Fig7_FEISTY_validation_x01.ipynb

{
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   "source": [
    "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\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7369f41-396e-4d39-8f5f-0564637da288",
   "metadata": {},
   "source": [
    "# Reading in SAU catch data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c26679bd-7891-43bd-a7cd-1362fcacd2ef",
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       "</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-68c68c06-d924-4459-b172-037d0815b013' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68c68c06-d924-4459-b172-037d0815b013' 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-3b671e36-779d-48b9-91ef-b12ac9455cfb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b671e36-779d-48b9-91ef-b12ac9455cfb' 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-1f48f4d0-cba6-490b-9c90-3f06d30ef2b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f48f4d0-cba6-490b-9c90-3f06d30ef2b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d676ce73-fe0a-4cbd-8035-878c148f9dab' class='xr-var-data-in' type='checkbox'><label for='data-d676ce73-fe0a-4cbd-8035-878c148f9dab' 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-4585ea08-7a9a-4b8f-b0ac-adfbc5786fa4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4585ea08-7a9a-4b8f-b0ac-adfbc5786fa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35dd03c3-b143-4ef7-9bde-e845daddfd29' class='xr-var-data-in' type='checkbox'><label for='data-35dd03c3-b143-4ef7-9bde-e845daddfd29' 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-f389d7f7-c876-4ff6-9d4d-c0e5f79c84bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f389d7f7-c876-4ff6-9d4d-c0e5f79c84bc' 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-17866d56-5a0e-4d2b-a30a-5e037df9381a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17866d56-5a0e-4d2b-a30a-5e037df9381a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-328a9406-6def-4b30-ba8f-19f5832bd994' class='xr-var-data-in' type='checkbox'><label for='data-328a9406-6def-4b30-ba8f-19f5832bd994' 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-eb8eaecf-3147-41c6-8bcf-615701512c70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb8eaecf-3147-41c6-8bcf-615701512c70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe22de3d-773f-497e-b9bf-cdc7ef245bd1' class='xr-var-data-in' type='checkbox'><label for='data-fe22de3d-773f-497e-b9bf-cdc7ef245bd1' 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-22c1d7ac-6106-47d4-b8d0-285682f9353e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22c1d7ac-6106-47d4-b8d0-285682f9353e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ee81537-cb7d-43d1-8d61-22ae87a4aa43' class='xr-var-data-in' type='checkbox'><label for='data-1ee81537-cb7d-43d1-8d61-22ae87a4aa43' 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-b552ce3d-7a9f-49b9-b252-0c8b234a10b4' class='xr-section-summary-in' type='checkbox'  ><label for='section-b552ce3d-7a9f-49b9-b252-0c8b234a10b4' 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-b302b6e3-94f7-4855-a329-1be66c1b103e' class='xr-index-data-in' type='checkbox'/><label for='index-b302b6e3-94f7-4855-a329-1be66c1b103e' 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-d25cf320-405b-45ab-a299-a0ce58e6fc2b' class='xr-index-data-in' type='checkbox'/><label for='index-d25cf320-405b-45ab-a299-a0ce58e6fc2b' 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-8a9789fe-7bb0-4637-9466-060c682c6bfe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8a9789fe-7bb0-4637-9466-060c682c6bfe' 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": 2,
     "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": "code",
   "execution_count": 3,
   "id": "6817b2bb-8f59-4fe5-87b0-3778adfab2fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "\n",
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       "\n",
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       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (LMEID: 66)\n",
       "Coordinates:\n",
       "  * LMEID       (LMEID) int64 1 2 3 4 5 6 7 8 9 ... 58 59 60 61 62 63 64 65 66\n",
       "Data variables:\n",
       "    LME Name    (LMEID) object &#x27;East Bering Sea&#x27; ... &#x27;Canadian High Arctic - ...\n",
       "    Area (km2)  (LMEID) int64 1156684 1406422 2198805 ... 3479735 982382 690651</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-c5963256-8de2-43f0-8a5e-3753aee468af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c5963256-8de2-43f0-8a5e-3753aee468af' 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'>LMEID</span>: 66</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f451a439-3188-447b-b24a-96d0271942d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f451a439-3188-447b-b24a-96d0271942d6' class='xr-section-summary' >Coordinates: <span>(1)</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'>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 ... 61 62 63 64 65 66</div><input id='attrs-f86ba5cc-e43c-4b30-a6a1-42e38004077a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f86ba5cc-e43c-4b30-a6a1-42e38004077a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec698133-d0ff-4c2a-8da5-7691a237e136' class='xr-var-data-in' type='checkbox'><label for='data-ec698133-d0ff-4c2a-8da5-7691a237e136' 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, 64, 65, 66])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bcd60e9d-acd9-4526-91bb-349b69ab849d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bcd60e9d-acd9-4526-91bb-349b69ab849d' class='xr-section-summary' >Data variables: <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>LME Name</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;East Bering Sea&#x27; ... &#x27;Canadian ...</div><input id='attrs-4587104d-6c93-435f-9788-61c0cece7051' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4587104d-6c93-435f-9788-61c0cece7051' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fae1dff0-07e7-4093-93c1-ec86c0ad97cd' class='xr-var-data-in' type='checkbox'><label for='data-fae1dff0-07e7-4093-93c1-ec86c0ad97cd' 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;East Bering Sea&#x27;, &#x27;Gulf of Alaska&#x27;, &#x27;California Current&#x27;,\n",
       "       &#x27;Gulf of California&#x27;, &#x27;Gulf of Mexico&#x27;,\n",
       "       &#x27;Southeast U.S. Continental Shelf&#x27;,\n",
       "       &#x27;Northeast U.S. Continental Shelf&#x27;, &#x27;Scotian Shelf&#x27;,\n",
       "       &#x27;Newfoundland-Labrador Shelf&#x27;, &#x27;Insular Pacific-Hawaiian&#x27;,\n",
       "       &#x27;Pacific Central-American Coastal&#x27;, &#x27;Caribbean Sea&#x27;,\n",
       "       &#x27;Humboldt Current&#x27;, &#x27;Patagonian Shelf&#x27;, &#x27;South Brazil Shelf&#x27;,\n",
       "       &#x27;East Brazil Shelf&#x27;, &#x27;North Brazil Shelf&#x27;, &#x27;West Greenland Shelf&#x27;,\n",
       "       &#x27;East Greenland Shelf&#x27;, &#x27;Barents Sea&#x27;, &#x27;Norwegian Sea&#x27;,\n",
       "       &#x27;North Sea&#x27;, &#x27;Baltic Sea&#x27;, &#x27;Celtic-Biscay Shelf&#x27;,\n",
       "       &#x27;Iberian Coastal&#x27;, &#x27;Mediterranean Sea&#x27;, &#x27;Canary Current&#x27;,\n",
       "       &#x27;Guinea Current&#x27;, &#x27;Benguela Current&#x27;, &#x27;Agulhas Current&#x27;,\n",
       "       &#x27;Somali Coastal Current&#x27;, &#x27;Arabian Sea&#x27;, &#x27;Red Sea&#x27;,\n",
       "       &#x27;Bay of Bengal&#x27;, &#x27;Gulf of Thailand&#x27;, &#x27;South China Sea&#x27;,\n",
       "       &#x27;Sulu-Celebes Sea&#x27;, &#x27;Indonesian Sea&#x27;, &#x27;North Australian Shelf&#x27;,\n",
       "       &#x27;Northeast Australian Shelf&#x27;, &#x27;East Central Australian Shelf&#x27;,\n",
       "       &#x27;Southeast Australian Shelf&#x27;, &#x27;Southwest Australian Shelf&#x27;,\n",
       "       &#x27;West-Central Australian Shelf&#x27;, &#x27;Northwest Australian Shelf&#x27;,\n",
       "       &#x27;New Zealand Shelf&#x27;, &#x27;East China Sea&#x27;, &#x27;Yellow Sea&#x27;,\n",
       "       &#x27;Kuroshio Current&#x27;, &#x27;Sea of Japan / East Sea&#x27;, &#x27;Oyashio Current&#x27;,\n",
       "       &#x27;Sea of Okhotsk&#x27;, &#x27;West Bering Sea&#x27;, &#x27;Chukchi Sea&#x27;, &#x27;Beaufort Sea&#x27;,\n",
       "       &#x27;East Siberian Sea&#x27;, &#x27;Laptev Sea&#x27;, &#x27;Kara Sea&#x27;, &#x27;Iceland Shelf&#x27;,\n",
       "       &#x27;Faroe Plateau&#x27;, &#x27;Antarctic&#x27;, &#x27;Black Sea&#x27;, &#x27;Hudson Bay&#x27;,\n",
