ECA&D indices#

icclim 5.2 comes with convenience functions to call each individual index from icclim namespace. These functions are autogenerated and are stored in _generated_api.py. Additionally, user custom indices can be called with the icclim.custom_index.

For example to use this new API with the index su you can do:

import glob
import icclim

summer_days = icclim.su(in_files=glob.glob("netcdf_files/tasmax*.nc"))

ECAD Generated Functions#

icclim’s API for ECAD indices and generic 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.

Functions

tg(in_files[, var_name, slice_mode, ...])

TG.

tn(in_files[, var_name, slice_mode, ...])

TN.

tx(in_files[, var_name, slice_mode, ...])

TX.

dtr(in_files[, var_name, slice_mode, ...])

DTR.

etr(in_files[, var_name, slice_mode, ...])

ETR.

vdtr(in_files[, var_name, slice_mode, ...])

vDTR.

su(in_files[, var_name, slice_mode, ...])

SU.

tr(in_files[, var_name, slice_mode, ...])

TR.

wsdi(in_files[, var_name, slice_mode, ...])

WSDI.

tg90p(in_files[, var_name, slice_mode, ...])

TG90p.

tn90p(in_files[, var_name, slice_mode, ...])

TN90p.

tx90p(in_files[, var_name, slice_mode, ...])

TX90p.

txx(in_files[, var_name, slice_mode, ...])

TXx.

tnx(in_files[, var_name, slice_mode, ...])

TNx.

csu(in_files[, var_name, slice_mode, ...])

CSU.

gd4(in_files[, var_name, slice_mode, ...])

GD4.

fd(in_files[, var_name, slice_mode, ...])

FD.

cfd(in_files[, var_name, slice_mode, ...])

CFD.

hd17(in_files[, var_name, slice_mode, ...])

HD17.

id(in_files[, var_name, slice_mode, ...])

ID.

tg10p(in_files[, var_name, slice_mode, ...])

TG10p.

tn10p(in_files[, var_name, slice_mode, ...])

TN10p.

tx10p(in_files[, var_name, slice_mode, ...])

TX10p.

txn(in_files[, var_name, slice_mode, ...])

TXn.

tnn(in_files[, var_name, slice_mode, ...])

TNn.

csdi(in_files[, var_name, slice_mode, ...])

CSDI.

cdd(in_files[, var_name, slice_mode, ...])

CDD.

prcptot(in_files[, var_name, slice_mode, ...])

PRCPTOT.

rr1(in_files[, var_name, slice_mode, ...])

RR1.

sdii(in_files[, var_name, slice_mode, ...])

SDII.

cwd(in_files[, var_name, slice_mode, ...])

CWD.

rr(in_files[, var_name, slice_mode, ...])

RR.

r10mm(in_files[, var_name, slice_mode, ...])

R10mm.

r20mm(in_files[, var_name, slice_mode, ...])

R20mm.

rx1day(in_files[, var_name, slice_mode, ...])

RX1day.

rx5day(in_files[, var_name, slice_mode, ...])

RX5day.

r75p(in_files[, var_name, slice_mode, ...])

R75p.

r75ptot(in_files[, var_name, slice_mode, ...])

R75pTOT.

r95p(in_files[, var_name, slice_mode, ...])

R95p.

r95ptot(in_files[, var_name, slice_mode, ...])

R95pTOT.

r99p(in_files[, var_name, slice_mode, ...])

R99p.

r99ptot(in_files[, var_name, slice_mode, ...])

R99pTOT.

sd(in_files[, var_name, slice_mode, ...])

SD.

sd1(in_files[, var_name, slice_mode, ...])

SD1.

sd5cm(in_files[, var_name, slice_mode, ...])

SD5cm.

sd50cm(in_files[, var_name, slice_mode, ...])

SD50cm.

cd(in_files[, var_name, slice_mode, ...])

cw(in_files[, var_name, slice_mode, ...])

CW.

wd(in_files[, var_name, slice_mode, ...])

WD.

ww(in_files[, var_name, slice_mode, ...])

WW.

fxx(in_files[, var_name, slice_mode, ...])

FXx.

fg6bft(in_files[, var_name, slice_mode, ...])

FG6Bft.

fgcalm(in_files[, var_name, slice_mode, ...])

FGcalm.

fg(in_files[, var_name, slice_mode, ...])

