examples/run_eddytools_example_ORCA_OW.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "from glob import glob\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib import pyplot as plt\n",
    "import xgcm\n",
    "from xorca.lib import load_xorca_dataset\n",
    "import pickle\n",
    "import eddytools as et\n",
    "from cmocean import cm\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=RuntimeWarning)\n",
    "# Obviously it is not a great idea to ignore warnings, however there are quite many\n",
    "# RuntimeWarnings because of division by 0 in some parts of this notebook. To keep\n",
    "# the instructive nature of this example notebook, these warnings are ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment the following cell to use `dask distributed` (i.e for use with `dask_bags` in the detection).  \n",
    "__This is experimental not thouroughly tested__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from dask.distributed import Client, LocalCluster\n",
    "#cluster = LocalCluster(n_workers=1, threads_per_worker=4)\n",
    "#client = Client(cluster)\n",
    "#client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paths\n",
    "datapath = '/home/jan/Data/ORCA_test_data/'\n",
    "meshpath = [datapath + 'test_ORCA_mesh_mask.nc']\n",
    "data_in = sorted(glob(datapath + 'test_data_year[12]_grid_[TUV].nc'))"
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    "### Loading data\n",
    "Loading data and later calculations are much quicker when:  \n",
    "1) The chunks on disk are the same size as the chunks specified in `load_xorca_dataset(..., input_ds_chunks, target_ds_chunks)`  \n",
    "2) Data on disk is not compressed  \n",
    "The files loaded in this notebook have been created with `ncks -7 -L 0 --cnk_dmn time_counter,73 --cnk_dmn deptht,46 --cnk_dmn y,100 --cnk_dmn x,100 ...` (for grid_T) etc.  \n",
    "3) If possible, use more than one cpu. (`xarray` will automatically invoke `dask` and thus at least the calculation of the Okubo-Weiss parameter, the spatial standard deviation thereof, and the interpolation benefit from the parallelization capabilities of `dask`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "data = load_xorca_dataset(data_files=data_in, aux_files=meshpath, model_config='NEST',\n",
    "           input_ds_chunks = {\"time_counter\": 1, \"t\": 1,\n",
    "                              \"z\": 11, \"deptht\": 11, \"depthu\": 11, \"depthv\": 11, \"depthw\": 11,\n",
    "                              \"x\": 1002, \"y\": 629},\n",
    "           target_ds_chunks = {\"t\": 1,\n",
    "                               \"z_c\": 11, \"z_l\": 11,\n",
    "                               \"x_c\": 1002, \"x_r\": 1002, \"y_c\": 629, \"y_r\": 629})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define metrics for xgcm (not strictly necessary)\n",
    "at, au = data['e1t'] * data['e2t'], data['e1u'] * data['e2u']\n",
    "av, af = data['e1v'] * data['e2v'], data['e1f'] * data['e2f']\n",
    "vt, vu, vv, vw = data['e3t'] * at, data['e3u'] * au, data['e3v'] * av, data['e3w'] * at\n",
    "\n",
    "data = data.update({'at': at, 'au': au, 'av': av, 'af': af, 'vt': vt, 'vu': vu, 'vv': vv, 'vw': vw})\n",
    "data = data.set_coords(['at', 'au', 'av', 'af', 'vt', 'vu', 'vv', 'vw'])\n",
    "\n",
    "metrics = {\n",
    "    ('X',): ['e1t', 'e1u', 'e1v', 'e1f'], # X distances\n",
    "    ('Y',): ['e2t', 'e2u', 'e2v', 'e2f'], # Y distances\n",
    "    ('Z',): ['e3t', 'e3u', 'e3v', 'e3w'], # Z distances\n",
    "    ('X', 'Y'): ['at', 'au', 'av', 'af'], # Areas\n",
    "    ('X', 'Y', 'Z'): ['vt', 'vu', 'vv', 'vw'] # Volumes\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add bathymetry to data to have depth information\n",
    "bathy = xr.open_mfdataset(datapath + 'test_ORCA_bathy_meter.nc')\n",
    "data = data.update({'bathymetry': (['y_c', 'x_c'], bathy['Bathymetry'].data)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = xgcm.Grid(data, metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    votemper    (t, z_c, y_c, x_c) float32 dask.array&lt;chunksize=(1, 11, 629, 1002), meta=np.ndarray&gt;\n",
       "    vomecrty    (t, z_c, y_r, x_c) float32 dask.array&lt;chunksize=(1, 11, 629, 1002), meta=np.ndarray&gt;\n",
       "    vozocrtx    (t, z_c, y_c, x_r) float32 dask.array&lt;chunksize=(1, 11, 629, 1002), meta=np.ndarray&gt;\n",
       "    bathymetry  (y_c, x_c) float32 dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7c76b86d-98b6-4a7c-8a5c-b8a7651158ae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c76b86d-98b6-4a7c-8a5c-b8a7651158ae' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>z_c</span>: 11</li><li><span class='xr-has-index'>z_l</span>: 11</li><li><span class='xr-has-index'>y_c</span>: 629</li><li><span class='xr-has-index'>y_r</span>: 629</li><li><span class='xr-has-index'>x_c</span>: 1002</li><li><span class='xr-has-index'>x_r</span>: 1002</li><li><span class='xr-has-index'>t</span>: 146</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f9d5f78f-18c8-410a-9c00-db33b8c772f1' class='xr-section-summary-in' type='checkbox'  ><label for='section-f9d5f78f-18c8-410a-9c00-db33b8c772f1' class='xr-section-summary' >Coordinates: <span>(41)</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_c</span></div><div class='xr-var-dims'>(z_c)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11</div><input id='attrs-d461f6cd-e7c0-46c3-86d6-c17efb0936f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d461f6cd-e7c0-46c3-86d6-c17efb0936f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9e5ddf4-3093-4f95-8839-27c5c8b488f0' class='xr-var-data-in' type='checkbox'><label for='data-d9e5ddf4-3093-4f95-8839-27c5c8b488f0' 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>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_l</span></div><div class='xr-var-dims'>(z_l)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 3.5 ... 8.5 9.5 10.5</div><input id='attrs-9a867c4b-bc83-4c77-992e-e5d631b50f7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a867c4b-bc83-4c77-992e-e5d631b50f7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a1127af-dbf6-4e13-a868-f65d4acfb857' class='xr-var-data-in' type='checkbox'><label for='data-2a1127af-dbf6-4e13-a868-f65d4acfb857' 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>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd></dl></div><div class='xr-var-data'><pre>array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_c</span></div><div class='xr-var-dims'>(y_c)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 625 626 627 628 629</div><input id='attrs-96418d05-1a4c-41f8-b40b-f85125573581' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96418d05-1a4c-41f8-b40b-f85125573581' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f052327-f4af-4725-a42b-4cfab94b380d' class='xr-var-data-in' type='checkbox'><label for='data-1f052327-f4af-4725-a42b-4cfab94b380d' 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>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3, ..., 627, 628, 629])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_r</span></div><div class='xr-var-dims'>(y_r)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 2.5 3.5 ... 627.5 628.5 629.5</div><input id='attrs-ce58352d-194d-4839-8075-180e326f00f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce58352d-194d-4839-8075-180e326f00f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96811ebe-658a-4e2b-92ac-507ce1122156' class='xr-var-data-in' type='checkbox'><label for='data-96811ebe-658a-4e2b-92ac-507ce1122156' 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>axis :</span></dt><dd>Y</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([  1.5,   2.5,   3.5, ..., 627.5, 628.5, 629.