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   "source": [
    "# Exemple d'utilisation de gridmarthe [FR]\n",
    "\n",
    "Le package gridmarthe est conçu pour faciliter la lecture/l'écriture de grille au format Marthe.\n",
    "Ce notebook permet d'explorer quelques fonctionnalités de base du package et de montrer un exemple\n",
    "de traitement des grilles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import du package\n",
    "import gridmarthe as gm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fonctionnalités de base\n",
    "\n",
    "Les grilles contiennent certaines métadonnées, dont l'index du pas de temps\n",
    "mais pas les dates en elles-même. Aussi, il est utile de fournir à la fonction de lecture\n",
    "soit une série de date (à construire manuellement) soit un fichier pastp pour que les dates soient\n",
    "lues automatiquement.\n",
    "\n",
    "En complément, plusieurs arguments peuvent être utiles à la lecture (retrait des valeurs nulles, transformation des xy, ajout de l'indicateur de maillage ou des numéros de colonnes/lignes, etc.).\n",
    "\n",
    "Pour évaluer les options possibles, on peut afficher l'aide de la fonction."
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
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    {
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     "text": [
      "Help on function load_marthe_grid in module gridmarthe.gridmarthe:\n",
      "\n",
      "load_marthe_grid(filename, varname=None, dates=None, fpastp=None, nanval=None, dropna=False, xyfactor=1, keepligcol=False, add_id_grid=False, title=None, var_attrs={}, model_attrs={'resolution_units': 'm', 'projection': 'epsg:27572', 'domain': 'FR-France'}, verbose=True)\n",
      "    LECSEM python wrapper\n",
      "    Fortran modules from marthe src wrapped for python module\n",
      "    \n",
      "    memo :  file.out read as seq => all layer then nested grid and all layers too. And so on for every timestep.\n",
      "            coords with no value read as 1e+20\n",
      "            zvar read as a single 1D array for every timestep\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "        filename    (str):  A path to marthegrid file (*.permh, *.out, etc.)\n",
      "        varname     (str):  variable to access in martgrid file, e.g `CHARGE` for groundwater head. See marthegrid file content.\n",
      "                            if None  is passed (default), function will scan all varnames in filename and keep first only\n",
      "                            if 'all' is passed,  function will scan all varnames in filename and keep all. All datavars are added to dataset, using recursive call to func\n",
      "                            if wrong variable name is passed, empty data will be returned.\n",
      "        dates       (sequence, Optionnal): Can be a pd.date_range, pd.Series, pd.DatetimeIndex, np.array or list of datetime/np.datetime objects.\n",
      "                                           If no dates (or no fpastp) is provided, a fake sequence of dates from 1850 to 1900 will be used for xarray object\n",
      "        fpastp      (str, Optionnal)     : A pastp file to read for dates\n",
      "        nanval      (float, Optionnal)   : A code value for nan values. Default is 9999.\n",
      "        dropna      (bool, Optionnal)    : Drop nan values (corresponding to nanval) in xarray object to return, default is False (keep nan values).\n",
      "        xyfactor    (int or float, Optionnal): factor to transform X and Y values. e.g.: 1000 to convert km XY to meters. Default is 1.\n",
      "        keepligcol  (bool, Optionnal): Add columns (col) and rows (lig) index (from 1 to n), Default is False.\n",
      "        add_id_grid (bool, Optionnal): Add grid id (from 0 to n), useful for nested grids. 0 is main grid, Default is False\n",
      "        title       (str , Optionnal): Title for grid attributes. Default is None (not used)\n",
      "        var_attrs   (dict, Optionnal): Dictionnary of attributes to add to variable DataArray.\n",
      "        model_attrs (dict, Optionnal): Dictionnary of attributes to add to Dataset.\n",
      "                                       by default, gis attrs are added and can be modified\n",
      "                                    'resolution_units': 'm',\n",
      "                                    'projection'      : 'epsg:27572',\n",
      "                                    'domain'          : 'FR-France',\n",
      "        verbose     (bool, Optionnal): Print some information about execution in stdout. Default is False.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "        ds (xr.Dataset): a xarray.Dataset object containing values and attributes read from Marthe grid file.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(gm.load_marthe_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lecture d'une grille marthe et stockage dans un Dataset (librairie xarray)\n",
    "ds = gm.load_marthe_grid('./data/chasim_hallue.out', 'CHARGE', fpastp='./data/hallue.pastp', drop_nan=True) # si la valeur des nans n'est pas 9999 (ex. permh, NaNs = 0), on peut spécifier la valeur"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 784kB\n",
       "Dimensions:  (time: 205, zone: 927)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n",
       "  * zone     (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "Data variables:\n",
       "    charge   (time, zone) float32 760kB 100.0 100.6 101.1 ... 27.0 26.0 26.35\n",
       "    x        (zone) float32 4kB 617.8 618.2 618.8 619.2 ... 606.8 607.2 607.8\n",
       "    y        (zone) float32 4kB 2.567e+03 2.567e+03 ... 2.543e+03 2.543e+03\n",
       "    dx       (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
       "    dy       (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
       "Attributes: (12/16)\n",
       "    conventions:          CF-1.10\n",
       "    title:                Modélisation du bassin de la SOMME Nappe_Libre\n",
       "    marthe_grid_version:  9.0\n",
       "    original_dimensions:  x,y,z [grids]: 53 54 1\n",
       "    lon_resolution:       0.5\n",
       "    lat_resolution:       0.5\n",
       "    ...                   ...\n",
       "    creation_date:        Created on 2024-06-18T20:44:48Z UTC\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    resolution_units:     m\n",
       "    projection:           epsg:27572\n",
