API reference#

dataprocessing basics#

geobricks#

Created on Thu 16 Dec 2021

@author: Alexandre Kenshilik Coche @contact: alexandre.co@hotmail.fr

This module is a collection of tools for manipulating hydrological space-time data, especially netCDF data. It has been originally developped to provide preprocessing tools for CWatM (https://cwatm.iiasa.ac.at/) and HydroModPy (Alex-Gauvain/HydroModPy), but most functions have been designed to be of general use.

geobricks.cell_area(data, src_crs=None, engine='pysheds')[source]#
geobricks.compute_Erefs_from_Epan(input_file)[source]#
geobricks.compute_ldd(dem_path, dirmap='1-9', engine: str = 'pysheds', src_crs=None)[source]#

Convert a Digital Elevation Model (DEM) into a Local Drain Direction map (LDD).

Parameters#

dem_pathstr, pathlib.Path, xarray.Dataset or xarray.DataArray

Digital Elevation Model data. Supported file formats are .tif, .asc and .nc.

dirmaptuple or str, optional, default ‘1-9’

Direction codes convention.

  • '1-9' (or 'keypad', or 'pcraster'): from 1 to 9, upward, from bottom-left corner, no-flow is 5 [pcraster convention]

  • 'D8' (or 'ESRI'): from 1 to 128 (base-2), clockwise, from middle-right position, no-flow is 0 [esri convention]

  • 'D8-WBT' (or 'WhiteBoxTools'): from 1 to 128 (base-2), clockwise, from top-right corner, no-flow is 0 [WhiteBoxTools convention]

engine{‘pysheds’, ‘whiteboxtools’}, optional, default ‘pyshed’

'whiteboxtools' has been deactivated to avoid the need to install whiteboxtools.

Returns#

LDD raster, xarray.Dataset.

geobricks.compute_relative_humidity(*, dewpoint_input_file, temperature_input_file, pressure_input_file, method='Penman-Monteith')[source]#

cf formula on https://en.wikipedia.org/wiki/Dew_point

gc.compute_relative_humidity(

dewpoint_input_file = r”D:- Postdoc- Travaux- Veille- Donnees8- MeteoERA5Brittany1-2021 Dewpoint temperature.nc”, temperature_input_file = r”D:- Postdoc- Travaux- Veille- Donnees8- MeteoERA5Brittany1-2021 Temperature.nc”, pressure_input_file = r”D:- Postdoc- Travaux- Veille- Donnees8- MeteoERA5Brittany1-2021 Surface pressure.nc”, method = “Sonntag”)

geobricks.compute_scale_and_offset(min, max, n)[source]#

Computes scale and offset necessary to pack a float32 (or float64?) set of values into a int16 or int8 set of values.

Parameters#

minfloat

Minimum value from the data

maxfloat

Maximum value from the data

nint

Number of bits into which you wish to pack (8 or 16)

Returns#

scale_factorfloat

Parameter for netCDF’s encoding

add_offsetfloat

Parameter for netCDF’s encoding

geobricks.compute_wind_speed(u_wind_data, v_wind_data)[source]#

U-component of wind is parallel to the x-axis V-component of wind is parallel to the y-axis

geobricks.convert_coord(pointXin, pointYin, inputEPSG=2154, outputEPSG=4326)[source]#

Il y a un soucis dans cette fonction. X et Y se retrouvent inversées. Il vaut mieux passer par les fonctions rasterio (voir plus haut) :

coords_conv = rasterio.warp.transform(rasterio.crs.CRS.from_epsg(inputEPSG),

rasterio.crs.CRS.from_epsg(outputEPSG), [pointXin], [pointYin])

pointXout = coords_conv[0][0] pointYout = coords_conv[1][0]

geobricks.convert_downwards_radiation(input_file, is_dailysum=False)[source]#
geobricks.convert_unit(data, operation, *, var=None)[source]#
geobricks.date_to_index(_start_date, _date, _freq)[source]#
geobricks.dummy_input(base, value)[source]#

Creates a dummy space-time map with the same properties as the base, but with a dummy value.

Parameters#

baseTYPE

DESCRIPTION.

valueTYPE

DESCRIPTION.

Returns#

None.

geobricks.export(data, output_filepath, **kwargs)[source]#
geobricks.extract_watershed(*, ldd, outlet, dirmap='1-9', engine: str = 'pysheds', src_crs=None, drop=False)[source]#

Parameters#

lddTYPE

DESCRIPTION.

outletsTYPE

DESCRIPTION.

engineTYPE

DESCRIPTION.

dropbool, default False

If True, only coordinate labels where the mask is are kept (coordinate labels outside from the mask are dropped from the result).

Returns#

None.

geobricks.format_xy_resolution(*, resolution=None, bounds=None, shape=None)[source]#

Format x_res and y_res from a resolution value/tuple/list, or from bounds and shape.

Parameters#

resolutionnumber | iterable, optional

xy_res or (x_res, y_res). The default is None.

boundsiterable, optional

(x_min, y_min, x_max, y_max). The default is None.

shapeiterable, optional

(height, width). The default is None.

Returns#

x_res and y_res

geobricks.georef(data, *, data_type='other', include_crs=True, export_opt=False, crs=None, **time_kwargs)[source]#

Description#

Il est fréquent que les données de source externe présentent des défauts de formattage (SCR non inclus, coordonnées non standard, incompatibilité avec QGIS…). Cette fonction permet de générer un raster ou shapefile standardisé, en particulier du point de vue de ses métadonnées, facilitant ainsi les opérations de géotraitement mais aussi la visualisation sous QGIS.

Exemple#

import geoconvert as gc gc.georef(data = r”D:CWatMraw_resultstest1modflow_watertable_monthavg.nc”,

data_type = ‘CWatM’)

Parametres#

datastr or xr.Dataset (or xr.DataArray)

Chemin d’accès au fichier à modifier (le fichier original ne sera pas altéré, un nouveau fichier ‘(…)_QGIS.nc’

sera créé.)

data_typestr
Type de données :

‘modflow’ | ‘DRIAS-Climat 2020’ | ‘DRIAS-Eau 2021’ ‘SIM 2021’ | ‘DRIAS-Climat 2022’ ‘Climat 2022’ | ‘DRIAS-Eau 2024’ ‘SIM 2024’ | ‘CWatM’ | ‘autre’ ‘other’ (case insensitive)

include_crsbool, optional

DESCRIPTION. The default is True.

export_optbool, optional

DESCRIPTION. The default is True. Le NetCDF crée est directement enregistré dans le même dossier que le fichier d’origine, en rajoutant ‘georef’ à son nom.

crsint, optional

Destination CRS, only necessary when data_type == ‘other’ The default is None.

**time_kwargs
Arguments for use_standard_time function:

  • var : time variable name (str), optional, default None

  • infer_from : {‘dims’, ‘coords’, ‘all’}, optional, default ‘dims’

Returns#

xarray.Dataset or geopandas.GeoDataFrame.

geobricks.get_filelist(data, *, extension=None, tag='')[source]#

This function converts a folder (or a file) in a list of relevant files.

Parameters#

data: str or iterable

Folder, filepath or iterable of filepaths

extension: str

Extension.

