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Accepting request 812430 from home:glaubitz:branches:devel:languages:python:numeric

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- Update to version 1.0.1
  + New features:
    - added thermal distribution model and lineshape (PR #620; @mpmdean)
    - introduced a new argument ``max_nfev`` to uniformly specify the maximum
      number of function evalutions (PR #610)
      **Please note: all other arguments (e.g., ``maxfev``, ``maxiter``, ...)
      will no longer be passed to the underlying solver. A warning will be emitted
      stating that one should use ``max_nfev``.**
    - the attribute ``call_kws`` was added to the ``MinimizerResult`` class and
      contains the keyword arguments that are supplied to the solver in SciPy.
  + Bug fixes:
    - fixes to the ``load`` and ``__setstate__`` methods of the Parameter class
    - fixed failure of ModelResult.dump() due to missing attributes
      (Issue #611, PR #623; @mpmdean)
    - ``guess_from_peak`` function now also works correctly with decreasing
       x-values or when using pandas (PRs #627 and #629; @mpmdean)
    - the ``Parameter.set()`` method now correctly first updates the boundaries
      and then the value (Issue #636, PR #637; @arunpersaud)
  + Various:
    - fixed typo for the use of expressions in the documentation
      (Issue #610; @jkrogager)
    - removal of PY2-compatibility and unused code and improved test
      coverage (PRs #619, #631, and #633)
    - removed deprecated ``isParameter`` function and automatic conversion of
      an ``uncertainties`` object (PR #626)
    - inaccurate FWHM calculations were removed from built-in models, others
      labeled as estimates (Issue #616 and PR #630)
    - corrected spelling mistake for the Doniach lineshape and model
      (Issue #634; @rayosborn)
    - removed unsupported/untested code for IPython notebooks in lmfit/ui/*

OBS-URL: https://build.opensuse.org/request/show/812430
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-lmfit?expand=0&rev=1
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Tomáš Chvátal 2020-06-16 08:37:37 +00:00 committed by Git OBS Bridge
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-------------------------------------------------------------------
Mon Jun 8 07:06:22 UTC 2020 - John Paul Adrian Glaubitz <adrian.glaubitz@suse.com>
- Update to version 1.0.1
+ New features:
- added thermal distribution model and lineshape (PR #620; @mpmdean)
- introduced a new argument ``max_nfev`` to uniformly specify the maximum
number of function evalutions (PR #610)
**Please note: all other arguments (e.g., ``maxfev``, ``maxiter``, ...)
will no longer be passed to the underlying solver. A warning will be emitted
stating that one should use ``max_nfev``.**
- the attribute ``call_kws`` was added to the ``MinimizerResult`` class and
contains the keyword arguments that are supplied to the solver in SciPy.
+ Bug fixes:
- fixes to the ``load`` and ``__setstate__`` methods of the Parameter class
- fixed failure of ModelResult.dump() due to missing attributes
(Issue #611, PR #623; @mpmdean)
- ``guess_from_peak`` function now also works correctly with decreasing
x-values or when using pandas (PRs #627 and #629; @mpmdean)
- the ``Parameter.set()`` method now correctly first updates the boundaries
and then the value (Issue #636, PR #637; @arunpersaud)
+ Various:
- fixed typo for the use of expressions in the documentation
(Issue #610; @jkrogager)
- removal of PY2-compatibility and unused code and improved test
coverage (PRs #619, #631, and #633)
- removed deprecated ``isParameter`` function and automatic conversion of
an ``uncertainties`` object (PR #626)
- inaccurate FWHM calculations were removed from built-in models, others
labeled as estimates (Issue #616 and PR #630)
- corrected spelling mistake for the Doniach lineshape and model
(Issue #634; @rayosborn)
- removed unsupported/untested code for IPython notebooks in lmfit/ui/*
- from version 1.0.0
+ New features:
- no new features are introduced in 1.0.0.
