Fig7_preprocessing.ipynb

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    "import xarray as xr\n",
    "import pandas as pd\n",
    "import pop_tools\n",
    "import numpy as np\n",
    "import cftime\n",
    "import matplotlib.pyplot as plt\n",
    "import dask\n",
    "from dask_jobqueue import PBSCluster\n",
    "from dask.distributed import Client\n",
    "from datetime import datetime\n",
    "import glob\n",
    "import flox"
   ]
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   "source": [
    "# Spinning up a Dask cluster"
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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='4:00:00')\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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   "execution_count": 3,
   "id": "234730b6-4240-4ac7-b487-db1fb0ef60c6",
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      "/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/dask_jobqueue/pbs.py:82: FutureWarning: project has been renamed to account as this kwarg was used wit -A option. You are still using it (please also check config files). If you did not set account yet, project will be respected for now, but it will be removed in a future release. If you already set account, project is ignored and you can remove it.\n",
      "  warnings.warn(warn, FutureWarning)\n",
      "/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/distributed/node.py:183: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 43499 instead\n",
      "  warnings.warn(\n",
      "/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/dask_jobqueue/pbs.py:82: FutureWarning: project has been renamed to account as this kwarg was used wit -A option. You are still using it (please also check config files). If you did not set account yet, project will be respected for now, but it will be removed in a future release. If you already set account, project is ignored and you can remove it.\n",
      "  warnings.warn(warn, FutureWarning)\n"
     ]
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    "cluster, client = get_ClusterClient()\n",
    "cluster.scale(30) "
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    "# Reading in SAU catch data"
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   "id": "c26679bd-7891-43bd-a7cd-1362fcacd2ef",
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       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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:  (Year: 61, LMEID: 65)\n",
       "Coordinates:\n",
       "  * Year     (Year) int64 1950 1951 1952 1953 1954 ... 2006 2007 2008 2009 2010\n",
       "  * LMEID    (LMEID) int64 1 2 3 4 5 6 7 8 9 10 ... 57 58 59 60 61 62 63 65 66\n",
       "Data variables:\n",
       "    FwCatch  (Year, LMEID) float64 26.91 154.4 5.209e+03 0.0 ... 63.51 202.2 0.0\n",
       "    PwCatch  (Year, LMEID) float64 5.118e+04 3.992e+05 ... 4.185e+04 0.008851\n",
       "    DwCatch  (Year, LMEID) float64 2.473e+04 2.227e+05 ... 8.225e+05 9.241</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dbbf8a4e-0a65-4d8e-9f1e-17d77c16d00f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dbbf8a4e-0a65-4d8e-9f1e-17d77c16d00f' 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'>Year</span>: 61</li><li><span class='xr-has-index'>LMEID</span>: 65</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-87508894-7adb-4043-b9d2-6e4aa5a0da3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87508894-7adb-4043-b9d2-6e4aa5a0da3a' 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 class='xr-has-index'>Year</span></div><div class='xr-var-dims'>(Year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1950 1951 1952 ... 2008 2009 2010</div><input id='attrs-ef3339bb-3cf8-441a-81de-698618cccff3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef3339bb-3cf8-441a-81de-698618cccff3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ae16e7e-19ee-4b3e-a186-277240f0210e' class='xr-var-data-in' type='checkbox'><label for='data-9ae16e7e-19ee-4b3e-a186-277240f0210e' 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([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
       "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,\n",
       "       1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
       "       1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LMEID</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 60 61 62 63 65 66</div><input id='attrs-c28743a2-0961-4e19-aed1-7c66e58f215a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c28743a2-0961-4e19-aed1-7c66e58f215a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d1c36f4-7b77-4600-b1c5-ac9dd3e50aa2' class='xr-var-data-in' type='checkbox'><label for='data-6d1c36f4-7b77-4600-b1c5-ac9dd3e50aa2' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1109c096-d364-47d6-b321-18cfc4aebfc0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1109c096-d364-47d6-b321-18cfc4aebfc0' 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>FwCatch</span></div><div class='xr-var-dims'>(Year, LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.91 154.4 5.209e+03 ... 202.2 0.0</div><input id='attrs-c611b8ee-fc63-4df8-9a4a-41b0f707250c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c611b8ee-fc63-4df8-9a4a-41b0f707250c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4c6736e-152e-4257-822a-c2946f290aa3' class='xr-var-data-in' type='checkbox'><label for='data-c4c6736e-152e-4257-822a-c2946f290aa3' 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([[2.69083192e+01, 1.54401086e+02, 5.20860467e+03, ...,\n",
       "        5.95618259e+01, 8.70667998e+00, 0.00000000e+00],\n",
       "       [3.15916453e+01, 2.01648602e+02, 9.02972306e+03, ...,\n",
       "        4.88418041e+01, 1.04334656e+01, 0.00000000e+00],\n",
       "       [3.79109282e+01, 5.59940846e+03, 3.78561702e+04, ...,\n",
       "        3.94754614e+01, 1.27811727e+01, 0.00000000e+00],\n",
       "       ...,\n",
       "       [1.80742178e+03, 7.81890543e+03, 1.10806915e+05, ...,\n",
       "        9.41522193e+01, 5.86495836e+02, 0.00000000e+00],\n",
       "       [1.45809344e+03, 2.08902993e+04, 1.69775284e+05, ...,\n",
       "        8.51792718e+01, 2.46480617e+02, 0.00000000e+00],\n",
       "       [1.55143914e+03, 3.49785695e+04, 1.95723670e+05, ...,\n",
