lmfit-py

Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.

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lmfit-py -

Published by newville over 6 years ago

lmfit-py - 0.9.10

Published by newville over 6 years ago

Major Changes for 0.9.10:

  • add AMPGO and basin-hopping global optimization methods.
  • aborting a fit from the objective function now raises AbortFitException
  • fit statistics are more uniformly calculated.
  • the uncertainties package is now an external dependency, and an out-dated copy is no longer kept in lmfit.
  • more exceptions when import matplotlib are now tolerated.
  • many documentation fixes.
lmfit-py -

Published by newville over 6 years ago

lmfit-py -

Published by newville over 6 years ago

This release provides a few small but important changes compared to 0.9.8:

  • asteval is now required as a separate install -- a copy is no longer included in lmfit. This makes it easier for asteval developments to happen independently from lmfit.
  • the formatting of floating point numbers in the fit report is now better, fixing at least one bug where the values reported were truncated and incorrect.
  • prefixes of Model components can be changed after the model is created.
  • saving models and modelresults can now include user-specified data (as long as it can be converted to json!).
lmfit-py -

Published by newville over 6 years ago

lmfit-py -

Published by newville over 6 years ago

lmfit-py -

Published by newville over 7 years ago

lmfit-py -

Published by newville over 7 years ago

lmfit-py -

Published by newville over 7 years ago

lmfit-py - 0.9.6

Published by newville over 7 years ago

Partial release notes for lmfit 0.9.6:

  • Support for SciPy 0.14 has been dropped: SciPy 0.15 is now required. This
    is especially important for lmfit maintenance, as it means we can now rely
    on SciPy having code for differential evolution and do not need to keep a
    local copy.

  • A brute force method was added, which can be used either with
    :meth:Minimizer.brute or using the method='brute' option to
    :meth:Minimizer.minimize. This method requires finite bounds on
    all varying parameters, or that parameters have a finite
    brute_step attribute set to specify the step size.

  • Custom cost functions can now be used for the scalar minimizers using the
    reduce_fcn option.

  • Many improvements to documentation and docstrings in the code were made.
    As part of that effort, all API documentation in this main Sphinx
    documentation now derives from the docstrings.

  • Uncertainties in the resulting best-fit for a model can now be calculated
    from the uncertainties in the model parameters.

  • Parameters have two new attributes: brute_step, to specify the step
    size when using the brute method, and user_data, which is unused but
    can be used to hold additional information the user may desire. This will
    be preserved on copy and pickling.

  • Several bug fixes and cleanups.

  • Versioneer was updated to 0.18.

  • Tests can now be run either with nose or pytest.

lmfit-py - 0.9.6rc1

Published by newville over 7 years ago

lmfit-py -

Published by newville about 8 years ago

lmfit-py - 0.9.4 release

Published by newville over 8 years ago

lmfit-py -

Published by newville over 8 years ago

lmfit-py - release 0.9.3

Published by newville over 8 years ago

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