PYME.localization.FitFactories.SplitterFitFusionR module¶

PYME.localization.FitFactories.SplitterFitFusionR.BlankResult(metadata)¶

PYME.localization.FitFactories.SplitterFitFusionR.FitFactory¶: alias of GaussianFitFactory

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

class PYME.localization.FitFactories.SplitterFitFusionR.GaussianFitFactory(data, metadata, fitfcn=<function f_gauss2d>, 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=10, 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.SplitterFitFusionR.GaussianFitResultR(fitResults, metadata, startParams, slicesUsed=None, resultCode=-1, fitErr=-5000.0, nchi2=-1, background=0)¶

PYME.localization.FitFactories.SplitterFitFusionR.f_gauss2d(p, X, Y)¶: 2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.SplitterFitFusionR.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.SplitterFitFusionR.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.SplitterFitFusionR.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.SplitterFitFusionR.genFitImage(fitResults, metadata)¶

PYME.localization.FitFactories.SplitterFitFusionR.splWrap(*args)¶