`icclim`#

Python library for climate indices calculation.

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icclim.index(in_files: icclim._core.model.icclim_types.InFileLike, index_name: str | icclim._core.generic.indicator.GenericIndicator | icclim._core.model.standard_index.StandardIndex | None = None, var_name: str | collections.abc.Sequence[str] | None = None, slice_mode: icclim._core.model.icclim_types.FrequencyLike | icclim._core.frequency.Frequency = 'year', time_range: collections.abc.Sequence[datetime.datetime | str] | None = None, out_file: str | None = None, threshold: str | icclim._core.model.threshold.Threshold | collections.abc.Sequence[str | icclim._core.model.threshold.Threshold] | None = None, callback: Callable[[int], None] = log.callback, callback_percentage_start_value: int = 0, callback_percentage_total: int = 100, base_period_time_range: collections.abc.Sequence[datetime.datetime] | collections.abc.Sequence[str] | None = None, doy_window_width: int = 5, only_leap_years: bool = False, ignore_Feb29th: bool = False, interpolation: str | icclim._core.model.quantile_interpolation.QuantileInterpolation = 'median_unbiased', out_unit: str | None = None, netcdf_version: str | icclim._core.model.netcdf_version.NetcdfVersion = 'NETCDF4', user_index: icclim._core.legacy.user_index.model.UserIndexDict | None = None, save_thresholds: bool = False, logs_verbosity: icclim.logger.Verbosity | str = 'LOW', date_event: bool = False, min_spell_length: int | None = 6, rolling_window_width: int | None = 5, sampling_method: icclim._core.model.icclim_types.SamplingMethodLike = RESAMPLE_METHOD, *, window_width: int | None = None, save_percentile: bool | None = None, indice_name: str | None = None, user_indice: icclim._core.legacy.user_index.model.UserIndexDict | None = None, transfer_limit_Mbytes: float | None = None) → xarray.core.dataset.Dataset[source]#

Compute climate index.

This is the main entry point for icclim.

Parameters:

in_files (str | list[str] | Dataset | DataArray | InputDictionary) – Absolute path(s) to NetCDF dataset(s), including OPeNDAP URLs, or path to zarr store, or xarray.Dataset or xarray.DataArray.
index_name (str | StandardIndex) – Climate index name. For ECA&D index, case insensitive name used to lookup the index. For user index, it’s the name of the output variable.
var_name (str | list[str] | None) – optional Target variable name to process corresponding to in_files. If None (default) on ECA&D index, the variable is guessed based on the climate index wanted. Mandatory for a user index.
slice_mode (FrequencyLike | Frequency) – Type of temporal aggregation: The possibles values are {"year", "month", "DJF", "MAM", "JJA", "SON", "ONDJFM" or "AMJJAS", ("season", [1,2,3]), ("month", [1,2,3,])} (where season and month lists can be customized) or any valid pandas frequency. A season can also be defined between two exact dates: ("season", ("19 july", "14 august")). Default is “year”. See slice_mode for details.
time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range: upper and lower bounds for temporal subsetting. If None, whole period of input files will be processed. The dates can either be given as instance of datetime.datetime or as string values. For strings, many format are accepted. Default is None.
out_file (str | None) – Output NetCDF file name (default: “icclim_out.nc” in the current directory). Default is “icclim_out.nc”. If the input in_files is a Dataset, out_file field is ignored. Use the function returned value instead to retrieve the computed value. If out_file already exists, icclim will overwrite it!
threshold (float | list[float] | None) – optional User defined threshold for certain indices. Default depend on the index, see their individual definition. When a list of threshold is provided, the index will be computed for each thresholds.
transfer_limit_Mbytes (float) – Deprecated, does not have any effect.
callback (Callable[[int], None]) – optional Progress bar printing. If None, progress bar will not be printed.
callback_percentage_start_value (int) – optional Initial value of percentage of the progress bar (default: 0).
callback_percentage_total (int) – optional Total percentage value (default: 100).
base_period_time_range (list[datetime.datetime ] | list[str] | tuple[str, str] | None) – optional Temporal range of the reference period. The dates can either be given as instance of datetime.datetime or as string values. It is used either: #. to compute percentiles if threshold is filled. When missing, the studied period is used to compute percentiles. The study period is either the dataset filtered by time_range or the whole dataset if time_range is missing. For day of year percentiles (doy_per), on extreme percentiles the overlapping period between base_period_time_range and the study period is bootstrapped. #. to compute a reference period for indices such as difference_of_mean (a.k.a anomaly) if a single variable is given in input.
doy_window_width (int) – optional Window width used to aggreagte day of year values when computing day of year percentiles (doy_per) Default: 5 (5 days).
min_spell_length (int) – optional Minimum spell duration to be taken into account when computing the sum_of_spell_lengths.
rolling_window_width (int) – optional Window width of the rolling window for indicators such as {max_of_rolling_sum, max_of_rolling_average, min_of_rolling_sum, min_of_rolling_average}
only_leap_years (bool) – optional Option for February 29th (default: False).
ignore_Feb29th (bool) – optional Ignoring or not February 29th (default: False).
interpolation (str | QuantileInterpolation | None) – optional Interpolation method to compute percentile values: {"linear", "median_unbiased"} Default is “median_unbiased”, a.k.a type 8 or method 8. Ignored for non percentile based indices.
out_unit (str | None) – optional Output unit for certain indices: “days” or “%” (default: “days”).
netcdf_version (str | NetcdfVersion) – optional NetCDF version to create (default: “NETCDF3_CLASSIC”).
user_index (UserIndexDict) – optional A dictionary with parameters for user defined index. See Create your own index with user_index. Ignored for ECA&D indices.
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”)
sampling_method (str) – Choose whether the output sampling configured in slice_mode is a groupby operation or a resample operation (as per xarray definitions). Possible values: {"groupby", "resample", "groupby_ref_and_resample_study"} (default: “resample”) groupby_ref_and_resample_study may only be used when computing the difference_of_means (a.k.a the anomaly).
indice_name (str | None) – DEPRECATED, use index_name instead.
user_indice (dict | None) – DEPRECATED, use user_index instead.
window_width (int) – DEPRECATED, use doy_window_width, min_spell_length or rolling_window_width instead.
save_percentile (bool) – DEPRECATED, use save_thresholds instead.

icclim.indice(*args, **kwargs) → xarray.core.dataset.Dataset[source]#

Proxy for icclim.index function.