FG.

ddnorth(in_files[, var_name, slice_mode, ...])

DDnorth.

ddeast(in_files[, var_name, slice_mode, ...])

DDeast.

ddsouth(in_files[, var_name, slice_mode, ...])

DDsouth.

ddwest(in_files[, var_name, slice_mode, ...])

DDwest.

gsl(in_files[, var_name, slice_mode, ...])

GSL.

spi6(in_files[, var_name, slice_mode, ...])

SPI6.

spi3(in_files[, var_name, slice_mode, ...])

SPI3.

pp(in_files[, var_name, slice_mode, ...])

PP.

ss(in_files[, var_name, slice_mode, ...])

SS.

rh(in_files[, var_name, slice_mode, ...])

RH.

custom_index(user_index, in_files[, ...])

Compute custom indices using simple operators.

icclim._generated_api.average(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

average.

Average of values that met threshold(s), if threshold(s) are given (e.g. Tx, Tn)

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.cd(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]

Days with TG < 25th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (cold/dry days). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.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]

CDD.

Maximum consecutive dry days (Precip < 1mm). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.cfd(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]

CFD.

Maximum number of consecutive frost days (Tmin < 0 C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.count_occurrences(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

count_occurrences.

Count occurrences where threshold(s) are met (e.g. SU, Tx90p, RR1).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.csdi(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]

CSDI.

Cold-spell duration index (days). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.csu(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]

CSU.

Maximum number of consecutive summer days (Tmax >25 C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.custom_index(user_index: UserIndexDict, 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, doy_window_width: int = 5, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | QuantileInterpolation = 'median_unbiased', out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False, min_spell_length: int | None = 6, rolling_window_width: int | None = 5, sampling_method: SamplingMethodLike = 'resample') Dataset[source]

Compute custom indices using simple operators.

Use the user_index parameter to describe how the index should be computed. You can find some examples in icclim documentation at Create your own index with user_index

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 (SliceMode) – 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.

  • doy_window_width (int) – optional Window width used to aggreagte day of year values when computing day of year percentiles (doy_per) Default: 5 (5 days).

  • 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.

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

  • min_spell_length (int) – optional Minimum spell duration to be taken into account when computing the sum_of_spell_lengths.

  • rolling_window_width (int) – optional Window width of the rolling window for indicators such as {max_of_rolling_sum, max_of_rolling_average, min_of_rolling_sum, min_of_rolling_average}

  • sampling_method (str) – Choose whether the output sampling configured in slice_mode is a groupby operation or a resample operation (as per xarray definitions). Possible values: {"groupby", "resample", "groupby_ref_and_resample_study"} (default: “resample”) groupby_ref_and_resample_study may only be used when computing the difference_of_means (a.k.a the anomaly).

Notes

This function has been auto-generated.

icclim._generated_api.cw(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]

CW.

Days with TG < 25th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (cold/wet days). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.cwd(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]

CWD.

Maximum consecutive wet days (Precip >= 1mm). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.ddeast(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]

DDeast.

Days with easterly winds (45° < DD <= 135°). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.ddnorth(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]

DDnorth.

Days with northerly winds (DD > 315° or DD ≤ 45°). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.ddsouth(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]

DDsouth.

Days with southerly winds (135° < DD <= 225°). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.ddwest(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]

DDwest.

Days with westerly winds (225° < DD <= 315°). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.deficit(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

deficit.

Compute the deficit below the given threshold. The deficit is sum(t - x[x<t]) where x is the studied variable and t the threshold (e.g. HD17).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.difference_of_extremes(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

difference_of_extremes.

Difference of extremes between two variables, or one variable and it’s reference period values. The extremes are always maximum for the first variable and minimum for the second variable (e.g. ETR: max(tasmax) - min(tasmin)).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.difference_of_means(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False, sampling_method: SamplingMethodLike = 'resample') Dataset[source]

difference_of_means.

Difference of the average between two variables, or one variable and it’s reference period values (e.g. anomaly: mean(tasmax) - mean(tasmax_ref])).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

  • sampling_method (str) – Choose whether the output sampling configured in slice_mode is a groupby operation or a resample operation (as per xarray definitions). Possible values: {"groupby", "resample", "groupby_ref_and_resample_study"} (default: “resample”) groupby_ref_and_resample_study may only be used when computing the difference_of_means (a.k.a the anomaly).

