xclim.core package¶

Core tools¶

Methods to build indicators and compute functions and to help handle climate data in general.

The top-level of this module contains exceptions and type descriptors used internally.

Subpackages¶

xclim.core.indicator package

Submodules¶

xclim.core._exceptions module¶

Exceptions and error handling utilities.

exception xclim.core._exceptions.MissingVariableError[source]

Bases: ValueError

Error raised when a dataset is passed to an indicator but one of the needed variable is missing.

exception xclim.core._exceptions.ValidationError[source]

Bases: ValueError

Error raised when input data to an indicator fails the validation tests.

property msg

xclim.core._exceptions.raise_warn_or_log(err, mode, msg=None, err_type=<class 'ValueError'>, stacklevel=1)[source]

Raise, warn or log an error according.

Parameters:

err (Exception) – An error.
mode ({‘ignore’, ‘log’, ‘warn’, ‘raise’}) – What to do with the error.
msg (str, optional) – The string used when logging or warning. Defaults to the msg attr of the error (if present) or to “Failed with <err>”.
err_type (type) – The type of error/exception to raise.
stacklevel (int) – Stacklevel when warning. Relative to the call of this function (1 is added).

xclim.core._types module¶

Type annotations and constants used throughout xclim.

xclim.core._types.Condition

Type annotation of the condition/comparison operators

alias of Literal[‘>’, ‘gt’, ‘<’, ‘lt’, ‘>=’, ‘ge’, ‘<=’, ‘le’]

class xclim.core._types.DateStr

Type annotation for strings representing full dates (YYYY[-MM[-DD[THH[:MM]]]]), may include time.

alias of str

class xclim.core._types.DayOfYearStr

Type annotation for strings representing dates without a year (MM-DD).

alias of str

xclim.core._types.Freq

Type annotation for frequency strings

alias of Literal[‘D’, ‘ME’, ‘MS’, ‘QE’, ‘QE-APR’, ‘QE-AUG’, ‘QE-DEC’, ‘QE-FEB’, ‘QE-JAN’, ‘QE-JUL’, ‘QE-JUN’, ‘QE-MAR’, ‘QE-MAY’, ‘QE-NOV’, ‘QE-OCT’, ‘QE-SEP’, ‘QS’, ‘QS-APR’, ‘QS-AUG’, ‘QS-DEC’, ‘QS-FEB’, ‘QS-JAN’, ‘QS-JUL’, ‘QS-JUN’, ‘QS-MAR’, ‘QS-MAY’, ‘QS-NOV’, ‘QS-OCT’, ‘QS-SEP’, ‘YE’, ‘YE-APR’, ‘YE-AUG’, ‘YE-DEC’, ‘YE-FEB’, ‘YE-JAN’, ‘YE-JUL’, ‘YE-JUN’, ‘YE-MAR’, ‘YE-MAY’, ‘YE-NOV’, ‘YE-OCT’, ‘YE-SEP’, ‘YS’, ‘YS-APR’, ‘YS-AUG’, ‘YS-DEC’, ‘YS-FEB’, ‘YS-JAN’, ‘YS-JUL’, ‘YS-JUN’, ‘YS-MAR’, ‘YS-MAY’, ‘YS-NOV’, ‘YS-OCT’, ‘YS-SEP’, ‘h’, ‘min’, ‘ms’, ‘s’, ‘us’]

class xclim.core._types.InputKind(*values)[source]

Bases: enum.IntEnum

Constants for defining types or kinds of indicator parameters.

For use by external parsers to determine what kind of data the indicator expects. On the creation of an indicator, the appropriate constant is stored in xclim.core.indicator.Indicator.parameters. The integer value is what gets stored in the output of xclim.core.indicator.Indicator.json().

For developers: For each constant, the docstring specifies the annotation a parameter of a compute function should use in order to be picked up by the indicator constructor. Notice that we are using the annotation format as described in PEP 604, i.e. with ‘|’ indicating a union and without import objects from typing.

BOOL = 9

A boolean flag.

Annotation : bool, may be optional.

DATASET = 70

An xarray dataset.

Developers : as compute functions only accept DataArrays, this should only be added by the indicator.

DATE = 7

A date in the YYYY-MM-DD format, may include a time.

Annotation : xclim.core.utils.DateStr (may be optional).

DAY_OF_YEAR = 6

A date, but without a year, in the MM-DD format.

Annotation : xclim.core.utils.DayOfYearStr (may be optional).

DICT = 10

A dictionary.

Annotation : dict or dict | None, may be optional.

FREQ_STR = 3

A string representing an “offset alias”, as defined by pandas.

See the Pandas documentation on Offset aliases for a list of valid aliases.

Annotation : str + freq as the parameter name.

KWARGS = 50

A mapping from argument name to value.

Developers : maps the **kwargs. Please use as little as possible.

MASK = 11: A mask or flag or scalar. Any value without units that might be passed as a non-temporal DataArray. Can be a DataArray, a single bool or a single float.

Annotation : xr.DataArray | bool or xr.DataArray | float, may be optional.

NUMBER = 4

A number.

Annotation : int, float and unions thereof, potentially optional.

NUMBER_SEQUENCE = 8

A sequence of numbers

Annotation : Sequence[int], Sequence[float] and unions thereof, may include single int and float, may be optional.

OPTIONAL_VARIABLE = 1

An optional data variable (DataArray or variable name).

Annotation : xr.DataArray | None. The default should be None.

OTHER_PARAMETER = 99

An object that fits None of the previous kinds.

Developers : This is the fallback kind, it will raise an error in xclim’s unit tests if used.

QUANTIFIED = 2

A quantity with units, either as a string (scalar), a pint.Quantity (scalar) or a DataArray (with units set).

Annotation : xclim.core.utils.Quantified and an entry in the xclim.core.units.declare_units() decorator. “Quantified” translates to str | xr.DataArray | pint.util.Quantity.

STRING = 5

A simple string.

Annotation : str or str | None. In most cases, this kind of parameter makes sense with choices indicated in the docstring’s version of the annotation with curly braces. See Defining new index-like compute functions.

VARIABLE = 0

A data variable (DataArray or variable name).

Annotation : xr.DataArray. May not include anything else, may not be optional.

xclim.core._types.KIND_ANNOTATION = {InputKind.VARIABLE: 'str or DataArray', InputKind.OPTIONAL_VARIABLE: 'str or DataArray, optional', InputKind.QUANTIFIED: 'quantity (string or DataArray, with units)', InputKind.FREQ_STR: 'offset alias (string)', InputKind.NUMBER: 'number', InputKind.STRING: 'str', InputKind.DAY_OF_YEAR: 'date (string, MM-DD)', InputKind.DATE: 'date (string, YYYY-MM-DD)', InputKind.NUMBER_SEQUENCE: 'number or sequence of numbers', InputKind.BOOL: 'boolean', InputKind.DICT: 'dict', InputKind.MASK: 'DataArray or scalar', InputKind.KWARGS: '', InputKind.DATASET: 'Dataset, optional', InputKind.OTHER_PARAMETER: 'Any'}: Mapping from InputKind to human-readable annotations, to use in the Parameters section of a numpydoc style docstring (and not for function signatures).

class xclim.core._types.Quantified

Type annotation for thresholds and other not-exactly-a-variable quantities

alias of TypeVar(‘Quantified’, xarray.DataArray, str, pint.registry.Quantity)

xclim.core._types.Reducer

Type annotation for reducing/resampling function names, or a function that reduces the “time” dimension.

alias of Literal[‘min’, ‘max’, ‘mean’, ‘std’, ‘var’, ‘count’, ‘sum’, ‘integral’, ‘doymin’, ‘doymax’]

xclim.core._types.TimeRange

Type annotation for a range between to full dates (YYYY[-MM[-DD]])

alias of tuple[xclim.core._types.DateStr, xclim.core._types.DateStr]

xclim.core._types.VARIABLES = {'air_density': {'canonical_units': 'kg m-3', 'cell_methods': 'time: mean', 'description': 'Air density.', 'dimensions': '[density]', 'standard_name': 'air_density'}, 'areacella': {'canonical_units': 'm2', 'cell_methods': 'area: sum', 'description': 'Atmospheric grid cell area.', 'dimensions': '[area]', 'standard_name': 'cell_area'}, 'areacello': {'canonical_units': 'm2', 'cell_methods': 'area: sum', 'description': 'Cell area (over the ocean).', 'dimensions': '[area]', 'standard_name': 'cell_area'}, 'ci': {'canonical_units': 'W m-2', 'cell_methods': 'time: mean', 'description': 'Clearness index.', 'dimensions': '[]'}, 'discharge': {'canonical_units': 'm3 s-1', 'cell_methods': 'time: mean', 'description': 'The amount of water, in all phases, flowing in the river channel and flood plain.', 'dimensions': '[length]**3/[time]', 'standard_name': 'water_volume_transport_in_river_channel'}, 'dtr': {'canonical_units': 'K', 'cell_methods': 'time: range within days', 'description': 'Diurnal temperature range.', 'dimensions': '[temperature]', 'standard_name': 'air_temperature'}, 'evspsbl': {'canonical_units': 'kg m-2 s-1', 'cell_methods': 'time: mean', 'description': 'Actual evapotranspiration flux.', 'dimensions': '[discharge]', 'standard_name': 'water_evapotranspiration_flux'}, 'evspsblpot': {'canonical_units': 'kg m-2 s-1', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Potential evapotranspiration flux.', 'dimensions': '[discharge]', 'standard_name': 'water_potential_evapotranspiration_flux'}, 'gwl': {'canonical_units': 'm', 'description': 'Groundwater level.', 'dimensions': '[length]'}, 'hurs': {'canonical_units': '%', 'cell_methods': 'time: mean', 'data_flags': [{'percentage_values_outside_of_bounds': None}], 'description': 'Relative humidity.', 'dimensions': '[]', 'standard_name': 'relative_humidity'}, 'huss': {'canonical_units': '1', 'cell_methods': 'time: mean', 'description': 'Specific humidity.', 'dimensions': '[]', 'standard_name': 'specific_humidity'}, 'lat': {'canonical_units': 'degrees_north', 'description': 'Latitude.', 'dimensions': '[]', 'standard_name': 'latitude'}, 'pr': {'canonical_units': 'kg m-2 s-1', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}, {'very_large_precipitation_events': {'thresh': '300 mm d-1'}}, {'values_op_thresh_repeating_for_n_or_more_days': {'n': 5, 'op': 'eq', 'thresh': '5 mm d-1'}}, {'values_op_thresh_repeating_for_n_or_more_days': {'n': 10, 'op': 'eq', 'thresh': '1 mm d-1'}}], 'description': 'Surface precipitation flux (all phases).', 'dimensions': '[precipitation]', 'standard_name': 'precipitation_flux'}, 'prc': {'canonical_units': 'kg m-2 s-1', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Precipitation flux due to the convection schemes of the model (all phases).', 'dimensions': '[precipitation]', 'standard_name': 'convective_precipitation_flux'}, 'prra': {'canonical_units': 'kg m-2 s-1', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Surface rain flux.', 'dimensions': '[precipitation]', 'standard_name': 'rainfall_flux'}, 'prsn': {'canonical_units': 'kg m-2 s-1', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Surface snowfall flux.', 'dimensions': '[mass]/([area]*[time])', 'standard_name': 'snowfall_flux'}, 'prsnd': {'canonical_units': 'm s-1', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Surface snowfall rate.', 'dimensions': '[length]/[time]'}, 'ps': {'canonical_units': 'Pa', 'cell_methods': 'time: mean', 'data_flags': [{'values_repeating_for_n_or_more_days': {'n': 5}}], 'description': 'Air pressure at surface', 'standard_name': 'surface_air_pressure'}, 'psl': {'canonical_units': 'Pa', 'cell_methods': 'time: mean', 'data_flags': [{'values_repeating_for_n_or_more_days': {'n': 5}}], 'description': 'Air pressure at sea level.', 'dimensions': '[pressure]', 'standard_name': 'air_pressure_at_sea_level'}, 'qspec': {'canonical_units': 'm s-1', 'cell_methods': 'time: mean', 'data_flags': [{'specific_discharge_extremely_high': {'thresh': '100 mm d-1'}}], 'description': 'The amount of water, in all phases, flowing in the river channel and flood plain per unit of drainage area.\n', 'dimensions': '[length]/[time]'}, 'rivo': {'canonical_units': 'm3 s-1', 'cell_methods': 'time: mean', 'description': 'The amount of water, in all phases, flowing in the river channel and flood plain.', 'dimensions': '[length]**3/[time]', 'standard_name': 'water_volume_transport_in_river_channel'}, 'rlds': {'canonical_units': 'W m-2', 'cell_methods': 'time: mean', 'description': 'Incoming longwave radiation.', 'dimensions': '[radiation]', 'standard_name': 'surface_downwelling_longwave_flux'}, 'rls': {'canonical_units': 'W m-2', 'cell_methods': 'time: mean', 'description': 'Net longwave radiation.', 'dimensions': '[radiation]', 'standard_name': 'surface_net_downward_longwave_flux'}, 'rlus': {'canonical_units': 'W m-2', 'cell_methods': 'time: mean', 'description': 'Outgoing longwave radiation.', 'dimensions': '[radiation]', 'standard_name': 'surface_upwelling_longwave_flux'}, 'rsds': {'canonical_units': 'W m-2', 'cell_methods': 'time: mean', 'description': 'Incoming shortwave radiation.', 'dimensions': '[radiation]', 'standard_name': 'surface_downwelling_shortwave_flux'}, 'rss': {'canonical_units': 'W m-2', 'cell_methods': 'time: mean', 'description': 'Net shortwave radiation.', 'dimensions': '[radiation]', 'standard_name': 'surface_net_downward_shortwave_flux'}, 'rsus': {'canonical_units': 'W m-2', 'cell_methods': 'time: mean', 'description': 'Outgoing shortwave radiation.', 'dimensions': '[radiation]', 'standard_name': 'surface_upwelling_shortwave_flux'}, 'sfcWind': {'canonical_units': 'm s-1', 'cell_methods': 'time: mean', 'data_flags': [{'wind_values_outside_of_bounds': {'lower': '0 m s-1', 'upper': '46.0 m s-1'}}, {'values_op_thresh_repeating_for_n_or_more_days': {'n': 6, 'op': 'gt', 'thresh': '2.0 m s-1'}}], 'description': 'Surface wind speed.', 'dimensions': '[speed]', 'standard_name': 'wind_speed'}, 'sfcWindfromdir': {'canonical_units': 'degree', 'cell_methods': 'time: mean', 'cmip6': False, 'description': 'Surface wind direction of provenance.', 'dimensions': '[]', 'standard_name': 'wind_from_direction'}, 'sfcWindmax': {'canonical_units': 'm s-1', 'cell_methods': 'time: max', 'data_flags': [{'wind_values_outside_of_bounds': {'lower': '0 m s-1', 'upper': '46.0 m s-1'}}, {'values_op_thresh_repeating_for_n_or_more_days': {'n': 6, 'op': 'gt', 'thresh': '2.0 m s-1'}}], 'description': 'Surface maximum wind speed.', 'dimensions': '[speed]', 'standard_name': 'wind_speed'}, 'siconc': {'canonical_units': '%', 'cell_methods': 'time: mean', 'data_flags': [{'percentage_values_outside_of_bounds': None}], 'description': 'Sea ice concentration (area fraction).', 'dimensions': '[]', 'standard_name': 'sea_ice_area_fraction'}, 'smd': {'canonical_units': 'mm d-1', 'cell_methods': 'time: mean', 'description': 'Soil moisture deficit.', 'dimensions': '[precipitation]', 'standard_name': 'soil_moisture_deficit'}, 'snc': {'canonical_units': '%', 'cell_methods': 'time: mean', 'data_flags': [{'percentage_values_outside_of_bounds': None}], 'description': 'Surface area fraction covered by snow.', 'dimensions': '[]', 'standard_name': 'surface_snow_area_fraction'}, 'snd': {'canonical_units': 'm', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Surface snow thickness.', 'dimensions': '[length]', 'standard_name': 'surface_snow_thickness'}, 'snr': {'canonical_units': 'kg m-3', 'cell_methods': 'time: mean', 'description': 'Surface snow density.', 'dimensions': '[density]', 'standard_name': 'surface_snow_density'}, 'snw': {'canonical_units': 'kg m-2', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Surface snow amount.', 'dimensions': '[mass]/[area]', 'standard_name': 'surface_snow_amount'}, 'sund': {'canonical_units': 's', 'cell_methods': 'time: mean', 'cmip6': False, 'description': 'Duration of sunshine.', 'dimensions': '[time]', 'standard_name': 'duration_of_sunshine'}, 'swe': {'canonical_units': 'm', 'cell_methods': 'time: mean', 'data_flags': [{'negative_accumulation_values': None}], 'description': 'Surface snow water equivalent amount', 'dimensions': '[length]', 'standard_name': 'lwe_thickness_of_surface_snow_amount'}, 'tas': {'canonical_units': 'K', 'cell_methods': 'time: mean', 'data_flags': [{'temperature_extremely_high': {'thresh': '60 degC'}}, {'temperature_extremely_low': {'thresh': '-90 degC'}}, {'tas_exceeds_tasmax': None}, {'tas_below_tasmin': None}, {'values_repeating_for_n_or_more_days': {'n': 5}}, {'outside_n_standard_deviations_of_climatology': {'n': 5, 'window': 5}}], 'description': 'Mean surface temperature.', 'dimensions': '[temperature]', 'standard_name': 'air_temperature'}, 'tasmax': {'canonical_units': 'K', 'cell_methods': 'time: maximum', 'data_flags': [{'temperature_extremely_high': {'thresh': '60 degC'}}, {'temperature_extremely_low': {'thresh': '-90 degC'}}, {'tas_exceeds_tasmax': None}, {'tasmax_below_tasmin': None}, {'values_repeating_for_n_or_more_days': {'n': 5}}, {'outside_n_standard_deviations_of_climatology': {'n': 5, 'window': 5}}], 'description': 'Maximum surface temperature.', 'dimensions': '[temperature]', 'standard_name': 'air_temperature'}, 'tasmin': {'canonical_units': 'K', 'cell_methods': 'time: minimum', 'data_flags': [{'temperature_extremely_high': {'thresh': '60 degC'}}, {'temperature_extremely_low': {'thresh': '-90 degC'}}, {'tasmax_below_tasmin': None}, {'tas_below_tasmin': None}, {'values_repeating_for_n_or_more_days': {'n': 5}}, {'outside_n_standard_deviations_of_climatology': {'n': 5, 'window': 5}}], 'description': 'Minimum surface temperature.', 'dimensions': '[temperature]', 'standard_name': 'air_temperature'}, 'tdps': {'canonical_units': 'K', 'cell_methods': 'time: mean', 'description': 'Mean surface dew point temperature.', 'dimensions': '[temperature]', 'standard_name': 'dew_point_temperature'}, 'thickness_of_rainfall_amount': {'canonical_units': 'm', 'cell_methods': 'time: sum', 'description': 'Accumulated depth of rainfall, i.e. the thickness of a layer of liquid water having the same mass per unit area as the rainfall amount.\n', 'dimensions': '[length]', 'standard_name': 'thickness_of_rainfall_amount'}, 'ua': {'canonical_units': 'm s-1', 'cell_methods': 'time: mean', 'description': 'Eastward component of the wind velocity (in the atmosphere).', 'dimensions': '[speed]', 'standard_name': 'eastward_wind'}, 'uas': {'canonical_units': 'm s-1', 'cell_methods': 'time: mean', 'description': 'Eastward component of the wind velocity (at the surface).', 'dimensions': '[speed]', 'standard_name': 'eastward_wind'}, 'vas': {'canonical_units': 'm s-1', 'cell_methods': 'time: mean', 'description': 'Northward component of the wind velocity (at the surface).', 'dimensions': '[speed]', 'standard_name': 'northward_wind'}, 'wind_speed': {'canonical_units': 'm s-1', 'cell_methods': 'time: mean', 'description': 'Wind speed.', 'dimensions': '[speed]', 'standard_name': 'wind_speed'}, 'wsgsmax': {'canonical_units': 'm s-1', 'cell_methods': 'time: maximum', 'cmip6': False, 'data_flags': [{'wind_values_outside_of_bounds': {'lower': '0 m s-1', 'upper': '76.0 m s-1'}}, {'values_op_thresh_repeating_for_n_or_more_days': {'n': 5, 'op': 'gt', 'thresh': '4.0 m s-1'}}], 'description': 'Maximum surface wind speed.', 'dimensions': '[speed]', 'standard_name': 'wind_speed_of_gust'}}