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_c</span></div><div class='xr-var-dims'>(x_c)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 999 1000 1001 1002</div><input id='attrs-7538c06d-c514-4339-833b-2fe916168da9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7538c06d-c514-4339-833b-2fe916168da9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e56893f2-cd36-4982-803c-9c8e1548f7ec' class='xr-var-data-in' type='checkbox'><label for='data-e56893f2-cd36-4982-803c-9c8e1548f7ec' 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>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 1000, 1001, 1002])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_r</span></div><div class='xr-var-dims'>(x_r)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 2.5 3.5 ... 1.002e+03 1.002e+03</div><input id='attrs-33ce0932-2533-4377-bb24-b901c9312648' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33ce0932-2533-4377-bb24-b901c9312648' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb1da49e-bf50-415f-9f20-96decaff69a2' class='xr-var-data-in' type='checkbox'><label for='data-cb1da49e-bf50-415f-9f20-96decaff69a2' 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>axis :</span></dt><dd>X</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([   1.5,    2.5,    3.5, ..., 1000.5, 1001.5, 1002.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_c</span></div><div class='xr-var-dims'>(z_c)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(11,), meta=np.ndarray&gt;</div><input id='attrs-72792797-a11b-49f5-a568-8e70b2fdecdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72792797-a11b-49f5-a568-8e70b2fdecdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f61144bd-a26e-4947-a39b-1ff12ad2e069' class='xr-var-data-in' type='checkbox'><label for='data-f61144bd-a26e-4947-a39b-1ff12ad2e069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 88 B </td>\n",
       "                        <td> 88 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (11,) </td>\n",
       "                        <td> (11,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 5 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_l</span></div><div class='xr-var-dims'>(z_l)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(11,), meta=np.ndarray&gt;</div><input id='attrs-b11f03ec-5104-48cd-a6c9-85b54c71ea21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b11f03ec-5104-48cd-a6c9-85b54c71ea21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e9847a5-fa69-486f-bfdc-1e65ee0a3ac6' class='xr-var-data-in' type='checkbox'><label for='data-9e9847a5-fa69-486f-bfdc-1e65ee0a3ac6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 88 B </td>\n",
       "                        <td> 88 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (11,) </td>\n",
       "                        <td> (11,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 5 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>llat_cc</span></div><div class='xr-var-dims'>(y_c, x_c)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</div><input id='attrs-1ac7600c-ecfc-43b4-b921-e41804c22ddd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ac7600c-ecfc-43b4-b921-e41804c22ddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fce040d1-beb6-430d-9da6-3fb3348f7a57' class='xr-var-data-in' type='checkbox'><label for='data-fce040d1-beb6-430d-9da6-3fb3348f7a57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.40 MiB </td>\n",
       "                        <td> 2.40 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (629, 1002) </td>\n",
       "                        <td> (629, 1002) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>llat_cr</span></div><div class='xr-var-dims'>(y_c, x_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</div><input id='attrs-6057a389-6048-4e8f-b309-0d85b942557f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6057a389-6048-4e8f-b309-0d85b942557f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-769ff657-8988-4691-81c5-bf383a1cabaf' class='xr-var-data-in' type='checkbox'><label for='data-769ff657-8988-4691-81c5-bf383a1cabaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.40 MiB </td>\n",
       "                        <td> 2.40 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (629, 1002) </td>\n",
       "                        <td> (629, 1002) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1976-01-03T12:00:00 ... 1977-12-...</div><input id='attrs-26d5a427-9044-45c5-bb44-abe796de9508' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26d5a427-9044-45c5-bb44-abe796de9508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91a86793-e5e0-43b0-89b8-2c2558c204ba' class='xr-var-data-in' type='checkbox'><label for='data-91a86793-e5e0-43b0-89b8-2c2558c204ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1976-01-03T12:00:00.000000000&#x27;, &#x27;1976-01-08T12:00:00.000000000&#x27;,\n",
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       "       &#x27;1977-10-15T12:00:00.000000000&#x27;, &#x27;1977-10-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;1977-10-25T12:00:00.000000000&#x27;, &#x27;1977-10-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;1977-11-04T12:00:00.000000000&#x27;, &#x27;1977-11-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;1977-11-14T12:00:00.000000000&#x27;, &#x27;1977-11-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;1977-11-24T12:00:00.000000000&#x27;, &#x27;1977-11-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;1977-12-04T12:00:00.000000000&#x27;, &#x27;1977-12-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;1977-12-14T12:00:00.000000000&#x27;, &#x27;1977-12-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;1977-12-24T12:00:00.000000000&#x27;, &#x27;1977-12-29T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e1t</span></div><div class='xr-var-dims'>(y_c, x_c)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</div><input id='attrs-dde1645c-c833-4928-8aa6-14097f5c3908' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dde1645c-c833-4928-8aa6-14097f5c3908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4c4bb4a-d269-4100-bc0f-65ccd9a730b4' class='xr-var-data-in' type='checkbox'><label for='data-f4c4bb4a-d269-4100-bc0f-65ccd9a730b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 4.81 MiB </td>\n",
       "                        <td> 4.81 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (629, 1002) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.81 MiB </td>\n",
       "                        <td> 4.81 MiB </td>\n",
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       "                        <td> (629, 1002) </td>\n",
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       "                <tbody>\n",
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       "                        <td> 4.81 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e1v</span></div><div class='xr-var-dims'>(y_r, x_c)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</div><input id='attrs-553445d6-040e-4c3c-b74b-e50572985900' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-553445d6-040e-4c3c-b74b-e50572985900' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bb9845b-804f-4c53-9fcd-5b26335f2d55' class='xr-var-data-in' type='checkbox'><label for='data-8bb9845b-804f-4c53-9fcd-5b26335f2d55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 4.81 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e2v</span></div><div class='xr-var-dims'>(y_r, x_c)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</div><input id='attrs-c8881c2d-e92c-4d29-b060-04102d7bcc6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8881c2d-e92c-4d29-b060-04102d7bcc6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eff249e-594a-41e1-903b-62454d66b981' class='xr-var-data-in' type='checkbox'><label for='data-9eff249e-594a-41e1-903b-62454d66b981' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 4.81 MiB </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>au</span></div><div class='xr-var-dims'>(y_c, x_r)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</div><input id='attrs-816b4a84-f94d-409d-a86c-d4cea092007a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-816b4a84-f94d-409d-a86c-d4cea092007a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da90007a-3acc-43bd-b565-46d58f340c8a' class='xr-var-data-in' type='checkbox'><label for='data-da90007a-3acc-43bd-b565-46d58f340c8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.81 MiB </td>\n",
