# ruff: noqa: A001, E501, N803
"""
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.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import icclim
from icclim.ecad.registry import EcadIndexRegistry
from icclim.threshold.factory import build_threshold
if TYPE_CHECKING:
import datetime as dt
from collections.abc import Sequence
from typing import FrequencyLike, InFileLike
from xarray import Dataset
from icclim._core.model.netcdf_version import NetcdfVersion
from icclim._core.model.quantile_interpolation import QuantileInterpolation
from icclim.frequency import Frequency
from icclim.logger import Verbosity
__all__ = [
"tg",
"tn",
"tx",
"dtr",
"etr",
"vdtr",
"su",
"tr",
"wsdi",
"tg90p",
"tn90p",
"tx90p",
"txx",
"tnx",
"csu",
"gd4",
"fd",
"cfd",
"hd17",
"id",
"tg10p",
"tn10p",
"tx10p",
"txn",
"tnn",
"csdi",
"cdd",
"prcptot",
"rr1",
"sdii",
"cwd",
"rr",
"r10mm",
"r20mm",
"rx1day",
"rx5day",
"r75p",
"r75ptot",
"r95p",
"r95ptot",
"r99p",
"r99ptot",
"sd",
"sd1",
"sd5cm",
"sd50cm",
"cd",
"cw",
"wd",
"ww",
"fxx",
"fg6bft",
"fgcalm",
"fg",
"ddnorth",
"ddeast",
"ddsouth",
"ddwest",
"gsl",
"spi6",
"spi3",
"pp",
"ss",
"rh",
]
[docs]
def tg(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean of daily mean temperature.
TG: Mean of daily mean temperature.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TG,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def tn(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean of daily minimum temperature.
TN: Mean of daily minimum temperature.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TN,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def tx(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean of daily maximum temperature.
TX: Mean of daily maximum temperature.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TX,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def dtr(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean Diurnal Temperature Range.
DTR: Mean Diurnal Temperature Range.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.DTR,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def etr(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Intra-period extreme temperature range.
ETR: Intra-period extreme temperature range.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.ETR,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def vdtr(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean day-to-day variation in Diurnal Temperature Range.
vDTR: Mean day-to-day variation in Diurnal Temperature Range.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.VDTR,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def su(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of Summer Days (Tmax > 25C).
SU: Number of Summer Days (Tmax > 25C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.SU,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 25 degree_Celsius",
),
out_unit="day",
)
[docs]
def tr(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of Tropical Nights (Tmin > 20C).
TR: Number of Tropical Nights (Tmin > 20C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.TR,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 20 degree_Celsius",
),
out_unit="day",
)
[docs]
def wsdi(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Warm-spell duration index (days).
WSDI: Warm-spell duration index (days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.WSDI,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 90 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def tg90p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days when Tmean > 90th percentile.
TG90p: Days when Tmean > 90th percentile.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TG90P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 90 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def tn90p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days when Tmin > 90th percentile.
TN90p: Days when Tmin > 90th percentile.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TN90P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 90 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def tx90p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days when Tmax > 90th daily percentile.
TX90p: Days when Tmax > 90th daily percentile.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TX90P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 90 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def txx(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum daily maximum temperature.
TXx: Maximum daily maximum temperature.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TXX,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def tnx(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum daily minimum temperature.
TNx: Maximum daily minimum temperature.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TNX,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def csu(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum number of consecutive summer days (Tmax >25 C).
CSU: Maximum number of consecutive summer days (Tmax >25 C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.CSU,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 25 degree_Celsius",
),
out_unit="day",
)
[docs]
def gd4(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Growing degree days (sum of Tmean > 4 C).
GD4: Growing degree days (sum of Tmean > 4 C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.GD4,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="4 degree_Celsius",
),
out_unit="degree_Celsius day",
)
[docs]
def fd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of Frost Days (Tmin < 0C).
FD: Number of Frost Days (Tmin < 0C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.FD,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 0 degree_Celsius",
),
out_unit="day",
)
[docs]
def cfd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum number of consecutive frost days (Tmin < 0 C).