Returns#

data_folderstr

Root of the files.

tiletypestr

Extension of files to retrieve.

geobricks.get_shape(x_res, y_res, bounds, x0=0, y0=0)[source]#
geobricks.gzip(data, complevel=3, shuffle=False)[source]#

Quick tool to apply lossless compression on a NetCDF file using gzip.

examples#

gc.gzip(filepath_comp99.8, complevel = 4, shuffle = True) gc.gzip(filepath_drias2022like, complevel = 5)

Parameters#

dataTYPE

DESCRIPTION.

Returns#

None.

geobricks.hourly_to_daily(data, *, mode='sum')[source]#
geobricks.hourly_to_daily_old(*, data, mode='mean', **kwargs)[source]#

Example#

import geoconvert as gc # full example: gc.hourly_to_daily(input_file = r”D:/2011-2021_hourly Temperature.nc”,

mode = ‘max’, output_path = r”D:/2011-2021_daily Temperature Max.nc”, fields = [‘t2m’, ‘tp’])

# input_file can also be a folder: gc.hourly_to_daily(input_file = r”D:- Postdoc- Travaux- Veille- Donnees8- MeteoERA5Brittany est”,

mode = ‘mean’)

Parameters#

input_filestr, or list of str

Can be a path to a file (or a list of paths), or a path to a folder, in which cas all the files in this folder will be processed.

modestr, or list of str, optional

= ‘mean’ (default) | ‘max’ | ‘min’ | ‘sum’

**kwargs#

fieldsstr or list of str, optional

e.g: [‘t2m’, ‘tp’, ‘u10’, ‘v10’, …] (if not specified, all fields are considered)

output_pathstr, optional

e.g: [r”D:/2011-2021_daily Temperature Max.nc”] (if not specified, output_name is made up according to arguments)

Returns#

None. Processed files are created in the output destination folder.

geobricks.index_to_date(_start_date, _time_index, _freq)[source]#
geobricks.load_any(data, *, rebuild_time_val=False, name=None, decode_coords='all', decode_times=True, **kwargs)[source]#

This function loads any common spatio-temporal file or variable, without the need to think about the file or variable type.

import geoconvert as gc data_ds = gc.load_any(r’D:data.nc’, decode_times = True, decode_coords = ‘all’)

Parameters#

dataTYPE

DESCRIPTION.

namestr, optional, default None

Name of main variable for tif or asc files.

**kwargs: keyword args

Argument passed to the xarray.open_dataset function call. May contain:

. decode_coords . decode_times . decode_cf > help(xr.open_dataset)

Returns#

data_dsTYPE

DESCRIPTION.

geobricks.main_space_dims(data_ds)[source]#
geobricks.main_time_dims(data_ds, all_coords=False, all_vars=False)[source]#

Parameters#

data_dsstr, pathlib.Path, xarray.Dataset, xarray.DataArray or geopandas.GeoDataFrame

Data to reproject. Supported file formats are .tif, .asc, .nc and vector formats supported by geopandas (.shp, .json, …) and pandas (*.csv…).

all_coordsbool, optional, defualt False

Only used if data_ds is a xarray variable. If False, only dimensions are considered as potential time coordinates. If True, even coordinates not associated to any dimension will be considered as well as potential time coordinates (along dims).

all_varsbool, optional, defualt False

Only used if data_ds is a xarray variable. If True, data variables (data_vars) will be considered as well as potential time coordinates (along dims).

Returns#

varstr or list

List of potential time coordinate names, the first one being the most relevant. If there is only one name, it is output directly as a string.

geobricks.main_var(data_ds)[source]#
geobricks.merge_data(data, *, extension=None, tag='', flatten=False)[source]#

This function merge all NetCDF inside a folder.

Parameters#

data: str or iterable

Folder, filepath or iterable of filepaths.

Returns#

Merged xarray.Dataset.

geobricks.nearest(x=None, y=None, x0=700012.5, y0=6600037.5, res=75)[source]#

Exemple#

import geoconvert as gc gc.nearest(x = 210054) gc.nearest(y = 6761020)

Parameters#

xfloat, optional

Valeur de la coordonnée x (ou longitude). The default is None.

yfloat, optional

Valeur de la coordonnée y (ou latitude). The default is None.

Returns#

Par défault, cette fonction retourne la plus proche valeur (de x ou de y) alignée sur la grille des cartes topo IGN de la BD ALTI. Il est possible de changer les valeurs de x0, y0 et res pour aligner sur d’autres grilles.

geobricks.pack(data, nbits=16)[source]#

examples#

Parameters#

dataTYPE

DESCRIPTION.

Returns#

None.

geobricks.pack_value(unpacked_value, scale_factor, add_offset)[source]#

Compute the packed value from the original value, a scale factor and an offset.

Parameters#

unpacked_valuenumeric

Original value.

scale_factornumeric

Scale factor, multiplied to the original value.

add_offsetnumeric

Offset added to the original value.

Returns#

numeric

Packed value.

geobricks.pick_dates_fields(*, input_file, output_format='NetCDF', **kwargs)[source]#

% DESCRIPTION: This function extracts the specified dates or fields from NetCDF files that contain multiple dates or fields, and exports it as a single file.

% EXAMPLE: import geoconvert as gc gc.pick_dates_fields(input_file = r”D:/path/test.nc”,

dates = [‘2020-10-15’, ‘2021-10-15’])

% OPTIONAL ARGUMENTS: > output_format = ‘NetCDF’ (default) | ‘GeoTIFF’ > kwargs:

> dates = [‘2021-10-15’, ‘2021-10-19’] > fields = [‘T2M’, ‘PRECIP’, …]

geobricks.reproject(data, *, src_crs=None, base_template=None, bounds=None, x0=None, y0=None, mask=None, rasterize=False, main_vars=None, rasterize_mode=['sum', 'dominant', 'and'], **rio_kwargs)[source]#

Reproject space-time data, and rasterize it if specified.

Parameters#

datastr, pathlib.Path, xarray.Dataset, xarray.DataArray or geopandas.GeoDataFrame

Data to reproject. Supported file formats are .tif, .asc, .nc and vector formats supported by geopandas (.shp, .json, …).

src_crsint or str or rasterio.crs.CRS, optional, default None

Coordinate reference system of the source (data). When passed as an integer, src_crs refers to the EPSG code. When passed as a string, src_crs can be OGC WKT string or Proj.4 string.

base_templatestr, pathlib.Path, xarra.Dataarray or geopandas.GeoDataFrame, optional, default None

Filepath, used as a template for spatial profile. Supported file formats are .tif, .nc and vector formats supported by geopandas (.shp, .json, …).

boundsiterable or None, optional, default None

Boundaries of the target domain as a tuple (x_min, y_min, x_max, y_max).

x0: number, optional, default None

Origin of the X-axis, used to align the reprojection grid.

y0: number, optional, default None

Origin of the Y-axis, used to align the reprojection grid.

maskstr, pathlib.Path, xarra.Dataarray or geopandas.GeoDataFrame, optional, default None

Filepath of mask used to clip the data.

rasterizebool, default False

Option to rasterize data (if data is a vector data).

main_varsiterable, default None

Data variables to rasterize. Only used if rasterize is True. If None, all variables in data are rasterized.

rasterize_modestr or list of str, or dict, default [‘sum’, ‘dominant’, ‘and’]

Defines the mode to rasterize data:

  • for numeric variables: 'mean' or 'sum' (default)

  • for categorical variables: 'percent' or 'dominant' (default)

    • 'dominant' rises the most frequent level for each cell

    • 'percent' creates a new variable per level, which stores

    the percentage (from 0 to 100) of occurence of this level compared to all levels, for each cell.

