calc_RMSE_spatial_correlations.ipynb

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    "import numpy as np\n",
    "import math\n",
    "import xarray as xr\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from glob import glob\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import metpy.calc as mpcalc\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from scipy import stats\n",
    "import pop_tools \n",
    "import matplotlib.path as mpath\n",
    "import dask\n",
    "import esmlab\n",
    "import utils\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.colors as colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First do nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/glade/campaign/cgd/oce/projects/FOSI_BGC/HR/g.e22.TL319_t13.G1850ECOIAF_JRA_HR.4p2z.001/ocn/proc/tseries/month_1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = 'g.e22.TL319_t13.G1850ECOIAF_JRA_HR.4p2z.001'\n",
    "path = '/glade/campaign/cgd/oce/projects/FOSI_BGC/HR/g.e22.TL319_t13.G1850ECOIAF_JRA_HR.4p2z.001/ocn/proc/tseries/month_1'\n",
    "variables = ['NO3','SiO3','PO4']\n",
    "coords = {'x':'TLONG','y':'TLAT'}\n",
    "keep_vars = variables + list(coords.values())+['dz','KMT']\n",
    "path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get monthly data and immediately convert it to annual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(ds):\n",
    "    ds=ds.isel(z_t=0)\n",
    "    #ds=ds.isel(z_t_150m=0)\n",
    "    return ds"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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     "text": [
      "1997\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "1998\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "1999\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2000\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2001\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2002\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2003\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2004\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2005\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2006\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2007\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2008\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2009\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2010\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2011\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2012\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2013\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2014\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2015\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2016\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2017\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "CPU times: user 9min 40s, sys: 1min 3s, total: 10min 43s\n",
      "Wall time: 12min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "ds_ann = xr.Dataset()\n",
    "\n",
    "#for year in np.arange(1997,2006,1):\n",
    "for year in np.arange(1997,2018,1):\n",
    "    yr4=\"{:04d}\".format(year)\n",
    "    ds = xr.Dataset()\n",
    "    print(year)\n",
    "    for v in variables:  \n",
    "        print(v)\n",
    "        files = sorted(glob(f'{path}/{case}.pop.h.{v}.{yr4}01-{yr4}12.nc'))\n",
    "        \n",
    "        dsv=xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\",\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True,preprocess=preprocess)\n",
    "\n",
    "        ds = xr.merge((ds, dsv))\n",
    "\n",
    "    ds = ds.drop([v for v in ds.variables if v not in keep_vars]).squeeze()\n",
    "    ds = ds.mean(dim='time')\n",
    "    \n",
    "    ## add it to the annual file\n",
    "    ds_ann = xr.concat([ds_ann, ds], \"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (nlat: 2400, nlon: 3600, time: 21)\n",
       "Coordinates:\n",
       "    TLAT     (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    TLONG    (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Dimensions without coordinates: nlat, nlon, time\n",
       "Data variables:\n",
       "    NO3      (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    PO4      (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    dz       (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    SiO3     (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    KMT      (time, nlat, nlon) float64 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-799cf46c-6e4a-4f4f-bbbb-d736ec135c0d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-799cf46c-6e4a-4f4f-bbbb-d736ec135c0d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nlat</span>: 2400</li><li><span>nlon</span>: 3600</li><li><span>time</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cf325515-90e7-4684-88fc-dc8e2605dc72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf325515-90e7-4684-88fc-dc8e2605dc72' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-6535dd81-c16a-4e18-98e4-7be844ebb3da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6535dd81-c16a-4e18-98e4-7be844ebb3da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-496693f0-8e2d-4429-9eee-53683d6d52ff' class='xr-var-data-in' type='checkbox'><label for='data-496693f0-8e2d-4429-9eee-53683d6d52ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-2b6bd6bc-312b-498e-9d49-32dccdc7fb5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b6bd6bc-312b-498e-9d49-32dccdc7fb5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c861d96-a09f-437d-b994-9a0621519ac3' class='xr-var-data-in' type='checkbox'><label for='data-9c861d96-a09f-437d-b994-9a0621519ac3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7979d1b-5b40-4a28-8b38-0ceee414af93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7979d1b-5b40-4a28-8b38-0ceee414af93' 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>NO3</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-225e14f1-f8c1-45ff-86f7-f2980d7ac683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-225e14f1-f8c1-45ff-86f7-f2980d7ac683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb40fd01-409d-4220-a89c-40ac5a62fcfc' class='xr-var-data-in' type='checkbox'><label for='data-cb40fd01-409d-4220-a89c-40ac5a62fcfc' 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>Dissolved Inorganic Nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 356 Tasks </td><td> 21 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-5a4ce517-8fd2-4f7d-b757-ea021d7598ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a4ce517-8fd2-4f7d-b757-ea021d7598ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aadbdcf9-4205-4bb2-8d48-c42861ca8bb3' class='xr-var-data-in' type='checkbox'><label for='data-aadbdcf9-4205-4bb2-8d48-c42861ca8bb3' 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>Dissolved Inorganic Phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></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>dz</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-2b31396f-f096-4b62-ac2c-5dfb11a32bfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b31396f-f096-4b62-ac2c-5dfb11a32bfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9e7788c-3ff3-4bcf-bd4d-891a46ee19dc' class='xr-var-data-in' type='checkbox'><label for='data-e9e7788c-3ff3-4bcf-bd4d-891a46ee19dc' 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>thickness of layer k</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    PO4      (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    dz       (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    SiO3     (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    KMT      (time, nlat, nlon) float64 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_ann"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### spin up some dask to make the processing faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ClusterClient():\n",
    "    import dask\n",
    "    from dask_jobqueue import PBSCluster\n",
    "    from dask.distributed import Client\n",
    "    cluster = PBSCluster(\n",
    "        cores=1,\n",
    "        memory='25GB',\n",
    "        processes=1,\n",
    "        queue='casper',\n",
    "        resource_spec='select=1:ncpus=1:mem=25GB',\n",
    "        project='NCGD0011',\n",
    "        walltime='03:00:00',\n",
    "        interface='ext',)\n",
    "\n",
    "    dask.config.set({\n",
    "        'distributed.dashboard.link':\n",
    "        'https://jupyterhub.hpc.ucar.edu/stable/user/{USER}/proxy/{port}/status'\n",
    "    })\n",
    "    client = Client(cluster)\n",
    "    return cluster, client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster, client = get_ClusterClient()\n",
    "cluster.scale(20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://128.117.208.86:35287</li>\n",
       "  <li><b>Dashboard: </b><a href='https://jupyterhub.hpc.ucar.edu/stable/user/kristenk/proxy/43039/status' target='_blank'>https://jupyterhub.hpc.ucar.edu/stable/user/kristenk/proxy/43039/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>0</li>\n",
       "  <li><b>Cores: </b>0</li>\n",
       "  <li><b>Memory: </b>0 B</li>\n",
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       "</td>\n",
       "</tr>\n",
       "</table>"
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      "text/plain": [
       "<Client: 'tcp://128.117.208.86:35287' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid = pop_tools.get_grid('POP_tx0.1v3')\n",
    "\n",
    "area = ds_grid.TAREA\n",
    "lats = ds_grid.TLAT\n",