       "       &#x27;Arctic Ocean&#x27;, &#x27;Aleutian Islands&#x27;,\n",
       "       &#x27;Canadian High Arctic - North Greenland&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Area (km2)</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1156684 1406422 ... 982382 690651</div><input id='attrs-0b2cab1d-2248-48de-b417-875407966b4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b2cab1d-2248-48de-b417-875407966b4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a3c8c6c-8dda-4d87-b374-5c5b84a16205' class='xr-var-data-in' type='checkbox'><label for='data-6a3c8c6c-8dda-4d87-b374-5c5b84a16205' 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([1156684, 1406422, 2198805,  187408, 1487243,  273604,  278780,\n",
       "        360362,  644710,  970483, 1945330, 3155197, 2468858, 1126103,\n",
       "        547393, 1042563,  992523,  315069,  499461, 1755845, 1078730,\n",
       "        656038,  343751,  714798,  280374, 2356569, 1086163, 1894861,\n",
       "       1450009, 2550561,  817048, 3843215,  409773, 3585440,  362568,\n",
       "       3100417,  940316, 2129162,  735694, 1264549,  649212, 1177870,\n",
       "       1017977,  539684,  888022,  940894,  744116,  409864, 1296824,\n",
       "        938298,  518772, 1497758, 2136750,  752459,  621896,  978514,\n",
       "        495700,  885286, 1095889,  149839, 3962791,  419518, 1135783,\n",
       "       3479735,  982382,  690651])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efc1a6c5-2b5c-4cd6-8fe1-24c41745b029' class='xr-section-summary-in' type='checkbox'  ><label for='section-efc1a6c5-2b5c-4cd6-8fe1-24c41745b029' class='xr-section-summary' >Indexes: <span>(1)</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>LMEID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2344b7db-c6e6-47fd-9ee6-db398bd06812' class='xr-index-data-in' type='checkbox'/><label for='index-2344b7db-c6e6-47fd-9ee6-db398bd06812' 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, 64, 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-a0a7a2ab-eb54-4865-82c3-fe3e87947927' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0a7a2ab-eb54-4865-82c3-fe3e87947927' 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:     (LMEID: 66)\n",
       "Coordinates:\n",
       "  * LMEID       (LMEID) int64 1 2 3 4 5 6 7 8 9 ... 58 59 60 61 62 63 64 65 66\n",
       "Data variables:\n",
       "    LME Name    (LMEID) object 'East Bering Sea' ... 'Canadian High Arctic - ...\n",
       "    Area (km2)  (LMEID) int64 1156684 1406422 2198805 ... 3479735 982382 690651"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lme_metadata_csv = pd.read_csv('data/LME_metadata.csv', thousands=',')\n",
    "\n",
    "lme_metadata = lme_metadata_csv.set_index([\"LME Number\"]).to_xarray().rename(name_dict={\"LME Number\" : \"LMEID\"})\n",
    "\n",
    "lme_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcdca06-f607-4880-a3a3-ec2f35d53c56",
   "metadata": {},
   "source": [
    "# Merging and fixing area issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38e78b06-9a0d-4c37-b08c-21092d9382a5",
   "metadata": {},
   "outputs": [
    {
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       "\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:     (Year: 61, LMEID: 66)\n",
       "Coordinates:\n",
       "  * Year        (Year) int64 1950 1951 1952 1953 1954 ... 2007 2008 2009 2010\n",
       "  * LMEID       (LMEID) int64 1 2 3 4 5 6 7 8 9 ... 58 59 60 61 62 63 64 65 66\n",
       "Data variables:\n",
       "    FwCatch     (Year, LMEID) float64 2.326e-05 0.0001098 ... 0.0002058 0.0\n",
       "    PwCatch     (Year, LMEID) float64 0.04425 0.2839 0.2842 ... 0.0426 1.282e-08\n",
       "    DwCatch     (Year, LMEID) float64 0.02138 0.1584 0.0797 ... 0.8373 1.338e-05\n",
       "    LME Name    (LMEID) object &#x27;East Bering Sea&#x27; ... &#x27;Canadian High Arctic - ...\n",
       "    Area (km2)  (LMEID) int64 1156684 1406422 2198805 ... 3479735 982382 690651</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-c985b7c0-ca68-49ea-92da-7fe15b3ded1e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c985b7c0-ca68-49ea-92da-7fe15b3ded1e' 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>: 66</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c6069f6e-085f-4474-8c13-ed2d37c3ac3c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6069f6e-085f-4474-8c13-ed2d37c3ac3c' 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-b1f63f82-5556-4b44-9d55-7c6cbcfb438b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1f63f82-5556-4b44-9d55-7c6cbcfb438b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f72ab306-84a4-45e0-b48a-68f3fd36fbf4' class='xr-var-data-in' type='checkbox'><label for='data-f72ab306-84a4-45e0-b48a-68f3fd36fbf4' 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 ... 61 62 63 64 65 66</div><input id='attrs-07d03880-1ee7-4411-9b5f-79c3ac9fb8a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07d03880-1ee7-4411-9b5f-79c3ac9fb8a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ef57775-5885-4ec4-b769-f847242d62f4' class='xr-var-data-in' type='checkbox'><label for='data-0ef57775-5885-4ec4-b769-f847242d62f4' 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, 64, 65, 66])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f659b59-aa2a-47e5-89bf-2500a4b0d62d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f659b59-aa2a-47e5-89bf-2500a4b0d62d' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>2.326e-05 0.0001098 ... 0.0</div><input id='attrs-e3973ab0-c30f-4025-98da-ccc70018287d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3973ab0-c30f-4025-98da-ccc70018287d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abc6bb31-71fe-48b5-b449-bd60671c1249' class='xr-var-data-in' type='checkbox'><label for='data-abc6bb31-71fe-48b5-b449-bd60671c1249' 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([[2.32633279e-05, 1.09782900e-04, 2.36883428e-03, ...,\n",
       "                   nan, 8.86282523e-06, 0.00000000e+00],\n",
       "       [2.73122524e-05, 1.43377025e-04, 4.10665023e-03, ...,\n",
       "                   nan, 1.06205790e-05, 0.00000000e+00],\n",
       "       [3.27755274e-05, 3.98131461e-03, 1.72167019e-02, ...,\n",
       "                   nan, 1.30103897e-05, 0.00000000e+00],\n",
       "       ...,\n",
       "       [1.56258907e-03, 5.55943055e-03, 5.03941529e-02, ...,\n",
       "                   nan, 5.97014030e-04, 0.00000000e+00],\n",
       "       [1.26058063e-03, 1.48535072e-02, 7.72125241e-02, ...,\n",
       "                   nan, 2.50900990e-04, 0.00000000e+00],\n",
       "       [1.34128175e-03, 2.48706075e-02, 8.90136549e-02, ...,\n",
       "                   nan, 2.05828950e-04, 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'>0.04425 0.2839 ... 0.0426 1.282e-08</div><input id='attrs-482c62e4-ee64-41e2-b855-eadabcc0780a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-482c62e4-ee64-41e2-b855-eadabcc0780a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7926a7f3-5fed-4dac-b7fd-aae4f023010b' class='xr-var-data-in' type='checkbox'><label for='data-7926a7f3-5fed-4dac-b7fd-aae4f023010b' 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([[4.42501349e-02, 2.83859378e-01, 2.84187147e-01, ...,\n",
       "                   nan, 3.00072664e-02, 1.20434644e-07],\n",
       "       [2.46977699e-02, 2.67754804e-01, 1.86314580e-01, ...,\n",
       "                   nan, 1.67725719e-02, 4.50402916e-07],\n",
       "       [2.49912285e-02, 1.58015167e-01, 1.27581549e-01, ...,\n",
       "                   nan, 1.72495911e-02, 5.63853105e-07],\n",
       "       ...,\n",
       "       [4.64091744e-02, 4.25694978e-02, 1.47438766e-01, ...,\n",
       "                   nan, 3.72114668e-02, 6.41582304e-09],\n",
       "       [5.17238310e-02, 4.55583014e-02, 1.43477303e-01, ...,\n",
       "                   nan, 4.12458622e-02, 1.28160605e-08],\n",
       "       [5.17686740e-02, 6.02316782e-02, 1.41732235e-01, ...,\n",
       "                   nan, 4.25994082e-02, 1.28161471e-08]])</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'>0.02138 0.1584 ... 0.8373 1.338e-05</div><input id='attrs-899ddcfc-811d-4050-a0ab-d580a56598fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-899ddcfc-811d-4050-a0ab-d580a56598fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d0094eb-9326-445b-ad1e-281bbd8a2d7e' class='xr-var-data-in' type='checkbox'><label for='data-6d0094eb-9326-445b-ad1e-281bbd8a2d7e' 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([[2.13840309e-02, 1.58359604e-01, 7.97013593e-02, ...,\n",