  • for boolean variables: 'or' or 'and' (default)

The modes can be specified for each variable by passing rasterize_mode as a dict: {'<var1>': 'mean', '<var2>': 'percent', ...}. This argument specification makes it possible to force a numeric variable to be rasterized as a categorical variable. Unspecified variables will be rasterized with the default mode.

**rio_kwargskeyword args, optional, defaults are None

Argument passed to the xarray.Dataset.rio.reproject() function call.

Note: These arguments are prioritary over base_template attributes.

May contain:

  • dst_crs : str

  • resolution : float or tuple

  • shape : tuple (int, int)

  • transform : Affine

  • nodata : float or None

  • resampling :

    • see help(rasterio.enums.Resampling)

    • most common are: 5 (average), 13 (sum), 0 (nearest), 9 (min), 8 (max), 1 (bilinear), 2 (cubic)…

    • the functionality 'std' (standard deviation) is also available

  • see help(xarray.Dataset.rio.reproject)

Returns#

Reprojected xarray.Dataset or geopandas.GeoDataFrame.

geobricks.river_pct(input_file, value)[source]#

Creates artificial modflow_river_percentage inputs (in *.nc) to use for drainage.

Parameters#

input_filestr

Original modflow_river_percentage.tif file to duplicate/modify

valuefloat

Value to impose on cells (from [0 to 1], not in percentage!) This value is added to original values as a fraction of the remaining “non-river” fraction:

For example, value = 0.3 (30%):

  • cells with 0 are filled with 0.3

  • cells with 1 remain the same

  • cells with 0.8 take the value 0.86, because 30% of what should

have been capillary rise become baseflow (0.8 + 0.3*(1-0.8)) - cells with 0.5 take the value 0.65 (0.5 + 0.3*(1-0.5))

Returns#

None.

geobricks.standard_fill_value(data_ds, *, var=None, attrs=None, encod=None)[source]#
geobricks.standard_grid_mapping(data, epsg=None)[source]#
QGIS needs a standard structure for grid_mapping information:

  • grid_mapping info should be in encodings and not in attrs

  • grid_mapping info should be stored in a coordinate names ‘spatial_ref’

  • …

In MeteoFrance data, these QGIS standards are not met. This function standardizes grid_mapping handling, so that it is compatible with QGIS.

Parameters#

dataTYPE

DESCRIPTION.

epsgTYPE

DESCRIPTION.

Returns#

data_dsTYPE

DESCRIPTION.

geobricks.switch_direction_map(input_file, input_mapping, output_mapping)[source]#
geobricks.time_series(*, input_file, epsg_coords=None, epsg_data=None, coords=None, mode='mean', dates=None, fields=None, cumul=False)[source]#

% DESCRIPTION: This function extracts the temporal data in one location given by coordinate.

% EXAMPLE: import geoconvert as gc era5 = gc.time_series(input_file = r”D:- Postdoc- Travaux- Veille- Donnees8- MeteoERA5Brittanydaily/2011-2021_Temperature_daily_mean.nc”,

coords = (-2.199337, 48.17824), epsg = 4326, fields = ‘t2m’)

cwatm_ds = gc.time_series(input_file = r”D:- Postdoc- Travaux_CWatM_EBR

esults aw_results_calib_groundwater_base_Ronansum_gwRecharge_daily.nc”,

coords = (3417964, 2858067), epsg = 3035)

% OPTIONAL ARGUMENTS:
> coords = coordinates of one point

/!Coordinates should be indicated in (X,Y) or (lon,lat) order (and not (lat,lon) !!!)

> coords can also indicate a mask:

coords = ‘all’ | filepath to a mask.tiff | filepath to a mask.shp

> epsg_coords = 4326 | 3035 | 2154 | 27572 | etc.

EPSG of the coords ! Useless if coords is a mask that includes a CRS

> epsg_data = same principle, for data without any included information about CRS > mode = ‘mean’ (standard) | ‘sum’ | ‘max’ | ‘min’

> dates = [‘2021-09’, ‘2021-12-01’ …] > fields = [‘T2M’, ‘PRECIP’, …]

geobricks.to_instant(data, derivative=False)[source]#
geobricks.transform_nc(*, input_file, x_shift=0, y_shift=0, x_size=1, y_size=1)[source]#
EXAMPLE:

import datatransform as dt dt.transform_nc(input_file = r”D:- Postdoc- Travaux_CWatM_EBRdatainput_1km_LeMeulandsurface opodemmin.nc”,

x_shift = 200, y_shift = 400)

geobricks.transform_tif(*, input_file, x_shift=0, y_shift=0, x_size=1, y_size=1)[source]#
EXAMPLE:

import datatransform as dt dt.transform_tif(input_file = r”D:- Postdoc- Travaux_CWatM_EBRdatainput_1km_LeMeureamapsmask_cwatm_LeMeu_1km.tif”,

x_shift = 200, y_shift = 300)

geobricks.tss_to_dataframe(*, input_file, skip_rows, start_date, cumul=False)[source]#

base = gc.tss_to_dataframe(input_file = r”D:- Postdoc- Travaux_CWatM_EBR

esults aw_results_prelim_cotech‚2-03-19_basedischarge_daily.tss”,

skip_rows = 4, start_date = ‘1991-08-01’)

precip = gc.tss_to_dataframe(input_file = r”D:- Postdoc- Travaux_CWatM_EBR

esults aw_results_artif‚2-03-25_basePrecipitation_daily.tss”,

skip_rows = 4, start_date = ‘2000-01-01’)

precip.val = precip.val*534000000/86400 # (le BV topographique du Meu fait 471 851 238 m2) precip[‘rolling_mean’] = precip[‘val’].rolling(10).mean()

input_filestr

Chemin d’accès au fichier d’entrée

skip_rowsint

Nombre de lignes à enlever en tête de fichier. /!ce nombre n’est ‘

start_datestr ou datetime

Date de la 1re valeur du fichier /!Si str, il faut qu’elle soit au format “%Y-%m-%d”

df : pandas.DataFrame

Récupérer la start_date à partir du fichier de settings indiqué au début du fichier *.tss., et regarder ensuite le SpinUp

geobricks.unpack_value(packed_value, scale_factor, add_offset)[source]#

Retrieve the original value from a packed value, a scale factor and an offset.

Parameters#

packed_valuenumeric

Value to unpack.

scale_factornumeric

Scale factor that was multiplied to the original value to retrieve.

add_offsetnumeric

Offset that was added to the original value to retrieve.

Returns#

numeric

Original unpacked value.

geobricks.unzip(data)[source]#

In some cases, especially for loading in QGIS, it is much quicker to load uncompressed netcdf than compressed netcdf. This function only applies to non-destructive compression.

Parameters#

dataTYPE

DESCRIPTION.

Returns#

None.

geobricks.use_standard_time(data_ds, *, var=None, infer_from='dims')[source]#

Use a standard time variable as the temporal coordinate. Standardize its names into ‘time’. If not the main time coordinate, swap it with the main time coordinate.