    "lons = ds_grid.TLONG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_global = xr.Dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a mean over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds_ann.mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds = ds.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "    TLAT     (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    TLONG    (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    NO3      (nlat, nlon) float32 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    PO4      (nlat, nlon) float32 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    dz       float32 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "    SiO3     (nlat, nlon) float32 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    KMT      (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c2a86a69-74e6-46ce-9dd4-aa4aafadddf6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c2a86a69-74e6-46ce-9dd4-aa4aafadddf6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nlat</span>: 2400</li><li><span>nlon</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39ca25b5-21d5-4d92-85ed-7f51e9c2c26b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39ca25b5-21d5-4d92-85ed-7f51e9c2c26b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-2cf0e880-2d42-4743-b3d4-7e9f0f85b1c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cf0e880-2d42-4743-b3d4-7e9f0f85b1c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b53f4f0-1093-463c-97bf-4e4ce7b985d3' class='xr-var-data-in' type='checkbox'><label for='data-8b53f4f0-1093-463c-97bf-4e4ce7b985d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-265d2e27-6832-4a85-99a2-c3eb29a19daf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-265d2e27-6832-4a85-99a2-c3eb29a19daf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c77ba752-09e3-4489-aae9-84f9f896513f' class='xr-var-data-in' type='checkbox'><label for='data-c77ba752-09e3-4489-aae9-84f9f896513f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-177458be-e1a4-4405-81a2-0c1174d8dca6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-177458be-e1a4-4405-81a2-0c1174d8dca6' 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>NO3</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-1526e570-1a5f-4099-afab-df70807f42b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1526e570-1a5f-4099-afab-df70807f42b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de188fab-64e6-4d49-98ca-405fe50ec465' class='xr-var-data-in' type='checkbox'><label for='data-de188fab-64e6-4d49-98ca-405fe50ec465' 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>Dissolved Inorganic Nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 32.96 MiB </td> <td> 32.96 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 386 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <text x=\"140.000000\" y=\"40.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,40.000000)\">2400</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-ce7ae49d-8f5b-4c3a-a80a-a47304711170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce7ae49d-8f5b-4c3a-a80a-a47304711170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e06206ad-bf8c-4072-a953-b2464799af62' class='xr-var-data-in' type='checkbox'><label for='data-e06206ad-bf8c-4072-a953-b2464799af62' 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>Dissolved Inorganic Phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 32.96 MiB </td> <td> 32.96 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 386 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\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>dz</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-2595c9d0-d802-46da-8e43-b9622f4b892a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2595c9d0-d802-46da-8e43-b9622f4b892a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-164f6e0a-1ec1-451b-80c8-b52415a384a2' class='xr-var-data-in' type='checkbox'><label for='data-164f6e0a-1ec1-451b-80c8-b52415a384a2' 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>thickness of layer k</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4 B </td> <td> 4.0 B </td></tr>\n",
       "    <tr><th> Shape </th><td> () </td> <td> () </td></tr>\n",
       "    <tr><th> Count </th><td> 264 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-36581774-4da7-4d3c-8c21-04e82340801f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36581774-4da7-4d3c-8c21-04e82340801f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b987cd96-0841-4136-827d-51d1554852a3' class='xr-var-data-in' type='checkbox'><label for='data-b987cd96-0841-4136-827d-51d1554852a3' 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>Dissolved Inorganic Silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 32.96 MiB </td> <td> 32.96 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 386 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"80\" x2=\"120\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,80.0 0.0,80.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"40.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,40.000000)\">2400</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-3a83b317-7722-4671-8fc6-2187f6004778' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a83b317-7722-4671-8fc6-2187f6004778' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f3763fd-2989-4fec-8b87-e3e974b51f49' class='xr-var-data-in' type='checkbox'><label for='data-1f3763fd-2989-4fec-8b87-e3e974b51f49' 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'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 65.92 MiB </td> <td> 65.92 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 388 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-42d22624-ea57-4771-9cec-586140ea6ed4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-42d22624-ea57-4771-9cec-586140ea6ed4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (nlat: 2400, nlon: 3600)\n",
       "Coordinates:\n",
       "    TLAT     (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    TLONG    (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    NO3      (nlat, nlon) float32 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    PO4      (nlat, nlon) float32 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    dz       float32 dask.array<chunksize=(), meta=np.ndarray>\n",
       "    SiO3     (nlat, nlon) float32 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    KMT      (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get WOA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = '/glade/work/kristenk/WOA_data/regrid_POP/WOA2018_x0.1POPgrid.nc' \n",
    "ds_woa = xr.load_dataset(file, decode_times=False, decode_coords=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_woa = ds_woa.isel(z_t=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds_woa['z_t'] = ds.z_t\n",
    "\n",
    "NO3_diff = ds.NO3 - ds_woa.NO3\n",
    "PO4_diff = ds.PO4 - ds_woa.PO4\n",
    "SiO3_diff = ds.SiO3 - ds_woa.SiO3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate RMSE for surface nuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_global['NO3']=esmlab.statistics.weighted_rmsd(ds.NO3, ds_woa.NO3, dim=['nlat', 'nlon'], weights=area)\n",
    "rmse_global['PO4']=esmlab.statistics.weighted_rmsd(ds.PO4, ds_woa.PO4, dim=['nlat', 'nlon'], weights=area)\n",
    "rmse_global['SiO3']=esmlab.statistics.weighted_rmsd(ds.SiO3, ds_woa.SiO3, dim=['nlat', 'nlon'], weights=area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(2.34632579)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['NO3'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.18605326)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['PO4'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(6.31096642)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['SiO3'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function esmlab.statistics.weighted_corr(x, y, dim=None, weights=None, return_p=True)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esmlab.statistics.weighted_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate spatial correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "no3_spat_corr=esmlab.statistics.weighted_corr(ds.NO3, ds_woa.NO3, dim=['nlat', 'nlon'], weights=area)\n",
    "po4_spat_corr=esmlab.statistics.weighted_corr(ds.PO4, ds_woa.PO4, dim=['nlat', 'nlon'], weights=area)\n",
    "sio3_spat_corr=esmlab.statistics.weighted_corr(ds.SiO3, ds_woa.SiO3, dim=['nlat', 'nlon'], weights=area)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.9644565)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no3_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no3_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.95323229)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "po4_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "po4_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.91999681)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sio3_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sio3_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do COPEPOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_copepod = xr.open_zarr('/glade/u/home/kristenk/Nuts2Fish/preprocessing/regrid/data/POP_tx0.1v3/copepod-2012__cmass-m00-qtr.zarr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14b6bc130210>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_copepod.copepod_C.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_chl_obs = xr.open_dataset('/glade/work/kristenk/satellite_data/globcolour_chl/POP_grid_x01_mean/POP_tx0.1v3/mean_glob_colour_chl.