PYME.localization.FitFactories.Dumbell3DFitR module

class PYME.localization.FitFactories.Dumbell3DFitR.Dumbell3DFitFactory(data, metadata, fitfcn=<function f_dumbell3d>, background=None, noiseSigma=None, **kwargs)

Bases: FFBase

Create a fit factory which will operate on image data (data), potentially using voxel sizes etc contained in metadata.

FromPoint(x, y, z=None, roiHalfSize=7, axialHalfSize=15)

This should be overridden in derived classes to actually do the fitting. The function which gets implemented should return a numpy record array, of the dtype defined in the module level FitResultsDType variable (the calling function uses FitResultsDType to pre-allocate an array for the results)

classmethod evalModel(params, md, x=0, y=0, z=0, roiHalfSize=7, axialHalfSize=15)

Evaluate the model that this factory fits - given metadata and fitted parameters.

Used for fit visualisation

PYME.localization.FitFactories.Dumbell3DFitR.Dumbell3DFitResultR(fitResults, metadata, slicesUsed=None, resultCode=-1, fitErr=None, background=0, length=0)

PYME.localization.FitFactories.Dumbell3DFitR.FitFactory

alias of Dumbell3DFitFactory

PYME.localization.FitFactories.Dumbell3DFitR.FitResult(fitResults, metadata, slicesUsed=None, resultCode=-1, fitErr=None, background=0, length=0)

PYME.localization.FitFactories.Dumbell3DFitR.f_dumbell3d(p, X, Y, Z)

Pair of 3D Gaussian model functions with linear background - parameter vector [A, x0, y0, z0, x1, y1, z1, wxy, wz, background] Note: Assumes sames sigma for both Gaussian (dumbell).