+ Improvements:
- support for Python 2 and use of the ``six`` package are removed. (PR #612)
+ Various:
- documentation updates to clarify the use of ``emcee``. (PR #614)
- from version 0.9.15
+ New features, improvements, and bug fixes:
- move application of parameter bounds to setter instead of getter (PR #587)
- add support for non-array Jacobian types in least_squares
(Issue #588, @ezwelty in PR #589)
- add more information (i.e., acor and acceptance_fraction) about
emcee fit (@j-zimmermann in PR #593)
- "name" is now a required positional argument for Parameter class,
update the magic methods (PR #595)
- fix nvars count and bound handling in confidence interval
calculations (Issue #597, PR #598)
- support Python 3.8; requires asteval >= 0.9.16 (PR #599)
- only support emcee version 3 (i.e., no PTSampler anymore) (PR #600)
- fix and refactor prob_bunc in confidence interval calculations (PR #604)
- fix adding Parameters with custom user-defined symbols
(Issue #607, PR #608; thanks to @gbouvignies for the report)
+ Various:
- bump requirements to LTS version of SciPy/ NumPy and code clean-up (PR #591)
- documentation updates (PR #596, and others)
- improve test coverage and Travis CI updates (PR #595, and others)
- update pre-commit hooks and configuration in setup.cfg
+ To-be deprecated:
- function Parameter.isParameter and conversion from
uncertainties.core.Variable to value in _getval (PR #595)
- from version 0.9.14
+ New features:
- the global optimizers ``shgo`` and ``dual_annealing`` (new in SciPy v1.2)
are now supported (Issue #527; PRs #545 and #556)
- ``eval`` method added to the Parameter class (PR #550 by @zobristnicholas)
- avoid ZeroDivisionError in ``printfuncs.params_html_table``
(PR #552 by @aaristov and PR #559)
- add parallelization to ``brute`` method (PR #564, requires SciPy v1.3)
+ Bug fixes:
- consider only varying parameters when reporting potential issues with calculating
errorbars (PR #549) and compare ``value`` to both ``min`` and ``max`` (PR #571)
- guard against division by zero in lineshape functions and ``FWHM``
and ``height`` expression calculations (PR #545)
- fix issues with restoring a saved Model (Issue #553; PR #554)
- always set ``result.method`` for ``emcee`` algorithm (PR #558)
- more careful adding of parameters to handle out-of-order
constraint expressions (Issue #560; PR #561)
- make sure all parameters in Model.guess() use prefixes (PRs #567 and #569)
- use ``inspect.signature`` for PY3 to support wrapped functions
(Issue #570; PR #576)
- fix ``result.nfev``` for ``brute`` method when using parallelization
(Issue #578; PR #579)
+ Various:
- remove "missing" in the Model class (replaced by nan_policy) and "drop"
as option to nan_policy (replaced by omit) deprecated since 0.9 (PR #565).
- deprecate 'report_errors' in printfuncs.py (PR #571)
- updates to the documentation to use ``jupyter-sphinx`` to include
examples/output (PRs #573 and #575)
- include a Gallery with examples in the documentation
using ``sphinx-gallery`` (PR #574 and #583)
- improve test-coverage (PRs #571, #572 and #585)
- add/clarify warning messages when NaN values are detected (PR #586)
- several updates to docstrings (Issue #584; PR #583, and others)
- update pre-commit hooks and several docstrings
- Update BuildRequires and Requires from setup.py
-------------------------------------------------------------------
Wed Apr 24 08:42:15 UTC 2019 - pgajdos@suse.com
- version update to 0.9.13
New features:
Clearer warning message in fit reports when uncertainties should
but cannot be estimated, including guesses of which Parameters
to examine (#521, #543)
SplitLorenztianModel and split_lorentzian function (#523)
HTML representations for Parameter, MinimizerResult, and Model
so that they can be printed better with Jupyter (#524, #548)
support parallelization for differential evolution (#526)
Bug fixes:
delay import of matplotlib (and so, the selection of its backend)
as late as possible (#528, #529)
fix for saving, loading, and reloading ModelResults (#534)
fix to leastsq to report the best-fit values, not the values tried
last (#535, #536)
fix synchronization of all parameter values on Model.guess() (#539, #542)
improve deprecation warnings for outdated nan_policy keywords (#540)
fix for edge case in gformat() (#547)
Project managements:
using pre-commit framework to improve and enforce coding style (#533)
added code coverage report to github main page
updated docs, github templates, added several tests.
dropped support and testing for Python 3.4.
- deleted patches
- lmfit-scipy.patch (upstreamed)
-------------------------------------------------------------------
Tue Mar 5 14:57:02 UTC 2019 - Todd R <toddrme2178@gmail.com>
- Fix spurious unit test errors.