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14b6bb391390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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su56HlbXbTMAEWMhMZxV5ZGsN+v/wvqwgxxdmhtEXmAnBAAAMgjdjhpjZupsJA2vCuRRoh7btFOZNSX1taxz9QNunzTUAn+sI0olpmxporiyji5RJyr8h9ttjluYv5bcAECCIs+kz4LqSo6lQADqwh+boAg7JMGSbhK9Q8BckFp06AJ/CRHFfgWSQhBYMgO9jCPsZehUKpg2dR1OZk+Ngxri1AEAwZdqmBblwGDJYc/PHwDikda6C9ZJCsMlZ6BfGjKBfbs9abQKGQru28wDq51yL5aM3Sq1ACD88lchfjWrHcyjUFUCEEE6bNurnvVZer2XVIcjRE+rPeYv/m9iLD9cFlUuKnK/oa4bKL0GVOfkb6Ix4HbIch4SEtLAm0QlkX79YkER6c2W5zRBm0eeQC4chQUb7SB6l2o6rY48xUUFAUDDYfz2v6wS5CIy2IKRgcMh3Vqo+yuOETHwD/EmjWAgUm2FdJY5F1Jyk6x8D/iSkfQ0ZtKgcyfjZ3bIgUv25b5c7ikX5fLU2VyioREbFYeU4/mcF3tiUZI+eB6dSAbckgaLRDrRzW9OnWOiYv6CgtQ4jEFR7gb4VizMVpSRB8NjJtHVsiegCIvosEX2LiO4noter/b9NRI8Q0X1q+/lh3tHs6XYTAJ/KgLNlfYY53sMvZwb+nEzXMBTQDJRcECmmVc8L8CfVtr/ahL5SsShrCEBGI7Hdr7AvQSfoaT4m1wFAQMEFKhWg0ZATQ46usOcl7zEvKhddkBbGmrpb0atDmYzMX8t0qIMFeGMTtGURcBzwyVMQJ0/BKZUAKIK/YkER/EUFRJpjOszoa/53HROBZ/xcrjPV1f/iwADE4NbabQC/wcxfJaItAL5CRIfUdx9m5pFQGs+a+J4MkIPlU58MCAa9+rfZSwGYVbsdLhgRFl3Gpsf1B9ocpGPdVTEZU5UNMLZotk0/etIJ9dlEVHnCp5S2Sfr0cZUKsGXRp+ju917OQBz80jtx8EvvxO6f+h2IeZUfUC4BpZJkcC0VpdDV2e2AFMg6OVFY2kVlTv6mrRacrYtwSiUV4VT0cyUQnOzDjmg7Gc72T2gY7UNrqKWinwujfFG15//WKB7dyMBMaLKbaevcFj/GzF9Vn08D+BaA84Z8CxHkb+oQENYW7PwBTVIXd2z45RsYbGGjV5SA70wOF+aBInWzTU6sImM85WjWzk7tRFfHysplli282fQFguMAjeD958iOQ3/3m2jPF8EFB2JrBWJhDjxXlEKiUAAcVwkFtU8lmWlTIDwhq/NpzaPVVtxYBZkoaQo4cewiRQuIOIEQoMMIL250BUAhsHT4o1h64INY/tp7h/WYxgYByrQB2ElE91rbgaQ2iehCAC8E8Pdq168R0deJ6BYi2j7M+8nNSkNGdWEfqFQCqXrMcQ7mWBVdw3U7Mramobbj6qA5CfBrKqg8BmhXQCjslNttEyLpd87XCoyJSWkXkiXU8Y/3PLBDIMGovuhdWHn4Iz3fRw6Jv7nnrXjFy38P7BCKxzbA5aKKQiJwwQGdXgdvrZjJt37ea+WJgjX9khQArbZPrCgYKBQiCxfbvGmEgusGTZTmUMusZFhWJY2KM1+ZqqI9vUA6pDOvtY8y8yWdDiKiRQB/DuANzHyKiG4A8F51ufcC+EMAPdImdEauOQwYkclfMMTqqhQMlb3dx/z3mSOwfOym4EpOs6hq0jwvxYdAJLmXhCUEVBsGehUaPsbGyVMgj/HSV85+HYdRoLWlAG/ehVD8VGK+BC4XISpl8MJ8YFW+9MjHQJWKTIDTwQSttk+n4XnKHxHMfA7TaNvFoALmJhWFZDKfVcY9lUqgUgkrq7fOvGCQGJxDGgCIqAgpGO5g5v8GAMx8hJk9lquymwC8eGi3g1xzGBh8hlUvUKuBVc2H6twVanUWDeWkpALx5MDZsth/5/SKjgXIleaGwPXsaCNzTlBQsLCYY4Vkr9dMr5pPqr7rmsRs8T0/8V6UTuRhrIPAFz7j13742d3vh3AJhXUX5DF4voTqC98FZ7OJpW/9HuoXXo+lhz+C+vmvk9nwG5t+noMOs9aZ8I7Kk7DDr+OCJRDSbhWFCi0uSGFTLGLpDNMSB+mQJiIC8CcAvsXMH7L2n8vMj6l/fwnANwdywQTkwqFHaA1hZe22ICWGykoNV77SNR1iS4GGX0D7q80BFI7Xqz0VSkiuK01GMZpC2LRkaLpt0j3XDVCAaz6pRBoRAOw6+OvPv73/e8kRwN8ceitevucD8OYKKKzLgABqt6Vg+KG3AqUS6hdeb6KYqFgwPFtsmww9T5qO2u0AiV4kAsne5/h5LHAcLD3ysRHe+eTBG1yC208B2AvgG0R0n9r3dgC/QkQvgJRFDwH4T4O6YBxy4dAFqov7/YlSOeyqlb3xETgsZFKSNWCqC/tik45iHdBKTR8IKZle7amJ3ggGh8CbMQ5iw/SphIWd9QzE1obohJxsb3gQRUJxVQoGLjggvZ4wIa4uUATQDmmngo1fgQUbf4LteDYICQRTKlYlU1JlbhS3OrFgEFo8mDHOzH8LxGbL/dVALpAR+RubAbWtV5pYfwk/kiOSx8BCJR6pl0lV8TIhrKHVeliTMC+ksgNriu1++h4QSHatasE+YyoQNCvZ0OGsPSYu7f7p96G9M6/fMAz8X7/4QbgCcrJuCZkD4TioPe8dIMOfBVm8WIW1UrkEbjR9ihPhgFz4GiZbJkS9LxKdpNouSk30TNcaunRITwVy4dABtW1XScGgV1eh1VSYZXVl8w5Zfa1Y8CN9XBe8vg5aXIjwJbHnRXlrdLy56/acbF+t7PXNVXqFZzQBRcjmeVIwxAmFJEd4F3kK9R9+G0AEt+hiLknw5OgL7BI+t/QmGcFUZKAlsGI5pOvPeqP8zUolE7qq6VNIETyCbf+TZeJ0HZBb9ClgdA6E9pEpLq00c+KZAgYN0qw0EciFQwrqu66RDmQlGIAwt4wwAgKAz6nPwnfgui642UysI20Lm2DikdOXSWll43bUdlwN1rxK9uSsi8ToLcyoGgelPXRFBigYXHYh5ktoL+SZ0cPA6jPkCv+ez74t9nudfFi/+M1S89OkicwyW9p1ZZlRQ55oJTCWStJctL4hw1wt3xM53RcOmnUMMEN6IjBbdzNgiLXOvPeBlVa7LaOUlG1fFzJJMwutbN4RqbFr7Lr9QkVKAQjWZPA8cKsVb06iAV0bwNK33w8xX8LBL/4WPnvwLQNpM0cQ933s+mwHFgsyq1pXXysVJd+RzaTrkGHh1cWElh75mBWmrBZISuMkyqcPDWYMNJR1EjC0nqaQR+0gokNE9F31d7t1ztuI6AEi+jYRVa39LyKib6jvPqpCvSYD5GBl43ZpWrK0CB3KmqkJu+aDVU2ttvNAImlfJ1QX9snKcRbfP5VLki5Zk6KFfQh2lJLjRL7vRZM5+KV34uXVD+Q5DmNGpG6C68qa1FaNaWd+HlQu+eHTnkBt+6v9sGfH1xp0cafcpCQhHdJupm1aMEyzUhJ51H8EcA8zv5+I3grgrQDeQkTPBXAZgOcBeAaAvyai5zCzB+AGAAcAfAnSY18DsDTEvkuHXqkEbjT8DOIERJzKrispM5hNQZXIOZajeWXj9oCPgAqSUK2veg5aG7FWd2xTV+iMZhvhwjz6s8ru7hWfXcm1hknA0gNBAV1/9puAhQqo0ZSahSZFLLimYqA4eSpgeiRNFV4shII0cuQO6YxQyRqPqc+niUiTR70SwMvUYbcC+ByAt6j9dzFzA8CDRPQAgBcT0UMAtjLzFwGAiG4D8IsYsnBYvv99qJ97nRQOgRuLUgcACJqCdE3flgwZrW27Klr/OY5plWQcuqTJ7s+BG6ADB3y/gl3wJ3DtGL+DFdIaew85pha1F7zTD5rQ45YcnyDRQv3c62S+jR43ul5EsWg0217Cm2cJjOkq5JMFI3FIh8ijduksP2Z+jIierg47D1Iz0Dis9rXU5/D+4UM5laVPwPP9C3ECgsiU1lw+fjNq219topViC/4oFb227apgDQVlYur3Zasu7g/4HHTMe6xgsO5Bg4WImp0SsOcl78GRf7cFX/tIRvt3jrFj+b73RPbVL/r12GOXHvtj1M9/HdBoghsNaVJaXAAA0MYmuN2WAmTnWVj+xvuG2u9JRq45dIkY8qjEQ2P2ccr+uGsdgDQ/4ZnPfGb3nQ0j6+rdmui101cLCBMuGO5rqRQMNzXhq07fWoMUDDor2gn6NJKgTAexAiGlDvTPvfR9cARywTADWHrwQ8nfHf4oAKB+zrWyHnmjCSzOAwCoVMTSY388kj5OKhiAmCJncxYMVTjEkUcBOKI5QojoXABPqP2HAVxgnX4+gEfV/vNj9kfAzDcCuBEALrnkkv4D6wsuwEWpQbAIhIMb2HkPofBTo0HEIcB06hdF6VUwGL+HTYxWKgYjlISIhiwmgSxKbiBYS9rCX3/hHdjz4ugqNMdsQpz/dKzc+9vm//q5153xgkFiukqAZsEwo5ViyaMAfAaArnu5H8BfWvsvI6IyEV0E4GIAX1YmqNNE9BLV5j7rnOFi+zbQWdtUPV7HN9FE/iasuKEifJhNhrSu7SCTz0LZpyoCpFsYwaDzJBzyyzLq0NRSUbKx6mL0YRi7c/D6JmopxRzlzefpMmcKbMFQfdG7gK1bpMlJYdaK+GQFA3m0UhdIIo96P4BPEdGvAvg+gFcBADPfT0SfAvBPkJFO16lIJQC4BsCfAqhAOqKH6ozW0OF/9fNeK2O9G0KWT7ShBYOiyYiD5qAJOHU9SzBo8jKNLgREtbLXDzEslcCtlinLaFb9SogxhMm6jsuMNgLOUGw4RuNwtm2Nvf7PvfR9uXA4A1A/59oI9bZz7DR4sQI8tSkFRGUOVHBRe8E7Y30ase2e/zpjsqrtuNpkX5PrYunJTwz8PoYFZsrNSlmRQh4FAK9IOOd9ACIeLWa+F8CPDK533WHpkY8l5hsYwVAqJsZ8m2zpYsEn6rMKopDmLXIdWac5HGIaQkAgFAsmmY2bTaMlcKsd69TWjnIjIID47GhLE0rjzimc3ETh+ABqXOeYbBBJAaBKlC498EFgY1O+4K4rndXbtoDnClj5yrszNVnbKQug1XZc7QdMqAJT7Hmon/2aqcqlmKYEtyyYrbsZIqgyJ0NNAz4GP3w1bQCTKvhOOlQViGYia3+AXdM5CQ6BymU489IhGCn9af2NQAjfzGTRcZMd0mhfnyiRcfNnfuGDQFuAmh0SQXJMNerPfTuwMC/HRbMJrK5JX8PjH8fSgx9SPEsEFJzMggGAr2lr9li7hrmK8uNmy69mN8GQ9RwylwmdCuTCISP47O3Sjq9AWjCoSbV+zrXJJ6uJWlNl60pssp2YLOWUBPDqwj5QuQxyXYiNDT80VZ1nagWn9UXXhrYzpe0t1I+kSl6f/x9vUiyg0f7+zC98EJf8anL0S47pwdI//S64XAJcx5QX5VYLtR1XB8Y9d8HaW9t5QI5TVcMcCIVPq5wcCAFuNCSB4ERjsJXgJgHT09MxY/m+98gVtF0WsVg0E2NsLoOG0DTekD4HTYGtoekt1EqeihbFdxiqdKdYXw+agwLEegIouIEXt37h9aif/Rq/zrPWNhQ5YOBc3Z7nyepeKWBXChHbEfmy+u+jVXFw75/Ex83nmEK4BGxsys/MMsHT82RluWYLEIyVf+ysNdR3XRP/BWtiSyUobMbgVhs4vTqQ2xgWZCgrZdqmBblw6AJLj38cpPhoqFSUf1WEUVouAc1XAPjcRLGJaMyyprNgVVo0Ooiqlb3JiWn28czg9Q3wxiZqO66W7KwnT8tsb70iC29CBOtAK4HViad/+Rvvg5iXhG7VH383XvHy38Pnlt6M//Op30g9L8f0oH7h9aATq8ESt/YCQ429+jPf0LGtpSM3SMdzoWBqlpiypcxSm9AssdZ+bjRQ23kA9QteL6/17Dd1uNJoMYvcSrlw6BLLR28Ebd0SYLmkQkFSISdg6bE/DjqH2VelDYQAPCuDOU4ImCLvGXIh9IvblsXkDQsrkbTrqi2WM6nLkFpvoSRzQRotFI9lYLLNMTWoX/TrsoJcsylNkMUCSJmYoMOl1Vhd+v4fmbDW+nPi+bTq51wr3wVjAlUagjYxsfDDpgWbd4KFkBQemw3pg2ikB22MAwJOpm1aMD09nSAsPfwRKRCKSijMz3dfUN1Woy2Hslk1CYHatqtMXkS1std/oQB1TLxZyZ789ZaE6uL+6E7Pky9ixgI9hZOboLYANdpob5vDS/99zsA6yaife12mVX79/NfJWg6bm/4krRzF5LoyRNqVmf3MQpqMPE/+VbXP6+dcm3wtYY15IEh/z8L/X/vRlAax9MjHZILqBEFSdlOmbVqQC4deUSwCc2Usff+PYsnKOsGesPWKXmoPnlztA4bVFUBwJZ+URW0X8AnvzwK1CvQFkDDhhkl4We0DQMsz1/3rL7wDlBd9m1jUd10jNQE1iSeZZ+rnvVaOQyGj7NhM1L7pU+8j1/XNoXqsNZvS58UsI5wA5fNqyf1tOc5N1j5rp3R47FoCQ12jtu0q8LZF2f8LJ4e2ZdZ8Dnn2Ug+oP+uNQLEILvX4+ARLsWxX1QKCq3/9ohHJ1b1mTU2j16CQ2cliVY0capmtWAjpXPc8xGoxCvULrwc2G4Hopc8tvwW1570jcNzn/8dk2YNzSNTPfg0wVwYsri+bxru+6xoZOGGTRaqSogCCiwxrPztC5tvYkXIqUAGeANR7YhYdzDJMNUGjDVZb1Nf2aWHY87By33tQ23kAtLbR07MYNCQr62yttXPh0AOWvvcHqP/QW8GFHgeDLidqI2QWMhOzFiJp4alx7cRFMoVYVwFFj6F5/HXeg3qJ2fNAhQLqZ78GS09+Ary65h9rgdRKtGN+Ro6hon7eaxUTr1pILC4Aa+tB7qPKnNQIPM/k5tTPvU6u5AFfa/WEyT0I5L/oCd2qHqehNQwiR56vvlt65GNS8GhTaqutalcHzUkRrcHaRw7J44QDODBsAzqcNincelSQ9BmzNf5n625GCDFfhqgkO6HTsLJ6q4nGCEcfRVZTyik3NLiuKWeqYSKvVNgsNxqSYXZxQdI3h2PO02jAc4wE9XOv86PMNE6eBtoe6hf9uozycR3g5ClACDOZ1nZcDW40wez7FLTPy/gYlEZpJnTNteWQMTsZzdEuJep5wI6zUNt5QH5W/gnYYd9xWkIMwoJDm5e0gBm/eUlqDlm2acH09HTCwHMFeJU+nGKK/M7EdXdwHA8KZIUgRsqAHrtJRqLoBDjbaU6EpYc+HOg7oExsWtMo5oroOFA//3WKfkWv5h0TGs2eBz69Ct5Q5pdCIZjNryd5HULaavsTu44g8kJjU5sfdUY/IK+l6p/rvzQ3B5w46TuvAeln0G17Vo0UxGsONlj7JnSYq452arf9sTlG5BnSOQAAra0l/M2ht/Z8/srqrSbpjT0vPn+hW4bWrI5nnU2tJv0Ab1RYS9G0HNbEz/Nl1H/4bdKZqWzBSw98EBs/sK27/uboGwFTkuPIkOWNTVnCU0/AgKkdHs5bobmyv9pvtaQvoNGIHwea3kKHn7bbUpjo6KW5OSMEqCx9G9rPwCyAZivigDbNd0NVr8NddQTVZkP6U8aIPFoph8Eg6iLT1i3B/8NZ00SKaylm6xGBiBIgEt209OQnAhQahv+GCLUf/U35uS3j0tefczZ4vmKitT7/V2/uuV85ukdt5wHfL9BuS0ezWlWb5Eb923oiaM5RWDpyQ5BXC4gPetCh01po2IlrdoXBuTKoUJBJmK22IpJsSSESwwQMdCcYWLDZjJBotyE2NqRpbYyYNbNSbgcYI5YOfxS1rVealZgWDvov2zbk8EsVFhBxNaDDiKPoFgJUmQs49agsi8tTuSzLQpZKkt6jISeX5fvP3FKQk4TlozfKsFBPyIQ0QE6WxYKhbgeRnJhdF1SI+sjqZ79Gaq7qN0+lgbEhWEYkAb6JSUVBsaqdDmYIbc6yI99YgAWDHOpOYwgh4Kz2BMTpVanNNlvg9XUsH72x57a77ssM1pCeHjE2y7Amdlt70IV2zEseR8hn708h7Iucoz/OV7D0+MdlHWDFfqlpQnQWLG1dBJ+1FUvffn9v95djKKif/Rop0BcqaP/ALt/XsLEZiVozDLwhsFp589q6XxwqrKGGtVZVzjag6arrcksJl3Zb8nhpSgztX+jCx5AVrHMsPAHx+BMQT9s2UsEAyGilNjuZtmnB9PR01tFhYg8U4gnxKGX2NWjTgHJGU2XOD3MMvahLR26QxVt2bId3zvZcW5hA6GI4Sw99GI/+TJQgkW1fBIuIH6G284CiVlFOaC1U7DHmOpEFSHjBYqgtCgXoLOZAJn+MfyEtMqlXaAHhPHFs4G1nwayZlaanp1OI+jnXyizOEBNl/ezXGNbVSKZ0CgJmJ/3CdqD4NgiHAuoXWrddKkZCUpce/BDQbOLgl97Zuf0cY8X9v3e9P5FbsKsBsjVJ13Ye8P0AgD+JC3+xsbJ2WyRB0/gnNJWL3j9XlmHPzaa/CEnRDHrSGpLoNULH8PoYEuMyZkdPk+kpFw5DQu35vwVs3QKU/WiQ2vZXo7btKoiNDSyf+qTMfLYoL+JyHuL4kSJCJIk2QyP8IqoJQJcsre08IMMBQ4lE1Re+C+Jp8eVBc0wO6herQIBWO96vBAQEA6Cil3RYMxA70da2XunX/DAmJ8dUa7MTIo0JKcD3lTCB9wpyou3pa1iJdGxlgI8KjMGFshLRBUT0WSL6FhHdT0SvV/t3ENEhIvqu+rt9mPeUC4choH7h9Vj+2nux9J0PYOn7fwS4juSDUaGCGiurt8qQP4VYzcGa+APRHrZACCUgBRDHVaNWiLVtV0mH+GYDaLUjFbec46e6quyVYzzg4yfkh3ASnEZoXNXPuVaOQ0Nnob6nkA9Bc32F/WE6ObLZDOYsWA7nJKEQcCJ3i6yCZphJoykYoObQBvAbzPzDAF4C4Doiei6AtwK4h5kvBnCP+n9oyKOVhgA7IcdkE+sKbEQyQ1ph+dhNQQ0iDvql0++uRU0QOcZKTIqFtfoygkpz5KxvyBrTngeUiiN36uXoEdoJXCwCTiMQ4Wai4ASDXNdEJ5nJP7zS19nOqtYC25O+fS37HOtzliikfqOU0oQE9Rnq3SsYGJjJiJkfA/CY+nyaiL4F4DwArwTwMnXYrQA+B6D/mPoE5JrDkCF2LAKA4sIvJhfrUbDNSOQ4PpWFtgfbQiDJrpv2nZ4A7PZ0HQnPkyr5kAVD/YffNrS2z0jY2mSS/0mxqHKzabiVZFZ0gpkmPIZC5ps47UBP+JkynbvFIM1TQwCD0BZOpg3ATiK619oSqY+J6EIALwTw9wB2KcGhBcjTh3lPueYwZKzc+9sAJPfN8pOfQG3bVagu7JPfrd0mD7JNQ1p26NW8CL2QQFD917BYKxNhC4YwPIFlS6MZJvjxJ1Fd3B/QoHL0htq2q8BCyAxh1wkGFajIIkBqiQTfQd05JyZ5Mo6tZDho9CMMBhQi2/Vls1NjHGXmSzodRESLAP4cwBuY+RRlDVUfEHLhMCKYkFGtOQiWBXwUeZn8joxKT47j+yDCE3o3L05YIITbIceo4dXF/XAWF4bPcKmpF6CKGFn9MAIzRybYpTu57TukTUQbs9QWChalth5nXU7Ag8pL6HAR+TdpEZMFYzArgQdnVgIAIipCCoY7mPm/qd1HiOhcZn6MiM4F8MTALhiD3Kw0JISL5NR2XC1ZJBPqQwcGtOY9AozqbygD0pAS3pd6jrYr6yzTc65Nv46F2vZXZz4WkJE1ekKrzl0RFF6eZ0J8c3SG1kDhkMx5UI5YIxigVvkFaw1owlWzT7yZxt6goLXfCTcjhaF9DoNwSJNUEf4EwLeY+UPWV58BsF993g/gLwd9HzZy4TBg1HYeUE5dObhrW69EdXG/CfWL8Mvol85w5pPvnFYMlhG6YotfJrypA7oPI9THex7E6lr281wHe4qXZT5cPPp4vJ1bf2w2pTaRIxXVhX2B3ITatqsiUUk6VNnA8CINceJN8ktkHZMZ+xYrsPS5gk0t61FigNFKPwVgL4CfJaL71PbzAN4PYDcRfRfAbvX/0JALh0Gj2ZIvqaoBbQsDkzQU6ygOCYyElVoWZ19EWFjnxX0Xab9DKGBAGDxD+sR