Official variables definitions.

A mapping from variable name to a dict with the following keys:

canonical_units [required] : The conventional units used by this variable.
cell_methods [optional] : The conventional cell_methods CF attribute
description [optional] : A description of the variable, to populate dynamically generated docstrings.
dimensions [optional] : The dimensionality of the variable, an abstract version of the units. See xclim.units.units._dimensions.keys() for available terms. This is especially useful for making xclim aware of “[precipitation]” variables.
standard_name [optional] : If it exists, the CF standard name.
data_flags [optional] : Data flags methods (xclim.core.dataflags) applicable to this variable. The method names are keys and values are dicts of keyword arguments to pass (an empty dict if there’s nothing to configure).

xclim.core._types.infer_kind_from_parameter(param)[source]

Return the appropriate InputKind constant from an inspect.Parameter object.

Parameters:: param (Parameter) – An inspect.Parameter instance.
Return type:: InputKind
Returns:: InputKind – The appropriate InputKind constant.

Notes

The correspondence between parameters and kinds is documented in xclim.core.utils.InputKind.

xclim.core._types.is_percentile_dataarray(source)[source]

Evaluate whether a DataArray is a Percentile.

A percentile DataArray must have ‘climatology_bounds’ attributes and either a quantile or percentiles coordinate, the window is not mandatory.

Parameters:: source (xr.DataArray) – The DataArray to evaluate.
Return type:: bool
Returns:: bool – True if the DataArray is a percentile.

xclim.core.bootstrapping module¶

Module comprising the bootstrapping algorithm for indicators.

xclim.core.bootstrapping._get_bootstrap_freq(freq)[source]

xclim.core.bootstrapping._get_year_label(year_dt)[source]

Return type:: str

xclim.core.bootstrapping.bootstrap_func(compute_index_func, **kwargs)[source]

Bootstrap the computation of percentile-based indicators.

Indicators measuring exceedance over percentile-based thresholds (such as tx90p) may contain artificial discontinuities at the beginning and end of the reference period used to calculate percentiles. The bootstrap procedure can reduce those discontinuities by iteratively computing the percentile estimate and the index on altered reference periods.

These altered reference periods are themselves built iteratively: When computing the index for year x, the bootstrapping creates as many altered reference periods as the number of years in the reference period. To build one altered reference period, the values of year x are replaced by the values of another year in the reference period, then the index is computed on this altered period. This is repeated for each year of the reference period, excluding year x. The final result of the index for year x is then the average of all the index results on altered years.

Parameters:

compute_index_func (Callable) – Index function.
**kwargs (dict) – Arguments to func.

Return type:

DataArray

Returns:

xr.DataArray – The result of func with bootstrapping.

Notes

This function is meant to be used by the percentile_bootstrap decorator. The parameters of the percentile calculation (percentile, window, reference_period) are stored in the attributes of the percentile DataArray. The bootstrap algorithm implemented here does the following:

For each temporal grouping in the calculation of the index
    If the group `g_t` is in the reference period
        For every other group `g_s` in the reference period
            Replace group `g_t` by `g_s`
            Compute percentile on resampled time series
            Compute index function using percentile
        Average output from index function over all resampled time series
    Else compute index function using original percentile

References

Zhang, Hegerl, Zwiers, and Kenyon [2005]

xclim.core.bootstrapping.build_bootstrap_year_da(da, groups, label, dim='time')[source]

Return an array where every other group replaces a group in the original along a new dimension.

Parameters:

da (DataArray) – Original input array over the reference period.
groups (dict) – Output of grouping functions, such as DataArrayResample.groups.
label (Any) – Key identifying the group item to replace.
dim (str) – Dimension recognised as time. Default: time.

Return type:

DataArray

Returns:

DataArray – Array where one group is replaced by values from every other group along the bootstrap dimension.

xclim.core.bootstrapping.percentile_bootstrap(func)[source]

Decorator applying a bootstrap step to the calculation of exceedance over a percentile threshold.

This feature is experimental.

Parameters:: func (Callable) – The function to decorate.
Return type:: Callable
Returns:: Callable – The decorated function.

Notes

Bootstrapping avoids discontinuities in the exceedance between the reference period over which percentiles are computed, and “out of reference” periods. See bootstrap_func for details.

Declaration example:

@declare_units(tas="[temperature]", t90="[temperature]")
@percentile_bootstrap
def tg90p(
    tas: xarray.DataArray,
    t90: xarray.DataArray,
    freq: Freq = "YS",
    bootstrap: bool = False,
) -> xarray.DataArray:
    pass

Examples

>>> from xclim.core.calendar import percentile_doy
>>> from xclim.compute import tg90p
>>> tas = xr.open_dataset(path_to_tas_file).tas
>>> # To start bootstrap reference period must not fully overlap the studied period.
>>> tas_ref = tas.sel(time=slice("1990-01-01", "1992-12-31"))
>>> t90 = percentile_doy(tas_ref, window=5, per=90)
>>> tas_90th_percentile = tg90p(tas=tas, tas_per=t90.sel(percentiles=90), freq="YS", bootstrap=True)

xclim.core.calendar module¶

Calendar Handling Utilities¶

Helper function to handle dates, times and different calendars with xarray.

xclim.core.calendar.add_season_coord(ds, freq)[source]

Add a season coordinates on a resampled dataset.

Parameters:

ds (xr.Dataset or xr.DataArray) – The xarray object with a “time” coordinate. Only supports daily or coarser frequencies (excluding weekly). The time axis must be complete and regular (xr.infer_freq(ds.time) doesn’t fail).
freq (str) – Resampling frequency. Must be between “MS” and “YS” and divide a year evenly.

Return type:

TypeVar(DataType, DataArray, Dataset)

Returns:

xr.DataArray or xr.Dataset – Input dataset with season coordinate.

xclim.core.calendar.adjust_doy_calendar(source, target)[source]

Interpolate from one set of dayofyear range to another calendar.

Interpolate an array defined over a dayofyear range (say 1 to 360) to another dayofyear range (say 1 to 365).

Parameters:

source (xr.DataArray or xr.Dataset) – Array with dayofyear coordinate.
target (xr.DataArray or xr.Dataset) – Array with time coordinate.

Return type:

TypeVar(DataType, DataArray, Dataset)

Returns:

xr.DataArray or xr.Dataset – Interpolated source array over coordinates spanning the target dayofyear range.

xclim.core.calendar.build_climatology_bounds(da)[source]

Build the climatology_bounds property with the start and end dates of input data.

Parameters:: da (xr.DataArray) – The input data. Must have a time dimension.
Return type:: list[str]
Returns:: list of str – The climatology bounds.

xclim.core.calendar.climatological_mean_doy(arr, window=5)[source]

Calculate the climatological mean and standard deviation for each day of the year.

Parameters:

arr (xarray.DataArray) – Input array.
window (int) – Window size in days.

Return type:

tuple[DataArray, DataArray]

Returns:

xarray.DataArray, xarray.DataArray – Mean and standard deviation.

xclim.core.calendar.common_calendar(calendars, join='outer')[source]

Return a calendar common to all calendars from a list.

Uses the hierarchy: 360_day < noleap < standard < all_leap.

Parameters:

calendars (Sequence of str) – List of calendar names.
join ({‘inner’, ‘outer’}) –
The criterion for the common calendar.
- ‘outer’: the common calendar is the biggest calendar (in number of days by year) that will include all the
  dates of the other calendars. When converting the data to this calendar, no timeseries will lose elements, but some might be missing (gaps or NaNs in the series).
- ‘inner’: the common calendar is the smallest calendar of the list.
  When converting the data to this calendar, no timeseries will have missing elements (no gaps or NaNs), but some might be dropped.

Return type:

str

Returns:

str – Returns “default” only if all calendars are “default”.

Examples

>>> common_calendar(["360_day", "noleap", "default"], join="outer")
'standard'
>>> common_calendar(["360_day", "noleap", "default"], join="inner")
'360_day'

xclim.core.calendar.compare_offsets(freqA, op, freqB)[source]

Compare offsets string based on their approximate length, according to a given operator.

Offsets are compared based on their length approximated for a period starting after 1970-01-01 00:00:00. If the offsets are from the same category (same first letter), only the multiplier prefix is compared (QS-DEC == QS-JAN, MS < 2MS). “Business” offsets are not implemented.

Parameters:

freqA (str) – RHS Date offset string (‘YS’, ‘1D’, ‘QS-DEC’, …).
op ({“>”, “gt”, “<”, “lt”, “>=”, “ge”, “<=”, “le”, “==”, “eq”, “!=”, “ne”}) – Operator to use.
freqB (str) – LHS Date offset string (‘YS’, ‘1D’, ‘QS-DEC’, …).

Return type:

bool

Returns:

bool – The result of freqA op freqB.

xclim.core.calendar.construct_offset(mult, base, start_anchored, anchor)[source]

Reconstruct an offset string from its parts.

Parameters:

mult (int) – The period multiplier (>= 1).
base (str) – The base period string (one char).
start_anchored (bool) – If True and base in [Y, Q, M], adds the “S” flag, False add “E”.
anchor (str, optional) – The month anchor of the offset. Defaults to JAN for bases YS and QS and to DEC for bases YE and QE.

Returns:

str – An offset string, conformant to pandas-like naming conventions.

