`icclim.dcsc`#

Direction de la Climatologie et des Services Climatiques (DCSC) indices.

Meteo France’s Direction de la Climatologie et des Services Climatiques (DCSC) specialized indices.

The DCSC indices public API, via the icclim.dcsc package, is generated from the icclim.dcsc.registry.DcscIndexRegistry registry definitions. The parameters of the functions are specialized to each index but are all taken from icclim.main.index general function. In other words, the DCSC indices in icclim.dcsc module are specializations of icclim.main.index for DCSC indices.

Submodules#

icclim.dcsc.registry

Package Contents#

icclim’s API for dcsc indices.

This module has been auto-generated. To modify these, edit the extractor tool in tools/extract-icclim-funs.py. This module exposes each climate index as individual functions for convenience.

`tav`	Moyenne de la température moyenne.
`txav`	Moyenne de la température maximale.
`trav`	Moyenne de l'amplitude thermique.
`tx10`	Extrême froid de la température maximale journalière (10e centile de la température maximale).
`tx90`	Extrême chaud de la température maximale journalière (90e centile de la température maximale).
`tn10`	Extrême froid de la température minimale journalière (10e centile de la température minimale).
`tn90`	Extrême chaud de la température minimale journalière (90e centile de la température minimale).
`tnfd`	Nombre de jours de gel (température minimale <= 0°C).
`txfd`	Nombre de jours sans dégel (température maximale <= 0°C).
`sd`	Nombre de journées d'été (température maximale > 25°C).
`tx35`	Nombre de jours de forte chaleur (température maximale > 35°C).
`tr`	Nombre de nuits tropicales (température minimale > 20°C).
`txnd`	Nombre de jours anormalement chauds (température maximale supérieure de plus de 5°C à la normale).
`tnht`	Nombre de nuits anormalement chaudes (température minimale supérieure de plus de 5°C à la normale).
`tnnd`	Nombre de jours anormalement froids (température minimale inférieure de plus de 5°C à la normale).
`tncwd`	Nombre de jours d'une vague de froid (température min < de plus de 5°C à la normale pdt au moins 5j consécutifs).
`txhwd`	Nombre de jours d'une vague de chaleur (température max > de plus de 5°C à la normale pdt au moins 5j consécutifs).
`hdd`	Degrés-jours de chauffage (Cumul sur la période des écarts négatifs au seuil de < 17°C par la température qt moyenne).
`cdd`	Degrés-jours de climatisation(Cumul sur la période des dépassements du seuil de > 18°C par la température qt moyenne).
`pav`	Précipitations quotidiennes moyennes.
`pint`	Précipitation moyenne des jours pluvieux (RR > 1 mm).
`rr`	Cumul de précipitation.
`rr1mm`	Nombre de jours de pluie (précipitations >= 1 mm).
`pn20mm`	Nombre de jours de fortes précipitations (précipitations >= 20 mm).
`pxcdd`	Période de sécheresse (Max [Nbj consécutifs RR < 1 mm]).
`pxcwd`	Nombre maximum de jours pluvieux consécutifs (Max [Nbj consécutifs RR > 1 mm]).
`r99`	Nombre de jours de précipitations extrêmes.
`pfl90`	Fraction des précipitations journalières intenses.
`pq90`	Précipitation quotidienne intense (90e centile des précipitations).
`pq99`	Précipitation quotidienne extrême (99e centile des précipitations).
`ffav`	Écart de la vitesse du vent moyenne journalière (par rapport à une periode de référence).
`ff98`	Nombre de jours de vent fort (vent ≥ 98e centile de la période de référence).

icclim._generated._dcsc.cdd(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Degrés-jours de climatisation(Cumul sur la période des dépassements du seuil de > 18°C par la température qt moyenne).

CDD: Degrés-jours de climatisation(Cumul sur la période des dépassements du seuil de > 18°C par la température qt moyenne). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.ff98(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de jours de vent fort (vent ≥ 98e centile de la période de référence).

