{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# [FR] Exemple d'utilisation de gridmarthe\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": 29, "metadata": {}, "outputs": [], "source": [ "# import du package\n", "import gridmarthe as gm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La librairie gridmarthe s'appuie sur `xarray`, qui permet de faciliter le traitement de grilles spatio-temporelles. La documentation complète de cette librairie est accessible : \n", "https://docs.xarray.dev/en/stable/index.html" ] }, { "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." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function load_marthe_grid in module gridmarthe.grid.io.gridmarthe:\n", "\n", "load_marthe_grid(filename: str, varname: Optional[str] = None, fpastp: Optional[str] = None, dates=None, drop_nan: bool = False, nan_value: Union[int, float, list, NoneType] = None, xyfactor: Union[int, float] = 1.0, shallow_only=False, add_col_row: bool = False, add_id_grid: bool = False, title: Optional[str] = None, var_attrs: dict = {}, epsg: int = 27572, full_3d: bool = False, drop_time: bool = False, model_attrs: dict = {'domain': 'FR-France', 'institution': 'BRGM, French Geological Survey, Orléans, France'}, engine: str = 'xarray', verbose: bool = False, **kwargs)\n", " Read Marthe Grid File as xarray.Dataset\n", " \n", " The gridfile is read as a sequence: the variable for all layer\n", " for main grid, then all layer for nested grids, is stored in\n", " a 1D vector for every timestep. A single spatial identifier\n", " ``zone`` is used to map spatial coordinates.\n", " \n", " Before plot operations, user can assign coordinates (set x,y\n", " as dimension coordinates and drop zone) to get 2-D arrays (or\n", " 3D arrays if multilayer) for every timesteps.\n", " \n", " Note\n", " ----\n", " \n", " A former known issue with some version of Marthe is that field name is\n", " not written in metadata, as number of nested grids or number of layers., which\n", " This can cause some bug when reading grids with gridmarthe.\n", " As of `gridmarthe` version 0.4, if no varname is scanned in file and/or number of layer/grids\n", " are missing, these informations are guessed when parsing data, which are stored in\n", " a variable named 'variable' and a warning is raised to alert user to rename the\n", " variable later. This is only valid for parameters grids (with only one timestep).\n", " In case of remaining errors, the command line tool `cleanmgrid`\n", " (provided with gridmarthe) can still be used to clean the file and add missing metadata.\n", " \n", " Parameters\n", " ----------\n", " filename: str\n", " A path to marthegrid file (.permh, .out, etc.)\n", " \n", " varname : str, optional\n", " Variable to access in martgrid file. See marthegrid (`filename`) file content.\n", " \n", " - If None is passed (default), function will scan all varnames in filename\n", " and keep first only.\n", " \n", " - If a varname is passed, e.g ``CHARGE`` for groundwater head, the returned\n", " dataset will contains only this variable.\n", " \n", " - If 'all' is passed, function will scan all varnames in filename and keep all.\n", " All datavars are added to dataset, using recursive call to func\n", " \n", " - If wrong variable name is passed, empty data will be returned.\n", " \n", " fpastp: str, optional\n", " A pastp file to read for dates\n", " \n", " dates: sequence, optional\n", " Can be a pd.date_range, pd.Series, pd.DatetimeIndex, np.array or list of\n", " datetime/np.datetime objects.\n", " If no dates (or no fpastp) is provided, a fake sequence of dates from\n", " 1850 to 1900 will be used for xarray object\n", " \n", " drop_nan: bool, optional\n", " Drop nan values (corresponding to nan_value) in xarray object to return.\n", " Default is False (keep nan values).\n", " \n", " nan_value: float or list of float, optional\n", " A code value for nan values. The default value is inferred from field name.