2u5emnmNghKEjN9ft7CvJEUC1sjew+l/ZuD1AlgiVw1DbemWUm0s/9wzoSmNIEfiBz0nmooyLmbhxHemjQyOvMc0geMLJtHVsi/lvmZmY+UeZ+QVq+ytmfoqZX8HMF6u/Q00Fz9/KQUMlCplazFoYaG78cOigfZ61P05j6AX2y5OlPVNxKwF7ipcB5BhhsPz13zG5GpkEhENKKDhY2bgdK2u3yclNUUqjUJD/50hGzES6fPxmqSkUC6qmsz05sy9MBp2Ypvtj/816Tpf9yTJ+Wb1jfPxk9r4MCLNWzyF3SA8I9XOuBTdboPmKFAyra7GhgpEkIGNzZ1MlKxIN0q0dtouV+MrmHR2Pqc5d4b+Y5OBg807sdi/FnuJlONi6Cyurt6Ja2QvRbGK3eynIdXGweWf89RIcznnkUjKMCQmIjAH799M1DSImzGE7n7txHg+pHwG+JhbZ2WUHBB6wQ3oSkGsOA0Bt5wHJX99sQpxehThxUjpdLUdygIM+vF9z7ycN6F78B1n7vu2qzs3ZwoqFFAAqkUmbmFY2bsch724A0ezXPaXLpfM5R9eoVvZGGE1tQQ3IKLP6OdfKIj3NVqxgSPRPxaDrwIe0MOoeTYY9O8HVvY4DzJRpmxbkwqFP1J/5Bskf32rLyd1muUxzEIfUan38wAZ2BrXdCKYUGP+CJdQOeXcHTFW2OYkcgmg2zf+73UvBngcRzqzNYVD7t+9A7XnviP/S5gwKjw9jzmGI1bVIzWdbMMQ3HTQ5xrbfyZfQyaTUg6bQ6zsQ1soN59RIMHvEe7lZqU/w6VUpGMKrOnOAvz9MGxDhlxl2FEkcUpzPWjAcbN0FwPcpxEUn7XYvDQgN/b/WJnY7r+q567OKPT/xXjirm6Cmh6Vvv9/Ugl76/h8BQCBqK5WLSDCAUK5M0niMQWARE39AxzYGgZ6c3zGaCQsGEYOfODqgnmXDNGkFWZBrDn2gfuH1krROq+22+h+jDWQNSY07Jg6D0DI6OX8Ptu7CbvfSgHaQ5bpaKNjIHNF0huDgF39L+pk8D7UdV4NPrwKNRsfzwmYltVNuWntIGjdhbXJYTuoMyGrmStWCU3J7uN0O1kcfImRNI8q0TQty4dAPNjYBwFfnsw72DkgKRe2m/bRcCPvc6uL+1Db0hG6vXMOftYaw23mVeVG1prDbeZWvNVgvsa19nMn+CG/LHJYe+CCWj90ETXRXf47kUgsL7vBCw3wfMj1FkDaxDhlxZqu+hEHaOfp62pSmfXkjQh6tlMNg44UXonTPPw5kBd8r4sxUWU0J5FCAfsHGnuJlEVMGOWRMTBphrSLwnfMqHBKfDpiUkj6fqTj0d78JAKif/zqpeW54oLV1/wAVCWT/pnGRYEnmzGEhPO76cnD7B/bbKYAcNbYFWDhAu436Ba/H0sMf6a/tTpfG7JmVcuHQB8qf+zrEGAVDGN0KKXm8Z6KPDrbuQrWyV5WXlEplUkgqYJmJUl5qLSD0ZxthwXGmwtCkC/ZrNMchJiqourDPrJYBDDSh0PaTpQmAvhdHQxBkLBjkQj7XzQZqOw8MmYJ+upzNWZALhx6xp3R5NNLHfkHTwvsmGCsbt5v8hU445N2dyY+QJABywSBBO3dAPHZE/SN9EPULXg9Y/qw4IV3becBExgHBBMZIPk0GxE3ySf6yvjEszcZ+71hqD2J1DVQqojp3Raa8np4vPTnrxIEgFw49oDp3hUV9nEAFYP9NSxLqVoD081KlXMsWBlkEA6BWvGNwZM4axNYKHOdc8ONPSL/D2rr0YbXaoGIBznw5cHwgKdF6/mmTOxAVFGMxh45ivBgB6Ujzkgeg3WeBoSyXnTHNYWhLWyK6hYieIKJvWvt+m4geCZFJ6e/eRkQPENG3iahq7X8REX1DffdRGjWpeQgdBUMc0o4LO986bUNAXGRRFiwfvxmHxKeN2SjSbsp34ePOZKx85d3gcgG04yygVJTaQKsNUhTmy8duAiD9QDpvRI+Hbia8QQVMdI0xR0SBhQk3D7MlD+w6jIFxK00KhtnTPwVQi9n/YZtMCgBUfdTLADxPnfNxItLMWTcAOADgYrXFtTkSVBf2QbTaQRvvNCGmz4Vzdw2k6fAEb/8fN/kXnnk+Ck8/2690d4aDy0WAGVQug4pSodeLkOrCPikU0hLWJgUjWtBk7gvU89LRhCrCMAszQNeX42zbtGBoZiVm/oIqcZcFrwRwFzM3ADxIRA8AeDERPQRgKzN/EQCI6DYAvwhgafA9zgC9YptmWDZZcl0sHf7owJpOcjzb3+VIQFsAG5tqEosP+R07pnHshxz1rLP3hzBL52al/vFrRPR1ZXbarvadB+Bh65jDat956nN4fyyI6ICuy/rkk09m7tCen3gvfuYXPmj+rz3vHahW9qI6dwXqF/26dYHpUQkzYVhmqiEJgvpFv+4XuJkxkOeBmy25sg0VeOpKQ+jWTDlmk+bIYJnh6ue9FsunPjnQBDlGNl6laRIgo57tbgDwgwBeAOAxAH+o9sc9MU7ZHwtmvpGZL2HmS84+++xMHao/8w2g+76Dub/+OurPeqMUBo8eAbdbYM+D98jjJlHM2bqYqc2JhtYaYnIWJh1LD35IRvLsumbcXRk8iMCtViDDOTNmZQIfEcRTx+SHAZcT5YzbqEBEZxPR24noRrUYv4WIbsl6/kiFAzMfYWaPmQWAmwC8WH11GMAF1qHnA3hU7T8/Zv9AUH/mG+A99ji43YJoNtH+14fhHX4U3qnT/qpNregAYOnIDYO69OhhFXlx5soTJRi6pdUQdpLYDKD2gncCDz0iSRstf9Yh7+6JX9FToTjW6/cCKpUAAMtHbxxcWVoGWFCmbYT4SwDbAPw1gP9lbZkwUuGgimJr/BIAHcn0GQCXEVGZiC6CdDx/mZkfA3CaiF6iopT2YYB1U8WTT0XU9kT6AQvuju3RY6YISTUVpgErm3cAngiYl4bhXBwl6OgJaVKyqR8wHXkg3I7PsJ9kiI0Nv/aF5w0sgmkCzUrzzPwWZv4UM/+53rKePMxQ1j8D8EUAP0REh4noVwH8vgpL/TqAlwO4HgCY+X4AnwLwTwCWAVzHzDrD7BoANwN4AMC/YJDOaDfl9q1VGQv2OYjIgTeGKlN9wfKVOJW5MXYkGd1MhCsbtwOCUa3sRW3bVdFayVMGcfyEFAphmvccg4cSvu1jx1HbeiVW1m4Dr20MpunJi1b6n3a6QLcYZrTSr8Ts/pOU498H4H0x++8F8CPdXv+7//hgx2O40ej8EuroHk9IFZQFDolPY0/xso61ECYCtmAoFiay4poh7esGLADXNYKhWtlrbMhUKkn7PQByHCyf+uRA+ztI1La/Gitrt2FP6XJ/Zy4Yhgv1TovNhv9/v01iIqOVXg/g7UTUANCC9OEyM2/NcvKMhd/4YA7y4ccek+b0C9Ehi8YmvNVVANJGPvG2VsvHQA6BXBfO9rPG26cO6Ep72LwjYB7TuQEgkuGKngA8mfw0qWYnSX8hhViA7j3H8KE0teri/sFQajAApmzbiMDMW5jZYeYKM29V/2cSDMAs02cwg9ut3gi3QiGrdmimrpE80VqD1X8tGFAoYOmxPx5jpzqgmzrEMdDaQXVxvyKuswreNBqykM58BUvf+cAgets3atuukmO0MbjVa47uwY1Gx0Vk5rYmMMFNpQtcDMDYk5n5C1nOnV3hoJDZnkiO4Z6hQhFwHYj1dVChiOrCPlChAO/06dREr0mDLRicSmXc3UmFJvHbU7o8lQk2EzgaYCCOPmVWiLXn/xaWv/berpqsX3i9FLAPfLDzwQn4mf/79zH/lYfgHTsRcDxPVKLbsNHnImCgUMR8bJW17R0jj0TqCCJ6NaRp6XwA9wF4CaQf+GeznD+zZqUsIIcC5hdAks6tbNxubPPcbkFsbMA7fRqALxQOiU9PZlJcuE/kSAFRmnAzmEI/Glla/oM2IQAAO9l/t/rZr0Ft5wGII0chHn+iu/6c/zpj0qrOXYHywa/Ce+oYwCJglpxIGoxhYVIEwzAwaYkOUjD8OIB/ZeaXA3ghgMzZwRM4uw0OnTiQ7Fh/IygUAg7CEEx1Mx2PnpFgbuQw1MWW+WIK0MtKuvb834JYXZNaQ0LhG262UK3sBYlsE1Rtx9XwTpyAOHkKotmE2NhMHRc26he/GeLoU0bYaU4uvQm1WiWHuqLVniVM5DvTK3giQ1k3mXkTAIiozMz/DOCHsp4808IBkCvGtBf6kHc3nGIB7kU/gNbLn2/2ZyaEI8ckcUWExKg1i7jrKccbmi3Un/v20fanS2h22J5W0g8elkIhLRSUBbjdAh090bG5amUvxGk/GdI0oeo9dwIfPQb2vI7RYSwYB1t3nZEEhNNgmu0Kk6c5HCaiswD8dwCHiOgv0UUS8ez7HFS1sz3FywByYu3ZcdEKorGZ9QIAVJZv3ISkfBmjdmDrwi8sHBAxmARodfIzi7Wzv5vCLLUdV/vRPnahpYR6GuLYifT2tl4p6VMShJRQJsY0LB+/GXtKl8uQ5071knGGmZZmFpOlATLzL6mPv01En4XMll7Oev7Maw4amgpjT/Gy7Cnz2h8R8kskXCB6jto/EsEQilAK9IsFwIyl7//R8PvRJw4274QzVwYyrKSri/tRXdwvTWaCg0LAFtQhod3p9xAbG6mTtfYX1J73jtR2DjbvNO3E/yZ+v3qtqZFjgiAybiMEEf00EV3JzJ+HdEYnEpeGMfOaQxgsGN7aOvYULwO5LqhSAbdaEfXfNg/VdlwN7+Spzo3HCZBQvoTZN2THnK77O5UgknkKKagu7g8ek+F5+hN1/LHVyt5UjcFG/VlvzLROdObKEJsN3/+VsMiYORPLpGJY757Oc5ggENG7AFwC6Wf4JIAigP8PwE9lOf+M0Rxs2JOmTkKqzl2ReLyuxNWh0WzH6OMG6Y8ItRWe3Ni2xU8BVlZvBRULsfHn1bkr5G/lWRqC9bJ35MnS7cS0nVUwiGYT3sOH4R3ubL4N8FilTEzkupMZ/TZNCGv6cVv4uAFiAukzfgnAvwewJvvHjwLYkvXkM240GsFgRfLAkzHne0qXxwqJ6twVyauNhEGmo1BSo1H6GZwZhEyAp2fKQgiXT33S0GFogRChmAibi0KV0sxxiBEUfT4PrQloAsCfe2mE+SUbrN8vNy31gCFN9D1hQA7pbkssp6DJzOaqRLTQze1MwBMdHWInaau6G9mTkb2yTDIXWRXVDnl3ByJOWNvA1WYS7MICYxACIgXT7OjUnEP2ZM+CfX+E+tupIE7cd1n3pYEFw9kqF2LOl+7vuRCRTVk+U+Gdw8IkCQQbg6PP+FNkLLHcAZ8iov8C4CwiuhqSujuDGURiwp7u8BAQDAkrfZADFApAoQAqFlA/9zoAMDHpAbCAUyzgkHc3DjbvRP3Zb8LB5p2B1R85BKcyB/ecpxsOeXsCCgiIbrcuMLH1hjOCHAIVCzjYvBMHm3f6ppokx7Ll8E277164/J1i0E3H6zIDnz3PJ3JLu0ZI49OLioOtuwZXW2AWMakCwQJxtq0TFL3FsX77w8x/AOC/AvhzSL/DO5n5Y1nPP+Mc0gCMY9A4bfWAcx2Q4wAkQ0/5+AkAIdZQy25sh1ratAph88Cel7wHzvo6EDN5kEOjmbiVCYRcd6JZSsOICz2ubX81gM6moqTnKn93ScqnUX/OWzJxZgnrHAAm7FZjT+ly0PN/CCv/8C7UdlwNsZFA36LGoA513VO8bKIKME0MJlgYBMAEZKfP2ElE91r/38jMWQjgfo2I9gG4F8BvMPPxjt1iPkREfw811xPRDmbOJHim5MkPAbFJUuz/tfwQAOBu24ZD4tMo/MAFOCQ+DaecvS7CwS+9Exsvfnai/2EUWbIsGNxsQmw2jEY0rWAd/RPYmU0wBI6xBAE/+VREECX9VoE2mq1gPgYLOEeOo37udRCra+khsdbCQAsInY+TA9P3HLL7HI7qcsZqyyIYkkosJ4KI/hMRHQHwdUiB8hX1NxPOGM0h1pyjksQAT+7zfEejTb2xp3S58Sfw0acAqIIzXeDzf/Vm2ZZaKQYyYi2fxzC1CJ0QqCO0phYdnMmZBIPSHqoL++Bs3QJWWp3+DZKEdZJQMr8dORBPPJleB1rxXdnCKaxtxoa2ThJp3SAwbZN/JwzRAMDMR/RnIroJwP/McNobATyPmY/2cs0Z+3Wywea4MbZpXdi93fKzbf0TjPkgbEboGi98rsyvcF1QqTiWEEbv+MnMHEGThmplb6rW0LVTudU2ldgAmeDWSYsLf1+t7PVNQiwMj1La+eS6gSCFamWvcUwn1tSeNsEwQL/ZVGCI9BkpJZbT8C8AeqZFyKQ5ENEHmPktnfZNIzpNJpGJos+V/cEvvzPwf/2ca8EbmxDr6yN1Guts8U527j2ly8Ge1KwmwiaeMkH29PxYgD0A5EjajAzZ7CwYTrEgjw2FomYhDaRSyTjV95QuhzNXhrcm3+FEGpZJxSxO8r1ggElwqsTyyyB9E4cBvAvAy4joBepKDwH4TxmaehuA/6N8Dsbhycyvy9KPrGal3QDCgqAes2/2MeAXd+nxj+MVP/t7KPyfb4JFu/MJA4TtCK3vugbi1KqZHDUflG0Try7uH2uZ0YjW0IfGYJvwNA+V7Tzu1B57noww2nZVgPGWXDf1dyw851lY+tbvBfZpwaAubHcydNExCI188s+MLJFIWdBtieUU/BcAfwPgG+iBuCNVOBDRNQCuBfAsIvq69dUWAH/X7cVmAcOgpbjnb96G+vmvg3jsiL8zPBEM6SVlwbK6nbonfX+iFZMH0GgY2hHnGedi6Xt/MJQ+JfZ1QBxV5Lp9CRZ9ji5MVNtxNernvw5Lhz8KWlwAjp+MXtMhUKEYEAzVyt7gPYWJA6ONjEZA5AKhN0xetHibmX+915M7jYI7AfwCgM+ov3p7ETP/h14vOi7YUUHhDOakTR0MwJ9EBlJzNoS1H7sA7qJKYEygmx4mIolmKcew50E8+thQ+xNGmo+kaz9DmrM49cQQVYea2JeP3YTmxdIkvHz0xsDiQfsWdBEpGxQuwGTlQIyUF2vWfQEjwqDyHAaIzxLRASI6l4h26C3ryakjgZlPMvNDzPwrzPyvADYg5eMiET2zz44PHUkUFrEvXtKLEWI7jbzQA8Lc4xtoP/8H4ZRKE++8C2QpDxnVuSvi7fijNLGk0I/ovt3z2beZfXYkmrMwn+irWT55C4BgpNIh724jTBLHab+Y4LE1DGReBPaLwWVIDwqXQ/kdIMNYuwplzTQyiOgXiOi7AB4E8HlIh8hStz2dSFj0FiZyKPTimAFEztBs7ge//E60FotwFhfiC7908RJn1Yp6eUn05BUglBsiwk7fyOp9mE78JKHQSTDp8eQQ+MJkhmQtWGzht9u91PiCZFMpC5k4MrlO6FIYpOXlTMs2EmSNVBqh5sDMF8Vsz9LfE9HutPOzjpTfgSxO/R1mvgjAKzArPgfrRV/ZuN0MpsDgUrQaVBxuWojbEFh68hNwd50taRpieP+zoB+6jE4v1G730pGtNk1CWMJzyHSP9rnhNuK+6+GZhzUbWqj4nx9ON78d8u4GCzYVCYHghEyFIg55dxvtQgvnwG9k/x4D1gqSOKiStjMaEyYcMuADaV9mHT0tZn4KgENEDjN/FjJTb6KRebCqpLfq3BXxdnelVQybduJv7nkrAGDp4Y/A2fm04Avew+TVz8sbFhC20ByFScf4GGKulele0p7RIFhqQ2yvNlEj/8C55ncTp1ZjT7d9KIe8u6XQtXIf9PkrG7cHypLudi+Fe84uU1o06XeydkQ1ryEiTXBM8jYIkMi2TRBS1aqswuEEES0C+AKAO4joIwBGG3fZI7oZDCb5LUzzTAS4Lmo7D4yq21g6/FGzYjwkPm2qjwU73P1Kt5eQz4BgkB/6SwTsgOrCvpGXVe0J1jjhtp91vvKVd8N9xi4TFVV/5hvwip8Nhq/ak/ie4mU45N0dIe7TvgqxugbAr3e+9PBH5PfNO3GwdRecYsEIirSs7EC/e9SScqRg+jSH1N5ktZO8EsAmgOsBXAFZi/Q9/fVrvIiEpKbVa4CMLFl6/OMj6Fk8Djbv7JwgZVeaS0FWARFrb9Z+GddBbfursXz85kxtdYM4dtOkPidOiCOmmrBDWwEAG5tGwIknnkThyadQrewFVeYAZvUbeYaSPNJ3WOYqciRpYqEQGymn9+0pXQ5yEphoOz2LDuM/RzrGEIk0dGT65Zl5jZk9Zm4z863M/FFlZppqpE6S4VrEI4rOSYPWIjq+sAOaFJMmLTgEKhZB8xXsefHw1ghZ1f5EH0mc3X3QUTqh0FY9oQsrsc3QsrRbEKtrEGvrRnBoYVJd2OcLujj/SqsNbrdRXdiH+kXxoeu6LVvbM76KXutE5FpGdkxetFInPJT2ZeobQkSniehUzHaaiE4NtJtjQkdTk2CAOZAFO25kqhg2jJdZExJ6HrjZBJ9ehfO9w4NrH+nlWuO7lMFmnCQQhhTKuad0OVZWb5VmobgsZ+t32VO8DOSQX2caMUJZHb+ydpvU3DYbiZXnDrbuwsHWXUYztvmaqFAczP3mAiMeU2BWsiOUmPmX047tlOewhZm3xmxbmHnroDo8KUicZAQDnkD94jePtkODQh/ROJHJNywg1hLqFfSIYSQYjhJ65a+FnPYLHGzdBXr+vzGTc6eEQ/0dC5bnForS56X8X3/9hXcknqe1l3Db7HnBcO1BIhcYk5gEF4fMNBy5QTENlmmJm03gxClUX/Su8fZpkOhCaASEBAs/y5iFKb4zteh3sowJsRWttqnPsKd0OaqL+0Hf/b5v+lHV3w55d5uEN+1YDlcT3O1eKk1SJ0+B2zIOpP6sN6K27SpzXP25bzcRULq9uHwZff1D3t3DT4IbkLAYab5Cr+DJiVYios8kbP8DwNOytnPG1HPIioijWkNI05LzZJQ3Z9SI5fofJFIc23HPZxhO6TSkRuX017D8O2CfDTkC3GiAocxmylmuHdh6pb+yeQf2FC8LUHZHVv+tNsRR391X23olQARut8Geh93upUbgxFF/J9KBjwJpzzVFSOkxN7TffVCYnK79XwD+A4BwHDUBeHHWRnLNIQaxZhSWZSXFseOo//Dbkk8eMkb6cmeIcGHBgRj/QaCTT6VnB3WnpLd+Vrgp59lal6kbosZUXHa0vgenVPLDVK26H7afTGw2sHzyFlnyVPVht3up9DHo82IwViERhw6/wUQLBY3J8Tl8CcA6M38+tH0OwLezNpILh4zwo0gYaI8xBn/UttxOL+oE9Cd6CEcnmwmwgYf9N0m5N1oQsOeBKhW427dhZfMOuNu2Rri9Ijko1v3qCKnAfv/iE/NcYjGFAmJSfA7MXFeJynHfvTRrO7lwyAI7KU4w+PgJ/MzP//7IuzF0c1IcYtT9MHVDtyVTe+6K68qQzGGt/vvFAK4btq8vH78ZS0duAAAsPfkJye1lC4AsE2acUJgGxP2ek/abTxmIKDPtUe5zSEBskpyyFfNmA5XDMxHJG48uCNycufKQO+PDJJilYdx1lvU4yXKcDZv9d3EBTqjGdORYdb42G1G5DFimpbFgmM8+S7u9XHuQDvnJVmw0npn1wKFpDkR0CxE9QUTftPbtIKJDRPRd9Xe79d3biOgBIvo2EVWt/S8iom+o7z5KRNnDFvpcUcStyliFtdKRqc8BjCIlesUQEer6164LKhZkxu8IUDjn6SO5zkDQg2Zjnm2phOWjN2LpyA1YPnpjcvsaagGzfPKWbPkvKeg5UU5jQIIhyU8yFAxK65igaKXOPc2GYZqV/hRALbTvrQDuYeaLAdyj/gcRPRfAZQCep875OBHpEXIDgAMALlZbuM3O6EP9jLWvs4BYXUNt+6tRf+Ybuu7ORCJlBRVH5kbFAqhUMiaPQcJOhHP/zbNxyLsbS498rPOJ49YaeoVFG5+FCt3UhyBnoMI5s9lyyJQaEZr2aaHwmBCHNBH9csL2/wCodGxAYWhmJWb+AhFdGNr9SsjC2QBwK4DPQdahfiWAu5i5AeBBInoAwIuJ6CEAW5n5iwBARLcB+EX0W0vCnkAyDDw7gkTaeR0QPPBmA9xqoX7OtUPnXaou7Btq++aZZAlfdWQiFg3BpFTfdQ1Ey+d0XL4/mAmcGhk1hYKBCkVZjzqLyczCwdZdqO08ENEuqFAMkAB2JTA7hPJq8kdut4YviMN+hgkHYSIS3DR+IeW7/5m1kVH7HHYx82MAwMyPEZG2FZwHGX6lcVjta6nP4f2xIKIDkFoG5jCfrUcptt/ooVbcugeAGMQC4sTJ2Bd1kOBWezQr4xQhYaC1KWewK7os4ZWi2RzoNccGHZbqeT0neGUab4McL1n9KcPANGiFEyIcmPnKQbQzKQ7puLeDU/bHgplvBHAjAGylHb39VBmEhVlJsxYSDqBolYeGUUdkhCYC+57BBG61QF4J9Yt+HUsPfqjvy2XWjCZ9gkhDqOSs+TxAG7tJqiMnqEGE+xHWnjOML2nu8bPkR4pJ/90ngxoDAEBE8cyMCsyc6YUd9TLgCBGdCwDq7xNq/2EAF1jHnQ/gUbX//Jj9o0OCr2LUsf5jifFOmzA8AV7fAJoJE1AXqF94fSxNdzfo25k6bFiCwSmVQOUy3F1Px8HWXQPnk9I+CadUij8g6TftJcoKI3YgTzJExm342GJtbwz9vyVrI6MWDp8BsF993g/gL639lxFRmYgugnQ8f1mZoE4T0UtUlNI+65zRI01ADBF7SpePPzwT/v3aGeNotvqm7V566MOZjktjbB1LDkiPcM4/Fyurt2Lp8EeHdo2DzTvBnicFRAaKd5M/0oPZaCoKM40AE5QE9269AThi/6/2ZcLQzEpE9GeQzuedRHQYwLsAvB/Ap4joVwF8H8CrAICZ7yeiTwH4J8gKc9cxsx5x10BGPlUgHdH9OaP7RdwLNEQ7bH3XNRP58kkzk+QMcp88MZJrzoK/od9w027g7tgO79jxTMfudl4Fpzw3E894bJgQs1IIPfdqmNFKv5Lw1SsSjn8fgAhJPTPfC+BHBti14WDAAqK6sE9mY7dbk2FvVUJRCwW5ywE8D9SaioqxZxyWjtyg6qJn+31EY3N6wkYnDRNQq2HQmBSH9FQiU5nRHlCdu8IP55wEwRBCILRVMNDq3+9wJmActNOa6ZWzKp8TON6mBRPkkP4GfFH1bCL6uv09M/9olnZy4TAoDGjFZfwLU/SScquN2vPeEclJyIJO/pTqwj6srN02cObXsWBMq/KDrbumyicztZgQ4QDglwHsAvBwaP8PoIuAnlyH7BYx/gZyyNAY9IPq4n5kJlObBLAq+tNogLjHPne4X7HZwJ7iZdNtC7eSy8ZRGMnUkBh2cZ8zHBNEn/FhAKeY+V/tDcC6+i4T8pEyIFChfyWMisWpEQyBqnCtNvDksf7a6fOYiYYV7TXqwkgA4Dzz/OAOW1DEbTm6R1bqjNEM5QuZ+evhncp/e2HWRvKRMAiQAwwg1punbHUcqE3QaGD3T3dvVjrTYBf3GRWWvvOB7k7IBUXXoC62jm11SVoagzTCrczcSvmv3yMCRVYcwvKxm/puk6c16ocFWAgUjnWXJT6OiTJHl8iFRHYMTnP4U2QkLU3APxDR1eGdKoXgK5l6gNwh3R2SXpIuWMSTUNt21VQ5oTVM5FK7DZw8nfm86sK+6TcXdQMrFHjUGIgzP2nsT+GYHRYGFa3UJWlpHN4A4C+I6Ar4wuASACUAv5S1H/mSYELAjUZw4pi0FVuHvrBgiBMnM2dLd21CyyehnjHUJMpJGqPjRnbNYScR3WttBzK0HiAtBZBY4ISZjzDzTwJ4N4CH1PZuZv4JZn486+3kmkMWhF4AE68+oAm8tuPq9KpfGpM8QbIABMM9vdHx0D3Fy84srQEI/I7VuSsy8SntKV0uP7AAFYq9l2PtYtyQQ2febzMIcFeRSEeZ+ZIh9gYAoOpIx9aSzoJcOKQhSSio77S/gfqgrq5f/GbA87K9kB349oeGjIWA2PPAT6ZXyKttu8qc19UklCaER/U8RrBKTsr7SGRY7YCwSSnwe1nJjPbn8DEdMQ102qPAcGXqESI6V5U6sElLh4ZcJ0xC1omACOgnjPX0KrjRJSPpsEMR+2iPU9hV6+deB7G+3m/voujmOXR6diMI8QxrDdoxrwUDC45s9nFpBIRhaKGiy7zaCNCGx+Xp6AVQx4vkggHAsIn3kkhLh4Zcc+gF+qVxXVCxCCp29xhf8bLfxT2fezvqz3ojeGMzUvClb7V+jHZgbrZQXdgnC94zA8pcxs1mxHRmr1qHggm1h+8pXmZotbWJrZOpLeyPqlb29m5mygoWONi6ywilsKZh9ycXEBiY5tANaekwkQuHLhApl0kEuA6QxJufAFFyZe3pjU2Ijc3I90mrtUmyBaetKLnVlpxLrgO02x0dokMXEhMGLQz0Z/tvJ+x2L8Uh727fH5GCgEkpo0kyUBIXUkuhcllmwYc0jVxABDHAaKWuSEuHhclcWk0C4oqaJEyISw9/pKumy4+cAmyhkNF0oU0Dk7DFdC74nFjIgkC2YOjy/kxbGe49qZ0sGDUhXq/CUN+TMS8t7k8/IUvJ14S+mSY8Dyurt2Yr6DOhmtpIwJikYj8DwRn8a3aPyMvMDOrlhXj0CFAqxvsa7PaG+bIN0r6eUP4yLBjIIf/YjMIw3GbasZ0ERVjoxP0/TISvkVXgxfaNhcwt6XzR9P8T+mkfX13Yh5XNO0CFIqhQjD+ui/ZnEYSh+xxGjjPzl8yCrIO83J1JqbbtKsnD5DhgIeJffHvijPvcy+Td7eTfh6PWOFGtlWvixBs3eQ15gol93tZ3gxYSvbbZ6Tz2POwpXW4c1LoOd3Xuiq6c1p2wsnab/Ltxu0nms/sY0/EzU0hMDrfSQJD7HJKQtWRihloG9ee+HeJfD4MKBdBcWUY3tVogxzFjJXUSEBx5IVk4fh97LO/otzWYEZs06RqNgYXf95jjAv0J27CHGcabYkIcxLOJ+KoGCRZgz8+J2FO6XJqArHuK9Q+oc1MREurVyl6/Hev3SXxW4wq9HhN6ZiaeUJyB4r0LZBnUWXIcWm2AyBcMlTnwukoWi9MKQiuvOPNKxEQzRqSaS1w30r8kU07kPuO0k140n2lGp/5bAjhOAKUK/7S29Xeu6zu2O7wPqf6oWfgt0jBZrKwDwQz/WgNAhsG89NgfJ36nHYbisSNyknQcwHXBTx0HALCwHIZOTE0I64VKEhBpNvMsjtl+V8aZJ4PQxNLJpt7RFp9m2koyVfUgTLt1bieh5+ecNiFb98OCpV+gWIiEuKb2PU7wqn3OXDm2D6nCO8v1spopp0yYzJrPITcr9YMOhHvkOKhtvVL+4zhKODhgZVKCVkPjXiaHZDiobCj4nW1GUqYa/5qhaJNhTEr+xSKhjz23pdpLO8e+l1hTSRbzWh8TTr8muEzPKltHEr/S/oHoKd1rEMunPgkAqG290mgP9mIkLZS1r2eVJiAm2EQ1okI+I8N0ieZJAjkdw/u0ZkClEqhcAkpFiCeOBg9KEgz6r71Z186CoQqG0HGBa9llTrt5mfV5dpawohYJ30vsvY1gpTnqsNesIIc6hram9T0SvVXyo5KMhptwjrWjix73iE4aR6eAiWH2MTcr5QAgOZVKJdTPvS7xEHJdoFSUEzsR+MQpgDpwMQ1o8omdPNMmamti7vJCgWsGKB9CbcX1KY4qwpwbEjBpuQ299rkXDMLE1Jc5L+l3UqGtOmopDllMdVQsYGX1VtmWFjiOZARAoRBZ2Sc9j1GEB4cuGP9cbEHRR/BGKjKalHKz0qwgLmxPaQzSh0CpPgftrCbXlb6GzYbMGoZajdlmqbhIh7QJJGGC6zjpDDPax345KVS7wJTK7NFMwAKAcm4rkxs5o6m3raONwuR0/V477vyuJtOYia6nEFa7DdcxgqG27Sq5kCGSOSueANgLTrTh+4iZoAcZEZcKezFhR0qlCY1Bvg9TNPFnQS4ceoHSBDqCSCbJpRHzMQfbyvISdbDHZzl+KLCvk3TNLvsSnHgY5EI9L99+37WW1CXsawx7kus67NUqItTTKj1BMAAAN1uG9yt43546lTI/55EJCHmx6Oc4bWGQYwTTpRVkQS4cuoR5AbWDOQa1nQfkylZpFwEwR224WeOje9EWJsSBNxBnrNYe1PNiz8vksOzl2gNzHveA8O+ZtQ9Z+xwQJHpl7VBAMAAwGe5J40u3Y18vNkfFuo+hCohuzEWD1hoA0Ixxg+XCoROSbJSUYlKyB4njSFtwuexPbr30IXb3EJOrErsSPwGF93eaUOKOyTKpod0yLzYLpUn4B3Tua8ZJQU9kk0AG2PF3DnEoZelz4NmocVld2AeQFBJZiP3C/cuSaDdSDaITBikgpszZnAW5Q7oXkIwequ+6Jvbr5WM3YfnYTXLlJYQUELospmC58tVbP93I8qJlST5KOcZuP/w5XG/A3p+GpGOytGNHMKkdsQ7aWHu+64JKRVCp5Dtf7SS90HOYpMikTIKqh4AC06Y2mzVb0m9hP9eUdm1zW1YqjUl6rgAG5qAmkW2bFuSaQ5dgwSZNfunIDanHkuvKl06PPb1S8az/Ad/0NGTbeXwn/WgTTmDWHuVKr9trhSelJIESyCj3PN/kZ2zqITt1J2qIMSHOOR5zkP8568SnxiU5lEyxHnb0htpPfUYxeRCZzhs2Bhm5NBlDZGCYbeEwBLsigExO4/q518nIJE9dX61addSHfAE9X4BoZDQhdf1ChRPMQs8myVbcrb17HMh0bRWiKTYbsffUSQubJHNIx9V68ODk7ywzVJoQjPgoQudnRoovwr7WtGLWHNKzb1bKYlbpBSmDuLbjavlBm42YpWmpWJAJccWCtVr3gltczD85cCpzsSYPzV/UVUx5KIIjYivu45mlxdGP1ZzAQmb8WoKRW0HK6079mzhzCILmvR5ONlvATKehxkAmDSUrOoyriRoz3YARNBenbVOC2dYcbAw8prmT/Zd9AeIJoESgQgHcbgdDV7spyGKr9Q4FnbGKpbXnFe6QE8PS8gNGsSpnoaqvmTwJB4CXbivvsMq1254E2E7mnqOtyPHL1lp5DIltdpNU1uUYixszk6TBhTFN/oQsmH3NwcagNAgWgOfFOqTru66R1AM63JKF3NbXfYESFwKb8uLQXBkoFmSGquvIjSi4WTb1pJVXQNuwNQ1rEuh2FdrLixob/jgikEMRM17AwR08ONOYmeTVbdfPNsTVFc7vyGTGStr6QHjMTNoz13kOs5QhfWYJB6B/M5OeSD0v2bTkSZMF6xdCsCzwQ4TlozcCejIKvzBxK1UtEABQuQwqFn1Op3Y7XoPRkTghYaA/+5fjgGAIdiU+Eil6Ker5eYZf8FG88LazNWBn55Rs6wxjZuRUESlICyNOOcl/Ngn3mmpeGoAAyIqJ1B6ympSmyKx05gkHjT6EBAtpMmIdnmph6cgNEBsbkUHAG5vg9Q3Utl0Ve16wa5a9vlSUWdYqBJat4kIsGPA8mCxrR/Hf6Jc4JTu006QfPtZeQcZqFwPw7YzCtAQoAWGR++nv9P5EdLi/iZuwFJJ+vyy/e1I7o0BYa0nq16Qg1xxmDb06YFmA223Uz3ttzHcs8xsEKz4aNSIUR02s1pDUL8DXQFTbhucGIQFhwxYQKTkAWV60sOYRWSWHV44Zn+XYXnKtJSRlU09IVvnYEArj7b+5eFbdtN8/LBj6cryPCpxxmxKcOQ7pLAiHe4b3wf6aQfDAjUa0mUJBOvUAfzLvZLoIt+2oyb/ZApotOIsLsk3XBTeb/iSmeXXgSa1BY0AvEVlCxjjAO02eGSbXcb3k/nUTYvlhPf8M1BzBNqcX3fqZ0gRHN2atWXh2GtOkFWTBWDQHInqIiL5BRPcR0b1q3w4iOkRE31V/t1vHv42IHiCibxNRdTSdjGoT4XA/Fgxe25BcShaWT94iv2+3ZbikFaYaaD/ummHnsDaBNBqA52H51CeTXyhl7ooIhqREJeV8jLP9B/brfo3QrjwR6OBQnfiVbAb02//Y0OszEQzA42zblGCcZqWXM/MLmPkS9f9bAdzDzBcDuEf9DyJ6LoDLADwPQA3Ax4moB4KiwSAiIDwPvLEZOU7SMlBAKERMMdHGAxO5PUFzswlut1Fd3O/vD8ehdxMpEpPsNiqn6iRPIJPkWB4Vuv09hp01Pq3Pf9Z8DpNkVnolgJepz7cC+ByAt6j9dzFzA8CDRPQAgBcD+OIY+gjAUqu1WafVxp7S5XAWFwBIwSA2No0ZyX+Z4snIwogQxWkIZbSMyW525spSS0mIPgpEeGRd/SeQp/V67KQLhU4IC/mJjJoZAQZ9z0kZ68O41lAxRZFIWTAuzYEBHCSirxCRtsnsYubHAED9fbrafx6Ah61zD6t9ERDRASK6l4jubSHqCxgkjAptZ5murwPttjQBxfkXrFV834OeghW42PNkfkVMrkPgelkd4WFO/DSnvS2sEo6bqpc8BmEn/rTfz6iRpJn24ruYVOSaw2DwU8z8KBE9HcAhIvrnlGPjRk/sI2bmGwHcCABbacfIfwZpYtrofFycQy9sIjKZvOz/DYNkli8AqTUQgYqFQKhsZHUbjl7qFFkUw8MUe25KO1PzkifcZ095AzkSkSQQUjOxMeHPe8oikbJgLMKBmR9Vf58gor+ANBMdIaJzmfkxIjoXwBPq8MMALrBOPx/AoyPtcAqSEsO6ChfVgkHnKAg2iW9Gt9MvSqq5iGW+QwwDZpIzPKy+Jx6bkhiVmF08Bc7riLktjpBwCjEJJq9ufAd2yGrSeZNwT0kgADRFzuYsGLlZiYgWiGiL/gxgD4BvAvgMgP3qsP0A/lJ9/gyAy4ioTEQXAbgYwJdH2+vu0GkAh30KmjjPlB917RW5dWzIjBU7+TqSfTRs3gkk1sUQ9XVr443LfYjDpL7MNiKmjkIxURjmSEe/gQ32OOyU/DZpjmtizrRNC8ahOewC8BckJ70CgDuZeZmI/gHAp4joVwF8H8CrAICZ7yeiTwH4JwBtANcxJ1UemGwETEb2PtdBopy2Cfz0rpD6rdtxFhfAmw19kH8dU1fCMk9pZtK4pK+YidFERyWZlyzSv7i+TaKQiDXtsQCcAgguuN0HkeEEYNT8Vb1M1ramkLZAiRtXcfvGhgGblYjoIQCnIe3GbSuqc2QYuXBg5u8BeH7M/qcAvCLhnPcBeN+QuzZ8xFXE6oHRMtYuq9g0IzWqw/UiHPKpqtPMPpaDOVEwpCQIpv0/CYgTDPK5yugzUyEuREQHBCeliZmc4jBE096gVu1xnF824syz9jOfnN+AhxGt9HJmPjroRrNikkJZZx46wzkAh4KDKkDnLbUGQ6Gs4boyIxowjmsqlcCbDZDjgBwnuojRq7KwYEiLQAp0PuTETjBrTU0IYppQZgFAmt7idFRbMOj//VMn475ZqPraIa1uEBiXOUffw6QuPqYpEikLcuEwYmjmSy0k7NyEwCrdnBCzWhcxuQ7NpuRYKpflNXQb9srLLm6TUMglNlJEXys8oaasTPXEOqnCIiBwQ2Y+k49iRYOFMWn27lSkaEDZmxji/domzhQkfT8ZmgO60Rx2amYIhRtVpGWgNchwfwbwX2K+Hzpy4TBCBFacLKKrUr1iLcqfxYSksgALxz9PsG/2APzi8IKlgAi0pz62ODLBR+gxOsE+P8s51jET8wIrpE125jvlvLd5rAzS6m/EaBJjuf84gW4hq+AeuiDMIBjSzHgTMa64q2iloxl8CJFwf2b+Qn+d7A65cBgRtEbglJRZyebPDx4IEMmKcfY+hYBWYHbaGkQraDJKyFNIcsamhb121Bw6CIxxCojME5xtMiNZYImIAQ5NpAkZ4WlJXuO4f//6bmTs+BNuMmvvCDqYap6M25fkA7I/j2WcDfCSCeH+IxUOebzeCGCHj65s3K53hg8CICd/TbIHpR0EVngq9BU69NU+X2dqC4u0T56ohI5j+qL/Nwg5vAOTn/4uhYAu3EZkErXbnhToZ2CbXMK+GAo+51ih2kXYayCkOGHL2k6nduXt+L+DNmWaMaBDqEMZ8P2EoiZ0NnlLuLdeTV7jHF+DCmVNCfcfKXLNYUiw1WAqFE0YaXVhHwCA2y374MC5xhRhebjIKuIT8FcAkdVf4GVRZpHEyKikfZ0imUImI1uYRF7sTkl5A0TYWZxwUOT/1HPSEg+BRO0s8dpdcGslxvaTE1nxx14r9D8VQ/dYKIA8X/jb/F89T7Q95IikaVzdYLxmvIFdLzbcf1CNZ0WuOWgMkI46oP6qF2Vl7TZ5mVYbKxu3S03C0gz0/2bl57qyP7oNMymoFaHn+cJCT4i6DavtgH/CsqXHdDqwkjb7ko5xyGg2cfcfWIlz/2aLLJNFL9FDqcfqso4xgjVwj7YfJmyGi1spd7GKjr1euP2k69kIa5qAGUt+XXLHv07GfqX2J+YeIp+7IXXM1IX4krhDBwMQGbdOTTF/j5mfr7bnqVD+kePM1hy6dbCmIOww0y+YXuVX564AC8bB5p3YU7zMFwwuLGoMx4+iMaYgApUKJodBdlut2FX4qr6+fQ9J/DTyOlZCXFIUkkPStKU/h5PodDv6eOvcwD2EMMyVXeT5x3yX1p/Aca4Du4IfNBtu8ORgm+reYyezTqHD4e9srSwOHYgOI9DPOqRV6rwYP/zZATwR0krcoOkwztcUeztBwRvJWUh63+LMnRmDAZL6MGwtgjBd2c9ZcGYLB9tha/+vP2duJrhSIduWq6AFAwA4i4typ5ClRkGkyomqyUbVggCUAGg2TVlQuCSZX2Ne1nA+RGBiSdIa7Pu0J38dLquPD3M9xQmLCeAkiuPoiQiGpCgesqrdBRqNmVjLZVnHI+yQ1hpfqD/6el1NVklCJqPjP3LPAKDNRg7BVCmEb6KEqxcDls9JmbC0zyuxb9b1/cvGj4+Yk5K/mxaI3heXk4gZ+EX6RJLpJINdOKLq26s4pTGsbNwOsDCCAQC41QLNVwJCwMCYlxxQsahOsARBu22Ogb1KpSCFt0m20xO8vo7+G+NEDiBshtAmFrKEjJ6IdaU7taWZ6LqZHMPPuFszQSIhoOW8D+zXUBOngRaE1v+6PKykSi9hZfOOyLXDFCJxwipOmNtaZyRpMkZ7ibtvLSAiSWP6vgX7AsH+zTwRvV913bgxH/7cjUkv8Z46fdeFOS7ct6GZmgZoVpoUnNmaQxo6RFJEVmbW8boKHBWVgFATR/3ZbwJKRTjPPA/YbIJPnPRNO/a5rgu4cnKQZgqCSYogJ56+O808JNhfBngieo59blgbAPzJgkKTZuiZpAnVJM4lo/WEzE2RZ2utYMPtdZqQIr6B0GcKC0J9nzEmNJory9V1q43lk7dgT/EyAEBt21VRk54OIjCRZ7Jdgl8dMNx/XzDAnBsrUO1nHfPbJ07M9nnqtw70wdYwHJI5gCwUDUu8s7rThBsYHwn97Qt2u5kOH46zetbMSrnm0AXslV1gwIcEg05iQ2TVJ81HTAQ+fsLstld4AV4kwdL+67pAoWCEBrRNvFDwP3d62eJWj/qzfW7SS6PDYm3Nw5p0jSktBbYAMJOgZX6LrKZDTlzjqLfOi7tmnEYXXoEHVpLh+9erZxuOrJVBc2UQOSYP5WDrLnCzKSv/wV8IULHgC1rlO4InzHO0tYLAOCJHRrIZc54LKpVApVLgd0tzGtv3RsWCP37kxfzjw3b/sF3frntu+yx6cFab305dJ9DH0JZ4foyWEvkd45z+o4LWrjttU4Jcc+gCAUezQwiTqRhHJhFWVm8NfFfbcTXorG3A6VWg0QQtLoCPt8Be218Bl4py8OiJuKQ0iEIBaDalCaNYlGYp1zX+CuNMZOtFjvMHBGghEF1pRUJQLU2BHN+sRBRcVlh5FGl0E3Gr5ETHeWjij4R0ui5WNu/AntLlgXuJbd8O0dTH6InKFnrKl2MmMbbKvApH/h5CAAXXRJ8BiJiUSE3oEELa9ZtNoC2wsnmHDGUmyYUFzwOaUGHNrm+SU9rhysbtqFb2guEZIRMUclGtQl5bmSNdV2XZszRH+g8zKHTd0P5QOLL8rK/lBsdWirYYEPJJDvYEJ39cFnQns1BEQ0lBZDz2be6Zrok/C2Zac0halYQHWE92yLCN35hI4gfI8rGb/NDT+QpQUH4Fvc+sqqK+AWYh6zS4cnIiR16HCgUYFtawPyCmvwHYL7T9OelZJKnser+b7YU0faFobkGaqm9Pggebd5oJ+WDzzujqP2wyijbmf1YOZHIcX5AogbGyeQcONu/0+6XvsZ3OGL+ycbuc+B0HUKVbdX9X1m6TWoZDQNFfm+m8l4Otu6R/Sj3XlY3bsbJ2G1Y2bpf3vXG78QeRo4RMoQAqFeHMlUELFZCqZa7pP8x9FgrmuQdMmcVCvPYZ95vHRD2FkZhFHtZSwqYxS2jF/W6dTEGJuSpx5taEa/QMBuBxtm1KcMZpDmkCIi6sMXKuLRQsGziVSua45ZO3xF6bnzouV3WlIrC6DrHZUD4GNdk71iD2PIhGQwoAuErFV4KB/EgTUuaCCBVHnFNRr/jSbLPmxVYaiRCAHcaphZCZMF1/fyebb2gF6a/yPLNPHxf+fUwYsNYULKxs3oHq4n5JHSIPjl3RmjbVc6BiQd4fyfukUjGi8QEIBBNkxfKpTwIAqpW9fla8wiHvbvnd4n7ZL6UFhe8pCbbWAgC1rVcCpSKIHKDgAsr8JU6tSgGxUAFaqrZ52JSnBJTRPr3QRG58PTqXxrqw5bOQbQTDuMPIlJyYkiyZ2bfRqe0B5TNFLjFjmsMZJxzSEFaD4xzOgYHPlmAoFADPSxQM8kACztoGABAbT8nznYJ8MZtNsJqQQWQ5olVkjHZgttu+/bjdBhzHUIEHBISO09fCwHZkx5kCwk5pwNdIwvdgEsNsAaKfkdWHGFs44CfkEbHv9ExY7euX3b3wAuwpXZ44UetJvbbtKnCzparhKY4qJSipVAKVpRDnRlOa7OBrbbFcV30iLBji+twvtCACgPo51wJrG7JuR7sFKpdl2K0nzHOXv4H6HZWjnNVChL34iTMy6VqLj8jCKiHCKJA7ocebrjei/yYIiESNwAoCsH2Bw3I6pyIXDrMNKujwUVITflDd1epvICKFpOOPU1Y21bkr4GzZIgfQ2joAwNm2FWi1/UHlCTCUsGm3pbYQchKyYEl3oF8KLUTMDSjHp4Pg6j4prDMsJPQ92APdtjFb5ivTT70/JUfE1pC0UIELwzwbpymABahc7moSDQvn2tYrgbJ2xjIwVwbmK6BmS/Z9syHvx3GCjLZTiqXHP45qZS9EQzrIHdcFb2xCrK3D+E4AAFJz0hqpEaSczpDKngdC0F8TibyKi0bSQkkkjCvtx0q4dqyAiPWNpGgYKf6RvsFIDuaYUsy2cLDDNDMMChNppLQAbrfVhEYmciNslqJiQU6SW7eA2m3Uf/htWPrW78X2hc97OujYKfDGppnwdruX+m0VHKDdhlDMqiYUkhR1dLMFO5QTgCz6Q45c8VlZvXIyhoxlTxIM9l8g2Y5MBLhkzF4mQ9t1pSmi2VTHuNLMZQtU4/BTZjPlN7Er1gVWeZaAOdi6K74/XcBeVQNA/VlvBBcccHkezqbqd7OFpUc+1ve1JgUrG7djt/MqAIDY2DTJkdIp75lxxULIXBqtIXohrS9BuzRFq7SGHUr8C/7uTnAM245x2/Rphw7HaARpQiCsXXQ0Xw0Fs+eQnm3h0IVgAGDMNSYjOTTgA1EdkCtnmq8AlTl5/FwZXC6itvMAlo8Ga3OsbNyO3e6lxt6scci7W77I5IBcB2KjaV1TxsSjUDD2Yv1y2v0yUS6esifbyXXW5BwbwaSFg3a2Jgxw4+vQjnHNVsqW01OvvpWJy5icmGV+gBAmi5Q9D/A8E3HkLMxDrK7KZubn081zfYCPPgVar4Dm56VDuFDA0oMfGsq1xolD4tORfbvdS/0Fh2CA5QIo1U4f+t+OAAP0RBzMUwkkXToI+MgiDm0rp4RKRd9vZC4ZneTjfIJhP2CUJ2sE2WczJhxmOloJQHRQZIl7brdltq/lbDPRFNpBpwVDoQC02lj63h8AJ06BGq2IYACUWalUwm730uj1VJ+EpmOwHGfseQFHq6kkp6OvwscLDpqAwojxo+ioFiqV/Inf2oxgsL8DAtdhZQaDpoIuFhUltGOc5jrOm5stGYFjRRwtH78ZVCiCBQ9NMABSk1h6/OPA2prUeBQ31ZmAQ97dMvqq1Qa3W2Z8x5lrIpuFSJReAsy4AeIXKwBM1B0Q1CoibXURHdXh+KGAofJYMmxTgtnWHJIQUlvlLpJCweK8D0RehMM9S0X1Yzex9OQnAABLj/1x4iWFoioIaw4BRMJL7Xh7nWgmrEOikSFmVRV+0ezVHDhmnzSR+TUgfPMUC2FpB0FNZPnIjcZRXK3sBQG+DV/Zsv3+CnCzlexUTnHeDhpLR24Y2bUmDfr57yldbsYWAF+TtMe7/qxNSiEzT8QBTEp7LOhCSWRII03QhOsEJklut4PcXwk5DuG/EadzwIdhZdMn+JIGa37KEK03ZZh94RAe6EBsNITczYBo+7ZMO+EotMpGq42lbla41jXD5qVD3t1S5S8UTby7Uyr56rud/ESOfLmsyT92gAsOOoAt2zLbTmGdMNVqS/+EI9sOh0tWF/dLM1BKqGfc5F5d3C8nhVZrYNE5OQYD44uwCRvj/AyAMSlF2gibdixTpU7WlKYs9f7YE3W4vRi/k/wjgu+o9TfCPRUKV40kxYUWheHPfWHGzEqzKxyIrAHtRlc3IQFh/qrEsoApRJPcmYQiGRZaXdyfecI7JD5tnIT62tW5K8Ceh4Otu0AO4WDzTmN2Eq220QpM5rUWCCr0M7HYCwvTV9K0G+pejBZQKsoY9Y3NiCCIQ68Tey4QJhdJWqzmi0qE8r/5ju7QO8UCgCN9Ttr31GoFzUwB86YVHp1QAz0R4cAH/a6arlKsn87OLpfJpZ0vld4P5NFK0wJyHDgL8+ofAikaA1kPWIBKFTkoiwXQFkWhzQw4rj8Rr67LcwoFYGFeRbY0gUIB4uQprKzeiurCvkyTKwBQoRjwPZDrggpFk9ilNYr6+a+Dd+QJf+U0Pw9uNHx1uliA0HUcbIGlVXVtLlK+AJ2cY5LolOYzTNt+jilGJ7+cNYmbRZflC5NfKNbXQM6KiEa8KQGSloMT1k5iV/pJ9dj1RxNRJReKjpW0SqUi0Iqe3jVyzWFK4DiSRmC+Ap4rgk6tyQzSjQ3jFOVmUw7O+Qqo0QRXSuBCASQERHkRbmUOdPI0eGMTNFcGL8yBSwU4q5ugHVtRv+D1mQUDAJz6fy/B9r87DBSLoMefkFFRDoEc5ZDbbJjIpUPe3cYmLE6fNqsf9gCy7L1UKvo0DEKF09mhokrzsVdu3Gx21e8cZxaSMtHt8Gnb5BmZrEPCxc7FMSt4O4JJF1OSFwlcL2Dusq5lfG3az2B/Di2UTJCFNqe6LmjnDqDVkgEJjgOc6uOBmQ7nwmEqwKUCePsWcFna1KntgU+diCZJbbsK1PaAZks6UxddiEoRIEJr5wIKRRfUbAMbm+CCA2p5EAtlcMHBwW90V73vrM98Q2og6xvAhefBWW8AJ09DrK7JPlomMHkTdoigzNgO1ITWmb0bmz7zpjJ/UbkEbGwaYWFqQ8xAoleO4UNXLAQQCXiIiw6ywZ4HYitQIrY6npXj4CVrCr4wsRgK9P/6fSBfI0FBvhdsqGcg34cdZwHNljIby5BqsWMLnI05+Y48mumxJCOcJzIDmFnhAAa4XMTm0yuYf+AYlh7+SOxhyydvQbWy1+c3cl24Z22DeNo2EBUh5ktAuQhsq8DZaAEPPw6nWADKZVR//N1wnjyBpYc+nK1PrgvxjJ1wTm+CGm254t++Dc7iPPjYCTloCwVAFZI52LrLaA+AWkkVHFDJsYqyOH5EEbN8CRpNcKsVcALm2kKOblBd2AfHMmcGVu4hxgC/7ogTXe0LP/op4EQOO6O1qcqmYNFt21F5jpVXo6LinC2LcqHUbMq/8xWQ1go8ASzOgysloFKCWJwDuw7cUxtwHj0qtZddTxvMQ8s1h+kAeQKi5KJ0siVX0ClICqGsn3sdeNfTsHzfe1B/7tuBYyfAnoelYzeZY2o7D6C27aps9nvXgXNSUmeIsxbQXizB3WzDPXwUdPbTgEoJ5DH4iaOozl0B0WrDmSsDgDSBqZfQKRYApQnQ4oLP1LrZkFuhIP0MRYI4vZo7hXN0DZvNlXSmvR3qaiEcEWSEQLFguL0CpicWQWZ3i/cr4jjWgRdKCzAEl9pEtDAv3wXXAW9ZAB49AtL8Y1u3+OYkoawJrgOv7MI7exHu1goK334YeOTIYB5aLhymBMwQJRfuZhu8ZQG1HVdL2uwuYOctLP3T78YeE5fwlgR62g6w4vnhoovW1iJaW4tYOLXo+wZcgJ6+E7y6pkJA24pEzg8lNPQFOlS1VJSmKteR2dp6kObhozl6RaCWh+IOU7k62rnr58QIMz4DPoFYc1JMfkEo78F0wdDHFILhsOocmp8HyiVzLjWaYCGwdOQG1La/WvrjXBdcLkn6l3YbEEWzcGxuLaKwczv4+48M4IFxHq00NWBG8dg6QITl+9+H+rPeOO4eQWyZQ2v7HKjNaG0poPxUA+6pDZljUCkBLU/6RkqSwrl+4fUQR4/5IawqzNY5+2n+yyTUqmrLok8kp/YNg2U0x2yjurBPliadn5d/CwWfFiVl8vPDruNjQgP5CKYGiGscxIZdmFTRKyvslUrSBwghgFJJChPtQ1MCQ2ytSK1cCbTl4zdHIgnrz34TXAG4LoFdF+1tZYjFucH4Chiy7soMgXjGVCGNbe5O/smnXwrecRYASBbOU6fHlhlbreyFc87ZADMaP7gL7QXXCAexUAa1BbyFEtxTDXDZhXtqU5rDyAEvlEFHT0g/gidU7oKd2MNAsQje3OxKk8mRQ6O2/dVyNa5rQqhsdr2qZ0WuaPwLOlRVaw3FAqgyJwWIop8BkV9WVXFpATAmIV3ZEFACQHGImT7Y41zTnHjC/04Iefz8vGyz2ezp/d5TuhyHWn/2FWa+pNfnt61wNv/ElldmOnblxJ/0da1RYWY1BxbCmIXqu64BFhYipF4jx+o6sFBB6YnTKCzOwSu7aF6wDXNPrGPjGbJ6V+VUA6JcQOPCs1A+3gBttoGCA9qyCDp+ElisyBdBlQjlRlP+r0LyqnNXpBaKyZEjDlQqAgsLhireUF+029KnVSr5q3wWYD1Zq+x9KijTz3xZOo1b8jwsKA2k1fYT4EzJ2wKoUgHmK9KZXJkDaKustLe+ATSbEGvrAZMTFQvgDTFQc+nB5p0g+rP+GtEh5DOEmRUOz3nRs8znSeDR0U7v2o6rQa023KYHp1KCu1kAF10UV9twNj2QECCP4TYE3BPrwNoGxK7tEFvmIHZuATHL/comTCdOSY0iwW6bI0cnVBf3wzlrm88ZVqwAp9dkPg4rZ3PBBdqeMZ3QXDkavllQdPcOAeWy77MQLM1BrZY0BxFJ7bct/YEousBcEdTyQOsbwMYmxOlVXzMBYqvlTRxmzAozs8JhUqGd4rVtV4GetgNU3AJqNEFtIW2mrgNnrYnC4+vA6hp4fQN08hToGbvky9MW8P7lX+E+Y5dfQUuZm5aP3zzmu8sxjdBFm7iyRWoMqxtSGBABbUeGcjsEOG1j/sHpVdDigtQgtE9CaRnGHOS4gLCKUWmKDO1DcEuSF1oJBX7quGIhbg6klseowbnmkGMQ0KGv9V3XAJU59YJ6wNo6nKNSE+CNTXhr63Aqc6BjJ0DlMlAowH3meeDFCujYKZ/GoNlMu1yOHImghXlgcR7e1grcExtyTBWL4LmizBlglglk8xVfO50ryyqGRU/6FFzlQG574M2Gb6bSaLbkQqbdlkKj6ErNd60BtFoQR49JnrEe6nVPBvJiPzkGjKUjN0gBoV86XURHV1Zz5UvET98hhYFiUaVjp6TtttHMNYYc/aHVAtoenPWWXMkXCuBSEVxSuQWrDaAozZ+8UIaz1pATPHmgORntQ4KkyagIUFvW9DClQYuK/FHTuGual7YqUrXZAFhMsWBATryXYzjQPpHajqtlxAeRVNefsQvOo0eAZ+ySKyzHMfWneU2SAk68HTbHdKDdlmVTPQG026CCC7QF0BbgggueK0BUSnICLBdlpv6mCk/VSaZl9b1KSgPLY9lxgEpJCpGmB2yuA03ITH5lipr2cczAzIWOT41wIKIagI9AVsC5mZnfP+YuDRzLx26SBXM0U6xStfHwo0C5LD87BDRbkdrIOXL0iqXHPy6TRDuUS6097x1Ss3AcUFtIQsvNlhQKgBQMc1YGMyAFg+vTaXMJ4O8fH2lhp5GA82I/YwERuQD+GMBuAIcB/AMRfYaZ/2m8PRs89EtTXdwfKFdKGxvmmGl01uWYbGRhD1i+P0g0Wdt5AJift6q4SSp4aRJVlBgOpG+h0QIaLeD0KpZnTTAoDKxo0IRgKoQDgBcDeICZvwcARHQXgFcCmDnhoJHTXuSYdGRNuKyf91osPfKxIfdmApBrDmPBeQAetv4/DODfhQ8iogMADqh/G0T0zRH0rRvsBHB03J0IIe9Tdkxiv6aiT0T/eUxdMej0nH6gn8ZP4/jKX/N/3Znx8En7vWIxLcIhrgp4RIdj5hsB3AgARHTvpKWo533KhknsEzCZ/cr7lA3D7hMz14bV9rjQoR7gxOAwgAus/89H/+U5cuTIkSNHAqZFOPwDgIuJ6CIiKgG4DMBnxtynHDly5JhZTIVZiZnbRPRrAFYgQ1lvYeb7O5w2ifSkeZ+yYRL7BExmv/I+ZcMk9mmiMbOU3Tly5MiRo3dMi1kpR44cOXKMELlwyJEjR44cEcyccCCiGhF9m4geIKK3jvjaDxHRN4joPiK6V+3bQUSHiOi76u926/i3qX5+m4iqA+zHLUT0hJ3n0Us/iOhF6n4eIKKPElFcSHE/ffptInpEPa/7iOjnR9ynC4jos0T0LSK6n4her/aP7Vml9Glsz4qI5ojoy0T0NdWnd0/Ac0rq01jH1EyBmWdmg3RW/wuAZwEoAfgagOeO8PoPAdgZ2vf7AN6qPr8VwAfU5+eq/pUBXKT67Q6oHy8F8GMAvtlPPwB8GcBPQOaZLAGoD7hPvw3gjTHHjqpP5wL4MfV5C4DvqGuP7Vml9Glsz0qdv6g+FwH8PYCXjPk5JfVprGNqlrZZ0xwMzQYzNwFomo1x4pUANBfGrQB+0dp/FzM3mPlBAA9A9r9vMPMXABzrpx9EdC6Arcz8RZZv0G3WOYPqUxJG1afHmPmr6vNpAN+CzMYf27NK6VMSRtEnZuZV9W9RbYzxPqekPiVhJGNqljBrwiGOZiPtxRo0GMBBIvoKSSoPANjFzI8B8sUH8HS1f9R97bYf56nPw+7frxHR15XZSZslRt4nIroQwAshV6AT8axCfQLG+KyIyCWi+wA8AeAQM4/9OSX0CZiQMTXtmDXhkIlmY4j4KWb+MQB1ANcR0UtTjh13XzWS+jGK/t0A4AcBvADAYwD+cBx9IqJFAH8O4A3MfCrt0FH1K6ZPY31WzOwx8wsg2QleTEQ/knL4OPs0EWNqFjBrwmGsNBvM/Kj6+wSAv4A0Ex1RqivU3yfG1Ndu+3FYfR5a/5j5iHrBBYCb4JvVRtYnIipCTsJ3MPN/U7vH+qzi+jQJz0r14wSAzwGoYULGlN2nSXlOs4BZEw5jo9kgogUi2qI/A9gD4Jvq+vvVYfsB/KX6/BkAlxFRmYguAnAxpGNsWOiqH8pMcJqIXqKiN/ZZ5wwEemJR+CXI5zWyPqk2/gTAt5jZrnQztmeV1KdxPisiOpuIzlKfKwB+DsA/Y7zPKbZP4x5TM4Vxe8QHvQH4ecgIj38B8I4RXvdZkNEQXwNwv742gKcBuAfAd9XfHdY571D9/DYGGCEB4M8gVeoW5MroV3vpB4BLIF+ufwHwn6Ey6gfYp9sBfAPA1yFf3nNH3KefhjQhfB3AfWr7+XE+q5Q+je1ZAfhRAP+orv1NAO/sdWyPoE9jHVOztOX0GTly5MiRI4JZMyvlyJEjR44BIBcOOXLkyJEjglw45MiRI0eOCHLhkCNHjhw5IsiFQ44cOXLkiCAXDjlmDkS02vmoHDlypCEXDjly5MiRI4JcOOSYWZDEB4nom4qv/1K1/2VE9Dki+q9E9M9EdEfO4Z8jRxCFcXcgR44h4pchCdieD2AngH8goi+o714I4HmQPDp/B+CnAPztGPqYI8dEItcccswyfhrAn7EkYjsC4PMAflx992VmPsySoO0+ABeOp4s5ckwmcuGQY5aRZipqWJ895Fp0jhwB5MIhxyzjCwAuVUVhzoYsVTpM5tscOWYG+WopxyzjLyBrA38Nkun0zcz8OBH9m/F2K0eOyUfOypojR44cOSLIzUo5cuTIkSOCXDjkyJEjR44IcuGQI0eOHDkiyIVDjhw5cuSIIBcOOXLkyJEjglw45MiRI0eOCHLhkCNHjhw5Ivj/ASaKO3yn21V+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_chl_obs.CHL1_mean.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_chl_cesm = xr.open_dataset('/glade/derecho/scratch/kristenk/hi-res-proc/FOSI-HR-surface_chl_1998-2005mean.nc')\n",
    "ds_mesozoo_cesm = xr.open_dataset('/glade/derecho/scratch/kristenk/hi-res-proc/FOSI-HR-mesozooC_1998-2005mean.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14b6bb23ecd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_chl_cesm.Chl_surf.plot(vmin=0,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14b6bab2a890>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_mesozoo_cesm.mesozooC.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "  --xr-background-color: #111111;\n",
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       ".xr-wrap {\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       ".xr-header {\n",
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       "  margin-bottom: 4px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;nlon&#x27; (nlon: 3600)&gt;\n",