Notes

This provides the mirror opposite functionality of parse_offset().

xclim.core.calendar.convert_doy(source, target_cal, source_cal=None, align_on='year', missing=nan, dim='time')[source]

Convert the calendar of day of year (doy) data.

Parameters:

source (xr.DataArray or xr.Dataset) – Day of year data (range [1, 366], max depending on the calendar). If a Dataset, the function is mapped to each variable with attribute is_day_of_year == 1.
target_cal (str) – Name of the calendar to convert to.
source_cal (str, optional) – Calendar the doys are in. If not given, will use the “calendar” attribute of source or, if absent, the calendar of its dim axis.
align_on ({‘date’, ‘year’}) – If ‘year’ (default), the doy is seen as a “percentage” of the year and is simply rescaled onto the new doy range. This always results in floating point data, changing the decimal part of the value. If ‘date’, the doy is seen as a specific date. See notes. This never changes the decimal part of the value.
missing (Any) – If align_on is “date” and the new doy doesn’t exist in the new calendar, this value is used.
dim (str) – Name of the temporal dimension.

Return type:

TypeVar(DataType, DataArray, Dataset)

Returns:

xr.DataArray or xr.Dataset – The converted doy data.

xclim.core.calendar.days_since_to_doy(da, start=None, calendar=None)[source]

Reverse the conversion made by doy_to_days_since().

Converts data given in days since a specific date to day-of-year.

Parameters:

da (xr.DataArray) – The result of doy_to_days_since().
start (DateOfYearStr, optional) – da is considered as days since that start date (in the year of the time index). If None (default), it is read from the attributes.
calendar (str, optional) – Calendar the “days since” were computed in. If None (default), it is read from the attributes.

Return type:

DataArray

Returns:

xr.DataArray – Same shape as da, values as day of year.

Examples

>>> time = xr.date_range("2020-07-01", "2021-07-01", freq="YS-JUL")
>>> da = xr.DataArray(
...     [-86, 92],
...     dims=("time",),
...     coords={"time": time},
...     attrs={"units": "days since 10-02"},
... )
>>> days_since_to_doy(da).values
array([190,   2])

xclim.core.calendar.doy_from_string(doy, year, calendar)[source]

Return the day-of-year corresponding to an “MM-DD” string for a given year and calendar.

Parameters:

doy (str) – The day of year in the format “MM-DD”.
year (int) – The year.
calendar (str) – The calendar name.

Return type:

int

Returns:

int – The day of year.

xclim.core.calendar.doy_to_days_since(da, start=None, calendar=None)[source]

Convert day-of-year data to days since a given date.

This is useful for computing meaningful statistics on doy data.

Parameters:

da (xr.DataArray) – Array of “day-of-year”, usually int dtype, must have a time dimension. Sampling frequency should be finer or similar to yearly and coarser than daily.
start (date of year str, optional) – A date in “MM-DD” format, the base day of the new array. If None (default), the time axis is used. Passing start only makes sense if da has a yearly sampling frequency.
calendar (str, optional) – The calendar to use when computing the new interval. If None (default), the calendar attribute of the data or of its time axis is used. All time coordinates of da must exist in this calendar. No check is done to ensure doy values exist in this calendar.

Return type:

DataArray

Returns:

xr.DataArray – Same shape as da, int dtype, day-of-year data translated to a number of days since a given date. If start is not None, there might be negative values.

Notes

The time coordinates of da are considered as the START of the period. For example, a doy value of 350 with a timestamp of ‘2020-12-31’ is understood as ‘2021-12-16’ (the 350th day of 2021). Passing start=None, will use the time coordinate as the base, so in this case the converted value will be 350 “days since time coordinate”.

Examples

>>> time = xr.date_range("2020-07-01", "2021-07-01", freq="YS-JUL")
>>> # July 8th 2020 and Jan 2nd 2022
>>> da = xr.DataArray([190, 2], dims=("time",), coords={"time": time})
>>> # Convert to days since Oct. 2nd, of the data's year.
>>> doy_to_days_since(da, start="10-02").values
array([-86,  92])

xclim.core.calendar.ensure_cftime_array(time)[source]

Convert an input 1D array to a numpy array of cftime objects.

Python datetimes are converted to cftime.DatetimeGregorian (“standard” calendar).

Parameters:: time (sequence) – A 1D array of datetime-like objects.
Return type:: ndarray | Sequence[datetime]
Returns:: np.ndarray – An array of cftime.datetime objects.
Raises:: ValueError – When unable to cast the input.:

xclim.core.calendar.get_calendar(obj, dim='time')[source]

Return the calendar of an object.

Parameters:

obj (Any) – An object defining some date. If obj is an array/dataset with a datetime coordinate, use dim to specify its name. Values must have either a datetime64 dtype or a cftime dtype. obj can also be a python datetime.datetime, a cftime object or a pandas Timestamp or an iterable of those, in which case the calendar is inferred from the first value.
dim (str) – Name of the coordinate to check (if obj is a DataArray or Dataset).

Return type:

str

Returns:

str – The Climate and Forecasting (CF) calendar name. Will always return “standard” instead of “gregorian”, following CF-Conventions v1.9.

Raises:

ValueError – If no calendar could be inferred.

xclim.core.calendar.is_offset_divisor(divisor, offset)[source]

Check that divisor is a divisor of offset.

A frequency is a “divisor” of another if a whole number of periods of the former fit within a single period of the latter.

Parameters:

divisor (str) – The divisor frequency.
offset (str) – The large frequency.

Returns:

bool – Whether divisor is a divisor of offset.

Examples

>>> is_offset_divisor("QS-JAN", "YS")
True
>>> is_offset_divisor("QS-DEC", "YS-JUL")
False
>>> is_offset_divisor("D", "ME")
True

xclim.core.calendar.parse_offset(freq)[source]

Parse an offset string.

Parse a frequency offset and, if needed, convert to cftime-compatible components.

Parameters:

freq (str) – Frequency offset.

Return type:

tuple[int, str, bool, str | None]

Returns:

multiplier (int) – Multiplier of the base frequency. “[n]W” is always replaced with “[7n]D”, as xarray doesn’t support “W” for cftime indexes.
offset_base (str) – Base frequency.
is_start_anchored (bool) – Whether coordinates of this frequency should correspond to the beginning of the period (True) or its end (False). Can only be False when base is Y, Q or M; in other words, xclim assumes frequencies finer than monthly are all start-anchored.
anchor (str, optional) – Anchor date for bases Y or Q. As xarray doesn’t support “W”, neither does xclim (anchor information is lost when given).

xclim.core.calendar.percentile_doy(arr, window=5, per=10.0, alpha=0.3333333333333333, beta=0.3333333333333333, copy=True)[source]

Percentile value for each day of the year.

Return the climatological percentile over a moving window around each day of the year. Different quantile estimators can be used by specifying alpha and beta according to specifications given by Hyndman and Fan [1996]. The default definition corresponds to method 8, which meets multiple desirable statistical properties for sample quantiles. Note that numpy.percentile corresponds to method 7, with alpha and beta set to 1.

Parameters:

arr (xr.DataArray) – Input data, a daily frequency (or coarser) is required.
window (int) – Number of time-steps around each day of the year to include in the calculation.
per (float or sequence of float) – Percentile(s) between [0, 100].
alpha (float) – Plotting position parameter.
beta (float) – Plotting position parameter.
copy (bool) – If True (default) the input array will be deep-copied. It’s a necessary step to keep the data integrity, but it can be costly. If False, no copy is made of the input array. It will be mutated and rendered unusable, but performances may significantly improve. Put this flag to False only if you understand the consequences.

Return type:

DataArray

Returns:

xr.DataArray – The percentiles indexed by the day of the year. For calendars with 366 days, percentiles of doys 1-365 are interpolated to the 1-366 range.

References

Hyndman and Fan [1996]

xclim.core.calendar.resample_doy(doy, arr)[source]

Create a temporal DataArray where each day takes the value defined by the day-of-year.

Parameters:

doy (xr.DataArray or xr.Dataset) – Array with dayofyear coordinate.
arr (xr.DataArray or xr.Dataset) – Array with time coordinate.

Return type:

TypeVar(DataType, DataArray, Dataset)

Returns:

xr.DataArray or xr.Dataset – An array with the same dimensions as doy, except for dayofyear, which is replaced by the time dimension of arr. Values are filled according to the day of year value in doy.

xclim.core.calendar.select_time(da, drop=False, season=None, month=None, doy_bounds=None, date_bounds=None, include_bounds=True, include_doy_bounds_nans=True, bounds_freq=None)[source]

Select entries according to a time period.

This conveniently improves xarray’s xarray.DataArray.where() and xarray.DataArray.sel() with fancier ways of indexing over time elements. In addition to the data da and argument drop, only one of season, month, doy_bounds or date_bounds may be passed.

Parameters:

da (xr.DataArray or xr.Dataset) – Input data.
drop (bool) – Whether to drop elements outside the period of interest (True) or to simply mask them (False, default). This option is incompatible with passing date_bounds or array-like doy_bounds.
season (str or sequence of str, optional) – One or more of ‘DJF’, ‘MAM’, ‘JJA’ and ‘SON’.
month (int or sequence of int, optional) – Sequence of month numbers (January = 1 … December = 12).
doy_bounds (2-tuple of optional integers or DataArray, optional) – The bounds as (start, end) of the period of interest expressed in day-of-year, integers going from 1 (January 1st) to 365 or 366 (December 31st). If DataArrays are passed, they must have the same coordinates on the dimensions they share. They may have a time dimension, in which case the selection is done independently for each period defined by the coordinate, which means the time coordinate must have an inferable frequency (see xr.infer_freq()) or the frequency must be passed explicitly with the bounds_freq argument. If None is passed as a bound, it is replaced by the start or end of the year (1 or 366) if the other bound is an integer, or by the start or end of the period defined by the inferred or passed frequency of DataArrays. Timesteps of the input not appearing in the time coordinate of the bounds are considered as “outside the bounds”.
date_bounds (2-tuple of optional strings, optional) – The bounds as (start, end) of the period of interest expressed as dates in the month-day (%m-%d) format. If None is passed as a bounds, it is replaced by the start or end of the period defined by the bounds_freq argument, corresponding to 1st January or 31st December for default “YS” bounds frequency.
include_bounds (bool or 2-tuple of bool, optional) – Whether the bounds of doy_bounds or date_bounds should be inclusive or not. Either one value for both or a tuple. Default is True, meaning bounds are inclusive.
include_doy_bounds_nans (bool, optional) – Whether to include values associated with NaN in doy_bounds. If True (default), missing values (NaN) in the start and end bounds are replaced by the start and end of the period, respectively.
bounds_freq (str, optional) – Needed with array-like doy_bounds without a time dimension or date_bounds, and corresponding to the frequency used to determine the start and end of the period (default “YS”). If doy_bounds have a time dimension, the frequency is first tried to be inferred from the time coordinate of the bounds; if it cannot be inferred, the frequency must be passed explicitly.

Return type:

TypeVar(DataType, DataArray, Dataset)

Returns:

xr.DataArray or xr.Dataset – Selected input values. If drop=False, this has the same length as da (along dimension ‘time’), but with masked (NaN) values outside the period of interest.

Examples

Keep only the values of fall and spring.

>>> ds = xr.open_dataset("ERA5/daily_surface_cancities_1990-1993.nc")
>>> ds.time.size
1461
>>> out = select_time(ds, drop=True, season=["MAM", "SON"])
>>> out.time.size
732

Or all values between two dates (included).

>>> out = select_time(ds, drop=True, date_bounds=("02-29", "03-02"))
>>> out.time.values
array(['1990-03-01T00:00:00.000000000', '1990-03-02T00:00:00.000000000',
       '1991-03-01T00:00:00.000000000', '1991-03-02T00:00:00.000000000',
       '1992-02-29T00:00:00.000000000', '1992-03-01T00:00:00.000000000',
       '1992-03-02T00:00:00.000000000', '1993-03-01T00:00:00.000000000',
       '1993-03-02T00:00:00.000000000'], dtype='datetime64[ns]')

xclim.core.calendar.split_time_to_season_year(ds, freq)[source]

Split a resampled dataset into a yearly time and a season coordinate.

Parameters:

ds (xr.Dataset or xr.DataArray) – The xarray object with a “time” coordinate. Only supports daily or coarser frequencies (excluding weekly). The time axis must be complete and regular (xr.infer_freq(ds.time) doesn’t fail).
freq (str) – Resampling frequency. Must be between “MS” and “YS” and divide a year evenly.

Return type:

TypeVar(DataType, DataArray, Dataset)

Returns:

xr.DataArray or xr.Dataset – Input dataset with season coordinate and yearly time.

xclim.core.calendar.stack_periods(da, window=30, stride=None, min_length=None, freq='YS', dim='period', start='1970-01-01', align_days=True, pad_value='<NA>')[source]

Construct a multi-period array.

Stack different equal-length periods of da into a new ‘period’ dimension.

This is similar to da.rolling(time=window).construct(dim, stride=stride), but adapted for arguments in terms of a base temporal frequency that might be non-uniform (years, months, etc.). It is reversible for some cases (see stride). A rolling-construct method will be much more performant for uniform periods (days, weeks).

Parameters:

da (xr.Dataset or xr.DataArray) – An xarray object with a time dimension. Must have a uniform timestep length. Output might be strange if this does not use a uniform calendar (noleap, 360_day, all_leap).
window (int) – The length of the moving window as a multiple of freq.
stride (int, optional) – At which interval to take the windows, as a multiple of freq. For the operation to be reversible with unstack_periods(), it must divide window into an odd number of parts. Default is window (no overlap between periods).
min_length (int, optional) – Windows shorter than this are not included in the output. Given as a multiple of freq. Default is window (every window must be complete). Similar to the min_periods argument of da.rolling. If freq is annual or quarterly and min_length == ``window, the first period is considered complete if the first timestep is in the first month of the period.
freq (str) – Units of window, stride and min_length, as a frequency string. Must be larger or equal to the data’s sampling frequency. Note that this function offers an easier interface for non-uniform period (like years or months) but is much slower than a rolling-construct method.
dim (str) – The new dimension name.
start (str) – The start argument passed to xarray.date_range() to generate the new placeholder time coordinate.
align_days (bool) – When True (default), an error is raised if the output would have unaligned days across periods. If freq = ‘YS’, day-of-year alignment is checked and if freq is “MS” or “QS”, we check day-in-month. Only uniform-calendar will pass the test for freq=’YS’. For other frequencies, only the 360_day calendar will work. This check is ignored if the sampling rate of the data is coarser than “D”.
pad_value (Any) – When some periods are shorter than others, this value is used to pad them at the end. Passed directly as argument fill_value to xarray.concat(), the default is the same as on that function.