FF98: Nombre de jours de vent fort (vent ≥ 98e centile de la période de référence). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.ffav(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Écart de la vitesse du vent moyenne journalière (par rapport à une periode de référence).

FFAV: Écart de la vitesse du vent moyenne journalière (par rapport à une periode de référence). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.hdd(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Degrés-jours de chauffage (Cumul sur la période des écarts négatifs au seuil de < 17°C par la température qt moyenne).

HDD: Degrés-jours de chauffage (Cumul sur la période des écarts négatifs au seuil de < 17°C par la température qt moyenne). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pav(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Précipitations quotidiennes moyennes.

PAV: Précipitations quotidiennes moyennes. Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pfl90(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Fraction des précipitations journalières intenses.

PFL90: Fraction des précipitations journalières intenses. Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pint(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Précipitation moyenne des jours pluvieux (RR > 1 mm).

PINT: Précipitation moyenne des jours pluvieux (RR > 1 mm). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pn20mm(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de jours de fortes précipitations (précipitations >= 20 mm).

PN20MM: Nombre de jours de fortes précipitations (précipitations >= 20 mm). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pq90(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Précipitation quotidienne intense (90e centile des précipitations).

PQ90: Précipitation quotidienne intense (90e centile des précipitations). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pq99(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Précipitation quotidienne extrême (99e centile des précipitations).

PQ99: Précipitation quotidienne extrême (99e centile des précipitations). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pxcdd(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Période de sécheresse (Max [Nbj consécutifs RR < 1 mm]).

PXCDD: Période de sécheresse (Max [Nbj consécutifs RR < 1 mm]). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.pxcwd(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre maximum de jours pluvieux consécutifs (Max [Nbj consécutifs RR > 1 mm]).

PXCWD: Nombre maximum de jours pluvieux consécutifs (Max [Nbj consécutifs RR > 1 mm]). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.r99(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de jours de précipitations extrêmes.

R99: Nombre de jours de précipitations extrêmes. Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.rr(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Cumul de précipitation.

RR: Cumul de précipitation. Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.rr1mm(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de jours de pluie (précipitations >= 1 mm).

RR1MM: Nombre de jours de pluie (précipitations >= 1 mm). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.sd(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de journées d’été (température maximale > 25°C).

SD: Nombre de journées d’été (température maximale > 25°C). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.tav(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Moyenne de la température moyenne.

TAV: Moyenne de la température moyenne. Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.tn10(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Extrême froid de la température minimale journalière (10e centile de la température minimale).

TN10: Extrême froid de la température minimale journalière (10e centile de la température minimale). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.tn90(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Extrême chaud de la température minimale journalière (90e centile de la température minimale).

TN90: Extrême chaud de la température minimale journalière (90e centile de la température minimale). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

Nombre de jours d’une vague de froid (température min < de plus de 5°C à la normale pdt au moins 5j consécutifs).

TNCWD: Nombre de jours d’une vague de froid (température min < de plus de 5°C à la normale pdt au moins 5j consécutifs). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)
normal (Union[str, Sequence[str], Dataset, DataArray, None]) – The normal to be compared to. Typically, the expected normal dataset should have one value per lat, lon couple. Can be a path or a list of paths to netCDF datasets or a xarray Dataset or DataArray.
normal_var_name (str | None, optional) – The name of the normal variable. If missing, icclim will try to guess which variable must be used in the normal dataset. Ignored if normal is a

Notes

This function has been auto-generated.

icclim._generated._dcsc.tnfd(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de jours de gel (température minimale <= 0°C).

TNFD: Nombre de jours de gel (température minimale <= 0°C). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

Nombre de nuits anormalement chaudes (température minimale supérieure de plus de 5°C à la normale).

