# ruff: noqa: A001, E501, N803
"""
icclim's API for dcsc indices.
This module has been auto-generated.
To modify these, edit the extractor tool in `tools/extract-icclim-funs.py`.
This module exposes each climate index as individual functions for convenience.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import icclim
from icclim._core.input_parsing import get_dataarray_from_dataset
from icclim.dcsc.registry import DcscIndexRegistry
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 DataArray, 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__ = [
"tav",
"txav",
"trav",
"tx10",
"tx90",
"tn10",
"tn90",
"tnfd",
"txfd",
"sd",
"tx35",
"tr",
"txnd",
"tnht",
"tnnd",
"tncwd",
"txhwd",
"hdd",
"cdd",
"pav",
"pint",
"rr",
"rr1mm",
"pn20mm",
"pxcdd",
"pxcwd",
"r99",
"pfl90",
"pq90",
"pq99",
"ffav",
"ff98",
]
[docs]
def tav(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Moyenne de la température moyenne.
TAV: Moyenne de la température moyenne.
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TAV,
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 txav(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Moyenne de la température maximale.
TXAV: Moyenne de la température maximale.
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TXAV,
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 trav(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Moyenne de l'amplitude thermique.
TRAV: Moyenne de l'amplitude thermique.
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TRAV,
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 tx10(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Extrême froid de la température maximale journalière (10e centile de la température maximale).
TX10: Extrême froid de la température maximale journalière (10e centile de la température maximale).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TX10,
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 tx90(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Extrême chaud de la température maximale journalière (90e centile de la température maximale).
TX90: Extrême chaud de la température maximale journalière (90e centile de la température maximale).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TX90,
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 tn10(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Extrême froid de la température minimale journalière (10e centile de la température minimale).
TN10: Extrême froid de la température minimale journalière (10e centile de la température minimale).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TN10,
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 tn90(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Extrême chaud de la température minimale journalière (90e centile de la température minimale).
TN90: Extrême chaud de la température minimale journalière (90e centile de la température minimale).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TN90,
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 tnfd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre de jours de gel (température minimale <= 0°C).
TNFD: Nombre de jours de gel (température minimale <= 0°C).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TNFD,
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 txfd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre de jours sans dégel (température maximale <= 0°C).
TXFD: Nombre de jours sans dégel (température maximale <= 0°C).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TXFD,
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 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:
"""Nombre de journées d'été (température maximale > 25°C).
SD: Nombre de journées d'été (température maximale > 25°C).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.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,
threshold=build_threshold(
query="> 25 degree_Celsius",
),
out_unit="day",
)
[docs]
def tx35(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre de jours de forte chaleur (température maximale > 35°C).
TX35: Nombre de jours de forte chaleur (température maximale > 35°C).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.TX35,
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="> 35 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:
"""Nombre de nuits tropicales (température minimale > 20°C).
TR: Nombre de nuits tropicales (température minimale > 20°C).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.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 txnd(
in_files: InFileLike,
normal: str | Sequence[str] | Dataset | DataArray,
var_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,
normal_var_name: str | None = None,
) -> Dataset:
"""Nombre de jours anormalement chauds (température maximale supérieure de plus de 5°C à la normale).
TXND: Nombre de jours anormalement chauds (température maximale supérieure de plus de 5°C à la normale).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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")
normal : Union[str, Sequence[str], Dataset, DataArray, None]
The normal to be compared to.
Typically, the expected normal dataset should have one value per `lat, lon`
couple.
Can be a path or a list of paths to netCDF datasets or a xarray Dataset or
DataArray.
normal_var_name : str | None, optional
The name of the normal variable.
If missing, icclim will try to guess which variable must be used in the
`normal` dataset.
Ignored if ``normal`` is a
Notes
-----
This function has been auto-generated.
"""
standard_index = DcscIndexRegistry.TXND
normal_da = get_dataarray_from_dataset(
normal_var_name, normal, standard_index.input_variables[0]
)
threshold = standard_index.threshold
threshold.prepare(normal_da)
return icclim.index(
index_name=DcscIndexRegistry.TXND,
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 tnht(
in_files: InFileLike,
normal: str | Sequence[str] | Dataset | DataArray,
var_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,
normal_var_name: str | None = None,
) -> Dataset:
"""Nombre de nuits anormalement chaudes (température minimale supérieure de plus de 5°C à la normale).
TNHT: Nombre de nuits anormalement chaudes (température minimale supérieure de plus de 5°C à la normale).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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")
normal : Union[str, Sequence[str], Dataset, DataArray, None]
The normal to be compared to.
Typically, the expected normal dataset should have one value per `lat, lon`
couple.
Can be a path or a list of paths to netCDF datasets or a xarray Dataset or
DataArray.
normal_var_name : str | None, optional
The name of the normal variable.
If missing, icclim will try to guess which variable must be used in the
`normal` dataset.
Ignored if ``normal`` is a
Notes
-----
This function has been auto-generated.
"""
standard_index = DcscIndexRegistry.TNHT
normal_da = get_dataarray_from_dataset(
normal_var_name, normal, standard_index.input_variables[0]
)
threshold = standard_index.threshold
threshold.prepare(normal_da)
return icclim.index(
index_name=DcscIndexRegistry.TNHT,
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 tnnd(
in_files: InFileLike,
normal: str | Sequence[str] | Dataset | DataArray,
var_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,
normal_var_name: str | None = None,
) -> Dataset:
"""Nombre de jours anormalement froids (température minimale inférieure de plus de 5°C à la normale).
TNND: Nombre de jours anormalement froids (température minimale inférieure de plus de 5°C à la normale).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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")
normal : Union[str, Sequence[str], Dataset, DataArray, None]
The normal to be compared to.
Typically, the expected normal dataset should have one value per `lat, lon`
couple.
Can be a path or a list of paths to netCDF datasets or a xarray Dataset or
DataArray.
normal_var_name : str | None, optional
The name of the normal variable.
If missing, icclim will try to guess which variable must be used in the
`normal` dataset.
Ignored if ``normal`` is a
Notes
-----
This function has been auto-generated.
"""
standard_index = DcscIndexRegistry.TNND
normal_da = get_dataarray_from_dataset(
normal_var_name, normal, standard_index.input_variables[0]
)
threshold = standard_index.threshold
threshold.prepare(normal_da)
return icclim.index(
index_name=DcscIndexRegistry.TNND,
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 tncwd(
in_files: InFileLike,
normal: str | Sequence[str] | Dataset | DataArray,
var_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,
normal_var_name: str | None = None,
) -> Dataset:
"""Nombre de jours d'une vague de froid (température min < de plus de 5°C à la normale pdt au moins 5j consécutifs).
TNCWD: Nombre de jours d'une vague de froid (température min < de plus de 5°C à la normale pdt au moins 5j consécutifs).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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")
normal : Union[str, Sequence[str], Dataset, DataArray, None]
The normal to be compared to.
Typically, the expected normal dataset should have one value per `lat, lon`
couple.
Can be a path or a list of paths to netCDF datasets or a xarray Dataset or
DataArray.
normal_var_name : str | None, optional
The name of the normal variable.
If missing, icclim will try to guess which variable must be used in the
`normal` dataset.
Ignored if ``normal`` is a
Notes
-----
This function has been auto-generated.
"""
standard_index = DcscIndexRegistry.TNCWD
normal_da = get_dataarray_from_dataset(
normal_var_name, normal, standard_index.input_variables[0]
)
threshold = standard_index.threshold
threshold.prepare(normal_da)
return icclim.index(
index_name=DcscIndexRegistry.TNCWD,
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 txhwd(
in_files: InFileLike,
normal: str | Sequence[str] | Dataset | DataArray,
var_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,
normal_var_name: str | None = None,
) -> Dataset:
"""Nombre de jours d'une vague de chaleur (température max > de plus de 5°C à la normale pdt au moins 5j consécutifs).
TXHWD: Nombre de jours d'une vague de chaleur (température max > de plus de 5°C à la normale pdt au moins 5j consécutifs).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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")
normal : Union[str, Sequence[str], Dataset, DataArray, None]
The normal to be compared to.
Typically, the expected normal dataset should have one value per `lat, lon`
couple.
Can be a path or a list of paths to netCDF datasets or a xarray Dataset or
DataArray.
normal_var_name : str | None, optional
The name of the normal variable.
If missing, icclim will try to guess which variable must be used in the
`normal` dataset.
Ignored if ``normal`` is a
Notes
-----
This function has been auto-generated.
"""
standard_index = DcscIndexRegistry.TXHWD
normal_da = get_dataarray_from_dataset(
normal_var_name, normal, standard_index.input_variables[0]
)
threshold = standard_index.threshold
threshold.prepare(normal_da)
return icclim.index(
index_name=DcscIndexRegistry.TXHWD,
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 hdd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Degrés-jours de chauffage (Cumul sur la période des écarts négatifs au seuil de < 17°C par la température qt moyenne).