       "        6.35053489e+01, 2.02202655e+02, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PwCatch</span></div><div class='xr-var-dims'>(Year, LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.118e+04 3.992e+05 ... 0.008851</div><input id='attrs-ba8114c8-2cd1-4119-8098-18c6c6957f9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba8114c8-2cd1-4119-8098-18c6c6957f9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29cf1255-502e-4d86-a10e-b68b2b8f0ea7' class='xr-var-data-in' type='checkbox'><label for='data-29cf1255-502e-4d86-a10e-b68b2b8f0ea7' 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([[5.11834231e+04, 3.99226074e+05, 6.24872119e+05, ...,\n",
       "        5.93021076e+01, 2.94785984e+04, 8.31783070e-02],\n",
       "       [2.85675152e+04, 3.76576247e+05, 4.09669430e+05, ...,\n",
       "        5.51540295e+01, 1.64770727e+04, 3.11071224e-01],\n",
       "       [2.89069541e+04, 2.22236008e+05, 2.80526947e+05, ...,\n",
       "        5.80890573e+01, 1.69456878e+04, 3.89425711e-01],\n",
       "       ...,\n",
       "       [5.36807495e+04, 5.98706783e+04, 3.24189096e+05, ...,\n",
       "        6.08046333e+01, 3.65558752e+04, 4.43109460e-03],\n",
       "       [5.98281278e+04, 6.40741974e+04, 3.15478610e+05, ...,\n",
       "        6.63065855e+01, 4.05191926e+04, 8.85142500e-03],\n",
       "       [5.98799969e+04, 8.47111574e+04, 3.11641547e+05, ...,\n",
       "        6.35307074e+01, 4.18488918e+04, 8.85148480e-03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DwCatch</span></div><div class='xr-var-dims'>(Year, LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.473e+04 2.227e+05 ... 9.241</div><input id='attrs-13084c0c-b490-40f1-af11-6ec44cc7db24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13084c0c-b490-40f1-af11-6ec44cc7db24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbcd0be3-491d-450b-b52f-436d26751071' class='xr-var-data-in' type='checkbox'><label for='data-bbcd0be3-491d-450b-b52f-436d26751071' 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([[2.47345664e+04, 2.22720431e+05, 1.75247747e+05, ...,\n",
       "        1.62789778e+03, 3.64907168e+04, 7.20763290e+00],\n",
       "       [2.83572473e+04, 2.51704876e+05, 1.91388117e+05, ...,\n",
       "        1.72104816e+03, 4.14785296e+04, 7.00475167e+00],\n",
       "       [2.41138355e+04, 1.94608465e+05, 1.65911180e+05, ...,\n",
       "        1.83308417e+03, 3.54033356e+04, 2.06611615e+02],\n",
       "       ...,\n",
       "       [7.09535156e+05, 3.70075996e+05, 3.82457003e+05, ...,\n",
       "        8.58334512e+02, 8.81697306e+05, 7.25609689e+00],\n",
       "       [6.19805097e+05, 3.59577527e+05, 2.61470091e+05, ...,\n",
       "        8.73830289e+02, 7.71034097e+05, 7.53970912e+00],\n",
       "       [6.58449486e+05, 3.67672838e+05, 3.05789493e+05, ...,\n",
       "        8.83446092e+02, 8.22525998e+05, 9.24133142e+00]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d148b353-981e-41b9-8be8-7b4fa908350d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d148b353-981e-41b9-8be8-7b4fa908350d' class='xr-section-summary' >Indexes: <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-index-name'><div>Year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a00636b7-61b1-4353-93e2-f71b07095c35' class='xr-index-data-in' type='checkbox'/><label for='index-a00636b7-61b1-4353-93e2-f71b07095c35' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
       "            1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
       "            1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
       "            1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
       "            1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
       "            2005, 2006, 2007, 2008, 2009, 2010],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;Year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>LMEID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1b2fdf7e-d2f8-4933-ba89-6249fa4e8ad7' class='xr-index-data-in' type='checkbox'/><label for='index-1b2fdf7e-d2f8-4933-ba89-6249fa4e8ad7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "            18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "            52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;LMEID&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-879d4539-51f6-433f-9d4c-4579735bbca7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-879d4539-51f6-433f-9d4c-4579735bbca7' 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:  (Year: 61, LMEID: 65)\n",
       "Coordinates:\n",
       "  * Year     (Year) int64 1950 1951 1952 1953 1954 ... 2006 2007 2008 2009 2010\n",
       "  * LMEID    (LMEID) int64 1 2 3 4 5 6 7 8 9 10 ... 57 58 59 60 61 62 63 65 66\n",
       "Data variables:\n",
       "    FwCatch  (Year, LMEID) float64 26.91 154.4 5.209e+03 0.0 ... 63.51 202.2 0.0\n",
       "    PwCatch  (Year, LMEID) float64 5.118e+04 3.992e+05 ... 4.185e+04 0.008851\n",
       "    DwCatch  (Year, LMEID) float64 2.473e+04 2.227e+05 ... 8.225e+05 9.241"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/SAUP_LME_Catch_annual_newF.csv')\n",
    "\n",
    "true_catch = df.set_index([\"Year\", \"LMEID\"]).to_xarray()\n",
    "\n",
    "true_catch\n",
    "\n",
    "### units on catch variables are g wet weight m-2 per year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc8fe47-232e-4ec7-9922-57d6096d08f1",
   "metadata": {},
   "source": [
    "# Reading in modeled FEISTY data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d26f1da-7dd4-450a-b821-dba4aeaaf607",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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:     (time: 42, group: 9, nlat: 2400, nlon: 3600, fish: 8)\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' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-68c767ce-78f3-4031-a891-5be8e0a9bc23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68c767ce-78f3-4031-a891-5be8e0a9bc23' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 42</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'>fish</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9a78d576-37a1-49bb-a7da-f8b20a4687ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a78d576-37a1-49bb-a7da-f8b20a4687ee' 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-7faa8d8b-a868-4da4-bf28-72af133a6a91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7faa8d8b-a868-4da4-bf28-72af133a6a91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52bfd88f-aa3f-42ce-9bf1-2a05d6b80835' class='xr-var-data-in' type='checkbox'><label for='data-52bfd88f-aa3f-42ce-9bf1-2a05d6b80835' 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-5cf468e8-36d9-4315-83c3-331fe57d5aa1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cf468e8-36d9-4315-83c3-331fe57d5aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6853e0a-6f4e-4383-8bda-245995f2de97' class='xr-var-data-in' type='checkbox'><label