ML_simpleNN_BodnerMethod.ipynb

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    "# Load data"
   ]
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   "source": [
    "from xgcm import Grid\n",
    "import pop_tools\n",
    "import gcsfs\n",
    "import fsspec as fs\n",
    "import numpy as np\n",
    "import xesmf as xe\n",
    "import xarray as xr\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from xgcm import Grid\n",
    "import importlib\n",
    "import preprocessing\n",
    "import os\n",
    "import xrft\n",
    "import gcm_filters\n",
    "import random\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "importlib.reload(preprocessing)\n",
    "from preprocessing import preprocess_data\n",
    "\n",
    "from gcm_filtering import filter_inputs_dataset\n",
    "from gcm_filtering import filter_inputs\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim"
   ]
  },
  {
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   "execution_count": 2,
   "id": "b53a9ebf-ae08-4b0c-8674-871b459519aa",
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     "text": [
      "Load Data\n",
      "Interpolating ocean velocities\n",
      "Modify units\n",
      "Load Data\n",
      "Align in time\n",
      "Interpolating ocean velocities\n",
      "Regrid Atmospheric Data\n",
      "Modify units\n",
      "Mask nans\n",
      "Calculate relative wind\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3TB\n",
       "Dimensions:       (time: 7305, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * time          (time) object 58kB 0181-01-01 12:00:00 ... 0200-12-31 12:00:00\n",
       "  * xt_ocean      (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.85 79.95\n",
       "  * yt_ocean      (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.94 89.98\n",
       "    area_t        (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    dxt           (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    dyt           (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    geolat_t      (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    geolon_t      (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    ht            (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    kmt           (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    wet           (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    slp           (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    t_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    q_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    wind          (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    surface_temp  (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    regrid_method:  bilinear</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-e2c20859-c167-4a49-8a77-a50c5f3b6b2d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2c20859-c167-4a49-8a77-a50c5f3b6b2d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7305</li><li><span class='xr-has-index'>yt_ocean</span>: 2700</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-462af08b-a2a9-4075-83f2-222975e70388' class='xr-section-summary-in' type='checkbox'  checked><label for='section-462af08b-a2a9-4075-83f2-222975e70388' class='xr-section-summary' >Coordinates: <span>(11)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00 ... 0200-12-...</div><input id='attrs-364ba5e6-3570-4470-b600-b496c5d55fdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-364ba5e6-3570-4470-b600-b496c5d55fdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17f74e55-0af6-46a3-bf7b-18cfdb5047de' class='xr-var-data-in' type='checkbox'><label for='data-17f74e55-0af6-46a3-bf7b-18cfdb5047de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(181, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 3, 12, 0, 0, 0, has_year_zero=False), ...,\n",
       "       cftime.DatetimeJulian(200, 12, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(200, 12, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(200, 12, 31, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-8b743894-fea6-41b9-963d-8bcb54ba2f3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b743894-fea6-41b9-963d-8bcb54ba2f3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4d7f3ac-4803-4acb-b070-fdb4dd10519a' class='xr-var-data-in' type='checkbox'><label for='data-a4d7f3ac-4803-4acb-b070-fdb4dd10519a' 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>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.11 -81.07 ... 89.94 89.98</div><input id='attrs-4fb52491-a726-48fa-b244-134f8dc4012e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fb52491-a726-48fa-b244-134f8dc4012e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54ab63d9-2b83-4723-821b-88577622441b' class='xr-var-data-in' type='checkbox'><label for='data-54ab63d9-2b83-4723-821b-88577622441b' 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>cartesian_axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,\n",
       "        89.978896])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-24cf7862-a405-4268-a94e-9998d8b3624b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24cf7862-a405-4268-a94e-9998d8b3624b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79ab078-2df2-459c-9c7a-1370212feb3a' class='xr-var-data-in' type='checkbox'><label for='data-b79ab078-2df2-459c-9c7a-1370212feb3a' 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>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.0, 999999986991104.0]</dd></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> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxt</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-fdb0b324-3d29-4dec-9851-5a1bef9dd12f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb0b324-3d29-4dec-9851-5a1bef9dd12f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c8a4e17-0fb4-45ec-a9a7-04e26e229df5' class='xr-var-data-in' type='checkbox'><label for='data-7c8a4e17-0fb4-45ec-a9a7-04e26e229df5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>width of T_cell</dd><dt><span>units :</span></dt><dd>m</dd></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> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyt</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-b615e814-9aab-4b0e-966f-29101e14256f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b615e814-9aab-4b0e-966f-29101e14256f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c4ba340-0fd9-4279-b8fa-1b2e9197acc0' class='xr-var-data-in' type='checkbox'><label for='data-7c4ba340-0fd9-4279-b8fa-1b2e9197acc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height of T_cell</dd><dt><span>units :</span></dt><dd>m</dd></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> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-f01dfc32-df52-43ba-83c9-4703bbf12f2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f01dfc32-df52-43ba-83c9-4703bbf12f2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5058ba1-1b79-4523-894e-151a91423854' class='xr-var-data-in' type='checkbox'><label for='data-e5058ba1-1b79-4523-894e-151a91423854' 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>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.0, 91.0]</dd></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> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-d7c56a94-ac4a-4e82-a8b4-6d5d8c4a078e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7c56a94-ac4a-4e82-a8b4-6d5d8c4a078e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32d86528-dfc9-4ce9-b63e-b8bbda887a26' class='xr-var-data-in' type='checkbox'><label for='data-32d86528-dfc9-4ce9-b63e-b8bbda887a26' 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>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.0, 361.0]</dd></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> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </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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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ht</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-d50ed177-9430-4468-80ff-7307c4db3138' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d50ed177-9430-4468-80ff-7307c4db3138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-790aede2-36b2-4582-8d4e-6d576bd0b5a5' class='xr-var-data-in' type='checkbox'><label for='data-790aede2-36b2-4582-8d4e-6d576bd0b5a5' 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>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>ocean depth on t-cells</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth_below_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_range :</span></dt><dd>[-1000000000.0, 1000000000.0]</dd></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> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kmt</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-fe9ca640-eded-4c9b-ae87-d2b9a1d73529' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe9ca640-eded-4c9b-ae87-d2b9a1d73529' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a4e9e74-3ded-4f0d-b0db-2ee99dc8d789' class='xr-var-data-in' type='checkbox'><label for='data-5a4e9e74-3ded-4f0d-b0db-2ee99dc8d789' 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>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>number of depth levels on t-grid</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>valid_range :</span></dt><dd>[-10.0, 1000000000.0]</dd></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> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </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",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wet</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-4ab30f54-89ee-4a4c-92aa-91f405a77eaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ab30f54-89ee-4a4c-92aa-91f405a77eaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21b5ddca-18fb-4e85-8117-a73a1b1973ba' class='xr-var-data-in' type='checkbox'><label for='data-21b5ddca-18fb-4e85-8117-a73a1b1973ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land/sea flag (0=land) for T-cell</dd><dt><span>units :</span></dt><dd>none</dd></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> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </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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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
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       "                    <tr>\n",