-------------------------------------------------------------------
Fri Jan 18 10:24:44 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com>
- Apply patch to fix build with new scipy:
* lmfit-scipy.patch
-------------------------------------------------------------------
Fri Jan 18 10:19:27 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com>
- Update to 0.9.12:
* make exceptions explicit
* use inspect.getfullargspec for Python3
* test-suite: use pytest features, improve coverage, fix mistakes
-------------------------------------------------------------------
Thu Mar 22 04:46:08 UTC 2018 - toddrme2178@gmail.com
- Initial version

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#
# spec file for package python-lmfit
#
# Copyright (c) 2020 SUSE LLC.
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
# upon. The license for this file, and modifications and additions to the
# file, is the same license as for the pristine package itself (unless the
# license for the pristine package is not an Open Source License, in which
# case the license is the MIT License). An "Open Source License" is a
# license that conforms to the Open Source Definition (Version 1.9)
# published by the Open Source Initiative.
# Please submit bugfixes or comments via https://bugs.opensuse.org/
#
%{?!python_module:%define python_module() python-%{**} python3-%{**}}
%define skip_python2 1
Name: python-lmfit
Version: 1.0.1
Release: 0
Summary: Least-Squares Minimization with Bounds and Constraints
License: MIT AND BSD-3-Clause
URL: https://lmfit.github.io/lmfit-py/
Source: https://files.pythonhosted.org/packages/source/l/lmfit/lmfit-%{version}.tar.gz
BuildRequires: %{python_module setuptools}
BuildRequires: fdupes
BuildRequires: python-rpm-macros
Requires: python-asteval >= 0.9.16
Requires: python-numpy >= 1.16
Requires: python-scipy >= 1.2
Recommends: python-dill
Recommends: python-emcee
Recommends: python-matplotlib
Recommends: python-pandas
Recommends: python-uncertainties >= 3.0.1
BuildArch: noarch
# SECTION test requirements
BuildRequires: %{python_module asteval >= 0.9.16}
BuildRequires: %{python_module numpy >= 1.16}
BuildRequires: %{python_module pytest}
BuildRequires: %{python_module scipy >= 1.2}
BuildRequires: %{python_module uncertainties >= 3.0.1}
# /SECTION
%python_subpackages
%description
A library for least-squares minimization and data fitting in
Python. Built on top of scipy.optimize, lmfit provides a Parameter object
which can be set as fixed or free, can have upper and/or lower bounds, or
can be written in terms of algebraic constraints of other Parameters. The
user writes a function to be minimized as a function of these Parameters,
and the scipy.optimize methods are used to find the optimal values for the
Parameters. The Levenberg-Marquardt (leastsq) is the default minimization
algorithm, and provides estimated standard errors and correlations between
varied Parameters. Other minimization methods, including Nelder-Mead's
downhill simplex, Powell's method, BFGS, Sequential Least Squares, and
others are also supported. Bounds and constraints can be placed on
Parameters for all of these methods.
In addition, methods for explicitly calculating confidence intervals are
provided for exploring minmization problems where the approximation of
estimating Parameter uncertainties from the covariance matrix is
questionable.
%prep
%setup -q -n lmfit-%{version}
sed -i -e '/^#!\//, 1d' lmfit/jsonutils.py
%build
%python_build
%install
%python_install
%python_expand %fdupes %{buildroot}%{$python_sitelib}
%check
python3 -c "import sys, lmfit, numpy, scipy, asteval, uncertainties, six;print('Python: {}\n\nlmfit: {}, scipy: {}, numpy: {}, asteval: {}, uncertainties: {}, six: {}'.format(sys.version, lmfit.__version__, scipy.__version__, numpy.__version__, asteval.__version__, uncertainties.__version__, six.__version__))"
cat << 'EOF' >> testexample.py
import numpy as np
import lmfit
from lmfit.lineshapes import gaussian
from lmfit.models import PseudoVoigtModel
x = np.linspace(0, 10, 201)
np.random.seed(0)
y = gaussian(x, 10.0, 6.15, 0.8)
y += gaussian(x, 8.0, 6.35, 1.1)
y += gaussian(x, 0.25, 6.00, 7.5)
y += np.random.normal(size=len(x), scale=0.5)
# with NaN values in the input data
y[55] = y[91] = np.nan
mod = PseudoVoigtModel()
params = mod.make_params(amplitude=20, center=5.5,
sigma=1, fraction=0.25)
params['fraction'].vary = False
# with propagate, should get no error, but bad results
result = mod.fit(y, params, x=x, nan_policy='propagate')
lmfit.report_fit(result)
print(result.__dict__)
EOF
cat testexample.py
python3 testexample.py
# We don't care about speed, and test_itercb is architecture-specific
%pytest -k 'not speed'
%files %{python_files}
%doc README.rst THANKS.txt
%license LICENSE
%{python_sitelib}/*
%changelog