       "array([   0,    1,    2, ..., 3597, 3598, 3599])\n",
       "Dimensions without coordinates: nlon</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'nlon'</div><ul class='xr-dim-list'><li><span>nlon</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e60de09b-8699-45b6-9a1d-82e3f107dce2' class='xr-array-in' type='checkbox' checked><label for='section-e60de09b-8699-45b6-9a1d-82e3f107dce2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 1 2 3 4 5 6 7 8 9 ... 3591 3592 3593 3594 3595 3596 3597 3598 3599</span></div><div class='xr-array-data'><pre>array([   0,    1,    2, ..., 3597, 3598, 3599])</pre></div></div></li><li class='xr-section-item'><input id='section-fa0f6a07-be16-41a6-9a4c-1c21f1f2403c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa0f6a07-be16-41a6-9a4c-1c21f1f2403c' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7663ec64-e433-4d9d-b4f7-d80c7506f71d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7663ec64-e433-4d9d-b4f7-d80c7506f71d' 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.DataArray 'nlon' (nlon: 3600)>\n",
       "array([   0,    1,    2, ..., 3597, 3598, 3599])\n",
       "Dimensions without coordinates: nlon"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_chl_cesm.nlon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_copepod = ds_copepod.rename({'lon':'nlon'})\n",
    "ds_copepod = ds_copepod.rename({'lat':'nlat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_chl_obs = ds_chl_obs.rename({'lon':'nlon'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_chl_obs = ds_chl_obs.rename({'lat':'nlat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_global['Chl']=esmlab.statistics.weighted_rmsd(ds_chl_cesm.Chl_surf, ds_chl_obs.CHL1_mean, dim=['nlat', 'nlon'], weights=area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_global['mesozoo']=esmlab.statistics.weighted_rmsd(ds_mesozoo_cesm.mesozooC, ds_copepod.copepod_C, dim=['nlat', 'nlon'], weights=area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.50800316)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['Chl'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(10.62872308)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['mesozoo'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "chl_spat_corr=esmlab.statistics.weighted_corr(ds_chl_cesm.Chl_surf, ds_chl_obs.CHL1_mean, dim=['nlat', 'nlon'], weights=area)\n",
    "mesozoo_spat_corr=esmlab.statistics.weighted_corr(ds_mesozoo_cesm.mesozooC, ds_copepod.copepod_C, dim=['nlat', 'nlon'], weights=area)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.37768537)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chl_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(3.10932429e-82)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chl_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.3493145)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesozoo_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(8.03812401e-70)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesozoo_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_cbpm = xr.open_dataset('/glade/work/kristenk/satellite_data/CBPM/POP_grid_x01_mean/POP_tx0.1v3/cbpm_npp_mean.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_cbpm = ds_cbpm.rename({'lon':'nlon'})\n",
    "ds_cbpm = ds_cbpm.rename({'lat':'nlat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_vgpm = xr.open_dataset('/glade/work/kristenk/satellite_data/VGPM/POP_grid_x01_mean/POP_tx0.1v3/vgpm_npp_mean.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_vgpm = ds_vgpm.rename({'lon':'nlon'})\n",
    "ds_vgpm = ds_vgpm.rename({'lat':'nlat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_cesm_npp = xr.open_dataset('/glade/derecho/scratch/kristenk/hi-res-proc/FOSI-HR-NPP_mean.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "}\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-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (nlat: 2400, nlon: 3600)\n",
       "Coordinates:\n",
       "    TLONG     (nlat, nlon) float64 ...\n",
       "    TLAT      (nlat, nlon) float64 ...\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    cesm_npp  (nlat, nlon) float32 ...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0834e3e1-e091-494c-a133-4baa9c48498e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0834e3e1-e091-494c-a133-4baa9c48498e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nlat</span>: 2400</li><li><span>nlon</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dc8d1d0b-fce1-495c-8c92-189b0679333c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc8d1d0b-fce1-495c-8c92-189b0679333c' 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>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-140d0265-e235-4e84-99ca-02d517e91f06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-140d0265-e235-4e84-99ca-02d517e91f06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d223c6-284e-4e26-99da-2cb45354a223' class='xr-var-data-in' type='checkbox'><label for='data-b9d223c6-284e-4e26-99da-2cb45354a223' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[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-d674d833-3aa7-4da7-ab98-8159224d4bf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d674d833-3aa7-4da7-ab98-8159224d4bf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acdfec57-5e8a-483d-9959-bd68b857adc9' class='xr-var-data-in' type='checkbox'><label for='data-acdfec57-5e8a-483d-9959-bd68b857adc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[8640000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e8516cb-ee54-498b-9969-5103d4d6a470' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e8516cb-ee54-498b-9969-5103d4d6a470' class='xr-section-summary' >Data variables: <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>cesm_npp</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6686097a-8385-4358-b704-c4741a9fcc92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6686097a-8385-4358-b704-c4741a9fcc92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebb8d896-2d0b-4a12-8e8a-a20f994810b1' class='xr-var-data-in' type='checkbox'><label for='data-ebb8d896-2d0b-4a12-8e8a-a20f994810b1' 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>Coccolithophores C Fixation Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>[8640000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ff1f20a-0ddd-4458-ae57-ff5f7d80690e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2ff1f20a-0ddd-4458-ae57-ff5f7d80690e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (nlat: 2400, nlon: 3600)\n",
       "Coordinates:\n",
       "    TLONG     (nlat, nlon) float64 ...\n",
       "    TLAT      (nlat, nlon) float64 ...\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    cesm_npp  (nlat, nlon) float32 ..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_cesm_npp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_global['NPP-VGPM']=esmlab.statistics.weighted_rmsd(ds_cesm_npp.cesm_npp, ds_vgpm.NPP, dim=['nlat', 'nlon'], weights=area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_global['NPP-CbPM']=esmlab.statistics.weighted_rmsd(ds_cesm_npp.cesm_npp, ds_cbpm.npp, dim=['nlat', 'nlon'], weights=area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(428.06580392)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['NPP-VGPM'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(275.0360726)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['NPP-CbPM'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgpm_spat_corr=esmlab.statistics.weighted_corr(ds_cesm_npp.cesm_npp, ds_vgpm.NPP, dim=['nlat', 'nlon'], weights=area)\n",
    "cbpm_spat_corr=esmlab.statistics.weighted_corr(ds_cesm_npp.cesm_npp, ds_cbpm.npp, dim=['nlat', 'nlon'], weights=area)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.2375735)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgpm_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(3.8124965e-32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgpm_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.40796359)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbpm_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(6.41673388e-97)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbpm_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now do deep nut biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/glade/campaign/cgd/oce/projects/FOSI_BGC/HR/g.e22.TL319_t13.G1850ECOIAF_JRA_HR.4p2z.001/ocn/proc/tseries/month_1'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = 'g.e22.TL319_t13.G1850ECOIAF_JRA_HR.4p2z.001'\n",
    "path = '/glade/campaign/cgd/oce/projects/FOSI_BGC/HR/g.e22.TL319_t13.G1850ECOIAF_JRA_HR.4p2z.001/ocn/proc/tseries/month_1'\n",
    "variables = ['NO3','SiO3','PO4']\n",
    "coords = {'x':'TLONG','y':'TLAT'}\n",
    "keep_vars = variables + list(coords.values())+['dz','KMT']\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(ds):\n",
    "    ds=ds.isel(z_t=32)\n",
    "    #ds=ds.isel(z_t_150m=0)\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1997\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "1998\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "1999\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2000\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2001\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2002\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2003\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2004\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2005\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2006\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2007\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2008\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2009\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2010\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2011\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2012\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2013\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2014\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2015\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2016\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "2017\n",