       "    domain:               FR-France</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-e5513f8c-151c-46d9-9281-9d897cb24823' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5513f8c-151c-46d9-9281-9d897cb24823' 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>: 205</li><li><span class='xr-has-index'>zone</span>: 927</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0fa505f4-9aa6-4ca9-b49e-13c318d15341' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0fa505f4-9aa6-4ca9-b49e-13c318d15341' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1995-07-31 ... 2012-07-01</div><input id='attrs-5f551af5-f25a-4cab-bb86-54cd9cf05131' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f551af5-f25a-4cab-bb86-54cd9cf05131' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0234204d-afda-4f9b-becc-15a5eae4ba7d' class='xr-var-data-in' type='checkbox'><label for='data-0234204d-afda-4f9b-becc-15a5eae4ba7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1995-07-31T00:00:00.000000000&#x27;, &#x27;1995-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-09-01T00:00:00.000000000&#x27;, ..., &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zone</span></div><div class='xr-var-dims'>(zone)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>255 256 257 258 ... 2724 2725 2726</div><input id='attrs-d14da7e6-64df-468e-82c9-6e2b156a1586' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d14da7e6-64df-468e-82c9-6e2b156a1586' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e52f7d5-cf61-4d28-ad0e-18dbb5a1874f' class='xr-var-data-in' type='checkbox'><label for='data-7e52f7d5-cf61-4d28-ad0e-18dbb5a1874f' 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([ 255,  256,  257, ..., 2724, 2725, 2726])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9959410-87b6-44f1-86c3-8f9f0246a598' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9959410-87b6-44f1-86c3-8f9f0246a598' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>charge</span></div><div class='xr-var-dims'>(time, zone)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 100.6 101.1 ... 26.0 26.35</div><input id='attrs-4e1a8c22-2297-4297-96ee-28b44fc47bc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e1a8c22-2297-4297-96ee-28b44fc47bc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4337725-a658-40e9-b38a-1342f026a85d' class='xr-var-data-in' type='checkbox'><label for='data-b4337725-a658-40e9-b38a-1342f026a85d' 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>varname :</span></dt><dd>CHARGE</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>missing_value :</span></dt><dd>9999.0</dd><dt><span>standard_name :</span></dt><dd>water_table_level</dd><dt><span>long_name :</span></dt><dd>groundwater head</dd></dl></div><div class='xr-var-data'><pre>array([[100.0473 , 100.6405 , 101.1256 , ...,  26.97066,  26.     ,\n",
       "         26.29721],\n",
       "       [100.0473 , 100.6405 , 101.1256 , ...,  26.97065,  26.     ,\n",
       "         26.29721],\n",
       "       [100.0473 , 100.6405 , 101.1256 , ...,  26.97384,  26.     ,\n",
       "         26.30182],\n",
       "       ...,\n",
       "       [100.0473 , 100.6405 , 101.1256 , ...,  26.99997,  26.     ,\n",
       "         26.36214],\n",
       "       [100.0473 , 100.6405 , 101.1256 , ...,  26.99997,  26.     ,\n",
       "         26.35616],\n",
       "       [100.0473 , 100.6405 , 101.1256 , ...,  26.99997,  26.     ,\n",
       "         26.34742]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(zone)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>617.8 618.2 618.8 ... 607.2 607.8</div><input id='attrs-5b9ad2b7-cdd1-4c3d-9003-f6809eeeec60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b9ad2b7-cdd1-4c3d-9003-f6809eeeec60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cdd9537-635d-46cf-b814-7caa27ebc175' class='xr-var-data-in' type='checkbox'><label for='data-4cdd9537-635d-46cf-b814-7caa27ebc175' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([617.75, 618.25, 618.75, 619.25, 619.75, 620.25, 620.75, 617.75,\n",
       "       618.25, 618.75, 619.25, 619.75, 620.25, 617.25, 617.75, 618.25,\n",
       "       618.75, 619.25, 619.75, 620.25, 617.25, 617.75, 618.25, 618.75,\n",
       "       619.25, 619.75, 620.25, 620.75, 614.75, 615.75, 616.25, 616.75,\n",
       "       617.25, 617.75, 618.25, 618.75, 619.25, 619.75, 620.25, 620.75,\n",
       "       609.25, 609.75, 614.25, 614.75, 615.25, 615.75, 616.25, 616.75,\n",
       "       617.25, 617.75, 618.25, 618.75, 619.25, 619.75, 620.25, 620.75,\n",
       "       608.25, 608.75, 609.25, 609.75, 610.25, 610.75, 611.25, 613.75,\n",
       "       614.25, 614.75, 615.25, 615.75, 616.25, 616.75, 617.25, 617.75,\n",
       "       618.25, 618.75, 619.25, 619.75, 620.25, 607.25, 607.75, 608.25,\n",
       "       608.75, 609.25, 609.75, 610.25, 610.75, 611.25, 613.75, 614.25,\n",
       "       614.75, 615.25, 615.75, 616.25, 616.75, 617.25, 617.75, 618.25,\n",
       "       618.75, 619.25, 619.75, 607.75, 608.25, 608.75, 609.25, 609.75,\n",
       "       610.25, 610.75, 611.25, 613.75, 614.25, 614.75, 615.25, 615.75,\n",
       "       616.25, 616.75, 617.25, 617.75, 618.25, 618.75, 619.25, 606.75,\n",
       "       607.25, 607.75, 608.25, 608.75, 609.25, 609.75, 610.25, 610.75,\n",
       "       611.25, 611.75, 612.25, 612.75, 613.25, 613.75, 614.25, 614.75,\n",
       "       615.25, 615.75, 616.25, 616.75, 617.25, 617.75, 618.25, 618.75,\n",
       "       604.25, 604.75, 605.25, 606.25, 606.75, 607.25, 607.75, 608.25,\n",
       "       608.75, 609.25, 609.75, 610.25, 610.75, 611.25, 611.75, 612.25,\n",
       "...\n",
       "       604.25, 604.75, 605.25, 605.75, 606.25, 606.75, 607.25, 607.75,\n",
       "       608.25, 608.75, 609.25, 602.75, 603.25, 603.75, 604.25, 604.75,\n",
       "       605.25, 605.75, 606.25, 606.75, 607.25, 607.75, 608.25, 608.75,\n",
       "       603.25, 603.75, 604.25, 604.75, 605.25, 605.75, 606.25, 606.75,\n",
       "       607.25, 607.75, 608.25, 608.75, 603.75, 604.25, 604.75, 605.25,\n",
       "       605.75, 606.25, 606.75, 607.25, 607.75, 608.25, 608.75, 603.75,\n",
       "       604.25, 604.75, 605.25, 605.75, 606.25, 606.75, 607.25, 607.75,\n",
       "       608.25, 608.75, 609.25, 603.75, 604.25, 604.75, 605.25, 605.75,\n",
       "       606.25, 606.75, 607.25, 607.75, 608.25, 608.75, 609.25, 609.75,\n",
       "       610.25, 603.75, 604.25, 604.75, 605.25, 605.75, 606.25, 606.75,\n",
       "       607.25, 607.75, 608.25, 608.75, 609.25, 609.75, 610.25, 603.75,\n",
       "       604.25, 604.75, 605.25, 605.75, 606.25, 606.75, 607.25, 607.75,\n",
       "       608.25, 608.75, 609.25, 603.25, 603.75, 604.25, 604.75, 605.25,\n",
       "       605.75, 606.25, 606.75, 607.25, 607.75, 608.25, 608.75, 603.25,\n",
       "       603.75, 604.25, 604.75, 605.25, 605.75, 606.25, 606.75, 607.25,\n",
       "       607.75, 608.25, 603.75, 604.25, 604.75, 605.25, 605.75, 606.25,\n",
       "       606.75, 607.25, 607.75, 608.25, 605.25, 605.75, 606.25, 606.75,\n",
       "       607.25, 607.75, 608.25, 605.25, 605.75, 606.25, 606.75, 607.25,\n",
       "       607.75, 608.25, 605.75, 606.25, 606.75, 607.25, 607.75],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(zone)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.567e+03 2.567e+03 ... 2.543e+03</div><input id='attrs-10d6cd5b-f6a7-4667-a6c2-872d2359f2f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10d6cd5b-f6a7-4667-a6c2-872d2359f2f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d52b608a-5c55-48b2-8614-ef95976de982' class='xr-var-data-in' type='checkbox'><label for='data-d52b608a-5c55-48b2-8614-ef95976de982' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([2566.75, 2566.75, 2566.75, 2566.75, 2566.75, 2566.75, 2566.75,\n",
       "       2566.25, 2566.25, 2566.25, 2566.25, 2566.25, 2566.25, 2565.75,\n",
       "       2565.75, 2565.75, 2565.75, 2565.75, 2565.75, 2565.75, 2565.25,\n",
       "       2565.25, 2565.25, 2565.25, 2565.25, 2565.25, 2565.25, 2565.25,\n",
       "       2564.75, 2564.75, 2564.75, 2564.75, 2564.75, 2564.75, 2564.75,\n",
