%global _empty_manifest_terminate_build 0 Name: python-pyimfit Version: 0.12.0 Release: 1 Summary: Python wrapper for astronomical image-fitting program Imfit License: GNU General Public License v3 (GPLv3) URL: https://github.com/perwin/pyimfit Source0: https://mirrors.aliyun.com/pypi/web/packages/2b/a8/054568ea68498fef20f9de6ae49a40a6f2cce8791dd9dff228d4387252fb/pyimfit-0.12.0.tar.gz BuildArch: noarch %description # Pyimfit This is a Python wrapper for the astronomical image-fitting program Imfit. Online documentation: [https://pyimfit.readthedocs.io/en/latest/](https://pyimfit.readthedocs.io/en/latest/). ## Sample Usage The following assumes an interactive Python session (such as an iPython session or Jupyter notebook): from astropy.io import fits import pyimfit imageFile = "/ic3478rss_256.fits" imfitConfigFile = "/config_exponential_ic3478_256.dat" # read in image data, convert to proper double-precisions, little-endian format image_data = fits.getdata(imageFile) # construct model from config file; construct new Imfit fitter based on model,; model_desc = pyimfit.ModelDescription.load(configFile) # create an Imfit object, using the previously loaded model configuration imfit_fitter = pyimfit.Imfit(model_desc) # load the image data and image characteristics and do a standard fit # (using default chi^2 statistics and Levenberg-Marquardt solver) result = imfit_fitter.fit(image_data, gain=4.725, read_noise=4.3, original_sky=130.14) # check the fit and print the resulting best-fit parameter values if result.fitConverged is True: print("Fit converged: chi^2 = {0}, reduced chi^2 = {1}".format(imfit_fitter.fitStatistic, result.reducedFitStat)) bestfit_params = result.params print("Best-fit parameter values:", bestfit_params) %package -n python3-pyimfit Summary: Python wrapper for astronomical image-fitting program Imfit Provides: python-pyimfit BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyimfit # Pyimfit This is a Python wrapper for the astronomical image-fitting program Imfit. Online documentation: [https://pyimfit.readthedocs.io/en/latest/](https://pyimfit.readthedocs.io/en/latest/). ## Sample Usage The following assumes an interactive Python session (such as an iPython session or Jupyter notebook): from astropy.io import fits import pyimfit imageFile = "/ic3478rss_256.fits" imfitConfigFile = "/config_exponential_ic3478_256.dat" # read in image data, convert to proper double-precisions, little-endian format image_data = fits.getdata(imageFile) # construct model from config file; construct new Imfit fitter based on model,; model_desc = pyimfit.ModelDescription.load(configFile) # create an Imfit object, using the previously loaded model configuration imfit_fitter = pyimfit.Imfit(model_desc) # load the image data and image characteristics and do a standard fit # (using default chi^2 statistics and Levenberg-Marquardt solver) result = imfit_fitter.fit(image_data, gain=4.725, read_noise=4.3, original_sky=130.14) # check the fit and print the resulting best-fit parameter values if result.fitConverged is True: print("Fit converged: chi^2 = {0}, reduced chi^2 = {1}".format(imfit_fitter.fitStatistic, result.reducedFitStat)) bestfit_params = result.params print("Best-fit parameter values:", bestfit_params) %package help Summary: Development documents and examples for pyimfit Provides: python3-pyimfit-doc %description help # Pyimfit This is a Python wrapper for the astronomical image-fitting program Imfit. Online documentation: [https://pyimfit.readthedocs.io/en/latest/](https://pyimfit.readthedocs.io/en/latest/). ## Sample Usage The following assumes an interactive Python session (such as an iPython session or Jupyter notebook): from astropy.io import fits import pyimfit imageFile = "/ic3478rss_256.fits" imfitConfigFile = "/config_exponential_ic3478_256.dat" # read in image data, convert to proper double-precisions, little-endian format image_data = fits.getdata(imageFile) # construct model from config file; construct new Imfit fitter based on model,; model_desc = pyimfit.ModelDescription.load(configFile) # create an Imfit object, using the previously loaded model configuration imfit_fitter = pyimfit.Imfit(model_desc) # load the image data and image characteristics and do a standard fit # (using default chi^2 statistics and Levenberg-Marquardt solver) result = imfit_fitter.fit(image_data, gain=4.725, read_noise=4.3, original_sky=130.14) # check the fit and print the resulting best-fit parameter values if result.fitConverged is True: print("Fit converged: chi^2 = {0}, reduced chi^2 = {1}".format(imfit_fitter.fitStatistic, result.reducedFitStat)) bestfit_params = result.params print("Best-fit parameter values:", bestfit_params) %prep %autosetup -n pyimfit-0.12.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pyimfit -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 0.12.0-1 - Package Spec generated