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       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_ref</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-1f8c9270-e485-4347-ad6e-cf7a8206b7c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f8c9270-e485-4347-ad6e-cf7a8206b7c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552917ce-f215-4dcb-81b0-58cef519597e' class='xr-var-data-in' type='checkbox'><label for='data-552917ce-f215-4dcb-81b0-58cef519597e' 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",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
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       "                        <th> Shape </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-7dc08fd3-f233-4d57-b292-9911dc7144d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7dc08fd3-f233-4d57-b292-9911dc7144d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5867d21-a856-4d55-8c18-d55ccd9dfdff' class='xr-var-data-in' type='checkbox'><label for='data-b5867d21-a856-4d55-8c18-d55ccd9dfdff' 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>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_relative</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-1652b596-ff81-45d3-879a-627e756bb3bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1652b596-ff81-45d3-879a-627e756bb3bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba6577b3-5e7d-48bf-8a43-4c28cbceb0d5' class='xr-var-data-in' type='checkbox'><label for='data-ba6577b3-5e7d-48bf-8a43-4c28cbceb0d5' 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> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (7305, 2700, 3600) </td>\n",
       "                        <td> (3, 2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2435 chunks in 37 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>v_relative</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-afc63652-91d7-4ca7-95b2-1f085173c7c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afc63652-91d7-4ca7-95b2-1f085173c7c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50391129-901e-431a-b3a7-a7c25cd54299' class='xr-var-data-in' type='checkbox'><label for='data-50391129-901e-431a-b3a7-a7c25cd54299' 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> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (7305, 2700, 3600) </td>\n",
       "                        <td> (3, 2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2435 chunks in 37 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b5b2bef-e3c2-4163-b559-ec771b012146' class='xr-section-summary-in' type='checkbox'  ><label for='section-7b5b2bef-e3c2-4163-b559-ec771b012146' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e8d38356-9992-4e8f-a4f9-f3ac2dcaed3a' class='xr-index-data-in' type='checkbox'/><label for='index-e8d38356-9992-4e8f-a4f9-f3ac2dcaed3a' 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(CFTimeIndex([0181-01-01 12:00:00, 0181-01-02 12:00:00, 0181-01-03 12:00:00,\n",
       "             0181-01-04 12:00:00, 0181-01-05 12:00:00, 0181-01-06 12:00:00,\n",
       "             0181-01-07 12:00:00, 0181-01-08 12:00:00, 0181-01-09 12:00:00,\n",
       "             0181-01-10 12:00:00,\n",
       "             ...\n",
       "             0200-12-22 12:00:00, 0200-12-23 12:00:00, 0200-12-24 12:00:00,\n",
       "             0200-12-25 12:00:00, 0200-12-26 12:00:00, 0200-12-27 12:00:00,\n",
       "             0200-12-28 12:00:00, 0200-12-29 12:00:00, 0200-12-30 12:00:00,\n",
       "             0200-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=7305, calendar=&#x27;julian&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-851b031e-055d-4c75-99d7-6c21690cab47' class='xr-index-data-in' type='checkbox'/><label for='index-851b031e-055d-4c75-99d7-6c21690cab47' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            -279.95, -279.84999999999997, -279.74999999999994,\n",
       "        -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
       "       -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
       "        -279.0499999999998,\n",
       "       ...\n",
       "         79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
       "         79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
       "         79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
       "          79.9499999999949],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-13bbf4ad-0683-4377-a6cb-5e67b60b8f9a' class='xr-index-data-in' type='checkbox'/><label for='index-13bbf4ad-0683-4377-a6cb-5e67b60b8f9a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,\n",
       "       -80.98191361521337, -80.93967426083289,  -80.8974349064524,\n",
       "       -80.85519555207192, -80.81295619769143, -80.77071684331095,\n",
       "       -80.72847748893047,\n",
       "       ...\n",
       "        89.59874190883218,   89.6409812632127,   89.6832206175932,\n",
       "        89.72545997197363,  89.76769932635409,  89.80993868073463,\n",
       "        89.85217803511516,  89.89441738949557,    89.936656743876,\n",
       "        89.97889609825653],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=2700))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af7c1dbf-dec5-4fd0-88ac-af41fcf6b0e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af7c1dbf-dec5-4fd0-88ac-af41fcf6b0e6' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 3TB\n",
       "Dimensions:       (time: 7305, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * time          (time) object 58kB 0181-01-01 12:00:00 ... 0200-12-31 12:00:00\n",
       "  * xt_ocean      (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.85 79.95\n",
       "  * yt_ocean      (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.94 89.98\n",
       "    area_t        (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    dxt           (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    dyt           (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    geolat_t      (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    geolon_t      (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    ht            (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    kmt           (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    wet           (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "Data variables:\n",
       "    slp           (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    v_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    u_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    t_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    q_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    wind          (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    surface_temp  (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    u_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    v_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    u_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    v_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_merged = preprocess_data()\n",
    "ds_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdfd0a91-5e06-41c6-afe4-271744d7bbde",
   "metadata": {},
   "source": [
    "# Training dataset creation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42636106-afa2-41a2-a1cc-90ca3318ad80",
   "metadata": {},
   "source": [
    "### Input data: filtered SGS heat flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbd8a310-367a-4bf2-b133-fe3ec7f699f4",
   "metadata": {},
   "outputs": [
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 14GB\n",
       "Dimensions:   (time: 365, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * time      (time) object 3kB 0181-01-01 12:00:00 ... 0181-12-31 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * yt_ocean  (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n",
       "Data variables:\n",
       "    data      (time, yt_ocean, xt_ocean) float32 14GB dask.array&lt;chunksize=(3, 2700, 3600), 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-6ca943b8-4103-47ea-ab02-2bcd3d176d58' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6ca943b8-4103-47ea-ab02-2bcd3d176d58' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>yt_ocean</span>: 2700</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e2ead672-ed1e-41f7-a26b-1d465dbdab7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2ead672-ed1e-41f7-a26b-1d465dbdab7d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00 ... 0181-12-...</div><input id='attrs-86a64bdf-aec6-41e5-8e04-ea40867ab3b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86a64bdf-aec6-41e5-8e04-ea40867ab3b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3ce7946-7ce2-4a42-b29d-32350fedef0a' class='xr-var-data-in' type='checkbox'><label for='data-f3ce7946-7ce2-4a42-b29d-32350fedef0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(181, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 3, 12, 0, 0, 0, has_year_zero=False), ...,\n",
       "       cftime.DatetimeJulian(181, 12, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 31, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-252c0741-ae7f-4632-afe1-2ea6d86ef196' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-252c0741-ae7f-4632-afe1-2ea6d86ef196' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4cd9d20-5ca1-45eb-8e17-0034c5467b51' class='xr-var-data-in' type='checkbox'><label for='data-f4cd9d20-5ca1-45eb-8e17-0034c5467b51' 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([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.11 -81.07 ... 89.94 89.98</div><input id='attrs-9f511ec2-f837-4b49-8218-621c608bf303' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f511ec2-f837-4b49-8218-621c608bf303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-213e2c23-bdc3-4a94-b753-17c799750531' class='xr-var-data-in' type='checkbox'><label for='data-213e2c23-bdc3-4a94-b753-17c799750531' 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([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,\n",
       "        89.978896])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c038b2b1-835b-407d-9248-83e65144df9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c038b2b1-835b-407d-9248-83e65144df9b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-a4f1f7e5-86e4-4de1-9912-00d065069aa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4f1f7e5-86e4-4de1-9912-00d065069aa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3ceb2e-c1f4-495d-8d88-7cbd08ead960' class='xr-var-data-in' type='checkbox'><label for='data-cc3ceb2e-c1f4-495d-8d88-7cbd08ead960' 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> 13.22 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 2700, 3600) </td>\n",
