pemtk.fit._plotters

Module Contents

Functions

BLMfitPlot(self[, keys, dataType, Etype, thres, col])

Wrap BLMplot for data + simulation results with default params.

lmPlotFit(self[, keys, dataType, Etype, thres])

Wrap lmPlot for data + simulation results with default params.

BLMsetPlot(self[, key, dataDict, agg, ref, overlay, ...])

Plot sets of BLM results from Xarray datasets with Holoviews.

hvSave(self[, key, pTypes, outStem, outPath, outTypes])

Wrapper for quick plot save routine from data dict.
pemtk.fit._plotters.BLMfitPlot(self, keys=None, dataType='AFBLM', Etype='t', thres=0.01, col=None, **kwargs)[source]

Wrap BLMplot for data + simulation results with default params.

TODO: - better plotting (HV?). - fix legend & colour mapping.

pemtk.fit._plotters.lmPlotFit(self, keys=None, dataType='AFBLM', Etype='t', thres=0.01, **kwargs)[source]

Wrap lmPlot for data + simulation results with default params.

pemtk.fit._plotters.BLMsetPlot(self, key='fits', dataDict='AFxr', agg=True, ref=True, overlay=['l', 'm'], pType='r', thres=0.001, sel=None, xDim=None, sq=True, drop=True, unstack=True, plotDict='plots')[source]

Plot sets of BLM results from Xarray datasets with Holoviews.

For plotting individual datasets with more control, see BLMfitPlot().

TODO: - add Seaborn plotting options. - Streamline, should be able to use recursively to stack additional plots…?

Parameters:

  • agg (bool, default = True) – If True, define reduced data as hv.reduce([‘Fit’], np.mean, spreadfn=np.std) NOTE: if False, rendering can be quite slow for large datasets. TODO: more options here.

  • ref (bool, default = True) – If True, include original fitted data in plots. TODO: more options here.

pemtk.fit._plotters.hvSave(self, key='plots', pTypes=None, outStem=None, outPath=None, outTypes=['png', 'html'])[source]

Wrapper for quick plot save routine from data dict.

If data is a HV object, set key=None to save directly Update: removed this, since it’s not very clear or useful (missing defaults).