       "       2564.75, 2564.75, 2564.75, 2564.75, 2564.75, 2564.25, 2564.25,\n",
       "       2564.25, 2564.25, 2564.25, 2564.25, 2564.25, 2564.25, 2564.25,\n",
       "       2564.25, 2564.25, 2564.25, 2564.25, 2564.25, 2564.25, 2564.25,\n",
       "       2563.75, 2563.75, 2563.75, 2563.75, 2563.75, 2563.75, 2563.75,\n",
       "       2563.75, 2563.75, 2563.75, 2563.75, 2563.75, 2563.75, 2563.75,\n",
       "       2563.75, 2563.75, 2563.75, 2563.75, 2563.75, 2563.75, 2563.75,\n",
       "       2563.25, 2563.25, 2563.25, 2563.25, 2563.25, 2563.25, 2563.25,\n",
       "       2563.25, 2563.25, 2563.25, 2563.25, 2563.25, 2563.25, 2563.25,\n",
       "       2563.25, 2563.25, 2563.25, 2563.25, 2563.25, 2563.25, 2563.25,\n",
       "       2563.25, 2562.75, 2562.75, 2562.75, 2562.75, 2562.75, 2562.75,\n",
       "       2562.75, 2562.75, 2562.75, 2562.75, 2562.75, 2562.75, 2562.75,\n",
       "       2562.75, 2562.75, 2562.75, 2562.75, 2562.75, 2562.75, 2562.75,\n",
       "       2562.25, 2562.25, 2562.25, 2562.25, 2562.25, 2562.25, 2562.25,\n",
       "       2562.25, 2562.25, 2562.25, 2562.25, 2562.25, 2562.25, 2562.25,\n",
       "       2562.25, 2562.25, 2562.25, 2562.25, 2562.25, 2562.25, 2562.25,\n",
       "...\n",
       "       2549.25, 2549.25, 2549.25, 2549.25, 2549.25, 2549.25, 2549.25,\n",
       "       2549.25, 2549.25, 2548.75, 2548.75, 2548.75, 2548.75, 2548.75,\n",
       "       2548.75, 2548.75, 2548.75, 2548.75, 2548.75, 2548.75, 2548.75,\n",
       "       2548.25, 2548.25, 2548.25, 2548.25, 2548.25, 2548.25, 2548.25,\n",
       "       2548.25, 2548.25, 2548.25, 2548.25, 2547.75, 2547.75, 2547.75,\n",
       "       2547.75, 2547.75, 2547.75, 2547.75, 2547.75, 2547.75, 2547.75,\n",
       "       2547.75, 2547.75, 2547.25, 2547.25, 2547.25, 2547.25, 2547.25,\n",
       "       2547.25, 2547.25, 2547.25, 2547.25, 2547.25, 2547.25, 2547.25,\n",
       "       2547.25, 2547.25, 2546.75, 2546.75, 2546.75, 2546.75, 2546.75,\n",
       "       2546.75, 2546.75, 2546.75, 2546.75, 2546.75, 2546.75, 2546.75,\n",
       "       2546.75, 2546.75, 2546.25, 2546.25, 2546.25, 2546.25, 2546.25,\n",
       "       2546.25, 2546.25, 2546.25, 2546.25, 2546.25, 2546.25, 2546.25,\n",
       "       2545.75, 2545.75, 2545.75, 2545.75, 2545.75, 2545.75, 2545.75,\n",
       "       2545.75, 2545.75, 2545.75, 2545.75, 2545.75, 2545.25, 2545.25,\n",
       "       2545.25, 2545.25, 2545.25, 2545.25, 2545.25, 2545.25, 2545.25,\n",
       "       2545.25, 2545.25, 2544.75, 2544.75, 2544.75, 2544.75, 2544.75,\n",
       "       2544.75, 2544.75, 2544.75, 2544.75, 2544.75, 2544.25, 2544.25,\n",
       "       2544.25, 2544.25, 2544.25, 2544.25, 2544.25, 2543.75, 2543.75,\n",
       "       2543.75, 2543.75, 2543.75, 2543.75, 2543.75, 2543.25, 2543.25,\n",
       "       2543.25, 2543.25, 2543.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dx</span></div><div class='xr-var-dims'>(zone)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-3039230c-ad62-433f-af18-496037fc239d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3039230c-ad62-433f-af18-496037fc239d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68dc334b-1dc3-4a03-a49e-5bb1a0e24719' class='xr-var-data-in' type='checkbox'><label for='data-68dc334b-1dc3-4a03-a49e-5bb1a0e24719' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "...\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
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       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dy</span></div><div class='xr-var-dims'>(zone)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-5d1db322-2ded-470c-97a2-8430fa675784' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d1db322-2ded-470c-97a2-8430fa675784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f138aec-581b-4e02-ab84-4998ffeda978' class='xr-var-data-in' type='checkbox'><label for='data-0f138aec-581b-4e02-ab84-4998ffeda978' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
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       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
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       "...\n",
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       "       0.5, 0.5, 0.5, 0.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ce83313-ffbf-4dad-9afa-8c1611127a38' class='xr-section-summary-in' type='checkbox'  ><label for='section-6ce83313-ffbf-4dad-9afa-8c1611127a38' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-73f58f65-0cca-443f-b78d-2c08d83a02cb' class='xr-index-data-in' type='checkbox'/><label for='index-73f58f65-0cca-443f-b78d-2c08d83a02cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1995-07-31&#x27;, &#x27;1995-08-01&#x27;, &#x27;1995-09-01&#x27;, &#x27;1995-10-01&#x27;,\n",
       "               &#x27;1995-11-01&#x27;, &#x27;1995-12-01&#x27;, &#x27;1996-01-01&#x27;, &#x27;1996-02-01&#x27;,\n",
       "               &#x27;1996-03-01&#x27;, &#x27;1996-04-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2011-10-01&#x27;, &#x27;2011-11-01&#x27;, &#x27;2011-12-01&#x27;, &#x27;2012-01-01&#x27;,\n",
       "               &#x27;2012-02-01&#x27;, &#x27;2012-03-01&#x27;, &#x27;2012-04-01&#x27;, &#x27;2012-05-01&#x27;,\n",
       "               &#x27;2012-06-01&#x27;, &#x27;2012-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=205, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zone</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6311313f-03e7-44e3-b832-73d4ad5c2994' class='xr-index-data-in' type='checkbox'/><label for='index-6311313f-03e7-44e3-b832-73d4ad5c2994' 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([ 255,  256,  257,  258,  259,  260,  261,  308,  309,  310,\n",
       "       ...\n",
       "       2670, 2671, 2672, 2673, 2674, 2722, 2723, 2724, 2725, 2726],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;zone&#x27;, length=927))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8c6db491-8248-4e70-b439-1cf0272de441' class='xr-section-summary-in' type='checkbox'  ><label for='section-8c6db491-8248-4e70-b439-1cf0272de441' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>CF-1.10</dd><dt><span>title :</span></dt><dd>Modélisation du bassin de la SOMME Nappe_Libre</dd><dt><span>marthe_grid_version :</span></dt><dd>9.0</dd><dt><span>original_dimensions :</span></dt><dd>x,y,z [grids]: 53 54 1</dd><dt><span>lon_resolution :</span></dt><dd>0.5</dd><dt><span>lat_resolution :</span></dt><dd>0.5</dd><dt><span>scale_factor :</span></dt><dd>1</dd><dt><span>extend :</span></dt><dd>xymin : 596.75 2542.25; xymax: 622.75 2568.75</dd><dt><span>frequency :</span></dt><dd>30 day(s)</dd><dt><span>period :</span></dt><dd>1995-2012</dd><dt><span>creation_date :</span></dt><dd>Created on 2024-06-18T20:44:48Z UTC</dd><dt><span>institution :</span></dt><dd>BRGM, French Geological Survey, Orléans, France</dd><dt><span>comment :</span></dt><dd>Hydrogeological model created with MARTHE code (Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code for hydro-systems modelling. report BRGM/RP-69660-FR).</dd><dt><span>resolution_units :</span></dt><dd>m</dd><dt><span>projection :</span></dt><dd>epsg:27572</dd><dt><span>domain :</span></dt><dd>FR-France</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 784kB\n",
       "Dimensions:  (time: 205, zone: 927)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n",