CFD: Maximum number of consecutive frost days (Tmin < 0 C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.CFD,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 0 degree_Celsius",
),
out_unit="day",
)
[docs]
def hd17(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Heating degree days (sum of Tmean < 17 C).
HD17: Heating degree days (sum of Tmean < 17 C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.HD17,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="17 degree_Celsius",
),
out_unit="degree_Celsius day",
)
[docs]
def id(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of sharp Ice Days (Tmax < 0C).
ID: Number of sharp Ice Days (Tmax < 0C).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.ID,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 0 degree_Celsius",
),
out_unit="day",
)
[docs]
def tg10p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days when Tmean < 10th percentile.
TG10p: Days when Tmean < 10th percentile.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TG10P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 10 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def tn10p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days when Tmin < 10th percentile.
TN10p: Days when Tmin < 10th percentile.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TN10P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 10 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def tx10p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days when Tmax < 10th percentile.
TX10p: Days when Tmax < 10th percentile.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TX10P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 10 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def txn(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Minimum daily maximum temperature.
TXn: Minimum daily maximum temperature.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TXN,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def tnn(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Minimum daily minimum temperature.
TNn: Minimum daily minimum temperature.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.TNN,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="degree_Celsius",
)
[docs]
def csdi(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Cold-spell duration index (days).
CSDI: Cold-spell duration index (days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.CSDI,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 10 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
out_unit="day",
)
[docs]
def cdd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum consecutive dry days (Precip < 1mm).
CDD: Maximum consecutive dry days (Precip < 1mm).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.CDD,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="< 1 mm/day",
),
out_unit="day",
)
[docs]
def prcptot(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Total precipitation during Wet Days.
PRCPTOT: Total precipitation during Wet Days.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.PRCPTOT,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 1 mm/day",
),
out_unit="mm",
)
[docs]
def rr1(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of Wet Days (precip >= 1 mm).
RR1: Number of Wet Days (precip >= 1 mm).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.RR1,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 1 mm/day",
),
out_unit="day",
)
[docs]
def sdii(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Average precipitation during Wet Days (SDII).
SDII: Average precipitation during Wet Days (SDII).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.SDII,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 1 mm/day",
),
out_unit="mm/day",
)
[docs]
def cwd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum consecutive wet days (Precip >= 1mm).
CWD: Maximum consecutive wet days (Precip >= 1mm).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.CWD,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 1 mm/day",
),
out_unit="day",
)
[docs]
def rr(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Precipitation sum (mm).
RR: Precipitation sum (mm).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.RR,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="mm",
)
[docs]
def r10mm(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of heavy precipitation days (Precip >=10mm).
R10mm: Number of heavy precipitation days (Precip >=10mm).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.R10MM,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 10 mm/day",
),
out_unit="day",
)
[docs]
def r20mm(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of very heavy precipitation days (Precip >= 20mm).
R20mm: Number of very heavy precipitation days (Precip >= 20mm).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.R20MM,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 20 mm/day",
),
out_unit="day",
)
[docs]
def rx1day(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum 1-day total precipitation.
RX1day: Maximum 1-day total precipitation.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.RX1DAY,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="mm/day",
)
[docs]
def rx5day(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum 5-day total precipitation.
RX5day: Maximum 5-day total precipitation.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.RX5DAY,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="mm",
)
[docs]
def r75p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with RR > 75th percentile of daily amounts (moderate wet days) (d).
R75p: Days with RR > 75th percentile of daily amounts (moderate wet days) (d).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.R75P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 75 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
out_unit="day",
)
[docs]
def r75ptot(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Precipitation fraction due to moderate wet days (> 75th percentile).
R75pTOT: Precipitation fraction due to moderate wet days (> 75th percentile).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.R75PTOT,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 75 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
out_unit="%",
)
[docs]
def r95p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with RR > 95th percentile of daily amounts (very wet days) (days).
R95p: Days with RR > 95th percentile of daily amounts (very wet days) (days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.R95P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 95 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
out_unit="day",
)
[docs]
def r95ptot(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Precipitation fraction due to very wet days (> 95th percentile).