       "                   nan, 3.71451399e-02, 1.04359987e-05],\n",
       "       [2.45159847e-02, 1.78968244e-01, 8.70418783e-02, ...,\n",
       "                   nan, 4.22224040e-02, 1.01422450e-05],\n",
       "       [2.08473840e-02, 1.38371317e-01, 7.54551586e-02, ...,\n",
       "                   nan, 3.60382576e-02, 2.99154877e-04],\n",
       "       ...,\n",
       "       [6.13421778e-01, 2.63132969e-01, 1.73938573e-01, ...,\n",
       "                   nan, 8.97509631e-01, 1.05061701e-05],\n",
       "       [5.35846521e-01, 2.55668304e-01, 1.18914634e-01, ...,\n",
       "                   nan, 7.84861792e-01, 1.09168149e-05],\n",
       "       [5.69256155e-01, 2.61424265e-01, 1.39070765e-01, ...,\n",
       "                   nan, 8.37277146e-01, 1.33806096e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LME Name</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;East Bering Sea&#x27; ... &#x27;Canadian ...</div><input id='attrs-93ff2af4-d3e5-40f6-b20b-ff6c14514542' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93ff2af4-d3e5-40f6-b20b-ff6c14514542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-506ccd2f-7704-4906-b659-ea8a0a6a76cb' class='xr-var-data-in' type='checkbox'><label for='data-506ccd2f-7704-4906-b659-ea8a0a6a76cb' 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;East Bering Sea&#x27;, &#x27;Gulf of Alaska&#x27;, &#x27;California Current&#x27;,\n",
       "       &#x27;Gulf of California&#x27;, &#x27;Gulf of Mexico&#x27;,\n",
       "       &#x27;Southeast U.S. Continental Shelf&#x27;,\n",
       "       &#x27;Northeast U.S. Continental Shelf&#x27;, &#x27;Scotian Shelf&#x27;,\n",
       "       &#x27;Newfoundland-Labrador Shelf&#x27;, &#x27;Insular Pacific-Hawaiian&#x27;,\n",
       "       &#x27;Pacific Central-American Coastal&#x27;, &#x27;Caribbean Sea&#x27;,\n",
       "       &#x27;Humboldt Current&#x27;, &#x27;Patagonian Shelf&#x27;, &#x27;South Brazil Shelf&#x27;,\n",
       "       &#x27;East Brazil Shelf&#x27;, &#x27;North Brazil Shelf&#x27;, &#x27;West Greenland Shelf&#x27;,\n",
       "       &#x27;East Greenland Shelf&#x27;, &#x27;Barents Sea&#x27;, &#x27;Norwegian Sea&#x27;,\n",
       "       &#x27;North Sea&#x27;, &#x27;Baltic Sea&#x27;, &#x27;Celtic-Biscay Shelf&#x27;,\n",
       "       &#x27;Iberian Coastal&#x27;, &#x27;Mediterranean Sea&#x27;, &#x27;Canary Current&#x27;,\n",
       "       &#x27;Guinea Current&#x27;, &#x27;Benguela Current&#x27;, &#x27;Agulhas Current&#x27;,\n",
       "       &#x27;Somali Coastal Current&#x27;, &#x27;Arabian Sea&#x27;, &#x27;Red Sea&#x27;,\n",
       "       &#x27;Bay of Bengal&#x27;, &#x27;Gulf of Thailand&#x27;, &#x27;South China Sea&#x27;,\n",
       "       &#x27;Sulu-Celebes Sea&#x27;, &#x27;Indonesian Sea&#x27;, &#x27;North Australian Shelf&#x27;,\n",
       "       &#x27;Northeast Australian Shelf&#x27;, &#x27;East Central Australian Shelf&#x27;,\n",
       "       &#x27;Southeast Australian Shelf&#x27;, &#x27;Southwest Australian Shelf&#x27;,\n",
       "       &#x27;West-Central Australian Shelf&#x27;, &#x27;Northwest Australian Shelf&#x27;,\n",
       "       &#x27;New Zealand Shelf&#x27;, &#x27;East China Sea&#x27;, &#x27;Yellow Sea&#x27;,\n",
       "       &#x27;Kuroshio Current&#x27;, &#x27;Sea of Japan / East Sea&#x27;, &#x27;Oyashio Current&#x27;,\n",
       "       &#x27;Sea of Okhotsk&#x27;, &#x27;West Bering Sea&#x27;, &#x27;Chukchi Sea&#x27;, &#x27;Beaufort Sea&#x27;,\n",
       "       &#x27;East Siberian Sea&#x27;, &#x27;Laptev Sea&#x27;, &#x27;Kara Sea&#x27;, &#x27;Iceland Shelf&#x27;,\n",
       "       &#x27;Faroe Plateau&#x27;, &#x27;Antarctic&#x27;, &#x27;Black Sea&#x27;, &#x27;Hudson Bay&#x27;,\n",
       "       &#x27;Arctic Ocean&#x27;, &#x27;Aleutian Islands&#x27;,\n",
       "       &#x27;Canadian High Arctic - North Greenland&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Area (km2)</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1156684 1406422 ... 982382 690651</div><input id='attrs-ba041510-03d6-4c01-a1b5-5fc7a25d31e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba041510-03d6-4c01-a1b5-5fc7a25d31e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e52824c-9541-4ce3-a793-eaec75690609' class='xr-var-data-in' type='checkbox'><label for='data-6e52824c-9541-4ce3-a793-eaec75690609' 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([1156684, 1406422, 2198805,  187408, 1487243,  273604,  278780,\n",
       "        360362,  644710,  970483, 1945330, 3155197, 2468858, 1126103,\n",
       "        547393, 1042563,  992523,  315069,  499461, 1755845, 1078730,\n",
       "        656038,  343751,  714798,  280374, 2356569, 1086163, 1894861,\n",
       "       1450009, 2550561,  817048, 3843215,  409773, 3585440,  362568,\n",
       "       3100417,  940316, 2129162,  735694, 1264549,  649212, 1177870,\n",
       "       1017977,  539684,  888022,  940894,  744116,  409864, 1296824,\n",
       "        938298,  518772, 1497758, 2136750,  752459,  621896,  978514,\n",
       "        495700,  885286, 1095889,  149839, 3962791,  419518, 1135783,\n",
       "       3479735,  982382,  690651])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4166793f-9e03-43cc-ae36-e18eaf7a02e4' class='xr-section-summary-in' type='checkbox'  ><label for='section-4166793f-9e03-43cc-ae36-e18eaf7a02e4' 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-d8b3428e-4211-41cc-9b94-2457e5e216e9' class='xr-index-data-in' type='checkbox'/><label for='index-d8b3428e-4211-41cc-9b94-2457e5e216e9' 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-615660c9-fc56-42fb-bf28-d5c412481358' class='xr-index-data-in' type='checkbox'/><label for='index-615660c9-fc56-42fb-bf28-d5c412481358' 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, 64, 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-f73e3a3d-f4ea-4173-b1e0-72595d7405de' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f73e3a3d-f4ea-4173-b1e0-72595d7405de' 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: 66)\n",
       "Coordinates:\n",
       "  * Year        (Year) int64 1950 1951 1952 1953 1954 ... 2007 2008 2009 2010\n",
       "  * LMEID       (LMEID) int64 1 2 3 4 5 6 7 8 9 ... 58 59 60 61 62 63 64 65 66\n",
       "Data variables:\n",
       "    FwCatch     (Year, LMEID) float64 2.326e-05 0.0001098 ... 0.0002058 0.0\n",
       "    PwCatch     (Year, LMEID) float64 0.04425 0.2839 0.2842 ... 0.0426 1.282e-08\n",
       "    DwCatch     (Year, LMEID) float64 0.02138 0.1584 0.0797 ... 0.8373 1.338e-05\n",
       "    LME Name    (LMEID) object 'East Bering Sea' ... 'Canadian High Arctic - ...\n",
       "    Area (km2)  (LMEID) int64 1156684 1406422 2198805 ... 3479735 982382 690651"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_catch = true_catch.merge(lme_metadata)\n",
    "\n",
    "true_catch[\"FwCatch\"] = true_catch.FwCatch/true_catch[\"Area (km2)\"]\n",
    "true_catch[\"PwCatch\"] = true_catch.PwCatch/true_catch[\"Area (km2)\"]\n",
    "true_catch[\"DwCatch\"] = true_catch.DwCatch/true_catch[\"Area (km2)\"]\n",
    "\n",
    "true_catch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc8fe47-232e-4ec7-9922-57d6096d08f1",
   "metadata": {},
   "source": [
    "# Reading in modeled FEISTY data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d26f1da-7dd4-450a-b821-dba4aeaaf607",
   "metadata": {
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   },
   "outputs": [