Return type:

TypeVar(DataType, DataArray, Dataset)

Returns:

xr.DataArray – A DataArray with a new period dimension and a time dimension with the length of the longest window. The new time coordinate has the same frequency as the input data but is generated using xarray.date_range() with the given start value. That coordinate is the same for all periods, depending on the choice of window and freq, it might make sense. But for unequal periods or non-uniform calendars, it will certainly not. If stride is a divisor of window, the correct timeseries can be reconstructed with unstack_periods(). The coordinate of period is the first timestep of each window.

xclim.core.calendar.time_bnds(time, freq=None)[source]

Find the time bounds for a datetime index by assuming an uniform sampling frequency.

As we are using datetime indices to stand in for period indices, assumptions regarding the period are made based on the given freq. This function does not implement finding bounds for an irregular time index.

Parameters:

time (DataArray, Dataset, CFTimeIndex, DatetimeIndex, DataArrayResample or DatasetResample) – Object which contains a time index as a proxy representation for a period index.
freq (str, optional) – String specifying the frequency/offset such as ‘MS’, ‘2D’, or ‘3min’ If not given, it is inferred from the time index, which means that index must have at least three elements.

Returns:

DataArray – The time bounds: start and end times of the periods inferred from the time index and a frequency. It has the original time index along it’s time coordinate and a new bnds coordinate. The dtype and calendar of the array are the same as the index. If a period follows another, its start is the same as the other’s end.

Notes

xclim assumes that indexes for greater-than-day frequencies are “floored” down to a daily resolution. For example, the coordinate “2000-01-31 00:00:00” with a “ME” frequency is assumed to mean a period going from “2000-01-01 00:00:00” to “2000-02-01 00:00:00”.

Similarly, it assumes that daily and finer frequencies yield indexes pointing to the period’s start. So “2000-01-31 00:00:00” with a “3h” frequency, means a period going from “2000-01-31 00:00:00” to “2000-01-31 03:00:00”.

See the relevant CF convention <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.13/cf-conventions.html#bounds-one-d>.

xclim.core.calendar.within_bnds_doy(arr, *, low, high)[source]

Return whether array values are within bounds for each day of the year.

Parameters:

arr (xarray.DataArray) – Input array.
low (xarray.DataArray) – Low bound with dayofyear coordinate.
high (xarray.DataArray) – High bound with dayofyear coordinate.

Return type:

DataArray

Returns:

xarray.DataArray – Boolean array of values within doy.

xclim.core.cfchecks module¶

CF-Convention Checking¶

Utilities designed to verify the compliance of metadata with the CF-Convention.

xclim.core.cfchecks._check_cell_methods(data_cell_methods, expected_method)[source]

Return type:: None

xclim.core.cfchecks.cfcheck_from_name(varname, vardata, attrs=None)[source]

Perform cfchecks on a DataArray using specifications from xclim’s default variables.

Only standard_name and cell_methods are supported, default is to only check standard_name.

Parameters:

varname (str) – The name of the variable to check.
vardata (xr.DataArray) – The variable to check.
attrs (list of str, optional) – Attributes to check. Default is [“standard_name”].

Raises:

ValidationError – If the variable does not meet the expected CF-Convention.

xclim.core.cfchecks.check_valid(var, key, expected)[source]

Check that a variable’s attribute has one of the expected values and raise a ValidationError if otherwise.

Parameters:

var (xr.DataArray) – The variable to check.
key (str) – The attribute to check.
expected (str or sequence of str) – The expected value(s).

Raises:

ValidationError – If the attribute is not present or does not match the expected value(s).

xclim.core.collection module¶

Indicator collections¶

An indicator collection is a structure holding multiple indicators. It can be created through a yaml configuration file.

YAML file structure¶

Indicator-defining yaml files are structured in the following way. Most entries of the indicators section are mirroring attributes of the xclim.core.indicator.Indicator, please refer to its documentation for more details on each.

module: <module name>  # Defaults to the file name
realm: <realm>  # If given here, applies to all indicators that do not already provide it.
keywords:
  - <keyword>  # Merged with indicator-specific keywords (appended to the list)
references: <references> # Merged with indicator-specific references (joined with a new line)
base: <base indicator class>  # Defaults to "Daily" and applies to all indicators that do not give it.
doc: <module docstring>  # Defaults to a minimal header, only valid if the module doesn't already exist.
variables:  # Optional section if indicators declared below rely on variables unknown to xclim
            # (not in `xclim.core.VARIABLES`)
            # The variables are not module-dependent and will overwrite any already existing with the same name.
  <varname>:
    canonical_units: <units> # required
    description: <description> # required
    standard_name: <expected standard_name> # optional
    cell_methods: <expected cell_methods> # optional
# The `bases` and `indicators` sections have the same syntax. Indicators defined in the `bases` section
# will only be created as classes and not instances. They will not be included in the IndicatorCollection's items,
# but rather in its `bases` property. This is useful for creating a base from which multiple indicators are declared
# in the `indicators`section.
bases:
indicators:
  <identifier>:  # The actual indicator identifier will be prepended by the module name.
    # From which Indicator to inherit
    base: <base indicator class>  # Defaults to module-wide base class
                                  # See :ref:`Base class specification` below.

    # General metadata, usually parsed from the `compute`s docstring when possible.
    realm: <realm>  # defaults to module-wide realm. One of "atmos", "land", "seaIce", "ocean".
    title: <title>
    abstract: <abstract>
    keywords:
      - <keyword>  # merged to module-wide keywords.
    references: <references>  # newline-seperated, merged to module-wide references.
    notes: <notes>

    # Other options (not all indicator classes support them)
    missing: <missing method name>
    missing_options: <missing options mapping>
    allowed_periods: [<list>, <of>, <allowed>, <periods>]
    context: <context> # A unit context enabled during the conversion of the compute's output to the requested units

    # Compute function
    compute: <function name>  # See :ref:`Compute function specification`, below.

    input:  # When "compute" is a generic function, this is a mapping from argument name to the expected variable.
            # It will change the expected name of the variable as well as its units/dimensionality.
            # Can refer to a variable declared in the `variables` section above or in `xclim.core.VARIABLES`.
            # See also :ref:`Inputs` below.
      <var name in compute> : <variable official name>
      ...

    # Parameters
    parameters:
      <param name>: <param data>  # Simplest case, to inject parameters in the compute function.
                                  # Kwargs-like parameters like ``indexer`` must be injected as a dictionary here.
      <param name>:  # To change parameters metadata or to declare units when "compute" is a generic function.
        default: <param default>
        description: <param description>
        name: <param name>  # Change the name of the parameter (similar to what `input` does for variables)
        kind: <param kind> # Override the parameter kind. This is mostly useful for transforming an
                           # optional variable into a required one by passing ``kind: 0``.
      ...

    # Output metadata
    outputs:   # List of mappings, one for each output.
        - var_name: <var name>  # Name to give to the output
          units: <units>        # Units to convert the output to and assign as attribute
          attrs:                # Mapping of attributes to assign to the output, can be templated strings.
              long_name: <...>
              description: <...>
        - ...

  ...  # and so on.

All fields are optional. Other fields found in the yaml file will trigger errors when validation is activated.

When a module is built from a yaml file, the yaml is first validated against the schema (see xclim/data/schema.yml) using the YAMALE library ([Lopker, 2022]). See the “Extending xclim” notebook for more info.

Base class specification¶

There are multiple ways to specify a base class when defining an indicator. In priority order:

If base starts with a ‘.’ (ex: .RXXp), the base class is taken from the current module.
- It is first searched in bases section.
- If not found, it is searched as another indicator declared _above_ the current definition.
The name is searched in the base class registry, xclim.core.indicator.base_registry (example: Daily).
The name is searched in the indicator registry, xclim.core.indicator.registry (example: prcptot).
If base contains a ‘.’ :
- If the first element is one of xclim’s indicators submodules (ex: atmos.precip_accumulation), that indicator is used as a base. Any submodule of xclim.indicators are possible.
- Otherwise, that path is loaded with python’s normal import mechanism. (ex: mymodule.submod.MyIndicator isloaded as from mymodule.submod import MyIndicator).

If the field base is not given, it defaults to the module-wide base, which itself defaults to xclim.core.indicator.Daily`.

Compute function specification¶

Similar to the base field, there are multiple ways to refer to a compute function in the compute field. In priority order:

If a module or mapping of compute functions was passed to IndicatorCollection.from_yaml(), the name is searched there (ex: extreme_precip_accumulation_and_days).
The name is searched in xclim.compute.generic (ex: statistics).
The name is searched in :py:mod:`xclim.compute (ex: corn_heat_units). It may contain a ‘.’ to denote a submodule (ex: generic.statistics).
Otherwise, it is loaded with python’s normal import mechanism (ex: mymodule.submod.my_function is loaded as from mymodule.submod import my_function).

Inputs¶

As xclim has strict definitions of possible input variables (see xclim.core.VARIABLES), the mapping of indicators.<identifier>.input simply links an argument name from the function given in “compute” to one of those official variables.

class xclim.core.collection.IndicatorCollection(indicators, name=None, bases=None, doc=None)[source]

Bases: dict

A collection of indicators.

static _find_base_class(name, mapping, bases)[source]

static _find_compute_function(name, computes)[source]

static _validate_yaml(schema_path, yml_path, encoding)[source]

classmethod from_yaml(filename, name=None, computes=None, translations=None, mode='raise', encoding='UTF8', validate=True, register=False)[source]

Build an indicator collection from a YAML file.

When given only a base filename (no ‘yml’ extension), this tries to find custom indicators in a module of the same name (.py) and translations in json files (.<lang>.json), see Notes.

Indicator created here will have the name of the module prepended to their identifier (ex: {mod}.{baseId}). The base identifier being the key name within the indicators mapping in the yaml.

Parameters:

filename (PathLike) – Path to a YAML file or to the stem of all module files. See Notes for behaviour when passing a basename only.
name (str, optional) – The name of the new or existing module, defaults to the basename of the file (e.g: atmos.yml -> atmos).
computes (Mapping of callables or module or path, optional) – A mapping or module of compute functions or a python file declaring such a module. When creating the indicator, the name in the compute field is first sought here, then the indicator class will search in xclim.compute.generic and finally in xclim.compute.
translations (Mapping of dicts or path, optional) – Translated metadata for the new indicators. Keys of the mapping must be two-character language tags. Values can be translations dictionaries as defined in xclim.core.locales. They can also be a path to a JSON file defining the translations.
mode ({‘raise’, ‘warn’, ‘ignore’}) – How to deal with broken indicator definitions.
encoding (str) – The encoding used to open the .yaml and .json files. It defaults to UTF-8, overriding python’s mechanism which is machine dependent.
validate (bool or PathLike) – If True (default), the yaml module is validated against the xclim schema. Can also be the path to a YAML schema against which to validate; Or False, in which case validation is simply skipped.
register (bool) – If True, the indicators created here are registered in xclim’s indicators registry registry upon creation, using the collection’s name prepended to their identifier as key, as explained above. Defaults to False, making collections independent from xclim’s registry. This does not change the behaviour of registering new variables, which are always added to xclim’s central xclim.core.VARIABLES.

Returns:

IndicatorCollection – A collection of indicators.

See also

xclim.core.indicator.Indicator: Indicator build logic.

Notes

When the given filename has no suffix (usually ‘.yaml’ or ‘.yml’), the function will try to load custom compute functions definitions from a file with the same name but with a .py extension. Similarly, it will try to load translations in *.<lang>.json files, where <lang> is the IETF language tag. Note that the file name can not contain a dot (.) for this logic to work.

For example. a set of custom indicators could be fully described by the following files:

example.yml : defining the indicator’s metadata.

example.py : defining a few compute functions.

example.fr.json : French translations

iter_indicators()[source]: Iterate over the (name, indicator) pairs in this collection.

xclim.core.datachecks module¶

Data Checks¶

Utilities designed to check the validity of data inputs.

xclim.core.datachecks.check_common_time(inputs)[source]

Raise an error if the list of inputs doesn’t have a single common frequency.

Parameters:

inputs (Sequence of xr.DataArray) – Input arrays.

Raises:

ValidationError –

if the frequency of any input can’t be inferred - if inputs have different frequencies - if inputs have a daily or hourly frequency, but they are not given at the same time of day.

Return type:

None

xclim.core.datachecks.check_daily(var)[source]

Raise an error if series has a frequency other that daily, or is not monotonically increasing.

Parameters:: var (xr.DataArray) – Input array.
Return type:: None

Notes

This does not check for gaps in series.

xclim.core.datachecks.check_freq(var, freq, strict=True)[source]

Raise an error if not series has not the expected temporal frequency or is not monotonically increasing.

Parameters:

var (xr.DataArray) – Input array.
freq (str or sequence of str) – The expected temporal frequencies, using Pandas frequency terminology (e.g. {‘Y’, ‘M’, ‘D’, ‘h’, ‘min’, ‘s’, ‘ms’, ‘us’}) and multiples thereof. To test strictly for ‘W’, pass ‘7D’ with strict=True. This ignores the start/end flag and the anchor (ex: ‘YS-JUL’ will validate against ‘Y’).
strict (bool) – Whether multiples of the frequencies are considered invalid or not. With strict set to False, a ‘3h’ series will not raise an error if freq is set to ‘h’.

Raises:

ValidationError –

If the frequency of var is not inferrable. - If the frequency of var does not match the requested freq.

Return type:

None

xclim.core.dataflags module¶

Data Flags¶

Pseudo-indicators designed to analyse supplied variables for suspicious/erroneous indicator values.

exception xclim.core.dataflags.DataQualityException(flag_array, message='Data quality flags indicate suspicious values. Flags raised are:\\n - ')[source]

Bases: Exception

Raised when any data evaluation checks are flagged as True.

Parameters:

flag_array (xarray.Dataset) – Xarray.Dataset of Data Flags.
message (str) – Message prepended to the error messages.

flag_array: xarray.Dataset | None = None

xclim.core.dataflags.data_flags(da, ds=None, flags=None, dims='all', freq=None, raise_flags=False)[source]

Evaluate the supplied DataArray for a set of data flag checks.

Test triggers depend on variable name and availability of extra variables within Dataset for comparison. If called with raise_flags=True, will raise a DataQualityException with comments for each failed quality check.

Parameters:

da (xarray.DataArray) – The variable to check. Must have a name that is a valid CMIP6 variable name and appears in xclim.core.VARIABLES.
ds (xarray.Dataset, optional) – An optional dataset with extra variables needed by some checks.
flags (dict, optional) – A dictionary where the keys are the name of the flags to check and the values are parameter dictionaries. The value can be None if there are no parameters to pass (i.e. default will be used). The default, None, means that the data flags list will be taken from xclim.core.VARIABLES.
dims ({“all”, None} or str or a sequence of strings) – Dimensions upon which the aggregation should be performed. Default: “all”.
freq (str, optional) – Resampling frequency to have data_flags aggregated over periods. Defaults to None, which means the “time” axis is treated as any other dimension (see dims).
raise_flags (bool) – Raise exception if any of the quality assessment flags are raised. Default: False.

Return type:

Dataset

Returns:

xarray.Dataset – The Dataset of boolean flag arrays.

