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|
%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
[](https://pyevaldata.readthedocs.io/en/latest/?badge=latest)
[](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
[](https://pyevaldata.readthedocs.io/en/latest/?badge=latest)
[](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
[](https://pyevaldata.readthedocs.io/en/latest/?badge=latest)
[](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 <Python_Bot@openeuler.org> - 1.5.1-1
- Package Spec generated
|