HDD: Degrés-jours de chauffage (Cumul sur la période des écarts négatifs au seuil de < 17°C par la température qt moyenne).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.HDD,
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 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:
"""Degrés-jours de climatisation(Cumul sur la période des dépassements du seuil de > 18°C par la température qt moyenne).
CDD: Degrés-jours de climatisation(Cumul sur la période des dépassements du seuil de > 18°C par la température qt moyenne).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.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="18 degree_Celsius",
),
out_unit="degree_Celsius day",
)
[docs]
def pav(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Précipitations quotidiennes moyennes.
PAV: Précipitations quotidiennes moyennes.
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PAV,
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 pint(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Précipitation moyenne des jours pluvieux (RR > 1 mm).
PINT: Précipitation moyenne des jours pluvieux (RR > 1 mm).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PINT,
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 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:
"""Cumul de précipitation.
RR: Cumul de précipitation.
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.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 rr1mm(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre de jours de pluie (précipitations >= 1 mm).
RR1MM: Nombre de jours de pluie (précipitations >= 1 mm).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.RR1MM,
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 pn20mm(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre de jours de fortes précipitations (précipitations >= 20 mm).
PN20MM: Nombre de jours de fortes précipitations (précipitations >= 20 mm).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PN20MM,
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 pxcdd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Période de sécheresse (Max [Nbj consécutifs RR < 1 mm]).
PXCDD: Période de sécheresse (Max [Nbj consécutifs RR < 1 mm]).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PXCDD,
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 pxcwd(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre maximum de jours pluvieux consécutifs (Max [Nbj consécutifs RR > 1 mm]).
PXCWD: Nombre maximum de jours pluvieux consécutifs (Max [Nbj consécutifs RR > 1 mm]).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PXCWD,
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 r99(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre de jours de précipitations extrêmes.
R99: Nombre de jours de précipitations extrêmes.
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.R99,
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 pfl90(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Fraction des précipitations journalières intenses.
PFL90: Fraction des précipitations journalières intenses.
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PFL90,
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 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 pq90(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Précipitation quotidienne intense (90e centile des précipitations).
PQ90: Précipitation quotidienne intense (90e centile des précipitations).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PQ90,
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,
threshold_min_value="1 mm d-1",
),
out_unit="%",
)
[docs]
def pq99(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Précipitation quotidienne extrême (99e centile des précipitations).
PQ99: Précipitation quotidienne extrême (99e centile des précipitations).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.PQ99,
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 doy_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 ffav(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
ignore_Feb29th: bool = False,
netcdf_version: str | NetcdfVersion = "NETCDF4",
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Écart de la vitesse du vent moyenne journalière (par rapport à une periode de référence).
FFAV: Écart de la vitesse du vent moyenne journalière (par rapport à une periode de référence).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.FFAV,
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="m s-1",
)
[docs]
def ff98(
in_files: InFileLike,
var_name: str | Sequence[str] | None = None,
slice_mode: FrequencyLike | Frequency = "year",
time_range: Sequence[dt.datetime | str] | None = None,
out_file: str | None = None,
base_period_time_range: Sequence[dt.datetime] | Sequence[str] | None = None,
only_leap_years: bool = False,
ignore_Feb29th: bool = False,
interpolation: str | QuantileInterpolation = "median_unbiased",
netcdf_version: str | NetcdfVersion = "NETCDF4",
save_thresholds: bool = False,
logs_verbosity: Verbosity | str = "LOW",
date_event: bool = False,
) -> Dataset:
"""Nombre de jours de vent fort (vent ≥ 98e centile de la période de référence).
FF98: Nombre de jours de vent fort (vent ≥ 98e centile de la période de référence).
Source: Portail DRIAS, DCSC, MeteoFrance.
Parameters
----------
in_files : str | list[str] | Dataset | DataArray | InputDictionary
Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs,
or path to zarr store, or xarray.Dataset or xarray.DataArray.
var_name : str | list[str] | None
``optional`` Target variable name to process corresponding to ``in_files``.
If None (default) on ECA&D index, the variable is guessed based on the
climate index wanted.
Mandatory for a user index.
slice_mode : FrequencyLike | Frequency
Type of temporal aggregation:
The possibles values are ``{"year", "month", "DJF", "MAM", "JJA", "SON",
"ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])}``
(where season and month lists can be customized) or any valid pandas
frequency.
A season can also be defined between two exact dates:
``("season", ("19 july", "14 august"))``.
Default is "year".
See :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=DcscIndexRegistry.FF98,
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="> 98 period_per",
doy_window_width=5,
only_leap_years=only_leap_years,
interpolation=interpolation,
reference_period=base_period_time_range,
threshold_min_value="1 kt",
),
out_unit="days",
)