Parameters#

data_dsxarray.dataset

Dataset whose temporal coordinate should be renamed.

varstr, optional, default None

…

infer_from{‘dims’, ‘coords’, ‘all’}

Only used for xarray variables. To specify if the time coordinate should be infered from dimensions, coordinates or all variables (coordinates and data variables).

Returns#

data_dsxarray.dataset

Dataset with the modified name for the temporal coordinate.

geobricks.xr_to_pd(xr_data)[source]#

Format xr objects (such as those from gc.time_series) into pandas.DataFrames formatted as in gc.tss_to_dataframe.

Parameters#

xr_dataxarray.DataSet or xarray.DataArary

Initial data to convert into pandas.DataFrame NB: xr_data needs to have only one dimension.

Returns#

Pandas.DataFrame

download_fr#

Created on Tue Sep 17 16:58:10 2024

@author: Alexandre Kenshilik Coche @contact: alexandre.co@hotmail.fr

download.py est un ensemble de fonctions permettant le téléchargement automatique de données françaises nécessaires à la modélisation hydrologique des socio-écosystèmes. Cet outil a notamment vocation à regrouper les données classiques nécessaires au modèle CWatM utilisé dans le cadre de la méthodologie Eau et Territoire (https://eau-et-territoire.org ).

download_fr.bnpe(*, dst_folder, start_year=None, end_year=None, masks=None, departments=None, communes=None, file_formats='geojson')[source]#
download_fr.hydrometry(*, dst_folder, masks, start_year, end_year=None, quantities='QmnJ', file_formats='geojson')[source]#

Parameters#

  • : TYPE

    DESCRIPTION.

dst_folderTYPE

DESCRIPTION.

masks(list of) str or pathlib.Paths

DESCRIPTION.

start_yearTYPE

DESCRIPTION.

end_yearTYPE, optional

DESCRIPTION. The default is None.

quantities : (list of) str, optional, default ‘QmnJ’

  • ‘QmnJ’: average daily discharge

  • ‘QmM’: average monthly discharge

  • ‘HIXM’: maximum instant height per month

  • ‘HIXnJ’: maximum instant height per day

  • ‘QINM’: minimum instant discharge per month

  • ‘QINnJ’: minimum instant discharge per day

  • ‘QixM’: maximum instant discharge per month

  • ‘QIXnJ’: maximum instant discharge per day

file_formatsTYPE, optional

DESCRIPTION. The default is ‘geojson’.

Returns#

None.

download_fr.sim2(*, var_list, dst_folder: str, first_year: int = None, last_year: int = None, extension='.nc', mask: str = None)[source]#

Depuis le 13/12/2023 ces données sont en accès ouvert sur https://meteo.data.gouv.fr (onglet “Données climatologiques de référence pour le changement climatique”,

puis “Données changement climatique - SIM quotidienne” : https://meteo.data.gouv.fr/datasets/6569b27598256cc583c917a7 )

Parameters#

var_listiterable

List of variable names. Works with SIM2 variable names: ‘DRAINC_Q’ | ‘RUNC_Q’ | ‘EVAP_Q’ | ‘PRETOT_Q’ | ‘T_Q’ … The ‘_Q’ is optional (‘DRAINC’, ‘RUNC’… also work)

dst_folderstr or pathlib.Path

Path to the output folder containing the clipped SIM2 netCDF files. According to the files already present in this folder, only the necessary complementary files will be downloaded.

first_yearint, optional, default None

Data will be extracted from 1st January of first_year to 31st December of last_year. If None, the earliest date will be considered.

last_yearint, optional, default None

End date of data to be extracted. If None, the current date will be used instead.

extension{‘.nc’, ‘.csv’}, optional, default ‘.nc’

Output file type. When extension = '.nc', the mask can be applied to clip data. The mask will also be taken into account to determine which data are necessary to download.

maskstr or pathlib.Path, optional, default None

Shapefile path to indicate how to clip the netcdf files that are stored on the dst_folder folder. Note: The only purpose of using mask at this step is to save space on disk.

Returns#

Create or update the netcdf files.

download_fr.valid_request(response, file_format, prev_frame=None)[source]#
download_fr.valid_single_request(response, file_format)[source]#

data-specific workflows#

standardize_fr#

Created on Thu 16 Dec 2021

@author: Alexandre Kenshilik Coche @contact: alexandre.co@hotmail.fr

This module is a collection of tools for manipulating hydrological space-time data, especially netcdf data. It has been originally developped to provide preprocessing tools for CWatM (https://cwatm.iiasa.ac.at/) and HydroModPy (Alex-Gauvain/HydroModPy), but most functions have been designed to be of general use.

standardize_fr.bdalti(data)[source]#
standardize_fr.bnpe(data, extension='.json')[source]#
standardize_fr.c3s(data)[source]#
standardize_fr.cesoso(data)[source]#
standardize_fr.convert_from_h5_newsurfex(*, input_file, mesh_file, scenario='historic', output_format='NetCDF', **kwargs)[source]#

% DESCRIPTION: This function converts Ronan *.h5 files from SURFEX into NetCDF files, or GeoTIFF images, in order to make output files readable by QGIS.

% EXAMPLE: >> import geoconvert as gc >> gc.convert_from_h5_newsurfex(input_file = r”D:2- Postdoc2- Travaux1- Veille4- Donnees8- MeteoSurfexBZHREA.h5”,

mesh_file = r”D:2- Postdoc2- Travaux1- Veille4- Donnees8- MeteoSurfexBZHshapefilemaille_meteo_fr_pr93.shp”, output_format = “NetCDF”, fields = [“REC”, “TAS”])

% ARGUMENTS: >

% OPTIONAL ARGUMENTS: > output_format = ‘NetCDF’ (défault) | ‘GeoTIFF’ > scenario = ‘historic’ | ‘RCP2.6’ | ‘RCP4.5’ | ‘RCP8.5’ > kwargs:

> fields = variable(s) to conserve (among all attributes in input file)

= [‘ETP’, ‘PPT’, ‘REC’, ‘RUN’, ‘TAS’]

(One file per variable will be created.)

> dates = for instance : [‘2019-01-01’, ‘2019-01-02’, …] (If no date is specified, all dates from input file are considered)

standardize_fr.convert_from_h5_oldsurfex(*, output_format='csv', **kwargs)[source]#

% DESCRIPTION : Cette fonction formate les fichiers Surfex de Quentin en fichiers *.csv organisés par dates (lignes) et identifiants de mailles (colonnes).

% EXEMPLES : >> import surfexconvert as sc #(NB : il faut au préalable que le dossier soit ajouté dans le PYTHONPATH)) >> sc.convert_from_h5_oldsurfex(output_format = “csv”,

start_years = list(range(2005, 2011, 1)), variables = [‘DRAIN’, ‘ETR’])

>> sc.convert_from_oldsurfex(output_format = “nc”,

mesh_file = r”D:2- Postdoc2- Travaux1- Veille4- Donnees8- MeteoSurfexBZHshapefilemaille_meteo_fr_pr93.shp”)

% ARGUMENTS (OPTIONNELS) : > output_format = ‘csv’ (défault) | ‘NetCDF’ | ‘GeoTIFF’ > kwargs:

> input_folder = dossier contenant les fichiers à traiter.