       "                        <td> 4.81 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (629, 1002) </td>\n",
       "                        <td> (629, 1002) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 4.81 MiB </td>\n",
       "                        <td> 4.81 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (629, 1002) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.81 MiB </td>\n",
       "                        <td> 4.81 MiB </td>\n",
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       "                        <td> (629, 1002) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                        <td> 52.89 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vosaline</span></div><div class='xr-var-dims'>(t, z_c, y_c, x_c)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 11, 629, 1002), meta=np.ndarray&gt;</div><input id='attrs-f2dfec6b-71ab-4738-82b8-55950b82ca8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2dfec6b-71ab-4738-82b8-55950b82ca8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53ba6580-813f-44d3-90be-9965b1d79cd9' class='xr-var-data-in' type='checkbox'><label for='data-53ba6580-813f-44d3-90be-9965b1d79cd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vomecrty</span></div><div class='xr-var-dims'>(t, z_c, y_r, x_c)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 11, 629, 1002), meta=np.ndarray&gt;</div><input id='attrs-06e13de8-bafc-4118-9276-6e0d01cc3ae1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06e13de8-bafc-4118-9276-6e0d01cc3ae1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-609d31f2-0c92-428c-bb88-4fd659720e2a' class='xr-var-data-in' type='checkbox'><label for='data-609d31f2-0c92-428c-bb88-4fd659720e2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vozocrtx</span></div><div class='xr-var-dims'>(t, z_c, y_c, x_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 11, 629, 1002), meta=np.ndarray&gt;</div><input id='attrs-8c1d1281-2b7d-4fc1-8e28-ae43e57a67c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c1d1281-2b7d-4fc1-8e28-ae43e57a67c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ab1790a-5d77-4627-a055-b522df379977' class='xr-var-data-in' type='checkbox'><label for='data-1ab1790a-5d77-4627-a055-b522df379977' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.77 GiB </td>\n",
       "                        <td> 26.45 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (146, 11, 629, 1002) </td>\n",
       "                        <td> (1, 11, 629, 1002) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 146 chunks in 5 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bathymetry</span></div><div class='xr-var-dims'>(y_c, x_c)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(629, 1002), meta=np.ndarray&gt;</div><input id='attrs-c33e0b3b-736c-433a-b3ee-d1f4a830e0a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c33e0b3b-736c-433a-b3ee-d1f4a830e0a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ec0c867-827c-43af-a863-67ac32dcd821' class='xr-var-data-in' type='checkbox'><label for='data-3ec0c867-827c-43af-a863-67ac32dcd821' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.40 MiB </td>\n",
       "                        <td> 2.40 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (629, 1002) </td>\n",
       "                        <td> (629, 1002) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9346ad2f-f69b-4bfe-bcc2-3029b639b281' class='xr-section-summary-in' type='checkbox'  ><label for='section-9346ad2f-f69b-4bfe-bcc2-3029b639b281' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>z_c</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60ba501f-e26b-4215-87b2-dad7c7b6aaea' class='xr-index-data-in' type='checkbox'/><label for='index-60ba501f-e26b-4215-87b2-dad7c7b6aaea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=&#x27;int64&#x27;, name=&#x27;z_c&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9742747-30fc-4141-9071-084105ef8395' class='xr-index-data-in' type='checkbox'/><label for='index-f9742747-30fc-4141-9071-084105ef8395' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5], dtype=&#x27;float64&#x27;, name=&#x27;z_l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_c</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e5ef8b54-1bfe-40a5-9cdd-0e6f9d07a255' class='xr-index-data-in' type='checkbox'/><label for='index-e5ef8b54-1bfe-40a5-9cdd-0e6f9d07a255' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "            ...\n",
       "            620, 621, 622, 623, 624, 625, 626, 627, 628, 629],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;y_c&#x27;, length=629))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_r</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fad32ff1-7b52-45b8-a103-b7790cc98183' class='xr-index-data-in' type='checkbox'/><label for='index-fad32ff1-7b52-45b8-a103-b7790cc98183' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([  1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "               10.5,\n",
       "              ...\n",
       "              620.5, 621.5, 622.5, 623.5, 624.5, 625.5, 626.5, 627.5, 628.5,\n",
       "              629.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y_r&#x27;, length=629))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x_c</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db5ce456-3f01-4557-9d15-a6974e3b261b' class='xr-index-data-in' type='checkbox'/><label for='index-db5ce456-3f01-4557-9d15-a6974e3b261b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "            ...\n",
       "             993,  994,  995,  996,  997,  998,  999, 1000, 1001, 1002],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;x_c&#x27;, length=1002))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x_r</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6aca1d7f-ed3c-4d4c-b5c5-619fc5bd9f66' class='xr-index-data-in' type='checkbox'/><label for='index-6aca1d7f-ed3c-4d4c-b5c5-619fc5bd9f66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([   1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,    8.5,\n",
       "                 9.5,   10.5,\n",
       "              ...\n",
       "               993.5,  994.5,  995.5,  996.5,  997.5,  998.5,  999.5, 1000.5,\n",
       "              1001.5, 1002.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x_r&#x27;, length=1002))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9df5c19e-0dcd-40f7-96b6-cc01f8ba056b' class='xr-index-data-in' type='checkbox'/><label for='index-9df5c19e-0dcd-40f7-96b6-cc01f8ba056b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1976-01-03 12:00:00&#x27;, &#x27;1976-01-08 12:00:00&#x27;,\n",
       "               &#x27;1976-01-13 12:00:00&#x27;, &#x27;1976-01-18 12:00:00&#x27;,\n",
       "               &#x27;1976-01-23 12:00:00&#x27;, &#x27;1976-01-28 12:00:00&#x27;,\n",
       "               &#x27;1976-02-02 12:00:00&#x27;, &#x27;1976-02-07 12:00:00&#x27;,\n",
       "               &#x27;1976-02-12 12:00:00&#x27;, &#x27;1976-02-17 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;1977-11-14 12:00:00&#x27;, &#x27;1977-11-19 12:00:00&#x27;,\n",
       "               &#x27;1977-11-24 12:00:00&#x27;, &#x27;1977-11-29 12:00:00&#x27;,\n",
       "               &#x27;1977-12-04 12:00:00&#x27;, &#x27;1977-12-09 12:00:00&#x27;,\n",
       "               &#x27;1977-12-14 12:00:00&#x27;, &#x27;1977-12-19 12:00:00&#x27;,\n",