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       "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-87365f56-73ac-45ac-a649-b0dd3f85159f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-87365f56-73ac-45ac-a649-b0dd3f85159f' 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-22f22464-9296-48f6-abad-32313b8d832d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22f22464-9296-48f6-abad-32313b8d832d' 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-28cbb657-5637-412b-a356-4a9abcb3464a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28cbb657-5637-412b-a356-4a9abcb3464a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ca883af-5b18-416e-997e-9571e61177e8' class='xr-var-data-in' type='checkbox'><label for='data-2ca883af-5b18-416e-997e-9571e61177e8' 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-edf916d1-77bd-4d18-8367-f8ea761e53f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edf916d1-77bd-4d18-8367-f8ea761e53f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84d79f2b-1ede-4def-840c-d5fbe3a9ee1b' class='xr-var-data-in' type='checkbox'><label for='data-84d79f2b-1ede-4def-840c-d5fbe3a9ee1b' 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-87f672bd-9d18-4b00-a71f-82d9e2f56362' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87f672bd-9d18-4b00-a71f-82d9e2f56362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d45bd5b2-470a-4313-b062-c54ca8de665b' class='xr-var-data-in' type='checkbox'><label for='data-d45bd5b2-470a-4313-b062-c54ca8de665b' 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-f96d596f-11ba-40a3-b833-6ff73059988e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f96d596f-11ba-40a3-b833-6ff73059988e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45f42376-abc2-43e7-9adf-653d617a403a' class='xr-var-data-in' type='checkbox'><label for='data-45f42376-abc2-43e7-9adf-653d617a403a' 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-9ae72199-12ca-479f-9fe2-8e150b773cbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ae72199-12ca-479f-9fe2-8e150b773cbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4e683b8-d822-4e1b-8ff5-16e853d124f5' class='xr-var-data-in' type='checkbox'><label for='data-c4e683b8-d822-4e1b-8ff5-16e853d124f5' 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-c0e05bb2-8e74-418e-9931-a905c74f6858' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0e05bb2-8e74-418e-9931-a905c74f6858' 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-69f7d7de-5879-4871-863c-82d9c3e4afa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69f7d7de-5879-4871-863c-82d9c3e4afa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25122a8b-7c6e-42ce-b765-96cf05daa1f1' class='xr-var-data-in' type='checkbox'><label for='data-25122a8b-7c6e-42ce-b765-96cf05daa1f1' 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",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 135000 chunks in 130 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\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-c389f8e4-d978-4398-9507-14e3d9f629b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c389f8e4-d978-4398-9507-14e3d9f629b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fb69c87-7b70-4ebd-b12f-acdabc02514e' class='xr-var-data-in' type='checkbox'><label for='data-9fb69c87-7b70-4ebd-b12f-acdabc02514e' 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",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </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-43e26f8a-0e3b-4cf4-b1be-7a98e8f8ffd6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43e26f8a-0e3b-4cf4-b1be-7a98e8f8ffd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8187942-dd80-47dc-a929-2b4d96f70a0b' class='xr-var-data-in' type='checkbox'><label for='data-f8187942-dd80-47dc-a929-2b4d96f70a0b' 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> 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",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfdd3497-d356-44e1-9ca7-1362a12816e1' class='xr-section-summary-in' type='checkbox'  ><label for='section-bfdd3497-d356-44e1-9ca7-1362a12816e1' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>fish</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41d04e03-e60b-417f-8dbb-a27f5908b549' class='xr-index-data-in' type='checkbox'/><label for='index-41d04e03-e60b-417f-8dbb-a27f5908b549' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#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=&#x27;object&#x27;, name=&#x27;fish&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>group</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e013b2e9-069c-421e-8b74-5e37311c844c' class='xr-index-data-in' type='checkbox'/><label for='index-e013b2e9-069c-421e-8b74-5e37311c844c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#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;], dtype=&#x27;object&#x27;, name=&#x27;group&#x27;))</pre></div></li><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-9d5591ad-7608-4cb0-8b86-9b4f4cb40355' class='xr-index-data-in' type='checkbox'/><label for='index-9d5591ad-7608-4cb0-8b86-9b4f4cb40355' 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-a05fda98-0e0d-4888-80cc-0ec4fc1818a6' class='xr-index-data-in' type='checkbox'/><label for='index-a05fda98-0e0d-4888-80cc-0ec4fc1818a6' 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-b0116548-93cf-4de8-bccd-cb9583ea1d8a' class='xr-index-data-in' type='checkbox'/><label for='index-b0116548-93cf-4de8-bccd-cb9583ea1d8a' 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-4591992f-c457-4f3b-b5fb-835afaf26e00' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4591992f-c457-4f3b-b5fb-835afaf26e00' 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>"
      ],
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       "<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": 5,
     "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": 6,
   "id": "6300a3f1-6733-4c5f-b30b-cf239c763cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "coords_dict = {'nlat': ds.nlat.data, 'nlon': ds.nlon.data}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebde9e4d-9168-4848-b2fd-fa741c8a80ad",
   "metadata": {},
   "outputs": [
    {
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       "</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-0a776965-9207-47d9-b12b-43a3fe49df84' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a776965-9207-47d9-b12b-43a3fe49df84' 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-e3813c90-59c8-4d5d-975b-ebe015dbb8f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3813c90-59c8-4d5d-975b-ebe015dbb8f3' 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-63b94e77-c998-4a6b-8063-75de8c39f070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63b94e77-c998-4a6b-8063-75de8c39f070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11feb0d4-3698-4179-b4db-0c137abb6dda' class='xr-var-data-in' type='checkbox'><label for='data-11feb0d4-3698-4179-b4db-0c137abb6dda' 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-2d5787ef-4232-4c7a-8713-107be529fd63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d5787ef-4232-4c7a-8713-107be529fd63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a2a3232-f9ea-448c-b66f-a3193d053350' class='xr-var-data-in' type='checkbox'><label for='data-0a2a3232-f9ea-448c-b66f-a3193d053350' 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-c5faea9b-4f8d-48ee-a00a-f59f346f4cf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5faea9b-4f8d-48ee-a00a-f59f346f4cf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec3224ed-afbd-4370-951c-43d917cc5361' class='xr-var-data-in' type='checkbox'><label for='data-ec3224ed-afbd-4370-951c-43d917cc5361' 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-e2713b78-7921-40b8-99e3-10911191d1ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2713b78-7921-40b8-99e3-10911191d1ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a02882dc-e6e7-4036-a690-d76639aa99e3' class='xr-var-data-in' type='checkbox'><label for='data-a02882dc-e6e7-4036-a690-d76639aa99e3' 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-22bcde06-bbce-4217-9af2-a768ad218f8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22bcde06-bbce-4217-9af2-a768ad218f8c' 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-b8d38ad8-f5a8-43e6-a05d-c2317638e6b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8d38ad8-f5a8-43e6-a05d-c2317638e6b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e151bf3-4a7c-488a-b90c-a95bf8a1f5c3' class='xr-var-data-in' type='checkbox'><label for='data-2e151bf3-4a7c-488a-b90c-a95bf8a1f5c3' 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-40b85644-b100-48f8-ac5d-daa90ceccc60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40b85644-b100-48f8-ac5d-daa90ceccc60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c7c4868-c673-4692-9f79-bd4a8dec7766' class='xr-var-data-in' type='checkbox'><label for='data-3c7c4868-c673-4692-9f79-bd4a8dec7766' 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-09573d01-3a13-4ecb-a652-e6767c0417a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09573d01-3a13-4ecb-a652-e6767c0417a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf5682e8-a20e-44c3-92e1-83f429c42581' class='xr-var-data-in' type='checkbox'><label for='data-bf5682e8-a20e-44c3-92e1-83f429c42581' 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-bd29325e-4446-44f6-9d3c-402aa5a3e13d' class='xr-section-summary-in' type='checkbox'  ><label for='section-bd29325e-4446-44f6-9d3c-402aa5a3e13d' 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-a979200a-9c86-4fbd-83bd-13be71e7e31f' class='xr-index-data-in' type='checkbox'/><label for='index-a979200a-9c86-4fbd-83bd-13be71e7e31f' 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-f53a7544-7d8e-4351-bd80-0e2531b667f4' class='xr-index-data-in' type='checkbox'/><label for='index-f53a7544-7d8e-4351-bd80-0e2531b667f4' 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-15831799-ade6-47fa-bd65-702743369b9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15831799-ade6-47fa-bd65-702743369b9e' 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": 8,