\n", " E.g. of default nan values:\n", " \n", " - hydraulic conductivity: 0 or -9999. (Warning: a value of +9999. is not\n", " a NaN value for hydraulic conductivity. See Marthe User Guide for explanation\n", " about this code, refering here to impervious layer);\n", " \n", " - hydraulic head: 9999.;\n", " \n", " - groundwater flow: 0. (9999. is used as special value for this field);\n", " \n", " - any other: 9999.\n", " \n", " xyfactor: int or float, optional\n", " factor to transform X and Y values. e.g.: 1000 to convert km XY to meters.\n", " Default is 1.\n", " \n", " shallow_only: bool, optional\n", " Boolean to read only the first layer. Default is False.\n", " Warning: only valid for NON nested grids for now.\n", " \n", " add_col_row: bool, optional\n", " Add columns (col) and rows (row, formerly lig (v<=0.1.3)) index (from 1 to n).\n", " Default is False.\n", " \n", " add_id_grid: bool, optional\n", " Add grid id (from 0 to n), useful for nested grids.\n", " 0 is main grid, Default is False\n", " \n", " title: str , optional\n", " Title for grid attributes. Default is None (not used)\n", " \n", " var_attrs: dict, optional\n", " Dictionnary of attributes to add to variable DataArray.\n", " \n", " epsg: int, optional\n", " EPSG code for projection. Default is 27572 for legacy reasons (Lambert 2 Etendu,\n", " for France). Used to write CRS information in attributes. Useful for GUI\n", " (eg visualisation in QGIS).\n", " \n", " full_3d: bool, optional\n", " Is z dimension an aquifer layer or real Z axis (in meters for exemple)\n", " Default is False (z is aquifer layer number)\n", " \n", " drop_time: bool, optional\n", " Drop time dimension even if only one timestep is present.\n", " Default is False. If True and only one timestep, time dimension is removed.\n", " Useful for parameters grids.\n", " \n", " model_attrs: dict, optional\n", " Dictionnary of attributes to add to Dataset.\n", " by default, gis attrs are added and can be modified\n", " \n", " >>> {\n", " ... 'domain': 'FR-France',\n", " ... 'institution': 'BRGM, French Geological Survey, Orléans, France'\n", " ... }\n", " \n", " For example, if your data is associated with a reference (report, paper, etc.):\n", " \n", " >>> {\n", " ... 'references': 'https://doi.org/...'\n", " ... }\n", " \n", " engine: str, optional\n", " Engine to use for returned object. Default is 'xarray', which return\n", " xarray.Dataset object.\n", " Another option is 'numpy', which return a list of numpy arrays :\n", " [zvar, zdates, isteps, zxcol, zylig, zdxlu, zdylu, ztitle, dims]\n", " \n", " verbose: bool, optional\n", " Print some information about execution in stdout.\n", " Default is False.\n", " \n", " Returns\n", " -------\n", " ds: xr.Dataset\n", " A xarray.Dataset object containing values and attributes read from Marthe\n", " grid file.\n", "\n" ] } ], "source": [ "help(gm.load_marthe_grid)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# lecture d'une grille marthe et stockage dans un Dataset (librairie xarray)\n", "ds = gm.load_marthe_grid(\n", " './data/chasim_hallue.out', 'CHARGE',\n", " fpastp='./data/hallue.pastp',\n", " drop_nan=True # si la valeur des nans n'est pas 9999 (ex. permh, NaNs = 0), on peut spécifier la valeur\n", ")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 2MB\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) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "Data variables:\n",
       "    charge   (time, zone) float64 2MB 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",
       "    izone    (zone) int32 4kB 1 2 3 4 5 6 7 8 ... 921 922 923 924 925 926 927\n",
       "Attributes: (12/17)\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",
       "    crs:                  {'crs_wkt': 'PROJCRS["NTF (Paris) / Lambert zone II...\n",
       "    lon_resolution:       0.5\n",
       "    ...                   ...\n",
       "    period:               1995-2012\n",
       "    frequency:            30 day(s)\n",
       "    creation_date:        Created on 2026-04-30T11:34:59Z UTC\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    domain:               FR-France\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France