TNHT: Nombre de nuits anormalement chaudes (température minimale supérieure de plus de 5°C à la normale). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)
normal (Union[str, Sequence[str], Dataset, DataArray, None]) – The normal to be compared to. Typically, the expected normal dataset should have one value per lat, lon couple. Can be a path or a list of paths to netCDF datasets or a xarray Dataset or DataArray.
normal_var_name (str | None, optional) – The name of the normal variable. If missing, icclim will try to guess which variable must be used in the normal dataset. Ignored if normal is a

Notes

This function has been auto-generated.

Nombre de jours anormalement froids (température minimale inférieure de plus de 5°C à la normale).

TNND: Nombre de jours anormalement froids (température minimale inférieure de plus de 5°C à la normale). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)
normal (Union[str, Sequence[str], Dataset, DataArray, None]) – The normal to be compared to. Typically, the expected normal dataset should have one value per lat, lon couple. Can be a path or a list of paths to netCDF datasets or a xarray Dataset or DataArray.
normal_var_name (str | None, optional) – The name of the normal variable. If missing, icclim will try to guess which variable must be used in the normal dataset. Ignored if normal is a

Notes

This function has been auto-generated.

icclim._generated._dcsc.tr(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de nuits tropicales (température minimale > 20°C).

TR: Nombre de nuits tropicales (température minimale > 20°C). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.trav(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Moyenne de l’amplitude thermique.

TRAV: Moyenne de l’amplitude thermique. Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.tx10(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Extrême froid de la température maximale journalière (10e centile de la température maximale).

TX10: Extrême froid de la température maximale journalière (10e centile de la température maximale). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.tx35(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de jours de forte chaleur (température maximale > 35°C).

TX35: Nombre de jours de forte chaleur (température maximale > 35°C). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.tx90(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Extrême chaud de la température maximale journalière (90e centile de la température maximale).

TX90: Extrême chaud de la température maximale journalière (90e centile de la température maximale). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.txav(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Moyenne de la température maximale.

TXAV: Moyenne de la température maximale. Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

icclim._generated._dcsc.txfd(in_files: InFileLike, var_name: str | Sequence[str] | None = None, slice_mode: FrequencyLike | Frequency = 'year', time_range: Sequence[dt.datetime | str] | None = None, out_file: str | None = None, ignore_Feb29th: bool = False, netcdf_version: str | NetcdfVersion = 'NETCDF4', logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) → Dataset[source]#

Nombre de jours sans dégel (température maximale <= 0°C).

TXFD: Nombre de jours sans dégel (température maximale <= 0°C). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

Notes

This function has been auto-generated.

Nombre de jours d’une vague de chaleur (température max > de plus de 5°C à la normale pdt au moins 5j consécutifs).

TXHWD: Nombre de jours d’une vague de chaleur (température max > de plus de 5°C à la normale pdt au moins 5j consécutifs). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)
normal (Union[str, Sequence[str], Dataset, DataArray, None]) – The normal to be compared to. Typically, the expected normal dataset should have one value per lat, lon couple. Can be a path or a list of paths to netCDF datasets or a xarray Dataset or DataArray.
normal_var_name (str | None, optional) – The name of the normal variable. If missing, icclim will try to guess which variable must be used in the normal dataset. Ignored if normal is a

Notes

This function has been auto-generated.

Nombre de jours anormalement chauds (température maximale supérieure de plus de 5°C à la normale).

TXND: Nombre de jours anormalement chauds (température maximale supérieure de plus de 5°C à la normale). Source: Portail DRIAS, DCSC, MeteoFrance.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.
logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)
normal (Union[str, Sequence[str], Dataset, DataArray, None]) – The normal to be compared to. Typically, the expected normal dataset should have one value per lat, lon couple. Can be a path or a list of paths to netCDF datasets or a xarray Dataset or DataArray.
normal_var_name (str | None, optional) – The name of the normal variable. If missing, icclim will try to guess which variable must be used in the normal dataset. Ignored if normal is a

Notes

This function has been auto-generated.

icclim.dcsc#

Submodules#

Package Contents#

`icclim.dcsc`#