for='data-f6853e0a-6f4e-4383-8bda-245995f2de97' 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-d8ce9eb7-ec19-4790-9e36-f718641e4d19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8ce9eb7-ec19-4790-9e36-f718641e4d19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d5b5734-2998-4a9e-9490-4e5900bb4c26' class='xr-var-data-in' type='checkbox'><label for='data-3d5b5734-2998-4a9e-9490-4e5900bb4c26' 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-c46d9f75-214a-4644-8bf2-4827e2831be0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c46d9f75-214a-4644-8bf2-4827e2831be0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bec099e7-3ab4-4ccf-b990-eca2cd0b66ff' class='xr-var-data-in' type='checkbox'><label for='data-bec099e7-3ab4-4ccf-b990-eca2cd0b66ff' 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-16126bb3-349d-4951-a79a-c73b57db065e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16126bb3-349d-4951-a79a-c73b57db065e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28ac722f-d498-4c20-a4fb-20acfec53719' class='xr-var-data-in' type='checkbox'><label for='data-28ac722f-d498-4c20-a4fb-20acfec53719' 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-b3bb1436-6833-479b-b1de-7b1cfa59837a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3bb1436-6833-479b-b1de-7b1cfa59837a' 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-a1704975-bd9f-444d-86fe-d54e90a483cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1704975-bd9f-444d-86fe-d54e90a483cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4bfae3d-d84f-4759-ab4d-5ffaa9662904' class='xr-var-data-in' type='checkbox'><label for='data-a4bfae3d-d84f-4759-ab4d-5ffaa9662904' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.33 GiB </td>\n",
       "                        <td> 189.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (42, 9, 2400, 3600) </td>\n",
       "                        <td> (42, 9, 8, 8) </td>\n",
       "                    </tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fish_yield</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-de90857c-f0ea-4c88-912f-e4f7285d93a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de90857c-f0ea-4c88-912f-e4f7285d93a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-303febe4-3af1-417d-be3b-4f4697098606' class='xr-var-data-in' type='checkbox'><label for='data-303febe4-3af1-417d-be3b-4f4697098606' 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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       "                        <th> Bytes </th>\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-bb786b30-01d3-4bc9-a44d-b4abceef9848' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb786b30-01d3-4bc9-a44d-b4abceef9848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c207fbd9-35e3-4c9f-b7ff-fdf85096f7dc' class='xr-var-data-in' type='checkbox'><label for='data-c207fbd9-35e3-4c9f-b7ff-fdf85096f7dc' 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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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 21.63 GiB </td>\n",
       "                        <td> 168.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (42, 8, 2400, 3600) </td>\n",
       "                        <td> (42, 8, 8, 8) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 135000 chunks in 130 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "            ...\n",
       "            2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;nlat&#x27;, length=2400))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nlon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-62d89c0b-1b6e-4f93-a09b-ed89b9213d32' class='xr-index-data-in' type='checkbox'/><label for='index-62d89c0b-1b6e-4f93-a09b-ed89b9213d32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "            ...\n",
       "            3590, 3591, 3592, 3593, 3594, 3595, 3596, 3597, 3598, 3599],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;nlon&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3dfcf22d-41fc-42f2-b696-0b0cca833cee' class='xr-index-data-in' type='checkbox'/><label for='index-3dfcf22d-41fc-42f2-b696-0b0cca833cee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,\n",
       "            1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,\n",
       "            2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,\n",
       "            2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b72fea41-cc4b-4718-8ba7-72e518047af8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b72fea41-cc4b-4718-8ba7-72e518047af8' 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:     (time: 42, group: 9, nlat: 2400, nlon: 3600, fish: 8)\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '/glade/scratch/kristenk/feisty_hires_proc/'\n",
    "files = sorted(glob.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": "markdown",
   "id": "3a5bcbc1-2b2a-49d8-866d-4bff2df1c427",
   "metadata": {},
   "source": [
    "# Resampling data by LMEs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b76a2b-717c-4b27-bba3-d45e7e5f44ef",
   "metadata": {},
   "source": [
    "## Reading in 0.1 degree LME mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6300a3f1-6733-4c5f-b30b-cf239c763cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "coords_dict = {'nlat': ds.nlat.data, 'nlon': ds.nlon.data}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebde9e4d-9168-4848-b2fd-fa741c8a80ad",
   "metadata": {},
   "outputs": [
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       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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)\n",
       "Coordinates:\n",
       "    TLONG     (nlat, nlon) float64 ...\n",
       "    TLAT      (nlat, nlon) float64 ...\n",
       "  * nlat      (nlat) int64 0 1 2 3 4 5 6 ... 2393 2394 2395 2396 2397 2398 2399\n",
       "  * nlon      (nlon) int64 0 1 2 3 4 5 6 ... 3593 3594 3595 3596 3597 3598 3599\n",
       "Data variables:\n",
       "    lme_mask  (nlat, nlon) float64 ...\n",
       "    KMT       (nlat, nlon) int32 ...\n",
       "    TAREA     (nlat, nlon) float64 ...\n",
       "Attributes:\n",
       "    regrid_method:  nearest_s2d</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1e3243c2-e111-40ac-899b-5448fe45769e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1e3243c2-e111-40ac-899b-5448fe45769e' 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></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-33873028-fc30-4343-b82d-215b97e5d11c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33873028-fc30-4343-b82d-215b97e5d11c' class='xr-section-summary' >Coordinates: <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>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'>...</div><input id='attrs-48f8e31a-26fa-447e-9faa-8c5548c82d37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48f8e31a-26fa-447e-9faa-8c5548c82d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46cdee05-a96c-4088-bed4-03027036288f' class='xr-var-data-in' type='checkbox'><label for='data-46cdee05-a96c-4088-bed4-03027036288f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>T-grid longitude</dd></dl></div><div class='xr-var-data'><pre>[8640000 values with dtype=float64]</pre></div></li><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'>...</div><input id='attrs-480845d4-0a9a-4e0b-b8f8-8c5e7332e39c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-480845d4-0a9a-4e0b-b8f8-8c5e7332e39c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ff993de-0cb4-4dc2-9b48-f125cf36c519' class='xr-var-data-in' type='checkbox'><label for='data-4ff993de-0cb4-4dc2-9b48-f125cf36c519' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>T-grid latitude</dd></dl></div><div class='xr-var-data'><pre>[8640000 values with dtype=float64]</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-b7cd100d-df29-4962-a9d9-a061f84f5025' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7cd100d-df29-4962-a9d9-a061f84f5025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f58adc3-ad63-443f-baa1-dd0f91eecba4' class='xr-var-data-in' type='checkbox'><label for='data-2f58adc3-ad63-443f-baa1-dd0f91eecba4' 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-80fb7095-4706-4d22-a7b7-e33314873763' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80fb7095-4706-4d22-a7b7-e33314873763' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41cd8df4-657d-49ee-8cee-024bc907b564' class='xr-var-data-in' type='checkbox'><label for='data-41cd8df4-657d-49ee-8cee-024bc907b564' 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></ul></div></li><li class='xr-section-item'><input id='section-5d9bbc89-0db2-4de2-885d-e59dfab0052b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d9bbc89-0db2-4de2-885d-e59dfab0052b' 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>lme_mask</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-12aee117-fe24-46b7-bea1-07334469e831' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12aee117-fe24-46b7-bea1-07334469e831' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7831bc62-0084-479c-9238-9b1bfc9d4fe9' class='xr-var-data-in' type='checkbox'><label for='data-7831bc62-0084-479c-9238-9b1bfc9d4fe9' 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>LME mask</dd></dl></div><div class='xr-var-data'><pre>[8640000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KMT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4d4c928-59dd-4e6a-b884-2ca07e0c761e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4d4c928-59dd-4e6a-b884-2ca07e0c761e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-023afc9e-017a-4a63-8f19-c91610d80b40' class='xr-var-data-in' type='checkbox'><label for='data-023afc9e-017a-4a63-8f19-c91610d80b40' 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>k Index of Deepest Grid Cell on T Grid</dd></dl></div><div class='xr-var-data'><pre>[8640000 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TAREA</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e5d23e3-2ecf-422d-b737-d4803fd4efab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e5d23e3-2ecf-422d-b737-d4803fd4efab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78caebc9-62dd-4858-a422-58a578b9591b' class='xr-var-data-in' type='checkbox'><label for='data-78caebc9-62dd-4858-a422-58a578b9591b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>cm^2</dd><dt><span>long_name :</span></dt><dd>area of T cells</dd></dl></div><div class='xr-var-data'><pre>[8640000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8171eb5-b1ab-41cb-8b2e-1a7e015885a0' class='xr-section-summary-in' type='checkbox'  ><label for='section-f8171eb5-b1ab-41cb-8b2e-1a7e015885a0' class='xr-section-summary' >Indexes: <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-index-name'><div>nlat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cb184df2-a972-46a8-8c0b-f604b3fc35ea' class='xr-index-data-in' type='checkbox'/><label for='index-cb184df2-a972-46a8-8c0b-f604b3fc35ea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "            ...\n",
       "            2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;nlat&#x27;, length=2400))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nlon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bdef50b7-1103-41c0-beed-58a82bcb1949' class='xr-index-data-in' type='checkbox'/><label for='index-bdef50b7-1103-41c0-beed-58a82bcb1949' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "            ...\n",
       "            3590, 3591, 3592, 3593, 3594, 3595, 3596, 3597, 3598, 3599],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;nlon&#x27;, length=3600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-304736a6-5909-4c14-8223-6118d30b4b17' class='xr-section-summary-in' type='checkbox'  checked><label for='section-304736a6-5909-4c14-8223-6118d30b4b17' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>nearest_s2d</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (nlat: 2400, nlon: 3600)\n",
       "Coordinates:\n",
       "    TLONG     (nlat, nlon) float64 ...\n",
       "    TLAT      (nlat, nlon) float64 ...\n",
       "  * nlat      (nlat) int64 0 1 2 3 4 5 6 ... 2393 2394 2395 2396 2397 2398 2399\n",
       "  * nlon      (nlon) int64 0 1 2 3 4 5 6 ... 3593 3594 3595 3596 3597 3598 3599\n",
       "Data variables:\n",
       "    lme_mask  (nlat, nlon) float64 ...\n",
       "    KMT       (nlat, nlon) int32 ...\n",
       "    TAREA     (nlat, nlon) float64 ...\n",
       "Attributes:\n",
       "    regrid_method:  nearest_s2d"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_lme_x01 = xr.open_dataset(\"data/LME-mask-POP_tx0.1v3.nc\").assign_coords(coords_dict)\n",
    "ds_lme_x01 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "191c5340-50b0-4842-94ae-770c1e1fb6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14722e27e730>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_lme_x01.lme_mask.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c7fbf4-313e-4047-a50a-bb5c0a040d9a",
   "metadata": {},
   "source": [