       "  * zone     (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "Data variables:\n",
       "    charge   (time, zone) float32 760kB 100.0 100.6 101.1 ... 27.0 26.0 26.35\n",
       "    x        (zone) float32 4kB 617.8 618.2 618.8 619.2 ... 606.8 607.2 607.8\n",
       "    y        (zone) float32 4kB 2.567e+03 2.567e+03 ... 2.543e+03 2.543e+03\n",
       "    dx       (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
       "    dy       (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
       "Attributes: (12/16)\n",
       "    conventions:          CF-1.10\n",
       "    title:                Modélisation du bassin de la SOMME Nappe_Libre\n",
       "    marthe_grid_version:  9.0\n",
       "    original_dimensions:  x,y,z [grids]: 53 54 1\n",
       "    lon_resolution:       0.5\n",
       "    lat_resolution:       0.5\n",
       "    ...                   ...\n",
       "    creation_date:        Created on 2024-06-18T20:44:48Z UTC\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    resolution_units:     m\n",
       "    projection:           epsg:27572\n",
       "    domain:               FR-France"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# affichage des valeurs et attributs\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La grille est chargée dans un objet xarray 2D, de dimension (time, zone).\n",
    "La dimension spatiale est donc 1D, contenue dans zone. Chaque zone possède un couple de coordonnées xy, stocké en variable (et non en dimension).\n",
    "Ce format permet d'effecture de nombreuses opérations de manière plus efficaces qu'en 2 ou 3D + time.\n",
    "Il suffit d'assigner les coordonnées (transformation en 2 ou 3D + temps) avant les fonctionnalités graphiques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
       "Dimensions:  (y: 48, x: 44, time: 205)\n",
       "Coordinates:\n",
       "  * y        (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n",
       "  * x        (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n",
       "Data variables:\n",
       "    charge   (time, y, x) float32 2MB nan nan nan nan ... 102.7 103.4 103.5\n",
       "    dx       (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n",
       "    dy       (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n",
       "Attributes: (12/16)\n",
       "    conventions:          CF-1.10\n",
       "    title:                Modélisation du bassin de la SOMME Nappe_Libre\n",
       "    marthe_grid_version:  9.0\n",
       "    original_dimensions:  x,y,z [grids]: 53 54 1\n",
       "    lon_resolution:       0.5\n",
       "    lat_resolution:       0.5\n",
       "    ...                   ...\n",
       "    creation_date:        Created on 2024-06-18T20:44:48Z UTC\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    resolution_units:     m\n",
       "    projection:           epsg:27572\n",
       "    domain:               FR-France</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-d06b724a-462b-4309-972d-12c16be356e3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d06b724a-462b-4309-972d-12c16be356e3' 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'>y</span>: 48</li><li><span class='xr-has-index'>x</span>: 44</li><li><span class='xr-has-index'>time</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-61c4c022-7cf6-42cf-855a-de0ffb6b2ea7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61c4c022-7cf6-42cf-855a-de0ffb6b2ea7' 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'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.543e+03 2.544e+03 ... 2.567e+03</div><input id='attrs-fb2431e0-95bd-44d9-a0f9-7c5c5012ceda' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb2431e0-95bd-44d9-a0f9-7c5c5012ceda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a51df62f-269b-4cd0-886c-3d7c2356764a' class='xr-var-data-in' type='checkbox'><label for='data-a51df62f-269b-4cd0-886c-3d7c2356764a' 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([2543.2, 2543.8, 2544.2, 2544.8, 2545.2, 2545.8, 2546.2, 2546.8, 2547.2,\n",
       "       2547.8, 2548.2, 2548.8, 2549.2, 2549.8, 2550.2, 2550.8, 2551.2, 2551.8,\n",
       "       2552.2, 2552.8, 2553.2, 2553.8, 2554.2, 2554.8, 2555.2, 2555.8, 2556.2,\n",
       "       2556.8, 2557.2, 2557.8, 2558.2, 2558.8, 2559.2, 2559.8, 2560.2, 2560.8,\n",
       "       2561.2, 2561.8, 2562.2, 2562.8, 2563.2, 2563.8, 2564.2, 2564.8, 2565.2,\n",
       "       2565.8, 2566.2, 2566.8], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>599.2 599.8 600.2 ... 620.2 620.8</div><input id='attrs-b570afeb-052c-4756-bef7-19f8051a1907' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b570afeb-052c-4756-bef7-19f8051a1907' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-768da6dd-cc7a-4914-ab5c-12390718a51b' class='xr-var-data-in' type='checkbox'><label for='data-768da6dd-cc7a-4914-ab5c-12390718a51b' 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([599.2, 599.8, 600.2, 600.8, 601.2, 601.8, 602.2, 602.8, 603.2, 603.8,\n",
       "       604.2, 604.8, 605.2, 605.8, 606.2, 606.8, 607.2, 607.8, 608.2, 608.8,\n",
       "       609.2, 609.8, 610.2, 610.8, 611.2, 611.8, 612.2, 612.8, 613.2, 613.8,\n",
       "       614.2, 614.8, 615.2, 615.8, 616.2, 616.8, 617.2, 617.8, 618.2, 618.8,\n",
       "       619.2, 619.8, 620.2, 620.8], dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1995-07-31 ... 2012-07-01</div><input id='attrs-fdff7c9c-a190-4c37-bf4c-5187b9a1adc2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdff7c9c-a190-4c37-bf4c-5187b9a1adc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e5ec314-c2d4-4d8b-97c3-704a862a2cde' class='xr-var-data-in' type='checkbox'><label for='data-9e5ec314-c2d4-4d8b-97c3-704a862a2cde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1995-07-31T00:00:00.000000000&#x27;, &#x27;1995-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-09-01T00:00:00.000000000&#x27;, ..., &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-28b71928-6a47-4555-ab64-8946b7e3b8f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28b71928-6a47-4555-ab64-8946b7e3b8f3' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>charge</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 102.7 103.4 103.5</div><input id='attrs-de69af8c-d34a-4253-b7b6-007178eb2877' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de69af8c-d34a-4253-b7b6-007178eb2877' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a7d2705-910e-4132-8526-aef72de0a7de' class='xr-var-data-in' type='checkbox'><label for='data-8a7d2705-910e-4132-8526-aef72de0a7de' 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>varname :</span></dt><dd>CHARGE</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>missing_value :</span></dt><dd>9999.0</dd><dt><span>standard_name :</span></dt><dd>water_table_level</dd><dt><span>long_name :</span></dt><dd>groundwater head</dd></dl></div><div class='xr-var-data'><pre>array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,  98.134  ,  98.3761 ,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 100.3159 , 100.7368 ,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 102.6527 , 103.3882 ,\n",
       "         103.5225 ]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "...\n",