       "                        <td> (3, 2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 122 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <text x=\"11.374461\" y=\"121.374461\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.374461,121.374461)\">365</text>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-391c30c0-cd86-41e7-b222-02f55ae83f4e' class='xr-section-summary-in' type='checkbox'  ><label for='section-391c30c0-cd86-41e7-b222-02f55ae83f4e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-42bcfd69-1583-4269-84c2-d5adab4b1ddb' class='xr-index-data-in' type='checkbox'/><label for='index-42bcfd69-1583-4269-84c2-d5adab4b1ddb' 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(CFTimeIndex([0181-01-01 12:00:00, 0181-01-02 12:00:00, 0181-01-03 12:00:00,\n",
       "             0181-01-04 12:00:00, 0181-01-05 12:00:00, 0181-01-06 12:00:00,\n",
       "             0181-01-07 12:00:00, 0181-01-08 12:00:00, 0181-01-09 12:00:00,\n",
       "             0181-01-10 12:00:00,\n",
       "             ...\n",
       "             0181-12-22 12:00:00, 0181-12-23 12:00:00, 0181-12-24 12:00:00,\n",
       "             0181-12-25 12:00:00, 0181-12-26 12:00:00, 0181-12-27 12:00:00,\n",
       "             0181-12-28 12:00:00, 0181-12-29 12:00:00, 0181-12-30 12:00:00,\n",
       "             0181-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=365, calendar=&#x27;julian&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-44399fe4-bd83-42a4-b920-932dd0fe4b7b' class='xr-index-data-in' type='checkbox'/><label for='index-44399fe4-bd83-42a4-b920-932dd0fe4b7b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            -279.95, -279.84999999999997, -279.74999999999994,\n",
       "        -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
       "       -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
       "        -279.0499999999998,\n",
       "       ...\n",
       "         79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
       "         79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
       "         79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
       "          79.9499999999949],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8d173599-9856-4378-ba35-a5dd6a9ee5d6' class='xr-index-data-in' type='checkbox'/><label for='index-8d173599-9856-4378-ba35-a5dd6a9ee5d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,\n",
       "       -80.98191361521337, -80.93967426083289,  -80.8974349064524,\n",
       "       -80.85519555207192, -80.81295619769143, -80.77071684331095,\n",
       "       -80.72847748893047,\n",
       "       ...\n",
       "        89.59874190883218,   89.6409812632127,   89.6832206175932,\n",
       "        89.72545997197363,  89.76769932635409,  89.80993868073463,\n",
       "        89.85217803511516,  89.89441738949557,    89.936656743876,\n",
       "        89.97889609825653],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=2700))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9536789f-d9ba-4454-b1fc-e1baf786c61b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9536789f-d9ba-4454-b1fc-e1baf786c61b' 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> Size: 14GB\n",
       "Dimensions:   (time: 365, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * time      (time) object 3kB 0181-01-01 12:00:00 ... 0181-12-31 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * yt_ocean  (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n",
       "Data variables:\n",
       "    data      (time, yt_ocean, xt_ocean) float32 14GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qstar = xr.open_dataset(\n",
    "    \"gs://leap-persistent/panalluri/HF_star.zarr\", engine=\"zarr\", chunks={}\n",
    ")\n",
    "Qstar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb10d498-be6e-4438-b6c7-18756ea41ca8",
   "metadata": {},
   "source": [
    "### Choose same regions as Abigail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4626a641-8d77-4cd8-9724-3282831a613d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example plot with Qstar.data\n",
    "Qstar.data.isel(time=0).plot(robust=True)\n",
    "\n",
    "# Define multiple latitude and longitude ranges\n",
    "# (conversion to ds coords by hand for now)\n",
    "boxes = [\n",
    "    (30, 45, -40, -55),  # gulf stream = (30, 45, -60, -45)\n",
    "    (-30, -15, -25, -10),  # south atlantic = (-30, -15, -25, -10)\n",
    "    (-8, 8, -30, -15), # equator atlantic = (-8, 8, -30, -15)    \n",
    "    (-55, -40, -60, -45), # malvinas current = (-55, -40, -60, -45)\n",
    "    (30, 45, -140, -125), # california current = (30, 45, -140, -125)\n",
    "    (-45, -30, -140, -125), # south pacific = (-45, -30, -140, -125)\n",
    "    (25, 40, -215, -200), # kuroshio extension = (25, 40, 145, 160)\n",
    "    (10, 25, -180, -165), # north pacific = (10, 25, -180, -165)\n",
    "    (-60, -45, -175, -160), # southern ocean, new zealand = (-60, -45, -175, -160)\n",
    "    (-55, -40, 20, 35), # agulhas current = (-55, -40, 20, 35)\n",
    "    (-25, -10, 70, 79.95), # indian ocean (west chunk) = (-25, -10, 70, 85)\n",
    "    (-25, -10, -279.95, -275), # indian ocean (east chunk) = (-25, -10, 70, 85)\n",
    "    (0, 15, 55, 70) # arabian sea = (0, 15, 55, 70)\n",
    "]\n",
    "\n",
    "for lat_min, lat_max, lon_min, lon_max in boxes:\n",
    "\n",
    "    # Draw the box on the plot\n",
    "    plt.gca().add_patch(\n",
    "        plt.Rectangle(\n",
    "            (lon_min, lat_min),  # (x, y) start point\n",
    "            lon_max - lon_min,  # width\n",
    "            lat_max - lat_min,  # height\n",
    "            fill=False,          # don't fill the box\n",
    "            edgecolor='red',     # color of the box\n",
    "            linewidth=2\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c78df3-2faa-4516-a4f0-1d54030acfb8",
   "metadata": {},
   "source": [
    "### Construct Q_star input and output data set for each region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ce46df6-9ba5-403a-8b9d-a51af3bd1d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering model\n"
     ]
    }
   ],
   "source": [
    "print(\"Filtering model\")\n",
    "Qstar_out = filter_inputs_dataset(Qstar, ['yt_ocean', 'xt_ocean'], filter_scale=50, filter_type='gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8cc2424-0abf-48df-adb9-c3ec24d6194b",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 12)&gt; Size: 96B\n",
       "array([cftime.DatetimeJulian(181, 1, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 2, 28, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 31, 0, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-25cee169-b45e-4fc8-bae1-57cf44d3d763' class='xr-array-in' type='checkbox' checked><label for='section-25cee169-b45e-4fc8-bae1-57cf44d3d763' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0181-01-31 00:00:00 0181-02-28 00:00:00 ... 0181-12-31 00:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeJulian(181, 1, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 2, 28, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 31, 0, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-6afc64bd-6450-45ad-b6b1-a7393e95c2fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6afc64bd-6450-45ad-b6b1-a7393e95c2fd' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-31 00:00:00 ... 0181-12-...</div><input id='attrs-e5f63d99-9a2b-44fd-b2d6-ebab0931a72a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5f63d99-9a2b-44fd-b2d6-ebab0931a72a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d6f4298-2584-43af-87a5-b16ca834a10a' class='xr-var-data-in' type='checkbox'><label for='data-5d6f4298-2584-43af-87a5-b16ca834a10a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(181, 1, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 2, 28, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 31, 0, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-163b94cb-ef9b-4600-ab83-5a398ca8e826' class='xr-section-summary-in' type='checkbox'  ><label for='section-163b94cb-ef9b-4600-ab83-5a398ca8e826' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c68bd73e-4731-415d-8174-0a6393068abe' class='xr-index-data-in' type='checkbox'/><label for='index-c68bd73e-4731-415d-8174-0a6393068abe' 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(CFTimeIndex([0181-01-31 00:00:00, 0181-02-28 00:00:00, 0181-03-31 00:00:00,\n",
       "             0181-04-30 00:00:00, 0181-05-31 00:00:00, 0181-06-30 00:00:00,\n",
       "             0181-07-31 00:00:00, 0181-08-31 00:00:00, 0181-09-30 00:00:00,\n",
       "             0181-10-31 00:00:00, 0181-11-30 00:00:00, 0181-12-31 00:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;julian&#x27;, freq=&#x27;ME&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bd78e6f-ef73-4964-bf68-775efd826038' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1bd78e6f-ef73-4964-bf68-775efd826038' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 12)> Size: 96B\n",
       "array([cftime.DatetimeJulian(181, 1, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 2, 28, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 31, 0, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtain monthly averaged data for the first year\n",
    "Qstar_time = Qstar_out.isel(time=slice(0,365)).resample(time='M').mean()\n",
    "Qstar_target_time = Qstar.isel(time=slice(0,365)).resample(time='M').mean()\n",
    "Qstar_time.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca93f7cb-a78e-4e29-8eff-d0ffa73ffad6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Qstar_southAtlantic': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 163, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -24.95 -24.85 -24.75 ... -10.15 -10.05\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -29.98 -29.89 -29.81 ... -15.17 -15.07\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_equatorAtlantic': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 160, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -29.95 -29.85 -29.75 ... -15.15 -15.05\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -7.925 -7.826 -7.726 ... 7.726 7.826 7.925\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_malvinasCurrent': <xarray.Dataset> Size: 2MB\n",