Examples

To evaluate all applicable data flags for a given variable:

>>> from xclim.core.dataflags import data_flags
>>> ds = xr.open_dataset(path_to_pr_file)
>>> flagged_multi = data_flags(ds.pr, ds)
>>> # The next example evaluates only one data flag, passing specific parameters. It also aggregates the flags
>>> # yearly over the "time" dimension only, such that a True means there is a bad data point for that year
>>> # at that location.
>>> flagged_single = data_flags(
...     ds.pr,
...     ds,
...     flags={"very_large_precipitation_events": {"thresh": "250 mm d-1"}},
...     dims=None,
...     freq="YS",
... )

xclim.core.dataflags.ecad_compliant(ds, dims='all', raise_flags=False, append=True)[source]

Run ECAD compliance tests.

Assert that file adheres to ECAD-based quality assurance checks.

Parameters:

ds (xarray.Dataset) – Variable-containing dataset.
dims ({“all”} or str or a sequence of strings, optional) – Dimensions upon which aggregation should be performed. Default: "all".
raise_flags (bool) – Raise exception if any of the quality assessment flags are raised, otherwise returns None. Default: False.
append (bool) – If True, return the Dataset with the ecad_qc_flag array appended to data_vars. If False, return the DataArray of the ecad_qc_flag variable.

Return type:

DataArray | Dataset | None

Returns:

xarray.DataArray or xarray.Dataset or None – Flag array or Dataset with flag array(s) appended.

xclim.core.dataflags.negative_accumulation_values(da)[source]

Check if variable values are negative for any given day.

Parameters:: da (xarray.DataArray) – Variable array.
Return type:: DataArray
Returns:: xarray.DataArray, [bool] – Boolean array of True where values are negative.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import negative_accumulation_values
>>> ds = xr.open_dataset(path_to_pr_file)
>>> flagged = negative_accumulation_values(ds.pr)

xclim.core.dataflags.outside_n_standard_deviations_of_climatology(da, *, n, window=5)[source]

Check if any daily value is outside n standard deviations from the day of year mean.

Parameters:

da (xarray.DataArray) – Variable array.
n (int) – Number of standard deviations.
window (int) – Moving window used in determining the climatological mean. Default: 5.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – The boolean array of True where values exceed the bounds.

Notes

A moving window of five (5) days is suggested for tas data flag calculations according to ICCLIM data quality standards.

References

Project team ECA&D and KNMI [2013]

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import outside_n_standard_deviations_of_climatology
>>> ds = xr.open_dataset(path_to_tas_file)
>>> std_devs = 5
>>> average_over = 5
>>> flagged = outside_n_standard_deviations_of_climatology(ds.tas, n=std_devs, window=average_over)

xclim.core.dataflags.percentage_values_outside_of_bounds(da)[source]

Check if variable values fall below 0% or exceed 100% for any given day.

Parameters:: da (xarray.DataArray) – Variable array.
Return type:: DataArray
Returns:: xarray.DataArray, [bool] – The boolean array of True where values exceed the bounds.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import percentage_values_outside_of_bounds
>>> flagged = percentage_values_outside_of_bounds(huss_dataset)

xclim.core.dataflags.register_methods(variable_name=None)[source]

Argument can be the output variable name template. The template may use any of the string-like input arguments. If not given, the function name is used instead, which may create variable conflicts.

Parameters:: variable_name (str, optional) – The output variable name template. Default is None.
Return type:: Callable
Returns:: callable – The function being registered.

xclim.core.dataflags.specific_discharge_extremely_high(da, *, thresh='100 mm d-1')[source]

Check if specific discharge values exceed 100 mm per day for any given day.

Parameters:

da (xarray.DataArray) – Specific discharge.
thresh (str) – Threshold above which specific discharges are considered problematic and a flag is raised.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where specific discharges are above the threshold.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import specific_discharge_extremely_high
>>> qspec = specific_discharge_dataset.qspec
>>> flagged = specific_discharge_extremely_high(qspec)

xclim.core.dataflags.tas_below_tasmin(tas, tasmin)[source]

Check if tas values are below tasmin values for any given day.

Parameters:

tas (xarray.DataArray) – Mean temperature.
tasmin (xarray.DataArray) – Minimum temperature.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where tas is below tasmin.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import tas_below_tasmin
>>> ds = xr.open_dataset(path_to_tas_file)
>>> flagged = tas_below_tasmin(ds.tas, ds.tasmin)

xclim.core.dataflags.tas_exceeds_tasmax(tas, tasmax)[source]

Check if tas values tasmax values for any given day.

Parameters:

tas (xarray.DataArray) – Mean temperature.
tasmax (xarray.DataArray) – Maximum temperature.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where tas is above tasmax.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import tas_exceeds_tasmax
>>> ds = xr.open_dataset(path_to_tas_file)
>>> flagged = tas_exceeds_tasmax(ds.tas, ds.tasmax)

xclim.core.dataflags.tasmax_below_tasmin(tasmax, tasmin)[source]

Check if tasmax values are below tasmin values for any given day.

Parameters:

tasmax (xarray.DataArray) – Maximum temperature.
tasmin (xarray.DataArray) – Minimum temperature.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where tasmax is below tasmin.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import tasmax_below_tasmin
>>> ds = xr.open_dataset(path_to_tas_file)
>>> flagged = tasmax_below_tasmin(ds.tasmax, ds.tasmin)

xclim.core.dataflags.temperature_extremely_high(da, *, thresh='60 degC')[source]

Check if temperature values exceed 60 degrees Celsius for any given day.

Parameters:

da (xarray.DataArray) – Temperature.
thresh (str) – Threshold above which temperatures are considered problematic and a flag is raised. Default is 60 degrees Celsius.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where temperatures are above the threshold.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import temperature_extremely_high
>>> ds = xr.open_dataset(path_to_tas_file)
>>> temperature = "60 degC"
>>> flagged = temperature_extremely_high(ds.tas, thresh=temperature)

xclim.core.dataflags.temperature_extremely_low(da, *, thresh='-90 degC')[source]

Check if temperature values are below -90 degrees Celsius for any given day.

Parameters:

da (xarray.DataArray) – Temperature.
thresh (str) – Threshold below which temperatures are considered problematic and a flag is raised. Default is -90 degrees Celsius.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where temperatures are below the threshold.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import temperature_extremely_low
>>> ds = xr.open_dataset(path_to_tas_file)
>>> temperature = "-90 degC"
>>> flagged = temperature_extremely_low(ds.tas, thresh=temperature)

xclim.core.dataflags.values_op_thresh_repeating_for_n_or_more_days(da, *, n, thresh, op='==')[source]

Check if array values repeat at a given threshold for N or more days.

Parameters:

da (xarray.DataArray) – Variable array.
n (int) – Number of repeating days needed to trigger data flag.
thresh (str) – Repeating values to search for that will trigger data flag.
op ({“>”, “gt”, “<”, “lt”, “>=”, “ge”, “<=”, “le”, “==”, “eq”, “!=”, “ne”}) – Operator used for comparison with thresh.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where values repeat at threshold for N or more days.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import values_op_thresh_repeating_for_n_or_more_days
>>> ds = xr.open_dataset(path_to_pr_file)
>>> units = "5 mm d-1"
>>> days = 5
>>> comparison = "eq"
>>> flagged = values_op_thresh_repeating_for_n_or_more_days(ds.pr, n=days, thresh=units, op=comparison)

xclim.core.dataflags.values_repeating_for_n_or_more_days(da, *, n)[source]

Check if exact values are found to be repeating for at least 5 or more days.

Parameters:

da (xarray.DataArray) – Variable array.
n (int) – Number of days to trigger flag.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – The boolean array of True where values repeat for n or more days.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import values_repeating_for_n_or_more_days
>>> ds = xr.open_dataset(path_to_pr_file)
>>> flagged = values_repeating_for_n_or_more_days(ds.pr, n=5)

xclim.core.dataflags.very_large_precipitation_events(da, *, thresh='300 mm d-1')[source]

Check if precipitation values exceed 300 mm/day for any given day.

Parameters:

da (xarray.DataArray) – Precipitation.
thresh (str) – Threshold to search an array for that will trigger flag if any day exceeds value.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – Boolean array of True where precipitation values exceed the threshold.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import very_large_precipitation_events
>>> ds = xr.open_dataset(path_to_pr_file)
>>> rate = "300 mm d-1"
>>> flagged = very_large_precipitation_events(ds.pr, thresh=rate)

xclim.core.dataflags.wind_values_outside_of_bounds(da, *, lower='0 m s-1', upper='46 m s-1')[source]

Check if wind speed values exceed reasonable bounds for any given day.

Parameters:

da (xarray.DataArray) – Wind speed.
lower (str) – The lower limit for wind speed. Default is 0 m s-1.
upper (str) – The upper limit for wind speed. Default is 46 m s-1.

Return type:

DataArray

Returns:

xarray.DataArray, [bool] – The boolean array of True where values exceed the bounds.

Examples

To gain access to the flag_array:

>>> from xclim.core.dataflags import wind_values_outside_of_bounds
>>> ceiling, floor = "46 m s-1", "0 m s-1"
>>> flagged = wind_values_outside_of_bounds(sfcWind_dataset, upper=ceiling, lower=floor)

xclim.core.formatting module¶

Formatting Utilities for Indicators¶

class xclim.core.formatting.AttrFormatter(mapping, modifiers)[source]

Bases: string.Formatter

A formatter for frequently used attribute values.

Parameters:

mapping (dict of str, sequence of str) – A mapping from values to their possible variations.
modifiers (sequence of str) – The list of modifiers. Must at least match the length of the longest value of mapping. Cannot include reserved modifier ‘r’.

Notes

See the doc of format_field() for more details.

_match_value(value)[source]

format(format_string, /, *args, **kwargs)[source]

Format a string.

Parameters:

format_string (str) – The string to format.
*args (Any) – Arguments to format.
**kwargs (Any) – Keyword arguments to format.

Return type:

str

Returns:

str – The formatted string.

format_field(value, format_spec)[source]

Format a value given a formatting spec.

If format_spec is in this Formatter’s modifiers, the corresponding variation of value is given. If format_spec is ‘r’ (raw), the value is returned unmodified. If format_spec is not specified but value is in the mapping, the first variation is returned.

Parameters:

value (Any) – The value to format.
format_spec (str) – The formatting spec.

Return type:

str

Returns:

str – The formatted value.

Examples

Let’s say the string “The dog is {adj1}, the goose is {adj2}” is to be translated to French and that we know that possible values of adj are nice and evil. In French, the genre of the noun changes the adjective (cat = chat is masculine, and goose = oie is feminine) so we initialize the formatter as:

>>> fmt = AttrFormatter(
...     {
...         "nice": ["beau", "belle"],
...         "evil": ["méchant", "méchante"],
...         "smart": ["intelligent", "intelligente"],
...     },
...     ["m", "f"],
... )
>>> fmt.format(
...     "Le chien est {adj1:m}, l'oie est {adj2:f}, le gecko est {adj3:r}",
...     adj1="nice",
...     adj2="evil",
...     adj3="smart",
... )
"Le chien est beau, l'oie est méchante, le gecko est smart"

The base values may be given using unix shell-like patterns:

>>> fmt = AttrFormatter(
...     {"YS-*": ["annuel", "annuelle"], "MS": ["mensuel", "mensuelle"]},
...     ["m", "f"],
... )
>>> fmt.format(
...     "La moyenne {freq:f} est faite sur un échantillon {src_timestep:m}",
...     freq="YS-JUL",
...     src_timestep="MS",
... )
'La moyenne annuelle est faite sur un échantillon mensuel'

xclim.core.formatting.capitalize_free_text(text, sep='. ')[source]

Ensure each sentence of the text begins with an uppercase letter.

Parameters:

text (str) – A string.
sep (str) – The separator indicating the end and the beginning of sentences, in addition to the first letter of the text.

Returns:

str – The capitalized text. In opposition to str.capitalize(), case of letters not at the beginning of a sentence is preserved.

xclim.core.formatting.gen_call_string(funcname, *args, **kwargs)[source]

Generate a signature string for use in the history attribute.

DataArrays and Dataset are replaced with their name, while Nones, floats, ints and strings are printed directly. All other objects have their type printed between < >.

Arguments given through positional arguments are printed positionnally and those given through keywords are printed prefixed by their name.

Parameters:

funcname (str) – Name of the function.
*args (Any) – Arguments given to the function.
**kwargs (Any) – Keyword arguments given to the function.

Return type:

str

Returns:

str – The formatted string.

Examples

>>> A = xr.DataArray([1], dims=("x",), name="A")
>>> gen_call_string("func", A, b=2.0, c="3", d=[10] * 100)
"func(<A array>, b=2.0, c='3', d=<list>)"

xclim.core.formatting.get_percentile_metadata(data, prefix)[source]

Get the metadata related to percentiles from the given DataArray as a dictionary.

Parameters:

data (xr.DataArray) – Must be a percentile DataArray, this means the necessary metadata must be available in its attributes and coordinates.
prefix (str) – The prefix to be used in the metadata key. Usually this takes the form of “tasmin_per” or equivalent.

Return type:

dict[str, str]

Returns:

dict – A mapping of the configuration used to compute these percentiles.

xclim.core.formatting.merge_attributes(attribute, *inputs_list, new_line='\\n', missing_str=None, **inputs_kws)[source]

Merge attributes from several DataArrays or Datasets.

If more than one input is given, its name (if available) is prepended as: “<input name> : <input attribute>”.

Parameters:

attribute (str) – The attribute to merge.
*inputs_list (xr.DataArray or xr.Dataset) – The datasets or variables that were used to produce the new object. Inputs given that way will be prefixed by their name attribute if available.
new_line (str) – The character to put between each instance of the attributes. Usually, in CF-conventions, the history attributes uses ‘\n’ while cell_methods uses ‘ ‘.
missing_str (str) – A string that is printed if an input doesn’t have the attribute. Defaults to None, in which case the input is simply skipped.
**inputs_kws (xr.DataArray or xr.Dataset) – Mapping from names to the datasets or variables that were used to produce the new object. Inputs given that way will be prefixes by the passed name.

Return type:

str

Returns:

str – The new attribute made from the combination of the ones from all the inputs.

xclim.core.formatting.prefix_attrs(source, keys, prefix)[source]

Rename some keys of a dictionary by adding a prefix.

Parameters:

source (dict) – Source dictionary, for example data attributes.
keys (sequence) – Names of keys to prefix.
prefix (str) – Prefix to prepend to keys.

Return type:

dict

Returns:

dict – Dictionary of attributes with some keys prefixed.

xclim.core.formatting.unprefix_attrs(source, keys, prefix)[source]

Remove prefix from keys in a dictionary.

Parameters:

source (dict) – Source dictionary, for example data attributes.
keys (sequence) – Names of original keys for which prefix should be removed.
prefix (str) – Prefix to remove from keys.

