Fig15_S11_vertical-xsection_nuts_npp.ipynb

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    "tags": []
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   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "from glob import glob\n",
    "from collections import OrderedDict\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import dask\n",
    "from ncar_jobqueue import NCARCluster\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy\n",
    "import cmocean\n",
    "import cartopy.crs as ccrs\n",
    "from scipy import stats\n",
    "import pop_tools \n",
    "\n",
    "from statistics import mean\n",
    "from scipy import stats\n",
    "import utils\n",
    "\n",
    "import metpy.calc as mpcalc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define case and path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = 'g.e22.TL319_t13.G1850ECOIAF_JRA_HR.4p2z.001'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/glade/campaign/cgd/oce/projects/FOSI_BGC/HR/' + case + '/ocn/proc/tseries/month_1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid = pop_tools.get_grid('POP_tx0.1v3')\n",
    "lons=ds_grid.TLONG\n",
    "lats=ds_grid.TLAT\n",
    "area=ds_grid.TAREA\n",
    "area_m=ds_grid.TAREA * 1e-4\n",
    "lons_norm = utils.normal_lons(lons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define variable list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = ['diatC','NO3','HMXL','Fe','TEMP','HMXL_DR']\n",
    "coords = {'x':'TLONG','y':'TLAT'}\n",
    "keepthese=['z_t','z_t_150m','time_bound','time','dz','TAREA','REGION_MASK'] + variables + list(coords.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(ds):\n",
    "    ds=ds.isel(z_t=slice(0,17))\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  diatC\n",
      "starting on  NO3\n",
      "starting on  HMXL\n",
      "starting on  Fe\n",
      "starting on  TEMP\n",
      "starting on  HMXL_DR\n"
     ]
    }
   ],
   "source": [
    "ds_fosi_x01 = xr.Dataset()\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print('starting on ', var)\n",
    "    \n",
    "    files = sorted(glob(f'{path}/{case}.pop.h.{var}.199901-199912.nc'))       \n",
    "    ds_tmp = xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\",combine='nested',\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True,preprocess=preprocess)\n",
    "\n",
    "    keep_vars=['z_t','z_t_150m','time_bound','dz','TLAT','TLONG','time'] + [var]\n",
    "\n",
    "    ds_tmp = ds_tmp.drop([v for v in ds_tmp.variables if v not in keep_vars])\n",
    "        \n",
    "    ds_fosi_x01 = xr.merge([ds_fosi_x01,ds_tmp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_fosi_x01[\"time\"] = ds_fosi_x01.time_bound.compute().mean(dim=\"d2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (d2: 2, nlat: 2400, nlon: 3600, time: 12, z_t: 17, z_t_150m: 15)\n",
       "Coordinates:\n",
       "  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 1.55e+04 1.651e+04\n",
       "  * z_t_150m    (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG       (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    TLAT        (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "  * time        (time) object 1999-01-16 12:00:00 ... 1999-12-16 12:00:00\n",
       "Dimensions without coordinates: d2, nlat, nlon\n",
       "Data variables:\n",
       "    dz          (z_t) float32 dask.array&lt;chunksize=(17,), meta=np.ndarray&gt;\n",
       "    time_bound  (time, d2) object dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "    diatC       (time, z_t_150m, nlat, nlon) float32 dask.array&lt;chunksize=(12, 15, 2400, 3600), meta=np.ndarray&gt;\n",
       "    NO3         (time, z_t, nlat, nlon) float32 dask.array&lt;chunksize=(12, 17, 2400, 3600), meta=np.ndarray&gt;\n",
       "    HMXL        (time, nlat, nlon) float32 dask.array&lt;chunksize=(12, 2400, 3600), meta=np.ndarray&gt;\n",
       "    Fe          (time, z_t, nlat, nlon) float32 dask.array&lt;chunksize=(12, 17, 2400, 3600), meta=np.ndarray&gt;\n",
       "    TEMP        (time, z_t, nlat, nlon) float32 dask.array&lt;chunksize=(12, 17, 2400, 3600), meta=np.ndarray&gt;\n",
       "    HMXL_DR     (time, nlat, nlon) float32 dask.array&lt;chunksize=(12, 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-79c98b6e-c1f1-44d2-baaa-e1f415d5cd85' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79c98b6e-c1f1-44d2-baaa-e1f415d5cd85' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>d2</span>: 2</li><li><span>nlat</span>: 2400</li><li><span>nlon</span>: 3600</li><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>z_t</span>: 17</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-33415481-415c-4cec-8976-7935f205d432' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33415481-415c-4cec-8976-7935f205d432' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.651e+04</div><input id='attrs-cad12162-6ac7-436b-8052-eac98b2dcfcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cad12162-6ac7-436b-8052-eac98b2dcfcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f5cf38c-c8d3-44fb-82a5-83a70b1be910' class='xr-var-data-in' type='checkbox'><label for='data-2f5cf38c-c8d3-44fb-82a5-83a70b1be910' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>587499.06</dd></dl></div><div class='xr-var-data'><pre>array([  500.  ,  1500.  ,  2500.  ,  3500.  ,  4500.  ,  5500.  ,  6500.  ,\n",
       "        7500.  ,  8500.  ,  9500.  , 10500.  , 11500.  , 12500.  , 13500.  ,\n",
       "       14500.  , 15500.  , 16509.84], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-3272f3f7-5304-49de-bb86-b45a6246d192' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3272f3f7-5304-49de-bb86-b45a6246d192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a822f237-8827-4692-b60e-4ce44fcfbaf3' class='xr-var-data-in' type='checkbox'><label for='data-a822f237-8827-4692-b60e-4ce44fcfbaf3' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</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'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-f67c18a5-befb-4ac8-b465-4a31b5e20e3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f67c18a5-befb-4ac8-b465-4a31b5e20e3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d9a510b-7f7f-4efc-90b8-eb801ac39c13' class='xr-var-data-in' type='checkbox'><label for='data-6d9a510b-7f7f-4efc-90b8-eb801ac39c13' 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'><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> 5 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",
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       "\n",
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       "  <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",
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       "\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>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'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-8eb18edb-c61d-4487-80c3-2d41a18c0cd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eb18edb-c61d-4487-80c3-2d41a18c0cd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a2cb98-1b96-412f-b2a0-9114094a7b4e' class='xr-var-data-in' type='checkbox'><label for='data-44a2cb98-1b96-412f-b2a0-9114094a7b4e' 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'><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> 5 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",
       "  <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",
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       "\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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1999-01-16 12:00:00 ... 1999-12-...</div><input id='attrs-67cb93f8-00b9-4b1e-bf53-012eab4ac870' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67cb93f8-00b9-4b1e-bf53-012eab4ac870' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-638fc2d4-a0d0-4063-bb4a-03cf2d8ce1af' class='xr-var-data-in' type='checkbox'><label for='data-638fc2d4-a0d0-4063-bb4a-03cf2d8ce1af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1999, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 3, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 4, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 5, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 6, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 7, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 8, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 9, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cdca7479-9f4f-426f-b05e-b1b7fa5f3622' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cdca7479-9f4f-426f-b05e-b1b7fa5f3622' class='xr-section-summary' >Data variables: <span>(8)</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>dz</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(17,), meta=np.ndarray&gt;</div><input id='attrs-3cab7607-f852-4e21-b0d8-a8aad7e93b2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cab7607-f852-4e21-b0d8-a8aad7e93b2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c09659e-a6fe-4eda-b57b-d6fb4abcaa91' class='xr-var-data-in' type='checkbox'><label for='data-5c09659e-a6fe-4eda-b57b-d6fb4abcaa91' 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> 68 B </td> <td> 68 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (17,) </td> <td> (17,) </td></tr>\n",
       "    <tr><th> Count </th><td> 6 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=\"85\" 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=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"55.492514\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >17</text>\n",
       "  <text x=\"140.000000\" y=\"17.746257\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.746257)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bound</span></div><div class='xr-var-dims'>(time, d2)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-6a1398d2-7665-43c8-b02a-a3f64aef9455' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a1398d2-7665-43c8-b02a-a3f64aef9455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dac9e2e5-d1e7-4635-be6e-68f26ff2cb22' class='xr-var-data-in' type='checkbox'><label for='data-dac9e2e5-d1e7-4635-be6e-68f26ff2cb22' 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>boundaries for time-averaging interval</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> 192 B </td> <td> 192 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (12, 2) </td> <td> (12, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"91\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"41\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <text x=\"20.601915\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"61.203829\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,61.203829,60.000000)\">12</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC</span></div><div class='xr-var-dims'>(time, z_t_150m, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 15, 2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-81398f6f-c5da-4b93-aa42-2a6dfa1a2fa8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81398f6f-c5da-4b93-aa42-2a6dfa1a2fa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efcb6b9c-9f2e-41c2-b382-ebca2910d57e' class='xr-var-data-in' type='checkbox'><label for='data-efcb6b9c-9f2e-41c2-b382-ebca2910d57e' 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>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</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> 5.79 GiB </td> <td> 5.79 GiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (12, 15, 2400, 3600) </td> <td> (12, 15, 2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(time, z_t, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 17, 2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-65e1ede0-7367-4c69-a208-3815c6298161' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65e1ede0-7367-4c69-a208-3815c6298161' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6a9c8cc-af66-4f5b-9873-19e4148b8a95' class='xr-var-data-in' type='checkbox'><label for='data-c6a9c8cc-af66-4f5b-9873-19e4148b8a95' 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>Potential Temperature</dd><dt><span>units :</span></dt><dd>degC</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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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (d2: 2, nlat: 2400, nlon: 3600, time: 12, z_t: 17, z_t_150m: 15)\n",
       "Coordinates:\n",
       "  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 1.55e+04 1.651e+04\n",
       "  * z_t_150m    (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG       (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    TLAT        (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "  * time        (time) object 1999-01-16 12:00:00 ... 1999-12-16 12:00:00\n",