" ], "text/plain": [ " Size: 2MB\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) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n", "Data variables:\n", " charge (time, zone) float64 2MB 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", " izone (zone) int32 4kB 1 2 3 4 5 6 7 8 ... 921 922 923 924 925 926 927\n", "Attributes: (12/17)\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", " crs: {'crs_wkt': 'PROJCRS[\"NTF (Paris) / Lambert zone II...\n", " lon_resolution: 0.5\n", " ... ...\n", " period: 1995-2012\n", " frequency: 30 day(s)\n", " creation_date: Created on 2026-04-30T11:34:59Z UTC\n", " comment: Hydrogeological model created with MARTHE code (Thi...\n", " domain: FR-France\n", " institution: BRGM, French Geological Survey, Orléans, France" ] }, "execution_count": 32, "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": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 3MB\n",
       "Dimensions:  (y: 48, x: 44, time: 205)\n",
       "Coordinates:\n",
       "  * y        (y) float32 192B 2.567e+03 2.566e+03 ... 2.544e+03 2.543e+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) float64 3MB nan nan nan nan nan ... nan nan nan nan\n",
       "    dx       (y, x) float32 8kB nan nan nan nan nan nan ... nan nan nan nan nan\n",
       "    dy       (y, x) float32 8kB nan nan nan nan nan nan ... nan nan nan nan nan\n",
       "    izone    (y, x) float64 17kB nan nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes: (12/17)\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",
       "    crs:                  {'crs_wkt': 'PROJCRS["NTF (Paris) / Lambert zone II...\n",
       "    lon_resolution:       0.5\n",
       "    ...                   ...\n",
       "    period:               1995-2012\n",
       "    frequency:            30 day(s)\n",
       "    creation_date:        Created on 2026-04-30T11:34:59Z UTC\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    domain:               FR-France\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France
" ], "text/plain": [ " Size: 3MB\n", "Dimensions: (y: 48, x: 44, time: 205)\n", "Coordinates:\n", " * y (y) float32 192B 2.567e+03 2.566e+03 ... 2.544e+03 2.543e+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) float64 3MB nan nan nan nan nan ... nan nan nan nan\n", " dx (y, x) float32 8kB nan nan nan nan nan nan ... nan nan nan nan nan\n", " dy (y, x) float32 8kB nan nan nan nan nan nan ... nan nan nan nan nan\n", " izone (y, x) float64 17kB nan nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes: (12/17)\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", " crs: {'crs_wkt': 'PROJCRS[\"NTF (Paris) / Lambert zone II...\n", " lon_resolution: 0.5\n", " ... ...\n", " period: 1995-2012\n", " frequency: 30 day(s)\n", " creation_date: Created on 2026-04-30T11:34:59Z UTC\n", " comment: Hydrogeological model created with MARTHE code (Thi...\n", " domain: FR-France\n", " institution: BRGM, French Geological Survey, Orléans, France" ] }, "execution_count": 33, "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" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "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": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "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 différentes opérations entre grilles, avec `xarray` et `gridmarthe`" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'charge' (zone: 927)> Size: 7kB\n",
       "array([ 0.00000e+00,  0.00000e+00,  0.00000e+00,  0.00000e+00,\n",
       "        0.00000e+00,  0.00000e+00,  0.00000e+00,  0.00000e+00,\n",
       "       -3.25440e-01, -4.60980e-01, -5.27480e-01, -5.59100e-01,\n",
       "       -5.74500e-01,  0.00000e+00, -3.74970e-01, -6.81720e-01,\n",
       "       -8.68420e-01, -9.77570e-01, -1.04721e+00, -1.09548e+00,\n",
       "        0.00000e+00, -6.41230e-01, -1.00733e+00, -1.22240e+00,\n",
       "       -1.35717e+00, -1.45930e+00, -1.57370e+00, -1.80513e+00,\n",
       "        0.00000e+00,  0.00000e+00,  0.00000e+00,  0.00000e+00,\n",
       "       -5.66100e-01, -1.02068e+00, -1.33316e+00, -1.53087e+00,\n",
       "       -1.65762e+00, -1.74995e+00, -1.82523e+00, -1.90350e+00,\n",
       "        0.00000e+00,  0.00000e+00, -1.43024e+00, -1.00962e+00,\n",
