PYME.Analysis.cramerRao module¶
tools for estimating the Fisher information matrix and Cramer-Rao lower bound
- PYME.Analysis.cramerRao.CalcCramerRao(FI)¶
Calculate the Cramer-Rao bound for a given Fisher information matrix
- Parameters
- FIarray
an NparamsxNparams Fisher information matrix
- Returns
- an array of lower bounds on variances (NB the CRLB is given as a variance, rather than std. deviation).
- PYME.Analysis.cramerRao.CalcCramerReoZ(FIz)¶
CRB is the diagonal elements of the inverse of the Fisher information matrix
- PYME.Analysis.cramerRao.CalcFisherInfoModel(params, param_delta, modelFunc, modelargs=())¶
Calculate the Cramer-Rao bound under Poisson noise for a given model function (at a given point), using a numerical derivative.
- Parameters
- paramsarray / list
The parameters
- param_deltaarray / list
The amount to add to the parameters when calculating the numerical gradient
- modelFuncfunction
A function describing the model. The output should be calibrated in photo-electrons
- modelargsiterable
A list of additional arguments to pass to the model function
- Returns
- The NparamsxNparams Fisher information matrix
- PYME.Analysis.cramerRao.CalcFisherInform2D(lam, voxelsize=[1, 1])¶
Calculate the Fisher Information of a 2D model
- Parameters
- lamndarray
model mean value in photoelectrons before Poisson noise process
- voxelsizeiterable
pixel dimensions of model in nm
- Returns
- a 2x2 Fisher information matrix
- PYME.Analysis.cramerRao.CalcFisherInformZ(lam, maxK=500, voxelsize=[1, 1, 1])¶
- PYME.Analysis.cramerRao.CalcFisherInformZn(lam, maxK=500, voxelsize=[1, 1, 1])¶
- PYME.Analysis.cramerRao.CalcFisherInformZn2(lam, maxK=500, voxelsize=[1, 1, 1])¶
- PYME.Analysis.cramerRao.FIkz(lam, k, voxelsize)¶
- PYME.Analysis.cramerRao.lp_poisson(lam, k)¶
log of poisson likelihood fcn
- PYME.Analysis.cramerRao.lp_poisson_n(lam, k)¶
log of poisson likelihood fcn
- PYME.Analysis.cramerRao.p_poisson(lam, k)¶
poisson likelihood fcn - calculated from log lhood for numerical stability