       "               &#x27;1977-12-24 12:00:00&#x27;, &#x27;1977-12-29 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;t&#x27;, length=146, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6f2d60e-5ffb-41bc-912d-6aeffb42babe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a6f2d60e-5ffb-41bc-912d-6aeffb42babe' 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:     (z_c: 11, z_l: 11, y_c: 629, y_r: 629, x_c: 1002, x_r: 1002,\n",
       "                 t: 146)\n",
       "Coordinates: (12/41)\n",
       "  * z_c         (z_c) int64 1 2 3 4 5 6 7 8 9 10 11\n",
       "  * z_l         (z_l) float64 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5\n",
       "  * y_c         (y_c) int64 1 2 3 4 5 6 7 8 ... 622 623 624 625 626 627 628 629\n",
       "  * y_r         (y_r) float64 1.5 2.5 3.5 4.5 5.5 ... 626.5 627.5 628.5 629.5\n",
       "  * x_c         (x_c) int64 1 2 3 4 5 6 7 8 ... 996 997 998 999 1000 1001 1002\n",
       "  * x_r         (x_r) float64 1.5 2.5 3.5 4.5 ... 1e+03 1.002e+03 1.002e+03\n",
       "    ...          ...\n",
       "    av          (y_r, x_c) float64 dask.array<chunksize=(629, 1002), meta=np.ndarray>\n",
       "    af          (y_r, x_r) float64 dask.array<chunksize=(629, 1002), meta=np.ndarray>\n",
       "    vt          (z_c, y_c, x_c) float64 dask.array<chunksize=(11, 629, 1002), meta=np.ndarray>\n",
       "    vu          (z_c, y_c, x_r) float64 dask.array<chunksize=(11, 629, 1002), meta=np.ndarray>\n",
       "    vv          (z_c, y_r, x_c) float64 dask.array<chunksize=(11, 629, 1002), meta=np.ndarray>\n",
       "    vw          (z_l, y_c, x_c) float64 dask.array<chunksize=(11, 629, 1002), meta=np.ndarray>\n",
       "Data variables:\n",
       "    sossheig    (t, y_c, x_c) float32 dask.array<chunksize=(1, 629, 1002), meta=np.ndarray>\n",
       "    vosaline    (t, z_c, y_c, x_c) float32 dask.array<chunksize=(1, 11, 629, 1002), meta=np.ndarray>\n",
       "    votemper    (t, z_c, y_c, x_c) float32 dask.array<chunksize=(1, 11, 629, 1002), meta=np.ndarray>\n",
       "    vomecrty    (t, z_c, y_r, x_c) float32 dask.array<chunksize=(1, 11, 629, 1002), meta=np.ndarray>\n",
       "    vozocrtx    (t, z_c, y_c, x_r) float32 dask.array<chunksize=(1, 11, 629, 1002), meta=np.ndarray>\n",
       "    bathymetry  (y_c, x_c) float32 dask.array<chunksize=(629, 1002), meta=np.ndarray>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OKUBO-WEISS\n",
    "Calculation of the Okubo-Weiss paramter is only done on one depth level, the depth at which we want to detect eddies. In this case we choose `z_c=9, z_l=9` which is ~93 m depth.  \n",
    "We use xgcm to define the grid for the interpolation and differentiation necessary for the calculation of vorticity and the Okubo-Weiss parameter.  \n",
    "For more information on the Okubo-Weiss parameter and its calculation see [Okubo, 1970](https://doi.org/10.1016/0011-7471(70)90059-8); [Weiss, 1991](https://doi.org/10.1016/0167-2789(91)90088-Q); [Basdevant and Philipovitch, 1994](https://doi.org/10.1016/0167-2789(94)90222-4); [Chelton et al., 2007](https://doi.org/10.1029/2007GL030812)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate vorticity and Okubo-Weiss parameter and make sure the chunk sizes are as before.\n",
    "data_OW = et.okuboweiss.calc(data.isel(z_c=9, z_l=9), grid,\n",
    "                             'vozocrtx', 'vomecrty').chunk({'x_c': 1002, 'x_r': 1002,\n",
    "                                                            'y_c': 629, 'y_r': 629})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the new variables `OW` and `vort` to the dataset `data`\n",
    "data = xr.merge([data, data_OW], compat='override')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPOLATION\n",
    "Now we interpolate the data. If the data is entirely on a grid, that has one-dimensional latitude and longitude dimensions (i.e. longitude at one x-index does not change with changing y-index and latitude at one y-index does not change with changing x-index), the fields are simply interpolated to the F-point of the grid (vorticity and OW paramter are calculated and stored already on the F-point). This behaviour can also be triggered/forced by setting `avoid_regrid = True` as an argument to `interp.horizontal()`, although this should only be done when absolutely necessary -> only when regridding does not work.\n",
    "If the grid is curvilinear, e.g. North of 20S or so on the ORCA grids, a regridding is performed (unless `avoid_regrid = True`). There are several methods available for this regridding which can be chosen by setting `regrid_method` in the `interpolation_parameters`. Please refer to [the xesmf documentation](https://xesmf.readthedocs.io/en/latest/notebooks/Compare_algorithms.html) for details on the regridding methods. More advanced regridding with masks and extrapolations can also be performed, again refer to  [the xesmf documentation](https://xesmf.readthedocs.io/en/latest/notebooks/Masking.html) and set the extrapolation method `ext_method` in `interpolation_parameters` if necessary.  \n",
    "\n",
    "Note that the regridding can take VERY long for large region and/or long time periods. If one has to deal with problems regarding the time it takes, one could split the regridding into several parts, let's say regrid one year at a time, store the results and later load the combined regridded data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The longitude in all the parameter definitions (`lon1` and `lon2`) needs to be in the range (-180, 180). Longitudes larger then 180 will result in an error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORTANT:  \n",
    "The region that gets interpolated needs to be larger than the region used later for detection and tracking. This is required to ensure that eddies close to the boundaries of the detection region can be detected, tracked and sampled, even if their surroundings extend beyond the limits of the detection region.  \n",
    "To handle this, the region is internally extended by 2 degrees (for a `latlon` grid) or 200 km (for a cartesian grid) in each longitude direction and 1 degree (`latlon`) or 100km (cartesian) in latitude direction. So you need to make sure that the region you specify is not closer to the limits of your data that that.  \n",
    "If you want to detect eddies in the region 140-170E and 50-60S, the interpolation will be carried out within 138-172E and 49-61S! However, you still need to define 140-170E and 50-60S in the `interpolation_parameters` (or something equivalent for the cartesian grids).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are not 100% sure yet how big the region will be in which you want to detect the eddies, you can specify a larger region for the interpolation and then later define smaller regions for the detection, tracking, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the parameters for the interpolation\n",
    "interpolation_parameters = {'model': 'ORCA',\n",
    "                            'grid': 'latlon',\n",
    "                            'start_time': '1976-01-01', # time range start\n",
    "                            'end_time': '1976-01-31', # time range end\n",
    "                            'calendar': 'standard', # calendar, must be either 360_day or standard\n",
    "                            'lon1': 172, # minimum longitude of detection region\n",
    "                            'lon2': -178, # maximum longitude\n",
    "                            'lat1': -58, # minimum latitude\n",
    "                            'lat2': -52, # maximum latitude\n",
    "                            'res': 1./10., # resolution of the fields in degrees\n",
    "                            'vars_to_interpolate': ['OW', 'vort', 'votemper', 'vosaline'], # variables to be interpolated \n",
    "                            'mask_to_interpolate': ['fmask', 'tmask', 'bathymetry'], # masks to interpolate\n",
    "                            'regrid_method': 'bilinear', # method used for regridding (default is 'bilinear')\n",