    "## Reading in grid variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb30996d-2461-4511-a22b-990f44b26763",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {'nlat': 8, 'nlon': 1200}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d20d22d6-a69c-49fd-82f0-5a2d78061285",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid = pop_tools.get_grid('POP_tx0.1v3').assign_coords(coords_dict)\n",
    "lons=ds_grid.TLONG\n",
    "lats=ds_grid.TLAT\n",
    "area=ds_grid.TAREA\n",
    "area_m=ds_grid.TAREA * 1e-4 # Converting area unit to m^2 (from cm^2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf16e8ad-8627-4461-be4b-85d25af1e701",
   "metadata": {},
   "source": [
    "## Getting list of all LME indices (removing 64, which doesn't have SAU data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f76a5869-79c6-4af6-a11f-140a8e3ab7d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66]\n"
     ]
    }
   ],
   "source": [
    "all_lmes = list(np.arange(1,67))\n",
    "all_lmes.remove(64)\n",
    "print(all_lmes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e038da4c-9493-4cb8-aa99-8dac2485d9f5",
   "metadata": {},
   "source": [
    "## Creating an empty dataset for the modeled catch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6005394c-1819-4b45-a314-37ad85999db2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "modeled_catch = xr.Dataset(coords={\"Year\": (\"Year\", np.arange(1980, 2022)),\n",
    "                \"LMEID\": (\"LMEID\", true_catch.LMEID.data)}, \n",
    "                           data_vars={'FwCatch': ([\"Year\", \"LMEID\"], np.zeros((42,65))),\n",
    "                                      'PwCatch': ([\"Year\", \"LMEID\"], np.zeros((42,65))),\n",
    "                                      'DwCatch': ([\"Year\", \"LMEID\"], np.zeros((42,65)))\n",
    "                                     })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e410c9a4-2120-4e92-9a2c-29084dd3f6f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lme =  1\n",
      "summing area\n",
      "1272275648600.4734\n",
      "summing masked var\n",
      "dividing by area\n",
      "1980\n",
      "Current Time = 16:59:20\n",
      "0.002787131559683013\n",
      "0.0011688477991032733\n",
      "0.003810405689598455\n",
      "1981\n",
      "Current Time = 16:59:41\n",
      "0.00290957464388598\n",
      "0.0011974845890466517\n",
      "0.003818691874168391\n",
      "1982\n",
      "Current Time = 16:59:59\n",
      "0.0029274306469769485\n",
      "0.0012448503306155096\n",
      "0.003726787992026247\n",
      "1983\n",
      "Current Time = 17:00:12\n",
      "0.0028296196425656134\n",
      "0.0012680008804506072\n",
      "0.0035723872246489715\n",
      "1984\n",
      "Current Time = 17:00:26\n",
      "0.0029358205574826937\n",
      "0.0013050512388703165\n",
      "0.0034310423245669133\n",
      "1985\n",
      "Current Time = 17:00:38\n",
      "0.0029356536857342514\n",
      "0.0013297048353971882\n",
      "0.0033193480603605353\n",
      "1986\n",
      "Current Time = 17:00:50\n",
      "0.002764331520212753\n",
      "0.0013062434568946227\n",
      "0.003325769793773598\n",
      "1987\n",
      "Current Time = 17:01:01\n",
      "0.002827948926694202\n",
      "0.0012865633550447036\n",
      "0.003467321304066955\n",
      "1988\n",
      "Current Time = 17:01:14\n",
      "0.0028033849207382794\n",
      "0.0012721370095930698\n",
      "0.0035969982388071196\n",
      "1989\n",
      "Current Time = 17:01:26\n",
      "0.0028591853329115965\n",
      "0.0012796719469869685\n",
      "0.0036169543818311447\n",
      "1990\n",
      "Current Time = 17:01:39\n",
      "0.0029333944133616164\n",
      "0.001301836503924719\n",
      "0.0036638702788270865\n",
      "1991\n",
      "Current Time = 17:01:51\n",
      "0.003074599751693558\n",
      "0.0013518492009570234\n",
      "0.0037009519425255217\n",
      "1992\n",
      "Current Time = 17:02:03\n",
      "0.0030278369165321075\n",
      "0.0013856126696136018\n",
      "0.0036733696135782586\n",
      "1993\n",
      "Current Time = 17:02:15\n",
      "0.0027912120218737267\n",
      "0.0013781610287141837\n",
      "0.0036131331510349174\n",
      "1994\n",
      "Current Time = 17:02:27\n",
      "0.0027748073272549326\n",
      "0.0013725644891946498\n",
      "0.0035120823055760125\n",
      "1995\n",
      "Current Time = 17:02:40\n",
      "0.0028778145970323405\n",
      "0.0013810327693464432\n",
      "0.0033630938723315383\n",
      "1996\n",
      "Current Time = 17:02:52\n",
      "0.00314205748340325\n",
      "0.0014311415028873098\n",
      "0.003328941900871814\n",
      "1997\n",
      "Current Time = 17:03:06\n",
      "0.003138669806752082\n",
      "0.0014818807594423776\n",
      "0.0033175050580160803\n",
      "1998\n",
      "Current Time = 17:03:17\n",
      "0.0028711318170060894\n",
      "0.0014845113655668333\n",
      "0.003181974442158189\n",
      "1999\n",
      "Current Time = 17:03:29\n",
      "0.002773022493662327\n",
      "0.001444625625345249\n",
      "0.0031507636038326198\n",
      "2000\n",
      "Current Time = 17:03:40\n",
      "0.0028247164297906277\n",
      "0.0013974844242339033\n",
      "0.003263096044423055\n",
      "2001\n",
      "Current Time = 17:03:53\n",
      "0.002830540106337578\n",
      "0.0013644859508751424\n",
      "0.0035079190987285323\n",
      "2002\n",
      "Current Time = 17:04:05\n",
      "0.002855687676244139\n",
      "0.0013489321560304786\n",
      "0.0037643605665048452\n",
      "2003\n",
      "Current Time = 17:04:16\n",
      "0.0029778016667052153\n",
      "0.0013697065579403713\n",
      "0.004005643242283541\n",
      "2004\n",
      "Current Time = 17:04:28\n",
      "0.002953526510768263\n",
      "0.0014121579700399607\n",
      "0.004039651392486601\n",
      "2005\n",
      "Current Time = 17:04:41\n",
      "0.002696496500865289\n",
      "0.001421684113086134\n",
      "0.003857677969500402\n",
      "2006\n",
      "Current Time = 17:04:53\n",
      "0.0027779847935533544\n",
      "0.0014310509821545579\n",
      "0.0037423879625028945\n",
      "2007\n",
      "Current Time = 17:05:05\n",
      "0.0029015412856792855\n",
      "0.001443310954940207\n",
      "0.0036709224319186043\n",
      "2008\n",
      "Current Time = 17:05:18\n",
      "0.0028497409760746308\n",
      "0.0014555167658245763\n",
      "0.0035044421436033705\n",
      "2009\n",
      "Current Time = 17:05:31\n",
      "0.0028878257935826624\n",
      "0.0014599107250064863\n",
      "0.0033987525264879107\n",
      "2010\n",
      "Current Time = 17:05:42\n",
      "0.0028761537582865713\n",
      "0.0014439655010981397\n",
      "0.0034054872942796384\n",
