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py-lmfit: updated to 1.0.3 Version 1.0.3 Release Notes (October 14, 2021) ============================================== Potentially breaking change: - argument ``x`` is now required for the ``guess`` method of Models To get reasonable estimates for starting values one should always supply both ``x`` and ``y`` values; in some cases it would work when only providing ``data`` (i.e., y-values). With the change above, ``x`` is now required in the ``guess`` method call, so scripts might need to be updated to explicitly supply ``x``. Bug fixes/enhancements: - do not overwrite user-specified figure titles in Model.plot() functions and allow setting with ``title`` keyword argument - preserve Parameters subclass in deepcopy - coerce ``data`` and ``indepdent_vars`` to NumPy array with ``dtype=float64`` or ``dtype=complex128`` where applicable - fix collision between parameter names in built-in models and user-specified parameters - correct error message in PolynomialModel - improved handling of altered JSON data - map ``max_nfev`` to ``maxiter`` when using ``differential_evolution`` - correct use of noise versus experimental uncertainty in the documentation - specify return type of ``eval`` method more precisely and allow for plotting of (Complex)ConstantModel by coercing their ``float``, ``int``, or ``complex`` return value to a ``numpy.ndarray`` - fix ``dho`` (Damped Harmonic Oscillator) lineshape - reset ``Minimizer._abort`` to ``False`` before starting a new fit - fix typo in ``guess_from_peak2d`` Various: - update asteval dependency to >= 0.9.22 to avoid DeprecationWarnings from NumPy v1.20.0 - remove incorrectly spelled ``DonaichModel`` and ``donaich`` lineshape, deprecated in version 1.0.1 - remove occurrences of OrderedDict throughout the code; dict is order-preserving since Python 3.6 - update the contributing instructions - (again) defer import of matplotlib to when it is needed - fix description of ``name`` argument in ``Parameters.add`` - update dependencies, make sure a functional development environment is installed on Windows - use ``setuptools_scm`` for version info instead of ``versioneer`` - transition to using ``f-strings`` - mark ``test_manypeaks_speed.py`` as flaky to avoid intermittent test failures - update scipy dependency to >= 1.14.0 - improvement to output of examples in sphinx-gallery and use higher resolution figures - remove deprecated functions ``lmfit.printfuncs.report_errors`` and ``asteval`` argument in ``Parameters`` class .. _whatsnew_102_label: Version 1.0.2 Release Notes (February 7, 2021) ============================================== 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 - all built-in models are now registered in ``lmfit.models.lmfit_models``; new Model class attribute ``valid_forms`` - added a SineModel - add the ``run_mcmc_kwargs argument`` to ``Minimizer.emcee`` to pass to the ``emcee.EnsembleSampler.run_mcmc`` function Bug fixes: - ``ModelResult.eval_uncertainty`` should use provided Parameters - center in lognormal model can be negative - restore best-fit values after calculation of covariance matrix - add helper-function ``not_zero`` to prevent ZeroDivisionError in lineshapes and use in exponential lineshape - save ``last_internal_values`` and use to restore internal values if fit is aborted - dumping a fit using the ``lbfgsb`` method now works, convert bytes to string if needed - fix use of callable Jacobian for scalar methods - preserve float/int types when encoding for JSON - better support for saving/loading of ExpressionModels and assure that ``init_params`` and ``init_fit`` are set when loading a ``ModelResult`` Various: - update minimum dependencies - improvements in coding style, docstrings, CI, and test coverage - fix typo in Oscillator - add example using SymPy - allow better custom pool for emcee() - update NIST Strd reference functions and tests - make building of documentation cross-platform - relax module name check in ``test_check_ast_errors`` for Python 3.9 - fix/update layout of documentation, now uses the sphinx13 theme - fixed DeprecationWarnings reported by NumPy v1.2.0 - increase value of ``tiny`` and check for it in bounded parameters to avoid "parameter not moving from initial value" - add ``max_nfev`` to ``basinhopping`` and ``brute`` (now supported everywhere in lmfit) and set to more uniform default values - use Azure Pipelines for CI, drop Travis
2021-11-07 12:58:47 +01:00
@comment $NetBSD: PLIST,v 1.7 2021/11/07 11:58:47 adam Exp $
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
${PYSITELIB}/${EGG_INFODIR}/requires.txt
${PYSITELIB}/${EGG_INFODIR}/top_level.txt
${PYSITELIB}/lmfit/__init__.py
${PYSITELIB}/lmfit/__init__.pyc
${PYSITELIB}/lmfit/__init__.pyo
py-lmfit: updated to 0.9.11 0.9.11: make exception explicit 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.