       "        [      nan,       nan,       nan, ...,  98.59094,  98.85855,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 100.5567 , 100.9858 ,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 102.6527 , 103.3882 ,\n",
       "         103.5225 ]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,  98.5106 ,  98.77482,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 100.5129 , 100.941  ,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 102.6527 , 103.3882 ,\n",
       "         103.5225 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dx</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.5 0.5 0.5 0.5</div><input id='attrs-06815d89-da50-4669-9583-14661701d992' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06815d89-da50-4669-9583-14661701d992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8003666d-f154-4fad-9bfb-46cf15504a39' class='xr-var-data-in' type='checkbox'><label for='data-8003666d-f154-4fad-9bfb-46cf15504a39' 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([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., 0.5, 0.5, nan],\n",
       "       [nan, nan, nan, ..., 0.5, 0.5, nan],\n",
       "       [nan, nan, nan, ..., 0.5, 0.5, 0.5]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dy</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.5 0.5 0.5 0.5</div><input id='attrs-9a1d7224-a8ec-466d-b715-f3cec39a772a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a1d7224-a8ec-466d-b715-f3cec39a772a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f22f7997-009e-40ce-908d-9e5c0ed87298' class='xr-var-data-in' type='checkbox'><label for='data-f22f7997-009e-40ce-908d-9e5c0ed87298' 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([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., 0.5, 0.5, nan],\n",
       "       [nan, nan, nan, ..., 0.5, 0.5, nan],\n",
       "       [nan, nan, nan, ..., 0.5, 0.5, 0.5]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2314d59a-965c-40c4-9f2b-cdc78d88a0bf' class='xr-section-summary-in' type='checkbox'  ><label for='section-2314d59a-965c-40c4-9f2b-cdc78d88a0bf' 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-17c8288a-cd4a-4dae-8603-386ac4a6eb83' class='xr-index-data-in' type='checkbox'/><label for='index-17c8288a-cd4a-4dae-8603-386ac4a6eb83' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1995-07-31&#x27;, &#x27;1995-08-01&#x27;, &#x27;1995-09-01&#x27;, &#x27;1995-10-01&#x27;,\n",
       "               &#x27;1995-11-01&#x27;, &#x27;1995-12-01&#x27;, &#x27;1996-01-01&#x27;, &#x27;1996-02-01&#x27;,\n",
       "               &#x27;1996-03-01&#x27;, &#x27;1996-04-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2011-10-01&#x27;, &#x27;2011-11-01&#x27;, &#x27;2011-12-01&#x27;, &#x27;2012-01-01&#x27;,\n",
       "               &#x27;2012-02-01&#x27;, &#x27;2012-03-01&#x27;, &#x27;2012-04-01&#x27;, &#x27;2012-05-01&#x27;,\n",
       "               &#x27;2012-06-01&#x27;, &#x27;2012-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=205, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ee34d820-5822-4712-a529-b51a100689ab' class='xr-index-data-in' type='checkbox'/><label for='index-ee34d820-5822-4712-a529-b51a100689ab' 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([2543.199951171875, 2543.800048828125, 2544.199951171875,\n",
       "       2544.800048828125, 2545.199951171875, 2545.800048828125,\n",
       "       2546.199951171875, 2546.800048828125, 2547.199951171875,\n",
       "       2547.800048828125, 2548.199951171875, 2548.800048828125,\n",
       "       2549.199951171875, 2549.800048828125, 2550.199951171875,\n",
       "       2550.800048828125, 2551.199951171875, 2551.800048828125,\n",
       "       2552.199951171875, 2552.800048828125, 2553.199951171875,\n",
       "       2553.800048828125, 2554.199951171875, 2554.800048828125,\n",
       "       2555.199951171875, 2555.800048828125, 2556.199951171875,\n",
       "       2556.800048828125, 2557.199951171875, 2557.800048828125,\n",
       "       2558.199951171875, 2558.800048828125, 2559.199951171875,\n",
       "       2559.800048828125, 2560.199951171875, 2560.800048828125,\n",
       "       2561.199951171875, 2561.800048828125, 2562.199951171875,\n",
       "       2562.800048828125, 2563.199951171875, 2563.800048828125,\n",
       "       2564.199951171875, 2564.800048828125, 2565.199951171875,\n",
       "       2565.800048828125, 2566.199951171875, 2566.800048828125],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1351db8e-e191-42a7-be1a-185373d01c56' class='xr-index-data-in' type='checkbox'/><label for='index-1351db8e-e191-42a7-be1a-185373d01c56' 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([599.2000122070312, 599.7999877929688, 600.2000122070312,\n",
       "       600.7999877929688, 601.2000122070312, 601.7999877929688,\n",
       "       602.2000122070312, 602.7999877929688, 603.2000122070312,\n",
       "       603.7999877929688, 604.2000122070312, 604.7999877929688,\n",
       "       605.2000122070312, 605.7999877929688, 606.2000122070312,\n",
       "       606.7999877929688, 607.2000122070312, 607.7999877929688,\n",
       "       608.2000122070312, 608.7999877929688, 609.2000122070312,\n",
       "       609.7999877929688, 610.2000122070312, 610.7999877929688,\n",
       "       611.2000122070312, 611.7999877929688, 612.2000122070312,\n",
       "       612.7999877929688, 613.2000122070312, 613.7999877929688,\n",
       "       614.2000122070312, 614.7999877929688, 615.2000122070312,\n",
       "       615.7999877929688, 616.2000122070312, 616.7999877929688,\n",
       "       617.2000122070312, 617.7999877929688, 618.2000122070312,\n",
       "       618.7999877929688, 619.2000122070312, 619.7999877929688,\n",
       "       620.2000122070312, 620.7999877929688],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39bda2dd-460b-4cd4-9e18-520432d49bd5' class='xr-section-summary-in' type='checkbox'  ><label for='section-39bda2dd-460b-4cd4-9e18-520432d49bd5' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>CF-1.10</dd><dt><span>title :</span></dt><dd>Modélisation du bassin de la SOMME Nappe_Libre</dd><dt><span>marthe_grid_version :</span></dt><dd>9.0</dd><dt><span>original_dimensions :</span></dt><dd>x,y,z [grids]: 53 54 1</dd><dt><span>lon_resolution :</span></dt><dd>0.5</dd><dt><span>lat_resolution :</span></dt><dd>0.5</dd><dt><span>scale_factor :</span></dt><dd>1</dd><dt><span>extend :</span></dt><dd>xymin : 596.75 2542.25; xymax: 622.75 2568.75</dd><dt><span>frequency :</span></dt><dd>30 day(s)</dd><dt><span>period :</span></dt><dd>1995-2012</dd><dt><span>creation_date :</span></dt><dd>Created on 2024-06-18T20:44:48Z UTC</dd><dt><span>institution :</span></dt><dd>BRGM, French Geological Survey, Orléans, France</dd><dt><span>comment :</span></dt><dd>Hydrogeological model created with MARTHE code (Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code for hydro-systems modelling. report BRGM/RP-69660-FR).</dd><dt><span>resolution_units :</span></dt><dd>m</dd><dt><span>projection :</span></dt><dd>epsg:27572</dd><dt><span>domain :</span></dt><dd>FR-France</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2MB\n",
       "Dimensions:  (y: 48, x: 44, time: 205)\n",
       "Coordinates:\n",
       "  * y        (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n",
       "  * x        (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n",
