        },\n",
    "    'North Atlantic': {\n",
    "        'latmin':  55,\n",
    "        'latmax':  68,\n",
    "        'lonmin': -45.,\n",
    "        'lonmax': -15.,\n",
    "        'plot_loc': 'upper_right',\n",
    "        'fish_to_show': ['tot_forage', 'tot_demersal']\n",
    "        },\n",
    "    'Canary Current': {\n",
    "        'latmin':  5,\n",
    "        'latmax':  25,\n",
    "        'lonmin': -30.,\n",
    "        'lonmax': -15.,\n",
    "        'plot_loc': 'upper_right',\n",
    "        'fish_to_show': ['tot_forage', 'tot_pelagic']\n",
    "        },\n",
    "    'Equatorial Indian': {\n",
    "        'latmin': -10.,\n",
    "        'latmax':  10.,\n",
    "        'lonmin':  60.,\n",
    "        'lonmax':  90.,\n",
    "        'plot_loc': 'upper_left',\n",
    "        'fish_to_show': ['tot_forage']\n",
    "        },\n",
    "}\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11377b9b-964a-4ab0-984b-08e24ffe4260",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_box_ts_data_dict(ds, box_info):\n",
    "\n",
    "    box_ts_data_dict = dict()\n",
    "\n",
    "    ones = np.full((2400,3600),1,dtype=int)\n",
    "    ones = xr.DataArray(ones,dims=('nlat','nlon'))\n",
    "    ones = ones.where(ds_grid.REGION_MASK>0)\n",
    "\n",
    "    for key, items in box_info.items():\n",
    "        box_ts_data_dict[key] = xr.Dataset()\n",
    "\n",
    "        ts_dataset = box_ts_data_dict[key]\n",
    "\n",
    "        lons_norm = utils.normal_lons(lons)\n",
    "\n",
    "        variables = [\"tot_forage\", \"tot_pelagic\", \"tot_demersal\"]\n",
    "\n",
    "        for var in variables:\n",
    "\n",
    "            mask = ones.where(lats<items['latmax']).where(lats>items['latmin']).where(lons_norm<items['lonmax']).where(lons_norm>items['lonmin'])\n",
    "            masked = ds[var].where(mask==1)\n",
    "            masked_area = area.where(mask==1)\n",
    "            ## reset nlon and nlat; otherwise there's an error\n",
    "            mask['nlat'] = ds_grid.nlat\n",
    "            masked['nlat'] = ds_grid.nlat\n",
    "            masked_area['nlat'] = ds_grid.nlat\n",
    "            mask['nlon'] = ds_grid.nlon\n",
    "            masked['nlon'] = ds_grid.nlon\n",
    "            masked_area['nlon'] = ds_grid.nlon\n",
    "            ts_dataset[var] = (masked * masked_area).sum(dim=('nlon','nlat')) / masked_area.sum(dim=('nlon','nlat'))\n",
    "    \n",
    "    return box_ts_data_dict\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4115be8d-b27c-44c7-bdec-f3ebfbd69cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "box_ts_data_dict = create_box_ts_data_dict(all_year_categories, box_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "721c1ab9-78f6-4245-b992-8040d1f18fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,18))\n",
    "\n",
    "grid = fig.add_gridspec(12, 2)\n",
    "\n",
    "yr_list = [1983, 1999, 2000, 2010]\n",
    "label_list = [\"a)\", \"b)\", \"c)\", \"d)\", \"e)\", \"f)\"]\n",
    "\n",
    "i = 0 \n",
    "\n",
    "for key, item in box_ts_data_dict.items():\n",
    "    info = box_info[key]\n",
    "    \n",
    "    ax = fig.add_subplot(grid[2*i:2*i+1, 0])\n",
    "    \n",
    "    ax.set_title(label_list[i] + \" \" + key, loc='left')\n",
    "    \n",
    "    color_fish_dict = {\"tot_forage\": 'orange',\n",
    "                           \"tot_pelagic\": 'royalblue',\n",
    "                           \"tot_demersal\": 'green'}\n",
    "\n",
    "    variables = info['fish_to_show']\n",
    "\n",