       " Dimensions:   (time: 12, yt_ocean: 224, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -59.95 -59.85 -59.75 ... -45.15 -45.05\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -54.95 -54.9 -54.84 ... -40.11 -40.03\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_californiaCurrent': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 190, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -140.0 -139.9 -139.8 ... -125.2 -125.1\n",
       "   * yt_ocean  (yt_ocean) float64 2kB 30.07 30.15 30.24 ... 44.82 44.89 44.97\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_southPacific': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 190, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -140.0 -139.9 -139.8 ... -125.2 -125.1\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -44.97 -44.89 -44.82 ... -30.15 -30.07\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_kuroshioExtension': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 179, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -214.9 -214.8 -214.7 ... -200.1 -200.0\n",
       "   * yt_ocean  (yt_ocean) float64 1kB 25.02 25.11 25.2 25.29 ... 39.8 39.88 39.95\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_northPacific': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 157, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -179.9 -179.8 -179.7 ... -165.1 -165.0\n",
       "   * yt_ocean  (yt_ocean) float64 1kB 10.1 10.2 10.29 10.39 ... 24.74 24.83 24.92\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_southernOcean': <xarray.Dataset> Size: 2MB\n",
       " Dimensions:   (time: 12, yt_ocean: 250, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -174.9 -174.8 -174.7 ... -160.2 -160.1\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -60.0 -59.95 -59.9 ... -45.18 -45.11 -45.04\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_agulhasCurrent': <xarray.Dataset> Size: 2MB\n",
       " Dimensions:   (time: 12, yt_ocean: 224, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB 20.05 20.15 20.25 ... 34.75 34.85 34.95\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -54.95 -54.9 -54.84 ... -40.11 -40.03\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_indianOceanWest': <xarray.Dataset> Size: 756kB\n",
       " Dimensions:   (time: 12, yt_ocean: 157, xt_ocean: 100)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 800B 70.05 70.15 70.25 ... 79.75 79.85 79.95\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -24.92 -24.83 -24.74 ... -10.29 -10.2 -10.1\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_indianOceanEast': <xarray.Dataset> Size: 379kB\n",
       " Dimensions:   (time: 12, yt_ocean: 157, xt_ocean: 50)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 400B -279.9 -279.8 -279.7 ... -275.1 -275.0\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -24.92 -24.83 -24.74 ... -10.29 -10.2 -10.1\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'Qstar_arabianSea': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 152, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB 55.05 55.15 55.25 ... 69.75 69.85 69.95\n",
       "   * yt_ocean  (yt_ocean) float64 1kB 0.05 0.15 0.25 0.35 ... 14.78 14.88 14.98\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data samples of regions in the ocean\n",
    "## ignore Qstar_gulfStream, not enough data for halo\n",
    "ds_Qstar_dict = {\n",
    "    # 'Qstar_gulfStream': Qstar_time.sel(yt_ocean=slice(30, 45), xt_ocean=slice(-40, -55)), \n",
    "    'Qstar_southAtlantic': Qstar_time.sel(yt_ocean=slice(-30, -15), xt_ocean=slice(-25, -10)),\n",
    "    'Qstar_equatorAtlantic': Qstar_time.sel(yt_ocean=slice(-8, 8), xt_ocean=slice(-30, -15)),\n",
    "    'Qstar_malvinasCurrent': Qstar_time.sel(yt_ocean=slice(-55, -40), xt_ocean=slice(-60, -45)),\n",
    "    'Qstar_californiaCurrent': Qstar_time.sel(yt_ocean=slice(30, 45), xt_ocean=slice(-140, -125)),\n",
    "    'Qstar_southPacific': Qstar_time.sel(yt_ocean=slice(-45, -30), xt_ocean=slice(-140, -125)),\n",
    "    'Qstar_kuroshioExtension': Qstar_time.sel(yt_ocean=slice(25, 40), xt_ocean=slice(-215, -200)),\n",
    "    'Qstar_northPacific': Qstar_time.sel(yt_ocean=slice(10, 25), xt_ocean=slice(-180, -165)),\n",
    "    'Qstar_southernOcean': Qstar_time.sel(yt_ocean=slice(-60, -45), xt_ocean=slice(-175, -160)),\n",
    "    'Qstar_agulhasCurrent': Qstar_time.sel(yt_ocean=slice(-55, -40), xt_ocean=slice(20, 35)),\n",
    "    'Qstar_indianOceanWest': Qstar_time.sel(yt_ocean=slice(-25, -10), xt_ocean=slice(70, 79.95)),\n",
    "    'Qstar_indianOceanEast': Qstar_time.sel(yt_ocean=slice(-25, -10), xt_ocean=slice(-279.95, -275)),\n",
    "    'Qstar_arabianSea': Qstar_time.sel(yt_ocean=slice(0, 15), xt_ocean=slice(55, 70))\n",
    "}\n",
    "ds_Qstar_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a2e3f0f-2236-478e-bb83-3779844ef3ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Qstar_southAtlantic': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 163, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -24.95 -24.85 -24.75 ... -10.15 -10.05\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -29.98 -29.89 -29.81 ... -15.17 -15.07\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>,\n",
       " 'Qstar_equatorAtlantic': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 160, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -29.95 -29.85 -29.75 ... -15.15 -15.05\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -7.925 -7.826 -7.726 ... 7.726 7.826 7.925\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>,\n",
       " 'Qstar_malvinasCurrent': <xarray.Dataset> Size: 2MB\n",
       " Dimensions:   (time: 12, yt_ocean: 224, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -59.95 -59.85 -59.75 ... -45.15 -45.05\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -54.95 -54.9 -54.84 ... -40.11 -40.03\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>,\n",
       " 'Qstar_californiaCurrent': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 190, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -140.0 -139.9 -139.8 ... -125.2 -125.1\n",
       "   * yt_ocean  (yt_ocean) float64 2kB 30.07 30.15 30.24 ... 44.82 44.89 44.97\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>,\n",
       " 'Qstar_southPacific': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 190, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -140.0 -139.9 -139.8 ... -125.2 -125.1\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -44.97 -44.89 -44.82 ... -30.15 -30.07\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>,\n",
       " 'Qstar_kuroshioExtension': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 179, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -214.9 -214.8 -214.7 ... -200.1 -200.0\n",
       "   * yt_ocean  (yt_ocean) float64 1kB 25.02 25.11 25.2 25.29 ... 39.8 39.88 39.95\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>,\n",
       " 'Qstar_northPacific': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 157, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -179.9 -179.8 -179.7 ... -165.1 -165.0\n",
       "   * yt_ocean  (yt_ocean) float64 1kB 10.1 10.2 10.29 10.39 ... 24.74 24.83 24.92\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>,\n",
       " 'Qstar_southernOcean': <xarray.Dataset> Size: 2MB\n",
       " Dimensions:   (time: 12, yt_ocean: 250, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB -174.9 -174.8 -174.7 ... -160.2 -160.1\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -60.0 -59.95 -59.9 ... -45.18 -45.11 -45.04\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>,\n",
       " 'Qstar_agulhasCurrent': <xarray.Dataset> Size: 2MB\n",
       " Dimensions:   (time: 12, yt_ocean: 224, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB 20.05 20.15 20.25 ... 34.75 34.85 34.95\n",
       "   * yt_ocean  (yt_ocean) float64 2kB -54.95 -54.9 -54.84 ... -40.11 -40.03\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>,\n",
       " 'Qstar_indianOceanWest': <xarray.Dataset> Size: 756kB\n",
       " Dimensions:   (time: 12, yt_ocean: 157, xt_ocean: 100)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 800B 70.05 70.15 70.25 ... 79.75 79.85 79.95\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -24.92 -24.83 -24.74 ... -10.29 -10.2 -10.1\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>,\n",
       " 'Qstar_indianOceanEast': <xarray.Dataset> Size: 379kB\n",
       " Dimensions:   (time: 12, yt_ocean: 157, xt_ocean: 50)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 400B -279.9 -279.8 -279.7 ... -275.1 -275.0\n",
       "   * yt_ocean  (yt_ocean) float64 1kB -24.92 -24.83 -24.74 ... -10.29 -10.2 -10.1\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>,\n",
       " 'Qstar_arabianSea': <xarray.Dataset> Size: 1MB\n",
       " Dimensions:   (time: 12, yt_ocean: 152, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean  (xt_ocean) float64 1kB 55.05 55.15 55.25 ... 69.75 69.85 69.95\n",
       "   * yt_ocean  (yt_ocean) float64 1kB 0.05 0.15 0.25 0.35 ... 14.78 14.88 14.98\n",
       "   * time      (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     data      (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data samples of regions in the ocean\n",
    "ds_Qstar_target_dict = {\n",
    "    'Qstar_southAtlantic': Qstar_target_time.sel(yt_ocean=slice(-30, -15), xt_ocean=slice(-25, -10)),\n",
    "    'Qstar_equatorAtlantic': Qstar_target_time.sel(yt_ocean=slice(-8, 8), xt_ocean=slice(-30, -15)),\n",
    "    'Qstar_malvinasCurrent': Qstar_target_time.sel(yt_ocean=slice(-55, -40), xt_ocean=slice(-60, -45)),\n",
    "    'Qstar_californiaCurrent': Qstar_target_time.sel(yt_ocean=slice(30, 45), xt_ocean=slice(-140, -125)),\n",
    "    'Qstar_southPacific': Qstar_target_time.sel(yt_ocean=slice(-45, -30), xt_ocean=slice(-140, -125)),\n",
    "    'Qstar_kuroshioExtension': Qstar_target_time.sel(yt_ocean=slice(25, 40), xt_ocean=slice(-215, -200)),\n",
    "    'Qstar_northPacific': Qstar_target_time.sel(yt_ocean=slice(10, 25), xt_ocean=slice(-180, -165)),\n",
    "    'Qstar_southernOcean': Qstar_target_time.sel(yt_ocean=slice(-60, -45), xt_ocean=slice(-175, -160)),\n",
    "    'Qstar_agulhasCurrent': Qstar_target_time.sel(yt_ocean=slice(-55, -40), xt_ocean=slice(20, 35)),\n",
    "    'Qstar_indianOceanWest': Qstar_target_time.sel(yt_ocean=slice(-25, -10), xt_ocean=slice(70, 79.95)),\n",
    "    'Qstar_indianOceanEast': Qstar_target_time.sel(yt_ocean=slice(-25, -10), xt_ocean=slice(-279.95, -275)),\n",
    "    'Qstar_arabianSea': Qstar_target_time.sel(yt_ocean=slice(0, 15), xt_ocean=slice(55, 70))\n",
