Lsqfit

Latest version: v13.2.2

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9.1.2

=========================
Very minor release:

- New option fitname in MultiFitter: fitname(x) creates new name
for chained fit link from old name x.

- Minor bug fix for MultiFitter.chained_lsqfit having to do with
svdcorrection.

9.1.1

========================
Fixes a bug that caused MultiFitter to crash in unusual circumstances.

9.1

=======================
The only significant change is the addition of MultiFitter.

- Added lsqfit.MultiFitter which is a framework from organizing
fits around a small number user-defined of models that are fit
simultaneously to many different pieces of data using different sets of
parameters. It was developed to support the corrfitter module, but is more
general.

- Tiny bug fixes to wavg to cover certain edge cases.

- Faster tests.

9.0.1

============================
Tiny fix to setup.py so lsqfit compiles without gsl. I didn't quite have
it right in v9.0.

9.0

=========================
This version offers a major overhaul of the underlying fitting
strategies used by lsqfit, offering more modern alternatives which can
be more robust and/or much faster than before.

- New 'fitter' parameter in nonlinear_fit to select the fitting package
used to do the actual minimization. Three choices are 'gsl_multifit'
(default), 'gsl_v1_multifit' (previous default), and 'scipy_least_squares'
(default if GSL not present). It is worth experimenting with different
fitters on any given problem, since they are optimized for different types
of problems. Sometimes one option will work where others fail. Also some of
the options can be 2-3 times faster (eg, alg='subspace2D' or either of the
dogleg options) than the defaults (which emphasize robustness). Additional
fitters are under consideration. See the Tutorial section on Faster Fitters
in the documentation.

- Extensively rewritten Tutorial with simpler examples, more plots, and better
organization.

- lsqfit will now compile without GSL, using scipy functions instead.
The GSL package (newly rewritten) is nevertheless recommended because of
its many options. Ideally both GSL and scipy are available, together
offering the most options.

- nonlinear_fit.set(...) is available to change default values for
parameters like svdcut, fitter, and tol.

- nonlinear_fit is almost but not quite backwards compatible. Setting
alg='lmder', for example, will not work unless fitter='gsl_v1_multifit',
which is not the default. Also analyzers aren't available for everything.

- nonlinear_fit ignores correlations in the data if udata=(x,y) is
specified rather than data=(x,y) in the argument list. Uncorrelated fits can
be much faster with 10,000s or 1,000,000s of y[i]s. Ignoring correlations
that are actually there could make answers wrong, however.

- BayesIntegrator.__call__ now accepts an extra flag adapt_to_pdf.
The default is True, which means that vegas adapts to the pdf.
Set it to False and vegas will adapt to the function times the pdf.
In either case, the results have an new attribute results.norm which
gives a vegas estimate for the norm of the pdf (won't be 1 if
the pdf is un-normalized, as is typically the case).

- lsqfit.empbayes_fit now accepts z0 arguments that are numbers, arrays
or dictionaries (as opposed to just arrays). z in fitargs(z) uses
the same layout as z0. This is a minor change but convenient since so many
applications involve just a single z. This function also now accepts
a prior distribution for z (see "plausibility" in the documentation).

- Slight enhancement of lsqfit.nonlinear_fit.format(...): setting
pstyle=None suppresses the parameter list. Otherwise unchanged.

8.1

========================
MPI support for BayesIntegrator is now automatic --- ie, no need to set
parameter mpi=True (the parameter has no effect). Older code should still
work, however. See the documentation for vegas 3.2 for information about
using multiple processors.

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