     "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": 9,
   "id": "191c5340-50b0-4842-94ae-770c1e1fb6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x146cf10fc190>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "bb30996d-2461-4511-a22b-990f44b26763",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {'nlat': 8, 'nlon': 1200}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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.chunk(chunks) * 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": 12,
   "id": "f76a5869-79c6-4af6-a11f-140a8e3ab7d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 46, 47, 49, 50, 51, 52, 53, 54, 59, 60, 65]\n"
     ]
    }
   ],
   "source": [
    "all_lmes = list(np.arange(1,67))\n",
    "all_lmes.remove(64)\n",
    "\n",
    "keep_lmes = [item for row in [np.arange(1,10), np.arange(11,23), \n",
    "             np.arange(24,33), [34], np.arange(36,39), [46, 47], np.arange(49,55), [59, 60, 65]] for item in row]\n",
    "\n",
    "# from file KristenSAU/'Stock_PNAS_catch_oceanprod_output.mat', 'notLELC'\n",
    "\n",
    "print(len(keep_lmes))\n",
    "print(keep_lmes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e038da4c-9493-4cb8-aa99-8dac2485d9f5",
   "metadata": {},
   "source": [
    "## Reading in previously created comparison catch dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d969a16e-3960-4338-bb4d-1372c0b6806c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (LMEID: 65, Year: 42)\n",
       "Coordinates:\n",
       "  * LMEID    (LMEID) int64 27 34 32 51 26 35 40 31 ... 22 28 57 17 52 49 58 21\n",
       "  * Year     (Year) int64 1980 1981 1982 1983 1984 ... 2017 2018 2019 2020 2021\n",
       "Data variables:\n",
       "    DwCatch  (LMEID, Year) float64 dask.array&lt;chunksize=(1, 42), meta=np.ndarray&gt;\n",
       "    FwCatch  (LMEID, Year) float64 dask.array&lt;chunksize=(1, 42), meta=np.ndarray&gt;\n",
       "    PwCatch  (LMEID, Year) float64 dask.array&lt;chunksize=(1, 42), 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-06daf6cb-3608-4528-b86f-697fedd94be0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-06daf6cb-3608-4528-b86f-697fedd94be0' 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'>LMEID</span>: 65</li><li><span class='xr-has-index'>Year</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5e701eed-eaf9-438e-bb31-3a66a93e04fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e701eed-eaf9-438e-bb31-3a66a93e04fd' 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'>LMEID</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>27 34 32 51 26 ... 17 52 49 58 21</div><input id='attrs-523d5e6c-2112-42c1-a474-b010bf085a43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-523d5e6c-2112-42c1-a474-b010bf085a43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a4443c8-84c7-4685-95d9-a7def88b16ee' class='xr-var-data-in' type='checkbox'><label for='data-6a4443c8-84c7-4685-95d9-a7def88b16ee' 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([27, 34, 32, 51, 26, 35, 40, 31, 61, 38, 62,  6, 24, 37, 10,  9, 25,  7,\n",
       "       56, 16, 47,  1, 30, 14, 59, 33, 45, 43, 50, 19, 55, 15, 48, 46, 23, 20,\n",
       "       63, 60,  2, 54, 44,  3, 13, 39, 18, 29, 36, 12, 11,  5, 42, 66,  8, 65,\n",
       "        4, 53, 41, 22, 28, 57, 17, 52, 49, 58, 21])</pre></div></li><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'>1980 1981 1982 ... 2019 2020 2021</div><input id='attrs-511a7ff8-6e30-4a51-aecf-a1c95c06f08e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-511a7ff8-6e30-4a51-aecf-a1c95c06f08e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61e06e41-9877-47e2-ab0e-9cd495ae3534' class='xr-var-data-in' type='checkbox'><label for='data-61e06e41-9877-47e2-ab0e-9cd495ae3534' 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-a4543c34-0f2d-437f-aeea-49bf0a84f9fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4543c34-0f2d-437f-aeea-49bf0a84f9fc' 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>DwCatch</span></div><div class='xr-var-dims'>(LMEID, Year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42), meta=np.ndarray&gt;</div><input id='attrs-ae9c24ae-3e9e-4aff-b81e-d4b07eff293e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae9c24ae-3e9e-4aff-b81e-d4b07eff293e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-228968ec-58fc-4285-ae8f-77270cc8ed71' class='xr-var-data-in' type='checkbox'><label for='data-228968ec-58fc-4285-ae8f-77270cc8ed71' 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> 21.33 kiB </td>\n",
       "                        <td> 336 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 42) </td>\n",
       "                        <td> (1, 42) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 65 chunks in 197 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FwCatch</span></div><div class='xr-var-dims'>(LMEID, Year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42), meta=np.ndarray&gt;</div><input id='attrs-eb6c639d-55b3-47ef-b58b-9e5cf8331fc7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb6c639d-55b3-47ef-b58b-9e5cf8331fc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-784328b3-8a50-4259-b15e-abb11894a31b' class='xr-var-data-in' type='checkbox'><label for='data-784328b3-8a50-4259-b15e-abb11894a31b' 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PwCatch</span></div><div class='xr-var-dims'>(LMEID, Year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42), meta=np.ndarray&gt;</div><input id='attrs-a32acac2-0408-4410-a19e-6c41a98ab91d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a32acac2-0408-4410-a19e-6c41a98ab91d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e0e69a1-726d-44bd-94e6-af6649e3e14f' class='xr-var-data-in' type='checkbox'><label for='data-1e0e69a1-726d-44bd-94e6-af6649e3e14f' 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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       "             7, 56, 16, 47,  1, 30, 14, 59, 33, 45, 43, 50, 19, 55, 15, 48, 46,\n",
       "            23, 20, 63, 60,  2, 54, 44,  3, 13, 39, 18, 29, 36, 12, 11,  5, 42,\n",
       "            66,  8, 65,  4, 53, 41, 22, 28, 57, 17, 52, 49, 58, 21],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;LMEID&#x27;))</pre></div></li><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-2d209031-7795-4013-9064-c31b8e7157df' class='xr-index-data-in' type='checkbox'/><label for='index-2d209031-7795-4013-9064-c31b8e7157df' 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;Year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c638319e-99da-4349-92dc-390a1e216838' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c638319e-99da-4349-92dc-390a1e216838' 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:  (LMEID: 65, Year: 42)\n",
       "Coordinates:\n",
       "  * LMEID    (LMEID) int64 27 34 32 51 26 35 40 31 ... 22 28 57 17 52 49 58 21\n",
       "  * Year     (Year) int64 1980 1981 1982 1983 1984 ... 2017 2018 2019 2020 2021\n",
       "Data variables:\n",
       "    DwCatch  (LMEID, Year) float64 dask.array<chunksize=(1, 42), meta=np.ndarray>\n",
       "    FwCatch  (LMEID, Year) float64 dask.array<chunksize=(1, 42), meta=np.ndarray>\n",
       "    PwCatch  (LMEID, Year) float64 dask.array<chunksize=(1, 42), meta=np.ndarray>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeled_catch = xr.open_mfdataset(glob.glob(\"data/FEISTY_SAU_comparison_x01/modeled_catch_by_lme_*.zarr\"), \n",
    "                                  concat_dim=\"LMEID\", coords='minimal', compat=\"override\", combine='nested', \n",
    "                                  engine='zarr')\n",
    "\n",
    "# Adjusting per day -> per year\n",
    "modeled_catch[\"FwCatch\"], modeled_catch[\"PwCatch\"], modeled_catch[\"DwCatch\"] = 365*modeled_catch.FwCatch, 365*modeled_catch.PwCatch, 365*modeled_catch.DwCatch\n",
    "\n",
    "modeled_catch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8b02bc-c0b9-4086-b9dc-8b049f08e154",
   "metadata": {},
   "source": [