    "}\n",
    "ds_Qstar_target_dict"
   ]
  },
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   "cell_type": "markdown",
   "id": "78598eec-2cf8-4fd5-a8a6-3640946579b6",
   "metadata": {},
   "source": [
    "### Construct input data: u_rel, v_rel, q_ref, t_ref, sst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9bbad4-10d5-44ec-8f06-477e2d3e73ee",
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   "source": [
    "u_rel = relative u_dir velocity b/n ocean and atmos \\\n",
    "v_rel = relative v_dir velocity b/n ocean and atmos \\\n",
    "q_ref = specific humidity at 2m in atmos \\\n",
    "t_ref = temperature at 2m in atmos \\\n",
    "sst = sea surface temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87820976-617b-47eb-8212-b371b0ce617a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering model\n"
     ]
    },
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       "Dimensions:       (time: 12, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * xt_ocean      (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.85 79.95\n",
       "  * yt_ocean      (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.94 89.98\n",
       "  * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       "Data variables:\n",
       "    slp           (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    t_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    q_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    wind          (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    surface_temp  (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_ocean       (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_ocean       (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_relative    (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_relative    (time, yt_ocean, xt_ocean) float32 467MB dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    filter_scale:  50\n",
       "    filter_type:   gaussian</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-b2d9849c-12df-4a25-b397-5ba091df66dd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2d9849c-12df-4a25-b397-5ba091df66dd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>yt_ocean</span>: 2700</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1828e7ad-4a94-45a2-93f5-89e987c5f00f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1828e7ad-4a94-45a2-93f5-89e987c5f00f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-3ea9cb64-bd4d-4063-85fa-dac496c7df7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ea9cb64-bd4d-4063-85fa-dac496c7df7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75451df5-4aec-4962-a1f8-1f12417f265b' class='xr-var-data-in' type='checkbox'><label for='data-75451df5-4aec-4962-a1f8-1f12417f265b' 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([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.11 -81.07 ... 89.94 89.98</div><input id='attrs-0b854ff4-0b2f-40fd-b5e4-0b0c0798d697' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b854ff4-0b2f-40fd-b5e4-0b0c0798d697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef951727-3083-4906-ba83-e06f74318002' class='xr-var-data-in' type='checkbox'><label for='data-ef951727-3083-4906-ba83-e06f74318002' 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([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,\n",
       "        89.978896])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-31 00:00:00 ... 0181-12-...</div><input id='attrs-30e4f774-096f-47be-9a22-5bd163f03b21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30e4f774-096f-47be-9a22-5bd163f03b21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24fb2279-5234-4156-a2be-1dd33a73261a' class='xr-var-data-in' type='checkbox'><label for='data-24fb2279-5234-4156-a2be-1dd33a73261a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(181, 1, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 2, 28, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 31, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 30, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 31, 0, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-205a2203-3be0-47b8-afe0-4a1b6cf6957e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-205a2203-3be0-47b8-afe0-4a1b6cf6957e' class='xr-section-summary' >Data variables: <span>(11)</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>slp</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-a7089e3d-a237-4385-a518-e7f4ad13dd86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7089e3d-a237-4385-a518-e7f4ad13dd86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe3e09d5-ccf1-45f3-8302-256337aa3006' class='xr-var-data-in' type='checkbox'><label for='data-fe3e09d5-ccf1-45f3-8302-256337aa3006' 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>filter_scale :</span></dt><dd>50</dd><dt><span>filter_type :</span></dt><dd>gaussian</dd></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> 444.95 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 2700, 3600) </td>\n",
       "                        <td> (1, 2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 195 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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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 444.95 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 444.95 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 444.95 MiB </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
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       "                        <td> 444.95 MiB </td>\n",
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       "                    <tr>\n",
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       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,104.9485979497544 24.9485979497544,104.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"84.948598\" y=\"124.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"164.948598\" y=\"59.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,59.948598)\">2700</text>\n",
       "  <text x=\"7.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,117.474299)\">12</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-84314329-ff88-4c1f-ad1a-f027491c465b' class='xr-section-summary-in' type='checkbox'  ><label for='section-84314329-ff88-4c1f-ad1a-f027491c465b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b611c4d5-84c4-4e96-b52e-b72fe25a7fe2' class='xr-index-data-in' type='checkbox'/><label for='index-b611c4d5-84c4-4e96-b52e-b72fe25a7fe2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            -279.95, -279.84999999999997, -279.74999999999994,\n",
       "        -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
       "       -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
       "        -279.0499999999998,\n",
       "       ...\n",
       "         79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
       "         79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
       "         79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
       "          79.9499999999949],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-57e3270a-e5bd-4585-b37a-139f6612b635' class='xr-index-data-in' type='checkbox'/><label for='index-57e3270a-e5bd-4585-b37a-139f6612b635' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,\n",
       "       -80.98191361521337, -80.93967426083289,  -80.8974349064524,\n",
       "       -80.85519555207192, -80.81295619769143, -80.77071684331095,\n",
       "       -80.72847748893047,\n",
       "       ...\n",
       "        89.59874190883218,   89.6409812632127,   89.6832206175932,\n",
       "        89.72545997197363,  89.76769932635409,  89.80993868073463,\n",
       "        89.85217803511516,  89.89441738949557,    89.936656743876,\n",
       "        89.97889609825653],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=2700))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a452059-e8fb-4612-ac10-68e57c366356' class='xr-index-data-in' type='checkbox'/><label for='index-7a452059-e8fb-4612-ac10-68e57c366356' 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(CFTimeIndex([0181-01-31 00:00:00, 0181-02-28 00:00:00, 0181-03-31 00:00:00,\n",
       "             0181-04-30 00:00:00, 0181-05-31 00:00:00, 0181-06-30 00:00:00,\n",
       "             0181-07-31 00:00:00, 0181-08-31 00:00:00, 0181-09-30 00:00:00,\n",
       "             0181-10-31 00:00:00, 0181-11-30 00:00:00, 0181-12-31 00:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;julian&#x27;, freq=&#x27;ME&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55150f05-02a9-4399-bd46-b27a6c5358d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55150f05-02a9-4399-bd46-b27a6c5358d0' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filter_scale :</span></dt><dd>50</dd><dt><span>filter_type :</span></dt><dd>gaussian</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 5GB\n",
       "Dimensions:       (time: 12, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * xt_ocean      (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.85 79.95\n",
       "  * yt_ocean      (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.94 89.98\n",
       "  * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       "Data variables:\n",
       "    slp           (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    v_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    u_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    t_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    q_ref         (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    wind          (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    surface_temp  (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    u_ocean       (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    v_ocean       (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    u_relative    (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "    v_relative    (time, yt_ocean, xt_ocean) float32 467MB dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>\n",
       "Attributes:\n",
       "    filter_scale:  50\n",
       "    filter_type:   gaussian"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_in = ds_merged\n",
    "ds_in = ds_in.drop(['area_t', 'dxt', 'dyt', 'geolat_t', 'geolon_t', 'ht', 'kmt', 'wet'])\n",
    "\n",
    "print(\"Filtering model\")\n",
    "ds_out = filter_inputs_dataset(ds_in, ['yt_ocean', 'xt_ocean'], filter_scale=50, filter_type='gaussian')\n",