      "2011\n",
      "Current Time = 17:05:55\n",
      "0.002869021333896347\n",
      "0.001430137183403334\n",
      "0.003441188527164845\n",
      "2012\n",
      "Current Time = 17:06:08\n",
      "0.0026769195796154514\n",
      "0.0014000881516794785\n",
      "0.0033869609761634735\n",
      "2013\n",
      "Current Time = 17:06:19\n",
      "0.0027660312025618713\n",
      "0.0013929414427086156\n",
      "0.0033142433207731642\n",
      "2014\n",
      "Current Time = 17:06:32\n",
      "0.0029220190100726967\n",
      "0.0014233693050692121\n",
      "0.003313252112216895\n",
      "2015\n",
      "Current Time = 17:06:48\n",
      "0.003084588658012942\n",
      "0.0014744117032391968\n",
      "0.003454863291157826\n",
      "2016\n",
      "Current Time = 17:06:59\n",
      "0.0028108424427914004\n",
      "0.001484036838265205\n",
      "0.00351228865083922\n",
      "2017\n",
      "Current Time = 17:07:11\n",
      "0.0027346887740261196\n",
      "0.0014671759134300356\n",
      "0.0035138890059928187\n",
      "2018\n",
      "Current Time = 17:07:23\n",
      "0.002820505710604656\n",
      "0.001466626886077546\n",
      "0.003552587099697696\n",
      "2019\n",
      "Current Time = 17:07:36\n",
      "0.0027850621707107143\n",
      "0.0014558087243481154\n",
      "0.003697114085506565\n",
      "2020\n",
      "Current Time = 17:07:47\n",
      "0.002785607915827361\n",
      "0.001435400725740807\n",
      "0.003788883961520991\n",
      "2021\n",
      "Current Time = 17:07:59\n",
      "0.0027363181604634873\n",
      "0.001414755887495282\n",
      "0.003822044099134528\n",
      "lme =  2\n",
      "summing area\n",
      "1464553514899.5095\n",
      "summing masked var\n",
      "dividing by area\n",
      "1980\n",
      "Current Time = 17:08:12\n",
      "0.002372927554230319\n",
      "0.0016238187228877368\n",
      "0.0019430872524619303\n",
      "1981\n",
      "Current Time = 17:08:24\n",
      "0.0025054201497785636\n",
      "0.0016459636397661997\n",
      "0.0019241929454310623\n",
      "1982\n",
      "Current Time = 17:08:36\n",
      "0.002496918716475411\n",
      "0.0016755326017475543\n",
      "0.001826871485116398\n",
      "1983\n",
      "Current Time = 17:08:48\n",
      "0.0024577927266480368\n",
      "0.0016861053562172285\n",
      "0.00180812700111166\n",
      "1984\n",
      "Current Time = 17:09:00\n",
      "0.0023774016000252156\n",
      "0.0016808356266809056\n",
      "0.0017453057310906977\n",
      "1985\n",
      "Current Time = 17:09:12\n",
      "0.0025222980296278675\n",
      "0.0016982055279397027\n",
      "0.0017432562849499856\n",
      "1986\n",
      "Current Time = 17:09:24\n",
      "0.002643380279953519\n",
      "0.001729051688421271\n",
      "0.0018709696632091815\n",
      "1987\n",
      "Current Time = 17:09:36\n",
      "0.002433218320094726\n",
      "0.0017313376207904036\n",
      "0.0019256086279601607\n",
      "1988\n",
      "Current Time = 17:09:48\n",
      "0.002295016389331113\n",
      "0.0017052127761499802\n",
      "0.0018735774200839732\n",
      "1989\n",
      "Current Time = 17:09:59\n",
      "0.0022952507698142787\n",
      "0.0016817081579543142\n",
      "0.0018225439101217733\n",
      "1990\n",
      "Current Time = 17:10:12\n",
      "0.0019496618945732684\n",
      "0.00160965916872866\n",
      "0.0018606938423349005\n",
      "1991\n",
      "Current Time = 17:10:23\n",
      "0.001591235861901075\n",
      "0.001454121881433343\n",
      "0.0018921034304724689\n",
      "1992\n",
      "Current Time = 17:10:35\n",
      "0.0016680761756709258\n",
      "0.0013147762402213254\n",
      "0.0019481228794910918\n",
      "1993\n",
      "Current Time = 17:10:47\n",
      "0.002071608297776556\n",
      "0.0013203275308452096\n",
      "0.0019222724697668882\n",
      "1994\n",
      "Current Time = 17:10:59\n",
      "0.0022842055556586377\n",
      "0.0014298464540983236\n",
      "0.0018209802864219235\n",
      "1995\n",
      "Current Time = 17:11:10\n",
      "0.0022705539659971436\n",
      "0.0015070642178673277\n",
      "0.0017173350591448743\n",
      "1996\n",
      "Current Time = 17:11:22\n",
      "0.002359050221370153\n",
      "0.001555676379487435\n",
      "0.0017236937002316592\n",
      "1997\n",
      "Current Time = 17:11:34\n",
      "0.0024563705768955633\n",
      "0.0016003903487603662\n",
      "0.0017427738448960014\n",
      "1998\n",
      "Current Time = 17:11:46\n",
      "0.0022456455495248814\n",
      "0.0016177164665375362\n",
      "0.001718135987152326\n",
      "1999\n",
      "Current Time = 17:11:58\n",
      "0.002186223279265078\n",
      "0.001598153960491984\n",
      "0.001658195597950416\n",
      "2000\n",
      "Current Time = 17:12:10\n",
      "0.002508057915490651\n",
      "0.0016203286215994608\n",
      "0.0017396960252180035\n",
      "2001\n",
      "Current Time = 17:12:22\n",
      "0.0025970099438848756\n",
      "0.0016684599675206976\n",
      "0.0018307050962751987\n",
      "2002\n",
      "Current Time = 17:12:35\n",
      "0.002620173222181006\n",
      "0.0017099024661365913\n",
      "0.0018791180099607299\n",
      "2003\n",
      "Current Time = 17:12:47\n",
      "0.00223023166230224\n",
      "0.001694887617887716\n",
      "0.0018805884050581616\n",
      "2004\n",
      "Current Time = 17:12:58\n",
      "0.0020102165730375462\n",
      "0.0016010881625802356\n",
      "0.001813752313192792\n",
      "2005\n",
      "Current Time = 17:13:10\n",
      "0.002155543284543426\n",
      "0.0015413993489742383\n",
      "0.0018027619053411486\n",
      "2006\n",
      "Current Time = 17:13:22\n",
      "0.0020427941230270995\n",
      "0.0015132260610317264\n",
      "0.001807344298908019\n",
      "2007\n",
      "Current Time = 17:13:34\n",
      "0.002042609781597839\n",
      "0.00148118590575095\n",
      "0.001845691816262678\n",
      "2008\n",
      "Current Time = 17:13:46\n",
      "0.002298764006444802\n",
      "0.0014920004880894045\n",
      "0.0019463459614072838\n",
      "2009\n",
      "Current Time = 17:13:58\n",
      "0.0025928045303358622\n",
      "0.0015615871368011992\n",
      "0.0020422004994146686\n",
      "2010\n",
      "Current Time = 17:14:10\n",
      "0.0025917079234696267\n",
      "0.0016403171163202083\n",
      "0.0020271156865770325\n",
      "2011\n",
      "Current Time = 17:14:23\n",
      "0.00253883796676354\n",
      "0.0016942229102983914\n",
      "0.001932737944570693\n",
      "2012\n",
      "Current Time = 17:14:34\n",
      "0.002404667911271508\n",