    "                            'ext_method': None} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpolating from model grid: ORCA\n",
      "No regridding necessary, just interpolating to vorticity grid point.\n",
      "Interpolating OW\n",
      "Interpolating vort\n",
      "Interpolating votemper\n",
      "Interpolating vosaline\n",
      "Interpolating fmask\n",
      "Interpolating tmask\n",
      "Interpolating bathymetry\n",
      "Interpolating e1f\n",
      "Interpolating e2f\n"
     ]
    }
   ],
   "source": [
    "data_int, regridder = et.interp.horizontal(data, metrics, interpolation_parameters, weights=None, avoid_regrid=False)\n",
    "\n",
    "# The variable `regridder` returns the weights if a regridding with xESMF has been performed. \n",
    "# If yes, execute the following line to save this regridder to disk, so it can be reused\n",
    "#regridder.to_netcdf(\"test_regridder.nc\")\n",
    "\n",
    "# Then the weights can be reused for future interpolation with the sam grid (i.e. the `lon1`, `lon2`, `lat1`, `lat2` \n",
    "# in `interpolation_paramters` must be identical)\n",
    "#weights = xr.open_dataset(\"test_regridder.nc\")\n",
    "#data_int, regridder = et.interp.horizontal(data, metrics, interpolation_parameters, weights=weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't be confused that interpolation of `e1f` and `e2f` is done although we did not request that. `fmask`, `e1f` and `e2f` are always interpolated, even if you do not request that because they are essential for the detection of the eddies!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Threshold for the Okubo-Weiss parameter\n",
    "Some sort of threshold needs to be defined for the Okubo-Weiss parameter to distinguish regions within and outside of eddies. This is usually defined as `OW_thr = a * sigma` ([Chelton et al., 2007](https://doi.org/10.1029/2007GL030812)), where `sigma` is the spatial standard deviation of the Okubo-Weiss parameter and `a` is some factor (more on the factor later).  \n",
    "In regions with highly variable dynamic conditions, detection based on a single values of this standard deviation often fails to detect eddies in more \"quiet\" areas of the ocean ([Isern-Fontanet et al., 2003](https://doi.org/10.1175/1520-0426(2003)20<772:IOMEFA>2.0.CO;2)).Thus we here define a spatially variable threshold for the Okubo-Weiss parameter by calculating the (2D) spatial standard deviation in moving boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load `OW` into memory so the `.rolling` operation is faster\n",
    "OW_tmp = data_int['OW'].compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Convert all land values to `NaN` so we don't have a lot of zeros when calculating\n",
    "# the standard deviation\n",
    "OW_tmp = OW_tmp.where(OW_tmp != 0)\n",
    "lon_tmp = OW_tmp['lon'].where(OW_tmp['lon'] > 0, other=OW_tmp['lon'] + 360.)\n",
    "OW_tmp = OW_tmp.assign_coords({'lon': lon_tmp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_OW_spatial_std = OW_tmp.rolling(\n",
    "                          lon=100, center=True, min_periods=1\n",
    "                          ).std(skipna=True).rolling(\n",
    "                          lat=100, center=True, min_periods=1\n",
    "                          ).std(skipna=True).mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to use the mean spatial standard deviation of the Okubo-Weiss parameter over the whole considered region. To do that execute the cell below instead of the one above! Please note that `'OW_thr'` in `detection_parameters` needs to be defined differently, depending on which method of calculating the standard deviation you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean_OW_spatial_std = OW_tmp.std(('lon', 'lat'), skipna=True).mean('time') # uncomment if necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use if OW_std is 2D\n",
    "data_int = data_int.update({'OW_std': (['lat', 'lon'], mean_OW_spatial_std.values)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use if OW_std is 1D\n",
    "#data_int = data_int.update({'OW_std': mean_OW_spatial_std})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Store the interpolated field on disk so we do not have to do the interpolation again\n",
    "data_int.to_netcdf(datapath + 'test_data_OW.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we already did the interpolation earlier and now start off with the interpolated fields,\n",
    "# we just uncomment the line below to load them\n",
    "data_int = xr.open_dataset(datapath + 'test_data_OW.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7faf1c1a15b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's have a look at the fields\n",
    "plt.pcolormesh(data_int['OW'].isel(time=0), vmin=-5e-10, vmax=5e-10, cmap=cm.balance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see several eddy-like features that have a low OW-parameter, now let's see whether the algorithm detects them as eddies!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DETECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to note for the setting of `detection_parameters`:  \n",
    "1. `'lon1'` and `'lon2'` need to be in the range (-180, 180). `'lon2'` can be smaller than `'lon1'` if you want to extract work in a region extending across the dateline (e.g. `'lon1': 160`, `'lon2': -170`). \n",
    "2. `'OW_thr': data_int` if the field `OW_std` in `data_int` is 2D, i.e. we have a spatially varying standard deviation of the Okubo-Weiss parameter. `'OW_thr': data_int['OW_std'].values` if the field `OW_std` in `data_int` is 1D.  \n",
    "3. `'OW_thr_factor'` is usually chosen in the range `(-0.5, -0.2)` ([Chelton et al., 2007](https://doi.org/10.1029/2007GL030812); [Isern-Fontanet et al., 2003](https://doi.org/10.1175/1520-0426(2003)20<772:IOMEFA>2.0.CO;2))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify parameters for eddy detection\n",
    "detection_parameters = {'model': 'ORCA',\n",
    "                        'grid': 'latlon',\n",
    "                        'start_time': '1976-01-01', # time range start\n",
    "                        'end_time': '1976-12-31', # time range end\n",
    "                        'calendar': 'standard', # calendar, must be either 360_day or standard\n",
    "                        'lon1': 172, # minimum longitude of detection region\n",
    "                        'lon2': -178, # maximum longitude\n",
    "                        'lat1': -58, # minimum latitude\n",
    "                        'lat2': -52, # maximum latitude\n",
    "                        'min_dep': 1000, # minimum ocean depth where to look for eddies in m\n",
    "                        'res': 1./10., # resolution of the fields in degree\n",
    "                        'OW_thr': data_int['OW_std'].values, # \n",
    "                        'OW_thr_name': 'OW_std', # Okubo-Weiss threshold for eddy detection\n",
    "                        'OW_thr_factor': -0.3, # Okubo-Weiss parameter threshold\n",
    "                        'Npix_min': 20, # minimum number of pixels (grid cells) to be considered as eddy\n",
    "                        'Npix_max': 500, # maximum number of pixels (grid cells)\n",
    "                        'no_long': False, # If True, elongated shapes will not be considered\n",
    "                        'no_two': False # If True, eddies with two minima in the OW\n",
    "                                        # parameter and a OW > OW_thr in between  will not\n",
    "                                        # be considered\n",
    "                       }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, when regridding was forced to be avoided in the interpolation (`avoid_regrid = True`), it will not work (yet) with `et.detection.detect_SSH`. Only detection based on the Okubo-Weiss parameter will work with the `avoid_regrid = True` option.  \n",