Si rien n’est spécifié, le dossier du script est pris en compte

> variables = variable(s) à traiter (parmi DRAIN, ETR, PRCP et RUNOFF)

Si ce n’est pas spécifié, toutes les variables sont considérées

> start_years = années à traiter

(par ex : 2012 correspond au fichier blabla_2012_2013) Si rien n’est spécifié, toutes les années sont considérées

> mesh_file = chemin d’accès au fichier de correspondances entre les id des tuiles et leurs coordonnées

(nécessaire uniquement pour NetCDF et GeoTIFF)

standardize_fr.corine(data)[source]#
standardize_fr.correct_bias(input_file, correct_factor=1, to_dailysum=True, progressive=False)[source]#

Fonction plutôt à utiliser sur les données finales formatées correct_factor:

  • for precipitations (hourly) : 1.8

  • for precipitations (daily sum): 0.087

  • for radiations (daily sum): 0.0715 (and progressive = True)

  • for potential evapotranspiration pos (daily sum): 0.04

standardize_fr.cropcoeff(data)[source]#
standardize_fr.drias(data)[source]#
standardize_fr.era5(data)[source]#
standardize_fr.explore2climat(data)[source]#
standardize_fr.explore2eau(data)[source]#
standardize_fr.georef(data, *, data_type='other', include_crs=True, export_opt=False, crs=None, **time_kwargs)[source]#

Description#

Il est fréquent que les données de source externe présentent des défauts de formattage (SCR non inclus, coordonnées non standard, incompatibilité avec QGIS…). Cette fonction permet de générer un raster ou shapefile standardisé, en particulier du point de vue de ses métadonnées, facilitant ainsi les opérations de géotraitement mais aussi la visualisation sous QGIS.

Exemple#

import geoconvert as gc gc.georef(data = r”D:CWatMraw_resultstest1modflow_watertable_monthavg.nc”,

data_type = ‘CWatM’)

Parametres#

datastr or xr.Dataset (or xr.DataArray)

Chemin d’accès au fichier à modifier (le fichier original ne sera pas altéré, un nouveau fichier ‘(…)_QGIS.nc’

sera créé.)

data_typestr
Type de données :

‘modflow’ | ‘DRIAS-Climat 2020’ | ‘DRIAS-Eau 2021’ ‘SIM 2021’ | ‘DRIAS-Climat 2022’ ‘Climat 2022’ | ‘DRIAS-Eau 2024’ ‘SIM 2024’ | ‘CWatM’ | ‘autre’ ‘other’ (case insensitive)

include_crsbool, optional

DESCRIPTION. The default is True.

export_optbool, optional

DESCRIPTION. The default is True. Le NetCDF crée est directement enregistré dans le même dossier que le fichier d’origine, en rajoutant ‘georef’ à son nom.

crsint, optional

Destination CRS, only necessary when data_type == ‘other’ The default is None.

**time_kwargs
Arguments for use_standard_time function:

  • var : time variable name (str), optional, default None

  • infer_from : {‘dims’, ‘coords’, ‘all’}, optional, default ‘dims’

Returns#

xarray.Dataset or geopandas.GeoDataFrame.

standardize_fr.hydrometry(data, extension='.json')[source]#
standardize_fr.name_xml_attributes(*, output_file, fields)[source]#
standardize_fr.obb(data)[source]#
standardize_fr.process_rht(shp_file, attrs_file, fields='all')[source]#

Pour rajouter les attributs dans le shapefile, de anière à pouvoir l’afficher facilement dans QGIS.

Parameters#

shp_filestr

Chemin vers le fichier shapefile

attrs_filestr

Chemin vers la table externe d’attributs

fieldslist

Liste des noms de colonnes que l’on veut insérer dans le shapefile

Returns#

Create a new file.

standardize_fr.remove_double(data_folder, data_type)[source]#

The data downloaded from DRIAS website contains some data sets available for different periods (it is the same data, but one version goes from 1950 to 2005, and another from 1970 to 2005). For some models, there are no duplicate of that sort. These duplicates are unnecessary. This script moves these duplicates to a subfolder named “doublons”.

Example#

folder_list = [r”Eau-SWIAV_Saisonnier_EXPLORE2-2024_historical”,

r”Eau-SWIAV_Saisonnier_EXPLORE2-2024_rcp45”, r”Eau-SWIAV_Saisonnier_EXPLORE2-2024_rcp85”]

root_folder = r”D:- Postdoc- Travaux- Veille- Donnees8- MeteoDRIASDRIAS-EauEXPLORE2-SIM2 2024Pays BasqueIndicateurs”

for folder in folder_list:

base_folder = os.path.join(root_folder, folder) subfolder_list = [os.path.join(base_folder, f)

for f in os.listdir(base_folder) if os.path.isdir(os.path.join(base_folder, f))]

for subfolder in subfolder_list:

gc.remove_double(subfolder, data_type = “indicateurs drias eau 2024”)

standardize_fr.secondary_era5_climvar(data)[source]#
This function computes the secondary data from ERA5-Land data, such as:

  • crop and water standard ETP from pan evaporation

  • wind speed from U- and V-components

  • relative humidity from T°, P and dewpoint

Parameters#

datafilepath or xarray.dataset (or xarray.dataarray)

Main dataset used to generate secondary quantities.

Returns#

None. Generates intended files.

standardize_fr.shortname(data, data_type, ext='nc')[source]#

ext : str

standardize_fr.sim2(data)[source]#
standardize_fr.standardize_data(data, data_name, **kwargs)[source]#
standardize_fr.ucsbretagne(data)[source]#

model-specific workflows#

cwatm#

Created on Tue Sep 17 16:58:10 2024

@author: Alexandre Kenshilik Coche @contact: alexandre.co@hotmail.fr

PAGAIE_interface est une interface regroupant les fonctions de geohydroconvert pertinentes pour géotraiter les données géographiques dans le cadre de la méthodologie Eau et Territoire (https://eau-et-territoire.org ). A la difference de geohydroconvert, trajectoire_toolbox propose une sélection des fonctions les plus utiles de geohydroconvert, au sein d’une interface traduite en Français.

cwatm.bdalti(data, **kwargs)[source]#
cwatm.bnpe(data, **kwargs)[source]#
cwatm.dummy_dzRel(base, out_path)[source]#
cwatm.dummy_fractionLandcover(base, first_year=2020, last_year=2020)[source]#
cwatm.dummy_lakeres(base, out_path)[source]#
cwatm.dummy_routing(base, out_path)[source]#
cwatm.explore2climat(data, **kwargs)[source]#
cwatm.explore2eau(data, **kwargs)[source]#
cwatm.format_data(data, data_name, *, mask=None, bounds=None, resolution=None, x0=None, y0=None, base_template=None, **rio_kwargs)[source]#
cwatm.formating(func)[source]#
cwatm.ocspaysbasque(data, **kwargs)[source]#
cwatm.sim2(data, **kwargs)[source]#
cwatm.zones_basses(mnt_raster)[source]#

graphics#

ncplot#

Created on Mon Mar 21 13:16:05 2022

@author: Alexandre Kenshilik Coche @contact: alexandre.co@hotmail.fr

ncplot.plot_time_series(*, figweb=None, data, labels, title='title', linecolors=None, fillcolors=None, cumul=False, date_ini_cumul=None, reference=None, ref_norm=None, mean_norm=False, mean_center=False, legendgroup=None, legendgrouptitle_text=None, stack=False, col=None, row=None, lwidths=None, lstyles=None, yaxis='y1', fill=None, mode='lines', markers=None, showlegend=True, visible=True, bar_widths=None)[source]#

Description#

This function provides a wrapper to facilitate the use of plotly.graph_objects class. It facilitates the input of several arguments:

  • data can be passed in any format

  • colors can be passed in any format (np.arrays, lists of strings…) which makes it possible to use indifferently plotly or matplotlib colormaps functions.