       "Data variables:\n",
       "    charge   (time, y, x) float32 2MB nan nan nan nan ... 102.7 103.4 103.5\n",
       "    dx       (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n",
       "    dy       (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n",
       "Attributes: (12/16)\n",
       "    conventions:          CF-1.10\n",
       "    title:                Modélisation du bassin de la SOMME Nappe_Libre\n",
       "    marthe_grid_version:  9.0\n",
       "    original_dimensions:  x,y,z [grids]: 53 54 1\n",
       "    lon_resolution:       0.5\n",
       "    lat_resolution:       0.5\n",
       "    ...                   ...\n",
       "    creation_date:        Created on 2024-06-18T20:44:48Z UTC\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    resolution_units:     m\n",
       "    projection:           epsg:27572\n",
       "    domain:               FR-France"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on ajoute les coordonées\n",
    "ds_3d = gm.assign_coords(ds)\n",
    "# a noter que les fonctions sont aussi accessibles par méthodes/attributs:\n",
    "# ds_3d = ds.mart.assign_coords() # equivalent\n",
    "ds_3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f7af53d79a0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# on plot à un instant t\n",
    "ds_3d.isel(time=-1)['charge'].plot.pcolormesh(x='x',y='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7af5802c20>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ou la chronique dans une maille\n",
    "ds.isel(zone=255)['charge'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour les gigognes, une fonction de plot spécifique est proposée (`gm.plot_nested_grids()`).\n",
    "\n",
    "Pour les modèles avec plusieurs couches, on peut récupérer les couches de surfaces~: `gm.get_min_layer(ds, [subset_layers=[3,5]])`\n",
    "\n",
    "ou même afficher ce résultat~: `gm.plot_outcrop()`\n",
    "\n",
    "\n",
    "Les fonctions natives de xarray, numpy permettent d'ores-et-déjà d'accéder à certaines fonctionnalités de Operasem (substitution de valeurs, transformation, opérations entre deux grilles, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fonctionnalités avancées\n",
    "\n",
    "Exemple de calcul du SPLI (package pyspli, https://gitlab.brgm.fr/brgm/si-eau/traitements-et-valorisations/py_spli)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import spli\n",
    "from multiprocessing import cpu_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_spli = spli.compute_spli(ds, n_jobs=cpu_count()) # monthly mean included, dummy ref_period: only for example\n",
    "#ds_spli = spli.compute_spli(ds, ref_period=[1995, 2010], n_jobs=cpu_count()) # monthly mean included, dummy ref_period: only for example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
       "Dimensions:  (zone: 927, time: 205)\n",
       "Coordinates:\n",
       "  * zone     (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n",
       "Data variables:\n",
       "    spli     (time, zone) float64 2MB 0.06557 0.06549 0.06554 ... 0.01162 0.6706\n",
       "Attributes:\n",
       "    varname:        CHARGE\n",
       "    units:          m\n",
       "    missing_value:  9999.0\n",
       "    standard_name:  water_table_level\n",
       "    long_name:      groundwater head</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-d2ade681-0195-4cdf-9d59-8dae1caeb77f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d2ade681-0195-4cdf-9d59-8dae1caeb77f' 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'>zone</span>: 927</li><li><span class='xr-has-index'>time</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5e5fb46d-f96c-4e31-98d6-f17b6d009417' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e5fb46d-f96c-4e31-98d6-f17b6d009417' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zone</span></div><div class='xr-var-dims'>(zone)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>255 256 257 258 ... 2724 2725 2726</div><input id='attrs-7ded64c3-e15b-4e73-be8c-78bdce7de6e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ded64c3-e15b-4e73-be8c-78bdce7de6e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90471223-94bd-4d70-9e38-38185a091307' class='xr-var-data-in' type='checkbox'><label for='data-90471223-94bd-4d70-9e38-38185a091307' 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([ 255,  256,  257, ..., 2724, 2725, 2726])</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1995-07-01 ... 2012-07-01</div><input id='attrs-84513e93-18d8-4c89-8103-ff3a3d208b3e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84513e93-18d8-4c89-8103-ff3a3d208b3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1f5c9d1-a244-4749-be65-38c4079a8de5' class='xr-var-data-in' type='checkbox'><label for='data-c1f5c9d1-a244-4749-be65-38c4079a8de5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1995-07-01T00:00:00.000000000&#x27;, &#x27;1995-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-09-01T00:00:00.000000000&#x27;, ..., &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a04cde5e-a7a4-462f-b93b-8466035b744d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a04cde5e-a7a4-462f-b93b-8466035b744d' 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>spli</span></div><div class='xr-var-dims'>(time, zone)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06557 0.06549 ... 0.01162 0.6706</div><input id='attrs-74b056ed-0c26-4263-90e6-4691ad2a53af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74b056ed-0c26-4263-90e6-4691ad2a53af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12bf42c3-11bd-4019-85e4-d5d4e684501b' class='xr-var-data-in' type='checkbox'><label for='data-12bf42c3-11bd-4019-85e4-d5d4e684501b' 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>varname :</span></dt><dd>CHARGE</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>missing_value :</span></dt><dd>9999.0</dd><dt><span>standard_name :</span></dt><dd>water_table_level</dd><dt><span>long_name :</span></dt><dd>groundwater head</dd></dl></div><div class='xr-var-data'><pre>array([[0.06557469, 0.0654901 , 0.06554286, ..., 0.43035806, 0.01161545,\n",
       "        0.23255949],\n",
       "       [0.06557469, 0.0654901 , 0.06554286, ..., 0.43455412, 0.01161545,\n",
       "        0.33211239],\n",
       "       [0.06557469, 0.0654901 , 0.06554286, ..., 0.47592143, 0.01161545,\n",
       "        0.44851163],\n",
       "       ...,\n",
       "       [0.06557469, 0.0654901 , 0.06554286, ..., 0.78400695, 0.01161545,\n",
       "        0.62140498],\n",
       "       [0.06557469, 0.0654901 , 0.06554286, ..., 0.78479654, 0.01161545,\n",
       "        0.63391474],\n",
       "       [0.06557469, 0.0654901 , 0.06554286, ..., 0.79772594, 0.01161545,\n",
       "        0.67064234]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6043eb94-cfef-46af-90fb-304e24da7bc0' class='xr-section-summary-in' type='checkbox'  ><label for='section-6043eb94-cfef-46af-90fb-304e24da7bc0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>zone</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d05281e5-7759-4f8f-a990-a0de914b9678' class='xr-index-data-in' type='checkbox'/><label for='index-d05281e5-7759-4f8f-a990-a0de914b9678' 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([ 255,  256,  257,  258,  259,  260,  261,  308,  309,  310,\n",