       "Dimensions without coordinates: d2, nlat, nlon\n",
       "Data variables:\n",
       "    dz          (z_t) float32 dask.array<chunksize=(17,), meta=np.ndarray>\n",
       "    time_bound  (time, d2) object dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "    diatC       (time, z_t_150m, nlat, nlon) float32 dask.array<chunksize=(12, 15, 2400, 3600), meta=np.ndarray>\n",
       "    NO3         (time, z_t, nlat, nlon) float32 dask.array<chunksize=(12, 17, 2400, 3600), meta=np.ndarray>\n",
       "    HMXL        (time, nlat, nlon) float32 dask.array<chunksize=(12, 2400, 3600), meta=np.ndarray>\n",
       "    Fe          (time, z_t, nlat, nlon) float32 dask.array<chunksize=(12, 17, 2400, 3600), meta=np.ndarray>\n",
       "    TEMP        (time, z_t, nlat, nlon) float32 dask.array<chunksize=(12, 17, 2400, 3600), meta=np.ndarray>\n",
       "    HMXL_DR     (time, nlat, nlon) float32 dask.array<chunksize=(12, 2400, 3600), meta=np.ndarray>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_fosi_x01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now grab year for eq Pac timeslice (Sept 1960)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  diatC\n",
      "starting on  NO3\n",
      "starting on  HMXL\n",
      "starting on  Fe\n",
      "starting on  TEMP\n",
      "starting on  HMXL_DR\n"
     ]
    }
   ],
   "source": [
    "ds_fosi_x01_2 = xr.Dataset()\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print('starting on ', var)\n",
    "    \n",
    "    files = sorted(glob(f'{path}/{case}.pop.h.{var}.196001-196012.nc'))       \n",
    "    ds_tmp = xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\",combine='nested',\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True,preprocess=preprocess)\n",
    "\n",
    "    keep_vars=['z_t','z_t_150m','time_bound','dz','TLAT','TLONG','time'] + [var]\n",
    "\n",
    "    ds_tmp = ds_tmp.drop([v for v in ds_tmp.variables if v not in keep_vars])\n",
    "        \n",
    "    ds_fosi_x01_2 = xr.merge([ds_fosi_x01_2,ds_tmp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_fosi_x01_2[\"time\"] = ds_fosi_x01_2.time_bound.compute().mean(dim=\"d2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now find at what latitude index we need to make transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x150105154c50>"
      ]
     },
     "execution_count": 14,
     "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_grid.REGION_MASK.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39.04835473, 39.0483857 , 39.04844762, ..., 39.04844762,\n",
       "       39.0483857 , 39.04835473])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid.TLAT.isel(nlat=1636).values ### Cross section through CA current at 39N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.00615491, 30.00615932, 30.00616816, ..., 30.00616816,\n",
       "       30.00615932, 30.00615491])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid.TLAT.isel(nlat=1500).values ### Cross section through SE US shelf current at 39N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.04959423, 2.04959423, 2.04959423, ..., 2.04959423, 2.04959423,\n",
       "       2.04959423])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid.TLAT.isel(nlat=1202).values ### Cross section through Eq. Pac at 2N\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### grab months for plots and make the trasect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_CAcurr = ds_fosi_x01.isel(time=2,nlat=1636)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_SEUS = ds_fosi_x01.isel(time=2,nlat=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_eqPac = ds_fosi_x01_2.isel(time=8,nlat=1202)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure out which longitudinal extents to show in the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x150105371090>"
      ]
     },
     "execution_count": 21,
     "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_fosi_x01.NO3.isel(time=2,z_t=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x15013b8aa710>"
      ]
     },
     "execution_count": 22,
     "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_CAcurr.NO3.isel(nlon=slice(3300,3500)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x15010503b090>"
      ]
     },
     "execution_count": 23,
     "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_CAcurr.diatC.isel(nlon=slice(3300,3500)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1501053c3850>"
      ]
     },
     "execution_count": 24,
     "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_SEUS.NO3.isel(nlon=slice(400,500)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x150105006550>"
      ]
     },
     "execution_count": 25,
     "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_SEUS.diatC.isel(nlon=slice(400,500)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds_eqPac.TLONG.isel(nlon=slice(2900,3600)).values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x150105267bd0>"
      ]
     },
     "execution_count": 27,
     "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_eqPac.NO3.isel(nlon=slice(2900,3600)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1501054b8b90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1hZSEhyXno2d3T7O4J+Zs6InWlkVXsBZI+swtZ+FiTxO1hepVUJLEbcss4lxCMSm0Vj0x57Kn3Vu2RHv5BPesWcYdnS25y7/k7rs2Y/PJnrHKYr0qmSJs3I2DJIhcOOv5pOeojikpKdr5ngdp3fT3BEkY5QyCok/kue8h9rSFVbMnCzyXHdxRKnPSFC1l0eWaeHNd4FlJ8HmaTSaJVw/R6qkLQrtwfhb9Ys/5+WhqudVNVCLRBUL5WXd/U+t5qrQ1m+X0OIo2yD3hauQkJJ3L6drTKpItVZFOdToLP3dFo8mflSyWTJk/A1V47aiEmafYWWRB5wmjJSXFRIdWq2TJRPpeut1Kos4dTfl+qGWKiZJWUVJkO90R9SVsXUHn6nM+WSQ721OFy6vPq4v173KMyShzQzi6IAiCeUQnFoyPnXB0QRAE8wQjJh0/y+MmnmgQBME8ISajzA3xRIMgCOYJRnTc7GiCpIMlXSdppaTjBsRL0gdz/JWS9pspr6SXSbpGUilp/1r4syRdJumq/PqM9XwcYyMcXRAEwTyipGh0zISkFvAR4BBgL+AVkvbqS3YIaZuzPYCjgI81yHs18GLgu31l3Q483/bvk7ZE++Ssb36OiK7LIAiCeYLNOJcXHACstH09gKQzgUNJ25tVHAqckbc/u0jStpJ2BHYdltf2tTmsz3ZfUbu8BlguaZntNeO6oXVlxicq6V1NwoIgCIL1I01GaTU6SHt3Xlo7juorbifg5tr1qhzWJE2TvKN4CXDFfHBy0KxF9yzgLX1hhwwIC4IgCNaTWUxGud32/iPiBw3k9a/UHJamSd7BlUp7A+8Cnt0k/YZgqKOTdDTwemB3SVfWorYC/meuDQuCIFhsGI1z49VVwC61652BXzZMs7RB3mlI2hk4Bzjc9s/WweY5YdRfh88AzwdW5NfqeLztgbt+rw+STpF0m6SrB8T9jSRL2qEWdnyeDXSdpOfUwh+fZ/2szLOJlMOXSfpcDv++pF3HfQ9BEATrS4ei0dGAS4A9JO0maSlwGOn3vM4K4PA8+/JA4E7btzTMOwVJ2wJfBY63Pa8aQ0Oflu07bd9o+xUkrz9JarpuKemhc2DLacDB/YGSdiF1n95UC9uL9OD3znk+mmcJQZo1dBS9mURVma8Bfmv7EcD7SU3rIAiCeYNJ0oFNjhnLstvAscD5wLXAWbavkfQ6Sa/Lyc4FrgdWAv9B6sUbmhdA0oskrQKeBHxV0vm5rGOBRwD/KOkH+XjQOJ7L+jLjGJ2kY4G3A7cCWYUOA/uO0xDb3x3Syno/8HfAl2phhwJn5oHOGyStBA6QdCOwte3vZdvPAF4InJfzvD3nPxv4sCTl2UZBEATzAHW1cseB7XNJzqwedlLt3MAxTfPm8HNI3ZP94e8E3rmeJs8JTSajvAl4pO075tiWaUh6AfAL2z/sm8q6E3BR7bqaETSZz/vDqzw3Q/q3IulO4AGktR/99R5FahWy5e9twc6b/Q6AQiVLipKtW/ezVWs1mxdrWO0llC66H84l6rAklzNJiyIr1BZ2FnRu0XGaWbWmnGB1Zwn3tZeyujPB2k6L1e0lrGlPcM/9y2ivbSWh2Swq645Qu5g6JDxF5LYSJq4r0/bdXJniK9FldXqizmVrus9XKbIWcK+qLO5LKYpOKrO1Foo1pOuqXkG5BDrLoLO64O77l3P75FZ86Z7dWX7dcibu6/TqSUrVU8Sgk711keWp6Yp2CaVR6fRqTxVlBlAS3vWUz497cfneu9msnsBzN6iuBtz/fHoPR6VxZ2q53fxZBHmkqHP/46/bIeEqQSUUXQkrQ1fg2YLuvp31j0EWhS5b4Gynsh1dAefqWVRizpVodH6saufrop5H0wSgqd1jJQjdtaOet/Z5rYSb6yLUGNSpPmtT76l7v4WnhEMSTl87OZE2MV09gde2ukLM1bOGJNjc6RSU+TmXnYKyVBKBrn3nKJPgtCaTnXTUFaLuCjoLGPD9mS2GakZlMEaaOLqbgTvn2pB+JG0OvJXBM3fWZaZQ41lEtk8GTgZ44F4PiBZfEAQbBFuNuiWD2dHE0V0PXCjpq0B3TYTt982ZVYmHA7sBVWtuZ+BySQcwfKbQqnzeH04tzypJE8A2wG/m8gaCIAhmS+xHN36aPNGbgG+QpptuVTvmFNtX2X6Q7V1t70pyVPvZ/hVp9s9heSblbqRJJxfn2UJ3Szowz7Y8nN7Y3gqSLA3AS4FvxfhcEATzibQfnRodQXNmbNHZfseGMETSZ4GnkVb7rwLeZvsTQ2y6RtJZJCmbNnCM7WrA52jSDM7NSJNQzsvhnwA+mSeu/IY0azMIgmAeETuMzwWjFoz/u+03SfoyA8aybL9gnIbkZQyj4nftuz4BOGFAukuBfQaErwZetn5WBkEQzB1peUG01sbNqBZdpTz9ng1hSBAEwWKn0roMxstQR2f7svz6nbwyfs8cdZ3tyQ1hXBAEwWKjyRY8wexosmD8acDpwI2kKfq7SDrCdv9eREEQBMF6kLbpia7LcdNkecF7gWfbvg5A0p7AZ4HHz6VhQRAEi5EYoxs/TRzdksrJAdj+iaQlozIEQRAEsyftXhBdl+OmiaO7VNIn6E1O+VPgsrkzKQiCYHGSJMDC0Y2bJo7uaJLo5xtIY3TfBT46l0YFQRAsThZPi07SHwC7UvNDts+Yi7qaLBhfI+nDwAUkadXrbK+dC2PmI0vV4YFL756iRNBSSQdxZ2dz7u4s74ZXfesdCkqLNeUEpQsm87Yaky5Ym4WcV7cnuHtyGZOdFmvaE6yZnKDdKZhcM0G5poXumUCToshCt+rUhG9highwPWwoOX0lnFvlUaeeSN200+jTRO6KQreTkHMxCUXbPVuAsgXtzZVEn1eL1fcs43/u2J3rb9uBB1/dYcm9nSn608k+9wR8YZrAMlUaG7WzkHPHqNOBMqXvijC3KnFl9YSWW8JkUeUBZU97bKoJMCsJKU99LlPHUyrR6V4BRhIuhJRtKwZUVLvnwZPu3LMnV1QJNSdB4Sz6XAkqM1WsOQlKG7Xoii27qMX3izLXxJm74s71fNATcq50xgeV2c+U8qeLW0+pn/w56zDlvenWX30vOsAkuCgoDWvbOUEpWN2imFT1yMDChfHanLaTVauVKy/B7aIr3lxUws2l0GR+7X4ne98DF+DWoBuePYtB9UTSJ0kyjz8A6lLwG8fRSfpj4CTgZ6SPw26SXmv7vNE5gyAIgtmwiGZd7g/staFkGJvOuny67ZUAkh5O2kU2HF0QBMGYWSRdl1cDvwfcsiEqa+LobqucXOZ64LY5sicIgmDRkmZdLooW3Q7AjyRdzNRdccYqLVnRxNFdI+lc4CxSH+rLgEskvTgb9sW5MCwIgmCxYaC9OFp0b9+QlTVxdMuBW4Gn5utfA9sDzye9L18EkHTXDOUIuMX2njOkC4IgWLQshq7LLC35YOAJOehi23PWU9hk1uWRDcv6me3HjUog6YqGZQVBECw+vDi6LiW9HPg34EJSI+hDkv7W9tlzUd+obXo+xIgJ67bf0Bf0kgb1NUkTBEGwKKk2Xl0EvBV4QtWKk/RA4JvAnDi6UW3kS0kKKMuB/YCf5uOx9NY9dLF9PYCkLSQV+XxPSS+oJMOqNEEQBMFgytyqm+nYxCn6uirvYLQ/Wr/KhkXYPt326cAepOUFH7L9IeAgkrMbxneB5ZJ2Ii0yP5K04/dIJJ0i6TZJV9fC/k3SjyVdKekcSdvW4o6XtFLSdZKeUwt/vKSrctwHpbTCVtIySZ/L4d+XtOtMNgVBEGxIqo1XF4Gj+5qk8yX9maQ/Iy1ZO3euKmviQR8CbFW73jKHDUO27wNeDHzI9ouAvRrUcxpwcF/YN4B9bO8L/AQ4HkDSXsBhwN45z0clVbsVfgw4iuSg96iV+Rrgt7YfAbwfeFcDm4IgCDYYRrTLotGxqZIbHx8EPg7sCzwGONn2W+aqziazLk8ErpD07Xz9VEZPDZWkJ5HEn1/TtB7b3+1vZdn+eu3yIuCl+fxQ4Ezba4AbJK0EDpB0I7C17e9lQ84AXkha3H5oze6zgQ9L0oZamR8EQdCEhT5GZ9uS/sv248mz9ueaJg7oVEnnAU/MQcfZ/tWILG8ktbzOsX2NpN2Bb49I35Q/Bz6Xz3ciOb6KVTlsMp/3h1d5bgaw3ZZ0J/AA4Pb+iiQdRWoVsvWOm3FXZ7OuLE9LZjL/m7q3s4z7O0tol61u3moNzNpOi/vaS+iUBZNli9LCFpNlwWS7xWS7RbvdouwUlG3hyRZMiuK+FkvuF601WUvPNa2/mral8nVdv7KufznqvFuOa/qUdf1L13QK+7QQpzynMufvgDru2VSaopPytJcXtDcTE2vE/WuW8pO7dmHzXxQsv/2+nk5j//Ov//VwTT/TTjqXHaOyRO0yaSZV56WhUNKWlJLWpYSLAhX0zrvPIVfUr5FZUVQSiFU5dLUt+7UaLSXdze5zzs8jp1eZtCklkv0DdTVz+nJ6HDZIqPvm9eqlALc1RSOy+2wFFDUdzLo+Jh6cJ5ef7tfTtDPrrz0NzL76Kxv7w+gro+i91o/6Z7T6fLqVHoMLcJle02dQlBPCa4SLFqimQ9nW1M++oGwJrzVuFVC495yq70lHve9d2Tuvvo+UVb3pHEG5JB3rjce7H52kg4EPAC3gP22f2BevHP9c4D7gz2xfPiqvpJeRGg2PBg6wfWmtvONJDZwO8Abb5w8x7SJJT7B9ybjudRSjZl0+yvaPJe2Xg27Orw+R9JDqYfTlaQHPr69uzxNQ+mdozgpJbwXawKeroAHJPCJ8VJ7pgfbJwMkAO+69XbT4giDYIFRjdOMg/x5/BHgW6U//JZJW2P5RLdkh9IZ5nkga+nniDHmvJg1NfbyvvvqQ0kOAb0ra0/a0yYvA04HXSvo5cC/5r0Yepho7o1p0bya1at47IM7AM6YF2h1JY915XNIRwPOAg2rdjKuAXWrJdgZ+mcN3HhBez7NK0gSwDfCbcdoaBEGwvoyxRXcAsLI2I/5M0hBO3dEdCpyRf1svkrStpB1J2+cMzGv72hzWX9/AISXgewNsO2Q8t9iMoY7O9lH59emzLPMKSSuAz5M8dVXerPtic9P5LcBT8wSXihXAZyS9j/TPYQ/SyvqOpLslHQh8Hzgc+FAtzxGkh/5S4FsxPhcEwXzCiE7ziSY7SLq0dn1y7o2q6A7XZFbRG4IalWanhnn7GTak1EXS9vn07hnKGitNtulpAX/M9A3y3jcky/akNRH1Fl9XKmxEPZ8FnkZ681YBbyON9S0DvpH/PVxk+3V57O8s0j+TNnBMrXl8NGkG52akSSjVLgufAD6Z/2X8htTEDoIgmFfMYjLK7bb3HxHfZLhmXYaB1qe+y2Yof/cZ6lgnmsy6/DKwGriKwcPkU5iFZFh/vlcMCP7EiPQnACcMCL8U2GdA+GqSIHUQBMG8xOOdjDJsiKdJmqUN8s66Ptu7zWj1HNDE0e08mwFCSXuSBjQfbHsfSfsCL7D9znU1MgiCYLHg8Tm6S4A9JO0G/ILUi/XKvjQrgGPzGNwTgTtt3yLp1w3y9jNwSGlcN7M+NOkMPk/Ss2dR5n+QuhwnAWxfSXQTBkEQNKCZKkqTVp/tNnAscD5wLXBWHvZ5naTX5WTnkvYYXUn67X79qLwAkl6Uh5eeBHxV0vk5zzWk7dx+BHyNqUNKG5UmLbqLgHOyfuUkvWmgWw9Jv7nti/tm5LTXz8wgCILFwRhbdNg+lz5pLdsn1c4NHNM0bw4/BzhnSJ6BQ0obmyYtuveSPPfmtre2vdUIJwdwu6SHkwchJb2UDbRdehAEwaaMDZ1SjY5NGUkHStqqdr2VpJlmda4zTVp0PwWunsVU/GNIi60fJekXwA0kObAgCIJgBha6BFjmY6RdcSruHRA2Npo4uluAC7MM2JoqcMTyAtt+pqQtSFsx3J0HNIMgCIIRJGW6ReHopugM2y6zkMec0KTr8gbSdjtLSbsYVMcwvgBg+17b1aLAOdlMLwiCYGExvsko85zrJb1B0pJ8vJE0KWZOaCLq/I4mBUl6FEnjbBtJL65FbU3avHWT5P7OEq658/dolwUTRUmBKfNWGve3l7BmcoKSJNjcKcX9q5d283baLcqOcNWfbqAU7iRVW7UFHVG0RWuNKNowcS+oDcVkT9C2K7pcE7jthbknNsvUtF1hYfrEfCsB29IUk1B0jDpJiLmi+h5VQsbTxHrrdZRJ1Lkqs7W2pFjTAcOyQpRLC5bdtYSJ+1q0by/Y8hclxdpaZf1SQv295Fl0OQ1gGHU6XWFnyiTqTLud4osWtApUJHVgS6goUx1FMb3sWvldAWdIfwHLngi0BUK4yCLHRRKOdqsn9qyOKdrVUlPhvFZWZX5GkNWI8/1U95pFqFWJOlfPvSqpu3rV3fRVGtWeX12cuXqfuqLPU8Lq72XtMwEwSMAZ0j2LrsBz3Y6ubdVZ32elEm6eEl+rt2wlweZyQkm4uSYsXRcbd6suIt0ryxP5uqhVkr83Uz//WRC61buP7v1Uz7sujl7//pX0vh9l7buY7UplMRYWiV7T60hb9fwD6alfQBbSnwvG2VR8JEmTclvg+bXwu4G/HGM9QRAEC5bF0HWZdxffYMvOxubobH8J+JKkJ1X7wQVBEATNSbMuN91NVWdC0t/ZfrekDzFAUsz2eu10M4y5GPy7Q9IFhDJKEATBrFngXZfX5tdLR6YaM01EnXcm7QDwhySty/8HvNH2qiFZ/gP4W/JeRbavlPQZIBxdEATBDCzkrkvbX86vp88mn6S7ZkoC3GJ7z0GRTVp0pwKfoSeI/Koc9qwh6UMZJQiCYB1wnti20MmayH/D9F1xpu1zmvmZ7cfNUOYVw+KaOLoH2j61dn2apDeNSB/KKEEQBOvIwu657PJ54CTgP4EmepgvWZ80TRzd7ZJeBXw2X7+CtN/cMAYpo7yqQT1BEASLG9NbjrSwadv+WNPE1U7n65qmyfSePwdeDvyK1DJ7KTB0zznb19t+JvBA4FG2/9D2jQ3qCYIgWPTYanRs4nxZ0usl7Shp++oYlljSoySdJ+mrkh4u6TRJv5N0saRHz1RZkxbdLrZf0Ffpk4Gbhhi0LXA4ue+1Gqubq2mjQRAEC4kFPuuy4oj8+re1sFE7jJ8M/BuwJfAt4C2kBtfzgA8DB42qrEmL7kMNwyrOJTm5q0jbplfHSCSdIuk2SVfXwraX9A1JP82v29Xijpe0UtJ1kp5TC3+8pKty3AeVPa2kZZI+l8O/L2nXmWwKgiDYkFRalwu9RWd7twHHMCcHsJXtL9v+LDBp+0wnvgxsNyIfMKJFJ+lJwB8AD5T05lrU1kBrRJnLbb95RPwwTiN55jNqYccBF9g+UdJx+fotkvYirarfm7ST7Tcl7Zk3+fsYSUrmIpLTPRg4D3gN8Fvbj5B0GPAu4E/Wwc4gCIK5wfRpqC1cJO0D7EVNItL2GUOS131O/4YCS5mBUS26paRm4gRTxZzvIo3TDeOTkv6yad9rhe3vAr/pCz4UqNZbnA68sBZ+pu01tm8g7Y57gKQdga1tfy8rY5/Rl6cq62zgoKq1FwRBMF+oZF1nOjZlJL2N1DP4IeDpwLuBF4zI8hFJWwLY/mitnEcA35ypvqEtOtvfAb4j6TTbP29mPgBrSX2pb6U3U3ZU3+soHmz7lmzPLZIelMN3IrXYKlblsMl83h9e5bk5l9WWdCfwAOD2/kolHUUWGJ3YYRt+csuDkEzRMq2ipFMWWay56Ik2d4TbBcX96b+Di54wbFGq+yQqgVgZikmhThJwLtaCOtCaTPFFm65Yc1cUuJwaVj3Zok33k98To02CzlMFdJMgsVUrt2OKtU4Cz51cZklXCNhFTfi2ej79X7IsHt0tb02HYk0WWS6hWK0kvsxS2svFZretoVjdhk4n1VVR/e0qmUZXBLlTJhFnO+VPmkm9b37h9PBb6Q+gAFpFEt4tS1T9W5ZSOdn+qk5VdkjpmDCmhQpjCkQWNy7d0zSWpvzyWEIYEOqUqOPu67RfqfysVdVX5a9sqD3j/uei6nnV8vVuohfnop5malyV1q2+MnI5roScK7FluSd6XJZTPmvdb3tNcHmKIPigv5VF7zNWtnrpuwLOVZWa/jms29h/3rWnCqsEnQtNFYauwjXoc10Lc69cdXr354n0tqgu+rxeaLHMunwp8BjgCttHSnowaanBQGx/fEj4SuBNM1XWZPeC2Tg5gDcDj7A9zYGMkUGfBI8IH5VneqB9Mmnwk+UP32kT/+8UBMEmxeL4xbk/70HXlrQ1cBsNGkOSdrL9i9lWNhfqodcA942prFtzdyT59bYcvgrYpZZuZ+CXOXznAeFT8uQN/rZheldpEATBxsOLYzIKcGmeof8fpMmKlwMXj8og6fdZx71N50LUuQP8QNK3mboj+bosL1hBmoZ6Yn79Ui38M5LeR5qMsgdwse2OpLslHQh8n7TM4UN9ZX2P1Gz+Vn2H2yAIgnnBIvhVsv36fHqSpK+R5lZcOSy9pKeTJiseui71NRF13pM0k7HpbgT/lY9ZIemzwNOAHSStAt5GcnBnSXoNad3eywBsXyPpLOBHJB3NY/KMS4CjSTM4NyPNtjwvh3+CNFFmJaklt8H2QgqCIGjOJt9amxFJ+w0Iezjwc9uDtJFXAE/MY3KzpkmLbla7EcxWlbqW7xVDogYuBLR9AnDCgPBLgX0GhK+mJ0wdBEEwPxkwGWsB8lFgP+BKkmffJ58/QNLrbH+9L/1ngH+S9Erbs35CTcboNrfd33c6zePmFhZ5sfaV/cdsDQuCIFh0VOvomhybNjcCj7O9v+3HA48DrgaeSVpqMAXbr83xn1qXypqKOjfZjeCN+fV562JIEARBsOmvkWvIo2xfU13Y/pGkx9m+ftjyZtvvlHT4ulTWpEV3DKnbstqN4E2kcbB+Iyrn93rbP68fwOv70wdBEAQDcMOjAZIOzjKJK7O6VH+8slTiytz7tt9MeYdJM0paIun03Kt3raTjR5j2E0kfk/TUfHw0hy0jrYce/GiGK6eMZEZHtw67EQzakPWQdTEuCIJg0TGmrktJLeAjpN/fvYBXZPnEOoeQZq3vQRLJ+FiDvJU04x7ABfka0hyIZbZ/H3g88NoRmsJHkBSt3gT8NXA98GckJ/f0Ge5rX0kvkPTi6hj9JJrNuvxX4N22f5evtwP+j+1/6Et3NKnltnvfmNxWwP/MVE8QBEEwQKFl3TkAWFnt0ybpTNL0/B/V0hwKnJGXWl0kadu8ZnnXEXkPJc2QhySreCFpNwEDW+R1ypuRVLLumnZ/yYl+OTeg3jvA7nuG3ZCkU4B9Seu1q0kpBr444jk0GqM7xPbfVxe2fyvpucA/9KX7DGkq//+l5+EB7rYdC7ODIAhmwoLxSYB1ZQ8zq4AnNkiz0wx5h0kznk1ygrcAmwN/Pei3P693vk/SNrbvnOU9HWi7v1U6I00cXUvSMttrACRtBizrT5QNvpPUxG0BD87lbylpS9sD968LgiAIajRv0e0g6dLa9clZvrCiiezhusgpDuMAkmDIQ0hb5/y3pG8O2fl7NXCVpG8A93YrmFlY5HuS9rL9oxnSTaGJo/sUcIGkU0k3+uf0dgGYhqRjgbcDtzK1abnvbAybL3iyoLxtOZ5wEvhtGa0p0sfAoI4oSlA7CTSrUxNw7gosV4VNDSsmk0Bs0c6izmUl0AxFxzUBZ7IwcAqfIjZLFZdFnStx4rKXoBJyRj3x3iTKm0WYO0btLDxc6Rzn0Vu3CjyhKWK/6nhK+UkgNwsYl0aTJWpnoeWyhDa0ioIlSwrUadG6aw26dzWsWdubYtY/06oSP+4KFdfiy1rZZYk7nZykgFYBExO9cosCnJ6dRRJ9rt1HKsdT7SiEJ/IDKEEqc+YyCTvb0EqCyCpNJVWQ3lsngWZnIe52Jz2LjlElQp3LHUZX0Ln/3vuelaXeKLuE6qLQfVP3umlVey9rotRuZbHv/vchf2bcnpqnEvBO34H8uanXWYlJ1+2v3UP/5yyl79UxRWR6inDzdHHqnng50ymmikpX34Xu57voexZTbKqFaWrdLsAt6KD0Fgg0aJnzutDc0d1ue/8R8cOkEpukWToi762Sdsytubo04yuBr9meBG6T9D/A/qTxt36+mo/ZcjrJ2f2KpLwlwLZH+pcmos7vlnQVaeG2gH+xff6ILG8CHmn7jqaWB0EQBJnxjdFdAuwhaTfgFyQ1qFf2pVkBHJvH4J4I3Jkd2K9H5B0mzXgT8AxJnyJ1XR4I/Psgw2yfLmkpsGcOui47yJk4BXg1aWPvxgvHG2ld2q5Lac3EzaQuzCAIgmA2VAvGx1FU2o7sWOB80salp2T5xNfl+JNIm1M/lzQD8j7gyFF5c9EDpRlJszRPJS3sFnDqMP1KSU8jtc5uzGl3kXRE3pd0FDfZXjGb5wDNZl0eSBJGfjSpOdsC7rW99ZAs1wMXSvoqU0Wd+3eFDYIgCPoY46xLbJ9Lcmb1sJNq5yatlW6UN4ffwQBpRtv30Fxm8b3As21fB11N5c+SliWM4sdZgvLLTPUv6z3r8sOkZuvnSf2thwOPGJH+pnwspcEW50EQBEGNxaGMsqRycgC2fyJpSYN8m5Ec3LNrYWNZXoDtlZJaeYeAUyX974i07wCQtIXte4elC4IgCKYzzhbdPOZSSZ8APpmv/5S0L91AJL0C+LrtI9elsiaO7r48aPgDSe8mrZHYYoRBTyJtibMl8FBJjwFeW9t/KAiCIBjGpi/Y3ISjSV2mbyCN0X2XtKPBMB4GfD63+i4gzRm5uOmeok20Ll+d0x1LWu+wC/CSEen/HXgOcAeA7R8Cf9TEmCAIgkVNU53LTbzVZ3uN7ffZfrHtF9l+f7VWe0j6E20/gzRx5oekZW6XS/qMpMMlPXhUfUNbdJIusH0QSaT5LaQFfu9oeBM3963H6QxLGwRBENTYxJ1YEyQ9mbTe+mHU/JDt3Ufls303cE4+yPqbhwBnkBpYAxnVdbmjpKcCL8hrLKZ4LtuXD8l3s6Q/AJy7PN8AXDvK+JmQ9NfAX5A+AleRpsBuDnyOpMl2I/By27/N6Y8HXkNysG+o1v1Jejy93cfPBd7YtOkbBEGwIVDj1WGbNJ8giTlfRoOGkAbsSF7j27YHaWZ2GeXo/omkWbkzaSpo3dEZeMaQfK8DPkDSSlsFfJ0h01ebIGknkrPcy/b9Shu8HkZS1L7A9ol5C4njgLdkD38YsDdJiuabkvbME2k+RlLovojk6A6m+frAIAiCuWdx/PW+M6/PbsooRzbKHwEjHJ3tsyV9AfhH2//c1Brbt5Nm0IyTCWAzSZOkltwvgeMZrKB9KHBm7u+9QdJK4ABJNwJb2/4egKQzgBcSji4IgnmCvGhmXX5b0r+RlgXU18MN7Cm0PXLrnpkYOevStiUdCjR2dJJOJ3UJ/i5fbwe81/afr4uBtn8h6T2ktXn3k6aYfl3SMAXtnUgttopKjXsyn/eHB0EQzB8Wx6zLaieEulbnjC2zPOvyaHoTHC8EPj6TfFiT5QUXSXqC7UsapAXYt3Jy0N3W53EN804jO8pDgd2A35GmmL5qVJYBYbNS45Z0FKmLk4ltt6NYI5hUEnYulcRbC7qizpUwc1ViV1i5EmXuMEWVTWUvvCvqPNkn5NwxRbsn6Fx0kkgwlXhzJSBskiBzWYnl5teczkUWrq0EaYveY5CTMK/aZRLmrUSOARdpQq4njDs1gWFyvZ2ewLBbQi6zHWUSL+50kiByWYJEsbZNsXaClkSxem0SdF5bE3WuUxq77J6TbZZ6k4TtsifG3OlkUeACXKQ3uhJrLgRFC1pFEizOos2UrolDG6r6Wq1uGS4KRAdcJKHkkqQL1KqEuz1VPLlTuy7z85zsoHYn1dWpfQhc/0D0TX4u+j6qpafHVyLO1f3k9wFqH/R6FdAVdaZ6b/tEobtlZFHqKrwSVu6mcyUs7t5noRLhruocNp+7+m4MeN+n2cPU8/54q+851UWqi+ljXe63qRKSroSrKwHpok84uorPz69s5dcl6XtbCTxPK39dWQQtuvVooX0MWEJvKcKrc9hfjMrUxNE9nbRT7M9JywtmUosuJG1XmxiyfcN6hvFM4Abbv87lfRH4A4YraA9T416Vz/vDp5G3ujgZYPnOuyyCj10QBPOFhdx1KenNo+IbSEU+wfZjatffkvTDmepttPFqgzR13gv8r6SzSf9NXg6cMMsy6twEHChpc1LX5UHApSSnO0hBewXwGUnvI01G2YO0sLAj6e6s3fl9kpTZh9bDriAIgvHi6S3RBcZW65m/I+nhtn8GIGl3GszabOLoZvX/wvYZeTPAZ5Bafy+e7SZ5feV9PzvNy4E2cAWptbUlAxS0szr3WaQt39vAMXnGJaS+3dNIywtmsyNDEATBhmEBt+gqicj14G9JE1muJ/mXh5F3XBhFE0f3VXpjXMtJY2XXkabvDyQ7tnV2bgPKexvwtr7gNQxQ0M7pT2BAK9L2pcA+47IrCIJg7CxgR7e+2L5A0h7AI0k+6cejFFUqmmy8+vv167xw77X96SRdbnvUor5GaYIgCBYzC3mMbn2R1CIpoOxK8l8HSZpxbG/Wk0RsXy7pCQOiHi1p4CZ7lY3ANrOtLwiCIAgyXybJUY53h/G+WTIFaWO8Xw9I+qgG9YXmZRAEwSgWcItuDLMudx4x438oTVp09VkybeArwBf6E9n++WwrD4IgCGosnlmXjwSeQJolD/B80lY9M3GepGfb/vpsKm0yRtedJaO0YndL26tnU0kQBEHQkAXcoqttzP11YL+8GwGS3g58vkERFwHnZF80SW9d99ajMs24lj/v97O1pC1IMymvk/S3DQwKgiAIZoHo6V3OdGziPBRYW7teS5pgMhPvBZ4EbG57a9tbzeTkoNnGq3vZvoskgHxuNvDVgxJKakn6ZoMygyAIgkEsgo1XgU8CF0t6u6S3kUQ8zmiQ76fA1bPdXq3JGN2SLKT5QuDDtielwf8nsvrIfZK2sX3nbAyZtxiKtpLuXQnFZF0rMutYVrqV+QOostKozEdZ63d3L2/RruJNMZnSFG3X8icNy6IDmiwpOp6qFeiUV+2yq3HZ1VusjkoDcpCGoHPeyQ60y67+oCXUSpqI7ggmh/8fcktQpvhkRypP7Zq2owSdDsXaPBep3YHJSdxuT9dxzHYBPS1KwGWZFnNWOo21uErTUq1Wrq8eJyiSRmn33ittybLT08us1y1B2Up1lQUunGQUW0IdJ23PqqxuPSSNz6z3qHbW+1w7mXU/azb1azQWRc22Wrn9z6GKq9LXtS6hZ1O/duaUunr1qK5pWdUtQaumhZnve4rNZaVDWsta2dsZMMDU/6yGMFDKuHvPPbunaFwWfWkrzc6cpqup2V93lSbfmyeKntZla5DOZlV+ei5JEzSFu0jljEWLeWG01mbE9gmSzgOekoOOtH1Fg6y3ABfmvPVdD9Z7ecHHSRub/hD4rqSHAXeNSL8auErSN0gyXZUhb2hQVxAEweJmYU9GqbM5cJftUyU9UNJutm+YIc8N+Viaj0Y0mYzyQeCD1bWkm0hCz8P4aj6CIAiCWbIYWnS5u3J/0uzLU0k7EnwKePKofOsqIbYuC8ZNWmYwLP50SZsBD7V93boYFQRBsGhZBI4OeBHwOJKGMbZ/KWlGwWdJ+wNvJWlcdv3XTGvr1mf7nGGGPB94D6lZuZukxwL/bPsF464rCIJgQbEwJpo0YW3e2NsAeVZ/Ez5NEnaelTLKuLYKrPN24ADSJqnY/gFJCDoIgiCYgXEuL5B0sKTrJK2UdNyAeEn6YI6/MmsZj8wraXtJ35D00/y6XS1uX0nfk3SNpKskLR9i2lmSPg5sK+kvgW8C/9Hgln5te4XtG2z/vDpmytREAqwF/DE9EU1g5CyXtu07NXVm2eL4jxIEQbC+jOnXMv92fwR4Fmnj6UskrejbNu0Q0p6dewBPJO3W/cQZ8h4HXGD7xOwAjwPeImmCNM72ats/lPQA0qLu6bdov0fSs0gTGx8J/JPtbzS4rbdJ+k/gAqbOuvziqExNui5nK6J5taRXAq28ncIbgP9tkC8IgmDRM0YJsAOAlbavB5B0JnAoU7dQOxQ4I8+9uEjStpJ2JDVshuU9FHhazn86cCHwFuDZwJW2fwhg+45hhkn6a+DzDZ1bnSNJuspL6PkjA+vt6GYrovlXpMHCNcBngPOBd84ifxAEweJkdmN0O+RNritOtn1y7Xon4Oba9SpSq40Z0uw0Q94H274FwPYtkh6Uw/cELOl84IHAmbbfPcT2rYHzJf0GOBM42/atw2+1y2P6t45rQhNHN1sRzd1tv5Xk7IIgCIKGiCEL5wdzu+39Zyiun343OixNk7z9TAB/SBJrvg+4QNJlti+YVlBaJvAOSfsCfwJ8R9Iq28+coY6LJO3V1/06I00mo1QimvdLukvS3ZJGLRg/SdLFkl4vadvZGDOM3Jw+W9KPJV0r6UkzDIgenwdQr5P0nFr44/MA6co8ADsOLYMgCILxMT4JsFXALrXrnYFfNkwzKu+tuXuT/Hpbrazv2L7d9n0kyciZNtq+DfgVcAfwoBnSQnKkP8i/7Vfm3/NR+6ACzRzdrEQ0bf8h8CrSQ7o0i0I/q0E9o/gA8DXbjwIeA1xLb0B0D9LA5HEAkvYCDgP2Bg4GPpoHViENtB5Fb/D14PW0KwiCYKyMcdblJcAeknaTtJT0u7iiL80K4PA8+/JA4M7cLTkq7wrgiHx+BPClfH4+sK+kzfPElKcydTywd4/S0ZIuJP127wD8ZcMhsoNJv93PJm3t87z8OpImXZezFtG0/RNJ/wBcSlJVeVxuPf39TLNj+pG0NfBHwJ/lstcCayUNGxA9lNQ3vAa4QdJK4ABJNwJb2/5eLvcMkn7nebOxJwiCYE4Z06xL221Jx5IcUAs4xfY1kl6X408itbqeC6wkdTceOSpvLvpE0vKA1wA3AS/LeX4r6X0kJ2ngXNvDVLIeBrwpLz+bEUmX295v1FKCKs2guCaOblYimrnP9UjSkoRvAM+3fbmkhwDfY4bZMQPYnbSj+amSHgNcBryR4QOiO5G6WyuqwdXJfN4fPugejiK1/JjYZrueKHNbFG1w1Q6uiTlX4s3VPKCik86LEtTOIs193Q5FOws2V2LO7fxaaf92jDpO6SZL1OkJNWPn656YMpBEnesCwtSEkKXUhq+0lstK0LnTE+qt0ruFZVRqqkhyVX91lEKFuwLR2GhNOwkZ9x4oagutbVPYMDmZ6is9NV3ffylXNtld8We327X42j3SwnSQhDsdRCuVXQkX10Wdq43uu0LEnV5cmQWT2x2YaCVx5+69FqAybYCVhX/VqcS0y574cpnvsd2GssSdTk+8uuj1lktFuq7us09wO9lj7OnT8Lp5+/MpiXF3zwcJKtfFoisG5OmWWgk9V3VW4tUVZV/ZdUHqehn9AtL98YPoF3WmJl49JV0xWLC6Xm9f/ereVxZprj+DEXRFpYta2nGNgnissy6xfS7JmdXDTqqdGzimad4cfgdw0JA8nyItMRiIpO3z6bv7rqv8vxmS9dEzdFEK2GZYZBNHN1sRzQ+TFv79ve37q8As8fIPDfIPsnE/4K9sf1/SB8jdlENY78HVPHPpZIDlO+0SawCDINhwLOxfnMvo3WH/b7JJDZtBPKpB2Z1hEY13GFfSIbPte2ZI/0cj4j45U30DWAWssv39fH02ydHdKmnH3JrrHxAdNri684DwIAiCecNCFnW2vU4qWU3UT0bRZIfxfSRdAVwNXCPpMkl7j0i/R54h+SNJ11fHuhpo+1fAzZIemYMOIg1wDhsQXQEcJmmZpN1IA5cX527OuyUdmMcLD6/lCYIgmB+Mb9blvEbSCyS9Jx/Pm8u6mnRdngy82fa3s3FPI3VN/sGQ9KcCbwPeT9rO50hmtTRkIH8FfDrP/rk+l1kweED0GklnkZxhGzjGdtWkPRo4DdiMNAklJqIEQTCvWMgtugpJJ5LW2306B71R0pNtHz8X9TVxdFtUTg7A9oUarTS9me0LJCk3N98u6b9Jzm+dyDNzBi2MHDYgegJwwoDwS4F91tWOIAiCOcUslo1Xnws81nmmlaTTgSuAjeborpf0j0A1vvYq0uSUYayWVAA/zdNTf0GzhYBBEASLGrE4WnSZbYFqluXQGZPjoImj+3PgHfSWBXyXvNZiCG8ibZH+BuBfgGfQG0sLgiAIRrE4HN3/Ba6Q9G2Sf/8j5qg1BzM4uqwo8vkG+mNdbF+ST+9htEMMgiAI+lBzbY5NFtufzcooTyA5urfkiYdzwkhHZ7sj6T5J29i+c1RaSV9mxH+R2GE8CIJgBhbIjMqZkPRk4Ae2V0h6FfB3kj6wvssIhtGk63I1cJWkbwD3VoG239CX7j3jNCwIgmAxskjG6D4GPCarXf0tcApwBkkfc+w0cXRfzcdIbH9n/c0JgiBY3IxTAmwe07btrFn8QdufkDRnczmGOjpJF9g+CNjL9lvmyoAgCIKgxuJo0d0t6XjSLP4/yvNBlsxVZaNadDtKeirwAqVt1Kcs+rZ9+VwZNe+oBJnL2jlZqLlDV4hVnXxNniZcxbmXv+i4KxKtTrrG0FqbXqt4ADqm6JhibYk6ZVfEGZwGrO1kT6eT7arCyq7YsCVwEi0W9NTgOrmMdhZ1roSDlVWfO0KFMAXVN0+VEHNdTHii6Ik+V4LSa9f2BHUBWi3olEn0uQQ6JZ5sd8WBXQ74C1sTNU7XZU/EeYDIMRbQ6goKJ2Fnkq11WyCJ91YCzFmE2S6TUDKdlKco8j2VSfgXwL2vi9b2/RpVQsedXO7kZBJzros9V8+sK7LtbHcV1RMq6go5lwMEkgFTDhcSzu9Pt7yiL11dKLrsK7s/bVVOURPyHiSS3FfOICHqKWkLTRO6rt//FHv6bazbWReuhp4w80xU+bKo81CR7HQzU7PW04x7S8vqt2Lh8yfAK4HX2P6VpIcC/zZXlY1ydP9E0pTcGejfqcCkZQNdJH3S9qslvdH2B8ZrZhAEwSJhcTi6u4EP5AmPe5JEmz87V5UNdXS2zwbOlvSPtv+lQVmPl/Qw4M/zXm/9LcBh2y8EQRAELKoF498FniJpO9Lmq5eSWnl/OheVNdm9oImTAzgJ+Bppm4XLmOroRm2/EARBEGQ0qKt24SHb92Wt4g/ZfrekH8xVZQ07tGfG9gdtP5q0E+3utnerHeHkgiAIZqLpzgWbvi+UpCeRWnDVrP7WXFXWZHnBrLB9dF4b8ZQc9F3bo3aGDYIgCDKLZHnBm0iSX+fkHWd2B749Osu608jR5amfD66nt33TkLRvAI6ip435aUkn2/7QetoaBEGw8Nn0W2szktddf6d2fT1JH3lOmNHRSfor0hY7t9LbQMLAvkOy/AXwRNv35vzvAr4HhKMLgiCYgYU8GUXSv9t+0zDJyLmSimzSonsj8EjbdzQsU/RWa5HPx7zYJAiCYAFiBq6bXEBU271tUMnIJo7uZmCkoHMfpwLfl3ROvn4h8IlZ2hUEQbAoWchjdLYvy6/fkfTAfP7rua53lATYm/Pp9cCFkr4KrKnibfcvIu+G5+0X/pDUkjvS9hXra2geJ7wU+IXt50naHvgcsCtwI/By27/NaY8HXkNqTb7B9vk5/PHAacBmwLnAG+2F/fcpCIJNh4W+jk6SSENhx5Jut5DUJi0x+Oe5qnfU8oKt8nET8A1gaS1sy1GF2r48Lzf4wDicXOaNwLW16+OAC2zvQVpweByApL2Aw4C9gYOBj2YnCUkx+yhgj3wcPCbbgiAI1p9Klq7JsWnyJuDJwBNsP8D2dsATgSdL+uu5qnSUMso7ACS9zPbn63GSXjZXBg1C0s7AHwMnAFVL81Dgafn8dOBC4C05/Ezba4AbJK0EDpB0I7C17e/lMs8gdauet0FuIgiCoAELuUUHHA48y/btVYDt6/OedF8H3j8XlTYZozse+HyDsLnk34G/I7UmKx5s+xYA27dIelAO3wm4qJZuVQ6bzOf94dOQdBSp5cfENtslUeaS7hyh6nyQqHNRm5eq+hzVmlhrlba11iiLOBftLJxciTo7CTon0eaSYrKc+i9uiojzAEHnnDaFq64dnG3IAs1lmUSSu0K9HaCVjS8QtQGDSqC47AnaqhJMzuLIVELG0Osv6KS5SZrsQCuX0ekk0eN+8eZ+sjiwB4k7V/dSifGWxipRq2/daf+/36reStw4Cye7ulcJinxvrVZPFFplT1B6muh0fuadDpRZtNrGk5NT66+eWVVPhyRELOFKULpeJqTn1YRK0LgS9K7fDzQru5pGVrPRkJ5DkcSPBwpCN2lhlH3PDnBZoqLAZYkBDRNl7hdPrqebIuo8RJR6St6aEHRb3ec/sL5R91UJQw8Tnl5XxliUpIOBD5AWY/+n7RP74pXjnwvcB/xZJdg/LO+oYaMc/1DgR8DbbfdPOllSd3IVtn8tac52LxjadSnpEEkfAnaS9MHacRrQniuDBtjxPOC2ahCzSZYBYR4RPj3QPtn2/rb3b22xRcNqgyAI1p9qt5OZjhnLSUM2HwEOAfYCXpGHduocQm8o5yjS8M5MeQcOG9V4P8N7ytaOMHlU3HoxqkX3S9LkjxeQtCsr7gbmrC91AE8mbRX0XGA5sLWkTwG3Stoxt+Z2BG7L6VcBu9Ty70y6l1X5vD88CIJgfmC6LfIxcACwMi/GJm+3diiptVVxKHBGnpR3kaRt8+/priPyDhs2QtILSRMY7x1i02Mk3TUgXKTf9zlhaIvO9g9tnw48grR9whXA5cBX6s3Uucb28bZ3tr0raZLJt2y/ClgBHJGTHQF8KZ+vAA6TtEzSbqR/Khfnbs67JR2Ym+uH1/IEQRDMC2bRottB0qW146i+onYiLQ+rGDRcMyzNqLxTho2ABwFI2oLk8N4x7N5st2xvPeDYyvZG2Xi14lnAx4GfkbzubpJea3tjT+I4ETgrq1/fBLwMIOumnUX659EGjrFdjTwcTW95wXnERJQgCOYbzWdU3m57/xHxTYZr1nuop8Y7gPfbvkfj3pB2PWni6N4HPN32SgBJDyepTW9wJ2H7QlIzmazUctCQdCeQZmj2h18K7DN3FgZBEKwfY5x1OWwYp0mapSPyDhs2eiLwUknvBrYFSkmrbX94HDezPjTZpue2ysllrqd3Y0EQBMG48CyOmbkE2EPSbpKWkoZ+VvSlWQEcrsSBwJ25O3JU3oHDRrafYnvXPMz078C/zgcnB81adNdIOhc4i/R4XwZcIunFALa/OCpzEARB0AyRlhiNA9ttSccC55OWCJySh3Zel+NPIilEPRdYSVpecOSovLnogcNG85kmjm45aeeCp+brXwPbA88nOb5wdEEQBGNCY1Q9sX0uyZnVw06qnRs4pmneHD502KiW5u3rYO6cMaOjs33khjAkCIJg0dO8WzKYBTOO0UnaU9IFkq7O1/tK+oe5Ny0IgmCxseC1LjcKTSaj/AdJ8msSwPaVpIHJIAiCYMyMSxkl6NFkjG5z2xf3rYvYYBJg84IB3QmVXqXKLAtZZp3LSl6wlqd7XjvkNOhctJO2pdoG9bQu02s+2u6qJUzpv8//7FT/l5d1Mqdo7xW1fNX7WOlclp6eXiVYWQdTNS3LnB4nfb+qzkrzL2tY9nQUe/X36qnKHVBv977K2qmnhLn0lPgUVoBMd4+KsoRWC7tEZdGztU6fzuV0PclW0uys7g2g7PSeXfX8+1/LsqfhWdPUrFmLq2dG1nas6i6KnirGAF3IgdQ1GTud6ZqNFfWyq/KHlO3+uqW0qMoFrv/CNtXi7NO2nJrXuFN7vzt9ZdW1K2v31mgcK6d3pYvZfVb52St/NspyqnbmENun2VFpZI57/7horY2dJo7u9rx2zgCSXgrcMqdWBUEQLEY8vlmXQY8mju4Y4GTgUZJ+AdwAvGpOrQqCIFishJ8bO01mXV4PPDPrmBW27557s4IgCBYn41xeECSGOjpJbx4SDoDt982RTUEQBIuXcHRjZ1SLrtrk9JHAE+jJvzwf+O5cGhUEQbAoMeOf3BIMd3S23wEg6evAflWXpaS3s2F3Fw+CIFgUCEfX5RzQZDLKQ5m68+ta0qZ8QRAEwbiZablGMGuaOLpPAhdLOofUsH4RaVfZIAiCYJxE1+Wc0GTW5QmSzgOekoOOtH3F3JoVBEGwOImuy/HTRAIM25fb/kA+NqiTk7SLpG9LulbSNZLemMO3l/QNST/Nr9vV8hwvaaWk6yQ9pxb+eElX5bgPar5tgxsEQRBal2OnkaPbyLSB/2P70cCBwDGS9gKOAy6wvQdwQb4mxx0G7A0cDHxU6opDfQw4CtgjHwdvyBsJgiAYTYg6zwXz3tHZvsX25fn8buBaYCfgUHpjhacDL8znhwJn2l5j+wbShoIH5C3ft7b9vbwH0xm1PEEQBBsfkzRJmxxBY5pMRpk3SNoVeBzwfeDBect3bN8i6UE52U