Notes

This function has been auto-generated.

icclim._generated_api.dtr(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]

DTR.

Mean Diurnal Temperature Range. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.etr(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]

ETR.

Intra-period extreme temperature range. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.excess(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

excess.

Compute the excess over the given threshold. The excess is sum(x[x>t] - t) where x is the studied variable and t the threshold (e.g. GD4).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.fd(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]

FD.

Number of Frost Days (Tmin < 0C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.fg(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]

FG.

Mean of daily mean wind strength. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.fg6bft(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]

FG6Bft.

Days with daily averaged wind ≥ 6 Bft (10.8 m s-1). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.fgcalm(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]

FGcalm.

Calm days, days with daily averaged wind <= 2 m s-1. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.fraction_of_total(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

fraction_of_total.

Compute the fraction of values meeting threshold(s) over the sum of every values (e.g. R75pTOT, R95pTOT).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.fxx(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]

FXx.

Maximum value of daily maximum wind gust. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.gd4(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]

GD4.

Growing degree days (sum of Tmean > 4 C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.gsl(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]

GSL.

Growing season length. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.hd17(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]

HD17.

Heating degree days (sum of Tmean < 17 C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.id(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]

ID.

Number of sharp Ice Days (Tmax < 0C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.max_consecutive_occurrence(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

max_consecutive_occurrence.

Count the maximum number of consecutive occurrences when threshold(s) are met (e.g. CDD, CSU, CWD).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.max_of_rolling_average(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False, rolling_window_width: int | None = 5) Dataset[source]

max_of_rolling_average.

Maximum of rolling average over time dimension.

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

  • rolling_window_width (int) – optional Window width of the rolling window for indicators such as {max_of_rolling_sum, max_of_rolling_average, min_of_rolling_sum, min_of_rolling_average}

Notes

This function has been auto-generated.

icclim._generated_api.max_of_rolling_sum(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False, rolling_window_width: int | None = 5) Dataset[source]

max_of_rolling_sum.

Maximum of rolling sum over time dimension (e.g. RX5DAY: maximum 5 days window of precipitation accumulation).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

  • rolling_window_width (int) – optional Window width of the rolling window for indicators such as {max_of_rolling_sum, max_of_rolling_average, min_of_rolling_sum, min_of_rolling_average}

Notes

This function has been auto-generated.

icclim._generated_api.maximum(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

maximum.

Maximum of values that met threshold(s), if threshold(s) are given (e.g. Txx, Tnx).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.mean_of_absolute_one_time_step_difference(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

mean_of_absolute_one_time_step_difference.

Average of the absolute one time step by one time step difference between two variables, or one variable and it’s reference period values (e.g. vDTR: mean((tasmax[i] - tasmin[i]) - (tasmax[i-1] - tasmin[i-1]) ; where i is the day of measure).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.mean_of_difference(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

mean_of_difference.

Average of the difference between two variables, or one variable and it’s reference period values (e.g. DTR: mean(tasmax - tasmin)).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.min_of_rolling_average(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False, rolling_window_width: int | None = 5) Dataset[source]

min_of_rolling_average.

Minimum of rolling average over time dimension.

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

  • rolling_window_width (int) – optional Window width of the rolling window for indicators such as {max_of_rolling_sum, max_of_rolling_average, min_of_rolling_sum, min_of_rolling_average}

Notes

This function has been auto-generated.

icclim._generated_api.min_of_rolling_sum(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False, rolling_window_width: int | None = 5) Dataset[source]

min_of_rolling_sum.

Minimum of rolling sum over time dimension.

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

  • rolling_window_width (int) – optional Window width of the rolling window for indicators such as {max_of_rolling_sum, max_of_rolling_average, min_of_rolling_sum, min_of_rolling_average}

Notes

This function has been auto-generated.

icclim._generated_api.minimum(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

minimum.

Minimum of values that met threshold(s), if threshold(s) are given (e.g. Txn, Tnn).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.pp(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]

PP.

Mean of daily sea level pressure (hPa) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.prcptot(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]

PRCPTOT.

Total precipitation during Wet Days. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r10mm(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]

R10mm.

Number of heavy precipitation days (Precip >=10mm). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r20mm(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]

R20mm.

Number of very heavy precipitation days (Precip >= 20mm). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r75p(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]

R75p.