       "       -1.14766e+00, -8.35060e-01, -7.87260e-01, -8.78120e-01,\n",
       "       -1.16742e+00, -1.45697e+00, -1.68467e+00, -1.83483e+00,\n",
       "       -1.92341e+00, -1.97372e+00, -1.99069e+00, -1.98500e+00,\n",
       "        0.00000e+00,  0.00000e+00, -3.44370e-01, -3.57610e-01,\n",
       "        0.00000e+00,  0.00000e+00,  0.00000e+00, -2.09688e+00,\n",
       "       -1.83277e+00, -1.61107e+00, -1.55613e+00, -1.44464e+00,\n",
       "       -1.43203e+00, -1.52037e+00, -1.68965e+00, -1.87151e+00,\n",
       "       -2.02142e+00, -2.11349e+00, -2.14897e+00, -2.14145e+00,\n",
       "       -2.09989e+00, -1.43919e+00, -1.36450e+00, -9.45770e-01,\n",
       "...\n",
       "       -6.78740e-01, -1.82235e+00, -1.73151e+00, -1.55717e+00,\n",
       "       -1.30703e+00, -9.77720e-01, -5.57570e-01, -4.35400e-02,\n",
       "       -1.44860e-01, -2.52990e-01, -3.26250e-01, -3.69900e-01,\n",
       "       -4.01230e-01, -5.87460e-01, -6.72990e-01, -1.76108e+00,\n",
       "       -1.67109e+00, -1.50200e+00, -1.26479e+00, -9.60430e-01,\n",
       "       -5.85770e-01, -1.32270e-01, -3.03400e-02, -1.52940e-01,\n",
       "       -1.90000e-01, -1.65750e-01,  0.00000e+00, -1.76839e+00,\n",
       "       -1.72637e+00, -1.62543e+00, -1.45758e+00, -1.23024e+00,\n",
       "       -9.55270e-01, -6.37120e-01, -3.04530e-01, -2.48100e-02,\n",
       "       -7.04900e-02,  0.00000e+00,  0.00000e+00, -1.75184e+00,\n",
       "       -1.68804e+00, -1.58243e+00, -1.41312e+00, -1.18093e+00,\n",
       "       -9.31090e-01, -6.64310e-01, -3.79320e-01, -4.50800e-02,\n",
       "       -1.84200e-02, -1.20000e-02, -1.64234e+00, -1.54945e+00,\n",
       "       -1.37045e+00, -1.08295e+00, -8.56250e-01, -6.43070e-01,\n",
       "       -4.37550e-01, -2.51870e-01, -1.76770e-01, -1.46430e-01,\n",
       "       -8.46730e-01, -6.94620e-01, -5.40770e-01, -4.04770e-01,\n",
       "       -2.89830e-01, -2.15860e-01, -1.52910e-01, -6.92440e-01,\n",
       "       -4.57850e-01, -3.42300e-01, -2.65000e-01, -2.01410e-01,\n",
       "       -1.59930e-01,  3.00000e-05,  0.00000e+00,  0.00000e+00,\n",
       "       -2.06900e-02,  0.00000e+00, -1.24820e-01])\n",
       "Coordinates:\n",
       "  * zone     (zone) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "Attributes:\n",
       "    varname:        CHARGE\n",
       "    units:          m\n",
       "    missing_value:  9999.0\n",
       "    standard_name:  water_table_level\n",
       "    long_name:      groundwater head
" ], "text/plain": [ " Size: 7kB\n", "array([ 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", " -3.25440e-01, -4.60980e-01, -5.27480e-01, -5.59100e-01,\n", " -5.74500e-01, 0.00000e+00, -3.74970e-01, -6.81720e-01,\n", " -8.68420e-01, -9.77570e-01, -1.04721e+00, -1.09548e+00,\n", " 0.00000e+00, -6.41230e-01, -1.00733e+00, -1.22240e+00,\n", " -1.35717e+00, -1.45930e+00, -1.57370e+00, -1.80513e+00,\n", " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", " -5.66100e-01, -1.02068e+00, -1.33316e+00, -1.53087e+00,\n", " -1.65762e+00, -1.74995e+00, -1.82523e+00, -1.90350e+00,\n", " 0.00000e+00, 0.00000e+00, -1.43024e+00, -1.00962e+00,\n", " -1.14766e+00, -8.35060e-01, -7.87260e-01, -8.78120e-01,\n", " -1.16742e+00, -1.45697e+00, -1.68467e+00, -1.83483e+00,\n", " -1.92341e+00, -1.97372e+00, -1.99069e+00, -1.98500e+00,\n", " 0.00000e+00, 0.00000e+00, -3.44370e-01, -3.57610e-01,\n", " 0.00000e+00, 0.00000e+00, 0.00000e+00, -2.09688e+00,\n", " -1.83277e+00, -1.61107e+00, -1.55613e+00, -1.44464e+00,\n", " -1.43203e+00, -1.52037e+00, -1.68965e+00, -1.87151e+00,\n", " -2.02142e+00, -2.11349e+00, -2.14897e+00, -2.14145e+00,\n", " -2.09989e+00, -1.43919e+00, -1.36450e+00, -9.45770e-01,\n", "...