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#
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.
Mean of daily mean temperature. |
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Mean of daily minimum temperature. |
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Mean of daily maximum temperature. |
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Mean Diurnal Temperature Range. |
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Intra-period extreme temperature range. |
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Mean day-to-day variation in Diurnal Temperature Range. |
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Number of Summer Days (Tmax > 25C). |
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Number of Tropical Nights (Tmin > 20C). |
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Warm-spell duration index (days). |
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Days when Tmean > 90th percentile. |
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Days when Tmin > 90th percentile. |
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Days when Tmax > 90th daily percentile. |
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Maximum daily maximum temperature. |
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Maximum daily minimum temperature. |
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Maximum number of consecutive summer days (Tmax >25 C). |
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Growing degree days (sum of Tmean > 4 C). |
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Number of Frost Days (Tmin < 0C). |
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Maximum number of consecutive frost days (Tmin < 0 C). |
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Heating degree days (sum of Tmean < 17 C). |
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Number of sharp Ice Days (Tmax < 0C). |
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Days when Tmean < 10th percentile. |
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Days when Tmin < 10th percentile. |
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Days when Tmax < 10th percentile. |
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Minimum daily maximum temperature. |
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Minimum daily minimum temperature. |
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Cold-spell duration index (days). |
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Maximum consecutive dry days (Precip < 1mm). |
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Total precipitation during Wet Days. |
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Number of Wet Days (precip >= 1 mm). |
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Average precipitation during Wet Days (SDII). |
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Maximum consecutive wet days (Precip >= 1mm). |
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Precipitation sum (mm). |
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Number of heavy precipitation days (Precip >=10mm). |
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Number of very heavy precipitation days (Precip >= 20mm). |
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Maximum 1-day total precipitation. |
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Maximum 5-day total precipitation. |
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Days with RR > 75th percentile of daily amounts (moderate wet days) (d). |
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Precipitation fraction due to moderate wet days (> 75th percentile). |
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Days with RR > 95th percentile of daily amounts (very wet days) (days). |
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Precipitation fraction due to very wet days (> 95th percentile). |
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Days with RR > 99th percentile of daily amounts (extremely wet days). |
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Precipitation fraction due to extremely wet days (> 99th percentile). |
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Mean of daily snow depth. |
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Snow days (SD >= 1 cm). |
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Number of days with snow depth >= 5 cm. |
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Number of days with snow depth >= 50 cm. |
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Days with TG < 25th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (cold/dry days). |
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Days with TG < 25th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (cold/wet days). |
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Days with TG > 75th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (warm/dry days). |
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Days with TG > 75th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (warm/wet days). |
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Maximum value of daily maximum wind gust. |
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Days with daily averaged wind ≥ 6 Bft (10.8 m s-1). |
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Calm days, days with daily averaged wind <= 2 m s-1. |
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Mean of daily mean wind strength. |
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Days with northerly winds (DD > 315° or DD ≤ 45°). |
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Days with easterly winds (45° < DD <= 135°). |
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Days with southerly winds (135° < DD <= 225°). |
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Days with westerly winds (225° < DD <= 315°). |
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Growing season length. |
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6-Month Standardized Precipitation Index. |
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3-Month Standardized Precipitation Index. |
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Mean of daily sea level pressure (hPa). |
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Sunshine duration (hours). |
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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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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 [source]#
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 toin_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. IfNone
, 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 isNone
.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 aDataset
,out_file
field is ignored. Use the function returned value instead to retrieve the computed value. Ifout_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.