    "\n",
    "ds_lowRes_time = ds_out.isel(time=slice(0,365)).resample(time='M').mean()\n",
    "ds_lowRes_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a260c50-af18-468a-8e09-542647602e31",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ds_lowRes_southAtlantic': <xarray.Dataset> Size: 13MB\n",
       " Dimensions:       (time: 12, yt_ocean: 163, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -24.95 -24.85 -24.75 ... -10.15 -10.05\n",
       "   * yt_ocean      (yt_ocean) float64 1kB -29.98 -29.89 -29.81 ... -15.17 -15.07\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 163, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_equatorAtlantic': <xarray.Dataset> Size: 13MB\n",
       " Dimensions:       (time: 12, yt_ocean: 160, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -29.95 -29.85 -29.75 ... -15.15 -15.05\n",
       "   * yt_ocean      (yt_ocean) float64 1kB -7.925 -7.826 -7.726 ... 7.826 7.925\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 160, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_malvinasCurrent': <xarray.Dataset> Size: 18MB\n",
       " Dimensions:       (time: 12, yt_ocean: 224, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -59.95 -59.85 -59.75 ... -45.15 -45.05\n",
       "   * yt_ocean      (yt_ocean) float64 2kB -54.95 -54.9 -54.84 ... -40.11 -40.03\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_californiaCurrent': <xarray.Dataset> Size: 15MB\n",
       " Dimensions:       (time: 12, yt_ocean: 190, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -140.0 -139.9 -139.8 ... -125.2 -125.1\n",
       "   * yt_ocean      (yt_ocean) float64 2kB 30.07 30.15 30.24 ... 44.82 44.89 44.97\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_southPacific': <xarray.Dataset> Size: 15MB\n",
       " Dimensions:       (time: 12, yt_ocean: 190, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -140.0 -139.9 -139.8 ... -125.2 -125.1\n",
       "   * yt_ocean      (yt_ocean) float64 2kB -44.97 -44.89 -44.82 ... -30.15 -30.07\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 190, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_kuroshioExtension': <xarray.Dataset> Size: 14MB\n",
       " Dimensions:       (time: 12, yt_ocean: 179, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -214.9 -214.8 -214.7 ... -200.1 -200.0\n",
       "   * yt_ocean      (yt_ocean) float64 1kB 25.02 25.11 25.2 ... 39.8 39.88 39.95\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 179, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_northPacific': <xarray.Dataset> Size: 12MB\n",
       " Dimensions:       (time: 12, yt_ocean: 157, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -179.9 -179.8 -179.7 ... -165.1 -165.0\n",
       "   * yt_ocean      (yt_ocean) float64 1kB 10.1 10.2 10.29 ... 24.74 24.83 24.92\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 157, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_southernOcean': <xarray.Dataset> Size: 20MB\n",
       " Dimensions:       (time: 12, yt_ocean: 250, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB -174.9 -174.8 -174.7 ... -160.2 -160.1\n",
       "   * yt_ocean      (yt_ocean) float64 2kB -60.0 -59.95 -59.9 ... -45.11 -45.04\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 250, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_agulhasCurrent': <xarray.Dataset> Size: 18MB\n",
       " Dimensions:       (time: 12, yt_ocean: 224, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB 20.05 20.15 20.25 ... 34.75 34.85 34.95\n",
       "   * yt_ocean      (yt_ocean) float64 2kB -54.95 -54.9 -54.84 ... -40.11 -40.03\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 2MB dask.array<chunksize=(1, 224, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_indianOceanWest': <xarray.Dataset> Size: 8MB\n",
       " Dimensions:       (time: 12, yt_ocean: 157, xt_ocean: 100)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 800B 70.05 70.15 70.25 ... 79.85 79.95\n",
       "   * yt_ocean      (yt_ocean) float64 1kB -24.92 -24.83 -24.74 ... -10.2 -10.1\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 754kB dask.array<chunksize=(1, 157, 100), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_indianOceanEast': <xarray.Dataset> Size: 4MB\n",
       " Dimensions:       (time: 12, yt_ocean: 157, xt_ocean: 50)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 400B -279.9 -279.8 -279.7 ... -275.1 -275.0\n",
       "   * yt_ocean      (yt_ocean) float64 1kB -24.92 -24.83 -24.74 ... -10.2 -10.1\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 377kB dask.array<chunksize=(1, 157, 50), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian,\n",
       " 'ds_lowRes_arabianSea': <xarray.Dataset> Size: 12MB\n",
       " Dimensions:       (time: 12, yt_ocean: 152, xt_ocean: 150)\n",
       " Coordinates:\n",
       "   * xt_ocean      (xt_ocean) float64 1kB 55.05 55.15 55.25 ... 69.75 69.85 69.95\n",
       "   * yt_ocean      (yt_ocean) float64 1kB 0.05 0.15 0.25 ... 14.78 14.88 14.98\n",
       "   * time          (time) object 96B 0181-01-31 00:00:00 ... 0181-12-31 00:00:00\n",
       " Data variables:\n",
       "     slp           (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     v_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     u_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     t_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     q_ref         (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     wind          (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     surface_temp  (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     u_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     v_ocean       (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     u_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       "     v_relative    (time, yt_ocean, xt_ocean) float32 1MB dask.array<chunksize=(1, 152, 150), meta=np.ndarray>\n",
       " Attributes:\n",
       "     filter_scale:  50\n",
       "     filter_type:   gaussian}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data samples of regions in the ocean\n",
    "ds_lowRes_dict = {\n",
    "    'ds_lowRes_southAtlantic': ds_lowRes_time.sel(yt_ocean=slice(-30, -15), xt_ocean=slice(-25, -10)),\n",
    "    'ds_lowRes_equatorAtlantic': ds_lowRes_time.sel(yt_ocean=slice(-8, 8), xt_ocean=slice(-30, -15)),\n",
    "    'ds_lowRes_malvinasCurrent': ds_lowRes_time.sel(yt_ocean=slice(-55, -40), xt_ocean=slice(-60, -45)),\n",
    "    'ds_lowRes_californiaCurrent': ds_lowRes_time.sel(yt_ocean=slice(30, 45), xt_ocean=slice(-140, -125)),\n",
    "    'ds_lowRes_southPacific': ds_lowRes_time.sel(yt_ocean=slice(-45, -30), xt_ocean=slice(-140, -125)),\n",
    "    'ds_lowRes_kuroshioExtension': ds_lowRes_time.sel(yt_ocean=slice(25, 40), xt_ocean=slice(-215, -200)),\n",
    "    'ds_lowRes_northPacific': ds_lowRes_time.sel(yt_ocean=slice(10, 25), xt_ocean=slice(-180, -165)),\n",
    "    'ds_lowRes_southernOcean': ds_lowRes_time.sel(yt_ocean=slice(-60, -45), xt_ocean=slice(-175, -160)),\n",
    "    'ds_lowRes_agulhasCurrent': ds_lowRes_time.sel(yt_ocean=slice(-55, -40), xt_ocean=slice(20, 35)),\n",
    "    'ds_lowRes_indianOceanWest': ds_lowRes_time.sel(yt_ocean=slice(-25, -10), xt_ocean=slice(70, 79.95)),\n",
    "    'ds_lowRes_indianOceanEast': ds_lowRes_time.sel(yt_ocean=slice(-25, -10), xt_ocean=slice(-279.95, -275)),\n",
    "    'ds_lowRes_arabianSea': ds_lowRes_time.sel(yt_ocean=slice(0, 15), xt_ocean=slice(55, 70))\n",
    "}\n",
    "ds_lowRes_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731d8249-6c55-4b19-8a4b-47da0dafcfb0",
   "metadata": {},
   "source": [
    "### Organize training/testing data into batches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3b1441-82fb-4a82-8d85-85f7038c4483",
   "metadata": {},
   "source": [
    "#### Timing steps in data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b612e9cf-e9e1-4c1b-acd6-a92daecb9ee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken for line 1: 0.000266 seconds\n",
      "Time taken for line 1: 238.303945 seconds\n",
      "Time taken for line 1: 0.001420 seconds\n",
      "Time taken for line 1: 0.000137 seconds\n",
      "Time taken for line 1: 0.000029 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "i=0\n",
    "ds = ds_Qstar_dict[names[i]]  # Same dict names for input and output Qstar\n",
    "ds_target = ds_Qstar_target_dict[names_target[i]] \n",
    "ds_lowRes_inputs = ds_lowRes_dict[names_lowRes[i]]\n",
    "time_size, x_size, y_size = ds.data.shape\n",
    "t=0\n",
    "x = np.random.randint(4, x_size - 4)\n",
    "y = np.random.randint(4, y_size - 4)\n",
    "\n",
    "end_time = time.time()\n",
    "print(f\"Time taken for line 1: {end_time - start_time:.6f} seconds\")\n",
    "\n",
    "start_time = time.time()\n",
    "halo_Qstar = ds.data[t, x-4:x+5, y-4:y+5].load()\n",
    "end_time = time.time()\n",
    "print(f\"Time taken for line 1: {end_time - start_time:.6f} seconds\")\n",
    "\n",
    "start_time = time.time()\n",
    "Qstar_stacked = halo_Qstar.stack(spatial=['xt_ocean', 'yt_ocean']).to_numpy()\n",
    "end_time = time.time()\n",
    "print(f\"Time taken for line 1: {end_time - start_time:.6f} seconds\")\n",
    "\n",
    "start_time = time.time()\n",
    "Qstar_tensor = torch.tensor(Qstar_stacked, dtype=torch.float32)\n",
    "end_time = time.time()\n",
    "print(f\"Time taken for line 1: {end_time - start_time:.6f} seconds\")\n",
    "\n",
    "start_time = time.time()\n",
    "Qstar_tensor\n",
    "end_time = time.time()\n",
    "print(f\"Time taken for line 1: {end_time - start_time:.6f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72a331e2-a2ae-4a60-a12b-1e7434af0872",
   "metadata": {},
   "source": [
    "#### Set up parallel processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e988847-330b-4390-96b3-53976886726b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from distributed import LocalCluster, Client\n",
    "#cluster = LocalCluster(n_workers=4, threads_per_worker=2) #default: n_workers=4, threads_per_worker=1\n",
    "#client = Client(cluster)\n",
    "#client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33452d44-7a47-4bc9-b9ff-b605127ff94e",
   "metadata": {},
   "source": [
    "### Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3595be71-8498-4fa2-960e-b9070b9c4f1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset: ds_lowRes_southAtlantic with shape (12, 163, 150)\n",
      "Extracting from southAtlantic_t0_sample at time 0\n",