    "    for group in variables:\n",
    "        ax.plot(item[group], linewidth=0.85, label=group, color=color_fish_dict[group])\n",
    "\n",
    "    ax.set_xlim([0, 41])\n",
    "    ax.set_ylabel(\"Fish biomass (g WW m$^{-2}$)\")\n",
    "    ax.set_xticks([0, 10, 20, 30, 40])\n",
    "    ax.set_xticklabels([\"1980\", \"1990\", \"2000\", \"2010\", \"2020\"])\n",
    "    \n",
    "    ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%.1f'))\n",
    "    \n",
    "    for year in yr_list:\n",
    "        ax.axvline(x = year-1980, color = 'grey', linestyle=\"dashed\", linewidth=1)\n",
    "        \n",
    "    i += 1\n",
    "        \n",
    "####################\n",
    "    \n",
    "label_list2 = [\"g)\", \"h)\", \"i)\", \"j)\"] \n",
    "\n",
    "for i in range(0,4):\n",
    "    ax = fig.add_subplot(grid[3*i:3*i+3, 1], projection=ccrs.Robinson(central_longitude=180))\n",
    "\n",
    "    year = yr_list[i]\n",
    "    ax.set_title(label_list2[i] + \" Dominant fish functional type, \" + str(year), loc='left')\n",
    "    lon, lat, field = utils.adjust_pop_grid(lons, lats, all_year_categories[\"dom_categories\"].sel(time=year))\n",
    "\n",
    "    lon, lat, field2 = utils.adjust_pop_grid(lons, lats, all_year_categories[\"df_dom\"].sel(time=year))\n",
    "    lon, lat, field3 = utils.adjust_pop_grid(lons, lats, all_year_categories[\"pf_dom\"].sel(time=year))\n",
    "\n",
    "    colorlist = ['paleturquoise', 'orange', 'green', 'royalblue', 'gold', 'firebrick']\n",
    "\n",
    "    cmap = mpl.colors.LinearSegmentedColormap.from_list('fish_cats', colorlist, N=6)\n",
    "\n",
    "\n",
    "    ax.add_feature(cartopy.feature.LAND, zorder=3, edgecolor='k', facecolor=\"white\")\n",
    "    pc=ax.pcolormesh(lon, lat, field, cmap=cmap, vmin=0.5, vmax=6.5,\n",
    "                     transform=ccrs.PlateCarree())\n",
    "    \n",
    "    ax.set_global()\n",
    "\n",
    "    ### Adding crosshatching regions\n",
    "\n",
    "    hatch1=ax.contourf(lon, lat, field2,levels=[0.5,1.5],transform=ccrs.PlateCarree(),colors=colorlist[4])\n",
    "    hatch2=ax.contourf(lon, lat, field3,levels=[0.5,1.5],transform=ccrs.PlateCarree(),colors=colorlist[5])\n",
    "\n",
    "    \n",
    "    for keys, info in box_info.items():\n",
    "        lon_pts = [info['lonmin'], info['lonmax'], info['lonmax'], info['lonmin'], info['lonmin']]\n",
    "        lat_pts = [info['latmax'], info['latmax'], info['latmin'], info['latmin'], info['latmax']]\n",
    "\n",
    "        ax.plot(lon_pts, lat_pts, color=\"black\", linewidth=1.5,transform=ccrs.PlateCarree(), zorder=100)\n",
    "\n",
    "cbar_ax = fig.add_axes([0.95, 0.16, 0.02, 0.7])\n",
    "cbar = fig.colorbar(pc, ticks=[1,2,3,4,5,6], pad=0.08, cax=cbar_ax)\n",
    "cbar.ax.set_yticklabels(['Mixed', 'Forage', 'Demersal', 'Lg Pelagic','Demersal &\\n Forage', 'Lg Pelagic &\\n Forage'], fontsize=10)\n",
    "\n",
    "forageline = plt.Line2D([0], [0], label='Forage', color='orange')\n",
    "pelagicline = plt.Line2D([0], [0], label='Pelagic', color='royalblue')\n",
    "demersalline = plt.Line2D([0], [0], label='Demersal', color='green')\n",
    "\n",
    "\n",
    "legend = fig.legend(handles=[forageline, pelagicline, demersalline], loc=[0.404, 0.076], framealpha=1)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"Fig11_fish_variability_x01.png\", dpi=300, bbox_inches='tight', facecolor=\"white\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fish-offline",
   "language": "python",
   "name": "fish-offline"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}