%global _empty_manifest_terminate_build 0 Name: python-pyEvalData Version: 1.5.1 Release: 1 Summary: Python module to evaluate experimental data License: MIT URL: https://github.com/dschick/pyEvalData Source0: https://mirrors.nju.edu.cn/pypi/web/packages/96/b6/4a1ba329db5fe52fc141f13bcbb7ae802f062611a22cbca226ed22732af1/pyEvalData-1.5.1.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-matplotlib Requires: python3-lmfit Requires: python3-scipy Requires: python3-uncertainties Requires: python3-xrayutilities Requires: python3-h5py Requires: python3-nexusformat Requires: python3-sphinx Requires: python3-nbsphinx Requires: python3-sphinxcontrib-napoleon Requires: python3-flake8 Requires: python3-pytest %description # Welcome to pyEvalData [![Documentation Status](https://readthedocs.org/projects/pyevaldata/badge/?version=latest)](https://pyevaldata.readthedocs.io/en/latest/?badge=latest) ![CI](https://github.com/dschick/pyEvalData/actions/workflows/main.yml/badge.svg) ![pypi](https://github.com/dschick/pyEvalData/actions/workflows/upload-to-pypi.yml/badge.svg) [![codecov](https://codecov.io/gh/dschick/pyEvalData/branch/develop/graph/badge.svg?token=B73OXF4YRI)](https://codecov.io/gh/dschick/pyEvalData) This is a Python module to read and evaluate experimental data. It can handle raw data from different sources such as [spec](https://certif.com/content/spec/), [hdf5](https://www.hdfgroup.org/solutions/hdf5/), [NeXus](https://www.nexusformat.org/) files which are common data formats at synchrotrons, FELs, as well as in a growing number of laboratories. The evaluation provides common functionalities such as binning, error calculation, and advanced data manipulation via algebraic expressions as well as pre- and post-data-filters. Furthermore, advanced wrapper functions allow for plotting and fitting sequences of one or multiple scans in dependence of an external parameter. A minimal code example would look like this: ```python import pyEvalData as ped # define your data source spec = ped.io.Spec(file_name='data.spec') # initialize the evaluation ev = ped.Evaluation(spec) # define the x- and y-data ev.xcol = 'motor1' ev.clist = ['ct1', 'ct2', 'ct1/ct2'] # create a plot for scans 1-3 ev.plot_scans([1, 2, 3]) ``` Please follow the [user guide](https://pyevaldata.readthedocs.io/en/latest/user_guide.html) and [examples](https://pyevaldata.readthedocs.io/en/latest/examples.html) for your first steps with `pyEvalData`. ## Features - reading of several pre-defined raw data formats * [spec](https://certif.com/content/spec/) * [hdf5](https://www.hdfgroup.org/solutions/hdf5/) * [NeXus](https://www.nexusformat.org/) * user-defined text files * camera images (Dectris Pilatus, Princeton MTE, Greateyes, ...) * composite sources - easy implementation of new raw-data formats using an `interface class` - common methods for plotting and fitting of experimental data, including: * data binning * error calculation * data manipulation via algebraic expressions * common data pre- and post-filters ## Installation You can either install directly from [pypi.org](https://www.pypi.org/project/pyEvalData) using the command $ pip install pyEvalData or if you want to work on the latest develop release you can clone `pyEvalData` from the main git repository: $ git clone https://github.com/dschick/pyEvalData.git pyEvalData To work in editable mode (source is only linked but not copied to the python site-packages), just do: $ pip install -e ./pyEvalData Or to do a normal install with $ pip install ./pyEvalData Optionally, you can also let pip install directly from the repository: $ pip install git+https://github.com/dschick/pyEvalData.git You can have the following optional installations to enable unit tests, as well as building the documentation: $ pip install pyEvalData[testing] $ pip install pyEvalData[documentation] ## Contribute & Support If you are having issues please let us know via the [issue tracker](https://github.com/dschick/pyEvalData/issues). You can contribute to the project via pull-requests following the [GitHub flow concept](https://docs.github.com/en/get-started/quickstart/github-flow). ## License The project is licensed under the MIT license. %package -n python3-pyEvalData Summary: Python module to evaluate experimental data Provides: python-pyEvalData BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyEvalData # Welcome to pyEvalData [![Documentation Status](https://readthedocs.org/projects/pyevaldata/badge/?version=latest)](https://pyevaldata.readthedocs.io/en/latest/?badge=latest) ![CI](https://github.com/dschick/pyEvalData/actions/workflows/main.yml/badge.svg) ![pypi](https://github.com/dschick/pyEvalData/actions/workflows/upload-to-pypi.yml/badge.svg) [![codecov](https://codecov.io/gh/dschick/pyEvalData/branch/develop/graph/badge.svg?token=B73OXF4YRI)](https://codecov.io/gh/dschick/pyEvalData) This is a Python module to read and evaluate experimental data. It can handle raw data from different sources such as [spec](https://certif.com/content/spec/), [hdf5](https://www.hdfgroup.org/solutions/hdf5/), [NeXus](https://www.nexusformat.org/) files which are common data formats at synchrotrons, FELs, as well as in a growing number of laboratories. The evaluation provides common functionalities such as binning, error calculation, and advanced data manipulation via algebraic expressions as well as pre- and post-data-filters. Furthermore, advanced wrapper functions allow for plotting and fitting sequences of one or multiple scans in dependence of an external parameter. A minimal code example would look like this: ```python import pyEvalData as ped # define your data source spec = ped.io.Spec(file_name='data.spec') # initialize the evaluation ev = ped.Evaluation(spec) # define the x- and y-data ev.xcol = 'motor1' ev.clist = ['ct1', 'ct2', 