      "0.0017025111780914455\n",
      "0.0018687081421177208\n",
      "2013\n",
      "Current Time = 17:14:46\n",
      "0.002249137311195946\n",
      "0.0016668608356886425\n",
      "0.001914212151167203\n",
      "2014\n",
      "Current Time = 17:14:58\n",
      "0.0019190461311707806\n",
      "0.00157964437176742\n",
      "0.0019683193043104983\n",
      "2015\n",
      "Current Time = 17:15:10\n",
      "0.0017579056030628914\n",
      "0.001454256050846647\n",
      "0.0019345826171637364\n",
      "2016\n",
      "Current Time = 17:15:22\n",
      "0.0019434306072550686\n",
      "0.0014193820705688525\n",
      "0.00179749293194237\n",
      "2017\n",
      "Current Time = 17:15:34\n",
      "0.0022063911029816794\n",
      "0.0014776929213353334\n",
      "0.0017343506629000087\n",
      "2018\n",
      "Current Time = 17:15:45\n",
      "0.0022839869074693115\n",
      "0.0015375855799769139\n",
      "0.001741421776928716\n",
      "2019\n",
      "Current Time = 17:15:57\n",
      "0.002153716284344015\n",
      "0.0015479804548343362\n",
      "0.0017790758181653074\n",
      "2020\n",
      "Current Time = 17:16:08\n",
      "0.0019473911057603162\n",
      "0.0015001367628818005\n",
      "0.0017618985449304337\n",
      "2021\n",
      "Current Time = 17:16:21\n",
      "0.0020720167785408805\n",
      "0.001460300527485642\n",
      "0.0017675132436937898\n",
      "lme =  3\n",
      "summing area\n",
      "2211117617859.141\n",
      "summing masked var\n",
      "dividing by area\n",
      "1980\n",
      "Current Time = 17:16:33\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [15], line 43\u001b[0m\n\u001b[1;32m     41\u001b[0m     total_pelagic \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(ds_tmp\u001b[38;5;241m.\u001b[39msel(fish\u001b[38;5;241m=\u001b[39mfish_type)\u001b[38;5;241m.\u001b[39msel(time\u001b[38;5;241m=\u001b[39myear))\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fish_type \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSd\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMd\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLd\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m---> 43\u001b[0m     total_demersal \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mds_tmp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfish\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfish_type\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myear\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28mprint\u001b[39m(total_forage)\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mprint\u001b[39m(total_pelagic)\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/xarray/core/common.py:150\u001b[0m, in \u001b[0;36mAbstractArray.__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__float__\u001b[39m(\u001b[38;5;28mself\u001b[39m: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m--> 150\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m)\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/xarray/core/dataarray.py:728\u001b[0m, in \u001b[0;36mDataArray.values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    719\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    720\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m    721\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    722\u001b[0m \u001b[38;5;124;03m    The array's data as a numpy.ndarray.\u001b[39;00m\n\u001b[1;32m    723\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    726\u001b[0m \u001b[38;5;124;03m    type does not support coercion like this (e.g. cupy).\u001b[39;00m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 728\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/xarray/core/variable.py:608\u001b[0m, in \u001b[0;36mVariable.values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    605\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    606\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    607\u001b[0m     \u001b[38;5;124;03m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[39;00m\n\u001b[0;32m--> 608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_as_array_or_item\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/xarray/core/variable.py:314\u001b[0m, in \u001b[0;36m_as_array_or_item\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_as_array_or_item\u001b[39m(data):\n\u001b[1;32m    301\u001b[0m     \u001b[38;5;124;03m\"\"\"Return the given values as a numpy array, or as an individual item if\u001b[39;00m\n\u001b[1;32m    302\u001b[0m \u001b[38;5;124;03m    it's a 0d datetime64 or timedelta64 array.\u001b[39;00m\n\u001b[1;32m    303\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[38;5;124;03m    TODO: remove this (replace with np.asarray) once these issues are fixed\u001b[39;00m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 314\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    315\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    316\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/dask/array/core.py:1700\u001b[0m, in \u001b[0;36mArray.__array__\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   1699\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 1700\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1701\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mand\u001b[39;00m x\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m dtype:\n\u001b[1;32m   1702\u001b[0m         x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mastype(dtype)\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/dask/base.py:315\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[1;32m    293\u001b[0m \n\u001b[1;32m    294\u001b[0m \u001b[38;5;124;03m    This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;124;03m    dask.base.compute\u001b[39;00m\n\u001b[1;32m    314\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m     (result,) \u001b[38;5;241m=\u001b[39m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/dask/base.py:600\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m    597\u001b[0m     keys\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_keys__())\n\u001b[1;32m    598\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m--> 600\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    601\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/distributed/client.py:3122\u001b[0m, in \u001b[0;36mClient.get\u001b[0;34m(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m   3120\u001b[0m         should_rejoin \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   3121\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3122\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgather\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpacked\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m   3124\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m futures\u001b[38;5;241m.