    "# Processing by years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "870de88a-1113-47e9-b94b-662983a91a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_catch_year_proc = xr.Dataset(coords={\"LMEID\": (\"LMEID\", all_lmes)}, \n",
    "                           data_vars={'FwCatch': ([\"LMEID\"], np.zeros((65))),\n",
    "                                      'PwCatch': ([\"LMEID\"], np.zeros((65))),\n",
    "                                      'DwCatch': ([\"LMEID\"], np.zeros((65)))\n",
    "                                     })\n",
    "\n",
    "for lme in all_lmes:\n",
    "    for fish_type in [\"FwCatch\", \"PwCatch\", \"DwCatch\"]:\n",
    "        top10 = np.sort(true_catch[fish_type].sel(LMEID=lme).data)[-10:]\n",
    "        meanoftop10 = np.mean(top10)\n",
    "        \n",
    "        true_catch_year_proc[fish_type].loc[dict(LMEID=lme)] = meanoftop10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7459d0eb-f221-4284-927e-e6226a9c4616",
   "metadata": {},
   "outputs": [
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       ".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:  (LMEID: 65)\n",
       "Coordinates:\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  (LMEID) float64 0.00169 0.007716 0.103 ... 0.000136 0.000745 0.0\n",
       "    PwCatch  (LMEID) float64 0.08869 0.3235 0.1598 ... 0.0573 1.017e-06\n",
       "    DwCatch  (LMEID) float64 1.415 0.3888 0.2114 ... 0.00289 1.167 0.0002402</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-d0e6185a-e479-4e95-9e08-cac5fbca6cf6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d0e6185a-e479-4e95-9e08-cac5fbca6cf6' 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'>LMEID</span>: 65</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8f50e63b-887b-4f32-b5e4-bed6a9a67f03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f50e63b-887b-4f32-b5e4-bed6a9a67f03' class='xr-section-summary' >Coordinates: <span>(1)</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'>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-5aa66cd3-7810-4bf1-90e6-808b3d13acb9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5aa66cd3-7810-4bf1-90e6-808b3d13acb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff49055d-36c4-4e6a-92ba-5d9f4c44c80c' class='xr-var-data-in' type='checkbox'><label for='data-ff49055d-36c4-4e6a-92ba-5d9f4c44c80c' 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-bc539201-1250-4484-896c-4c22078e63a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bc539201-1250-4484-896c-4c22078e63a2' 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'>(LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00169 0.007716 ... 0.000745 0.0</div><input id='attrs-5047a34b-71e1-4aed-b64b-b97984942927' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5047a34b-71e1-4aed-b64b-b97984942927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb6a0b9b-1776-4d6e-98f8-4e945ae90bd9' class='xr-var-data-in' type='checkbox'><label for='data-fb6a0b9b-1776-4d6e-98f8-4e945ae90bd9' 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([1.69020855e-03, 7.71554447e-03, 1.03035562e-01, 4.47233182e-01,\n",
       "       6.13984420e-01, 6.37809668e-03, 8.63583500e-02, 1.09021568e-01,\n",
       "       2.24377144e-01, 6.96489882e-04, 2.56045942e-01, 8.73980953e-03,\n",
       "       5.83846849e+00, 2.77084364e-01, 6.96562692e-02, 1.20182484e-01,\n",
       "       9.20093912e-02, 3.34674317e-02, 1.98175656e-01, 1.13825150e+00,\n",
       "       2.22272602e-01, 7.32822612e-01, 1.46849211e+00, 1.22852039e-01,\n",
       "       9.32973233e-01, 2.89070829e-01, 2.45883294e+00, 1.28261178e-01,\n",
       "       6.14758318e-01, 3.32395090e-02, 1.60173623e-02, 2.11316078e-01,\n",
       "       6.13161785e-02, 1.50081274e-01, 4.91113784e-01, 3.49661230e-01,\n",
       "       1.55086532e-01, 2.47411717e-01, 2.78364791e-03, 1.34413066e-03,\n",
       "       1.87687658e-03, 6.76785352e-03, 6.37639725e-03, 1.20846741e-02,\n",
       "       1.32317664e-02, 4.94333483e-02, 1.22459241e+00, 9.97228663e-01,\n",
       "       3.05039691e-01, 5.73822230e-01, 2.14099292e-01, 7.93305589e-02,\n",
       "       1.30120581e-02, 1.39737643e-03, 4.73864000e-06, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.27934319e-03, 8.60632084e-01, 6.37936796e-02,\n",
       "                  nan, 1.93760740e+00, 1.36036377e-04, 7.44986311e-04,\n",
       "       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'>(LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08869 0.3235 ... 0.0573 1.017e-06</div><input id='attrs-5c159fc1-45e7-48da-8c52-9f70849f5923' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c159fc1-45e7-48da-8c52-9f70849f5923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc622c33-f7ec-4b8a-a690-aceb4c77a50b' class='xr-var-data-in' type='checkbox'><label for='data-dc622c33-f7ec-4b8a-a690-aceb4c77a50b' 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([8.86922296e-02, 3.23519142e-01, 1.59774113e-01, 1.47230813e+00,\n",
       "       1.09878141e-01, 5.03752848e-01, 1.74282067e+00, 4.36792215e-01,\n",
       "       6.55072824e-01, 4.61181485e-03, 5.69662644e-01, 9.62931689e-02,\n",
       "       2.76269527e+00, 1.35511154e-02, 1.30518766e-01, 3.20445868e-02,\n",
       "       5.38133940e-02, 4.48566157e-01, 8.85776324e-02, 2.81955096e-01,\n",
       "       8.20620871e-01, 2.32471531e+00, 1.38771039e+00, 1.04284724e+00,\n",
       "       7.15557020e-01, 1.72125639e-01, 1.06111292e+00, 5.26574540e-01,\n",
       "       1.61570657e+00, 5.22372767e-02, 1.16352985e-01, 3.04192262e-01,\n",
       "       1.53515235e-01, 5.19342087e-01, 2.87462510e+00, 9.95967185e-01,\n",
       "       6.85581571e-01, 4.76283458e-01, 1.58299415e-02, 1.03013649e-02,\n",
       "       1.77610383e-02, 3.09540484e-02, 2.83093350e-02, 2.46647119e-02,\n",
       "       1.62592123e-02, 1.11809831e-01, 2.64916670e+00, 1.33908136e+00,\n",
       "       1.63833813e+00, 1.60840268e+00, 1.34950690e+00, 5.46758603e-01,\n",
       "       3.75740328e-02, 4.51703128e-02, 2.41067973e-05, 1.48426787e-03,\n",
       "       3.04665568e-03, 7.86918493e-03, 8.78265274e-02, 1.91157962e-02,\n",
       "                  nan, 5.36228221e-01, 1.33175794e-04, 5.72983786e-02,\n",
       "       1.01748423e-06])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DwCatch</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.415 0.3888 ... 1.167 0.0002402</div><input id='attrs-10b590aa-2c12-4562-a6ad-04586021bc90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10b590aa-2c12-4562-a6ad-04586021bc90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-184696f9-db6a-42fa-9983-5025d040f647' class='xr-var-data-in' type='checkbox'><label for='data-184696f9-db6a-42fa-9983-5025d040f647' 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([1.41458695e+00, 3.88754960e-01, 2.11410669e-01, 8.04966939e-01,\n",
       "       3.12355291e-01, 2.38865708e-01, 3.21883816e+00, 3.94288544e+00,\n",
       "       5.11504591e+00, 4.47312616e-03, 1.30207461e-01, 1.02309280e-01,\n",
       "       2.52913833e-01, 1.04899293e+00, 3.89019978e-01, 2.00467541e-01,\n",
       "       2.04207621e-01, 2.42517679e+00, 4.70192739e-01, 8.13932325e-01,\n",
       "       4.67560066e-01, 2.54060762e+00, 1.33438397e+00, 1.07675437e+00,\n",
       "       1.59413569e+00, 2.66434861e-01, 5.73982362e-01, 3.12620400e-01,\n",
       "       9.34221451e-01, 4.37559563e-02, 8.04251095e-02, 3.77718413e-01,\n",
       "       1.09441102e-01, 4.83981463e-01, 2.24531240e+00, 1.13859367e+00,\n",
       "       2.11526596e-01, 3.84076205e-01, 4.17842430e-02, 1.54642374e-02,\n",
       "       4.68258471e-02, 2.25649945e-02, 2.47660882e-02, 2.53877192e-02,\n",
       "       1.90715328e-02, 4.69842540e-01, 2.07704412e+00, 1.69342002e+00,\n",
       "       6.32370739e-01, 2.05294093e+00, 3.83583174e+00, 2.59944242e+00,\n",
       "       6.45192609e-01, 2.54387479e-01, 1.08844550e-03, 6.76387453e-04,\n",
       "       7.01773065e-04, 3.60536729e-03, 7.13966435e-01, 2.10330433e+00,\n",
       "                  nan, 3.13872102e-01, 2.89047467e-03, 1.16656512e+00,\n",
       "       2.40180567e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97cff910-779d-49d7-8014-10e022642788' class='xr-section-summary-in' type='checkbox'  ><label for='section-97cff910-779d-49d7-8014-10e022642788' class='xr-section-summary' >Indexes: <span>(1)</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>LMEID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7424d4f1-9701-440e-bd36-7341b710a98a' class='xr-index-data-in' type='checkbox'/><label for='index-7424d4f1-9701-440e-bd36-7341b710a98a' 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-601e0aac-eb5d-4e96-991c-655736a4c37f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-601e0aac-eb5d-4e96-991c-655736a4c37f' 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:  (LMEID: 65)\n",