    "\n",
    "### Parallel detection\n",
    "There are two ways to parallelize the detection (if the necessary packages are installed.)  \n",
    "   1. [**dask_bags** ](https://docs.dask.org/en/stable/bag.html). To use `dask_bags`, simply set the argument `use_bags=True` in the call to `et.detection.detect_OW()`.  \n",
    "   2. [**multiprocessing**](https://docs.python.org/3/library/multiprocessing.html). To use `multiprocessing`, set argument `use_mp=True` in the call to `et.detection.detect_OW()` and specify how many CPUs to use by setting `mp_cpu=N`, with `N` being an integer.  \n",
    "   \n",
    "Note that `dask_bags` also uses `multiprocessing` under the hood. In most cases using `multiprocessing` directly will be faster, but tests should be conducted for each operating system and/or dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing data for eddy detection (masking and region extracting etc.)\n",
      "detection at time step  1  of  6\n",
      "detection at time step  2  of  6\n",
      "detection at time step  3  of  6\n",
      "detection at time step  4  of  6\n",
      "detection at time step  5  of  6\n",
      "detection at time step  6  of  6\n"
     ]
    }
   ],
   "source": [
    "# We detect eddies at level 10 [.isel(z=9)] (as we only calculated the Okubow-Weiss\n",
    "# parameter at this level)\n",
    "eddies = et.detection.detect_OW(data_int.isel(z=9), detection_parameters, 'OW', 'vort', regrid_avoided=False, \n",
    "                                 use_bags=False, use_mp=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': numpy.datetime64('1976-01-03T12:00:00.000000000'),\n",
       " 'lon': array([177.1605505]),\n",
       " 'lat': array([-57.71053964]),\n",
       " 'amp': array([2.5957213e-06]),\n",
       " 'eddy_j': array([20, 20, 20, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24,\n",
       "        24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 28, 28, 28, 29, 29]),\n",
       " 'eddy_i': array([71, 72, 73, 71, 72, 73, 70, 71, 72, 73, 70, 71, 72, 73, 70, 71, 72,\n",
       "        73, 70, 71, 72, 73, 70, 71, 72, 73, 71, 72, 73, 71, 72, 73, 72, 73]),\n",
       " 'area': array([1200.00489166]),\n",
       " 'scale': array([[19.54414031]]),\n",
       " 'type': 'cyclonic'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this format, information for every detected eddy is stored\n",
    "# (eddies[t][ed], where t is the time step and ed the eddy number)\n",
    "eddies[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a single time step to see what the detection method detected\n",
    "t=0\n",
    "d_p = detection_parameters\n",
    "plot_lon = data_int['lon'].where(data_int['lon'].values > 0, other=data_int['lon'].values + 360)\n",
    "plt.pcolormesh(plot_lon, data_int['lat'].values, data_int.OW.isel(time=t).values,\n",
    "               vmin=-1e-9, vmax=1e-9, cmap=cm.balance, shading='auto')\n",
    "for c, l in zip(['whitesmoke', 'dimgray'], ['-', '--']):\n",
    "    plt.plot([d_p['lon1'], d_p['lon2'], d_p['lon2'], d_p['lon1'], d_p['lon1']],\n",
    "             [d_p['lat1'], d_p['lat1'], d_p['lat2'], d_p['lat2'], d_p['lat1']],\n",
    "             color=c, linestyle=l)\n",
    "\n",
    "for i in np.arange(0, len(eddies[t])):\n",
    "    if eddies[t][i]['lon'] < 0:\n",
    "        eddy_lon = eddies[t][i]['lon'] + 360\n",
    "    else:\n",
    "        eddy_lon = eddies[t][i]['lon']\n",
    "    if eddies[t][i]['type'] == 'cyclonic':\n",
    "        for s, c in zip([7, 6], ['k', 'gold']):\n",
    "            plt.plot(eddy_lon, eddies[t][i]['lat'], marker='o', color=c, markersize=s)\n",
    "    elif eddies[t][i]['type'] == 'anticyclonic':\n",
    "        for s, c in zip([7, 6], ['k', 'silver']):\n",
    "            plt.plot(eddy_lon, eddies[t][i]['lat'], marker='o', color=c, markersize=s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that all features below a certain OW threshold have been detected. Some of them, we might not consider as an eddy. If you have the feeling, that too many features are detected, that are not eddies, change parameters like `OW_thr_factor`, `Npix_min`, `Npix_max`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For every time step, we store one file on disk with all the information of the detected\n",
    "# eddies at this time step\n",
    "for i in np.arange(0, len(eddies)):\n",
    "    datestring = str(eddies[i][0]['time'])[0:10]\n",
    "    with open(datapath + 'test_'\n",
    "          + str(datestring) + '_eddies_OW0.3_test.pickle', 'wb') as f:\n",
    "        pickle.dump(eddies[i], f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRACKING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to note for the setting of `tracking_parameters`:  \n",
    "1. `'start_time'` is required to be no earlier than the earliest actual date of the detected eddies. In our case here, for the year 0002 and a 5-day temporal resolution of the data, this is `'0002-01-05'` (The `MITgcm` stores the 5-daily averages at the end of the 5-day period).\n",
    "2. `'lon1'` and `'lon2'` need to be identical to `'lon1'` and `'lon2'` in `detection_parameters`.  \n",
    "3. If you stored the detected eddies in files and want to track these, set `'dict': 0`, make sure `'data_path'`, `'file_root'` and `'file_spec'` are set accordingly and use `in_file=True` as an argument to `et.tracking.track()`.. The method will look for files `datapath + file_root + 'YYYYMMDD' + file_spec + '.pickle'`, the date `'YYYYMMDD'` is automatically calculated from `'start_time'`, `'dt'`, and `'end_time'`. You have to make sure that the stored, detected eddies contain that date in their filename (e.g. as defined in the cell above)!  \n",
    "\n",
    "Some notes on the search distance `search_dist` that is used to determine in what radius to look for a similar eddy at the next time step.  \n",
    "    - If `search_circle: True`, the algorithm simply searches for similar eddies within a radius of `search_dist` kilometers around the center of the current eddy. This is the simplest method.\n",
    "    - If `search_circle: False`, the algorithm will determine where to look for similar eddies based on an ellipse with a minor axis of `search_dist` kilometers. If `search_dist: 0`, the minor and major axes of the ellipse are calculated based on the propagation of Rossby waves to account for the fact that eddies will move towards the West (see [Chelton et al., 2011](https://www.sciencedirect.com/science/article/pii/S0079661111000036) for details). In regions with strong currents this might lead to a loss of a lot of tracks, but no sensitivity studies have been conducted... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify parameters for eddy tracking\n",
    "tracking_parameters = {'model': 'ORCA',\n",
    "                       'grid': 'latlon',\n",
    "                       'start_time': '1976-01-03', # time range start\n",
    "                       'end_time': '1976-12-31', # time range end\n",
    "                       'calendar': 'standard', # calendar, must be either 360_day or standard\n",
    "                       'dt': 5, # temporal resolution of the data in days\n",
    "                       'lon1': 172, # minimum longitude of detection region\n",
    "                       'lon2': -178, # maximum longitude\n",
    "                       'lat1': -58, # minimum latitude\n",
    "                       'lat2': -52, # maximum latitude\n",
    "                       'search_dist': 0., # maximum distance of search ellipse/circle from eddy center in km\n",
    "                                          # if ellipse: towards the east (if set to 0, it\n",
    "                                          # will be calculated as (150. / (7. / dt)))\n",
    "                       'search_circle': False, # if True, search in a circle. otherwise use ellipse\n",