Return type:

dict

Returns:

dict – Dictionary of attributes whose keys were prefixed, with prefix removed.

xclim.core.formatting.update_history(hist_str, *inputs_list, new_name=None, **inputs_kws)[source]

Return a history string with the timestamped message and the combination of the history of all inputs.

The new history entry is formatted as “[<timestamp>] <new_name>: <hist_str> - xclim version: <xclim.__version__>.”

Parameters:

hist_str (str) – The string describing what has been done on the data.
*inputs_list (xr.DataArray or xr.Dataset) – The datasets or variables that were used to produce the new object. Inputs given that way will be prefixed by their “name” attribute if available.
new_name (str, optional) – The name of the newly created variable or dataset to prefix hist_msg.
**inputs_kws (xr.DataArray or xr.Dataset) – Mapping from names to the datasets or variables that were used to produce the new object. Inputs given that way will be prefixes by the passed name.

Return type:

str

Returns:

str – The combine history of all inputs starting with hist_str.

See also

merge_attributes: Merge attributes from several DataArrays or Datasets.

xclim.core.formatting.update_xclim_history(func)[source]

Decorator that auto-generates and fills the history attribute.

The history is generated from the signature of the function and added to the first output. Because of a limitation of the boltons wrapper, all arguments passed to the wrapped function will be printed as keyword arguments.

Parameters:: func (Callable) – The function to decorate.
Return type:: Callable
Returns:: Callable – The decorated function.

xclim.core.locales module¶

Internationalization module¶

This module defines methods and object to help the internationalization of metadata for climate indicators computed by xclim. Go to Adding translated metadata to see how to use this feature.

All the methods and objects in this module use localization data given in JSON files. These files are expected to be defined as in this example for French:

{
    "attrs_mapping": {
        "modifiers": ["", "f", "mpl", "fpl"],
        "YS": ["annuel", "annuelle", "annuels", "annuelles"],
        "YS-*": ["annuel", "annuelle", "annuels", "annuelles"],
        # ... and so on for other frequent parameters translation...
    },
    "dtrvar": {
        "long_name": "Variabilité de l'amplitude de la température diurne",
        "description": "Variabilité {freq:f} de l'amplitude de la température diurne (définie comme la moyenne de la variation journalière de l'amplitude de température sur une période donnée)",
        "title": "Variation quotidienne absolue moyenne de l'amplitude de la température diurne",
        "comment": "",
        "abstract": "La valeur absolue de la moyenne de l'amplitude de la température diurne.",
    },
    # ... and so on for other indicators...
}

Indicators are named by their identifier, the same as in the indicator registry (xclim.core.indicators.registry), but which can differ from the callable name. In the example above, the indicator is usually called in python code using atmos.daily_temperature_range_variability, but its identifier is dtrvar. Use the ind.identifier accessor to get its registry name, they are case-insensitive.

Accordingly, when writing a translation file for an IndicatorCollection, the keys to use are the same as the ones in the indicators section of the collection’s YAML file.

Here, the usual parameter passed to the formatting of “description” is “freq” and is usually translated from “YS” to “annual”. However, in French and in this sentence, the feminine form should be used, so the “f” modifier is added by the translator so that the formatting function knows which translation to use. Acceptable entries for the mappings are limited to what is already defined in xclim.core.indicators.utils.default_formatter.

For user-provided internationalization dictionaries, only the “attrs_mapping” and its “modifiers” key are mandatory, all other entries (translations of frequent parameters and all indicator entries) are optional. For xclim-provided translations (for now only French), all indicators must have en entry and the “attrs_mapping” entries must match exactly the default formatter. Those default translations are found in the xclim/locales folder.

xclim.core.locales.TRANSLATABLE_ATTRS = ['long_name', 'description', 'comment', 'title', 'abstract']: List of attributes to consider translatable when generating locale dictionaries.

exception xclim.core.locales.UnavailableLocaleError(locale)[source]

Bases: ValueError

Error raised when a locale is requested but doesn’t exist.

Parameters:: locale (str) – The locale code.

xclim.core.locales._valid_locales(locales)[source]: Check if the locales are valid.

xclim.core.locales.generate_local_dict(locale, init_english=False)[source]

Generate a dictionary with keys for each indicator and translatable attributes.

Parameters:

locale (str) – Locale in the IETF format.
init_english (bool) – If True, fills the initial dictionary with the english versions of the attributes. Defaults to False.

Return type:

CaseInsensitiveDict

Returns:

dict – Indicator translation dictionary.

xclim.core.locales.get_local_attrs(indicator, locale, var_name=None, names=None, append_locale_name=True)[source]

Get all attributes of an indicator in the requested locale.

Parameters:

indicator (str or sequence of strings) – Indicator’s identifier, usually the same as in xc.core.indicator.registry. If multiple names are passed, the attrs from each indicator are merged, with the highest priority set to the first name.
locale (str) – IETF language tag or a tuple of the language tag and a translation dict, or a tuple of the language tag and a path to a json file defining translation of attributes.
var_name (str, optional) – For multi-output indicator, this is the name of the variable for which we request attributes.
names (sequence of str, optional) – If given, only returns translations of attributes in this list.
append_locale_name (bool) – If True (default), append the language tag (as “{attr_name}_{locale}”) to the returned attributes.

Return type:

dict

Returns:

dict – All attributes available for given indicator and locales. Warns and returns an empty dict if none were available.

Raises:

ValueError – If append_locale_name is False and multiple locales are requested.

xclim.core.locales.get_local_dict(locale)[source]

Return all translated metadata for a given locale.

Parameters:

locale (str or sequence of str) – IETF language tag or a tuple of the language tag and a translation dict, or a tuple of the language tag and a path to a json file defining translation of attributes.

Return type:

tuple[str, dict]

Returns:

str – The best fitting locale string.
dict – The available translations in this locale.

Raises:

UnavailableLocaleError – If the given locale is not available.

xclim.core.locales.get_local_formatter(locale)[source]

Return an AttrFormatter instance for the given locale.

Parameters:: locale (str or tuple of str) – IETF language tag or a tuple of the language tag and a translation dict, or a tuple of the language tag and a path to a json file defining translation of attributes.
Return type:: AttrFormatter
Returns:: AttrFormatter – A locale-based formatter object instance.

xclim.core.locales.list_locales()[source]

List of loaded locales.

Includes all loaded locales, no matter how complete the translations are.

Return type:: list
Returns:: list – A list of available locales.

xclim.core.locales.load_locale(locdata, locale)[source]

Load translations from a json file into xclim.

Parameters:

locdata (str or Path or dictionary) – Either a loaded locale dictionary or a path to a json file.
locale (str) – The locale name (IETF tag).

Return type:

None

xclim.core.locales.read_locale_file(filename, module=None, encoding='UTF8')[source]

Read a locale file (.json) and return its dictionary.

Parameters:

filename (PathLike) – The file to read.
module (str, optional) – If the module is a string, this module name is added to all identifiers translated in this file. Defaults to None, and no module name is added (as if the indicator was an official xclim indicator).
encoding (str) – The encoding to use when reading the file. Defaults to UTF-8, overriding Python’s default mechanism which is machine-dependent.

Return type:

dict[str, dict]

Returns:

dict – The locale dictionary. All entries are lowercase.

xclim.core.missing module¶

Missing Values Identification¶

Indicators may use different criteria to determine whether a computed indicator value should be considered missing. In some cases, the presence of any missing value in the input time series should result in a missing indicator value for that period. In other cases, a minimum number of valid values or a percentage of missing values should be enforced. The World Meteorological Organisation (WMO) suggests criteria based on the number of consecutive and overall missing values per month.

xclim has a registry of missing value detection algorithms that can be extended by users to customize the behavior of indicators. Once registered, algorithms can be used by setting the global option as xc.set_options(check_missing="method") or within indicators by setting the missing attribute of an Indicator subclass. By default, xclim registers the following algorithms:

any: A result is missing if any input value is missing.

some_but_not_all: A result is missing if some but not all input values are missing.

at_least_n: A result is missing if less than a given number of valid values are present.

pct: A result is missing if more than a given fraction of its values are missing.

wmo: A result is missing if 11 days are missing, or 5 consecutive values are missing in a month.

To define another missing value algorithm, subclass MissingBase and decorate it with xclim.core.options.register_missing_method(). See subclassing guidelines in MissingBase’s doc.

xclim.core.missing.at_least_n_valid(da, freq, src_timestep=None, n=20, subfreq=None, **indexer)[source]

Mask periods as missing if they don’t have at least a given number of valid values.

Ignores the expected count of elements.

Parameters:

da (xr.DataArray) – Input data, must have a “time” coordinate.
freq (str, optional) – Target resampling frequency. If None, a collapse of the temporal dimension is assumed.
src_timestep (str, optional) – The expected source input frequency. If not given, it will be inferred from the input array.
n (float) – The minimum number of valid values needed.
subfreq (str, optional) – If given, computes a mask at this frequency using this method and then resample at the target frequency using the “any” method on subgroups.
**indexer (Indexer) – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If no indexer is given, all values are considered. See xclim.core.calendar.select_time().

Return type:

DataArray

Returns:

DataArray – Boolean array at the resampled frequency, True on the periods that should be considered missing or invalid.

xclim.core.missing.expected_count(time, freq=None, src_timestep=None, **indexer)[source]

Get expected number of step of length src_timestep per each resampling period freq that time covers.

The determination of the resampling periods intersecting with the input array are done following xarray’s and pandas’ heuristics. The input coordinate needs not be continuous if src_timestep is given.

Parameters:

time (xr.DataArray, optional) – Input time coordinate from which the final resample time coordinate is guessed.
freq (str, optional.) – Resampling frequency. If not given or None, the count for the full time range is returned.
src_timestep (str, Optional) – The expected input frequency. If not given, it will be inferred from the input array.
**indexer (Indexer) – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are considered. See xc.core.calendar.select_time().

Return type:

DataArray

Returns:

xr.DataArray – Integer array at the resampling frequency with the number of expected elements in each period.

xclim.core.missing.missing_any(da, freq, src_timestep=None, **indexer)[source]

Mask periods as missing if any of its elements is missing or invalid.

Parameters:

da (xr.DataArray) – Input data, must have a “time” coordinate.
freq (str, optional) – Resampling frequency. If None, a collapse of the temporal dimension is assumed.
src_timestep (str, optional) – The expected source input frequency. If not given, it will be inferred from the input array.
**indexer (Indexer) – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are considered. See xclim.core.calendar.select_time().

Return type:

DataArray

Returns:

DataArray – Boolean array at the resampled frequency, True on the periods that should be considered missing or invalid.

xclim.core.missing.missing_from_context(da, freq, src_timestep=None, **indexer)[source]

Mask periods as missing according to the algorithm and options set in xclim’s global options.

The options can be manipulated with xclim.core.options.set_options().

Parameters:

da (xr.DataArray) – Input data, must have a “time” coordinate.
freq (str, optional) – Resampling frequency. If absent, a collapse of the temporal dimension is assumed.
src_timestep (str, optional) – The expected source input frequency. If not given, it will be inferred from the input array.
**indexer (Indexer) – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are considered. See xclim.core.calendar.select_time().

Return type:

DataArray

Returns:

DataArray – Boolean array at the resampled frequency, True on the periods that should be considered missing or invalid.

xclim.core.missing.missing_pct(da, freq, src_timestep=None, tolerance=0.1, subfreq=None, **indexer)[source]

Mask periods as missing when there are more than a given percentage of missing days.

Parameters:

da (xr.DataArray) – Input data, must have a “time” coordinate.
freq (str, optional) – Target resampling frequency. If None, a collapse of the temporal dimension is assumed.
src_timestep (str, optional) – The expected source input frequency. If not given, it will be inferred from the input array.
tolerance (float) – The maximum tolerated proportion of missing values, given as a number between 0 and 1.
subfreq (str, optional) – If given, computes a mask at this frequency using this method and then resample at the target frequency using the “any” method on subgroups.
**indexer (Indexer) – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If no indexer is given, all values are considered. See xclim.core.calendar.select_time().

Return type:

DataArray

Returns:

DataArray – Boolean array at the resampled frequency, True on the periods that should be considered missing or invalid.

xclim.core.missing.missing_some_but_not_all(da, freq, src_timestep=None, **indexer)[source]

Return whether there are some missing days in the array, but not all are missing.

Return type:: DataArray

xclim.core.missing.missing_wmo(da, freq, src_timestep=None, nm=11, nc=5, **indexer)[source]

Mask periods as missing using the WMO criteria for missing days.

The World Meteorological Organisation recommends that where monthly means are computed from daily values, it should be considered missing if either of these two criteria are met:

– observations are missing for 11 or more days during the month; – observations are missing for a period of 5 or more consecutive days during the month.

Stricter criteria are sometimes used in practice, with a tolerance of 5 missing values or 3 consecutive missing values.

Notes

If used at frequencies larger than a month, for example on an annual or seasonal basis, the function will return True if any month within a period is masked.

Parameters:

da (xr.DataArray) – Input data, must have a “time” coordinate.
freq (str, optional) – Target resampling frequency. If None, a collapse of the temporal dimension is assumed.
src_timestep (str, optional) – The expected source input frequency. If not given, it will be inferred from the input array.
nm (int) – Minimal number of missing elements for a month to be masked.
nc (int) – Minimal number of consecutive missing elements for a month to be masked.
**indexer (Indexer) – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If no indexer is given, all values are considered. See xclim.core.calendar.select_time().

Return type:

DataArray

Returns:

DataArray – Boolean array at the resampled frequency, True on the periods that should be considered missing or invalid.

xclim.core.missing.register_missing_method(name)[source]

Parameters:: name (str) – Name of missing method.
Return type:: Callable
Returns:: Callable – Decorator function.

xclim.core.options module¶

Options Submodule¶

Global or contextual options for xclim, similar to xarray.set_options.

xclim.core.options._set_metadata_locales(locales)[source]

xclim.core.options._set_missing_options(mopts)[source]

xclim.core.options._valid_missing_options(mopts)[source]: Check if all methods and their options in mopts are valid.

xclim.core.options.cfcheck(func)[source]

Decorate functions checking CF-compliance of DataArray attributes.

Functions should raise ValidationError exceptions whenever attributes are non-conformant.

Parameters:: func (Callable) – Function to decorate.
Return type:: Callable
Returns:: Callable – Decorated function.

xclim.core.options.datacheck(func)[source]

Decorate functions checking data inputs validity.

Parameters:: func (Callable) – Function to decorate.
Return type:: Callable
Returns:: Callable – Decorated function.

xclim.core.options.register_missing_method(name)[source]

Parameters:: name (str) – Name of missing method.
Return type:: Callable
Returns:: Callable – Decorator function.

xclim.core.options.run_check(func, option, *args, **kwargs)[source]

Run function and customize exception handling based on option.

Parameters:

func (Callable) – Function to run.
option (str) – Option to use.
*args (tuple) – Positional arguments to pass to the function.
**kwargs (dict) – Keyword arguments to pass to the function.