7ARbVsq3LYZD7vDx9Uz1Gklh8T22zXi6hUw6dq9PbCK4Fn1cKqMmsCz6kMp/NKwLku9uwk5oydxZ3LXr99v6hzJeLc/y+vfl6SxWvd+2vTLa8mbDylXENrgBiyS1DREyqu/1Wq2zAlX58Yb1kmcd+62LGnj8APFHRuit2rd0C+aYLO0+4ziT7LxRQB4inl9z/z6p7Kvnvsv7ey6D63SuRYRTFVlDmXOyV+6H2OuO7vnR8gFu3+WX6DhKjJ792g3v4GrYtBYs6D3vNplLX7rp1Os3mYPdJ0QXOngiyDlcSdKac9++m2VJ/5WjppuCD0OhJjdONn3rfoKiRtCXwBeJPtu0YlHRDmEeHTA+2Tbe9ve//W5lvM3tggCIJ1Jboux84m4egkLSE5uU/b/mIOvjV3R5Jfb8vhq4Bdatl3Bn6Zw3ceEB4EQTA/MLVegRmOoDHz3tHlmZGfAK7t09dcARyRz48AvlQLP0zSMkm7kSadXJy7Oe+WdGAu8/BaniAIgnlATEaZCzaFMbonA68GrpL0gxz298CJwFmSXgPcBLwMwPY1ks4CfkSasXmM7bxjJkcDpwGbAeflIwiCYP4QTmzszHtHZ/v/MXh8DeCgIXlOAE4YEH4psM/4rAuCIBgjJu1sH4yVee/ogiAIFg8NZ6MGsyIcXRAEwXwiui7HTji6IAiC+UI16zIYK/N+1mUQBMGiYoyzLiUdnDV/V0o6bkC8su7vSklXStpvprzDdIYlPUvSZVlP+DJJzxjD0xgL4eiCIAjmE2NydFnj9yPAIcBewCuyFnCdQ+hp/x5F0gOeKe9AnWHgduD5tn+ftOTrk+v6CMZNOLogCIL5gp2k4JocM3MAsNL29bbXAmeStIDrHAqc4cRFwLZZgGNU3kMZoDNs+wrblQjHNcByScvW6TmMmXB0QRAE84nxdV3uBNxcux6k7zsszai8U3SGgQcxnZcAV9he08TQuSYmo4wRNes276Wt0k/Vz82vAwrrD5vNLOS5HuCeJgQ8C1saTKfuF3gekohZ/Xcb9WNRltAqhr8PM70/M/0QlU7iwFkY2GWZhJv7xJy7xeX4/tcZqYSH6+XNJOg8teIk4m2nOlutgenr5dTtcn+9Q97H6v1V0bdktqofes+sfl/D7K4932nC2GWZhJizKLqLEtWEtqeUMYh+QfNRAtPrQvNZlztIurR2fbLtk2vXTfR911sbuB9JewPvAp7dJP2GIBxdEATBvGFWOpa3295/RPww3d8maZaOyHurpB3zrjF1nWEk7QycAxxu+2dNb2Suia7LIAiC+YLBLhsdDbgE2EPSbpKWkjakXtGXZgVweJ59eSBwZ+6OHJV3oM6wpG2BrwLH2/6fdX4Gc0C06IIgCOYTY5IAs92WdCxwPtACTslawK/L8ScB5wLPJW1QfR9w5Ki8ueiBOsPAscAjgH+U9I857Nm2uy2+jUU4uiAIgvlCtRHw2IrzuSRnVg87qXZu4JimeXP4HQzQGbb9TuCd62nynBCOLgiCYD4REmBjJxxdEATBPGLaTNhgvQlHFwRBMG+ITVXngnB0QRAE84UQdZ4TwtEFQRDMEwy4mbxXMAsW3Tq6mdS8gyAINhrOG682OYLGLKoWXU2R+1kkRYBLJK2w/aONa1kQBEHC0XU5dhZbi66JmncQBMHGI1p0Y2dRtegYrMj9xP5Eko4i7c0EsOYnb3vz1RvAttmyA2n/p/nGhrfL9ASuR2ulxzObHWHX7HnY+mS+m9+e/02fvUPD5PP1Gcw7Fpuja6TInRXATwaQdOkMwqkbhbBr9sxX28Ku2TFf7RoHtg/e2DYsRBZb12UTNe8gCIJgAbHYHF0TNe8gCIJgAbGoui5nUOQexskzxG8swq7ZM19tC7tmx3y1K5inyCE3EwRBECxgFlvXZRAEQbDICEcXBEEQLGjC0Q1hY0uFSTpF0m2Srq6FbS/pG5J+ml+3q8Udn229TtJz5tCuXSR9W9K1kq6R9Mb5YJuk5ZIulvTDbNc75oNdtbpakq6Q9JX5YpekGyVdJekHki6dL3bluraVdLakH+fP2pPmi23BJojtOPoO0kSVnwG7A0uBHwJ7bWAb/gjYD7i6FvZu4Lh8fhzwrny+V7ZxGbBbtr01R3btCOyXz7cCfpLr36i2kdZIbpnPlwDfBw7c2HbV7Hsz8BngK/PovbwR2KEvbKPbles7HfiLfL4U2Ha+2BbHpndEi24wG10qzPZ3gd/0BR9K+gEgv76wFn6m7TW2bwBWku5hLuy6xfbl+fxu4FqS4sxGtc2Je/Llknx4Y9sFIGln4I+B/6wFb3S7hrDR7ZK0NemP3icAbK+1/bv5YFuwaRKObjCDpMJ22ki21Hmw7VsgORzgQTl8o9graVfgcaTW00a3LXcP/gC4DfiG7XlhF/DvwN/REypjnthl4OuSLsuyd/PFrt2BXwOn5u7e/5S0xTyxLdgECUc3mEZSYfOIDW6vpC2BLwBvsn3XqKQDwubENtsd248lKd4cIGmfjW2XpOcBt9m+rGmWAWFz9V4+2fZ+wCHAMZL+aETaDWnXBKnb/mO2HwfcS+qqHMam9n0NNjDh6AYzX6XCbpW0I0B+vS2Hb1B7JS0hOblP2/7ifLINIHdzXQgcPA/sejLwAkk3krrAnyHpU/PALmz/Mr/eBpxD6u7b6HblulblFjnA2STHNx9sCzZBwtENZr5Kha0AjsjnRwBfqoUfJmmZpN2APYCL58IASSKNnVxr+33zxTZJD5S0bT7fDHgm8OONbZft423vbHtX0ufoW7ZftbHtkrSFpK2qc+DZwNUb2y4A278Cbpb0yBx0EPCj+WBbsImysWfDzNcDeC5pRuHPgLduhPo/C9wCTJL+sb4GeABwAfDT/Lp9Lf1bs63XAYfMoV1/SOoWuhL4QT6eu7FtA/YFrsh2XQ38Uw7f6M+sVt/T6M263NjPa3fSTMUfAtdUn/GNbVetrscCl+b387+A7eaLbXFsekdIgAVBEAQLmui6DIIgCBY04eiCIAiCBU04uiAIgmBBE44uCIIgWNCEowuCIAgWNOHogqABkk6T9NKNbUcQBLMnHF0QBEGwoAlHFwQ1JO2a9z/7j7yv3dez0ko9zUFZbPgqpX0Dl+XwGyW9Q9LlOe5RG+cugiCoE44uCKazB/AR23sDvwNeUkVIWg6cBvyJ7d8nCRAfXct7u5NQ8seAv9lQBgdBMJxwdEEwnRts/yCfXwbsWot7ZI7/Sb4+nbR3WsUXh+QLgmAjEY4uCKazpnbeIbXaKgZtCTMob3++IAg2EuHogmB2/BjYVdIj8vWrge9sRHuCIJiBcHRBMAtsrwaOBD4v6SrSruEnbVyrgiAYRexeEARBECxookUXBEEQLGjC0QVBEAQLmnB0QRAEwYImHF0QBEGwoAlHFwRBECxowtEFQRAEC5pwdEEQBMGC5v8DbmljUfE48rEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_eqPac.Fe.isel(nlon=slice(2900,3600)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x150105508f50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CgeM4fYPUlxI0pwHPJyywugV4sZndJWln4DJgSkeTR735lcBvgS8AXwR+I+nlnducH0kHAx8CDjWzjZlLVwBHSJonaRdgN+B6M3sEWC/pAEkCjgQuz5Q5Kp6/lSCZ7b0Vx3H6iBD4LM8xk5jZo2Z2H/CAmd0V035HzlAzefponwRem1YuaXfg64Rlwl1D0teBVxLCk64meNFTgHnA1cFvcJ2ZvdfM7pB0CfArwpDa8XHFGcBxjC9vvioeAOcAF8aFA78nrFpzHMfpH/pwHw2EORozSwhz5WlakZwrkfM4moHUyQCY2W8kDbRt6RSY2TsaJJ/TIv8ZwBkN0lcRJqrq00eAt03HRsdxnN4iVOg7R3MswaGMmNn1mfTlwJl5KsjjaFZJOofxxQF/AdzYjpWO4zjO1EiiMNj1/+OnhZnd0CT9fuD+PHXkcTTHAccDJxBWbv2UMFfjOI7jdBNBof96NABIeo2ZXd1J2TwbNkclfR64hqDefJeZlTtpzHEcx2lNn87RHAW8g6DW0jZ5wgS8EfgSQWtMwC6S3mNmV7Uu6TiO47SF1HeORtKHgdcAb+y0jryrzl5lZnfHRp8NfIfx1VyO4zhOFxD042KA04Dn1W0xaYs8d/R46mQi9wKPd9qg4ziO0wT15T6ao4FLJS3ttII8PZo7JH0XuIQg2fI24AZJbwYws2922rjjOI6TQVAc7C8JGjO7SNKjwH8Df9xJHXnuaD4hjPMr4vsngG0IcWIM+CaApHVT1CPgETPbfYp8juM4cxKpL/fRYGY/ktTxSFaeVWdH56zrHjN7YasMkn6Zs67NF0tA0/+idEttud9Um7tFHsXjRnl6rdrcUJHZksz1eJ5kylCYkH/CeZc/vnr15DxqyolZ03z16fWqxvUK0a3qatV+s/Y6Ia0tMagP/WKZ9tpRt+7mx9RviwFSzOz2Tsu2ChPwOVo8PzM7oS7pLTnay5PHcRxnbtKHq85SJG1F0I9cQcZ3NPAFk2jVo1kVX18G7Ml4LJi30UAZwMzujcYsBDbFuAW7A3sAV5lZJc3jOI7jNKYfh84i3yXEAbuNCX3yqWkVJuB8AEl/SVjeXInvvwT8oFk5gnLAn0jamrDJcxUhZPJftGOY4zjOXEMShWJxts1oxnwzO6mTgnkWA+wALGY8dsuimNYMmdlGSccAnzOzj8+JuRnHcZzpIij02aqzDBdK+mvgSmA0TcwTZTPPHZ0J/FLSj+P7VwCnt8gvSS8h9GCOaaMdx3GcOU5/rjqLlIF/B05lfP7egF2nKphn1dm5kq4C/igmnWxmj7YociIhjsy3YtyYXYEft8jvOI7jECJs9utiAOAk4Dlm9mS7BVutOtvDzH4tad+Y9GB83UHSDmZ2U4MyReAQMzs0TYsLAKZcleA4jjPn6eNVZ8AdQEcyNK16NCcRAt58ssE1A149KdGsJqmrkTcdx3HmEt0aOpO0HLgA+APCKrGVZvaZaVRZA26O0yjZOZrOlzeb2bHx9VVtGvNLSVcA3wCGM/W5VI3jOE4rJFTKFR05D1XgA2Z2k6TFwI2SrjazX3VY33/Ho23yhAkoEuShVzBxk85ZTYpsAzzFxB7PmFSN4ziO0wxBl3o0ZvYI8Eg8Xy/pTmBHoC1HI2klQa3/m2a2vhNb8qwG+zYwQs5NOm1I1jiO4zhZBMq/j2appFWZ9yvNbGXDaqUVwAuBX3Rg1VeAg4GTJJUJ+yi/Z2a35K0gj6NZZmb75K0wqgGcDWxnZntL2gc41Mz+ZYpyXwHeRAhLsHdM24agSLCCEJv67Wb2dLx2CmH5dA04wcy+H9P3A84Dhgg7WU80M5M0jzBeuR+hx/VnMea14zhOnyAo5HY0T5rZi6asUVoEXAa838ymEj+ehJldR1AEOF3SM4HXAh+Iv+03EZzOJa3qyNNHu0rSa9uw6z8Jy5sr0chbgSNylDuP4DWznAxcY2a7EVQGTgaQtGesc69Y5otxiA+CkzsW2C0eaZ3HAE+b2XOATwEfa+OeHMdxeo8IjibPkac6aYDgZL7WjXlyM3vKzL5uZkea2QuALxB+Z1uSp0dzHfAtSQWC81Boz5Y0yb/AzK7XRJXV6lSNmNlPY/cuy2HAK+P5+cC1wIdi+sVmNgrcJ+luYH9J9wNLzOznAJIuAA4njC8exvhG00uBz0uS2RYqb+w4zmaHurhhU+FH+BzgzhZz6nnqaSk7Y2ZnTFVH3lDOLwFuy/mj/GQM92zRyLcSJ6Q6YLs4oYWZPSJp25i+I8EBpqyOaZV4Xp+elnkw1lWVtBZ4JjBp85GkYwm9IpYvXz4u895K/j8jBd+tUAG5yLY1k+02swWmtsESUHFaIQx6Ff6g3TACzewYS28UIsCScJ4+p6QKhdKk59bNkAZpVfXmTohm0OSRFqSxfDIbsyubP71eQGPvldRQkqDiwFj+xKztUAHTCQ1QbxeMhwEYKEy8r4SJ7bQKEdCz/04l6N6qs5cB7wJuk3RzTPuwmX23zXoWT9eQPI7mt8DtbfznfzywEthD0kPAfXRfULPRN89apLcqMzkxTKitBNhv3329x+M4zozRrR6Nmf0vjX/32q3nI9OtI4+jeQS4NsrQZDfpNOuKmZkdFMMFFOKyul06tO8xSdvH3sz2QBrhbTWwPJNvGfBwTF/WID1bZrWkEvAMxoVCHcdxZh+1tRhgRpG0DPgcoadkwP8SFlutblmQfIsB7iNMxA8SulDp0YzLAMxsOLPm+tIc7TTiCuCoeH4UcHkm/QhJ86IT2w24Pg6zrZd0QByfPLKuTFrXW4Ef+fyM4zj9hbq6GKDLnEv4Hd2BMBXx7Zg2JXlENXN1myTtQVgF9gxJb85cWgLMz1H+64SJ/6WSVgOnEZSjL4khBx4gBF0jinVeQth4VAWON7NarOo4xpc3XxUPCJNiF8aFA78n30o4x3GcmaO9fTQzzbPMLOtYzpP0/jwFuynf/1zCPpitgEMy6euBv56qsJm9o8mlA5vkPwOYtNrBzFYBezdIHyE6KsdxnP6ke8oAPeBJSe8Evh7fv4OwJ3FKuuZozOxy4HJJL0mXFzuO4zht0F2ts27zbuDzhH2IBvwspk1JLwKSPSXpGtpUBnAcx3Ho2x6NmT0AHDplxgZMeUeSlkn6lqQnJD0m6bK4+qAZnSoDOI7jzG0kVCjmOmbeNO0i6SxJ35R0RXrkKZunR3MucBHj8xvvjGmvaZK/I2UAx3Ecp3+XNxNCBJxDWG02pcByljyOpt2VBt1UBnAcx5k7iL4dOgNGzOyznRTM42jaXWnQSBngnZ0Y5ziOM5eQhAb6djHAZySdRggTkN28f9NUBfM4mkYrDZrGnDGze4EJygA52nAcx3H6e+jsDwnaaa9mfOjMmBjksiF5HM1yM5uw0kDSywgbKCchaSvCjvwVQEljAnxTx5V2HMeZ63RL66wH/Cmwq5mV2y2Yx9F8Dtg3R1rKdwnKyrkicvY/hpIqVihNVke2ibeXqvNatxWU6xWaG12P7YeJsSbtZ8uqMFFtOVUUTrOqMPF+8igy50SWgE2zvh6pVLetCp159jD5sw/3mkyWRrYEZOPl66stlJDR1n+3aRPNxI5lNuGizML/o/H71Ux90aSoxFwFFbCoxpxVPk7rERmV6loFJVUKSQ2kcE9199PM5kbK0K3IqkpnKVRHIakFmyojYf4jSSbOg6gAhRIqDmDFQUwaaz9Vjk6VnBt9O8REhetp0cdaZ8AthA35j0+RbxJNHY2klwAvBZ5VF49gCdDqScw3s5bxCxzHcZwmzGaoj9ZsB/xa0g1MnKOZcm9Nqx7NILAo5smKaK4jiFI240JJfw1cWWeMKyU7juO0RP3saE7rtGBTR2NmPwF+Iuk8M/tdG3WWgX8HTmW8p2nArp0a6TiOMydQGDbtR6JPQNIS2lSVyaPe3I6TATgJeI6ZTYpc6TiO47RC+SalZoEYefijwCbC/LvI2Ynoheu8A9jYg3odx3G2fPp31dn/A/bqpBPRC0dTA26W9GMmztH48mbHcZwWGD1Ytdo97qHDTsSUjkbS7sDZ5Fdj/u94OI7jOO2gvl4McArwM0m/oM1ORJ4ezX8Sukz/ESu9VdJFQENHY2bn57HYcRzHqUfQp4sBCD7gR3SwRzLPHeVSY5Z0iZm9XdJtNNjXZGb7tGOY4zjOXKSPh86qne6RzCuqmUeN+cT4+qZODHEcx3Ho56GzH8eVZ9+mzT2SeRxNLjVmM0udz9+Y2Yey1yR9DPhQfRnHcRwng/p3eTPw5/H1lExaruXNU7pOM7vXzA4CngXsYWZ/bGb3tyjSKCDa66dqpxWS/k7SHZJul/R1SfMlbSPpakm/ja9bZ/KfIuluSXdJel0mfT9Jt8Vrn5X69xN1HGeOkmoLTnXMMGa2S4Mj10b8PKGc/1XSVmY2bGbrJW0tadJCAEnHxfmZ50q6NXPcB9za/m2N1bsjcALwIjPbm6CzdgRwMnCNme0GXBPfI2nPeH0v4GDgi5JSbbazgWOB3eJxcKd2OY7j9AJTIdcxU0hqJqCcO08ea19vZmvSN2b2NPCGBvkuAg4Broiv6bGfmU038FkJGJJUAhYADwOHAekKt/OBw+P5YcDFZjZqZvcBdwP7S9oeWGJmPzczAy7IlHEcx5l9JCiW8h0zx7mxg7FNs4MQ4rkpeawtSppnZqMAkoaAefWZzGwtsBZ4R+xBbBfrXyRpkZk1jF8zFWb2kKRPEOLfbAJ+YGY/kLRdOi9kZo9I2jYW2ZEQpiBldUyrxPP69EnECa9jAZYvX9bEsIny8GOy9mZQ7I6kfla2f1KZrMR/+t4sKP63qitKwls2PamO256mUxuXj29UZzbMQKYua5A+qVxSC/W2EVogS7aN+tAG9WkNy2ZtGbvQOORDs7Jj15PahDoKLcI4pO2NhWBIqpnvTWlCXqkS8hUHpvzvVZYECX6ipH+DkASpxH+z597wOxPLKP1uxXAZWdn/sftK2zAbDzlRK0MS0wulsXsaozgwFn5DtfAdtEIp2pm5jzSUQeYZTrAxPU/vJdqlWhmNrKdQHg7n5U3Y6EZs03DIWBqgMLQQGxjCBodI5i1GA/Ox0jxMBYqWYBRIVKRYELXExtpIwwdIhBAKterE70LH9OU+mmcANzI5mkWWJ1pVkMfRfBW4RtK5hI/83Yz3JCYh6X3A6cBjTIzC1tHy5jj3chiwC7AG+EYMLd20SIM0a5E+OdFsJWEBBPu98AVtBihxHMeZBn3maMxsxXTryCOq+fE493Ig4cf6o2b2/RZF3g8818yemq5xkYOA+8zsCQBJ3yTEyXlM0vaxN7M948F4VgPLM+WXEYbaVsfz+nTHcZy+oY/30XRMroE+M7sKuCpnnQ8ShtC6xQPAAZIWEIbODgRWAcPAUcCZ8fXymP8K4CJJZwE7ECb9rzezmqT1kg4AfkEIN/25LtrpOI4zPfpbgqZj8midHUD4QX4eIRhaERg2syVNitwLXCvpO0zc1HNWJwaa2S8kXQrcRFAk+CVhWGsRcImkYwjO6G0x/x2SLgF+FfMfb2bp4OlxwHnAEMFx5nWejuM4M0MXd11IOhj4DOF3+8tmdmbXKm+DPD2azxOWC38DeBGhJ/CcFvkfiMdgPKaNmZ3G5Ohuo4TeTaP8ZwBnNEhfBezdDZscx3G6j7oW+CwuyvoCYW/jauAGSVeY2a+mUeeOwM5kfIeZ/XSqcnmHzu6WVIw9g3Ml/axF3o9Egxaa2XCe+h3HcZxI94bO9gfuNrN7ASRdTFhY1ZGjiQovfxbLp6NEBnTF0WyUNEiIMfNxgs7ZwhbGvISwpnoRsJOk5wPvMbO/ydGW4zjOnMUkLP/Q2VJJqzLvV8YVsyk7EubMU1YDfzQN8w4nLPQanSpjPXkczbsIGzvfB/wdYUXXW1rk/zTwOsKkPGZ2i6SXt2uY4zjOnMMmbGebiifN7EUtrufe0pGTe4EBMnPveWnqaCRdY2YHMi6SOQJ8JE+lZvZgnYxYN3YyOY7jbOEYSRueZgqabfXolI2Eka1r6GLgs+0lvQI4NI7tTfAcZnZTk3IPSnopYHHI7QTgzqkMcRzHmesYUOveFvEbgN0k7QI8RFjU9eeti7Tkini0TStH808EocplwCeZ6GgMeHWTcu8lLKfbkeBRf0AINeA4juNMgXWpR2Nm1ajU8n3C8uavmNkd06jv/Nh52D0m3WVmlTxlmzoaM7tU0mXAP5rZP7dhzJPAX+TN7ziO4wQMSLooemVm3wW+2426JL2SID92P6HjsVzSUXmWN7dcRxdVjg9r05jzJW2Veb+1pK+0U4fjOM5cxXIes8Angdea2SvM7OWERV+fylMwz6qz6yS92MxuyGnMPvVhBSS9MGfZvqWRMnFQtU3GFWujuu0kmikDp2WhsfREej1dWNFAlXfSOosEpGRifmmCqq6pgFKl41RBOD2yQZVSledsndn0tHwmfUpF5tjOhHJN86bqvROVe0Wt4e7pSW3He84qTSsqA49dzz737GfZSBU6WzYq9U5oM6lCrTr+vZhg3HgMkdTyCflSG1IFYAkKJSgNThyzzny/sm0rnZtN1bkLpbHPXUk12KZCqLPumWW/D+MNJahWhlp1Qt4xeXoVgmJxsYSV5oe2ktpYmdBmbSwvhep4HamKdWkQpfZEpWfmLYJCcfJyqUZq5dnnVXdP6X2rOkphZD02soHa+jUkw+tINqwJtg7ORwuWUFy8FdpmewqFEpZ+P9PKSvMoEm0vlKLqdvzs0+eauadpY93t0XSZATO7K31jZr+RNJCnYB5H8yrgPZJ+R9AXU2jDmqkxFyRtHePWEGMVzGjwBMdxnM2Vbs3R9IBVks4BLozv/4IQPmBK8jiAdsMwfxL4WdQnM+DtNJCDcRzHcSbS5VVn3eY4wsKuEwgdjp8CX8xTMI+jaeu2zeyCuFv11dGYN09HW8dxHGcu0a9DZ2Y2KunzwDWEWGN3mVk5T9k8juY7jAcOm08IQHYXsFcLg35Fh3o6juM4cxWz/h06k/RG4EvAPQR/sIuk98QwMi3JE/jsD+sa2xd4TwMjbjKzfacwdMo8juM4c5nOApzPCJ8EXmVmdwNIejahIzJ9R1OPmd0k6cUNLj1P0q0tiooQe9pxHMdpQp92aAAeT51M5F7GIxu3JE/gs5MybwvAfsATDbLukaM91zxzHMdpQtiw2V+eRtKb4+kdkr4LXEIw9W0EmZspydOjWZw5rwJXApfVZzKz3+Vp0HEcx2lOH646OyRz/hjwinj+BLB1ngryzNGMKTYr7OpaZGYjbRjpOI7j5KTPOjSY2dExWucJZpZLCaCeKbezSrpI0hJJCwkrye6S9P86acxxHMdpjmEkOY8ZtStEVz600/J5dBP2NLN1hOhq3wV2IgRDm4SkoqQfdmqM4zjOnMbSJc5TH7PAzyR9XtKfSNo3PfIUzONoBqKezeHA5VEWuuFtRq+3UVJXV5dJ2krSpZJ+LelOSS+RtI2kqyX9Nr5uncl/iqS7Jd0l6XWZ9P0k3RavfVbKHzPVcRxnJkgs3zELvJSwf/KfCUudPwl8Ik/BPIsB/oMgC30L8FNJOwPrWuQfAW6TdDVBGw3IF4WtBZ8Bvmdmb43xEBYAHwauMbMzJZ1MiJ3zIUl7EgL87AXsAPxQ0u7RCZ4NHAtcR+idHUyONeCO4zgzQZCg6bNJmoiZvarTsnkWA3wW+Gz6XtIDBKHNZnwnHl1B0hLg5cBfRnvKQFnSYcArY7bzgWuBDxHCGlxsZqPAfZLuBvaXdD+wxMx+Huu9gNBLc0fjOE7f0Kd+BhhTB9iLoBIDQJ54ZZ1s2DSgqcZ7jMI2BOyUlZSeBrsSltGdK+n5BLXQE4HtzOyR2OYjkraN+Xck9FhSVse0SjyvT5+EpGMJPR92WrZj/OSTxtLlSS1Ih9fKqFqGwQUY8yaHE8iWSaXbs/L8hVJjCfhU9l2FxtL6dTL4WYn/CfLlGUl6pe8Bkoxkf/yGW3EACiFcgGLYAJtQb21cqn/CX0X2PieGJUjvKb2PsTAD0Y5m4RUaSulnpeaz8ux1Uu1jzzh7v41IQyNk8iuVx7cEpfdfmDzSnM2XPosJ7WbsUrS3YSiFCaEjog3FQawa5f8LxfC9aRB+YEKb6XepOIBlZe0tCXWUBifaHu+PaBuFKP+f1CiUN6DRYaw8EqT7B+aRzFs46blZaQQrDUKSUKhsQuWN4e+isgkbGIJCMYQEqJbHnqEVB8cl/seeeRmzJNSVDVcR81ihND7Yn5XqT+tIwxPE775qFQqb1pKsfZJkwxqqjz5AdcMGRtesJ6lUKS2Yz9D22wEwsHgrbGBe/FseDxopDY/b1+Bzs9K88NxKpcbf4Tbpx300KZK+RBhNehXwZeCtwPV5ynYpiMIEYw4Bbga+F9+/QFJHcaYjJWBf4GwzeyFhOO7kViY0SLMW6ZMTzVaa2YvM7EVLlz6zXXsdx3E6w6CW5DtmgZea2ZHA03Hby0uA5XkKdt3RAKcD+wNrAMzsZoIQZ6esBlab2S/i+0sJjucxSdsDxNfHM/mzN78MeDimL2uQ7jiO0xekPZo8xyywKb5ulLQDYZQo1297HgmaIvBGYEU2v5md1aRI1czW1i3o6vipmNmjkh6U9Nw4FHcgYT/Pr4CjgDPj6+WxyBXARZLOIiwG2A243sxqktZLOgD4BXAk8LlO7XIcx+k+1reLAYArJW0F/DtwE+F3/ct5CuYZVPw2cSUZ+YRFb5f050BR0m6EIDk/y2NMC/4W+FpccXYvcDShN3aJpGOABwi6O5jZHZIuITiiKnB8XHEGIXDPecAQYRGALwRwHKdvMINKH2rQAJjZR+PpZZKuBOab2do8ZfM4mmUtwjY34m+BU4FR4CLg+8C/tFF+EnH47UUNLh3YJP8ZNIjqaWargL2nY4vjOE6v6MfFAJLeCcjM0hDOaRC0IyUNm9lFU9WRx9FcJem1ZvaDnHbtamanEpyN4ziO0wZ9OHT2AcIWk3ouJmwr6YqjuQ74VhTUrBBXaZrZkib5vxSHuM4DLjKzNTnacBzHmfOEHs1sWzGJopmtr080s/VRNWZK8qw6+yRhGdsCM1tiZotbOBnM7I+BdxJWfq2KopyvyWOM4zjOnMaglliuYwYZiKLKE5C0GBhskH8SeRzNb4HbrY1A1mb2G+AfCDv1XwF8NuqUvbl1ScdxnLmLkW9p8wzP45wDXCppRZoQzy+O16Ykz9DZI8C1kq4iTPADzZc3S9qHsCrsjcDVwCEx/PMOwM+Bb+YxzHEcZ65hQKXPxs7M7BOSNgA/kbSIYOYwcKaZnZ2njjyO5r54DJKvm/R54D+BD5tZusEHM3tY0j/kMcpxHGdOEofO+g0z+xJh/n0RYQXapDmbVuSOsBnH48zMNkyRv9HqhPTahc2uOY7jzHX6cXlzlql+/5uRRxlgb+BCYJv4/kngSDO7o0n+3YB/A/ZkosLnrp0Y6DiOM5fo0/2a0yLP0NlK4CQz+zGApFcShsZe2iT/ucBpwKcIKp9H01jQcvMgKi0HZdaoGpxRnVV1ZEy1llo5KB4X4whjvbpxqkacKj0ntbG6ZUlQxk2VZ6vlCarNqlMmTlWArS6dQgGSOgGHYimq2dapCmfVhqN9Y6+F4rhqc7EUPsBsW6lKbqE0phpsxcGJqtPxUKoknSQoVQcuxq9eRtk5fT+mcGxJaKcZGXXptM0J6sixfKpGPEnBuU4deNKzyKhPK73XjDpwmn+szfS5p9+PlEJx/LxYaqzeXKuNqVKrVg4mFEZRaSCeZ/5Us2rCSRI+81ptTMHZioNQGqCQXk9qYwrKJLWJis/pd7ZaDt+HYhGqUb140zqqa5/ChteRjGxEg/NRaYDiM56JhhaiwfnjCs7p92B0GKplbHSEJKmhQhEtXBL2RIyOjaSjeUMUBuaF26mMQjXcsxYvDXYWSuG7p0L4O0pqQUU7VRbPfMeVVFGtEv4WazVUHiZZv4ba+jVU1jxO5emnGXlqLcOP/p7yumGSShUVC5TmD1IbKTM0vAErj1BYvFWwtzSAJTWsWoFqBRvdhMX2NDifwtBCVBpAC5dQGBwKStnR5unS7z2aTsnzZBamTgbAzK5ttNQtw5CZXSNJZvY74HRJ/0NwPo7jOE4TzGxGJGgk/TtwCFAG7gGOnmrPYwe6l2PkWd58r6R/lLQiHv9AWBzQjJG4ufO3kt4n6U+BbVvkdxzHcSIztLz5amDvKC/2G+CUHGW+TQhA+UxgceaYkjw9mncDH2F8WfJPCcNhzXg/ITjOCcBHgVcT1JUdx3GcFsxUKOc6SbHrCEHMpqJd3csxWjqa2FX6hpkdlLdCM7shnm6gtUNyHMdxshgk+Zc3L5W0KvN+pZmt7KDVdwP/lSNfu7qXY7R0NDGGy0ZJz5hKDlrSt2kRd8bMDm3XOMdxnLlE6NHkzv6kmTVStQdA0g+BP2hw6VQzuzzmOZUQTuVrOdprV/dyjDxDZyPAbZKuJuwGhVD7CXX5PpGjLsdxHKcF3Vp1NtVIlKSjgDcBB+aUGEt1L29rR5IM8jma78SjJWb2k3YadhzHcSZiZpRreeJLTg9JBxO1KM1sY85ibetepjR1NJKuMbMDgT3N7EPtVuw4juO0hzFjEjSfB+YBV0sCuM7M3jtFmbZ0L7O06tFsL+kVwKGSLqZu06WZ3TRV5Y7jOE5+bIa0zszsOR0Ua1f3coxWjuafgJOBZUC9xzLCsuUxJF1oZu+SdKKZfaYdIxzHcZxAP4pqQvu6l1maOhozu5QQg+AfzeyjOeraT9LOwLslXcDkHtDv8xrlOI4zFzFmPKhZbtrVvcwypTJATicD8CXge8AewI11x6oW5XIhqSjpl5KujO+3kXS1pN/G160zeU+RdLekuyS9LpO+n6Tb4rXPKg5OOo7j9ANmUK4muY5ZINW93NnMdgY+QNC9nJI8EjS5MLPPmtnzgK+Y2a5mtkvm6IZy84nAnZn3JwPXmNluwDXxPZL2BI4A9gIOBr4YN54CnA0cC+wWj4O7YJfjOE5XSOdo+iyUc8ok3Uugle7lGF1zNJnGj5P0/Khz9r4YcXNaSFpGEHP7cib5MOD8eH4+cHgm/WIzGzWz+4C7gf0lbQ8sMbOfx+V5F2TKOI7j9AV97Gja1b0cI5eudewRbMdExc4HmuQ9gdBrSLXRviZppZl9Lk9bTfg08EEmCrhtZ2aPRFsekZQKd+5I2MGasjqmVeJ5fXqjezg23gM7LdthXBqeApCMyaurVkXVUVQdQZVRSKokxcExmXfqJfxT2frKKKpsRBt+D0mNwuD8IN+ebIOV5gfZ89GwN9YG5oX2kgQrlmAsBEGUek9l1MfaGJewD+8NqqNRUr46blMqdZ+RwydJQv7SQJA8z4YBSO1P60wl9Aulie3XqmMy7kG23savm6FKNTyDaIeV5o1L4BcK49L/lkCtOi7bP8HmWKcK4ZlknrMyIQPGQiPUKuEzyd5nloysv2rlIA+fpheKFOLzSEM2THgWSS1MRsZQAFYeGa8vDQ+QCRNQKBQmpQGhzVjOkhpWC+eFofgP48DQhGcwIRSCCpBUx+sojAaboz1WKaOBQQrzF2GF0YmfR3wu2XtO1q/ByiMkw+uwkWHKTz1Fed0wAwuHGFi8ABsZRguWoIEBVBoMnxtAkpBsGg7PZGAwVldDI/G7vGl4TG7fRoZDmIG0zU3DqFik+Mzt0fwFY3L9mjcUQiXE76GV5kGhMBYeQ5aEvz1LSNY8QTIyTO2pR6k89SQjT61lw0NPUF6/kcpwmdG1o5SHK1jNKA2VKA4UGHl6mKGli1m8/GkKgyVUKGAxnEVSqVJetxFLEqyWUBgoYUl4LQ6WmLfVYuZt+6zwLEoDY/c8Hfp5jobGupd/madgnsBnf0uQ+H8MSP9CDWjWU/kr4I/MbDiW/xjwc6AjRyPpTcDjZnZjjIUzZZEGadYifXJi0AtaCbDfC/bp20/dcZwtCzOo9q+jOaheEUbS24BvTFUwT4/mROC5ZvZUTmMEZCM/1Zhe4LOXEfbyvIEQsXOJpK8Cj0naPvZmtgcej/lXA8sz5ZcBD8f0ZQ3SHcdx+oY+7tGcwmSn0ihtEnkczYNAS0HNOs4FfiHpW/H94cA5bZSfgJmdQoyVEHs0f29m74yBe44Czoyvl8ciVwAXSToL2IEw6X99FAhdL+kA4BfAkXTYy3Icx+kFZsyIBE07SHo98AZgR0mfzVxaQhDknJJWEjQnxdN7CbID3yGH7ICZnSXpWuCPCT2Zo83sl3mMaZMzgUskHQM8ALwttn+HpEuAXxEewvFmlvawjgPOA4aAq+LhOI7TF/TpHM3DhC0qhxK2q6SsB/4uTwWtejTpxPsD8cjKDrR8ElGepusSNXE53bXx/CngwCb5zgDOaJC+Cti723Y5juN0g5mSoGkHM7sFuEXSRWZW6aSOVsoAqdzA28xswhhcnAByHMdxuky/OZoMKyT9G7AnYb4cgDz7JPPso2kUSzpPfGnHcRynDYJ6c5LrmAXOJWx6rwKvIuxFvDBPwVZzNNOeAHIcx3HawPpyjiZlyMyukSQz+x1wuqT/IWx/aUmrOZppTwA5juM4+UkMRmdHxywPIzGM828lvQ94CNh2ijJA6zmasQkgwuqxPQg9u7vMrDx9mx3HcZwsMxj4rBPeDywATgA+SggVc1Segnn20bwG+A/gHoLD2UXSe8zMlwY7juN0kz5cdZZiZjfE0w3A0e2UzeNozgJeZWZ3A0h6NvAdfA+K4zhOV+nHfTSSPm1m75f0bRpsbTGzQ6eqI4+jeTx1MpF7GZd7cRzHcbpIvzkaxleWfaLTCvI4mjskfRe4hODN3gbcIOnNAGb2zVaFN3uySsUAKob31TIqD6NqUAZO1gUlZuYvDirJEFR1swrOUTlYUcG59vTj2KZhKBQpLt6KQnEAK2xCSZXq4w9SWLAYDQwG9d1ikcLQYqxUxYqlcZViFcbVjwFkY21pTMG4DLVaUCYuDkIxKgdHteagDl0dUw9WbQAbHApqxZZgVopVj6s9p8rIMoNiESuNK/GmysSqVUK9qX1Jdezex5R4AQqxTEK4l4z6s2qVMYXiCerJ8b7HFKQhlMsqVydJUBJOqqhaDufZe0hqY2rCQT27RjI6QlIeCWnVChQKFOYvhNIAKhZJarWxeidQKIbyjdSbMyh9NqUBLFOHVStQrWC1GioWsUqYBrWFURl4Yfa+auNlU9XoSjkqRydQKKBCMahAjyk6FymMjqChhUENmaDsnIyOYNWgWK1CkWTTMLZxHVYeYfiB1Yw+vYGNjz5FUkuYt/ViBhcvYMG2Gxnceis0b/4EBeasAnWaPvZ8q2WsUiEpj5CUq5TXR0XnWkJSqcZnU2Bo3VqKCxcFJeRCMfwNzJuPSoNocD6FhYuhODAmnmiVUapPP44Nr6P6xENUN46w/oHHWP/AY2x8chPrH9lAoSjKGypUhstURmsUonp1aajEwm0XMLqujNUSSguHKA6UqI6USSoVKsMjJOUaSS2hOhJtLBYoDhQoDQ0wb6vFzH9qLYOLFzCwcIjCYC4x/JaYQbXPFgOY2Y3x9SeSnhXPn2injjxPZj5BufkV8f0ThFCehxAcz5btaBzHcWYIM0j6rEcTIxGfBryPME9fkFQFPmdm/5ynjikdjZm1NenjOI7jdIoR4jL2Fe8nqOi/OAaTRNKuwNmS/s7MPjVVBVMqA0jaXdI1km6P7/eJkdUcx3GcLmOJ5TpmkCOBd6ROBsDM7gXeGa9NSR4Jmv8kSM5UYgO3Ake0barjOI7Tmjh0lueYQQbM7MlJpoZ5moE8FeSZo1lgZtdLE2KXuQSN4zhOlzEmrmfpE1pt0M+1eT+Po3ky7p0Ji66ktwKP5KnccRzHaQODWp8FPgOeL2ldg3SRUXFuRR5HczywEthD0kPAfYSxOcdxHKerzPj8y5SY2eR1+m2SZ9XZvcBBkhYCBTNbP91GHcdxnMmEobP+cjTdIE8o5/p0oHkoZ8dxHKdDDJL+W948bfKEcn4u8GLgivj+EOCnvTTKcRxnrjKnejSZUM4/APZNh8wknQ58o1k5x3Ecp3O2REeTZx/NTkxcwlYGVvTEmgZIWi7px5LulHSHpBNj+jaSrpb02/i6dabMKZLulnSXpNdl0veTdFu89lnVrdl2HMeZTcyMWi3JdXQDSX8vySQt7UqFTcjjaC4Erpd0uqTTgF8A5/fSqDqqwAfM7HnAAcDxkvYETgauMbPdgGvie+K1I4C9gIOBL0pKV02cDRwL7BaPg2fwPhzHcaakTru26TFdJC0nxBt7YPq1tWZKR2NmZxCC3DwNrAGONrN/67Fd2fYfMbOb4vl64E5gR+Awxh3e+cDh8fww4GIzG42SCXcD+0vaHlhiZj+3ICZ0QaaM4zjOrGMzqwzwKeCDNIgx021y6VrHH/qbemzLlEhaAbyQ0KvazswegeCMJKWxq3cErssUWx3TKvG8Pr1RO8cSej7stOMOQXq9wUpyWZCyt8ooyUiQPS/GUADhzWQ/Pibdn1ShWiHZuB4VCtjQQqwyikoJVq2QrF8T5NyrlSC/XhqA0iAUSkEivRDqNhXGJNNTKf1siACSaggRkFSx0Y1ofgGLsvtk7E9l3AE0b35oRwUoZuXpq+P3kCTjYQCSUpT3L4X6Yt3UykHGPw1pYAnUaiTrf4+KRTTEmLS/MQhpWIIkbaM2FlrAVEBpmIAkCaESYDxUgArjz73uWZNUUXUUavFZEmT1qVbG5PZTiXvbNDx+niThs6lU0MBAkOSvVoIkf7WC4mdAoRjCOdRqQaq/GkeaS4OTwwmkYRTqQwgkNWxkeCzcQNqmlUcoLFyCkhqK9VnGbhWKUChg5ZHw+VXL4/VXy0Gmv1oOtlQrIbxFGlqgWgnlRjeN5Us2DVNZs4byuuEgt//Q02x4eAO1So3iwBMsWDrEM3ZZyvxnPoMF225Ncf4gKhaojQSpfYtDOqWFMUxALcGShOqmUSxJKK/bSFKpBgn+SgWrJdTKVZJKFRULLFq/cYLs/sCidWho4Vj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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_eqPac.diatC.isel(nlon=slice(2900,3600)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.    ,  15.    ,  25.    ,  35.    ,  45.    ,  55.    ,\n",
       "        65.    ,  75.    ,  85.    ,  95.    , 105.    , 115.    ,\n",
       "       125.    , 135.    , 145.    , 155.    , 165.0984], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depths = (ds_fosi_x01.z_t[0:18] /100).values\n",
    "depths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
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       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\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",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\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",
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       "\n",
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       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
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       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
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       "\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",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\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",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\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",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
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       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
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       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
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       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
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       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
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       "\n",
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       "  display: grid;\n",
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       "}\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.DataArray &#x27;diatC&#x27; (z_t_150m: 15, nlon: 700)&gt;\n",
       "dask.array&lt;getitem, shape=(15, 700), dtype=float32, chunksize=(15, 700), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * z_t_150m  (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG     (nlon) float64 dask.array&lt;chunksize=(700,), meta=np.ndarray&gt;\n",
       "    TLAT      (nlon) float64 dask.array&lt;chunksize=(700,), meta=np.ndarray&gt;\n",
       "    time      object 1960-09-16 00:00:00\n",
       "Dimensions without coordinates: nlon\n",
       "Attributes:\n",
       "    long_name:     Diatom Carbon\n",
       "    units:         mmol/m^3\n",
       "    grid_loc:      3114\n",
       "    cell_methods:  time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'diatC'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>z_t_150m</span>: 15</li><li><span>nlon</span>: 700</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-06b46152-32ca-419b-b3cf-e427ae307669' class='xr-array-in' type='checkbox' checked><label for='section-06b46152-32ca-419b-b3cf-e427ae307669' 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>dask.array&lt;chunksize=(15, 700), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 41.02 kiB </td> <td> 41.02 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (15, 700) </td> <td> (15, 700) </td></tr>\n",
       "    <tr><th> Count </th><td> 4 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",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-9d7f007d-61b3-4ff5-80fe-1482755c6ddc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d7f007d-61b3-4ff5-80fe-1482755c6ddc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-6d913470-c7e4-4973-bd9c-2d7319ac5a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d913470-c7e4-4973-bd9c-2d7319ac5a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f352b83e-8cc4-4ced-9de4-42684326e2e0' class='xr-var-data-in' type='checkbox'><label for='data-f352b83e-8cc4-4ced-9de4-42684326e2e0' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLONG</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(700,), meta=np.ndarray&gt;</div><input id='attrs-12d35574-9054-4759-bb77-27e06d6fdcbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12d35574-9054-4759-bb77-27e06d6fdcbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a083b032-52c4-4752-a69e-84380221232a' class='xr-var-data-in' type='checkbox'><label for='data-a083b032-52c4-4752-a69e-84380221232a' 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'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 5.47 kiB </td> <td> 5.47 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (700,) </td> <td> (700,) </td></tr>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(700,), meta=np.ndarray&gt;</div><input id='attrs-30e5a8b4-8ded-4a29-af44-be620bf8d626' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30e5a8b4-8ded-4a29-af44-be620bf8d626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b9e0c9e-5699-40e3-90e0-eceb9e3a66dc' class='xr-var-data-in' type='checkbox'><label for='data-8b9e0c9e-5699-40e3-90e0-eceb9e3a66dc' 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'><table>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1960-09-16 00:00:00</div><input id='attrs-cc383a50-9d0e-4433-9363-a1ece198bc28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc383a50-9d0e-4433-9363-a1ece198bc28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a10d927-9236-4c2a-bd09-42f419959c43' class='xr-var-data-in' type='checkbox'><label for='data-0a10d927-9236-4c2a-bd09-42f419959c43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1960, 9, 16, 0, 0, 0, 0), dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86568669-2fe7-4103-a73a-fc2e28eaf45c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-86568669-2fe7-4103-a73a-fc2e28eaf45c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'diatC' (z_t_150m: 15, nlon: 700)>\n",
       "dask.array<getitem, shape=(15, 700), dtype=float32, chunksize=(15, 700), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * z_t_150m  (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG     (nlon) float64 dask.array<chunksize=(700,), meta=np.ndarray>\n",
       "    TLAT      (nlon) float64 dask.array<chunksize=(700,), meta=np.ndarray>\n",
       "    time      object 1960-09-16 00:00:00\n",
       "Dimensions without coordinates: nlon\n",
       "Attributes:\n",
       "    long_name:     Diatom Carbon\n",
       "    units:         mmol/m^3\n",
       "    grid_loc:      3114\n",
       "    cell_methods:  time: mean"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,10))\n",
    "\n",
    "############# Eq Pac\n",
    "\n",
    "### NO3\n",
    "var = 'NO3'\n",
    "cmap='Spectral_r'\n",
    "ax = fig.add_subplot(3,2,1)\n",