    "                       'eddy_scale_min': 0.5, # minimum factor by which eddy amplitude and area are allowed to change in one timestep\n",
    "                       'eddy_scale_max': 1.5, # maximum factor by which eddy amplitude and area are allowed to change in one timestep\n",
    "                       'dict': eddies, # dictionary containing detected eddies to be used when not stored in files (set to 0 otherwise)\n",
    "                       'data_path': datapath, # path to the detected eddies pickle files\n",
    "                       'file_root': 'test',\n",
    "                       'file_spec': 'eddies_OW0.3_test',\n",
    "                       'ross_path': datapath} # path to rossrad.dat containing Chelton et a1. 1998 Rossby radii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tracking at time step  9  of  73\n",
      "tracking at time step  17  of  73\n",
      "tracking at time step  25  of  73\n",
      "tracking at time step  33  of  73\n",
      "tracking at time step  42  of  73\n",
      "tracking at time step  50  of  73\n",
      "tracking at time step  58  of  73\n",
      "tracking at time step  66  of  73\n"
     ]
    }
   ],
   "source": [
    "# Now we track the eddies, all information needed has to be added to `tracking_parameters`\n",
    "tracks = et.tracking.track(tracking_parameters, in_file=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have tracked all eddies that met the criteria specified in `tracking_parameters`. Every entry `i` in `tracks[i]` corresponds to one complete track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': array(['1976-01-03T12:00:00.000000000', '1976-01-08T12:00:00.000000000'],\n",
       "       dtype='datetime64[ns]'),\n",
       " 'lon': array([173.81230268, 174.06338099]),\n",
       " 'lat': array([-57.474193  , -57.65866633]),\n",
       " 'amp': array([4.72767688e-06, 2.79983036e-06]),\n",
       " 'eddy_j': {0: array([24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27,\n",
       "         28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30,\n",
       "         30, 30, 30, 31, 31, 31, 31, 31, 32, 32, 32, 32, 33, 33]),\n",
       "  1: array([22, 22, 22, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 25, 25, 25, 25,\n",
       "         25, 25, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 28, 28, 28,\n",
       "         28])},\n",
       " 'eddy_i': {0: array([37, 38, 36, 37, 38, 39, 36, 37, 38, 39, 40, 35, 36, 37, 38, 39, 40,\n",
       "         35, 36, 37, 38, 39, 40, 41, 35, 36, 37, 38, 39, 40, 41, 36, 37, 38,\n",
       "         39, 40, 41, 36, 37, 38, 39, 40, 36, 37, 38, 39, 37, 38]),\n",
       "  1: array([37, 38, 39, 37, 38, 39, 40, 41, 38, 39, 40, 41, 42, 38, 39, 40, 41,\n",
       "         42, 43, 39, 40, 41, 42, 43, 44, 39, 40, 41, 42, 43, 44, 40, 41, 42,\n",
       "         43])},\n",
       " 'area': array([1715.24583437, 1238.48461381]),\n",
       " 'scale': array([23.36620864, 19.85502195]),\n",
       " 'type': 'cyclonic',\n",
       " 'exist_at_start': True,\n",
       " 'terminated': True}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The entries in `track` look like this\n",
    "tracks[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To have a look at how the tracking performs, just pick two eddies and see whether they are tracked.\n",
    "ed1 = 0\n",
    "ed2 = 4\n",
    "t = 0\n",
    "j = 5\n",
    "\n",
    "plt.figure(figsize=(18, j))\n",
    "\n",
    "plot_lon = data_int['lon'].where(data_int['lon'].values > 0, other=data_int['lon'].values + 360)\n",
    "\n",
    "ed1_lon = tracks[ed1]['lon']\n",
    "ed1_lon[ed1_lon < 0] = ed1_lon[ed1_lon < 0] + 360\n",
    "ed2_lon = tracks[ed2]['lon']\n",
    "ed2_lon[ed2_lon < 0] = ed2_lon[ed2_lon < 0] + 360\n",
    "\n",
    "for i in np.arange(0, j):\n",
    "    plt.subplot(1, j, i + 1)\n",
    "    plt.pcolormesh(plot_lon, data_int.lat, data_int.OW.isel(time=t + i).values,\n",
    "                   vmin=-5e-10, vmax=5e-10, cmap=cm.balance, shading='auto')\n",
    "    plt.plot(ed1_lon[t:t + i+1], tracks[ed1]['lat'][t:t + i+1], marker='o', color='m')\n",
    "    plt.plot(ed2_lon[t:t + i+1], tracks[ed2]['lat'][t:t + i+1], marker='o', color='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " # We save the tracks for later use\n",
    "with open(datapath\n",
    "          + 'test_19760101_19761231_tracks_OW0.3'\n",
    "          + '_test.pickle', 'wb') as f:\n",
    "    pickle.dump(tracks, f, pickle.HIGHEST_PROTOCOL)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is how to open the tracks-file again (no need to do that if we just saved it)\n",
    "with open(datapath\n",
    "          + 'test_19760101_19761231_tracks_OW0.3'\n",
    "          + '_test.pickle', 'rb') as f:\n",
    "    tracks = pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAMPLING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to note for the setting of `sample_parameters`:  \n",
    "1. `'start_time'` is required to be no earlier than the earliest actual date of the tracked eddies. In our case here, for the year 2018 and a 5-day temporal resolution of the data, this is `'2018-01-03'` (if we had daily data, it would be `'2018-01-01'`).\n",
    "2. `'lon1'` and `'lon2'` need to be identical to `'lon1'` and `'lon2'` in `detection_parameters`.  \n",
    "3. Right now, the usage of `'range'` and `'split'` has not been thouroughly tested! It seems to work for most cases though.  \n",
    "\n",
    "`'range'`: Set to `True` if you only want to sample eddies within a certain range `'values_range'` of a property `'var_range'` in the dataset `'ds_range'`. `'var_range'` needs to be 2D (thus the `.isel(z=9)` in the example below) and interpolated to the same grid as `OW` used above. It is most likely that, if you follow this example, `'var_range'` is stored in the same dataset as `OW`. In the example below, only eddies that have a center temperature between 4 and 7 degrees C at depth level 10 (`z=9`) will be sampled and stored.  \n",
    "\n",
    "`'split'`: Set to `True` if you want to split the sampled eddies into two categories, above and below a certain threshold value `'value_split'` of a variable `'var_split'` in the dataset `'ds_split'`. As for `'range'`, `'var_split'` needs to be 2D and interpolated to the same grid as `OW` used above. In the example below the eddies will be put into two categories: In the first category, the eddies must have a center surface salinity above 34.0 and in the second category, below 34.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the sampling we again have to specify some parameters, defining when, where and which \n",
    "# eddies to sample.\n",
    "# Because the dataset containing the sampled eddies can grow huge for larger regions and/or\n",
    "# longer time periods, `eddytools.sample.sample()` writes the samples for each individual\n",
    "# eddy to individual netcdf-files on disk!\n",
    "sample_parameters = {'model': 'ORCA',\n",
    "                     'grid': 'latlon',\n",
    "                     'start_time': '1976-01-03', # time range start\n",
    "                     'end_time': '1976-12-31', # time range end\n",
    "                     'calendar': 'standard', # calendar, must be either 360_day or standard\n",
    "                     'max_time': 73, # maximum length of tracks to consider\n",
    "                                     # (model time steps)\n",
    "                     'lon1': 172, # minimum longitude of detection region\n",
    "                     'lon2': -178, # maximum longitude\n",
    "                     'lat1': -58, # minimum latitude\n",
    "                     'lat2': -52, # maximum latitude\n",
    "                     'type': 'anticyclonic', # type of eddy\n",
    "                     'lifetime': 5, # length of the eddy's track in days\n",
    "                     'size': 25, # eddy size (radius in km)\n",