R95pTOT: Precipitation fraction due to very wet days (> 95th percentile).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.R95PTOT,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 95 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
out_unit="%",
)
[docs]
def r99p(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with RR > 99th percentile of daily amounts (extremely wet days).
R99p: Days with RR > 99th percentile of daily amounts (extremely wet days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.R99P,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 99 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
out_unit="day",
)
[docs]
def r99ptot(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Precipitation fraction due to extremely wet days (> 99th percentile).
R99pTOT: Precipitation fraction due to extremely wet days (> 99th percentile).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.R99PTOT,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 99 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
out_unit="%",
)
[docs]
def sd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean of daily snow depth.
SD: Mean of daily snow depth.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.SD,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="cm",
)
[docs]
def sd1(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Snow days (SD >= 1 cm).
SD1: Snow days (SD >= 1 cm).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.SD1,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 1 cm",
),
out_unit="day",
)
[docs]
def sd5cm(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of days with snow depth >= 5 cm.
SD5cm: Number of days with snow depth >= 5 cm.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.SD5CM,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 5 cm",
),
out_unit="day",
)
[docs]
def sd50cm(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Number of days with snow depth >= 50 cm.
SD50cm: Number of days with snow depth >= 50 cm.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
""" # noqa: D401
return icclim.index(
index_name=EcadIndexRegistry.SD50CM,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 50 cm",
),
out_unit="day",
)
[docs]
def cd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with TG < 25th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (cold/dry days).
CD: Days with TG < 25th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (cold/dry days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.CD,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=[
build_threshold(
query="< 25 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
build_threshold(
query="< 25 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
],
out_unit="day",
)
[docs]
def cw(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with TG < 25th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (cold/wet days).
CW: Days with TG < 25th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (cold/wet days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.CW,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=[
build_threshold(
query="< 25 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
build_threshold(
query="> 75 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
],
out_unit="day",
)
[docs]
def wd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with TG > 75th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (warm/dry days).
WD: Days with TG > 75th percentile of daily mean temperature and RR <25th percentile of daily precipitation sum (warm/dry days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.WD,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=[
build_threshold(
query="> 75 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
build_threshold(
query="< 25 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
],
out_unit="day",
)
[docs]
def ww(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with TG > 75th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (warm/wet days).
WW: Days with TG > 75th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (warm/wet days).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
only_leap_years : bool
``optional`` Option for February 29th (default: False).
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
interpolation : str | QuantileInterpolation | None
``optional`` Interpolation method to compute percentile values:
``{"linear", "median_unbiased"}``
Default is "median_unbiased", a.k.a type 8 or method 8.
Ignored for non percentile based indices.
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
save_thresholds : bool
``optional`` True if the thresholds should be saved within the resulting
netcdf file (default: False).
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.WW,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
only_leap_years=only_leap_years,
ignore_Feb29th=ignore_Feb29th,
interpolation=interpolation,
netcdf_version=netcdf_version,
save_thresholds=save_thresholds,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=[
build_threshold(
query="> 75 doy_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
),
build_threshold(
query="> 75 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 mm d-1",
),
],
out_unit="day",
)
[docs]
def fxx(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Maximum value of daily maximum wind gust.
FXx: Maximum value of daily maximum wind gust.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.FXX,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="m s-1",
)
[docs]
def fg6bft(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with daily averaged wind ≥ 6 Bft (10.8 m s-1).
FG6Bft: Days with daily averaged wind ≥ 6 Bft (10.8 m s-1).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.FG6BFT,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query=">= 10.8 m s-1",
),
out_unit="day",
)
[docs]
def fgcalm(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Calm days, days with daily averaged wind <= 2 m s-1.
FGcalm: Calm days, days with daily averaged wind <= 2 m s-1.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.FGCALM,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="<= 2 m s-1",
),
out_unit="day",
)
[docs]
def fg(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean of daily mean wind strength.
FG: Mean of daily mean wind strength.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.FG,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="m s-1",
)
[docs]
def ddnorth(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with northerly winds (DD > 315° or DD ≤ 45°).