    "ax.set_title('a) Eq. Pacific, Fe',fontsize=12, loc='left')\n",
    "#tmp = ds_eqPac.NO3.isel(nlon=slice(2900,3600)) * 1000 \n",
    "tmp = ds_eqPac.Fe.isel(nlon=slice(2900,3600)) \n",
    "lon = lons_norm.isel(nlon=slice(2900,3600),nlat=1202)\n",
    "mld = ds_eqPac.HMXL.isel(nlon=slice(2900,3600)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap) #, vmin=0.15,vmax=0.6)\n",
    "#pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.15,vmax=0.6)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "#fig.colorbar(pc, label='Fe (µmol m$^{-3}$)',extend='both');\n",
    "########### diatC\n",
    "# var = 'NO3'\n",
    "# cmap='RdYlGn'\n",
    "# ax = fig.add_subplot(3,2,2)\n",
    "# ax.set_title('b) Eq. Pacific, diatom carbon',fontsize=12, loc='left')\n",
    "# tmp = ds_eqPac.diatC.isel(nlon=slice(2900,3600))\n",
    "# lon = lons_norm.isel(nlon=slice(2900,3600),nlat=1202)\n",
    "# pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "# plt.gca().invert_yaxis()\n",
    "# mld = ds_eqPac.HMXL.isel(nlon=slice(2900,3600)) * 0.01\n",
    "# ml = ax.plot(lon,mld,color='black')\n",
    "# ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "# fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "############################### Ca CUrrent\n",
    "\n",
    "### NO3\n",
    "var = 'NO3'\n",
    "cmap='Spectral_r'\n",
    "ax = fig.add_subplot(3,2,3)\n",
    "ax.set_title('c) CA current, NO$_3$',fontsize=12, loc='left')\n",
    "tmp = ds_CAcurr.NO3.isel(nlon=slice(3300,3460))\n",
    "lon = lons_norm.isel(nlon=slice(3300,3460),nlat=1636)\n",
    "mld = ds_CAcurr.HMXL.isel(nlon=slice(3300,3460)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=1,vmax=10)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "########### diatC\n",
    "# var = 'NO3'\n",
    "# cmap='Greens'\n",
    "# ax = fig.add_subplot(3,2,4)\n",
    "# ax.set_title('d) CA current, diatom carbon',fontsize=12, loc='left')\n",
    "# tmp = ds_CAcurr.diatC.isel(nlon=slice(3300,3460))\n",
    "# lon = lons_norm.isel(nlon=slice(3300,3460),nlat=1636)\n",
    "# pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "# plt.gca().invert_yaxis()\n",
    "# mld = ds_CAcurr.HMXL.isel(nlon=slice(3300,3460)) * 0.01\n",
    "# ml = ax.plot(lon,mld,color='black')\n",
    "# ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "# fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "############################### SE US shelf\n",
    "\n",
    "### NO3\n",
    "var = 'NO3'\n",
    "cmap='Spectral_r'\n",
    "ax = fig.add_subplot(3,2,5)\n",
    "ax.set_title('e) SE US shelf, NO$_3$',fontsize=12, loc='left')\n",
    "tmp = ds_SEUS.NO3.isel(nlon=slice(400,500))\n",
    "lon = lons_norm.isel(nlon=slice(400,500),nlat=1500)\n",
    "mld = ds_SEUS.HMXL.isel(nlon=slice(400,500)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.5,vmax=2)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "########### diatC\n",
    "# var = 'NO3'\n",
    "# cmap='Greens'\n",
    "# ax = fig.add_subplot(3,2,6)\n",
    "# ax.set_title('f) SE US shelf, diatom carbon',fontsize=12, loc='left')\n",
    "# tmp = ds_SEUS.diatC.isel(nlon=slice(400,500))\n",
    "# lon = lons_norm.isel(nlon=slice(400,500),nlat=1500)\n",
    "# pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "# plt.gca().invert_yaxis()\n",
    "# mld = ds_SEUS.HMXL.isel(nlon=slice(400,500)) * 0.01\n",
    "# ml = ax.plot(lon,mld,color='black')\n",
    "# ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "# fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "fig.subplots_adjust(hspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "depths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "depths[0:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,10))\n",
    "\n",
    "############# Eq Pac\n",
    "\n",
    "### NO3\n",
    "\n",
    "cmap='YlOrBr'\n",
    "ax = fig.add_subplot(3,2,1)\n",
    "ax.set_title('a) Eq. Pacific, Fe',fontsize=12, loc='left')\n",
    "#tmp = ds_eqPac.NO3.isel(nlon=slice(2900,3600))\n",
    "tmp = ds_eqPac.Fe.isel(nlon=slice(3400,3600),z_t=slice(0,8))  * 1000 \n",
    "lon = lons_norm.isel(nlon=slice(3400,3600),nlat=1202)\n",
    "mld = ds_eqPac.HMXL.isel(nlon=slice(3400,3600)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths[0:8],tmp, cmap=cmap, vmin=0.15,vmax=0.25)\n",
    "#pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.15,vmax=0.6)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "#fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "fig.colorbar(pc, label='Fe (µmol m$^{-3}$)',extend='both');\n",
    "########### diatC\n",
    "\n",
    "# cmap='Greens'\n",
    "# ax = fig.add_subplot(3,2,2)\n",
    "# ax.set_title('b) Eq. Pacific, diatom carbon',fontsize=12, loc='left')\n",
    "# tmp = ds_eqPac.diatC.isel(nlon=slice(3400,3600),z_t_150m=slice(0,8))\n",
    "# lon = lons_norm.isel(nlon=slice(3400,3600),nlat=1202)\n",
    "# pc = ax.pcolormesh(lon,depths[0:8],tmp, cmap=cmap)\n",
    "# plt.gca().invert_yaxis()\n",
    "# mld = ds_eqPac.HMXL.isel(nlon=slice(3400,3600)) * 0.01\n",
    "# ml = ax.plot(lon,mld,color='black')\n",
    "# ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "# fig.colorbar(pc, label='Diatom C (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "# ############################### Ca CUrrent\n",
    "\n",
    "### NO3\n",
    "var = 'NO3'\n",
    "cmap='Spectral_r'\n",
    "ax = fig.add_subplot(3,2,3)\n",
    "ax.set_title('c) CA current, NO$_3$',fontsize=12, loc='left')\n",
    "tmp = ds_CAcurr.NO3.isel(nlon=slice(3380,3460))\n",
    "lon = lons_norm.isel(nlon=slice(3380,3460),nlat=1636)\n",
    "mld = ds_CAcurr.HMXL.isel(nlon=slice(3380,3460)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=1,vmax=10)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "# ########### diatC\n",
    "# var = 'NO3'\n",
    "# cmap='Greens'\n",
    "# ax = fig.add_subplot(3,2,4)\n",
    "# ax.set_title('d) CA current, diatom carbon',fontsize=12, loc='left')\n",
    "# tmp = ds_CAcurr.diatC.isel(nlon=slice(3380,3460))\n",
    "# lon = lons_norm.isel(nlon=slice(3380,3460),nlat=1636)\n",
    "# pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "# plt.gca().invert_yaxis()\n",
    "# mld = ds_CAcurr.HMXL.isel(nlon=slice(3380,3460)) * 0.01\n",
    "# ml = ax.plot(lon,mld,color='black')\n",
    "# ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "# fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "############################### SE US shelf\n",
    "\n",
    "### NO3\n",
    "var = 'NO3'\n",
    "cmap='Spectral_r'\n",
    "ax = fig.add_subplot(3,2,5)\n",
    "ax.set_title('e) SE US shelf, NO$_3$',fontsize=12, loc='left')\n",
    "tmp = ds_SEUS.NO3.isel(nlon=slice(400,500))\n",
    "lon = lons_norm.isel(nlon=slice(400,500),nlat=1500)\n",
    "mld = ds_SEUS.HMXL.isel(nlon=slice(400,500)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.5,vmax=2)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "# ########### diatC\n",
    "# var = 'NO3'\n",
    "# cmap='Greens'\n",
    "# ax = fig.add_subplot(3,2,6)\n",
    "# ax.set_title('f) SE US shelf, diatom carbon',fontsize=12, loc='left')\n",
    "# tmp = ds_SEUS.diatC.isel(nlon=slice(400,500))\n",
    "# lon = lons_norm.isel(nlon=slice(400,500),nlat=1500)\n",
    "# pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "# plt.gca().invert_yaxis()\n",
    "# mld = ds_SEUS.HMXL.isel(nlon=slice(400,500)) * 0.01\n",
    "# ml = ax.plot(lon,mld,color='black')\n",
    "# ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "# fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "# fig.subplots_adjust(hspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nuts, TEMP, and MLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x792 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,11))\n",
    "\n",
    "############# Eq Pac\n",
    "\n",
    "cmap='YlOrBr'\n",
    "ax = fig.add_subplot(3,2,1)\n",
    "ax.set_title('a) Eq. Pacific 2°N, Fe (Sept. 1960)',fontsize=12, loc='left')\n",
    "#tmp = ds_eqPac.NO3.isel(nlon=slice(2900,3600))\n",
    "tmp = ds_eqPac.Fe.isel(nlon=slice(3200,3600),z_t=slice(0,11))  * 1000 \n",
    "lon = lons_norm.isel(nlon=slice(3200,3600),nlat=1202)\n",
    "mld = ds_eqPac.HMXL.isel(nlon=slice(3200,3600)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths[0:11],tmp, cmap=cmap, vmin=0.15,vmax=0.25)\n",
    "#pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.15,vmax=0.6)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "#fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "fig.colorbar(pc, label='Fe (µmol m$^{-3}$)',extend='both');\n",
    "\n",
    "\n",
    "########### TEMP\n",
    "cmap='YlGnBu_r'\n",
    "ax = fig.add_subplot(3,2,2)\n",
    "ax.set_title('b) Eq. Pacific 2°N, Temperature (Sept. 1960)',fontsize=12, loc='left')\n",
    "tmp = ds_eqPac.TEMP.isel(nlon=slice(3200,3600),z_t=slice(0,11))\n",
    "lon = lons_norm.isel(nlon=slice(3200,3600),nlat=1202)\n",
    "pc = ax.pcolormesh(lon,depths[0:11],tmp, cmap=cmap, vmin=23, vmax=26)\n",
    "plt.gca().invert_yaxis()\n",
    "mld = ds_eqPac.HMXL.isel(nlon=slice(3200,3600)) * 0.01\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='Diatom C (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "############################### Ca CUrrent\n",
    "\n",
    "### NO3\n",
    "var = 'NO3'\n",
    "cmap='Purples'\n",
    "ax = fig.add_subplot(3,2,3)\n",
    "ax.set_title('c) CA current 39°N, NO$_3$ (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_CAcurr.NO3.isel(nlon=slice(3300,3460))\n",
    "lon = lons_norm.isel(nlon=slice(3300,3460),nlat=1636)\n",
    "mld = ds_CAcurr.HMXL.isel(nlon=slice(3300,3460)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=1,vmax=4)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "########### TEMP\n",
    "cmap='YlGnBu_r'\n",
    "ax = fig.add_subplot(3,2,4)\n",
    "ax.set_title('d) CA current 39°N, Temperature (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_CAcurr.TEMP.isel(nlon=slice(3300,3460))\n",
    "lon = lons_norm.isel(nlon=slice(3300,3460),nlat=1636)\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "plt.gca().invert_yaxis()\n",
    "mld = ds_CAcurr.HMXL.isel(nlon=slice(3300,3460)) * 0.01\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "############################### SE US shelf\n",
    "\n",
    "### NO3\n",
    "cmap='Purples'\n",
    "ax = fig.add_subplot(3,2,5)\n",
    "ax.set_title('e) SE US shelf 30°N, NO$_3$ (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_SEUS.NO3.isel(nlon=slice(400,500))\n",
    "lon = lons_norm.isel(nlon=slice(400,500),nlat=1500)\n",
    "mld = ds_SEUS.HMXL.isel(nlon=slice(400,500)) * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.5,vmax=1.5)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "########### TEMP\n",
    "var = 'NO3'\n",
    "cmap='YlGnBu_r'\n",
    "ax = fig.add_subplot(3,2,6)\n",
    "ax.set_title('f) SE US shelf 30°N, Temperature (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_SEUS.TEMP.isel(nlon=slice(400,500))\n",
    "lon = lons_norm.isel(nlon=slice(400,500),nlat=1500)\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "plt.gca().invert_yaxis()\n",
    "mld = ds_SEUS.HMXL.isel(nlon=slice(400,500)) * 0.01\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "fig.savefig('/glade/work/kristenk/figs/Regional_transects_x01.png', bbox_inches='tight', dpi=300, format='png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = 'g.e22.GOMIPECOIAF_JRA-1p4-2018.TL319_g17.4p2z.002branch'\n",
    "path = '/glade/campaign/cesm/development/bgcwg/projects/CESM2-OMIP2-like-4p2z-run/' + case + '/ocn/proc/tseries/month_1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  diatC\n",
      "starting on  NO3\n",
      "starting on  HMXL\n",
      "starting on  Fe\n",
      "starting on  TEMP\n",
      "starting on  HMXL_DR\n"
     ]
    }
   ],
   "source": [
    "ds_fosi = xr.Dataset()\n",
    "#ds_fosi_seas = xr.Dataset()\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print('starting on ', var)\n",
    "    ################ FOSI\n",
    "    \n",
    "    ds_tmp = xr.open_dataset(path + case + '.pop.h.' + var + '.195801-202112.nc')\n",
    "    \n",
    "    keep_vars=['z_t','z_t_150m','time_bound','dz','TLAT','TLONG','time'] + [var]\n",
    "\n",
    "    ds_tmp = ds_tmp.drop([v for v in ds_tmp.variables if v not in keep_vars])\n",
    "    \n",
    "    ds_fosi = xr.merge([ds_fosi,ds_tmp],combine_attrs='override')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (d2: 2, nlat: 384, nlon: 320, time: 768, z_t: 60, z_t_150m: 15)\n",
       "Coordinates:\n",
       "    TLAT        (nlat, nlon) float64 -79.22 -79.22 -79.22 -79.22 ... nan nan nan\n",
       "    TLONG       (nlat, nlon) float64 320.6 321.7 322.8 323.9 ... nan nan nan nan\n",
       "  * time        (time) object 1958-02-01 00:00:00 ... 2022-01-01 00:00:00\n",