\u001b[39mvalues():\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/distributed/client.py:2291\u001b[0m, in \u001b[0;36mClient.gather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m   2289\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2290\u001b[0m     local_worker \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2291\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2292\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gather\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2293\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2294\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2295\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2296\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocal_worker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_worker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2297\u001b[0m \u001b[43m    \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2298\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/distributed/utils.py:339\u001b[0m, in \u001b[0;36mSyncMethodMixin.sync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m future\n\u001b[1;32m    338\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 339\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    340\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback_timeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/distributed/utils.py:402\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    400\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    401\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m e\u001b[38;5;241m.\u001b[39mis_set():\n\u001b[0;32m--> 402\u001b[0m         \u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error:\n\u001b[1;32m    405\u001b[0m     typ, exc, tb \u001b[38;5;241m=\u001b[39m error\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/site-packages/distributed/utils.py:391\u001b[0m, in \u001b[0;36msync.<locals>.wait\u001b[0;34m(timeout)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwait\u001b[39m(timeout):\n\u001b[1;32m    390\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 391\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43me\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    392\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m    393\u001b[0m         loop\u001b[38;5;241m.\u001b[39madd_callback(cancel)\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/threading.py:581\u001b[0m, in \u001b[0;36mEvent.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    579\u001b[0m signaled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flag\n\u001b[1;32m    580\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m signaled:\n\u001b[0;32m--> 581\u001b[0m     signaled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cond\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    582\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m signaled\n",
      "File \u001b[0;32m/glade/work/eromashkova/miniconda3/envs/diag-sandbox-moc-pop-tools/lib/python3.9/threading.py:316\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    314\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    315\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 316\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    318\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ones = np.full((2400,3600),1,dtype=int)\n",
    "ones = xr.DataArray(ones,dims=('nlat','nlon')).assign_coords(coords_dict)\n",
    "ones = ones.where(ds_grid.REGION_MASK>0, 0)\n",
    "\n",
    "\n",
    "for lme in all_lmes[:]:\n",
    "    print('lme = ', lme)\n",
    "        \n",
    "    # Setting up LME masking\n",
    "    mask_x01 = ones.where(ds_lme_x01.lme_mask == lme)\n",
    "    masked_area_x01 = area_m.where(ds_lme_x01.lme_mask == lme)\n",
    "    print('summing area')\n",
    "    tot_masked_area_x01 = masked_area_x01.sum(dim=('nlon','nlat'))\n",
    "    print(tot_masked_area_x01.values)\n",
    "    \n",
    "    masked_var = ds[\"fish_yield\"].where(mask_x01==1)\n",
    "    \n",
    "    # double check that this is working over each fish type\n",
    "    masked_var = masked_var.where(masked_var < 0.5)\n",
    "    masked_var = masked_var.where(masked_var > -0.001)\n",
    "    print('summing masked var')\n",
    "    tot_masked_var = (masked_var * masked_area_x01).sum(dim=('nlon','nlat'))\n",
    "    #tot_masked_var = (masked_var).sum(dim=('nlon','nlat'))\n",
    "    print('dividing by area')\n",
    "    ds_tmp =  tot_masked_var / tot_masked_area_x01\n",
    "    \n",
    "    for year in np.arange(1980, 2022):\n",
    "        print(year)\n",
    "        \n",
    "        now = datetime.now()\n",
    "        current_time = now.strftime(\"%H:%M:%S\")\n",
    "        print(\"Current Time =\", current_time)\n",
    "                \n",
    "        total_forage = 0\n",
    "        total_pelagic = 0\n",
    "        total_demersal = 0\n",
    "        \n",
    "        for fish_type in [\"Sf\", \"Mf\"]:\n",
    "            total_forage += float(ds_tmp.sel(fish=fish_type).sel(time=year))\n",
    "        for fish_type in [\"Sp\", \"Mp\", \"Lp\"]:\n",
    "            total_pelagic += float(ds_tmp.sel(fish=fish_type).sel(time=year))\n",
    "        for fish_type in [\"Sd\", \"Md\", \"Ld\"]:\n",
    "            total_demersal += float(ds_tmp.sel(fish=fish_type).sel(time=year))\n",
    "            \n",
    "        print(total_forage)\n",
    "        print(total_pelagic)\n",
    "        print(total_demersal)\n",
    "\n",
    " \n",
    "        modeled_catch.FwCatch.loc[dict(Year=year, LMEID=lme)] = total_forage\n",
    "        modeled_catch.PwCatch.loc[dict(Year=year, LMEID=lme)] = total_pelagic\n",
    "        modeled_catch.DwCatch.loc[dict(Year=year, LMEID=lme)] = total_demersal\n",
    "        \n",
    "    modeled_catch.sel(LMEID=lme).to_zarr(store=\"data/FEISTY_SAU_comparison_x01/modeled_catch_by_lme_\" + str(lme) + \".zarr\")\n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bc3ea49-c85b-4f9b-8f6f-6d431e1323c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.scatter(true_catch.FwCatch.sel(Year=1981), modeled_catch.FwCatch.sel(Year=1981))\n",
    "plt.xlabel(\"SAU\")\n",
    "plt.ylabel(\"FEISTY\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  }
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