       "       ...\n",
       "       2670, 2671, 2672, 2673, 2674, 2722, 2723, 2724, 2725, 2726],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;zone&#x27;, length=927))</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-2fb2f711-832a-4378-a3a2-29a7ae881201' class='xr-index-data-in' type='checkbox'/><label for='index-2fb2f711-832a-4378-a3a2-29a7ae881201' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1995-07-01&#x27;, &#x27;1995-08-01&#x27;, &#x27;1995-09-01&#x27;, &#x27;1995-10-01&#x27;,\n",
       "               &#x27;1995-11-01&#x27;, &#x27;1995-12-01&#x27;, &#x27;1996-01-01&#x27;, &#x27;1996-02-01&#x27;,\n",
       "               &#x27;1996-03-01&#x27;, &#x27;1996-04-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2011-10-01&#x27;, &#x27;2011-11-01&#x27;, &#x27;2011-12-01&#x27;, &#x27;2012-01-01&#x27;,\n",
       "               &#x27;2012-02-01&#x27;, &#x27;2012-03-01&#x27;, &#x27;2012-04-01&#x27;, &#x27;2012-05-01&#x27;,\n",
       "               &#x27;2012-06-01&#x27;, &#x27;2012-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=205, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c76aad56-bf0a-4b45-a9ee-a0f9d122c2f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c76aad56-bf0a-4b45-a9ee-a0f9d122c2f9' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>varname :</span></dt><dd>CHARGE</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>missing_value :</span></dt><dd>9999.0</dd><dt><span>standard_name :</span></dt><dd>water_table_level</dd><dt><span>long_name :</span></dt><dd>groundwater head</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2MB\n",
       "Dimensions:  (zone: 927, time: 205)\n",
       "Coordinates:\n",
       "  * zone     (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n",
       "Data variables:\n",
       "    spli     (time, zone) float64 2MB 0.06557 0.06549 0.06554 ... 0.01162 0.6706\n",
       "Attributes:\n",
       "    varname:        CHARGE\n",
       "    units:          m\n",
       "    missing_value:  9999.0\n",
       "    standard_name:  water_table_level\n",
       "    long_name:      groundwater head"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_spli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "  padding-right: 10px;\n",
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       "}\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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
       "Dimensions:  (y: 48, x: 44, time: 205)\n",
       "Coordinates:\n",
       "  * y        (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n",
       "  * x        (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n",
       "Data variables:\n",
       "    spli     (time, y, x) float64 3MB nan nan nan nan ... 0.1627 0.519 0.6357\n",
       "Attributes:\n",
       "    varname:        CHARGE\n",
       "    units:          m\n",
       "    missing_value:  9999.0\n",
       "    standard_name:  water_table_level\n",
       "    long_name:      groundwater head</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-777e7919-e7b0-4924-abd8-2e89a5bc8fb8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-777e7919-e7b0-4924-abd8-2e89a5bc8fb8' 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'>y</span>: 48</li><li><span class='xr-has-index'>x</span>: 44</li><li><span class='xr-has-index'>time</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6590d376-c03d-43e1-ae81-015d614f2a17' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6590d376-c03d-43e1-ae81-015d614f2a17' 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'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.543e+03 2.544e+03 ... 2.567e+03</div><input id='attrs-b19a00be-b272-4f06-bc4d-46a725bb49f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b19a00be-b272-4f06-bc4d-46a725bb49f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ed58a77-66a1-4c48-86f6-7758c75ca512' class='xr-var-data-in' type='checkbox'><label for='data-6ed58a77-66a1-4c48-86f6-7758c75ca512' 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([2543.2, 2543.8, 2544.2, 2544.8, 2545.2, 2545.8, 2546.2, 2546.8, 2547.2,\n",
       "       2547.8, 2548.2, 2548.8, 2549.2, 2549.8, 2550.2, 2550.8, 2551.2, 2551.8,\n",
       "       2552.2, 2552.8, 2553.2, 2553.8, 2554.2, 2554.8, 2555.2, 2555.8, 2556.2,\n",
       "       2556.8, 2557.2, 2557.8, 2558.2, 2558.8, 2559.2, 2559.8, 2560.2, 2560.8,\n",
       "       2561.2, 2561.8, 2562.2, 2562.8, 2563.2, 2563.8, 2564.2, 2564.8, 2565.2,\n",
       "       2565.8, 2566.2, 2566.8], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>599.2 599.8 600.2 ... 620.2 620.8</div><input id='attrs-7adc983e-fbc6-46c3-a01e-f8bbe5910029' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7adc983e-fbc6-46c3-a01e-f8bbe5910029' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40f258b0-ea59-496c-8aee-79809d2ca142' class='xr-var-data-in' type='checkbox'><label for='data-40f258b0-ea59-496c-8aee-79809d2ca142' 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([599.2, 599.8, 600.2, 600.8, 601.2, 601.8, 602.2, 602.8, 603.2, 603.8,\n",
       "       604.2, 604.8, 605.2, 605.8, 606.2, 606.8, 607.2, 607.8, 608.2, 608.8,\n",
       "       609.2, 609.8, 610.2, 610.8, 611.2, 611.8, 612.2, 612.8, 613.2, 613.8,\n",
       "       614.2, 614.8, 615.2, 615.8, 616.2, 616.8, 617.2, 617.8, 618.2, 618.8,\n",
       "       619.2, 619.8, 620.2, 620.8], dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1995-07-01 ... 2012-07-01</div><input id='attrs-3ca94979-b746-4eb6-8ed7-ea3774fcb312' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ca94979-b746-4eb6-8ed7-ea3774fcb312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f4845ea-8c3f-412b-99b3-51fd39726835' class='xr-var-data-in' type='checkbox'><label for='data-4f4845ea-8c3f-412b-99b3-51fd39726835' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1995-07-01T00:00:00.000000000&#x27;, &#x27;1995-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-09-01T00:00:00.000000000&#x27;, ..., &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-306ae617-a511-48b0-8b2a-84bd9bcada0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-306ae617-a511-48b0-8b2a-84bd9bcada0b' 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>spli</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.1627 0.519 0.6357</div><input id='attrs-0f484591-bcf7-4308-b66e-a936cefaebd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f484591-bcf7-4308-b66e-a936cefaebd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e148374-ccfc-4caa-b222-93983e494f36' class='xr-var-data-in' type='checkbox'><label for='data-6e148374-ccfc-4caa-b222-93983e494f36' 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>varname :</span></dt><dd>CHARGE</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>missing_value :</span></dt><dd>9999.0</dd><dt><span>standard_name :</span></dt><dd>water_table_level</dd><dt><span>long_name :</span></dt><dd>groundwater head</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ..., -1.24301456,\n",
       "         -1.2682733 ,         nan],\n",
       "        [        nan,         nan,         nan, ..., -0.90344083,\n",
       "         -0.87273404,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.16269731,\n",