       "  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "  * z_t_150m    (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "Dimensions without coordinates: d2, nlat, nlon\n",
       "Data variables:\n",
       "    diatC       (time, z_t_150m, nlat, nlon) float32 ...\n",
       "    dz          (z_t) float32 1e+03 1e+03 1e+03 ... 2.499e+04 2.5e+04 2.5e+04\n",
       "    time_bound  (time, d2) object 1958-01-01 02:00:00.000003 ... 2022-01-01 0...\n",
       "    NO3         (time, z_t, nlat, nlon) float32 ...\n",
       "    HMXL        (time, nlat, nlon) float32 ...\n",
       "    Fe          (time, z_t, nlat, nlon) float32 ...\n",
       "    TEMP        (time, z_t, nlat, nlon) float32 ...\n",
       "    HMXL_DR     (time, 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-cd69b38a-9981-422d-8ff4-417c4a39c67e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd69b38a-9981-422d-8ff4-417c4a39c67e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>d2</span>: 2</li><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li><li><span class='xr-has-index'>time</span>: 768</li><li><span class='xr-has-index'>z_t</span>: 60</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b1e5d7aa-dc9d-4fd8-b5b3-70d8708f2df9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1e5d7aa-dc9d-4fd8-b5b3-70d8708f2df9' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.22 -79.22 -79.22 ... nan nan</div><input id='attrs-b42c26bc-7359-4449-a499-f9a0815e5c8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b42c26bc-7359-4449-a499-f9a0815e5c8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b50bcc32-283e-4dcd-82e9-612fe6bcf9f7' class='xr-var-data-in' type='checkbox'><label for='data-b50bcc32-283e-4dcd-82e9-612fe6bcf9f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[-79.22052261, -79.22052261, -79.22052261, ..., -79.22052261,\n",
       "        -79.22052261, -79.22052261],\n",
       "       [-78.68630626, -78.68630626, -78.68630626, ..., -78.68630626,\n",
       "        -78.68630626, -78.68630626],\n",
       "       [-78.15208992, -78.15208992, -78.15208992, ..., -78.15208992,\n",
       "        -78.15208992, -78.15208992],\n",
       "       ...,\n",
       "       [ 71.29031715,  71.29408252,  71.30160692, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [ 71.73524335,  71.73881845,  71.74596231, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [ 72.18597561,  72.18933231,  72.19603941, ...,          nan,\n",
       "                 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'>320.6 321.7 322.8 ... nan nan nan</div><input id='attrs-c2348039-12e0-4657-8442-05923b36a844' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2348039-12e0-4657-8442-05923b36a844' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9826e60b-8f8d-45a5-a258-b5738f0995f1' class='xr-var-data-in' type='checkbox'><label for='data-9826e60b-8f8d-45a5-a258-b5738f0995f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       ...,\n",
       "       [320.25133086, 320.75380113, 321.25577325, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [320.23459477, 320.70358949, 321.17207442, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [320.21650899, 320.6493303 , 321.08163473, ...,          nan,\n",
       "                 nan,          nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1958-02-01 00:00:00 ... 2022-01-...</div><input id='attrs-0b260465-774e-403d-8dc8-94f8d22a3eed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b260465-774e-403d-8dc8-94f8d22a3eed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb8d79d4-3a66-4d8a-a402-6bda22b13a47' class='xr-var-data-in' type='checkbox'><label for='data-bb8d79d4-3a66-4d8a-a402-6bda22b13a47' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bound</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1958, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1958, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1958, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2021, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2021, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2022, 1, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-d469b8cb-e536-4a0e-b317-eb164108dc68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d469b8cb-e536-4a0e-b317-eb164108dc68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4d95d3f-34bd-45a3-9254-a415655600b3' class='xr-var-data-in' type='checkbox'><label for='data-a4d95d3f-34bd-45a3-9254-a415655600b3' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-84e292f9-a8a0-46d2-abf0-7c12760c51d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84e292f9-a8a0-46d2-abf0-7c12760c51d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea7b59d2-6ae9-44c7-ab56-d7fe6cf91587' class='xr-var-data-in' type='checkbox'><label for='data-ea7b59d2-6ae9-44c7-ab56-d7fe6cf91587' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d6f5693-1713-4179-a2b1-1ebd20688d98' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d6f5693-1713-4179-a2b1-1ebd20688d98' class='xr-section-summary' >Data variables: <span>(8)</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>diatC</span></div><div class='xr-var-dims'>(time, z_t_150m, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a89b0ff0-b6f8-4531-86e0-99cbef7ca300' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a89b0ff0-b6f8-4531-86e0-99cbef7ca300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f5197b2-eab2-4bc6-9bb9-ead0d5e71853' class='xr-var-data-in' type='checkbox'><label for='data-1f5197b2-eab2-4bc6-9bb9-ead0d5e71853' 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>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>[1415577600 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+03 1e+03 ... 2.5e+04 2.5e+04</div><input id='attrs-4e528e93-475c-4671-8d6f-5867f836c7bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e528e93-475c-4671-8d6f-5867f836c7bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-141fc5b2-7af4-48a7-848a-946f4e8e33dd' class='xr-var-data-in' type='checkbox'><label for='data-141fc5b2-7af4-48a7-848a-946f4e8e33dd' 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'><pre>array([ 1000.    ,  1000.    ,  1000.    ,  1000.    ,  1000.    ,\n",
       "        1000.    ,  1000.    ,  1000.    ,  1000.    ,  1000.    ,\n",
       "        1000.    ,  1000.    ,  1000.    ,  1000.    ,  1000.    ,\n",
       "        1000.    ,  1019.6808,  1056.4484,  1105.9951,  1167.807 ,\n",
       "        1242.4133,  1330.9678,  1435.141 ,  1557.1259,  1699.6796,\n",
       "        1866.2124,  2060.9023,  2288.852 ,  2556.247 ,  2870.575 ,\n",
       "        3240.8372,  3677.7725,  4194.031 ,  4804.2236,  5524.7544,\n",
       "        6373.192 ,  7366.945 ,  8520.893 ,  9843.658 , 11332.466 ,\n",
       "       12967.199 , 14705.344 , 16480.709 , 18209.135 , 19802.234 ,\n",
       "       21185.957 , 22316.51  , 23186.494 , 23819.45  , 24257.217 ,\n",
       "       24546.78  , 24731.014 , 24844.328 , 24911.975 , 24951.291 ,\n",
       "       24973.594 , 24985.96  , 24992.674 , 24996.244 , 24998.11  ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bound</span></div><div class='xr-var-dims'>(time, d2)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1958-01-01 02:00:00.000003 ... 2...</div><input id='attrs-3fc7f111-5fe7-4644-a785-ecfb037bd950' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fc7f111-5fe7-4644-a785-ecfb037bd950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e34751b-f960-433a-b99d-1bd07310ed16' class='xr-var-data-in' type='checkbox'><label for='data-3e34751b-f960-433a-b99d-1bd07310ed16' 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>boundaries for time-averaging interval</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeNoLeap(1958, 1, 1, 2, 0, 0, 3),\n",
       "        cftime.DatetimeNoLeap(1958, 2, 1, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(1958, 2, 1, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(1958, 3, 1, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(1958, 3, 1, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(1958, 4, 1, 0, 0, 0, 0)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2021, 10, 1, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2021, 11, 1, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2021, 11, 1, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2021, 12, 1, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2021, 12, 1, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2022, 1, 1, 0, 0, 0, 0)]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3</span></div><div class='xr-var-dims'>(time, z_t, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7ecab14-656e-4549-9183-51d2c9970584' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7ecab14-656e-4549-9183-51d2c9970584' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d46a526-17d8-4d2d-bd52-8e431e116974' class='xr-var-data-in' type='checkbox'><label for='data-4d46a526-17d8-4d2d-bd52-8e431e116974' 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'><pre>[5662310400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HMXL</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'>...</div><input id='attrs-fddd8be3-214d-47d7-bd0b-315c51319f23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fddd8be3-214d-47d7-bd0b-315c51319f23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebc4b9f9-3655-48c6-ae66-7ffaaaa6a0f5' class='xr-var-data-in' type='checkbox'><label for='data-ebc4b9f9-3655-48c6-ae66-7ffaaaa6a0f5' 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>Mixed-Layer Depth</dd><dt><span>units :</span></dt><dd>centimeter</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>[94371840 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe</span></div><div class='xr-var-dims'>(time, z_t, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ebac97c-95c9-4d23-b53e-ee316034df33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ebac97c-95c9-4d23-b53e-ee316034df33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68a148fd-37f9-4820-a8c7-c4585fdecdd3' class='xr-var-data-in' type='checkbox'><label for='data-68a148fd-37f9-4820-a8c7-c4585fdecdd3' 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 Iron</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'><pre>[5662310400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(time, z_t, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5b06c3e6-84fe-4e16-ba08-49ba73e19de4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b06c3e6-84fe-4e16-ba08-49ba73e19de4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5f5d757-231a-4763-8150-e6cfae565360' class='xr-var-data-in' type='checkbox'><label for='data-d5f5d757-231a-4763-8150-e6cfae565360' 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>Potential Temperature</dd><dt><span>units :</span></dt><dd>degC</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'><pre>[5662310400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HMXL_DR</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'>...</div><input id='attrs-ac012faf-b9c6-46d7-b9c0-591aa0142604' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac012faf-b9c6-46d7-b9c0-591aa0142604' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fa42ce7-5e1b-418a-91a1-079319057eef' class='xr-var-data-in' type='checkbox'><label for='data-0fa42ce7-5e1b-418a-91a1-079319057eef' 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>Mixed-Layer Depth (density)</dd><dt><span>units :</span></dt><dd>centimeter</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>[94371840 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84528a5b-f3df-4b3e-8dc5-46bf2fda3151' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-84528a5b-f3df-4b3e-8dc5-46bf2fda3151' 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:     (d2: 2, nlat: 384, nlon: 320, time: 768, z_t: 60, z_t_150m: 15)\n",
       "Coordinates:\n",
       "    TLAT        (nlat, nlon) float64 -79.22 -79.22 -79.22 -79.22 ... nan nan nan\n",
       "    TLONG       (nlat, nlon) float64 320.6 321.7 322.8 323.9 ... nan nan nan nan\n",
       "  * time        (time) object 1958-02-01 00:00:00 ... 2022-01-01 00:00:00\n",
       "  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "  * z_t_150m    (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "Dimensions without coordinates: d2, nlat, nlon\n",
       "Data variables:\n",
       "    diatC       (time, z_t_150m, nlat, nlon) float32 ...\n",
       "    dz          (z_t) float32 1e+03 1e+03 1e+03 ... 2.499e+04 2.5e+04 2.5e+04\n",
       "    time_bound  (time, d2) object 1958-01-01 02:00:00.000003 ... 2022-01-01 0...\n",
       "    NO3         (time, z_t, nlat, nlon) float32 ...\n",
       "    HMXL        (time, nlat, nlon) float32 ...\n",
       "    Fe          (time, z_t, nlat, nlon) float32 ...\n",
       "    TEMP        (time, z_t, nlat, nlon) float32 ...\n",
       "    HMXL_DR     (time, nlat, nlon) float32 ..."