2018-07-13 08:22:35 +02:00
${PYSITELIB}/lmfit/_ampgo.py
${PYSITELIB}/lmfit/_ampgo.pyc
${PYSITELIB}/lmfit/_ampgo.pyo
Update py-lmfit to 0.9.7 Changes to 0.9.7 not immediately obvious Version 0.9.6 Release Notes 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 Minimizer.brute() or using the method='brute' option to 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.
2017-08-31 10:27:42 +02:00
${PYSITELIB}/lmfit/confidence.py
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/lmfit/confidence.pyc
${PYSITELIB}/lmfit/confidence.pyo
py-lmfit: updated to 0.9.8 0.9.8: update doc for 5 digit-precision fit statistics increase default precision for chi-square, etc from 3 to 5
2018-02-27 07:40:07 +01:00
${PYSITELIB}/lmfit/jsonutils.py
${PYSITELIB}/lmfit/jsonutils.pyc
${PYSITELIB}/lmfit/jsonutils.pyo
Update py-lmfit to 0.9.7 Changes to 0.9.7 not immediately obvious Version 0.9.6 Release Notes 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 Minimizer.brute() or using the method='brute' option to 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.
2017-08-31 10:27:42 +02:00
${PYSITELIB}/lmfit/lineshapes.py
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/lmfit/lineshapes.pyc
${PYSITELIB}/lmfit/lineshapes.pyo
Update py-lmfit to 0.9.7 Changes to 0.9.7 not immediately obvious Version 0.9.6 Release Notes 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 Minimizer.brute() or using the method='brute' option to 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.
2017-08-31 10:27:42 +02:00
${PYSITELIB}/lmfit/minimizer.py
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/lmfit/minimizer.pyc
${PYSITELIB}/lmfit/minimizer.pyo
Update py-lmfit to 0.9.7 Changes to 0.9.7 not immediately obvious Version 0.9.6 Release Notes 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 Minimizer.brute() or using the method='brute' option to 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.
2017-08-31 10:27:42 +02:00
${PYSITELIB}/lmfit/model.py
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/lmfit/model.pyc
${PYSITELIB}/lmfit/model.pyo
Update py-lmfit to 0.9.7 Changes to 0.9.7 not immediately obvious Version 0.9.6 Release Notes 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 Minimizer.brute() or using the method='brute' option to 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.
2017-08-31 10:27:42 +02:00
${PYSITELIB}/lmfit/models.py
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/lmfit/models.pyc
${PYSITELIB}/lmfit/models.pyo
Update py-lmfit to 0.9.7 Changes to 0.9.7 not immediately obvious Version 0.9.6 Release Notes 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 Minimizer.brute() or using the method='brute' option to 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.
2017-08-31 10:27:42 +02:00
${PYSITELIB}/lmfit/parameter.py
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/lmfit/parameter.pyc
${PYSITELIB}/lmfit/parameter.pyo
Update py-lmfit to 0.9.7 Changes to 0.9.7 not immediately obvious Version 0.9.6 Release Notes 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 Minimizer.brute() or using the method='brute' option to 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.
