fancytools.fit package¶

Submodules¶

fancytools.fit.fit2dArrayToFn module¶

fancytools.fit.fit2dArrayToFn.fit2dArrayToFn(arr, fn, mask=None, down_scale_factor=1, output_shape=None, guess=None)[source]¶

Fit a 2d array to a 2d function

*Ignore masked values * [down_scale_factor] map to speed up fitting procedure * [output_shape] shape of the output array * [guess] must be scaled using [scale_factor]

Returns:	Fitted map, fitting params (scaled), error

fancytools.fit.linregressIgnoringOutliers module¶

fancytools.fit.linregressIgnoringOutliers.linregressIgnoringOutliers(x, y, n_iter=3, nstd=2)[source]¶: do linear regression [n_iter] times successive removing [outliers] return result of normal linregress

fancytools.fit.polyFitIgnoringOutliers module¶

fancytools.fit.polyFitIgnoringOutliers.polyFitIgnoringOutliers(x, y, deg=2, niter=3, nstd=2)[source]¶

Returns:	callable function of polynomal fit excluding all outliers
Return type:	(np.poly1d)
Parameters:	deg (int) – degree of polynomal fit n_iter (int) – do linear regression n times successive removing nstd (float) – exclude outlioers, if their deviation is > [nstd] * standard deviation