DDnorth: Days with northerly winds (DD > 315° or DD ≤ 45°).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.DDNORTH,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 315 degree OR <= 45 degree",
),
out_unit="day",
)
[docs]
def ddeast(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with easterly winds (45° < DD <= 135°).
DDeast: Days with easterly winds (45° < DD <= 135°).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.DDEAST,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 45 degree AND <= 135 degree",
),
out_unit="day",
)
[docs]
def ddsouth(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with southerly winds (135° < DD <= 225°).
DDsouth: Days with southerly winds (135° < DD <= 225°).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.DDSOUTH,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 135 degree AND <= 225 degree",
),
out_unit="day",
)
[docs]
def ddwest(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Days with westerly winds (225° < DD <= 315°).
DDwest: Days with westerly winds (225° < DD <= 315°).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.DDWEST,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
threshold=build_threshold(
query="> 225 degree AND <= 315 degree",
),
out_unit="day",
)
[docs]
def gsl(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Growing season length.
GSL: Growing season length.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.GSL,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="day",
)
[docs]
def spi6(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""6-Month Standardized Precipitation Index.
SPI6: 6-Month Standardized Precipitation Index.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.SPI6,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="",
)
[docs]
def spi3(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""3-Month Standardized Precipitation Index.
SPI3: 3-Month Standardized Precipitation Index.
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
base_period_time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range of the reference period.
The dates can either be given as instance of datetime.datetime or as string
values.
It is used either:
#. to compute percentiles if threshold is filled.
When missing, the studied period is used to compute percentiles.
The study period is either the dataset filtered by `time_range` or the whole
dataset if `time_range` is missing.
For day of year percentiles (doy_per), on extreme percentiles the
overlapping period between `base_period_time_range` and the study period is
bootstrapped.
#. to compute a reference period for indices such as difference_of_mean
(a.k.a anomaly) if a single variable is given in input.
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.SPI3,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
base_period_time_range=base_period_time_range,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="",
)
[docs]
def pp(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean of daily sea level pressure (hPa).
PP: Mean of daily sea level pressure (hPa).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.PP,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="hPa",
)
[docs]
def ss(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Sunshine duration (hours).
SS: Sunshine duration (hours).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.SS,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="hours",
)
[docs]
def rh(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Mean of daily relative humidity (%).
RH: Mean of daily relative humidity (%).
Source: ECA&D, Algorithm Theoretical Basis Document (ATBD) v11.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :ref:`slice_mode` for details.
time_range : list[datetime.datetime ] | list[str] | tuple[str, str] | None
``optional`` Temporal range: upper and lower bounds for temporal subsetting.
If ``None``, whole period of input files will be processed.
The dates can either be given as instance of datetime.datetime or as string
values. For strings, many format are accepted.
Default is ``None``.
out_file : str | None
Output NetCDF file name (default: "icclim_out.nc" in the current directory).
Default is "icclim_out.nc".
If the input ``in_files`` is a ``Dataset``, ``out_file`` field is ignored.
Use the function returned value instead to retrieve the computed value.
If ``out_file`` already exists, icclim will overwrite it!
ignore_Feb29th : bool
``optional`` Ignoring or not February 29th (default: False).
netcdf_version : str | NetcdfVersion
``optional`` NetCDF version to create (default: "NETCDF3_CLASSIC").
date_event : bool
When True the date of the event (such as when a maximum is reached) will be
stored in coordinates variables.
**warning** This option may significantly slow down computation.
logs_verbosity : str | Verbosity
``optional`` Configure how verbose icclim is.
Possible values: ``{"LOW", "HIGH", "SILENT"}`` (default: "LOW")
Notes
-----
This function has been auto-generated.
"""
return icclim.index(
index_name=EcadIndexRegistry.RH,
in_files=in_files,
var_name=var_name,
slice_mode=slice_mode,
time_range=time_range,
out_file=out_file,
ignore_Feb29th=ignore_Feb29th,
netcdf_version=netcdf_version,
logs_verbosity=logs_verbosity,
date_event=date_event,
out_unit="%",
)