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_fosi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_fosi[\"time\"] = ds_fosi.time_bound.compute().mean(dim=\"d2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find closest lats cross sections in x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid_x1 = pop_tools.get_grid('POP_gx1v7')\n",
    "lats_x1 = ds_grid_x1.TLAT\n",
    "lons_x1 = ds_grid_x1.TLONG\n",
    "area_x1 = ds_grid_x1.TAREA\n",
    "lons_norm_x1 = utils.normal_lons(lons_x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x15012bae5050>"
      ]
     },
     "execution_count": 48,
     "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_grid_x1.REGION_MASK.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CA current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39.76472518, 39.69905896, 39.63316061, 39.56705522, 39.50076797,\n",
       "       39.43432413, 39.36774902, 39.30106807, 39.23430673, 39.16749051,\n",
       "       39.10064498, 39.0337957 , 38.9669683 , 38.90018837, 38.83348155])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid_x1.TLAT.isel(nlat=292,nlon=slice(230,245)).values ### Cross section through CA current at 39N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-139.64524521, -138.52001209, -137.39521012, -136.2708353 ,\n",
       "       -135.14688342, -134.02335007, -132.90023069, -131.77752054,\n",
       "       -130.65521469, -129.5333081 , -128.41179555, -127.2906717 ,\n",
       "       -126.16993106, -125.04956804, -123.92957692])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lons_norm_x1.isel(nlat=292,nlon=slice(230,245)).values ### Cross section through CA current at 39N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SE US shelf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.29841574, 30.27424885, 30.25082555, 30.22815506, 30.20624635,\n",
       "       30.18510805, 30.16474852, 30.14517581, 30.12639765, 30.10842146,\n",
       "       30.09125436, 30.07490313, 30.05937425, 30.04467386, 30.03080779,\n",
       "       30.01778153, 30.00560023, 29.99426873, 29.98379151, 29.97417273])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid_x1.TLAT.isel(nlat=279,nlon=slice(290,310)).values ### Cross section through SE US shelf at 30N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-71.82306916, -70.70595309, -69.58889397, -68.47188946,\n",
       "       -67.35493725, -66.23803504, -65.12118056, -64.00437159,\n",
       "       -62.8876059 , -61.7708813 , -60.65419563, -59.53754675,\n",
       "       -58.42093253, -57.30435087, -56.18779969, -55.07127692,\n",
       "       -53.95478054, -52.8383085 , -51.7218588 ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lons_norm_x1.isel(nlat=279,nlon=slice(291,310)).values ### Cross section through SE US shelf at 30N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eq Pacific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00600659, 2.00585935, 2.0057112 , 2.0055622 , 2.0054124 ,\n",
       "       2.00526186, 2.00511064, 2.0049588 , 2.0048064 , 2.00465349,\n",
       "       2.00450013, 2.00434639, 2.00419232, 2.00403798, 2.00388343,\n",
       "       2.00372874, 2.00357395, 2.00341914, 2.00326435, 2.00310965,\n",
       "       2.00295511, 2.00280077, 2.0026467 , 2.00249295, 2.0023396 ,\n",
       "       2.00218669, 2.00203428, 2.00188244, 2.00173122, 2.00158069,\n",
       "       2.00143089, 2.00128188, 2.00113373])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid_x1.TLAT.isel(nlat=194,nlon=slice(223,256)).values ### Cross section through Eq. Pac at 2N\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-148.56240426, -147.43740366, -146.3124031 , -145.18740257,\n",
       "       -144.06240208, -142.93740163, -141.81240121, -140.68740082,\n",
       "       -139.56240048, -138.43740016, -137.31239989, -136.18739965,\n",
       "       -135.06239945, -133.93739928, -132.81239915, -131.68739906,\n",
       "       -130.562399  , -129.43739898, -128.312399  , -127.18739905,\n",
       "       -126.06239914, -124.93739926, -123.81239942, -122.68739962,\n",
       "       -121.56239985, -120.43740012, -119.31240043, -118.18740077,\n",
       "       -117.06240115, -115.93740156, -114.81240201, -113.68740249,\n",
       "       -112.56240301])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lons_norm_x1.isel(nlat=194,nlon=slice(223,256)).values ### Cross section through Eq. Pac at 2N\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(cftime.DatetimeNoLeap(1999, 3, 16, 12, 0, 0, 0), dtype=object)\n",
       "Coordinates:\n",
       "    time     object 1999-03-16 12:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-356c2d0e-c080-40f3-922e-b1bf0eaa179c' class='xr-array-in' type='checkbox' checked><label for='section-356c2d0e-c080-40f3-922e-b1bf0eaa179c' 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>1999-03-16 12:00:00</span></div><div class='xr-array-data'><pre>array(cftime.DatetimeNoLeap(1999, 3, 16, 12, 0, 0, 0), dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-5da474d1-69fa-4893-aad5-e7a81242c5d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5da474d1-69fa-4893-aad5-e7a81242c5d6' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1999-03-16 12:00:00</div><input id='attrs-95cf4508-7f33-425e-add2-99770c9a5c8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95cf4508-7f33-425e-add2-99770c9a5c8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43e14f14-136e-4858-829a-9b12de493f29' class='xr-var-data-in' type='checkbox'><label for='data-43e14f14-136e-4858-829a-9b12de493f29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1999, 3, 16, 12, 0, 0, 0), dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4e1b1ff-1fd2-47aa-b5f0-babb7a9fe034' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f4e1b1ff-1fd2-47aa-b5f0-babb7a9fe034' 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 'time' ()>\n",
       "array(cftime.DatetimeNoLeap(1999, 3, 16, 12, 0, 0, 0), dtype=object)\n",
       "Coordinates:\n",
       "    time     object 1999-03-16 12:00:00"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_fosi.time[494]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(cftime.DatetimeNoLeap(1960, 9, 16, 0, 0, 0, 0), dtype=object)\n",
       "Coordinates:\n",
       "    time     object 1960-09-16 00:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2ab7146a-290e-4691-8321-0dd3493ad587' class='xr-array-in' type='checkbox' checked><label for='section-2ab7146a-290e-4691-8321-0dd3493ad587' 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>1960-09-16 00:00:00</span></div><div class='xr-array-data'><pre>array(cftime.DatetimeNoLeap(1960, 9, 16, 0, 0, 0, 0), dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-ce47f8d7-f0a6-4d41-b0f6-ce449763e7d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce47f8d7-f0a6-4d41-b0f6-ce449763e7d7' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1960-09-16 00:00:00</div><input id='attrs-9b398d4d-1a09-4c82-b8e8-d7d968c1ac02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b398d4d-1a09-4c82-b8e8-d7d968c1ac02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c9d8893-198a-4eda-9bf0-c6596914f290' class='xr-var-data-in' type='checkbox'><label for='data-3c9d8893-198a-4eda-9bf0-c6596914f290' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1960, 9, 16, 0, 0, 0, 0), dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da864d48-7422-4273-916c-165540ec0152' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-da864d48-7422-4273-916c-165540ec0152' 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 'time' ()>\n",
       "array(cftime.DatetimeNoLeap(1960, 9, 16, 0, 0, 0, 0), dtype=object)\n",
       "Coordinates:\n",
       "    time     object 1960-09-16 00:00:00"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_fosi.time[32]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### trim datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_CAcurr_x1 = ds_fosi.isel(time=494,nlat=292,nlon=slice(230,245),z_t=slice(0,17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_SEUS_x1 = ds_fosi.isel(time=494,nlat=279,nlon=slice(290,310),z_t=slice(0,17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_eqPac_x1 = ds_fosi.isel(time=32,nlat=194,nlon=slice(223,257),z_t=slice(0,17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
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       "}\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:     (d2: 2, nlon: 15, z_t: 17, z_t_150m: 15)\n",
       "Coordinates:\n",
       "    TLAT        (nlon) float64 39.76 39.7 39.63 39.57 ... 39.03 38.97 38.9 38.83\n",
       "    TLONG       (nlon) float64 220.4 221.5 222.6 223.7 ... 233.8 235.0 236.1\n",
       "    time        object 1999-03-16 12:00:00\n",
       "  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 1.55e+04 1.651e+04\n",
       "  * z_t_150m    (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "Dimensions without coordinates: d2, nlon\n",
       "Data variables:\n",
       "    diatC       (z_t_150m, nlon) float32 0.7629 0.7318 ... -0.01495 -0.009929\n",
       "    dz          (z_t) float32 1e+03 1e+03 1e+03 1e+03 ... 1e+03 1e+03 1.02e+03\n",
       "    time_bound  (d2) object 1999-03-01 00:00:00 1999-04-01 00:00:00\n",
       "    NO3         (z_t, nlon) float32 2.641 2.482 2.339 ... 16.55 18.22 18.46\n",
       "    HMXL        (nlon) float32 1.452e+04 1.479e+04 ... 9.13e+03 8.647e+03\n",
       "    Fe          (z_t, nlon) float32 0.0003924 0.0003883 ... 0.001298 0.001498\n",
       "    TEMP        (z_t, nlon) float32 11.3 11.47 11.63 11.79 ... 9.279 9.095 9.196\n",
       "    HMXL_DR     (nlon) float32 1.477e+04 1.489e+04 ... 7.481e+03 6.64e+03</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-1567a956-b4c5-4fe1-a5a8-ab899f3336f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1567a956-b4c5-4fe1-a5a8-ab899f3336f2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>d2</span>: 2</li><li><span>nlon</span>: 15</li><li><span class='xr-has-index'>z_t</span>: 17</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5ca16957-77d2-4552-8a8f-2b36f890790c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ca16957-77d2-4552-8a8f-2b36f890790c' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>39.76 39.7 39.63 ... 38.9 38.83</div><input id='attrs-60fadbe7-e451-4ee7-8210-320cfca797af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60fadbe7-e451-4ee7-8210-320cfca797af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8450f55-ade9-4508-b353-5fb30ea019e8' class='xr-var-data-in' type='checkbox'><label for='data-c8450f55-ade9-4508-b353-5fb30ea019e8' 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([39.76472518, 39.69905896, 39.63316061, 39.56705522, 39.50076797,\n",
       "       39.43432413, 39.36774902, 39.30106807, 39.23430673, 39.16749051,\n",
       "       39.10064498, 39.0337957 , 38.9669683 , 38.90018837, 38.83348155])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLONG</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>220.4 221.5 222.6 ... 235.0 236.1</div><input id='attrs-94215652-278e-4069-9481-f0290d6bbb79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94215652-278e-4069-9481-f0290d6bbb79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e18073c1-6786-4543-850e-3f3f243705e3' class='xr-var-data-in' type='checkbox'><label for='data-e18073c1-6786-4543-850e-3f3f243705e3' 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([220.35475479, 221.47998791, 222.60478988, 223.7291647 ,\n",
       "       224.85311658, 225.97664993, 227.09976931, 228.22247946,\n",
       "       229.34478531, 230.4666919 , 231.58820445, 232.7093283 ,\n",
       "       233.83006894, 234.95043196, 236.07042308])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1999-03-16 12:00:00</div><input id='attrs-308e9d4b-f914-4d1c-9b56-2bba9830bed0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-308e9d4b-f914-4d1c-9b56-2bba9830bed0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edc20faa-c097-4427-a6ad-de603cca7d77' class='xr-var-data-in' type='checkbox'><label for='data-edc20faa-c097-4427-a6ad-de603cca7d77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1999, 3, 16, 12, 0, 0, 0), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.651e+04</div><input id='attrs-d1e37d82-6d64-4758-8a8a-6a6624b70c58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1e37d82-6d64-4758-8a8a-6a6624b70c58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7f65c2c-7430-4bc7-8991-32bde7d64e0f' class='xr-var-data-in' type='checkbox'><label for='data-a7f65c2c-7430-4bc7-8991-32bde7d64e0f' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.  ,  1500.  ,  2500.  ,  3500.  ,  4500.  ,  5500.  ,  6500.  ,\n",
       "        7500.  ,  8500.  ,  9500.  , 10500.  , 11500.  , 12500.  , 13500.  ,\n",
       "       14500.  , 15500.  , 16509.84], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-ddda5491-4675-4ef5-bd1c-aeddd67a1787' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddda5491-4675-4ef5-bd1c-aeddd67a1787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-006c3af8-f2ca-4848-bdea-6c528615e5f8' class='xr-var-data-in' type='checkbox'><label for='data-006c3af8-f2ca-4848-bdea-6c528615e5f8' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96e585d0-0640-4130-b051-864fefd3b732' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96e585d0-0640-4130-b051-864fefd3b732' class='xr-section-summary' >Data variables: <span>(8)</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>diatC</span></div><div class='xr-var-dims'>(z_t_150m, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-275fb713-4036-4892-ba41-269fb23f9f34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-275fb713-4036-4892-ba41-269fb23f9f34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-462b9841-acee-4074-93f6-27b85d41fbc8' class='xr-var-data-in' type='checkbox'><label for='data-462b9841-acee-4074-93f6-27b85d41fbc8' 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>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[ 7.628604e-01,  7.317940e-01,  6.647476e-01, ...,  3.226151e+00,\n",
       "         3.301094e+00,  4.303741e+00],\n",
       "       [ 7.545522e-01,  7.240278e-01,  6.578478e-01, ...,  3.145796e+00,\n",
       "         3.215435e+00,  4.168707e+00],\n",
       "       [ 7.342504e-01,  7.061209e-01,  6.419184e-01, ...,  2.844694e+00,\n",
       "         2.907197e+00,  3.731339e+00],\n",
       "       ...,\n",
       "       [ 4.015933e-01,  3.575891e-01,  3.210109e-01, ..., -8.768223e-04,\n",
       "        -1.156020e-02, -9.241790e-03],\n",
       "       [ 3.718382e-01,  3.381674e-01,  2.999829e-01, ..., -1.441353e-02,\n",
       "        -1.541581e-02, -1.056244e-02],\n",
       "       [ 3.334016e-01,  3.130040e-01,  2.799187e-01, ..., -1.919774e-02,\n",
       "        -1.494712e-02, -9.929016e-03]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+03 1e+03 ... 1e+03 1.02e+03</div><input id='attrs-e35c9f2f-142d-4fa2-bd13-97810866c055' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e35c9f2f-142d-4fa2-bd13-97810866c055' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-250df655-f776-4dbf-af3c-8b428a7ea132' class='xr-var-data-in' type='checkbox'><label for='data-250df655-f776-4dbf-af3c-8b428a7ea132' 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'><pre>array([1000.    , 1000.    , 1000.    , 1000.    , 1000.    , 1000.    ,\n",
       "       1000.    , 1000.    , 1000.    , 1000.    , 1000.    , 1000.    ,\n",
       "       1000.    , 1000.    , 1000.    , 1000.    , 1019.6808],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bound</span></div><div class='xr-var-dims'>(d2)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1999-03-01 00:00:00 1999-04-01 0...</div><input id='attrs-f8791259-4d36-4c47-a986-ce6d31486896' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8791259-4d36-4c47-a986-ce6d31486896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a853e6c-7220-4b2c-9e90-146694db644b' class='xr-var-data-in' type='checkbox'><label for='data-8a853e6c-7220-4b2c-9e90-146694db644b' 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>boundaries for time-averaging interval</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1999, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1999, 4, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3</span></div><div class='xr-var-dims'>(z_t, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-077301c6-f570-4754-a2ff-cf48555e3ca6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-077301c6-f570-4754-a2ff-cf48555e3ca6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7b7731d-1a37-42d1-a544-f3fac6ce874c' class='xr-var-data-in' type='checkbox'><label for='data-f7b7731d-1a37-42d1-a544-f3fac6ce874c' 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'><pre>array([[ 2.640827,  2.481701,  2.338799, ...,  1.86273 ,  1.96112 ,  1.785559],\n",
       "       [ 2.642399,  2.48321 ,  2.340283, ...,  1.872791,  1.97141 ,  1.801842],\n",
       "       [ 2.646326,  2.486785,  2.34366 , ...,  1.910314,  2.009172,  1.851253],\n",
       "       ...,\n",
       "       [ 2.872096,  2.681173,  2.515121, ..., 13.726141, 16.201866, 16.915287],\n",
       "       [ 4.396124,  3.553983,  3.037989, ..., 15.376578, 17.397612, 17.802847],\n",
       "       [ 7.509044,  6.637398,  5.801031, ..., 16.548151, 18.218082, 18.462938]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HMXL</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0106517d-7eb2-4d24-b8eb-03fbe45326a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0106517d-7eb2-4d24-b8eb-03fbe45326a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e9aa507-96b9-401c-b5f5-973da2b41a3d' class='xr-var-data-in' type='checkbox'><label for='data-0e9aa507-96b9-401c-b5f5-973da2b41a3d' 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>Mixed-Layer Depth</dd><dt><span>units :</span></dt><dd>centimeter</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>array([14519.04 , 14793.916, 14868.252, 15141.565, 15218.236, 15070.314,\n",
       "       14873.983, 14405.981, 14040.531, 13255.036, 12285.346, 11203.997,\n",
       "       10059.549,  9129.584,  8647.191], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe</span></div><div class='xr-var-dims'>(z_t, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1fb4558c-f35e-4e1d-9bd1-81039c543fc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fb4558c-f35e-4e1d-9bd1-81039c543fc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15e0ed76-8578-4d28-a16a-ddd79cfdc525' class='xr-var-data-in' type='checkbox'><label for='data-15e0ed76-8578-4d28-a16a-ddd79cfdc525' 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 Iron</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'><pre>array([[0.000392, 0.000388, 0.000386, ..., 0.000568, 0.000641, 0.000669],\n",
       "       [0.000392, 0.000388, 0.000386, ..., 0.00057 , 0.000644, 0.000673],\n",
       "       [0.000393, 0.000389, 0.000387, ..., 0.000579, 0.000653, 0.000686],\n",
       "       ...,\n",
       "       [0.000412, 0.000407, 0.000404, ..., 0.000994, 0.001242, 0.001439],\n",
       "       [0.000442, 0.000424, 0.000414, ..., 0.001036, 0.001283, 0.001481],\n",
       "       [0.000494, 0.000476, 0.00046 , ..., 0.001058, 0.001298, 0.001498]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(z_t, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7afdad39-de27-4266-b1ef-ae0ee4916bbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7afdad39-de27-4266-b1ef-ae0ee4916bbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-008e3e9a-bbfe-4c06-abe3-789898454f42' class='xr-var-data-in' type='checkbox'><label for='data-008e3e9a-bbfe-4c06-abe3-789898454f42' 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>Potential Temperature</dd><dt><span>units :</span></dt><dd>degC</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'><pre>array([[11.298875, 11.46649 , 11.629368, ..., 11.623432, 11.612587, 11.672486],\n",
       "       [11.294234, 11.461858, 11.625053, ..., 11.605483, 11.596598, 11.657639],\n",
       "       [11.288581, 11.456303, 11.619492, ..., 11.589419, 11.582561, 11.645413],\n",
       "       ...,\n",
       "       [11.24589 , 11.414494, 11.583319, ...,  9.86711 ,  9.514043,  9.519577],\n",
       "       [11.004624, 11.275482, 11.508546, ...,  9.529781,  9.267293,  9.336539],\n",
       "       [10.512658, 10.802348, 11.097185, ...,  9.27949 ,  9.094782,  9.19606 ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HMXL_DR</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11ca11ae-46a7-464e-93dc-417aed2f29dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11ca11ae-46a7-464e-93dc-417aed2f29dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f1db0c9-32ca-4a34-94ea-fb5f5be71c25' class='xr-var-data-in' type='checkbox'><label for='data-7f1db0c9-32ca-4a34-94ea-fb5f5be71c25' 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>Mixed-Layer Depth (density)</dd><dt><span>units :</span></dt><dd>centimeter</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>array([14772.357 , 14887.422 , 14746.794 , 15065.761 , 15417.569 , 15021.089 ,\n",
       "       13697.065 , 11716.963 , 10403.582 ,  9728.277 ,  8978.508 ,  8068.756 ,\n",
       "        7778.7476,  7481.    ,  6639.681 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23dad269-1c3e-40a9-af34-3036ad796205' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-23dad269-1c3e-40a9-af34-3036ad796205' 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:     (d2: 2, nlon: 15, z_t: 17, z_t_150m: 15)\n",
       "Coordinates:\n",
       "    TLAT        (nlon) float64 39.76 39.7 39.63 39.57 ... 39.03 38.97 38.9 38.83\n",
       "    TLONG       (nlon) float64 220.4 221.5 222.6 223.7 ... 233.8 235.0 236.1\n",
       "    time        object 1999-03-16 12:00:00\n",
       "  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 1.55e+04 1.651e+04\n",
       "  * z_t_150m    (z_t_150m) float32 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04\n",
       "Dimensions without coordinates: d2, nlon\n",
       "Data variables:\n",
       "    diatC       (z_t_150m, nlon) float32 ...\n",
       "    dz          (z_t) float32 1e+03 1e+03 1e+03 1e+03 ... 1e+03 1e+03 1.02e+03\n",
       "    time_bound  (d2) object 1999-03-01 00:00:00 1999-04-01 00:00:00\n",
       "    NO3         (z_t, nlon) float32 ...\n",
       "    HMXL        (nlon) float32 ...\n",
       "    Fe          (z_t, nlon) float32 ...\n",
       "    TEMP        (z_t, nlon) float32 ...\n",
       "    HMXL_DR     (nlon) float32 ..."
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_CAcurr_x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(depths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x792 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,11))\n",
    "\n",
    "############# Eq Pac\n",
    "\n",
    "cmap='YlOrBr'\n",
    "ax = fig.add_subplot(3,2,1)\n",
    "ax.set_title('a) Eq. Pacific 2°N, Fe (Sept. 1960)',fontsize=12, loc='left')\n",
    "#tmp = ds_eqPac.NO3.isel(nlon=slice(2900,3600))\n",
    "tmp = ds_eqPac_x1.Fe.isel(z_t=slice(0,11))  * 1000 \n",
    "lon = lons_norm_x1.isel(nlat=194,nlon=slice(223,257))\n",
    "mld = ds_eqPac_x1.HMXL * 0.01\n",
    "pc = ax.pcolormesh(lon,depths[0:11],tmp, cmap=cmap, vmin=0.15,vmax=0.25)\n",
    "#pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.15,vmax=0.6)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "#fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "fig.colorbar(pc, label='Fe (µmol m$^{-3}$)',extend='both');\n",
    "\n",
    "\n",
    "########### TEMP\n",
    "cmap='YlGnBu_r'\n",
    "ax = fig.add_subplot(3,2,2)\n",
    "ax.set_title('b) Eq. Pacific 2°N, Temperature (Sept. 1960)',fontsize=12, loc='left')\n",
    "tmp = ds_eqPac_x1.TEMP.isel(z_t=slice(0,11))\n",
    "lon = lons_norm_x1.isel(nlat=194,nlon=slice(223,257))\n",
    "pc = ax.pcolormesh(lon,depths[0:11],tmp, cmap=cmap, vmin=23, vmax=26)\n",
    "plt.gca().invert_yaxis()\n",
    "mld = ds_eqPac_x1.HMXL * 0.01\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='Diatom C (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "# ############################### Ca CUrrent\n",
    "\n",
    "### NO3\n",
    "cmap='Purples'\n",
    "ax = fig.add_subplot(3,2,3)\n",
    "ax.set_title('c) CA current 39°N, NO$_3$ (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_CAcurr_x1.NO3\n",
    "lon = lons_norm_x1.isel(nlat=292,nlon=slice(230,245))\n",
    "mld = ds_CAcurr_x1.HMXL * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=1,vmax=4)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "########### TEMP\n",
    "cmap='YlGnBu_r'\n",
    "ax = fig.add_subplot(3,2,4)\n",
    "ax.set_title('d) CA current 39°N, Temperature (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_CAcurr_x1.TEMP\n",
    "lon = lons_norm_x1.isel(nlat=292,nlon=slice(230,245))\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "plt.gca().invert_yaxis()\n",
    "mld = ds_CAcurr_x1.HMXL * 0.01\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "# ############################### SE US shelf\n",
    "\n",
    "### NO3\n",
    "cmap='Purples'\n",
    "ax = fig.add_subplot(3,2,5)\n",
    "ax.set_title('e) SE US shelf 30°N, NO$_3$ (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_SEUS_x1.NO3\n",
    "lon = lons_norm_x1.isel(nlat=279,nlon=slice(290,310))\n",
    "mld = ds_SEUS_x1.HMXL * 0.01\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap, vmin=0.5,vmax=1.5)\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "ax.set_ylim(0,165)\n",
    "plt.gca().invert_yaxis()\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "# ########### TEMP\n",
    "var = 'NO3'\n",
    "cmap='YlGnBu_r'\n",
    "ax = fig.add_subplot(3,2,6)\n",
    "ax.set_title('f) SE US shelf 30°N, Temperature (March 1999)',fontsize=12, loc='left')\n",
    "tmp = ds_SEUS_x1.TEMP\n",
    "lon = lons_norm_x1.isel(nlat=279,nlon=slice(290,310))\n",
    "pc = ax.pcolormesh(lon,depths,tmp, cmap=cmap)\n",
    "ax.set_ylim(0,165)\n",
    "plt.gca().invert_yaxis()\n",
    "mld = ds_SEUS_x1.HMXL * 0.01\n",
    "ml = ax.plot(lon,mld,color='black')\n",
    "ax.set(xlabel='Longitude',ylabel='Depth (m)') #, xlabel='time (simulation years)')\n",
    "fig.colorbar(pc, label='NO$_3$ (mmol m$^{-3}$)',extend='both');\n",
    "\n",
    "fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "fig.savefig('/glade/work/kristenk/figs/Regional_transects_x1.png', bbox_inches='tight', dpi=300, format='png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda-analysis5]",
   "language": "python",
   "name": "conda-env-miniconda-analysis5-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}