    "                     'd_surr': 1, # surroundings size\n",
    "                     'range': False, # sample eddy within a range of `var_range`\n",
    "                     'ds_range': data_int.isel(z=9), # dataset of `var_range`\n",
    "                     'var_range': ['votemper'], # variable to base the range on\n",
    "                     'value_range': [[4, 7],], # range of `var_range`\n",
    "                     'split': False, # split eddies at a threshold in below and above\n",
    "                     'ds_split': data_int.isel(z=0), # dataset of `var_split`\n",
    "                     'var_split': ['vosaline'], # variable to base split on\n",
    "                     'value_split': [34.0,], # split eddies at this value\n",
    "                     'sample_vars': ['votemper'], # variables to sample\n",
    "                     'save_location': datapath, # where to store the netcdf files \n",
    "                     'save_name': 'test'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampling can take quite long as for every eddy that fits the criteria we need to read data from disk at every time step  \n",
    "The names of the files will be defined by `save_name`, the criteria you specify and the eddy number  \n",
    "For this sampling parameters, the file name of the first eddy will be  \n",
    "`test.anticyclonic.larger_25.longer_5.0000001.nc`  \n",
    "\n",
    "If you set `'split'` to `True`, the file names will differ for the two categories.  \n",
    "`test.anticyclonic.larger_25.longer_5.0000001.above_thr.nc` for eddies that are above `'value_split'`  \n",
    "`test.anticyclonic.larger_25.longer_5.0000001.below_thr.nc`\n",
    "for eddies that are below `'value_split'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data prepared, now sampling\n",
      "sampling eddy number 0 of 18\n",
      "sampling eddy number 2 of 18\n",
      "sampling eddy number 4 of 18\n",
      "sampling eddy number 6 of 18\n",
      "sampling eddy number 8 of 18\n",
      "sampling eddy number 10 of 18\n",
      "sampling eddy number 12 of 18\n",
      "sampling eddy number 14 of 18\n",
      "sampling eddy number 16 of 18\n"
     ]
    }
   ],
   "source": [
    "et.sample.sample(tracks, data_int, sample_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AVERAGING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now average over the sampled eddies.  \n",
    "As each eddy has its own file, we first need to find out how many files/samples there are, so we can loop over them and then store the datasets in a dictionary.  \n",
    "Note that for large region, i.e. a lot of samples the resulting dataset could be too large to fit into memory. The `chunks={}` argument to `xr.open_dataset()` is an attempt to overcome this, however I do not know yet whether this has a lot of effect! One could also split the samples into several parts and then work on each part seperately (the number of eddies going into each average is stored, so one could later do a weighted average over the different parts!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find out number of samples\n",
    "samples = {}\n",
    "num_samples = len(glob(sample_parameters['save_location']\n",
    "                       + sample_parameters['save_name'] + '.anticyclonic.larger_25.longer_5.*.nc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we load each stored sample\n",
    "for i in np.arange(1, num_samples + 1):\n",
    "    snum = \"%07d\" % (i,)\n",
    "    samples[i] = xr.open_dataset(sample_parameters['save_location'] + sample_parameters['save_name'] +\n",
    "                                 '.anticyclonic.larger_25.longer_5.' + str(snum) + '.nc', chunks={})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First a \"preparation\" is performed: Basically, all eddies are interpolated onto a normalized (in length) section crossing them through the eddy center. This section's orientation can be defined by the argument `section`, which can be either `'zonal'` (default) or `'meridional'`. The length of this section can be defined with argument `interp_vec`. A larger number gives a finer resolution of the interpolated data, but might not necessarily be useful (if input data is coarser resolution for example).    \n",
    "For every variable specified, at every time step, the values and anomalies (with respect to surroundings) are interpolated (with method `method`) onto the normalized section and stored according to the month the eddy was first detected. The depth profile of the surroundings is stored as well.  \n",
    "For available interpolation methods please have a look in the documentation of the underlying function [`scipy.interpolate.interp1d`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "votemper\n"
     ]
    }
   ],
   "source": [
    "normalized_eddies = et.average.prepare(samples, ['votemper'], interp_vec=41, interp_method='nearest', section='zonal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 36, 11, 41)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_eddies['votemper_anom']['07'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here for example, 5 eddies originating in July have been stored with a maximum length of 37 time steps. 11 is the length of the depth dimension, 41 the length of the normalized section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can average these samples in different ways.  \n",
    "Three possibilities are given with `average`  \n",
    "1. seasonal -> bins the samples (of all available years) into four seasons (DJF, MAM, JJA, SON) and averages them into a seasonal climatology  \n",
    "2. monthly  -> does the same for each month (so you end up with a monthly climatology)  \n",
    "3. total    -> averages over all available eddies  \n",
    "The three methods all return the means, standard deviations (across eddies), and the number of eddies that went into the derived quantities for a) the variables specified, b) their anomalies to the surroundings, c) the surroundings. These results are stored in the output dictionary under `output['ave']['mean'][period]['variable']`, `output['ave']['mean'][period]['variable_anom']`, `output['ave']['mean'][period]['variable_around']`, respectively. `period` refers to 1. `DJF`, `MAM`, `JJA` or `SON`, 2. `01`, `02`, `03`, etc., representing the month, or in case of the total average thie layer in the dictionary does not exist.  \n",
    "Additionally, the averaged temporal evolution of the eddy centers will be stored under `output['evo'][...]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "season_eddies = et.average.seasonal(normalized_eddies, ['votemper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f018d6089d0>"
      ]
     },
     "execution_count": 34,
     "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": [
    "plt.pcolormesh(season_eddies['ave']['mean']['DJF']['votemper_anom'], vmin=-1, vmax=1, cmap=cm.balance)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_eddies = et.average.monthly(normalized_eddies, ['votemper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f01a4410040>"
      ]
     },
     "execution_count": 36,
     "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": [
    "plt.pcolormesh(monthly_eddies['ave']['mean']['03']['votemper_anom'], vmin=-1, vmax=1, cmap=cm.balance)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_eddies = et.average.total(normalized_eddies, ['votemper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f018d4fe7c0>"
      ]
     },
     "execution_count": 38,
     "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": [
    "plt.pcolormesh(total_eddies['ave']['mean']['votemper_anom'], vmin=-1, vmax=1, cmap=cm.balance)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:eddytools]",
   "language": "python",
   "name": "conda-env-eddytools-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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}