PYME.localization.FitFactories.GaussFitConstrR module

PYME.localization.FitFactories.GaussFitConstrR.FitFactory

alias of GaussianFitFactory

PYME.localization.FitFactories.GaussFitConstrR.FitResult(fitResults, metadata, slicesUsed=None, resultCode=-1, fitErr=None, background=0)
class PYME.localization.FitFactories.GaussFitConstrR.GaussianFitFactory(data, metadata, fitfcn=<function f_gauss2d>, background=None, noiseSigma=None)

Bases: PYME.localization.FitFactories.FFBase.FFBase

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

Methods

FromPoint(x, y[, z, roiHalfSize, axialHalfSize])
evalModel(params, md[, x, y, roiHalfSize]) Evaluate the model that this factory fits - given metadata and fitted parameters.
FromPoint(x, y, z=None, roiHalfSize=5, axialHalfSize=15)
classmethod evalModel(params, md, x=0, y=0, roiHalfSize=5)

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

Used for fit visualisation

PYME.localization.FitFactories.GaussFitConstrR.GaussianFitResultR(fitResults, metadata, slicesUsed=None, resultCode=-1, fitErr=None, background=0)
PYME.localization.FitFactories.GaussFitConstrR.f_J_gauss2d(p, X, Y)

generate the jacobian for a 2d Gaussian - for use with _fithelpers.weightedJacF

PYME.localization.FitFactories.GaussFitConstrR.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.GaussFitConstrR.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.GaussFitConstrR.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.GaussFitConstrR.f_j_gauss2d(p, func, d, w, X, Y)

generate the jacobian for a 2d Gaussian