2017-08-31 10:27:42 +02:00
${PYSITELIB}/lmfit/printfuncs.py
Add py-lmfit 0.9.5 A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
2016-09-16 17:20:31 +02:00
${PYSITELIB}/lmfit/printfuncs.pyc
${PYSITELIB}/lmfit/printfuncs.pyo
py-lmfit: updated to 1.0.3 Version 1.0.3 Release Notes (October 14, 2021) ============================================== Potentially breaking change: - argument ``x`` is now required for the ``guess`` method of Models To get reasonable estimates for starting values one should always supply both ``x`` and ``y`` values; in some cases it would work when only providing ``data`` (i.e., y-values). With the change above, ``x`` is now required in the ``guess`` method call, so scripts might need to be updated to explicitly supply ``x``. Bug fixes/enhancements: - do not overwrite user-specified figure titles in Model.plot() functions and allow setting with ``title`` keyword argument - preserve Parameters subclass in deepcopy - coerce ``data`` and ``indepdent_vars`` to NumPy array with ``dtype=float64`` or ``dtype=complex128`` where applicable - fix collision between parameter names in built-in models and user-specified parameters - correct error message in PolynomialModel - improved handling of altered JSON data - map ``max_nfev`` to ``maxiter`` when using ``differential_evolution`` - correct use of noise versus experimental uncertainty in the documentation - specify return type of ``eval`` method more precisely and allow for plotting of (Complex)ConstantModel by coercing their ``float``, ``int``, or ``complex`` return value to a ``numpy.ndarray`` - fix ``dho`` (Damped Harmonic Oscillator) lineshape - reset ``Minimizer._abort`` to ``False`` before starting a new fit - fix typo in ``guess_from_peak2d`` Various: - update asteval dependency to >= 0.9.22 to avoid DeprecationWarnings from NumPy v1.20.0 - remove incorrectly spelled ``DonaichModel`` and ``donaich`` lineshape, deprecated in version 1.0.1 - remove occurrences of OrderedDict throughout the code; dict is order-preserving since Python 3.6 - update the contributing instructions - (again) defer import of matplotlib to when it is needed - fix description of ``name`` argument in ``Parameters.add`` - update dependencies, make sure a functional development environment is installed on Windows - use ``setuptools_scm`` for version info instead of ``versioneer`` - transition to using ``f-strings`` - mark ``test_manypeaks_speed.py`` as flaky to avoid intermittent test failures - update scipy dependency to >= 1.14.0 - improvement to output of examples in sphinx-gallery and use higher resolution figures - remove deprecated functions ``lmfit.printfuncs.report_errors`` and ``asteval`` argument in ``Parameters`` class .. _whatsnew_102_label: Version 1.0.2 Release Notes (February 7, 2021) ============================================== 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 - all built-in models are now registered in ``lmfit.models.lmfit_models``; new Model class attribute ``valid_forms`` - added a SineModel - add the ``run_mcmc_kwargs argument`` to ``Minimizer.emcee`` to pass to the ``emcee.EnsembleSampler.run_mcmc`` function Bug fixes: - ``ModelResult.eval_uncertainty`` should use provided Parameters - center in lognormal model can be negative - restore best-fit values after calculation of covariance matrix - add helper-function ``not_zero`` to prevent ZeroDivisionError in lineshapes and use in exponential lineshape - save ``last_internal_values`` and use to restore internal values if fit is aborted - dumping a fit using the ``lbfgsb`` method now works, convert bytes to string if needed - fix use of callable Jacobian for scalar methods - preserve float/int types when encoding for JSON - better support for saving/loading of ExpressionModels and assure that ``init_params`` and ``init_fit`` are set when loading a ``ModelResult`` Various: - update minimum dependencies - improvements in coding style, docstrings, CI, and test coverage - fix typo in Oscillator - add example using SymPy - allow better custom pool for emcee() - update NIST Strd reference functions and tests - make building of documentation cross-platform - relax module name check in ``test_check_ast_errors`` for Python 3.9 - fix/update layout of documentation, now uses the sphinx13 theme - fixed DeprecationWarnings reported by NumPy v1.2.0 - increase value of ``tiny`` and check for it in bounded parameters to avoid "parameter not moving from initial value" - add ``max_nfev`` to ``basinhopping`` and ``brute`` (now supported everywhere in lmfit) and set to more uniform default values - use Azure Pipelines for CI, drop Travis
2021-11-07 12:58:47 +01:00
${PYSITELIB}/lmfit/version.py
${PYSITELIB}/lmfit/version.pyc
${PYSITELIB}/lmfit/version.pyo
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