Replace sas_temper_engine.py

# perform the traditional estimation of the uncertainties in the

# fitting parameters by looking at the partial derivatives using the

# approach that is used in Paul Kienzle's "bumps", as is noted below.

# https://github.com/bumps/bumps/blob/master/LICENSE.txt 

# Jacobian matrix.  The approach is not perfect, and can give odd

# values that do not seem consistent with what a person using it 

# might expect.  This may be removed from future releases, or it will

# only be available if a single model is being generated.

#

# This approach is similar to one of a few that is implemented in bumps.

# bumps is by Paul Kienzle, who works at NIST's NCNR

# https://github.com/bumps/

# see https://github.com/bumps/bumps/blob/master/bumps/lsqerror.py 

# bumps is the fitting engine that Sasview uses with sasmodels,

# but it is an independent package.

def est_uncerts(d, f, modconf, best_model):

    # this is our ugly way of getting at this matrix of derivatives

    loc = modelconfig.ModelConfig(f.name,f.category,f.params,f.sq)

    # print("Jacobian")

    # print(J)

    # this is the singlular value decomposition of J

    # see https://github.com/bumps/bumps/blob/master/bumps/lsqerror.py 

    # bumps is by Paul Kienzle, who works at NIST's NCNR

    # https://github.com/bumps/bumps/blob/master/LICENSE.txt

    # This is the singlular value decomposition of J.

    # The tolerance shown cuts down on singlular values

    # bumps as a whole may not use this directly.

    U, S, V = np.linalg.svd(J, 0)