      "NO3\n",
      "SiO3\n",
      "PO4\n",
      "CPU times: user 9min 15s, sys: 1min 1s, total: 10min 17s\n",
      "Wall time: 12min 49s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "ds_ann = xr.Dataset()\n",
    "\n",
    "#for year in np.arange(1997,2006,1):\n",
    "for year in np.arange(1997,2018,1):\n",
    "    yr4=\"{:04d}\".format(year)\n",
    "    ds = xr.Dataset()\n",
    "    print(year)\n",
    "    for v in variables:  \n",
    "        print(v)\n",
    "        files = sorted(glob(f'{path}/{case}.pop.h.{v}.{yr4}01-{yr4}12.nc'))\n",
    "        \n",
    "        dsv=xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\",\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True,preprocess=preprocess)\n",
    "\n",
    "        ds = xr.merge((ds, dsv))\n",
    "\n",
    "    ds = ds.drop([v for v in ds.variables if v not in keep_vars]).squeeze()\n",
    "    ds = ds.mean(dim='time')\n",
    "    \n",
    "    ## add it to the annual file\n",
    "    ds_ann = xr.concat([ds_ann, ds], \"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds_ann.mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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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 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    TLAT     (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    NO3      (nlat, nlon) float32 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    SiO3     (nlat, nlon) float32 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    KMT      (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    dz       float32 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "    PO4      (nlat, nlon) float32 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-17da8dc9-7804-42ec-994b-e773d1ec7e80' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17da8dc9-7804-42ec-994b-e773d1ec7e80' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nlat</span>: 2400</li><li><span>nlon</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b1686957-5a0d-48b5-9d20-b7da1d6c92fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1686957-5a0d-48b5-9d20-b7da1d6c92fd' 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>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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-1bda7b6c-60fc-4122-bb7f-4ab62cf5f41e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bda7b6c-60fc-4122-bb7f-4ab62cf5f41e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06b93b96-5f63-4ec7-b2c4-78dfd574724d' class='xr-var-data-in' type='checkbox'><label for='data-06b93b96-5f63-4ec7-b2c4-78dfd574724d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-61340323-c842-4e6b-8501-841f591858fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61340323-c842-4e6b-8501-841f591858fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45512f58-87f4-480d-ac4a-ed53cffda993' class='xr-var-data-in' type='checkbox'><label for='data-45512f58-87f4-480d-ac4a-ed53cffda993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aae9d13f-d999-4c45-a17a-07d412480f22' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aae9d13f-d999-4c45-a17a-07d412480f22' 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>NO3</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-7867d088-a180-4e4a-bbff-7110d0abbe27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7867d088-a180-4e4a-bbff-7110d0abbe27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d9a6bed-209a-4e5c-8ca2-2ebcfe757810' class='xr-var-data-in' type='checkbox'><label for='data-6d9a6bed-209a-4e5c-8ca2-2ebcfe757810' 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>Dissolved Inorganic Nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 32.96 MiB </td> <td> 32.96 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 386 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\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>SiO3</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-9f1f2118-63fb-4314-b144-3d7a3c27579e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f1f2118-63fb-4314-b144-3d7a3c27579e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf379c9-d4e2-4578-ac39-c974b553b5f7' class='xr-var-data-in' type='checkbox'><label for='data-aaf379c9-d4e2-4578-ac39-c974b553b5f7' 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>Dissolved Inorganic Silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 32.96 MiB </td> <td> 32.96 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 386 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\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>KMT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-de3486d3-7549-4c72-a8d5-19b62259c27c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de3486d3-7549-4c72-a8d5-19b62259c27c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d26fbe1-c7bb-44c1-9a3f-b620e949d93c' class='xr-var-data-in' type='checkbox'><label for='data-5d26fbe1-c7bb-44c1-9a3f-b620e949d93c' 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'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 65.92 MiB </td> <td> 65.92 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 388 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"40.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,40.000000)\">2400</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-4256b2e2-be21-41e8-acea-d7c466193be2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4256b2e2-be21-41e8-acea-d7c466193be2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5dd801e-caa5-4ce5-bb03-abee4541dac6' class='xr-var-data-in' type='checkbox'><label for='data-b5dd801e-caa5-4ce5-bb03-abee4541dac6' 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>thickness of layer k</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4 B </td> <td> 4.0 B </td></tr>\n",
       "    <tr><th> Shape </th><td> () </td> <td> () </td></tr>\n",
       "    <tr><th> Count </th><td> 264 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-168f6525-778b-4497-aa9e-c588b5bf3fa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-168f6525-778b-4497-aa9e-c588b5bf3fa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8323d7eb-6b5b-438b-910e-5f843c6697d2' class='xr-var-data-in' type='checkbox'><label for='data-8323d7eb-6b5b-438b-910e-5f843c6697d2' 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>Dissolved Inorganic Phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 32.96 MiB </td> <td> 32.96 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 386 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-83ab02ba-7b23-4f02-a3ec-4e3ddeb46d19' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83ab02ba-7b23-4f02-a3ec-4e3ddeb46d19' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (nlat: 2400, nlon: 3600)\n",
       "Coordinates:\n",
       "    TLONG    (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    TLAT     (nlat, nlon) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    NO3      (nlat, nlon) float32 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    SiO3     (nlat, nlon) float32 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    KMT      (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    dz       float32 dask.array<chunksize=(), meta=np.ndarray>\n",
       "    PO4      (nlat, nlon) float32 dask.array<chunksize=(2400, 3600), meta=np.ndarray>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[read in WOA data above]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_global['NO3']=esmlab.statistics.weighted_rmsd(ds.NO3, ds_woa.NO3, dim=['nlat', 'nlon'], weights=area)\n",
    "rmse_global['PO4']=esmlab.statistics.weighted_rmsd(ds.PO4, ds_woa.PO4, dim=['nlat', 'nlon'], weights=area)\n",
    "rmse_global['SiO3']=esmlab.statistics.weighted_rmsd(ds.SiO3, ds_woa.SiO3, dim=['nlat', 'nlon'], weights=area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(17.52758871)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['NO3'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(1.37989054)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['PO4'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(31.2410961)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_global['SiO3'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "no3_spat_corr=esmlab.statistics.weighted_corr(ds.NO3, ds_woa.NO3, dim=['nlat', 'nlon'], weights=area)\n",
    "po4_spat_corr=esmlab.statistics.weighted_corr(ds.PO4, ds_woa.PO4, dim=['nlat', 'nlon'], weights=area)\n",
    "sio3_spat_corr=esmlab.statistics.weighted_corr(ds.SiO3, ds_woa.SiO3, dim=['nlat', 'nlon'], weights=area)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.50770765)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no3_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(1.9211884e-157)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no3_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.3200982)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "po4_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(2.60003242e-58)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "po4_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.7067292)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sio3_spat_corr.r.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sio3_spat_corr.p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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