\n", " -6.78740e-01, -1.82235e+00, -1.73151e+00, -1.55717e+00,\n", " -1.30703e+00, -9.77720e-01, -5.57570e-01, -4.35400e-02,\n", " -1.44860e-01, -2.52990e-01, -3.26250e-01, -3.69900e-01,\n", " -4.01230e-01, -5.87460e-01, -6.72990e-01, -1.76108e+00,\n", " -1.67109e+00, -1.50200e+00, -1.26479e+00, -9.60430e-01,\n", " -5.85770e-01, -1.32270e-01, -3.03400e-02, -1.52940e-01,\n", " -1.90000e-01, -1.65750e-01, 0.00000e+00, -1.76839e+00,\n", " -1.72637e+00, -1.62543e+00, -1.45758e+00, -1.23024e+00,\n", " -9.55270e-01, -6.37120e-01, -3.04530e-01, -2.48100e-02,\n", " -7.04900e-02, 0.00000e+00, 0.00000e+00, -1.75184e+00,\n", " -1.68804e+00, -1.58243e+00, -1.41312e+00, -1.18093e+00,\n", " -9.31090e-01, -6.64310e-01, -3.79320e-01, -4.50800e-02,\n", " -1.84200e-02, -1.20000e-02, -1.64234e+00, -1.54945e+00,\n", " -1.37045e+00, -1.08295e+00, -8.56250e-01, -6.43070e-01,\n", " -4.37550e-01, -2.51870e-01, -1.76770e-01, -1.46430e-01,\n", " -8.46730e-01, -6.94620e-01, -5.40770e-01, -4.04770e-01,\n", " -2.89830e-01, -2.15860e-01, -1.52910e-01, -6.92440e-01,\n", " -4.57850e-01, -3.42300e-01, -2.65000e-01, -2.01410e-01,\n", " -1.59930e-01, 3.00000e-05, 0.00000e+00, 0.00000e+00,\n", " -2.06900e-02, 0.00000e+00, -1.24820e-01])\n", "Coordinates:\n", " * zone (zone) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\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": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute diff between grids\n", "ds0 = ds.sel(time='1995-11-01')\n", "ds1 = ds.sel(time='2001-02-01')\n", "\n", "ds0['charge'] - ds1['charge']" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 2MB\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) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "    quantile  float64 8B 0.05\n",
       "Data variables:\n",
       "    charge    (time, zone) float64 2MB 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",
       "    izone     (zone) int32 4kB 1 2 3 4 5 6 7 8 ... 921 922 923 924 925 926 927\n",
       "    q5        (zone) float64 7kB 100.0 100.6 101.1 101.6 ... 26.98 26.0 26.31\n",
       "    tresh     (time, zone) bool 190kB False False False ... False False False\n",
       "Attributes: (12/17)\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",
       "    crs:                  {'crs_wkt': 'PROJCRS["NTF (Paris) / Lambert zone II...\n",
       "    lon_resolution:       0.5\n",
       "    ...                   ...\n",
       "    period:               1995-2012\n",
       "    frequency:            30 day(s)\n",
       "    creation_date:        Created on 2026-04-30T11:34:59Z UTC\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    domain:               FR-France\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France
" ], "text/plain": [ " Size: 2MB\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) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n", " quantile float64 8B 0.05\n", "Data variables:\n", " charge (time, zone) float64 2MB 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", " izone (zone) int32 4kB 1 2 3 4 5 6 7 8 ... 921 922 923 924 925 926 927\n", " q5 (zone) float64 7kB 100.0 100.6 101.1 101.6 ... 26.98 26.0 26.31\n", " tresh (time, zone) bool 190kB False False False ... False False False\n", "Attributes: (12/17)\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", " crs: {'crs_wkt': 'PROJCRS[\"NTF (Paris) / Lambert zone II...\n", " lon_resolution: 0.5\n", " ... ...\n", " period: 1995-2012\n", " frequency: 30 day(s)\n", " creation_date: Created on 2026-04-30T11:34:59Z UTC\n", " comment: Hydrogeological model created with MARTHE code (Thi...\n", " domain: FR-France\n", " institution: BRGM, French Geological Survey, Orléans, France" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute quantile\n", "ds_5perc = ds.copy()\n", "ds_5perc['q5'] = ds_5perc['charge'].quantile(0.05, dim='time')\n", "ds_5perc['tresh'] = ds_5perc['charge'] < ds_5perc['q5']\n", "ds_5perc" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# compute anomaly\n", "anom = ds.groupby(\"time.month\") - ds.groupby(\"time.month\").median(dim=\"time\")\n", "\n", "# rename new variable\n", "ds_anom = ds.copy()\n", "ds_anom['anom'] = anom['charge']" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# compute seasonal mean\n", "# 1st: mean head over each season...\n", "ds_season = ds_anom.resample(time=\"QS-DEC\").median(dim='time') # or .agg({\"time\": 'mean'})" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 152kB\n",
       "Dimensions:  (season: 4, zone: 927)\n",