  • colors can be passed universally as linecolors and fillcolors argument, no matter what graphical function is used then by plotly (for instance go.Bar normally needs colors to be passed in the marker dict whereas go.Scatter needs colors to be passed in the line dict as well as through an fillcolor argument)

It also offers additional treatments in an easy way:

  • plot cumulative values

  • normalized values

This function is particularly appropriate to plot time series.

Example#

import cwatplot as cwp [_, _, figweb] = cwp.plot_time_series(data = [dfA, dfV],

labels = [‘Altrui’, ‘Vergogn’])

Parameters#

figweb: plotly figure

Can plot on top of a previous plotly figure.

data: array of pandas.DataFrames

Data to plot.

labels: array of strings

Texts for legend.

title: string

Title used for the matplotlib figure (fig1, ax1). Not used for plotly figure (figweb).

linecolors: np.array

Colors are stored in [R, G, B, Alpha]. For instance: linecolors = [[1.0, 0.5, 0.4, 1],[…]].

cumul: bool

Option to plot cumulated curves (True).

date_ini_cumul: string

The string should indicate the date in the format ‘YYYY-MM-DD’. (for instance: date_ini_cumul = ‘2000-07-31’) Only used when cumul = True.

reference: pandas.DataFrame

Used for displaying metrics (NSE, NSElog, VOLerr), computed against the reference data provided here.

ref_norm: pandas.DataFrame (or xarray.DataSet, beta version…)

Used for plotting values normalized against the provided reference data.

mean_norm: bool

Option to normalize each curve against its mean (True).

mean_center: bool

Option to center each curve on its mean (True).

legendgroup: string

To group curves under the provided group identifier. One group at a time.

legendgrouptitle_text:

Text associated with the group identifier.

stack: bool

Option to plot stacked curves (True).

col: int

Column number for subplots.

row: int

Row number for subplots.

visible: {True, False, “legendonly”}, optional, default True

Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible).

mode: {“markers”, “lines”, “lines+markers”, “bar”}, optional, default “lines”

To select the representation mode.

markers : list of dict

Returns#

fig1: matplotlib figure

OBSOLETE: recent developments (normalized curves, stacked curves…) have not been implemented in this figure.

ax1: matplotlib axis

Related to the previous figure. OBSOLETE.

figweb: plotly figure

This figure version includes all options.

ncplot.precip_like_discharge(*, input_file)[source]#

% EXEMPLE : import cwatplot as cwp cwp.precip_like_discharge(input_file = input_file)

% ARGUMENTS > input_file = fichier des précipitations > freq = ‘daily’ | ‘monthly’

ncplot.tss_to_dataframe(*, input_file, skip_rows, start_date)[source]#

# Base base = cwp.tss_to_dataframe(input_file = r”D:- Postdoc- Travaux_CWatM_EBR

esults aw_results_prelim_cotech‚2-03-19_basedischarge_daily.tss”,

skip_rows = 4, start_date = ‘1991-08-01’)

# Virginie virg = cwp.tss_to_dataframe(input_file = r”D:- Postdoc- Travaux_CWatM_EBRdata%ARCHIVE fichiers Virginiesimulationssim0discharge_daily.tss”,

skip_rows = 0, start_date = ‘1991-01-01’)

# Nouvelle base base = cwp.tss_to_dataframe(input_file = r”D:- Postdoc- Travaux_CWatM_EBR

esults aw_results_artif‚2-03-25_basedischarge_daily.tss”,

skip_rows = 4, start_date = ‘2000-01-01’)

# Données data = cwp.tss_to_dataframe(input_file = r”D:- Postdoc- Travaux- Veille- Donnees- Stations et debitsDebitsHydroDataPyStations_Bretagnemeu_montfort.csv”,

skip_rows = 0, start_date = ‘1969-01-01’)

# Precip precip = cwp.tss_to_dataframe(input_file = r”D:- Postdoc- Travaux_CWatM_EBR

esults aw_results_artif‚2-03-25_basePrecipitation_daily.tss”,

skip_rows = 4, start_date = ‘2000-01-01’)

precip.val = precip.val*534000000/86400 precip[‘rolling_mean’] = precip[‘val’].rolling(10).mean()

  • : TYPE

    DESCRIPTION.

input_fileTYPE

DESCRIPTION.

skip_rowsTYPE

DESCRIPTION.

start_dateTYPE

DESCRIPTION.

dfTYPE

DESCRIPTION.

trajplot#

class trajplot.Figure(var: str, root_folder, scenario: str = 'RCP 8.5', epsg_data=None, coords='all', epsg_coords=None, rolling_days: int = 1, period_years: int = 10, annuality='calendar', plot_type: str = None, repres: None | str = 'area', cumul: bool = False, relative: bool = False, language: str = 'fr', color='scale', plotsize='wide', name: str = '', credit: None | str = 'auto', showlegend: bool = True, shadow: None | str = None, verbose: bool = False)[source]#

Bases: object

Main data available as Figure attributes#

Main variables

Description

self.model_names_list
List of model names.
For example in EXPLORE2 it corresponds to the identifier of the
climatic experiment: ‘Model1’, ‘Model2’, ‘Model3’…, ‘Model17’.

self.original_data
List of the pandas.Dataframes retrieved from the NetCDF data,
for each model (climatic experiments). NetCDF data are converted
into time series by considering the spatial average over the
coords argument (mask).

self.relative_ref
Equivalent to self.original_data, but contains the time
series used as reference (historic) to compute relative
values (if user-chosen).

self.rea_data
Equivalent to self.original_data, but contains the
reanalysis time series, which are added to the plots in
order to provide a historic reference.

self.all_res
List of pd.Dataframes for each period (according to
period_years argument), containing timeseries averaged
over a year (365 days) for each model (climatic experiments)
(one column per model). The year starts on the month defined by
annuality argument.

self.graph_res
Results formated for the plots.
Either in the form of a list of pd.Dataframes, one for each
period, each pd.Dataframe containing the aggregated result
(min, mean, sum…) from self.all_res [in case of
plot_type = 'temporality'].
Or in the form of a single pd.Dataframe containing the
aggregated values for each day [in case of plot_type is
a metric].

