sunpeek.components.fluids_wpd_models.WPDModelPure#

class sunpeek.components.fluids_wpd_models.WPDModelPure(unit)#

Bases: WPDModel

Methods

`__init__`(unit)
`csv2df`(csv_file)	Read WebPlotDigitizer csv with single dataset into dataframe.
`plot_fit`(prop_to_plot, fluid_name[, ...])	Plot model fit and original / ground truth data from the WebPlotDigitizer csv dataset.
`predict`(*args)	Compute model prediction (fluid density, heat capacity) based on trained sklearn model, self.sk_model.
`train`([fit_type])	Fits polynomial interpolation model to fluid raw data in df.

csv2df(csv_file)#: Read WebPlotDigitizer csv with single dataset into dataframe.

plot_fit(prop_to_plot, fluid_name, settings=None, n_points=50)#

Plot model fit and original / ground truth data from the WebPlotDigitizer csv dataset. This is useful to check quality of model fit, after calling self.train().

Return type:: Figure

predict(*args)#

Compute model prediction (fluid density, heat capacity) based on trained sklearn model, self.sk_model.

Parameters:: args (pint Quantity) – Inputs required for prediciton: temperature and (for mixed fluids) concentration. Fluid temperature in unit self.unit[‘te’], typically ‘degC’ Fluid concentration in unit self.unit[‘c’], typically ‘percent’
Returns:: pint Quantity
Return type:: Calculated fluid property (density or heat capacity) in unit self.unit[``’out’``]

train(fit_type='polynomial')#

Fits polynomial interpolation model to fluid raw data in df.

Parameters:: fit_type (str) – Type of sklearn fit. Currently, only ‘polynomial’ implemented, works good enough.
Return type:: Trained sklearn model.