Raises:

ValidationError – If the function raises a ValidationError and the option is set to “raise”.

class xclim.core.options.set_options(**kwargs)[source]

Bases: object

Set options for xclim in a controlled context.

Parameters:

metadata_locales (list[Any]) – List of IETF language tags or tuples of language tags and a translation dict, or tuples of language tags and a path to a json file defining translation of attributes. Default: [].
data_validation ({“log”, “raise”, “error”}) – Whether to “log”, “raise” an error or ‘warn’ the user on inputs that fail the data checks in xclim.core.datachecks(). Default: "raise".
cf_compliance ({“log”, “raise”, “error”}) – Whether to “log”, “raise” an error or “warn” the user on inputs that fail the CF compliance checks in xclim.core.cfchecks(). Default: "warn".
check_missing ({“any”, “wmo”, “pct”, “at_least_n”, “skip”}) – How to check for missing data and flag computed indicators. Available methods are “any”, “wmo”, “pct”, “at_least_n” and “skip”. Missing method can be registered through the xclim.core.options.register_missing_method decorator. Default: "any"
missing_options (dict) – Dictionary of options to pass to the missing method. Keys must the name of missing method and values must be mappings from option names to values.
run_length_ufunc (str) – Whether to use the 1D ufunc version of run length algorithms or the dask-ready broadcasting version. Default is "auto", which means the latter is used for dask-backed and large arrays.
as_dataset (bool) – If True, indicators output datasets. If False, they output DataArrays or tuple of DataArrays. The output dataset inherits attributes from the input dataset (if any) according to xarray’s keep_attrs option, which defaults to preserving attributes. Default :True.
resample_map_blocks (bool) – If True, some indicators will wrap their resampling operations with xr.map_blocks, using xclim.compute.helpers.resample_map(). This requires flox to be installed in order to ensure the chunking is appropriate.

Examples

You can use set_options either as a context manager:

>>> import xclim
>>> ds = xr.open_dataset(path_to_tas_file).tas
>>> with xclim.set_options(metadata_locales=["fr"]):
...     out = xclim.atmos.tg_mean(ds)

Or to set global options:

import xclim

xclim.set_options(missing_options={"pct": {"tolerance": 0.04}})

static _update(kwargs)[source]: Update values.

xclim.core.units module¶

Units Handling Submodule¶

xclim’s pint-based unit registry is an extension of the registry defined in cf-xarray. This module defines most unit handling methods.

xclim.core.units.amount2lwethickness(amount, out_units=None)[source]

Convert a liquid water amount (mass over area) to its equivalent area-averaged thickness (length).

This will simply divide the amount by the density of liquid water, 1000 kg/m³. This is equivalent to using the “hydro” context of xclim.core.units.units.

Parameters:

amount (xr.DataArray) – A DataArray storing a liquid water amount quantity.
out_units (str, optional) – Specific output units, if needed.

Return type:

Union[DataArray, TypeVar(Quantified, DataArray, str, Quantity)]

Returns:

xr.DataArray or Quantified – The standard_name of amount is modified if a conversion is found (see xclim.core.units.cf_conversion()), it is removed otherwise. Other attributes are left untouched.

See also

lwethickness2amount: Convert a liquid water equivalent thickness to an amount.

xclim.core.units.amount2rate(amount, dim='time', sampling_rate_from_coord=False, out_units=None)[source]

Convert an amount variable to a rate by dividing by the sampling period length.

If the sampling period length cannot be inferred, the amount values are divided by the duration between their time coordinate and the next one. The last period is estimated with the duration of the one just before.

This is the inverse operation of xclim.core.units.rate2amount().

Parameters:

amount (xr.DataArray or pint.Quantity or str) – “amount” variable. Ex: Precipitation amount in “mm”.
dim (str or xr.DataArray) – The name of the time dimension or the time coordinate itself.
sampling_rate_from_coord (bool) – For data with irregular time coordinates. If True, the diff of the time coordinate will be used as the sampling rate, meaning each data point will be assumed to span the interval ending at the next point. See notes of xclim.core.units.rate2amount(). Defaults to False, which raises an error if the time coordinate is irregular.
out_units (str, optional) – Specific output units, if needed.

Return type:

DataArray

Returns:

xr.DataArray or Quantity – The converted variable. The standard_name of amount is modified if a conversion is found.

Raises:

ValueError – If the time coordinate is irregular and sampling_rate_from_coord is False (default).

See also

rate2amount: Convert a rate to an amount.
is_temporal_rate: Determine if a variable is a rate based on its CF attributes.

xclim.core.units.cf_conversion(standard_name, conversion, direction)[source]

Get the standard name of the specific conversion for the given standard name.

Parameters:

standard_name (str) – Standard name of the input.
conversion ({‘amount2rate’, ‘amount2lwethickness’}) – Type of conversion. Available conversions are the keys of the conversions entry in xclim/data/variables.yml. See xclim.core.units.CF_CONVERSIONS. They also correspond to functions in this module.
direction ({‘to’, ‘from’}) – The direction of the requested conversion. “to” means the conversion as given by the conversion name, while “from” means the reverse operation. For example conversion=”amount2rate” and direction=”from” will search for a conversion from a rate or flux to an amount or thickness for the given standard name.

Return type:

str | None

Returns:

str or None – If a string, this means the conversion is possible and the result should have this standard name. If None, the conversion is not possible within the CF standards.

xclim.core.units.check_units(val, dim=None)[source]

Check that units are compatible with dimensions, otherwise raise a ValidationError.

Parameters:

val (str or xr.DataArray, optional) – Value to check.
dim (str or xr.DataArray, optional) – Expected dimension, e.g. [temperature]. If a quantity or DataArray is given, the dimensionality is extracted.

Return type:

None

xclim.core.units.convert_units_to(source, target, context=None)[source]

Convert a mathematical expression into a value with the same units as a DataArray.

If the dimensionalities of source and target units differ, automatic CF conversions will be applied when possible. See xclim.core.units.cf_conversion().

Parameters:

source (str or xr.DataArray or units.Quantity or xr.Dataset or xr.DataTree) – The value to be converted, e.g. ‘4C’ or ‘1 mm/d’. If a Dataset, target must also be a mapping from variable name to target units. If a DataTree, this function will be applied over nodes with xarray.DataTree.map_over_datasets().
target (str or xr.DataArray or units.Quantity or units.Unit or dict) – Target array of values to which units must conform. If source is a Dataset, it must be mapping from variable name to target units.
context ({“infer”, “hydro”, “none”}, optional) – The unit definition context. Default: None. If “infer”, it will be inferred with xclim.core.units.infer_context() using the standard name from the source or, if none is found, from the target. This means that the “hydro” context could be activated if any one of the standard names allows it.

Return type:

DataArray | float | Dataset

Returns:

xr.DataArray or float or xr.Dataset – The source value converted to target’s units. The outputted type is always similar to source initial type. Attributes are preserved unless an automatic CF conversion is performed, in which case only the new standard_name appears in the result.

See also

cf_conversion: Get the standard name of the specific conversion for the given standard name.
amount2rate: Convert an amount to a rate.
rate2amount: Convert a rate to an amount.
amount2lwethickness: Convert an amount to a liquid water equivalent thickness.
lwethickness2amount: Convert a liquid water equivalent thickness to an amount.

xclim.core.units.declare_relative_units(**units_by_name)[source]

Function decorator checking the units of arguments.

The decorator checks that input values have units that are compatible with each other. It also stores the input units as a ‘relative_units’ attribute.

Parameters:: **units_by_name (str) – Mapping from the input parameter names to dimensions relative to other parameters. The dimensions can be a single parameter name as <other_var> or more complex expressions, such as <other_var> * [time].
Return type:: Callable
Returns:: Callable – The decorated function.

See also

declare_units: A decorator to check units of function arguments.

Examples

In the following function definition:

@declare_relative_units(thresh="<da>", thresh2="<da> / [time]")
def func(da, thresh, thresh2): ...

The decorator will check that thresh has units compatible with those of da and that thresh2 has units compatible with the time derivative of da.

Usually, the function would be decorated further by declare_units() to create a unit-aware index:

temperature_func = declare_units(da="[temperature]")(func)

This call will replace the “<da>” by “[temperature]” everywhere needed.

xclim.core.units.declare_units(**units_by_name)[source]

Create a decorator to check units of function arguments.

The decorator checks that input and output values have units that are compatible with expected dimensions. It also stores the input units as an ‘in_units’ attribute.

Parameters:: **units_by_name (str) – Mapping from the input parameter names to their units or dimensionality (“[…]”). If this decorates a function previously decorated with declare_relative_units(), the relative unit declarations are made absolute with the information passed here.
Return type:: Callable
Returns:: Callable – The decorated function.

See also

declare_relative_units: A decorator to check for relative units of function arguments.

Examples

In the following function definition:

@declare_units(tas="[temperature]")
def func(tas): ...

The decorator will check that tas has units of temperature (C, K, F).

xclim.core.units.ensure_absolute_temperature(units)[source]

Convert temperature units to their absolute counterpart, assuming they represented a difference (delta).

Celsius becomes Kelvin, Fahrenheit becomes Rankine. Does nothing for other units.

Parameters:: units (str) – Units to transform.
Return type:: str
Returns:: str – The transformed units.

See also

ensure_delta: Ensure a unit is a delta unit.

xclim.core.units.ensure_cf_units(ustr)[source]

Ensure the passed unit string is CF-compliant.

The string will be parsed to pint then recast to a string by xclim.core.units.pint2cfunits().

Parameters:: ustr (str) – A unit string.
Return type:: str
Returns:: str – The unit string in CF-compliant form.

xclim.core.units.ensure_delta(unit)[source]

Return delta units for temperature.

For dimensions where delta exist in pint (Temperature), it replaces the temperature unit by delta_degC or delta_degF based on the input unit. For other dimensionality, it just gives back the input units.

Parameters:: unit (str) – Unit to transform in delta (or not).
Return type:: str
Returns:: str – The transformed units.

xclim.core.units.flux2rate(flux, density, out_units=None)[source]

Convert a flux variable to a rate by dividing with a density.

This is the inverse operation of xclim.core.units.rate2flux().

Parameters:

flux (xr.DataArray) – “flux” variable, e.g. Snowfall flux in “kg m-2 s-1”.
density (Quantified) – Density used to convert from a flux to a rate, e.g. Snowfall density “312 kg m-3”. Density can also be an array with the same shape as flux.
out_units (str, optional) – Specific output units, if needed.

Return type:

DataArray

Returns:

xr.DataArray – The converted rate value.

See also

rate2flux: Convert a rate to a flux.

Examples

The following converts an array of snowfall flux in kg m-2 s-1 to snowfall flux in mm/s, assuming a density of 100 kg m-3:

>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> prsn = xr.DataArray(
...     [0.1] * 365,
...     dims=("time",),
...     coords={"time": time},
...     attrs={"units": "kg m-2 s-1"},
... )
>>> prsnd = flux2rate(prsn, density="100 kg m-3", out_units="mm/s")
>>> prsnd.units
'mm s-1'
>>> float(prsnd[0])
1.0

xclim.core.units.infer_context(standard_name=None, dimension=None)[source]

Return units context based on either the variable’s standard name or the pint dimension.

Valid standard names for the hydro context are those including the terms “rainfall”, “lwe” (liquid water equivalent) and “precipitation”. The latter is technically incorrect, as any phase of precipitation could be referenced. Standard names for evapotranspiration, evaporation and canopy water amounts are also associated with the hydro context.

Parameters:

standard_name (str, optional) – CF-Convention standard name.
dimension (str, optional) – Pint dimension, e.g. ‘[time]’.

Return type:

str

Returns:

str – “hydro” if variable refers to liquid water or to a mass of water in any phase, otherwise “none”.

xclim.core.units.infer_sampling_units(da, deffreq=None, dim='time')[source]

Infer a multiplier and the units corresponding to one sampling period.

Parameters:

da (xr.DataArray) – A DataArray from which to take coordinate dim.
deffreq (str, optional) – If no frequency is inferred from da[dim], take this one.
dim (str) – Dimension from which to infer the frequency.

Return type:

tuple[int, str]

Returns:

int – The magnitude (number of base periods per period).
str – Units as a string, understandable by pint.

Raises:

ValueError – If the frequency has no corresponding units.

xclim.core.units.lwethickness2amount(thickness, out_units=None)[source]

Convert a liquid water thickness (length) to its equivalent amount (mass over area).

This will simply multiply the thickness by the density of liquid water, 1000 kg/m³. This is equivalent to using the “hydro” context of xclim.core.units.units.

Parameters:

thickness (xr.DataArray) – A DataArray storing a liquid water thickness quantity.
out_units (str, optional) – Specific output units, if needed.

Return type:

Union[DataArray, TypeVar(Quantified, DataArray, str, Quantity)]

Returns:

See also

amount2lwethickness: Convert an amount to a liquid water equivalent thickness.

xclim.core.units.pint2cfattrs(value, is_difference=None)[source]

Return CF-compliant units attributes from a pint unit.

Parameters:

value (pint.Unit) – Input unit.
is_difference (bool, optional) – Whether the value represent a difference in temperature, which is ambiguous in the case of absolute temperature scales like Kelvin or Rankine. Default is to guess, see notes.

Return type:

dict[str, str]

Returns:

dict – Units following CF-Convention, using symbols.

Notes

Temperatures are understood as differences if the input unit contains any of the known difference units (see TEMPERATURE_DELTA_UNITS). They are understood as on-scale if the input unit is exactly one of the known on-scale units (°C, °F or °Re). Otherwise, “unknown” is given as the “units metadata” (this usually happens with K or °R, or with composed units like °C d). Of course, units_metadata is not added if the input has no temperature dimension.

xclim.core.units.pint2cfunits(value)[source]

Return a CF-compliant unit string from a pint unit.

Parameters:: value (pint.Unit) – Input unit.
Return type:: str
Returns:: str – Units following CF-Convention, using symbols.

xclim.core.units.pint_multiply(da, q, out_units=None)[source]

Multiply xarray.DataArray by pint.Quantity.

Parameters:

da (xr.DataArray) – Input array.
q (pint.Quantity) – Multiplicative factor.
out_units (str, optional) – Units the output array should be converted into.

Return type:

DataArray

Returns:

xr.DataArray – The product DataArray.

xclim.core.units.rate2amount(rate, dim='time', sampling_rate_from_coord=False, out_units=None)[source]

Convert a rate variable to an amount by multiplying by the sampling period length.

If the sampling period length cannot be inferred, the rate values are multiplied by the duration between their time coordinate and the next one. The last period is estimated with the duration of the one just before.

This is the inverse operation of xclim.core.units.amount2rate().

Parameters:

rate (xr.DataArray or pint.Quantity or str) – “Rate” variable, with units of “amount” per time. Ex: Precipitation in “mm / d”.
dim (str or DataArray) – The name of time dimension or the coordinate itself.
sampling_rate_from_coord (bool) – For data with irregular time coordinates. If True, the diff of the time coordinate will be used as the sampling rate, meaning each data point will be assumed to apply for the interval ending at the next point. See notes. Defaults to False, which raises an error if the time coordinate is irregular.
out_units (str, optional) – Specific output units, if needed.