       "          0.51899633,  0.63566888]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,  0.25804849,\n",
       "          0.24961875,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.50485408,\n",
       "          0.57742817,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.16269731,\n",
       "          0.51899633,  0.63566888]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,  0.34080593,\n",
       "          0.34316097,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.73412719,\n",
       "          0.6049981 ,         nan],\n",
       "        [        nan,         nan,         nan, ...,  0.16269731,\n",
       "          0.51899633,  0.63566888]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1819aa51-0f83-4fe1-80b9-c9f9bf891c85' class='xr-section-summary-in' type='checkbox'  ><label for='section-1819aa51-0f83-4fe1-80b9-c9f9bf891c85' 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-b398a753-6984-45e1-b48a-cc8488093b4c' class='xr-index-data-in' type='checkbox'/><label for='index-b398a753-6984-45e1-b48a-cc8488093b4c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1995-07-01&#x27;, &#x27;1995-08-01&#x27;, &#x27;1995-09-01&#x27;, &#x27;1995-10-01&#x27;,\n",
       "               &#x27;1995-11-01&#x27;, &#x27;1995-12-01&#x27;, &#x27;1996-01-01&#x27;, &#x27;1996-02-01&#x27;,\n",
       "               &#x27;1996-03-01&#x27;, &#x27;1996-04-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2011-10-01&#x27;, &#x27;2011-11-01&#x27;, &#x27;2011-12-01&#x27;, &#x27;2012-01-01&#x27;,\n",
       "               &#x27;2012-02-01&#x27;, &#x27;2012-03-01&#x27;, &#x27;2012-04-01&#x27;, &#x27;2012-05-01&#x27;,\n",
       "               &#x27;2012-06-01&#x27;, &#x27;2012-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=205, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9194289-5cc5-4a28-8b74-984477305aff' class='xr-index-data-in' type='checkbox'/><label for='index-e9194289-5cc5-4a28-8b74-984477305aff' 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([2543.199951171875, 2543.800048828125, 2544.199951171875,\n",
       "       2544.800048828125, 2545.199951171875, 2545.800048828125,\n",
       "       2546.199951171875, 2546.800048828125, 2547.199951171875,\n",
       "       2547.800048828125, 2548.199951171875, 2548.800048828125,\n",
       "       2549.199951171875, 2549.800048828125, 2550.199951171875,\n",
       "       2550.800048828125, 2551.199951171875, 2551.800048828125,\n",
       "       2552.199951171875, 2552.800048828125, 2553.199951171875,\n",
       "       2553.800048828125, 2554.199951171875, 2554.800048828125,\n",
       "       2555.199951171875, 2555.800048828125, 2556.199951171875,\n",
       "       2556.800048828125, 2557.199951171875, 2557.800048828125,\n",
       "       2558.199951171875, 2558.800048828125, 2559.199951171875,\n",
       "       2559.800048828125, 2560.199951171875, 2560.800048828125,\n",
       "       2561.199951171875, 2561.800048828125, 2562.199951171875,\n",
       "       2562.800048828125, 2563.199951171875, 2563.800048828125,\n",
       "       2564.199951171875, 2564.800048828125, 2565.199951171875,\n",
       "       2565.800048828125, 2566.199951171875, 2566.800048828125],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d673a48d-e753-487e-8658-5b4c2a2e7c1f' class='xr-index-data-in' type='checkbox'/><label for='index-d673a48d-e753-487e-8658-5b4c2a2e7c1f' 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([599.2000122070312, 599.7999877929688, 600.2000122070312,\n",
       "       600.7999877929688, 601.2000122070312, 601.7999877929688,\n",
       "       602.2000122070312, 602.7999877929688, 603.2000122070312,\n",
       "       603.7999877929688, 604.2000122070312, 604.7999877929688,\n",
       "       605.2000122070312, 605.7999877929688, 606.2000122070312,\n",
       "       606.7999877929688, 607.2000122070312, 607.7999877929688,\n",
       "       608.2000122070312, 608.7999877929688, 609.2000122070312,\n",
       "       609.7999877929688, 610.2000122070312, 610.7999877929688,\n",
       "       611.2000122070312, 611.7999877929688, 612.2000122070312,\n",
       "       612.7999877929688, 613.2000122070312, 613.7999877929688,\n",
       "       614.2000122070312, 614.7999877929688, 615.2000122070312,\n",
       "       615.7999877929688, 616.2000122070312, 616.7999877929688,\n",
       "       617.2000122070312, 617.7999877929688, 618.2000122070312,\n",
       "       618.7999877929688, 619.2000122070312, 619.7999877929688,\n",
       "       620.2000122070312, 620.7999877929688],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f37752fd-e4bd-49b7-a579-bc97bc822e9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f37752fd-e4bd-49b7-a579-bc97bc822e9d' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>varname :</span></dt><dd>CHARGE</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>missing_value :</span></dt><dd>9999.0</dd><dt><span>standard_name :</span></dt><dd>water_table_level</dd><dt><span>long_name :</span></dt><dd>groundwater head</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 3MB\n",
       "Dimensions:  (y: 48, x: 44, time: 205)\n",
       "Coordinates:\n",
       "  * y        (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n",
       "  * x        (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n",
       "  * time     (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n",
       "Data variables:\n",
       "    spli     (time, y, x) float64 3MB nan nan nan nan ... 0.1627 0.519 0.6357\n",
       "Attributes:\n",
       "    varname:        CHARGE\n",
       "    units:          m\n",
       "    missing_value:  9999.0\n",
       "    standard_name:  water_table_level\n",
       "    long_name:      groundwater head"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get coords back\n",
    "ds_spli['x'] = (\"zone\", ds['x'].data)\n",
    "ds_spli['y'] = (\"zone\", ds['y'].data)\n",
    "if \"z\" in ds_spli.data_vars.keys():\n",
    "    ds_spli['z'] = (\"zone\", ds['z'].data)\n",
    "\n",
    "# assign coords for plot\n",
    "ds_xy_spli = ds_spli.assign_coords(\n",
    "    x=('zone', np.around(ds_spli['x'].data, 1) ),\n",
    "    y=('zone', np.around(ds_spli['y'].data, 1) ),\n",
    ")\n",
    "ds_xy_spli = ds_xy_spli.set_index(zone=['y', 'x']).unstack('zone').sortby('y', 'x')\n",
    "ds_xy_spli\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7f7b284d8070>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAA6JxBUAAAAAAAAcEokrAAAAAAAAOCQSVwAAAAAAAHBIJK4AAAAAAADgkEhcAQAAAAAAwCH9PwnHb4fpczQYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1300x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot res\n",
    "da=ds_xy_spli['spli'].sel(time=['1997-10-01', '2001-05-01', '2005-02-01','2012-01-01'])\n",
    "da.plot.pcolormesh(\n",
    "    x=\"x\", y=\"y\",\n",
    "    extend='both',\n",
    "    levels=[-3, -1.2815, -0.8416, -0.253, 0.253, 0.8416, 1.2815, 3],\n",
    "    colors=['red', 'red', 'orange', 'yellow', 'lime', 'cyan', 'blue', 'navy', 'navy'], # double extrema for extended colors\n",
    "    col='time',\n",
    "    cbar_kwargs={\n",
    "        'label': 'SPLI', \n",
    "        # 'ticks_label': ['Très bas', 'Bas', 'Modérément\\nbas', 'Autour de la\\nmoyenne', 'Modérément\\nhaut', 'Haut', 'Très haut']\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}