forked from pool/python-lmfit
Markéta Machová
635dd2b355
- Update to version 1.0.2
* officially supports Python 3.9 and has dropped support for
Python 3.5. The minimum version of the following dependencies
were updated: asteval>=0.9.21, numpy>=1.18, and scipy>=1.3.
New features:
* added two-dimensional Gaussian lineshape and model (PR #642;
@mpmdean)
* all built-in models are now registered in lmfit.models.
lmfit_models; new Model class attribute valid_forms (PR #663;
@rayosborn)
* added a SineModel (PR #676; @lneuhaus)
* add the run_mcmc_kwargs argument to Minimizer.emcee to pass to
the emcee.EnsembleSampler.run_mcmc function (PR #694; @rbnvrw)
- Skip python36 build: Numpy 1.20 dropped support for Python 3.6
(NEP 29)
OBS-URL: https://build.opensuse.org/request/show/873450
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-lmfit?expand=0&rev=4
136 lines
4.5 KiB
RPMSpec
136 lines
4.5 KiB
RPMSpec
#
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# spec file for package python-lmfit
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#
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# Copyright (c) 2021 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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%{?!python_module:%define python_module() python-%{**} python3-%{**}}
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%define skip_python2 1
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%define skip_python36 1
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Name: python-lmfit
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Version: 1.0.2
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Release: 0
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Summary: Least-Squares Minimization with Bounds and Constraints
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License: MIT AND BSD-3-Clause
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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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BuildRequires: %{python_module setuptools}
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BuildRequires: fdupes
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BuildRequires: python-rpm-macros
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Requires: python-asteval >= 0.9.21
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Requires: python-numpy >= 1.18
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Requires: python-scipy >= 1.3
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Recommends: python-dill
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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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Recommends: python-uncertainties >= 3.0.1
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BuildArch: noarch
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# SECTION test requirements
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BuildRequires: %{python_module asteval >= 0.9.21}
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BuildRequires: %{python_module numpy >= 1.18}
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BuildRequires: %{python_module pytest}
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BuildRequires: %{python_module scipy >= 1.3}
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BuildRequires: %{python_module uncertainties >= 3.0.1}
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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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%setup -q -n lmfit-%{version}
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sed -i -e '/^#!\//, 1d' lmfit/jsonutils.py
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%build
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%python_build
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%install
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%python_install
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%python_expand %fdupes %{buildroot}%{$python_sitelib}
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%check
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%{python_exec -c "import sys, lmfit, numpy, scipy, asteval, uncertainties, six;
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print('Python: {}\n\n'
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'lmfit: {}, scipy: {}, numpy: {}, asteval: {}, uncertainties: {}, six: {}'.format(
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sys.version,
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lmfit.__version__,
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scipy.__version__,
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numpy.__version__,
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asteval.__version__,
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uncertainties.__version__,
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six.__version__))"}
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cat << 'EOF' >> testexample.py
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import numpy as np
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import lmfit
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from lmfit.lineshapes import gaussian
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from lmfit.models import PseudoVoigtModel
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x = np.linspace(0, 10, 201)
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np.random.seed(0)
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y = gaussian(x, 10.0, 6.15, 0.8)
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y += gaussian(x, 8.0, 6.35, 1.1)
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y += gaussian(x, 0.25, 6.00, 7.5)
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y += np.random.normal(size=len(x), scale=0.5)
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# with NaN values in the input data
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y[55] = y[91] = np.nan
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mod = PseudoVoigtModel()
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params = mod.make_params(amplitude=20, center=5.5,
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sigma=1, fraction=0.25)
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params['fraction'].vary = False
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# with propagate, should get no error, but bad results
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result = mod.fit(y, params, x=x, nan_policy='propagate')
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lmfit.report_fit(result)
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print(result.__dict__)
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EOF
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cat testexample.py
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%python_exec testexample.py
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# We don't care about speed, and test_itercb is architecture-specific
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# test_model_nan_policy - fails on non x86_64
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# test_shgo_scipy_vs_lmfit_2 - fails on non x86_64
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%pytest -k 'not speed and not (test_model_nan_policy or test_shgo_scipy_vs_lmfit_2)'
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%files %{python_files}
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%doc README.rst THANKS.txt
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%license LICENSE
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%{python_sitelib}/lmfit
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%{python_sitelib}/lmfit-%{version}*-info
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%changelog
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