pywr.recorders.NormalisedGaussianKDEStorageRecorder

class pywr.recorders.NormalisedGaussianKDEStorageRecorder(*args, **kwargs)

A recorder that fits a KDE to a normalised time-series of volume.

This recorder is an extension to NumpyArrayNormalisedStorageRecorder which, at the end of a simulation, uses kernel density estimation (KDE) to estimate the probability density function of the storage time-series. It returns the probability of being at or below zero of the normalised values in the aggregated_value() method (i.e. used for optimisation). The recorder flattens data from all scenarios before computing the KDE. Therefore, a single PDF is produced and returned via .to_dataframe().

The user can specify an optional resampling (e.g. to create annual minimum time-series) prior to fitting the KDE. By default the KDE is reflected at the normalised storage bounds (-1.0 and 1.0) to represent the boundedness of the distribution. This can be disabled if required.

Parameters:

resample_freqstr or None

If not None the resampling frequency used by prior to distribution fitting.

resample_funcstr or None

If not None the resampling function used prior to distribution fitting.

num_pdfint

Number of points in the PDF estimate. Defaults to 101.

use_reflectionbool

Whether to reflect the PDF at the upper and lower normalised bounds (i.e. -1.0 and 1.0 volume) to account for the boundedness of the distribution. Defaults to true.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
after(self)
aggregated_value(self)
before(self)
finish(self)
is_constraint_violated(self)	Returns true if the value from this Recorder violates its constraint bounds.
load(cls, model, data)
register(cls)
reset(self)
setup(self)
to_dataframe()	Return a pandas.DataFrame of the estimated PDF.
unregister(cls)
values()	Return the estimated PDF values.

Attributes

agg_func
children
comment	comment: str
constraint_lower_bounds
constraint_upper_bounds
data
epsilon	epsilon: 'double'
ignore_nan	ignore_nan: 'bool'
is_constraint	Returns true if either upper or lower constraint bounds is defined.
is_double_bounded_constraint	Returns true if upper and lower constraint bounds are both defined and not-equal to one another.
is_equality_constraint	Returns true if upper and lower constraint bounds are both defined and equal to one another.
is_lower_bounded_constraint	Returns true if lower constraint bounds is defined and upper constraint bounds is not.
is_objective
is_upper_bounded_constraint	Returns true if upper constraint bounds is defined and lower constraint bounds is not.
model
name
node
parameter
parents
tags	tags: dict
temporal_agg_func