Days with RR > 75th percentile of daily amounts. (moderate wet days) (d) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r75ptot(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]

R75pTOT.

Precipitation fraction due to moderate wet days. (> 75th percentile) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r95p(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]

R95p.

Days with RR > 95th percentile of daily amounts. (very wet days) (days) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r95ptot(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]

R95pTOT.

Precipitation fraction due to very wet days (> 95th percentile). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r99p(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]

R99p.

Days with RR > 99th percentile of daily amounts. (extremely wet days) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.r99ptot(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]

R99pTOT.

Precipitation fraction due to extremely wet days. (> 99th percentile) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.rh(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]

RH.

Mean of daily relative humidity (%) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.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]

RR.

Precipitation sum (mm). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.rr1(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]

RR1.

Number of Wet Days (precip >= 1 mm). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.rx1day(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]

RX1day.

maximum 1-day total precipitation. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.rx5day(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]

RX5day.

maximum 5-day total precipitation. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.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]

SD.

Mean of daily snow depth. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.sd1(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]

SD1.

Snow days (SD >= 1 cm). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.sd50cm(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]

SD50cm.

Number of days with snow depth >= 50 cm. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.sd5cm(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]

SD5cm.

Number of days with snow depth >= 5 cm. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.sdii(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]

SDII.

Average precipitation during Wet Days (SDII). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.spi3(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]

SPI3.

3-Month Standardized Precipitation Index. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.spi6(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]

SPI6.

6-Month Standardized Precipitation Index. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.ss(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]

SS.

Sunshine duration (hours) Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.standard_deviation(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

standard_deviation.

Standard deviation of values that met threshold(s), if threshold(s) are given.

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.su(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]

SU.

Number of Summer Days (Tmax > 25C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.sum(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False) Dataset[source]

sum.

Sum of values that met threshold(s), if threshold(s) are given (e.g. PRCPTOT, RR).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.sum_of_spell_lengths(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, threshold: str | Threshold | Sequence[str | Threshold] = None, ignore_Feb29th: bool = False, out_unit: str | None = None, netcdf_version: str | NetcdfVersion = 'NETCDF4', save_thresholds: bool = False, logs_verbosity: Verbosity | str = 'LOW', date_event: bool = False, min_spell_length: int | None = 6) Dataset[source]

sum_of_spell_lengths.

Sum the lengths of each consecutive occurrence spell when threshold(s) are met. The minimum spell length is controlled by min_spell_length (e.g. WSDI, CSDI).

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 (SliceMode) – 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!

  • threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.

  • ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).

  • out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).

  • 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

  • min_spell_length (int) – optional Minimum spell duration to be taken into account when computing the sum_of_spell_lengths.

Notes

This function has been auto-generated.

icclim._generated_api.tg(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]

TG.

Mean of daily mean temperature. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tg10p(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]

TG10p.

Days when Tmean < 10th percentile. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tg90p(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]

TG90p.

Days when Tmean > 90th percentile. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tn(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]

TN.

Mean of daily minimum temperature. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tn10p(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]

TN10p.

Days when Tmin < 10th percentile. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tn90p(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]

TN90p.

Days when Tmin > 90th percentile. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tnn(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]

TNn.

Minimum daily minimum temperature. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tnx(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]

TNx.

Maximum daily minimum temperature. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.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]

TR.

Number of Tropical Nights (Tmin > 20C). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tx(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]

TX.

Mean of daily maximum temperature. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tx10p(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]

TX10p.

Days when Tmax < 10th percentile. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.tx90p(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]

TX90p.

Days when Tmax > 90th daily percentile. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.txn(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]

TXn.

Minimum daily maximum temperature. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.txx(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]

TXx.

Maximum daily maximum temperature. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.vdtr(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]

vDTR.

Mean day-to-day variation in Diurnal Temperature Range. Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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”).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.wd(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]

WD.

Days with TG > 75th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (warm/dry days). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.wsdi(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]

WSDI.

Warm-spell duration index (days). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.

icclim._generated_api.ww(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]

WW.

Days with TG > 75th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (warm/wet days). Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.

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 (SliceMode) – 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).

  • logs_verbosity (str | Verbosity) – optional Configure how verbose icclim is. Possible values: {"LOW", "HIGH", "SILENT"} (default: “LOW”)

  • 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.

Notes

This function has been auto-generated.