skdownscale.pointwise_models.BcsdPrecipitation¶

class skdownscale.pointwise_models.BcsdPrecipitation(time_grouper=<function MONTH_GROUPER>, climate_trend_grouper=<function DAY_GROUPER>, climate_trend=<function MONTH_GROUPER>, return_anoms=True, qm_kwargs=None)[source]¶

Bases: BcsdBase

Classic BCSD model for Precipitation

Parameters:

time_grouper (str or pd.Grouper, optional) – Pandas time frequency str or Grouper object. Specifies how to group time periods. Default is ‘M’ (e.g. Monthly).
qm_kwargs (dict) – Keyword arguments to pass to QuantileMapper.

time_grouper¶

Linear Regression object.

Type:: pd.Grouper

quantile_mappers_¶

QuantileMapper objects (one for each time group).

Type:: dict

__init__(time_grouper=<function MONTH_GROUPER>, climate_trend_grouper=<function DAY_GROUPER>, climate_trend=<function MONTH_GROUPER>, return_anoms=True, qm_kwargs=None)¶

Methods

`__init__`([time_grouper, ...])
`fit`(X, y)	Fit BcsdPrecipitation model
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict using the BcsdPrecipitation model
`set_params`(**params)	Set the parameters of this estimator.

fit(X, y)[source]¶

Fit BcsdPrecipitation model

Parameters:

X (pd.Series or pd.DataFrame, shape (n_samples, 1)) – Training data
y (pd.Series or pd.DataFrame, shape (n_samples, 1)) – Target values.

Returns:

self (returns an instance of self.)

predict(X)[source]¶

Predict using the BcsdPrecipitation model

Parameters:: X (pd.Series or pd.DataFrame, shape (n_samples, 1)) – Samples.
Returns:: C (pd.DataFrame, shape (n_samples, 1)) – Returns predicted values.