Examples#

from watertrajectories_pytools.src.graphics import trajplot as tjp

mask = r"D:- Postdoc- Travaux- Veille- Donnees- Territoire Annecy\masque_zone_etude.shp"

F = tjp.Figure(
    var = 'T',
    root_folder = r"E:\Inputs\Climat",
    scenario = 'rcp8.5',
    coords = mask,
    rolling_days = 30,
    period_years = 10,
    annuality = 3,
    name = 'Annecy',
    )

for m in mask_dict:
    for scenario in ['SIM2', 'rcp8.5']:
        figweb, _, _ = tjp.temporality(
            var = 'PRETOT', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], name = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 3, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)
        figweb, _, _ = tjp.temporality(
            var = 'DRAIN', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], name = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 10, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)
        figweb, _, _ = tjp.temporality(
            var = 'SWI', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], name = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 3, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)
        figweb, _, _ = tjp.temporality(
            var = 'T', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], name = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 10, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)

Parameters#

varstr

‘PRETOT’ | ‘PRENEI’ | ‘ETP’ | ‘EVAPC’ | ‘RUNOFFC’ | ‘DRAINC’ | ‘T’ | ‘SWI’ | …

scenariostr

‘SIM2’ | ‘historical’ | ‘RCP 4.5’ | ‘RCP 8.5’

root_folderstr, path
Path to the folder containing climatic data.

r”D:- Postdoc- Travaux- Veille- Donnees8- Meteo” (on my PC) r”E:InputsClimat” (on the external harddrive)

epsg_dataTYPE, optional

DESCRIPTION. The default is None.

coordsTYPE, optional

DESCRIPTION. The default is ‘all’.

epsg_coordsTYPE, optional

DESCRIPTION. The default is None.

languageTYPE, optional

DESCRIPTION. The default is ‘fr’.

plotsizeTYPE, optional

DESCRIPTION. The default is ‘wide’.

rolling_daysTYPE, optional

DESCRIPTION. The default is 1.

period_yearsTYPE, optional

DESCRIPTION. The default is 10.

cumulTYPE, optional

DESCRIPTION. The default is False.

plot_typeTYPE

DESCRIPTION.

repres{None, “area” or “bar”}, optional, default None

Only used when plot_type is a metric. Defines the type of graphical representation of the metric plot.

  • "area": curves representing rolling averages

  • "bar": rectangles representing period averages

nameint

Suffix to add in the filename. Especially usefull to indicate the name of the site.

creditstr, optional, default ‘auto’

To display the acknowledgement for data and conception. If 'auto', the standard info about data source and conception author will be displayed. To remove all mention of credits, pass credit=''.

color‘scale’, ‘discrete’, <colormap> (str) or list of colors, optional, default ‘scale’

Colors for the plots (for now, only for temporality plots)

relativebool

Whether the values should be computed as absolute or relatively to the reference period.

showlegendbool, optional, default True

Whether to display the legend or not.

shadow{None, ‘last’, ‘first’, ‘firstlast’, ‘lastfirst’, ‘all’}, optional, default = None

Whether to display dialy values in grey shadows, and for which period.

annualityint or str
Type of year

‘calendar’ | ‘meteorological’ | ‘meteo’ | ‘hydrological’ | ‘hydro’ 1 | 9 | 10

verbose: bool

Whether or not to display memory diagnotics.

agg_labels = {'decrease': ['decrease', 'diminution'], 'increase': ['increase', 'augmentation'], 'max': ['maximum', 'maximum'], 'mean': ['mean', 'moyenne'], 'min': ['minimum', 'minimum'], 'range': ['range', 'amplitude'], 'sum': ['sum', 'somme']}#
classmethod get_plot_mode(plot_type)[source]#
layout(*, plotsize=None, name=None, credit=None, color=None, language=None, showlegend=None, shadow=None, repres=None, cumul=None)[source]#
load()[source]#
Results#

This method also update the instance attributes all_res and graph_res, which respectively store the whole results and the final results used for the plot.

Warning#

This method can lead to memory size issues. It seems to appear when the garbage collector is not doing its job fast enough in the xarray variable data from C:ProgramDataMiniconda3envscwatenvLibsite-packagesxarraycodingvariables.py.

To solve this, you might just need to open this folder on Windows.

Examples (pipeline)#

import os mask_folder = r”D:2- Postdoc2- Travaux1- Veille4- Donnees15- Territoire Pays Basque” mask_dict = dict() mask_dict[‘cotier’] = os.path.join(mask_folder, r”Sage-cotier-basque.shp”) mask_dict[‘CAPB’] = os.path.join(mask_folder, r”zone_etude_fusion.shp”) mask_dict[‘Nive-Nivelle’] = os.path.join(mask_folder, r”Nive-Nivelle.shp”) for var in [‘PRETOT’, ‘DRAIN’, ‘SWI’, ‘T’]:

for scenario in [‘SIM2’, ‘rcp8.5’]:
for m in mask_dict:
F = tjp.Figure(

var = var, root_folder = r”E:InputsClimat”, scenario = scenario, coords = mask_dict[m], name = m, rolling_days = 30, period_years = 10, )

F.plot(plot_type = ‘annual_sum’) F.plot(plot_type = ‘annual_sum’, period_years = 30) F.plot(plot_type = ‘annual_sum’, period_years = 1) F.plot(plot_type = ‘annual_scatter’, agg_rule = ‘sum’) F.plot(plot_type = ‘15/11’, period_years = 10) F.plot(plot_type = ‘15/05’, period_years = 10) if var == ‘DRAIN’:

F.plot(plot_type = ‘> 1.5’)

classmethod metric(merge_res, startdate, plot_type)[source]#
metric_labels = {'annual': ['annual {}', '{} annuelle'], 'date': ['date of {}', 'date de {}']}#
metric_list = ['annual', 'date']#
plot(*, rolling_days=None, period_years=None, annuality=None, plot_type=None, plotsize=None, name=None, credit=None, color=None, language=None, showlegend=None, shadow=None, repres=None, cumul=None)[source]#
F = tjp.Figure(

var = ‘DRAINC’, root_folder = r”E:InputsClimat”, scenario = ‘rcp8.5’, coords = r”C:

ile.shp”,

rolling_days = 30, annuality = 10, name = ‘myCatchment’, )

F.plot(plot_type = ‘> 1.5’, period_years = 10) F.plot(plot_type = ‘annual_sum’) F.plot(plot_type = ‘annual_sum’, period_years = 30) F.plot(plot_type = ‘annual_scatter’, period_years = 1) F.plot(plot_type = ‘15/11’, period_years = 10)

plot_typestr

‘temporality’ | ‘annual_sum’

None.

static timeseries(*, data_type, var, scenario, season, domain='Pays-Basque', epsg_data=None, coords='all', epsg_coords=None, language='fr', plotsize='wide', rolling_window=1, plot_type='2series')[source]#
Examples#

import climatic_plot as tjp

tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024 SWIAV”,

scenario = ‘historical’, season = ‘JJA’, domain = ‘Pays Basque’, rolling_window = 10, plot_type = ‘2series’)

# —- Original and rolled series for SWIAV for season in [‘JJA’, ‘SON’, ‘DJF’, ‘MAM’]:

for scenario in [‘historical’, ‘rcp45’, ‘rcp85’]:
for domain in [‘Pays Basque’, ‘Annecy’]:
tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,

var = ‘SWIAV’, scenario = scenario, season = season, domain = domain, rolling_window = 10, plot_type = ‘2series’)