'ct1/ct2'] # create a plot for scans 1-3 ev.plot_scans([1, 2, 3]) ``` Please follow the [user guide](https://pyevaldata.readthedocs.io/en/latest/user_guide.html) and [examples](https://pyevaldata.readthedocs.io/en/latest/examples.html) for your first steps with `pyEvalData`. ## Features - reading of several pre-defined raw data formats * [spec](https://certif.com/content/spec/) * [hdf5](https://www.hdfgroup.org/solutions/hdf5/) * [NeXus](https://www.nexusformat.org/) * user-defined text files * camera images (Dectris Pilatus, Princeton MTE, Greateyes, ...) * composite sources - easy implementation of new raw-data formats using an `interface class` - common methods for plotting and fitting of experimental data, including: * data binning * error calculation * data manipulation via algebraic expressions * common data pre- and post-filters ## Installation You can either install directly from [pypi.org](https://www.pypi.org/project/pyEvalData) using the command $ pip install pyEvalData or if you want to work on the latest develop release you can clone `pyEvalData` from the main git repository: $ git clone https://github.com/dschick/pyEvalData.git pyEvalData To work in editable mode (source is only linked but not copied to the python site-packages), just do: $ pip install -e ./pyEvalData Or to do a normal install with $ pip install ./pyEvalData Optionally, you can also let pip install directly from the repository: $ pip install git+https://github.com/dschick/pyEvalData.git You can have the following optional installations to enable unit tests, as well as building the documentation: $ pip install pyEvalData[testing] $ pip install pyEvalData[documentation] ## Contribute & Support If you are having issues please let us know via the [issue tracker](https://github.com/dschick/pyEvalData/issues). You can contribute to the project via pull-requests following the [GitHub flow concept](https://docs.github.com/en/get-started/quickstart/github-flow). ## License The project is licensed under the MIT license. %package help Summary: Development documents and examples for pyEvalData Provides: python3-pyEvalData-doc %description help # Welcome to pyEvalData [![Documentation Status](https://readthedocs.org/projects/pyevaldata/badge/?version=latest)](https://pyevaldata.readthedocs.io/en/latest/?badge=latest) ![CI](https://github.com/dschick/pyEvalData/actions/workflows/main.yml/badge.svg) ![pypi](https://github.com/dschick/pyEvalData/actions/workflows/upload-to-pypi.yml/badge.svg) [![codecov](https://codecov.io/gh/dschick/pyEvalData/branch/develop/graph/badge.svg?token=B73OXF4YRI)](https://codecov.io/gh/dschick/pyEvalData) This is a Python module to read and evaluate experimental data. It can handle raw data from different sources such as [spec](https://certif.com/content/spec/), [hdf5](https://www.hdfgroup.org/solutions/hdf5/), [NeXus](https://www.nexusformat.org/) files which are common data formats at synchrotrons, FELs, as well as in a growing number of laboratories. The evaluation provides common functionalities such as binning, error calculation, and advanced data manipulation via algebraic expressions as well as pre- and post-data-filters. Furthermore, advanced wrapper functions allow for plotting and fitting sequences of one or multiple scans in dependence of an external parameter. A minimal code example would look like this: ```python import pyEvalData as ped # define your data source spec = ped.io.Spec(file_name='data.spec') # initialize the evaluation ev = ped.Evaluation(spec) # define the x- and y-data ev.xcol = 'motor1' ev.clist = ['ct1', 'ct2', 'ct1/ct2'] # create a plot for scans 1-3 ev.plot_scans([1, 2, 3]) ``` Please follow the [user guide](https://pyevaldata.readthedocs.io/en/latest/user_guide.html) and [examples](https://pyevaldata.readthedocs.io/en/latest/examples.html) for your first steps with `pyEvalData`. ## Features - reading of several pre-defined raw data formats * [spec](https://certif.com/content/spec/) * [hdf5](https://www.hdfgroup.org/solutions/hdf5/) * [NeXus](https://www.nexusformat.org/) * user-defined text files * camera images (Dectris Pilatus, Princeton MTE, Greateyes, ...) * composite sources - easy implementation of new raw-data formats using an `interface class` - common methods for plotting and fitting of experimental data, including: * data binning * error calculation * data manipulation via algebraic expressions * common data pre- and post-filters ## Installation You can either install directly from [pypi.org](https://www.pypi.org/project/pyEvalData) using the command $ pip install pyEvalData or if you want to work on the latest develop release you can clone `pyEvalData` from the main git repository: $ git clone https://github.com/dschick/pyEvalData.git pyEvalData To work in editable mode (source is only linked but not copied to the python site-packages), just do: $ pip install -e ./pyEvalData Or to do a normal install with $ pip install ./pyEvalData Optionally, you can also let pip install directly from the repository: $ pip install git+https://github.com/dschick/pyEvalData.git You can have the following optional installations to enable unit tests, as well as building the documentation: $ pip install pyEvalData[testing] $ pip install pyEvalData[documentation] ## Contribute & Support If you are having issues please let us know via the [issue tracker](https://github.com/dschick/pyEvalData/issues). You can contribute to the project via pull-requests following the [GitHub flow concept](https://docs.github.com/en/get-started/quickstart/github-flow). ## License The project is licensed under the MIT license. %prep %autosetup -n pyEvalData-1.5.1 %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-pyEvalData -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu May 18 2023 Python_Bot - 1.5.1-1 - Package Spec generated