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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
@comment $NetBSD: PLIST,v 1.5 2018/07/13 06:22:35 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/_version.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/_version.pyc
${PYSITELIB}/lmfit/_version.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
${PYSITELIB}/lmfit/ui/__init__.py
${PYSITELIB}/lmfit/ui/__init__.pyc
${PYSITELIB}/lmfit/ui/__init__.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/ui/basefitter.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/ui/basefitter.pyc
${PYSITELIB}/lmfit/ui/basefitter.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/ui/ipy_fitter.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/ui/ipy_fitter.pyc
${PYSITELIB}/lmfit/ui/ipy_fitter.pyo
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