PYME.localization.FitFactories.GaussMultifitR module¶

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

PYME.localization.FitFactories.GaussMultifitR.FitResult(fitResults, metadata, resultCode=-1, fitErr=None)¶

class PYME.localization.FitFactories.GaussMultifitR.GaussianFitFactory(data, metadata, fitfcn=<function f_multiGauss>, background=None, **kwargs)¶

Bases: object

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

FindAndFit(threshold=2)¶

X = None¶

Y = None¶

classmethod evalModel(params, md, x=0, y=0, roiHalfSize=5)¶

PYME.localization.FitFactories.GaussMultifitR.GaussianFitResultR(fitResults, metadata, resultCode=-1, fitErr=None)¶

PYME.localization.FitFactories.GaussMultifitR.f_J_gauss2d(p, X, Y)¶: generate the jacobian for a 2d Gaussian - for use with _fithelpers.weightedJacF

PYME.localization.FitFactories.GaussMultifitR.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.GaussMultifitR.f_gauss2dF(p, X, Y)¶: 2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y] - uses fast exponential approx

PYME.localization.FitFactories.GaussMultifitR.f_gauss2dSlow(p, X, Y)¶: 2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.GaussMultifitR.f_j_gauss2d(p, func, d, w, X, Y)¶: generate the jacobian for a 2d Gaussian

PYME.localization.FitFactories.GaussMultifitR.f_multiGauss(p, X, Y, s)¶

PYME.localization.FitFactories.GaussMultifitR.f_multiGaussJ(p, X, Y, s)¶

PYME.localization.FitFactories.GaussMultifitR.f_multiGaussS(p, X, Y, s)¶