       "Coordinates:\n",
       "  * season   (season) object 32B 'DJF' 'JJA' 'MAM' 'SON'\n",
       "  * zone     (zone) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
       "Data variables:\n",
       "    charge   (season, zone) float64 30kB 100.0 100.6 101.1 ... 26.99 26.0 26.33\n",
       "    x        (season, zone) float32 15kB 617.8 618.2 618.8 ... 606.8 607.2 607.8\n",
       "    y        (season, zone) float32 15kB 2.567e+03 2.567e+03 ... 2.543e+03\n",
       "    dx       (season, zone) float32 15kB 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n",
       "    dy       (season, zone) float32 15kB 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n",
       "    izone    (season, zone) float64 30kB 1.0 2.0 3.0 4.0 ... 925.0 926.0 927.0\n",
       "    anom     (season, zone) float64 30kB 0.0 0.0 0.0 ... -0.002924 0.0 0.001059\n",
       "Attributes: (12/17)\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",
       "    crs:                  {'crs_wkt': 'PROJCRS["NTF (Paris) / Lambert zone II...\n",
       "    lon_resolution:       0.5\n",
       "    ...                   ...\n",
       "    period:               1995-2012\n",
       "    frequency:            30 day(s)\n",
       "    creation_date:        Created on 2026-04-30T11:34:59Z UTC\n",
       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
       "    domain:               FR-France\n",
       "    institution:          BRGM, French Geological Survey, Orléans, France
" ], "text/plain": [ " Size: 152kB\n", "Dimensions: (season: 4, zone: 927)\n", "Coordinates:\n", " * season (season) object 32B 'DJF' 'JJA' 'MAM' 'SON'\n", " * zone (zone) int32 4kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n", "Data variables:\n", " charge (season, zone) float64 30kB 100.0 100.6 101.1 ... 26.99 26.0 26.33\n", " x (season, zone) float32 15kB 617.8 618.2 618.8 ... 606.8 607.2 607.8\n", " y (season, zone) float32 15kB 2.567e+03 2.567e+03 ... 2.543e+03\n", " dx (season, zone) float32 15kB 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n", " dy (season, zone) float32 15kB 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n", " izone (season, zone) float64 30kB 1.0 2.0 3.0 4.0 ... 925.0 926.0 927.0\n", " anom (season, zone) float64 30kB 0.0 0.0 0.0 ... -0.002924 0.0 0.001059\n", "Attributes: (12/17)\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", " crs: {'crs_wkt': 'PROJCRS[\"NTF (Paris) / Lambert zone II...\n", " lon_resolution: 0.5\n", " ... ...\n", " period: 1995-2012\n", " frequency: 30 day(s)\n", " creation_date: Created on 2026-04-30T11:34:59Z UTC\n", " comment: Hydrogeological model created with MARTHE code (Thi...\n", " domain: FR-France\n", " institution: BRGM, French Geological Survey, Orléans, France" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ... then aggregate all seasons\n", "ds_season_agg = ds_season.groupby(\"time.season\").mean(dim=\"time\")\n", "ds_season_agg # mind the new dimension 'season'" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# set coords for plot\n", "ds_season_agg.update({\n", " v: ds_season_agg[v].isel(season=0) # remove season dim for coordinates, appears in computation\n", " for v in ['x', 'y', 'dx', 'dy']\n", "})\n", "ds_season_agg_2d = gm.assign_coords(ds_season_agg)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Seasonal plot\n", "import numpy as np\n", "from matplotlib import pyplot as plt, colors\n", "\n", "var = 'anom'\n", "minval, maxval = ds_season_agg_2d[var].min(skipna=True).values, ds_season_agg_2d[var].max(skipna=True).values\n", "fig, axes = plt.subplots(nrows=2, ncols=2)\n", "axes = axes.ravel()\n", "for i, season in enumerate((\"DJF\", \"MAM\", \"JJA\", \"SON\")):\n", " ds_season_agg_2d[var].sel(season=season).plot.pcolormesh(\n", " # x=\"x\", y=\"lat\",\n", " ax=axes[i],\n", " # cmap=\"Spectral\",\n", " # vmin=minval, vmax=maxval,\n", " norm=colors.CenteredNorm(halfrange=np.max(np.abs([minval, maxval]))),\n", " add_colorbar=True,\n", " extend=\"both\",\n", " )\n", "plt.tight_layout()" ] } ], "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": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 2 }