Return type:

DataArray

Returns:

xr.DataArray or Quantity – The converted variable. The standard_name of rate is modified if a conversion is found.

Raises:

ValueError – If the time coordinate is irregular and sampling_rate_from_coord is False (default).

See also

amount2rate: Convert an amount to a rate.
is_temporal_rate: Determine if a variable is a rate based on its CF attributes.

Notes

Floating-point precision can have surprising results. For example, a daily series of 1 mm/d precipitation rates might not convert to exactly 1 mm daily amounts. This is because a float multiplication is still happening in the background and the time step duration might have been stored in [nano]seconds at one point.

Examples

The following converts a daily array of precipitation in mm/h to the daily amounts in mm:

>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> pr = xr.DataArray([1] * 365, dims=("time",), coords={"time": time}, attrs={"units": "mm/h"})
>>> pram = rate2amount(pr)
>>> pram.units
'mm'
>>> float(pram[0])
24.0

Also works if the time axis is irregular : the rates are assumed constant for the whole period starting on the values timestamp to the next timestamp. This option is activated with sampling_rate_from_coord=True.

>>> time = time[[0, 9, 30]]  # The time axis is Jan 1st, Jan 10th, Jan 31st
>>> pr = xr.DataArray([1] * 3, dims=("time",), coords={"time": time}, attrs={"units": "mm/h"})
>>> pram = rate2amount(pr, sampling_rate_from_coord=True)
>>> pram.values
array([216., 504., 504.])

Finally, we can force output units:

>>> pram = rate2amount(pr, out_units="pc")  # Get rain amount in parsecs. Why not.
>>> pram.values
array([7.00008327e-18, 1.63335276e-17, 1.63335276e-17])

xclim.core.units.rate2flux(rate, density, out_units=None)[source]

Convert a rate variable to a flux by multiplying with a density.

This is the inverse operation of xclim.core.units.flux2rate().

Parameters:

rate (xr.DataArray) – “Rate” variable, e.g. Snowfall rate in “mm / d”.
density (Quantified) – Density used to convert from a rate to a flux, e.g. Snowfall density “312 kg m-3”. Density can also be an array with the same shape as rate.
out_units (str, optional) – Specific output units, if needed.

Return type:

DataArray

Returns:

xr.DataArray – The converted flux value.

See also

flux2rate: Convert a flux to a rate.

Examples

The following converts an array of snowfall rate in mm/s to snowfall flux in kg m-2 s-1, assuming a density of 100 kg m-3:

>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> prsnd = xr.DataArray([1] * 365, dims=("time",), coords={"time": time}, attrs={"units": "mm/s"})
>>> prsn = rate2flux(prsnd, density="100 kg m-3", out_units="kg m-2 s-1")
>>> prsn.units
'kg m-2 s-1'
>>> float(prsn[0])
0.1

xclim.core.units.str2pint(val)[source]

Convert a string to a pint.Quantity, splitting the magnitude and the units.

Parameters:: val (str) – A quantity in the form “[{magnitude} ]{units}”, where magnitude can be cast to a float and units is understood by xclim.core.units.units2pint().
Return type:: Quantity
Returns:: pint.Quantity – Magnitude is 1 if no magnitude was present in the string.

xclim.core.units.to_agg_units(out, orig, statistic, dim='time', deffreq='D')[source]

Set and convert units of an array after an aggregation operation along the sampling dimension (time).

Parameters:

out (xr.DataArray) – The output array of the aggregation operation, no units operation done yet.
orig (xr.DataArray) – The original array before the aggregation operation, used to infer the sampling units and get the variable units.
statistic ({‘min’, ‘max’, ‘mean’, ‘std’, ‘var’, ‘doymin’, ‘doymax’, ‘count’, ‘integral’, ‘sum’} or Callable) – The type of aggregation operation performed. “integral” is mathematically equivalent to “sum”, but the units are multiplied by the timestep of the data (requires an inferrable frequency).
dim (str) – The time dimension along which the aggregation was performed.
deffreq (str, optional) – For operations count and integral, this gives the default source frequency to assume, if it can’t be inferred from out[dim].

Return type:

DataArray

Returns:

xr.DataArray – The DataArray with aggregated values. Depending on configurations, units may also be converted or simplified.

Examples

Take a daily array of temperature and count number of days above a threshold. to_agg_units will infer the units from the sampling rate along “time”, so we ensure the final units are correct:

>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> tas = xr.DataArray(
...     np.arange(365),
...     dims=("time",),
...     coords={"time": time},
...     attrs={"units": "degC"},
... )
>>> cond = tas > 100  # Which days are boiling
>>> Ndays = cond.sum("time")  # Number of boiling days

# Note: older xarray drops units while modern xarray preserves them >>> Ndays.attrs.get(“units”) # doctest: +SKIP ‘degC’ >>> Ndays = to_agg_units(Ndays, tas, “count”) >>> Ndays.units ‘d’

Similarly, here we compute the total heating degree-days, but we have weekly data:

>>> time = xr.date_range("2001-01-01", freq="7D", periods=52)
>>> tas = xr.DataArray(
...     np.arange(52) + 10,
...     dims=("time",),
...     coords={"time": time},
... )
>>> dt = (tas - 16).assign_attrs(units="degC", units_metadata="temperature: difference")
>>> degdays = dt.clip(0).sum("time")  # Integral of temperature above a threshold
>>> degdays = to_agg_units(degdays, dt, statistic="integral")
>>> degdays.units
'°C week'

Which we can always convert to the more common “K days”:

>>> degdays = convert_units_to(degdays, "K days")
>>> degdays.units
'd K'

xclim.core.units.units2pint(value)[source]

Return the pint Unit for the DataArray units.

Parameters:: value (xr.DataArray or pint.Unit or pint.Quantity or dict or str) – Input data array or string representing a unit (with no magnitude).
Return type:: Unit
Returns:: pint.Unit – Units of the data array.

Notes

To avoid ambiguity related to differences in temperature vs absolute temperatures, set the units_metadata attribute to “temperature: difference” or “temperature: on_scale” on the DataArray.

xclim.core.utils module¶

Miscellaneous Utilities¶

Helper functions for the computations, indicator construction and other things.

class xclim.core.utils.CaseInsensitiveDict(data=None)[source]

Bases: collections.abc.MutableMapping[str, Any]

A basic dictionary but keys are strings and case-insensitive, stored all lowercase.

_abc_impl = <_abc._abc_data object>

static _casefold(key)[source]

Return type:: str

copy()[source]

Return type:: CaseInsensitiveDict

get(key, default=None)[source]

Return type:: Any

items()[source]

Return type:: Iterator[tuple[str, Any]]

keys()[source]

Return type:: Iterator[str]

pop(key)[source]

If key is not found, d is returned if given, otherwise KeyError is raised.

Return type:: Any

popitem()[source]

as a 2-tuple; but raise KeyError if D is empty.

Return type:: tuple[str, Any]

setdefault(key, default=None)[source]

Return type:: Any

update(other, **kwargs)[source]

If E present and has a .keys() method, does: for k in E.keys(): D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

xclim.core.utils._chunk_like(*inputs, chunks)[source]

Helper function that (re-)chunks inputs according to a single chunking dictionary.

Will also ensure passed inputs are not IndexVariable types, so that they can be chunked.

xclim.core.utils._compute_virtual_index(n, quantiles, alpha, beta)[source]

Compute the floating point indexes of an array for the linear interpolation of quantiles.

Based on the approach used by Hyndman and Fan [1996].

Parameters:

n (array_like) – The sample sizes.
quantiles (array_like) – The quantiles values.
alpha (float) – A constant used to correct the index computed.
beta (float) – A constant used to correct the index computed.

Notes

alpha and beta values depend on the chosen method (see quantile documentation).

References

Hyndman and Fan [1996]

xclim.core.utils._get_gamma(virtual_indexes, previous_indexes)[source]

Compute gamma (AKA ‘m’ or ‘weight’) for the linear interpolation of quantiles.

Parameters:

virtual_indexes (array_like) – The indexes where the percentile is supposed to be found in the sorted sample.
previous_indexes (array_like) – The floor values of virtual_indexes.

Notes

gamma is usually the fractional part of virtual_indexes but can be modified by the interpolation method.

xclim.core.utils._get_indexes(arr, virtual_indexes, valid_values_count)[source]

Get the valid indexes of arr neighbouring virtual_indexes.

Parameters:

arr (array_like) – The input array.
virtual_indexes (array_like) – The indexes where the percentile is supposed to be found in the sorted sample.
valid_values_count (array_like) – The number of valid values in the sorted array.

Return type:

tuple[ndarray, ndarray]

Returns:

array_like, array_like – A tuple of virtual_indexes neighbouring indexes (previous and next).

Notes

This is a companion function to linear interpolation of quantiles.

xclim.core.utils._linear_interpolation(left, right, gamma)[source]

Compute the linear interpolation weighted by gamma on each point of two same shape arrays.

Parameters:

left (array_like) – Left bound.
right (array_like) – Right bound.
gamma (array_like) – The interpolation weight.

Return type:

ndarray

Returns:

array_like – The linearly interpolated array.

xclim.core.utils._nan_quantile(arr, quantiles, axis=0, alpha=1.0, beta=1.0)[source]

Get the quantiles of the array for the given axis.

A linear interpolation is performed using alpha and beta.

Return type:: float | ndarray

Notes

By default, alpha == beta == 1 which performs the 7th method of Hyndman and Fan [1996]. With alpha == beta == 1/3 we get the 8th method.

xclim.core.utils.calc_perc(arr, percentiles=None, alpha=1.0, beta=1.0, copy=True)[source]

Compute percentiles using nan_calc_percentiles and move the percentiles’ axis to the end.

Parameters:

arr (array_like) – The input array.
percentiles (sequence of float, optional) – The percentiles to compute. If None, only the median is computed.
alpha (float) – A constant used to correct the index computed.
beta (float) – A constant used to correct the index computed.
copy (bool) – If True, the input array is copied before computation. Default is True.

Return type:

ndarray

Returns:

np.ndarray – The percentiles along the last axis.

xclim.core.utils.deprecated(from_version, suggested=None)[source]

Mark an index as deprecated and optionally suggest a replacement.

Parameters:

from_version (str, optional) – The version of xclim from which the function is deprecated.
suggested (str, optional) – The name of the function to use instead.

Return type:

Callable

Returns:

Callable – The decorated function.

xclim.core.utils.ensure_chunk_size(da, **minchunks)[source]

Ensure that the input DataArray has chunks of at least the given size.

If only one chunk is too small, it is merged with an adjacent chunk. If many chunks are too small, they are grouped together by merging adjacent chunks.

Parameters:

da (xr.DataArray) – The input DataArray, with or without the dask backend. Does nothing when passed a non-dask array.
**minchunks (dict[str, int]) – A kwarg mapping from dimension name to minimum chunk size. Pass -1 to force a single chunk along that dimension.

Return type:

DataArray

Returns:

xr.DataArray – The input DataArray, possibly rechunked.

xclim.core.utils.get_temp_dimname(dims, new_dim)[source]

Get an new dimension name based on new_dim, that is not used in dims.

Parameters:

dims (sequence of str) – The dimension names that already exist.
new_dim (str) – The new name we want.

Return type:

str

Returns:

str – The new dimension name with as many underscores prepended as necessary to make it unique.

xclim.core.utils.lazy_indexing(da, index, dim=None)[source]

Get values of da at indices index in a NaN-aware and lazy manner.

Parameters:

da (xr.DataArray) – Input array. If not 1D, dim must be given and must not appear in index.
index (xr.DataArray) – N-d integer indices, if DataArray is not 1D, all dimensions of index must be in DataArray.
dim (str, optional) – Dimension along which to index, unused if da is 1D, should not be present in index.

Return type:

DataArray

Returns:

xr.DataArray – Values of da at indices index.

xclim.core.utils.load_module(path, name=None)[source]

Load a python module from a python file, optionally changing its name.

Parameters:

path (os.PathLike) – The path to the python file.
name (str, optional) – The name to give to the module. If None, the module name will be the stem of the path.

Return type:

ModuleType

Returns:

ModuleType – The loaded module.

Examples

Given a path to a module file (.py):

from pathlib import Path
import os

path = Path("path/to/example.py")

The two following imports are equivalent, the second uses this method.

os.chdir(path.parent)
import example as mod1

os.chdir(previous_working_dir)
mod2 = load_module(path)
mod1 == mod2

xclim.core.utils.make_clix_meta_yaml(raw, adapted)[source]

Read in the clix-meta “index_definitions.yml” file and adapt it to a xclim virtual module yaml.

Parameters:

raw (os.PathLike or StringIO or str) – The path to the clix-meta “index_definitions.yml” file or the string representation of the yaml.
adapted (os.PathLike) – The path where to write the adapted yaml.

Return type:

None

xclim.core.utils.nan_calc_percentiles(arr, percentiles=None, axis=-1, alpha=1.0, beta=1.0, copy=True)[source]

Convert the percentiles to quantiles and compute them using _nan_quantile.

Parameters:

arr (array_like) – The input array.
percentiles (sequence of float, optional) – The percentiles to compute. If None, only the median is computed.
axis (int) – The axis along which to compute the percentiles.
alpha (float) – A constant used to correct the index computed.
beta (float) – A constant used to correct the index computed.
copy (bool) – If True, the input array is copied before computation. Default is True.

Return type:

ndarray

Returns:

np.ndarray – The percentiles along the specified axis.

xclim.core.utils.split_auxiliary_coordinates(obj)[source]

Split auxiliary coords from the dataset.

An auxiliary coordinate is a coordinate variable that does not define a dimension and thus is not necessarily needed for dataset alignment. Any coordinate that has a name different from its dimension(s) is flagged as auxiliary. All scalar coordinates are flagged as auxiliary.

Parameters:

obj (xr.DataArray or xr.Dataset) – An xarray object.

Return type:

tuple[DataArray | Dataset, DataArray]

Returns:

clean_obj (xr.DataArray or xr.Dataset) – Same as obj but without any auxiliary coordinate.
aux_crd_ds (xr.Dataset) – The auxiliary coordinates as a dataset. Might be empty.

Notes

This is useful to circumvent xarray’s alignment checks that will sometimes look the auxiliary coordinate’s data, which can trigger unwanted dask computations.

The auxiliary coordinates can be merged back with the dataset with xarray.Dataset.assign_coords() or xarray.DataArray.assign_coords().

clean, aux = split_auxiliary_coordinates(ds)
merged = clean.assign_coords(da.coords)
merged.identical(ds)  # -> True

xclim.core.utils.uses_dask(*das)[source]

Evaluate whether dask is installed and array is loaded as a dask array.

Parameters:: *das (xr.DataArray or xr.Dataset) – DataArrays or Datasets to check.
Return type:: bool
Returns:: bool – True if any of the passed objects is using dask.