forked from pool/python-lmfit
5c0159d016
* New features:
+ add 'min_rel_change' as optional variable in calculation of confidence
intervals with Model.conf_interval().
+ Model.eval_uncertainty now takes an optional dscale parameter (default
value of 0.01) to set the step size for calculating derivatives
+ add calculation of predicted_interval to Model.eval_uncertainty
* Bug fixes/enhancements:
+ allow Model.eval_uncertainty to be performed with single points for x
independent variables
+ allow Model._parse_param to handle older-style passed-in 'argnames' and
'kwargs' as for variadic function
+ better allow (or re-allow) Model function independent variables / keyword
arguments to be given non-default values at model creation time
+ add form as independent variable for builtin Step, Rectangle, Thermal
Distribution models.
+ use Model._reprstring(long=True) for model Model.__repr__().
+ restore best-fit parameter values for high accuracy values of constrained
values
+ improvement to Model for the difference between Parameter, "independent
variable", and "option".
+ better saving/loading saved states of Model now use dill, have several
cleanups, and are now versioned for future-proofing.
+ fix bug calculating r-squared for fits with weights
+ fix bug in modelresult.eval_uncertainty() after load_modelresult()
+ add test for MinimizerResult.uvars after successful fit
* Maintenance/Deprecations:
+ drop support for Python 3.7
+ fix tests for Python 3.12 and Python 3.13-dev
+ increase minimum numpy verstio to 1.23 and scipy to 1.8.
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-lmfit?expand=0&rev=11
106 lines
3.9 KiB
RPMSpec
106 lines
3.9 KiB
RPMSpec
#
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# spec file for package python-lmfit
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#
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# Copyright (c) 2024 SUSE LLC
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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#
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Name: python-lmfit
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Version: 1.3.1
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Release: 0
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Summary: Least-Squares Minimization with Bounds and Constraints
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License: BSD-3-Clause AND MIT
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URL: https://lmfit.github.io/lmfit-py/
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Source: https://files.pythonhosted.org/packages/source/l/lmfit/lmfit-%{version}.tar.gz
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# PATCH-FIX-UPSTREAM gh#lmfit/lmfit-py#959
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Patch0: support-numpy-2.patch
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BuildRequires: %{python_module base >= 3.8}
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BuildRequires: %{python_module pip}
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BuildRequires: %{python_module setuptools_scm}
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BuildRequires: %{python_module setuptools}
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BuildRequires: %{python_module wheel}
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BuildRequires: fdupes
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BuildRequires: python-rpm-macros
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Requires: python-asteval >= 0.9.28
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Requires: python-dill >= 0.3.4
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Requires: python-numpy >= 1.23
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Requires: python-scipy >= 1.8
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Requires: python-uncertainties >= 3.1.4
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Recommends: python-emcee
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Recommends: python-matplotlib
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Recommends: python-pandas
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BuildArch: noarch
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# SECTION test requirements
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BuildRequires: %{python_module asteval >= 0.9.28}
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BuildRequires: %{python_module dill >= 0.3.4}
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BuildRequires: %{python_module flaky}
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BuildRequires: %{python_module numpy >= 1.23}
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BuildRequires: %{python_module pytest-cov}
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BuildRequires: %{python_module pytest}
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BuildRequires: %{python_module scipy >= 1.8}
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BuildRequires: %{python_module uncertainties >= 3.1.4}
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# /SECTION
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%python_subpackages
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%description
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A library for least-squares minimization and data fitting in
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Python. Built on top of scipy.optimize, lmfit provides a Parameter object
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which can be set as fixed or free, can have upper and/or lower bounds, or
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can be written in terms of algebraic constraints of other Parameters. The
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user writes a function to be minimized as a function of these Parameters,
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and the scipy.optimize methods are used to find the optimal values for the
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Parameters. The Levenberg-Marquardt (leastsq) is the default minimization
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algorithm, and provides estimated standard errors and correlations between
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varied Parameters. Other minimization methods, including Nelder-Mead's
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downhill simplex, Powell's method, BFGS, Sequential Least Squares, and
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others are also supported. Bounds and constraints can be placed on
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Parameters for all of these methods.
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In addition, methods for explicitly calculating confidence intervals are
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provided for exploring minmization problems where the approximation of
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estimating Parameter uncertainties from the covariance matrix is
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questionable.
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%prep
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%autosetup -p1 -n lmfit-%{version}
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sed -i -e '/^#!\//, 1d' lmfit/jsonutils.py
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%build
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%pyproject_wheel
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%install
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%pyproject_install
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%python_expand %fdupes %{buildroot}%{$python_sitelib}
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%check
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# We don't care about speed on obs
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donttest="speed"
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# these tests fail on non x86_64. Upstream does not care: https://github.com/lmfit/lmfit-py/issues/692
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donttest+=" or test_model_nan_policy"
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donttest+=" or test_shgo_scipy_vs_lmfit_2"
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# fails on 32-bit
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if [ $(getconf LONG_BIT) -ne 64 ]; then
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donttest+=" or (test_itercb_minimizer_class and leastsq and False)"
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donttest+=" or (test_aborted_solvers and brute)"
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fi
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%pytest -k "not ($donttest)"
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%files %{python_files}
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%doc README.rst AUTHORS.txt
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%license LICENSE
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%{python_sitelib}/lmfit
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%{python_sitelib}/lmfit-%{version}.dist-info
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%changelog
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