# —- Same for SSWI for season in [‘JJA’, ‘SON’, ‘DJF’, ‘MAM’]:

for scenario in [‘rcp45’, ‘rcp85’]:
for domain in [‘Pays Basque’, ‘Annecy’]:
tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,

var = ‘SSWI’, scenario = scenario, season = season, domain = domain, rolling_window = 10, plot_type = ‘2series’)

# —- All different colors with SIM2 tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,

var = ‘SWIAV’, scenario = ‘historical’, season = ‘JJA’, domain = ‘Pays Basque’, rolling_window = 10, plot_type = ‘all with sim2’)

# —- Narratifs for season in [‘JJA’, ‘SON’, ‘DJF’, ‘MAM’]:

for domain in [‘Pays Basque’, ‘Annecy’]:
tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,

var = ‘SWIAV’, scenario = ‘rcp85’, season = season, domain = domain, rolling_window = 10, plot_type = ‘narratifs’)

Parameters#
  • : TYPE

    DESCRIPTION.

root_folderTYPE

DESCRIPTION.

scenariostr

‘historical’ | ‘rcp45’ | ‘rcp85’

seasonstr

‘DJF’ | ‘MAM’ | ‘JJA’ | ‘SON’ | ‘NDJFMA’ | ‘MAMJJASO’ | ‘JJASO’ | ‘SONDJFM’

data_typeTYPE

DESCRIPTION.

epsg_dataTYPE, optional

DESCRIPTION. The default is None.

coordsTYPE, optional

DESCRIPTION. The default is ‘all’.

epsg_coordsTYPE, optional

DESCRIPTION. The default is None.

languageTYPE, optional

DESCRIPTION. The default is ‘fr’.

plotsizeTYPE, optional

DESCRIPTION. The default is ‘wide’.

rolling_windowTYPE, optional

DESCRIPTION. The default is 10.

plot_typeTYPE, optional

DESCRIPTION. The default is ‘2series’.

: TYPE

DESCRIPTION.

Returns#

None.

update(*, var=None, root_folder=None, scenario=None, epsg_data=None, coords=None, epsg_coords=None, relative=None, rolling_days=None, period_years=None, annuality=None, plot_type=None, repres=None, cumul=None, language=None, plotsize=None, color=None, name=None, credit=None, showlegend=None, shadow=None, verbose=None)[source]#
classmethod update_verbose(verbose: bool)[source]#
verbose: bool = False#

cmapgenerator#

Created on Thu Sep 5 15:55:24 2024

@author: Alexandre Kenshilik Coche

cmapgenerator.custom(n_steps, *args)[source]#
args:

color1, color2, color3… en format [Rouge Vert Bleu Alpha] (valeurs entre 0 et 1)

cmapgenerator.custom_2_colors(n_steps, first_color, last_color)[source]#
cmapgenerator.discrete(sequence_name='ibm', alpha=1, black=True, alternate=True, color_format='float')[source]#

Generate a standardized colorscale, based on predefined color maps.

Parameters#

sequence_name{“trio”, “duo”, “uno”, “ibm”, “wong”}, optional, default “ibm”

A flag to choose the colorscale among the available ones.

  • "trio": a 3x9-color scale (+ grays) based on 9 distinct hues.

    • Two other colorscales can be derived from this one:

    • "duo": only the dark and light variations of each hue are returned.

    • "uno": only the middle variation of each hue is returned.

  • "wong": a 9-color scale (+ black) extended from Wong, adapted for colorblindness.

  • "ibm": a 6-color scale (+ black) extended from IBM colorscale, adapted for colorblindness.

alphaNone or float, optional, default 1

Transparency (from 0 to 1). If None, colors are returned without the 4th value.

blackbool, optional, default True

If False, the black color (and related gray variations) are not included in the colorscale.

alternatebool, optional, default True

If True, the colorscale is not in rainbow order.

color_format{“float”, “rbg_str”, “rgba_tuple”}, optional, default “float”

The way to define colors:

  • "float": [0.22, 0.5, 0.99, 0.85]

  • "rgba_str": "rgba(56.1, 127.5, 252.45, 0.82)"

  • "rgba_tuple": (56.1, 127.5, 252.45, 0.82)

Returns#

Return a numpy.array where each row is a 1D-array [red, green, blue, alpha], with values between 0 and 1, or corresponding list with values converted to rgba tuples or strings.

cmapgenerator.to_rgba_str(color)[source]#

This function can convert a color variable of any format ('float', 'rgba_tuple', 'rgba_str') and any shape (one color or several colors) into a color variable in the 'rgba_str' format, for example:

'rgb(239.95, 227.97, 66.045)'

or:

['rgb(239.95, 227.97, 66.045)',
 'rgb(135.92, 33.915, 84.915)',
 'rgb(188.95, 133.11, 254.49)',
 'rgb(212.92, 94.095, 0.51)',
 'rgb(120.105, 195.08, 236.895)']

Parameters#

colorlist, numpy.array, tuple or str

Input color variable to convert.

Returns#

A color variable similar to the input, but in the 'rgba_str' format.

SIM2_tools#

Created on Fri Apr 5 18:08:57 2024

@author: Alexandre Kenshilik Coche @contact: alexandre.co@hotmail.fr

Based on the work of Ronan Abhervé and Loic Duffar (loicduffar)

SIM2_tools.clip(filepath, maskpath)[source]#
SIM2_tools.clip_folder(folder, maskpath)[source]#
SIM2_tools.compress(filepath)[source]#
SIM2_tools.compress_folder(folder)[source]#
SIM2_tools.folder_to_netcdf(folder)[source]#

Parameters#

folderstr

Folder containing the .csv files.

Returns#

None. Creates the .nc files in the folder ‘netcdf’

SIM2_tools.merge(filelist)[source]#
SIM2_tools.merge_folder(folder)[source]#
SIM2_tools.plot_map(var, *, file_folder=None, mode='sum', timemode='annual')[source]#

Generates interactive maps from SIM2 data (html).

Example#

import SIM2_tools as smt

smt.plot_map(‘PRETOT’, mode = “sum”, timemode = ‘annual’)

for timemode in [‘JJA’, ‘SON’, ‘DJF’, ‘MAM’]:

smt.plot_map(‘SWI’, mode = “mean”, timemode = timemode, file_folder = folder)

Parameters#

varstr
SIM2 variables:

‘ETP’ | ‘EVAP’ | ‘PRELIQ’ | ‘PRENEI’ | ‘PRETOT’ | ‘DRAINC’ | ‘RUNC’ | ‘T’ | ‘TINF_H’ | ‘TSUP_H’ | ‘WG_RACINE’ | ‘WGI_RACINE’ | ‘SWI’ …

modestr, optional

‘sum’ | ‘min’ | ‘max’ | ‘mean’ | ‘ratio’ | ‘ratio_precip’ | ‘mean_cumdiff’ | ‘sum_cumdiff’ | ‘min_cumdiff’ | ‘max_cumdiff’ ‘mean_cumdiff_ratio’ | ‘sum_cumdiff_ratio’ ‘mean_deficit’ | ‘sum_deficit’ The default is “sum”.

timemodestr, optional

‘annual’ | ‘ONDJFM’ | ‘AMJJAS’ | ‘DJF’ | ‘MAM’ | ‘JJA’ | ‘SON’. The default is ‘annual’.

Returns#

None. Creates the html maps.

SIM2_tools.to_netcdf(csv_file_path)[source]#
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