      "Extracting from southAtlantic_t1_sample at time 1\n",
      "Extracting from southAtlantic_t2_sample at time 2\n",
      "Extracting from southAtlantic_t3_sample at time 3\n",
      "Extracting from southAtlantic_t4_sample at time 4\n",
      "Extracting from southAtlantic_t5_sample at time 5\n"
     ]
    }
   ],
   "source": [
    "names = list(ds_Qstar_dict.keys())  # Get the list of dataset names\n",
    "names_target = list(ds_Qstar_target_dict.keys())\n",
    "training_samples_Qstar = {}  # To store samples for training (time 0 to 10)\n",
    "testing_samples_Qstar = {}   # To store samples for testing (time 11 and 12)\n",
    "training_samples_Qstar_target = {}  \n",
    "testing_samples_Qstar_target = {} \n",
    "\n",
    "names_lowRes = list(ds_lowRes_dict.keys())  # Get the list of dataset names\n",
    "training_samples_urel = {}  \n",
    "testing_samples_urel = {}   \n",
    "training_samples_vrel = {}  \n",
    "testing_samples_vrel = {}   \n",
    "training_samples_qref = {} \n",
    "testing_samples_qref = {}   \n",
    "training_samples_sst = {}  \n",
    "testing_samples_sst = {}   \n",
    "training_samples_tref = {}  \n",
    "testing_samples_tref = {} \n",
    "\n",
    "sample_num = len(names)\n",
    "\n",
    "# Loop over each dataset\n",
    "for i in range(sample_num):\n",
    "    ds = ds_Qstar_dict[names[i]]  # Same dict names for input and output Qstar\n",
    "    ds_target = ds_Qstar_target_dict[names_target[i]] \n",
    "    ds_lowRes_inputs = ds_lowRes_dict[names_lowRes[i]]  # Access the dataset by its name\n",
    "    \n",
    "    # Get the shape of the dataset (assuming it's 3D: time t x y)\n",
    "    time_size, x_size, y_size = ds.data.shape # same shape for all inputs\n",
    "\n",
    "    name_LR = names_lowRes[i].replace(\"ds_lowRes_\", \"\")\n",
    "    print(f\"Processing dataset: {names_lowRes[i]} with shape {ds.data.shape}\")\n",
    "    \n",
    "    # Loop over each time point\n",
    "    for t in range(time_size):\n",
    "        # Randomly select a point that allows for a 4-point halo\n",
    "        x = np.random.randint(4, x_size - 4)\n",
    "        y = np.random.randint(4, y_size - 4)\n",
    "        \n",
    "        # Extract the 9x9 block at time point t\n",
    "        halo_Qstar = ds.data[t, x-4:x+5, y-4:y+5].load()  # Assuming the structure is (time, y, x)\n",
    "        halo_urel = ds_lowRes_inputs.u_relative[t, x-4:x+5, y-4:y+5].load()\n",
    "        halo_vrel = ds_lowRes_inputs.v_relative[t, x-4:x+5, y-4:y+5].load()\n",
    "        halo_sst = ds_lowRes_inputs.surface_temp[t, x-4:x+5, y-4:y+5].load()\n",
    "        halo_qref = ds_lowRes_inputs.q_ref[t, x-4:x+5, y-4:y+5].load()\n",
    "        halo_tref = ds_lowRes_inputs.t_ref[t, x-4:x+5, y-4:y+5].load()\n",
    "\n",
    "        # Target data value\n",
    "        halo_Qstar_target = ds_target.data[t, x, y] # Only want a single point output for Qstar comparison\n",
    "\n",
    "        # Stack the data\n",
    "        Qstar_stacked = halo_Qstar.stack(spatial=['xt_ocean', 'yt_ocean'])  \n",
    "        urel_stacked = halo_urel.stack(spatial=['xt_ocean', 'yt_ocean']) \n",
    "        vrel_stacked = halo_vrel.stack(spatial=['xt_ocean', 'yt_ocean']) \n",
    "        qref_stacked = halo_qref.stack(spatial=['xt_ocean', 'yt_ocean']) \n",
    "        tref_stacked = halo_tref.stack(spatial=['xt_ocean', 'yt_ocean']) \n",
    "        sst_stacked = halo_sst.stack(spatial=['xt_ocean', 'yt_ocean']) \n",
    "\n",
    "        # Convert to PyTorch tensor form\n",
    "        Qstar_tensor = torch.tensor(Qstar_stacked.values, dtype=torch.float32)\n",
    "        Qstar_target_tensor = torch.tensor(halo_Qstar_target.values, dtype=torch.float32)\n",
    "        urel_tensor = torch.tensor(urel_stacked.values, dtype=torch.float32)\n",
    "        vrel_tensor = torch.tensor(vrel_stacked.values, dtype=torch.float32)\n",
    "        qref_tensor = torch.tensor(qref_stacked.values, dtype=torch.float32)\n",
    "        tref_tensor = torch.tensor(tref_stacked.values, dtype=torch.float32)\n",
    "        sst_tensor = torch.tensor(sst_stacked.values, dtype=torch.float32)        \n",
    "        \n",
    "        # Customize the name for the samples dictionary\n",
    "        sample_name = f\"{names[i]}_t{t}_sample\"  # Example format: dataset_t0_sample\n",
    "        final_sample_name = sample_name.replace(\"Qstar_\", \"\")\n",
    "        print(f\"Extracting from {final_sample_name} at time {t}\")\n",
    "        \n",
    "        # Store samples based on the time point\n",
    "        if 0 <= t <= 9:\n",
    "            training_samples_Qstar[final_sample_name] = Qstar_tensor\n",
    "            training_samples_Qstar_target[final_sample_name] = Qstar_target_tensor\n",
    "            training_samples_urel[final_sample_name] = urel_tensor\n",
    "            training_samples_vrel[final_sample_name] = vrel_tensor\n",
    "            training_samples_qref[final_sample_name] = qref_tensor\n",
    "            training_samples_tref[final_sample_name] = tref_tensor\n",
    "            training_samples_sst[final_sample_name] = sst_tensor\n",
    "        elif t >= 10:\n",
    "            testing_samples_Qstar[final_sample_name] = Qstar_tensor\n",
    "            testing_samples_Qstar_target[final_sample_name] = Qstar_target_tensor\n",
    "            testing_samples_urel[final_sample_name] = urel_tensor\n",
    "            testing_samples_vrel[final_sample_name] = vrel_tensor\n",
    "            testing_samples_qref[final_sample_name] = qref_tensor\n",
    "            testing_samples_tref[final_sample_name] = tref_tensor\n",
    "            testing_samples_sst[final_sample_name] = sst_tensor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97e2322-d038-4926-941c-d052e832bca3",
   "metadata": {},
   "source": [
    "### Input data: Final form for NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d02d04a-f544-4be5-ab4d-7afc11e84694",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = len(list(ds_lowRes_dict.keys()))\n",
    "\n",
    "names_Qstar_input = list(ds_Qstar_dict.keys())\n",
    "names_inputs = list(ds_lowRes_dict.keys())\n",
    "\n",
    "NN_training_inputs = {}  \n",
    "\n",
    "for i in range(num_samples): \n",
    "\n",
    "    # Final relevant samples\n",
    "    sample_name = names_Qstar_input[i]\n",
    "    final_sample_name = sample_name.replace(\"Qstar_\", \"\")\n",
    "    final_sample_string = f\"{final_sample_name}_t{i}_sample\"\n",
    "        \n",
    "    # Prepare batch data\n",
    "    x_urel = training_samples_urel[final_sample_string]\n",
    "    x_vrel = training_samples_vrel[final_sample_string]\n",
    "    x_qref = training_samples_qref[final_sample_string]\n",
    "    x_tref = training_samples_tref[final_sample_string]\n",
    "    x_sst = training_samples_sst[final_sample_string]\n",
    "    x_Qstar = training_samples_Qstar[final_sample_string]\n",
    "    \n",
    "    # Concatenate features along the feature dimension (dim=1)\n",
    "    Xbatch = torch.stack((x_urel, x_vrel, x_sst, x_tref), dim=1)\n",
    "\n",
    "    # Add sample to dictionary\n",
    "    NN_training_inputs[final_sample_string] = Xbatch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5301a1-deda-478b-ae4e-778929e843b9",
   "metadata": {},
   "source": [
    "## NN architecture setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f3dcef9-63b4-4eda-be52-40c179b2a6f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SGS_HF(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.hidden1 = nn.Linear(5, 10)\n",
    "        self.act1 = nn.ReLU()\n",
    "        self.hidden2 = nn.Linear(10, 5)\n",
    "        self.act2 = nn.ReLU()\n",
    "        self.output = nn.Linear(5, 1)\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.act1(self.hidden1(x))\n",
    "        x = self.act2(self.hidden2(x))\n",
    "        x = self.output(x)\n",
    "        return x\n",
    " \n",
    "model = SGS_HF()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0bf656c-b11d-4c80-bb06-2247fac3cf94",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.MSELoss()  \n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e368050f-c99d-45b5-a6c1-344266feaeeb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "n_epochs = 5\n",
    "batch_size = 12\n",
    "\n",
    "sample_region = list(NN_training_inputs.keys())\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    for i in range(batch_size): \n",
    "        \n",
    "        # Forward pass\n",
    "        y_pred = model(NN_training_inputs[sample_region[i]]) \n",
    "        \n",
    "        # Prepare target data\n",
    "        ybatch = training_samples_Qstar_target[sample_region[i]]\n",
    "        \n",
    "        # Compute loss\n",
    "        loss = loss_fn(y_pred, ybatch)  \n",
    "\n",
    "        # Backward pass and optimization\n",
    "        optimizer.zero_grad()  # Clear previous gradients\n",
    "        loss.backward()        # Compute gradients (backpropagation)\n",
    "        optimizer.step()       # Update model parameters\n",
    "        \n",
    "        print(f'Batch {i // batch_size}, loss {loss.item()}')\n",
    "        \n",
    "    print(f'Finished epoch {epoch}, latest loss {loss.item()}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "920d3d13-3c37-445f-88f5-f5737fdc5157",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'leap-persistent/panalluri/airSeaNN_temporal.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4601af96-48fe-4c68-940d-1b05d4a1ff3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, 'leap-persistent/panalluri/airSeaNN_temporal_wholeModel.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab53b5a2-05cc-4328-ad2a-ecd92ca25a5f",
   "metadata": {},
   "source": [
    "## NN training losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "691ed73a-f84c-4162-8771-e2f3f9e0bb3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example data for multiple lines\n",
    "epochs = [0, 1, 2]\n",
    "batch_0_losses = np.array([1.6868, 0.3770, 0.3138])\n",
    "batch_1_losses = np.array([5.2102, 0.6825, 0.8711])\n",
    "batch_2_losses = np.array([0.1358, 1.9022, 1.5184])\n",
    "batch_3_losses = np.array([3.8071, 1.5627, 2.0439])\n",
    "batch_4_losses = np.array([2.7307, 4.0232, 3.2725])\n",
    "batch_5_losses = np.array([0.2974, 0.4911, 0.2400])\n",
    "batch_6_losses = np.array([24.4274, 23.5716, 26.7460])\n",
    "batch_7_losses = np.array([31.1967, 31.1470, 26.6861])\n",
    "\n",
