icclim.ecad

European Climate Assesment & Dataset (ECAD) indices.

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

Submodules

• icclim.ecad.binding
• icclim.ecad.registry

Package Contents

icclim’s API for ECAD 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.

tg	Mean of daily mean temperature.
tn	Mean of daily minimum temperature.
tx	Mean of daily maximum temperature.
dtr	Mean Diurnal Temperature Range.
etr	Intra-period extreme temperature range.
vdtr	Mean day-to-day variation in Diurnal Temperature Range.
su	Number of Summer Days (Tmax > 25C).
tr	Number of Tropical Nights (Tmin > 20C).
wsdi	Warm-spell duration index (days).
tg90p	Days when Tmean > 90th percentile.
tn90p	Days when Tmin > 90th percentile.
tx90p	Days when Tmax > 90th daily percentile.
txx	Maximum daily maximum temperature.
tnx	Maximum daily minimum temperature.
csu	Maximum number of consecutive summer days (Tmax >25 C).
gd4	Growing degree days (sum of Tmean > 4 C).
fd	Number of Frost Days (Tmin < 0C).
cfd	Maximum number of consecutive frost days (Tmin < 0 C).
hd17	Heating degree days (sum of Tmean < 17 C).
id	Number of sharp Ice Days (Tmax < 0C).
tg10p	Days when Tmean < 10th percentile.
tn10p	Days when Tmin < 10th percentile.
tx10p	Days when Tmax < 10th percentile.
txn	Minimum daily maximum temperature.
tnn	Minimum daily minimum temperature.
csdi	Cold-spell duration index (days).
cdd	Maximum consecutive dry days (Precip < 1mm).
prcptot	Total precipitation during Wet Days.
rr1	Number of Wet Days (precip >= 1 mm).
sdii	Average precipitation during Wet Days (SDII).
cwd	Maximum consecutive wet days (Precip >= 1mm).
rr	Precipitation sum (mm).
r10mm	Number of heavy precipitation days (Precip >=10mm).
r20mm	Number of very heavy precipitation days (Precip >= 20mm).
rx1day	Maximum 1-day total precipitation.
rx5day	Maximum 5-day total precipitation.
r75p	Days with RR > 75th percentile of daily amounts (moderate wet days) (d).
r75ptot	Precipitation fraction due to moderate wet days (> 75th percentile).
r95p	Days with RR > 95th percentile of daily amounts (very wet days) (days).
r95ptot	Precipitation fraction due to very wet days (> 95th percentile).
r99p	Days with RR > 99th percentile of daily amounts (extremely wet days).
r99ptot	Precipitation fraction due to extremely wet days (> 99th percentile).
sd	Mean of daily snow depth.
sd1	Snow days (SD >= 1 cm).
sd5cm	Number of days with snow depth >= 5 cm.
sd50cm	Number of days with snow depth >= 50 cm.
cd	Days with TG < 25th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (cold/dry days).
cw	Days with TG < 25th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (cold/wet days).
wd	Days with TG > 75th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (warm/dry days).
ww	Days with TG > 75th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (warm/wet days).
fxx	Maximum value of daily maximum wind gust.
fg6bft	Days with daily averaged wind ≥ 6 Bft (10.8 m s-1).
fgcalm	Calm days, days with daily averaged wind <= 2 m s-1.
fg	Mean of daily mean wind strength.
ddnorth	Days with northerly winds (DD > 315° or DD ≤ 45°).
ddeast	Days with easterly winds (45° < DD <= 135°).
ddsouth	Days with southerly winds (135° < DD <= 225°).
ddwest	Days with westerly winds (225° < DD <= 315°).
gsl	Growing season length.
spi6	6-Month Standardized Precipitation Index.
spi3	3-Month Standardized Precipitation Index.
pp	Mean of daily sea level pressure (hPa).
ss	Sunshine duration (hours).
rh	Mean of daily relative humidity (%).

icclim._generated._ecad.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

Days with TG < 25th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (cold/dry days).

CD: 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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum consecutive dry days (Precip < 1mm).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum number of consecutive frost days (Tmin < 0 C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Cold-spell duration index (days).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum number of consecutive summer days (Tmax >25 C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with TG < 25th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (cold/wet days).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum consecutive wet days (Precip >= 1mm).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with easterly winds (45° < DD <= 135°).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with northerly winds (DD > 315° or DD ≤ 45°).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with southerly winds (135° < DD <= 225°).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with westerly winds (225° < DD <= 315°).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean Diurnal Temperature Range.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Intra-period extreme temperature range.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of Frost Days (Tmin < 0C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean of daily mean wind strength.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with daily averaged wind ≥ 6 Bft (10.8 m s-1).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Calm days, days with daily averaged wind <= 2 m s-1.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum value of daily maximum wind gust.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Growing degree days (sum of Tmean > 4 C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Growing season length.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Heating degree days (sum of Tmean < 17 C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of sharp Ice Days (Tmax < 0C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean of daily sea level pressure (hPa).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Total precipitation during Wet Days.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of heavy precipitation days (Precip >=10mm).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of very heavy precipitation days (Precip >= 20mm).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with RR > 75th percentile of daily amounts (moderate wet days) (d).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Precipitation fraction due to moderate wet days (> 75th percentile).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with RR > 95th percentile of daily amounts (very wet days) (days).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Precipitation fraction due to very wet days (> 95th percentile).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with RR > 99th percentile of daily amounts (extremely wet days).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Precipitation fraction due to extremely wet days (> 99th percentile).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean of daily relative humidity (%).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Precipitation sum (mm).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of Wet Days (precip >= 1 mm).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum 1-day total precipitation.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum 5-day total precipitation.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean of daily snow depth.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Snow days (SD >= 1 cm).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of days with snow depth >= 50 cm.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of days with snow depth >= 5 cm.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Average precipitation during Wet Days (SDII).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

3-Month Standardized Precipitation Index.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

6-Month Standardized Precipitation Index.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Sunshine duration (hours).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of Summer Days (Tmax > 25C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean of daily mean temperature.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days when Tmean < 10th percentile.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days when Tmean > 90th percentile.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean of daily minimum temperature.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days when Tmin < 10th percentile.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days when Tmin > 90th percentile.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Minimum daily minimum temperature.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum daily minimum temperature.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Number of Tropical Nights (Tmin > 20C).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean of daily maximum temperature.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days when Tmax < 10th percentile.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days when Tmax > 90th daily percentile.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Minimum daily maximum temperature.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Maximum daily maximum temperature.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Mean day-to-day variation in Diurnal Temperature Range.

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

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

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with TG > 75th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (warm/dry days).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Warm-spell duration index (days).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.

icclim._generated._ecad.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

Days with TG > 75th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (warm/wet days).

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 (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.

• time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.

• out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!

• base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.

• only_leap_years (bool) – optional Option for February 29th (default: False).

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

• interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.

• netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).

• save_thresholds (bool) – optional True if the thresholds should be saved within the resulting netcdf file (default: False).

• date_event (bool) – When True the date of the event (such as when a maximum is reached) will be stored in coordinates variables. warning This option may significantly slow down computation.

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

Notes

This function has been auto-generated.