       "Coordinates:\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  (LMEID) float64 0.00169 0.007716 0.103 ... 0.000136 0.000745 0.0\n",
       "    PwCatch  (LMEID) float64 0.08869 0.3235 0.1598 ... 0.0573 1.017e-06\n",
       "    DwCatch  (LMEID) float64 1.415 0.3888 0.2114 ... 0.00289 1.167 0.0002402"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_catch_year_proc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "33556cf8-17c0-43e9-9f7d-924dba6799f3",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\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",
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       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
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       "  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",
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       "  width: 1em;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (LMEID: 65)\n",
       "Coordinates:\n",
       "  * LMEID    (LMEID) int64 27 34 32 51 26 35 40 31 ... 22 28 57 17 52 49 58 21\n",
       "Data variables:\n",
       "    DwCatch  (LMEID) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    FwCatch  (LMEID) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    PwCatch  (LMEID) float64 dask.array&lt;chunksize=(1,), 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-ad6e5193-7566-45c2-be93-3eb4e58cc1f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad6e5193-7566-45c2-be93-3eb4e58cc1f6' 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'>LMEID</span>: 65</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-06725e9c-4c8f-46f3-a422-d540c7770676' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06725e9c-4c8f-46f3-a422-d540c7770676' class='xr-section-summary' >Coordinates: <span>(1)</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'>LMEID</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>27 34 32 51 26 ... 17 52 49 58 21</div><input id='attrs-ac03077f-9bd4-44e6-9634-eaf3ff9d4861' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac03077f-9bd4-44e6-9634-eaf3ff9d4861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4974ec8-16da-4a9e-9616-4e584dc0af9d' class='xr-var-data-in' type='checkbox'><label for='data-d4974ec8-16da-4a9e-9616-4e584dc0af9d' 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([27, 34, 32, 51, 26, 35, 40, 31, 61, 38, 62,  6, 24, 37, 10,  9, 25,  7,\n",
       "       56, 16, 47,  1, 30, 14, 59, 33, 45, 43, 50, 19, 55, 15, 48, 46, 23, 20,\n",
       "       63, 60,  2, 54, 44,  3, 13, 39, 18, 29, 36, 12, 11,  5, 42, 66,  8, 65,\n",
       "        4, 53, 41, 22, 28, 57, 17, 52, 49, 58, 21])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-852ade7d-1b2f-49a0-a09f-c963116ff71c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-852ade7d-1b2f-49a0-a09f-c963116ff71c' 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>DwCatch</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-2dbcfedc-216b-44aa-b5fe-120542eebfc0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2dbcfedc-216b-44aa-b5fe-120542eebfc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71694368-343c-4cc8-b553-e9af729df7e5' class='xr-var-data-in' type='checkbox'><label for='data-71694368-343c-4cc8-b553-e9af729df7e5' 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> 520 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65,) </td>\n",
       "                        <td> (1,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 65 chunks in 200 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"27\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FwCatch</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-996a03a3-261f-4a6f-a23f-fa5921573025' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-996a03a3-261f-4a6f-a23f-fa5921573025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6521e217-b64b-40cf-9e33-bb4b356a57bb' class='xr-var-data-in' type='checkbox'><label for='data-6521e217-b64b-40cf-9e33-bb4b356a57bb' 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> 520 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65,) </td>\n",
       "                        <td> (1,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 65 chunks in 200 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"27\" />\n",
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       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"27\" />\n",
       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"27\" />\n",
       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"27\" />\n",
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       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"27\" />\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PwCatch</span></div><div class='xr-var-dims'>(LMEID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-04b2e430-a45a-4cfb-a021-36c78bc69c2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04b2e430-a45a-4cfb-a021-36c78bc69c2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc719604-a4e9-4bfd-8a49-b06c4b1a2549' class='xr-var-data-in' type='checkbox'><label for='data-dc719604-a4e9-4bfd-8a49-b06c4b1a2549' 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> 520 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65,) </td>\n",
       "                        <td> (1,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 65 chunks in 200 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"27\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"27\" />\n",
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       "  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"27\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"27\" />\n",
       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"27\" />\n",
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       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"27\" />\n",
       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"27\" />\n",
       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"27\" />\n",
       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"27\" />\n",
       "  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"27\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"27\" />\n",
       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"27\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.592633883233734 0.0,27.592633883233734\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"47.592634\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >65</text>\n",
       "  <text x=\"140.000000\" y=\"13.796317\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.796317)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6284e7e9-89b3-4632-9288-2c352355615e' class='xr-section-summary-in' type='checkbox'  ><label for='section-6284e7e9-89b3-4632-9288-2c352355615e' class='xr-section-summary' >Indexes: <span>(1)</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>LMEID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6163539a-6f9b-4914-9e28-650e0c597f27' class='xr-index-data-in' type='checkbox'/><label for='index-6163539a-6f9b-4914-9e28-650e0c597f27' 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([27, 34, 32, 51, 26, 35, 40, 31, 61, 38, 62,  6, 24, 37, 10,  9, 25,\n",
       "             7, 56, 16, 47,  1, 30, 14, 59, 33, 45, 43, 50, 19, 55, 15, 48, 46,\n",
       "            23, 20, 63, 60,  2, 54, 44,  3, 13, 39, 18, 29, 36, 12, 11,  5, 42,\n",
       "            66,  8, 65,  4, 53, 41, 22, 28, 57, 17, 52, 49, 58, 21],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;LMEID&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31e873fe-cadf-4ec9-af53-1b2ced7cae46' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31e873fe-cadf-4ec9-af53-1b2ced7cae46' 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:  (LMEID: 65)\n",
       "Coordinates:\n",
       "  * LMEID    (LMEID) int64 27 34 32 51 26 35 40 31 ... 22 28 57 17 52 49 58 21\n",
       "Data variables:\n",
       "    DwCatch  (LMEID) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    FwCatch  (LMEID) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    PwCatch  (LMEID) float64 dask.array<chunksize=(1,), meta=np.ndarray>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeled_catch_year_proc = modeled_catch.sel(Year=np.arange(1980, 2011)).mean(\"Year\")\n",