    "# Create a new figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot multiple lines\n",
    "plt.plot(epochs, batch_0_losses, marker='o', color='b', label='Batch 0')  \n",
    "plt.plot(epochs, batch_1_losses, marker='x', color='g', label='Batch 1')  \n",
    "plt.plot(epochs, batch_2_losses, marker='s', color='r', label='Batch 2') \n",
    "plt.plot(epochs, batch_3_losses, marker='^', color='c', label='Batch 3')  \n",
    "plt.plot(epochs, batch_4_losses, marker='v', color='m', label='Batch 4')  \n",
    "plt.plot(epochs, batch_5_losses, marker='>', color='y', label='Batch 5') \n",
    "plt.plot(epochs, batch_6_losses, marker='<', color='k', label='Batch 6')  \n",
    "plt.plot(epochs, batch_7_losses, marker='D', color='pink', label='Batch 7')   \n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Losses')\n",
    "plt.title('Losses across epochs for each batch')\n",
    "plt.legend()  # Add legend to distinguish lines\n",
    "plt.grid(True)  # Add grid for better readability\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b972d2ba-46d4-4b7c-bddc-0f7271620bc6",
   "metadata": {},
   "source": [
    "## Evaluate NN performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf0e61b-f924-4227-b31a-cae4dd773c9f",
   "metadata": {},
   "source": [
    "### Trial 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b69aee22-2736-4844-8e04-f23516464618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGS_HF(\n",
       "  (hidden1): Linear(in_features=4, out_features=8, bias=True)\n",
       "  (act1): ReLU()\n",
       "  (hidden2): Linear(in_features=8, out_features=4, bias=True)\n",
       "  (act2): ReLU()\n",
       "  (output): Linear(in_features=4, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SGS_HF()  # Recreate the model instance\n",
    "model.load_state_dict(torch.load('leap-persistent/panalluri/airSeaNN_temporal.pth'))\n",
    "model.eval()  # Set the model to evaluation mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec16f8fb-2fef-44c4-9807-f5c22f487252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-22.7881, -23.2886, -23.7266, -21.8767, -22.3571, -22.7773, -20.9902,\n",
       "        -21.4496, -21.8518])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ground truth data that NN is trying to predict\n",
    "y_true = torch.tensor(Qstar_stacked.isel(time=8).data.values, dtype=torch.float32)\n",
    "y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c89b3ed9-da0f-419c-9a2c-254b44bc11cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input info for NN\n",
    "x_urel = torch.tensor(u_rel_stacked.isel(time=8).values, dtype=torch.float32)\n",
    "x_vrel = torch.tensor(v_rel_stacked.isel(time=8).values, dtype=torch.float32)\n",
    "x_sst = torch.tensor(sst_stacked.isel(time=8).values, dtype=torch.float32)\n",
    "x_tref = torch.tensor(t_ref_stacked.isel(time=8).values, dtype=torch.float32)\n",
    "        \n",
    "Xbatch = torch.stack((x_urel, x_vrel, x_sst, x_tref), dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64ecf4b7-eb4c-4948-8fd0-0b5af15ec39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad(): #avoid extra computation in NN evaluation\n",
    "    predictions = model(Xbatch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ea3bc37-8e2b-4a3f-8291-1f3c81097263",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert predictions to numpy for metric calculation \n",
    "predictions = predictions.numpy()\n",
    "y_true = y_true.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2f1b7bf5-d3b0-4381-ac7e-bc4f53404289",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 257.4769592285156\n",
      "Mean Absolute Error: 16.02436637878418\n",
      "R-squared: -367.8670349121094\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "mse = mean_squared_error(y_true, predictions)\n",
    "mae = mean_absolute_error(y_true, predictions)\n",
    "r2 = r2_score(y_true, predictions)\n",
    "\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'Mean Absolute Error: {mae}')\n",
    "print(f'R-squared: {r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb848fe-5a89-40f4-ad38-23ee40e4aaac",
   "metadata": {},
   "source": [
    "### Plot comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4c8f5aed-705d-43d5-b0ff-894685ecfaa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c863dc5a-8982-4f3c-b670-09e3666b0e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and a grid specification\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "gs = gridspec.GridSpec(1, 4, width_ratios=[1, 0.05, 1, 0.05], wspace=0.6)\n",
    "\n",
    "# Create subplots\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax2 = plt.subplot(gs[2])\n",
    "cax1 = plt.subplot(gs[1])\n",
    "cax2 = plt.subplot(gs[3])\n",
    "\n",
    "# Plot data on each subplot\n",
    "cimg1 = ax1.imshow(y_true.reshape((3,3)), cmap='viridis')  # First subplot\n",
    "ax1.set_title('True Data')\n",
    "\n",
    "cimg2 = ax2.imshow(predictions.reshape((3,3)), cmap='viridis')  # Second subplot\n",
    "ax2.set_title('Predicted Data')\n",
    "\n",
    "# Add a colorbar\n",
    "fig.colorbar(cimg1, cax=cax1)\n",
    "fig.colorbar(cimg2, cax=cax2)\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff692a3c-9b71-4a4f-ae74-b98428e929a8",
   "metadata": {},
   "source": [
    "### Trial 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48ae5812-5da5-4c88-bf83-defec236c507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 19.150434494018555\n",
      "Mean Absolute Error: 4.370053291320801\n",
      "R-squared: -349.7537841796875\n"
     ]
    }
   ],
   "source": [
    "#ground truth data that NN is trying to predict\n",
    "y_true = torch.tensor(Qstar_stacked.isel(time=9).data.values, dtype=torch.float32)\n",
    "\n",
    "# input info for NN\n",
    "x_urel = torch.tensor(u_rel_stacked.isel(time=9).values, dtype=torch.float32)\n",
    "x_vrel = torch.tensor(v_rel_stacked.isel(time=9).values, dtype=torch.float32)\n",
    "x_sst = torch.tensor(sst_stacked.isel(time=9).values, dtype=torch.float32)\n",
    "x_tref = torch.tensor(t_ref_stacked.isel(time=9).values, dtype=torch.float32)\n",
    "        \n",
    "Xbatch = torch.stack((x_urel, x_vrel, x_sst, x_tref), dim=1)\n",
    "\n",
    "with torch.no_grad(): #avoid extra computation in NN evaluation\n",
    "    predictions = model(Xbatch)\n",
    "\n",
    "# Convert predictions to numpy for metric calculation \n",
    "predictions = predictions.numpy()\n",
    "y_true = y_true.numpy()\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "mse = mean_squared_error(y_true, predictions)\n",
    "mae = mean_absolute_error(y_true, predictions)\n",
    "r2 = r2_score(y_true, predictions)\n",
    "\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'Mean Absolute Error: {mae}')\n",
    "print(f'R-squared: {r2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "06e5027a-1001-4b8b-8d1a-9100cfb5f668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and a grid specification\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "gs = gridspec.GridSpec(1, 4, width_ratios=[1, 0.05, 1, 0.05], wspace=0.6)\n",
    "\n",
    "# Create subplots\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax2 = plt.subplot(gs[2])\n",
    "cax1 = plt.subplot(gs[1])\n",
    "cax2 = plt.subplot(gs[3])\n",
    "\n",
    "# Plot data on each subplot\n",
    "cimg1 = ax1.imshow(y_true.reshape((3,3)), cmap='viridis')  # First subplot\n",
    "ax1.set_title('True Data')\n",
    "\n",
    "cimg2 = ax2.imshow(predictions.reshape((3,3)), cmap='viridis')  # Second subplot\n",
    "ax2.set_title('Predicted Data')\n",
    "\n",
    "# Add a colorbar\n",
    "fig.colorbar(cimg1, cax=cax1)\n",
    "fig.colorbar(cimg2, cax=cax2)\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7afbc6cd-c644-4469-b0c0-ff5d57da958f",
   "metadata": {},
   "source": [
    "### Trial 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0bec162e-4944-4337-a9d9-603c2636f0d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 97.55026245117188\n",
      "Mean Absolute Error: 9.867952346801758\n",
      "R-squared: -549.0772705078125\n"
     ]
    }
   ],
   "source": [
    "#ground truth data that NN is trying to predict\n",
    "y_true = torch.tensor(Qstar_stacked.isel(time=10).data.values, dtype=torch.float32)\n",
    "\n",
    "# input info for NN\n",
    "x_urel = torch.tensor(u_rel_stacked.isel(time=10).values, dtype=torch.float32)\n",
    "x_vrel = torch.tensor(v_rel_stacked.isel(time=10).values, dtype=torch.float32)\n",
    "x_sst = torch.tensor(sst_stacked.isel(time=10).values, dtype=torch.float32)\n",
    "x_tref = torch.tensor(t_ref_stacked.isel(time=10).values, dtype=torch.float32)\n",
    "        \n",
    "Xbatch = torch.stack((x_urel, x_vrel, x_sst, x_tref), dim=1)\n",
    "\n",
    "with torch.no_grad(): #avoid extra computation in NN evaluation\n",
    "    predictions = model(Xbatch)\n",
    "\n",
    "# Convert predictions to numpy for metric calculation \n",
    "predictions = predictions.numpy()\n",
    "y_true = y_true.numpy()\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "mse = mean_squared_error(y_true, predictions)\n",
    "mae = mean_absolute_error(y_true, predictions)\n",
    "r2 = r2_score(y_true, predictions)\n",
    "\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'Mean Absolute Error: {mae}')\n",
    "print(f'R-squared: {r2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1bea966d-ae36-4ca4-b4a0-8a27f27160bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and a grid specification\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "gs = gridspec.GridSpec(1, 4, width_ratios=[1, 0.05, 1, 0.05], wspace=0.6)\n",
    "\n",
    "# Create subplots\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax2 = plt.subplot(gs[2])\n",
    "cax1 = plt.subplot(gs[1])\n",
    "cax2 = plt.subplot(gs[3])\n",
    "\n",
    "# Plot data on each subplot\n",
    "cimg1 = ax1.imshow(y_true.reshape((3,3)), cmap='viridis')  # First subplot\n",
    "ax1.set_title('True Data')\n",
    "\n",
    "cimg2 = ax2.imshow(predictions.reshape((3,3)), cmap='viridis')  # Second subplot\n",
    "ax2.set_title('Predicted Data')\n",
    "\n",
    "# Add a colorbar\n",
    "fig.colorbar(cimg1, cax=cax1)\n",
    "fig.colorbar(cimg2, cax=cax2)\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
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   "id": "7ec27a8e-ed91-470a-ad08-7ae54bfd870e",
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   "outputs": [],
   "source": []
  }
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