Deprecated: to be deleted in a future release.

icclim.indices(index_group: collections.abc.Sequence[str] | str | icclim._core.model.index_group.IndexGroup | icclim._core.model.standard_index.StandardIndex, *, ignore_error: bool = False, **kwargs) → xarray.core.dataset.Dataset[source]#

Compute multiple indices at the same time.

The input dataset(s) must include all the necessary variables. It can only be used with keyword arguments (kwargs).

Parameters:

index_group ("all" | str | IndexGroup | list[str]) – Either the name of an IndexGroup or an instance of IndexGroup or a list of index short names or the name(s) of standard variable(s) (such as ‘tasmax’). The value “all” can also be used to compute every indices. Note that the input given by in_files must include all the necessary variables to compute the indices of this group.
ignore_error (bool) – When True, ignore indices that fails to compute. This is option is particularly useful when used with index_group=’all’ to compute everything that can be computed given the input.
kwargs (Dict) – icclim.index keyword arguments.

Returns:

A Dataset with one data variable per index.

Return type:

xr.Dataset

Notes

If output_file is part of kwargs, the result is written in a single netCDF file, which will contain all the index results of this group.

Build a Threshold.

This function is a factory for Threshold instances. It can build a BasicThreshold, a PercentileThreshold or a BoundedThreshold. See Generic indices recipes for how to combine thresholds with generic indices.

Parameters:

query (str | None = None) – string query describing a threshold. It must include: an operator, a threshold value and optionally a unit such as “> 10 degC”. It acts as a shorthand for operator, value and unit parameters for simple threshold. Don’t use query when value is a DataArray, a Dataset or a path to a netcdf/zarr storage, instead use operator, value and unit. query supersede operator, value and unit parameters.
operator (Operator | str = None) – keyword argument only. The operator either as an instance of Operator or as a compatible string. See OperatorRegistry for the list of all operators. When query is None and operator is None, the default Operator.REACH is used.
value (str | float | int | Dataset | DataArray | Sequence[float | int | str] | None) – keyword argument only. The threshold value(s), default to None. It can be: * a simple scalar threshold * a percentile that will be computed per-grid cell (in combinaison with unit) * per-grid cell thresholds defined by a DataArray, a Dataset or a string path to a netcdf/zarr. * a sequence of scalars, the indicator will then be computed for each value and a specific dimension will be created (also work with percentiles).
unit (str | None = None) – Keyword argument only. The threshold unit. When unit is None, if value is a dataset and a “units” can be read from its attrs, this unit will be used. If value is a scalar or a sequence of scalar, the exceedance will be computed by assuming threshold has the same unit as the studied value is it compared to. When unit is a string it must be a valid unit of our shared pint registry with xclim or a special percentile unit: * “doy_per” for day of year percentiles (in ECAD, used for temperature indices such as TX90p) * “period_per” for per period percentiles (in ECAD, used for rain indices such as R75p)
threshold_min_value (str | float | pint.Quantity) – A minimum value used to pre-filter computed threshold values. This is particularly useful to compute a percentile threshold for rain where dry days are usually ignored. In that case threshold_min_value would be set to “1 mm/day”. If threshold_min_value is a number, unit is used to quantify threshold_min_value.
offset (float | None) – Optional. An offset applied to the threshold. This is particularly useful when the threshold is an existing dataset (netcdf file or zarr store) and the data should be compared to this dataset + an offset (e.g. to compute days when T > 5 degC above normal)
kwargs – Additional arguments to build a PercentileThreshold. See PercentileThreshold constructor for the complete list of possible arguments.

Examples

# -- Scalar threshold
scalar_t = build_threshold(">= 30 degC")
assert isinstance(scalar_t, BasicThreshold)

# -- Daily percentile threshold
doy_t = build_threshold(">= 30 doy_per")
assert isinstance(doy_t, PercentileThreshold)

# -- Per grid-cell threshold, with offset
grided_t = build_threshold(
    operator=">=", value="path/to/tasmax_thresholds.nc", unit="K", offset=5
)
assert isinstance(grided_t, BasicThreshold)

# -- Daily percentile threshold, from a file
tasmax = xarray.open_dataset("path/to/tasmax_thresholds.nc").tasmax
doys = xclim.core.calendar.percentile_doy(tasmax)
doy_file_t = build_threshold(operator=">=", value=doys)
assert isinstance(doy_file_t, PercentileThreshold)

# -- Bounded threshold
bounded_t = build_threshold(">= -20 degree AND <= 20 degree ")
# equivalent to:
x = build_threshold(">= -20 degree")
y = build_threshold("<= 20 degree")
bounded_t2 = x & y
assert bounded_t == bounded_t2
# equivalent to:
bounded_t3 = build_threshold(thresholds=[x, y], logical_link="AND")
assert bounded_t == bounded_t3
assert isinstance(bounded_t, BoundedThreshold)

icclim#

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`icclim`#