    "\n",
    "modeled_catch_year_proc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7061dfcd-0a44-48d5-b584-0e9a5f431d9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lme_range = keep_lmes\n",
    "year_range = np.arange(1980, 2011)\n",
    "\n",
    "fig = plt.figure(figsize=(8,10))\n",
    "\n",
    "gs = fig.add_gridspec(4, 2)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "\n",
    "ax1.scatter(np.log10(true_catch_year_proc.FwCatch.sel(LMEID=lme_range)), \n",
    "            np.log10(modeled_catch_year_proc.FwCatch.sel(LMEID=lme_range)), color=\"orange\", s=6)\n",
    "ax1.axline((0,0), slope=1, color=\"k\", linestyle=\"--\", lw=0.5)\n",
    "ax1.set_title(\"a) Forage Fishes\", loc='left')\n",
    "ax1.set_xlim(-4,1.5)\n",
    "ax1.set_ylim(-4,1.5)\n",
    "\n",
    "slope, intercept, r, p, se = scipy.stats.linregress(np.log10(true_catch_year_proc.FwCatch.sel(LMEID=lme_range)),\n",
    "                                        np.log10(modeled_catch_year_proc.FwCatch.sel(LMEID=lme_range)))\n",
    "\n",
    "ax1.text(.05, .95, \"r = {:.2f}\\np = {:.2e}\".format(round(r, 2),round(p,3)), ha='left', va='top', transform=ax1.transAxes)\n",
    "\n",
    "\n",
    "###########\n",
    "\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "\n",
    "ax2.scatter(np.log10(true_catch_year_proc.PwCatch.sel(LMEID=lme_range)), \n",
    "            np.log10(modeled_catch_year_proc.PwCatch.sel(LMEID=lme_range)), color=\"blue\", s=6)\n",
    "ax2.axline((0,0), slope=1, color=\"k\", linestyle=\"--\", lw=0.5)\n",
    "ax2.set_title(\"b) Large Pelagics\", loc='left')\n",
    "ax2.set_xlim(-4,1.5)\n",
    "ax2.set_ylim(-4,1.5)\n",
    "\n",
    "slope, intercept, r, p, se = scipy.stats.linregress(np.log10(true_catch_year_proc.PwCatch.sel(LMEID=lme_range)),\n",
    "                                        np.log10(modeled_catch_year_proc.PwCatch.sel(LMEID=lme_range)))\n",
    "\n",
    "ax2.text(.05, .95, \"r = {:.2f}\\np = {:.2e}\".format(round(r, 2),round(p,4)), ha='left', va='top', transform=ax2.transAxes)\n",
    "\n",
    "###########\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(gs[1, 0])\n",
    "\n",
    "ax3.scatter(np.log10(true_catch_year_proc.DwCatch.sel(LMEID=lme_range)), \n",
    "            np.log10(modeled_catch_year_proc.DwCatch.sel(LMEID=lme_range)), color=\"green\", s=6)\n",
    "ax3.axline((0,0), slope=1, color=\"k\", linestyle=\"--\", lw=0.5)\n",
    "ax3.set_title(\"c) Demersals\", loc='left')\n",
    "ax3.set_xlim(-2,1.5)\n",
    "ax3.set_ylim(-2,1.5)\n",
    "\n",
    "slope, intercept, r, p, se = scipy.stats.linregress(np.log10(true_catch_year_proc.DwCatch.sel(LMEID=lme_range)),\n",
    "                                        np.log10(modeled_catch_year_proc.DwCatch.sel(LMEID=lme_range)))\n",
    "\n",
    "ax3.text(.05, .95, \"r = {:.2f}\\np = {:.2e}\".format(round(r, 2),round(p,9)), ha='left', va='top', transform=ax3.transAxes)\n",
    "\n",
    "###########\n",
    "\n",
    "ax4 = fig.add_subplot(gs[1, 1])\n",
    "\n",
    "ax4.scatter(np.log10(true_catch_year_proc.FwCatch.sel(LMEID=lme_range) + \n",
    "                true_catch_year_proc.PwCatch.sel(LMEID=lme_range) + \n",
    "                true_catch_year_proc.DwCatch.sel(LMEID=lme_range)), \n",
    "                np.log10(modeled_catch_year_proc.FwCatch.sel(LMEID=lme_range) +\n",
    "               modeled_catch_year_proc.PwCatch.sel(LMEID=lme_range) + \n",
    "               modeled_catch_year_proc.DwCatch.sel(LMEID=lme_range)), color=\"black\", s=6)\n",
    "ax4.axline((0,0), slope=1, color=\"k\", linestyle=\"--\", lw=0.5, label=\"Reference 1:1\")\n",
    "ax4.set_title(\"d) All fishes\", loc='left')\n",
    "ax4.set_xlim(-2,1.5)\n",
    "ax4.set_ylim(-2,1.5)\n",
    "\n",
    "slope, intercept, r, p, se = scipy.stats.linregress(np.log10(true_catch_year_proc.FwCatch.sel(LMEID=lme_range) + \n",
    "                true_catch_year_proc.PwCatch.sel(LMEID=lme_range) + \n",
    "                true_catch_year_proc.DwCatch.sel(LMEID=lme_range)),\n",
    "                np.log10(modeled_catch_year_proc.FwCatch.sel(LMEID=lme_range) +\n",
    "                modeled_catch_year_proc.PwCatch.sel(LMEID=lme_range) + \n",
    "                modeled_catch_year_proc.DwCatch.sel(LMEID=lme_range)))\n",
    "\n",
    "ax4.text(.05, .95, \"r = {:.2f}\\np = {:.2e}\".format(round(r, 2),round(p,5)), ha='left', va='top', transform=ax4.transAxes)\n",
    "\n",
    "\n",
    "for i in [ax1, ax2, ax3, ax4]:\n",
    "    i.set_xlabel(\"SAU log$_{10}$(MT WW km$^{-2}$ y$^{-1}$)\")\n",
    "    i.set_ylabel(\"FEISTY log$_{10}$(MT WW km$^{-2}$ y$^{-1}$)\")\n",
    "\n",
    "        \n",
    "############################\n",
    "\n",
    "bottom_ax = fig.add_subplot(gs[2, :])\n",
    "        \n",
    "true_catch_ts = (true_catch.FwCatch.sel(Year=year_range).sel(LMEID=3) + \n",
    "                true_catch.PwCatch.sel(Year=year_range).sel(LMEID=3) + \n",
    "                true_catch.DwCatch.sel(Year=year_range).sel(LMEID=3))\n",
    "\n",
    "modeled_catch_ts = (modeled_catch.FwCatch.sel(Year=year_range).sel(LMEID=3) +\n",
    "               modeled_catch.PwCatch.sel(Year=year_range).sel(LMEID=3) + \n",
    "               modeled_catch.DwCatch.sel(Year=year_range).sel(LMEID=3))\n",
    "\n",
    "bottom_ax2 = bottom_ax.twinx()\n",
    "\n",
    "l1 = bottom_ax.plot(year_range, modeled_catch_ts, label=\"FEISTY\", color=\"black\")\n",
    "l2 = bottom_ax2.plot(year_range, true_catch_ts, \"--\", label=\"SAU\", color=\"dimgrey\")\n",
    "\n",
    "bottom_ax2.set_title(\"e) All fishes - California Current\", loc=\"left\")\n",
    "\n",
    "\n",
    "bottom_ax.set_xlabel(\"Year\")\n",
    "bottom_ax.set_ylabel(\"FEISTY (MT WW km$^{-2}$ y$^{-1}$)\")\n",
    "bottom_ax2.set_ylabel(\"SAU (MT WW km$^{-2}$ y$^{-1}$)\", labelpad=15, rotation=-90)\n",
    "\n",
    "bottom_ax2.set_ylim(0.18,0.5)\n",
    "\n",
    "\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "bottom_ax.xaxis.set_minor_locator(MultipleLocator(1))\n",
    "        \n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "bottom_ax2.legend( handles=l1+l2, loc=\"lower right\")\n",
    "\n",
    "slope, intercept, r, p, se = scipy.stats.linregress(true_catch_ts, modeled_catch_ts)\n",
    "\n",
    "bottom_ax.text(.01, .23, \"r = {:.2f}\\np = {:.2e}\".format(round(r, 2),round(p,6)), ha='left', va='top', transform=bottom_ax.transAxes)\n",
    "\n",
    "\n",
    "##############\n",
    "\n",
    "        \n",
    "bottom_ax3 = fig.add_subplot(gs[3, :])\n",
    "        \n",
    "true_catch_ts = (true_catch.FwCatch.sel(Year=year_range).sel(LMEID=6) + \n",
    "                true_catch.PwCatch.sel(Year=year_range).sel(LMEID=6) + \n",
    "                true_catch.DwCatch.sel(Year=year_range).sel(LMEID=6))\n",
    "\n",
    "modeled_catch_ts = (modeled_catch.FwCatch.sel(Year=year_range).sel(LMEID=6) +\n",
    "               modeled_catch.PwCatch.sel(Year=year_range).sel(LMEID=6) + \n",
    "               modeled_catch.DwCatch.sel(Year=year_range).sel(LMEID=6))\n",
    "\n",
    "bottom_ax4 = bottom_ax3.twinx()\n",
    "\n",
    "l3 = bottom_ax3.plot(year_range, modeled_catch_ts, label=\"FEISTY\", color=\"black\")\n",
    "l4 = bottom_ax4.plot(year_range, true_catch_ts, \"--\", label=\"SAU\", color=\"dimgrey\")\n",
    "\n",
    "bottom_ax3.set_title(\"f) All fishes - SE U.S. Continental Shelf\", loc=\"left\")\n",
    "\n",
    "\n",
    "bottom_ax3.set_xlabel(\"Year\")\n",
    "bottom_ax3.set_ylabel(\"FEISTY (MT WW km$^{-2}$ y$^{-1}$)\")\n",
    "bottom_ax4.set_ylabel(\"SAU (MT WW km$^{-2}$ y$^{-1}$)\", labelpad=15, rotation=-90)\n",
    "\n",
    "\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "bottom_ax3.xaxis.set_minor_locator(MultipleLocator(1))\n",
    "        \n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "bottom_ax3.legend( handles=l3+l4, loc=\"upper right\")\n",
    "\n",
    "slope, intercept, r, p, se = scipy.stats.linregress(true_catch_ts, modeled_catch_ts)\n",
    "\n",
    "bottom_ax3.text(.01, .23, \"r = {:.2f}\\np = {:.2e}\".format(round(r, 2),round(p,5)), ha='left', va='top', transform=bottom_ax3.transAxes)\n",
    "\n",
    "\n",
    "\n",
    "##############\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(\"Fig5_FEISTY_validation_x01.png\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  }
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