PYME.localization.FitFactories.SplitterFitFNR module

PYME.localization.FitFactories.SplitterFitFNR.BlankResult(metadata)

PYME.localization.FitFactories.SplitterFitFNR.FitFactory

alias of GaussianFitFactory

PYME.localization.FitFactories.SplitterFitFNR.FitResult(fitResults, metadata, startParams, slicesUsed=None, resultCode=-1, fitErr=-5000.0, nchi2=-1, background=0, sumIntensity=0)

class PYME.localization.FitFactories.SplitterFitFNR.GaussianFitFactory(data, metadata, fitfcn=<built-in function genSplitGaussInArrayPVec>, 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=5, 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, roiHalfSize=5)

PYME.localization.FitFactories.SplitterFitFNR.GaussianFitResultR(fitResults, metadata, startParams, slicesUsed=None, resultCode=-1, fitErr=-5000.0, nchi2=-1, background=0, sumIntensity=0)

PYME.localization.FitFactories.SplitterFitFNR.f_gauss2d2c(p, Xg, Yg, Xr, Yr)

2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.SplitterFitFNR.f_gauss2d2cA(p, Xg, Yg, Xr, Yr, Arr)

2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.SplitterFitFNR.f_gauss2d2ccb(p, Xg, Yg, Xr, Yr)

2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.SplitterFitFNR.genFitImage(fitResults, metadata)

PYME.localization.FitFactories.SplitterFitFNR.splWrap(*args)