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|
%global _empty_manifest_terminate_build 0
Name: python-rampy
Version: 0.4.9
Release: 1
Summary: A Python module containing functions to treat spectroscopic (XANES, Raman, IR...) data
License: GNU-GPLv2
URL: https://github.com/charlesll/rampy
Source0: https://mirrors.aliyun.com/pypi/web/packages/a0/2c/61dd47122b4e024d523c02c01297a7425b71dde91ba09925fd23fb44afa0/rampy-0.4.9.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-scikit-learn
Requires: python3-pandas
Requires: python3-xlrd
Requires: python3-gcvspline
Requires: python3-cvxpy
%description
Copyright (2015-2021) C. Le Losq and co.
lelosq@ipgp.fr
[](https://travis-ci.org/charlesll/rampy) [](https://doi.org/10.5281/zenodo.1168729) [](https://mybinder.org/v2/gh/charlesll/rampy.git/master)
Rampy is a Python library that aims at helping processing spectroscopic data, such as Raman, Infrared or XAS spectra. It offers, for instance, functions to subtract baselines as well as to stack, resample or smooth spectra. It aims at facilitating the use of Python in processing spectroscopic data. It integrates within a workflow that uses Numpy/Scipy as well as optimisation libraries such as lmfit or emcee, for instance.
The /examples/ folder contain various examples.
# REQUIREMENTS
Rampy is tested on Python 3.8 (see Travis badge; no garantee that it works on other Python versions)
The following libraries are required and indicated in setup.cfg:
- Scipy
- Numpy >= 1.12
- sklearn
- pandas & xlrd
Optional dependencies:
- gcvspline (you need a working FORTRAN compiler for its installation. To avoid this problem under Windows, wheels for Python 2.7, 3.4 and 3.6 are provided for 64 bit Windows, and a wheel for Python 3.6 is provided for Windows 32 bits. If installation fails, please check if is due to a fortran compiler issue.)
- xlrd and matplotlib
*Installation of gcvspline as well as matplotlib and xlrd are necessary for use of the `rampy.rameau()` class.*
- cvxpy v 1.1 or higher. As for gcvspline, the installation of cvxpy can cause problems for Windows users due to missing compiler. See instructions from cvxpy in this case.
*Installation of cvxpy is necessary for use of the `rampy.mixing()` class.*
Additional libraries for model fitting may be wanted:
- lmfit & aeval (http://cars9.uchicago.edu/software/python/lmfit/)
- emcee
# INSTALLATION
Install with pip:
`pip install rampy`
If you want to use gcvspline and cvxpy, also install it:
`pip install gcvspline`
`pip install cvxpy`
# EXAMPLES
Given a signal [x y] containing a peak, and recorded in a text file myspectrum.txt.
You can import it, remove a automatic background, plot the result, and print the centroid of the peak as:
```
import matplotlib.pyplot as plt
import numpy as np
import rampy as rp
spectrum = np.genfromtxt("myspectrum.txt")
bir = np.array([[0,100., 200., 1000]]) # the frequency regions devoid of signal, used by rp.baseline()
y_corrected, background = rp.baseline(spectrum[:,0],spectrum[:,1],bir,"arPLS",lam=10**10)
plt.figure()
plt.plot(spectrum[:,0],spectrum[:,1],"k",label="raw data")
plt.plot(spectrum[:,0],background,"k",label="background")
plt.plot(spectrum[:,0],y_corrected,"k",label="corrected signal")
plt.show()
print("Signal centroid is %.2f" % rp.centroid(spectrum[:,0],y_corrected))
```
See the /example folder for further examples.
# Other packages
rampy can be used also to analyse the output of the [RADIS](https://radis.readthedocs.io/en/latest/) package.
See for instance https://github.com/charlesll/rampy/issues/13
Updated September 2021
%package -n python3-rampy
Summary: A Python module containing functions to treat spectroscopic (XANES, Raman, IR...) data
Provides: python-rampy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-rampy
Copyright (2015-2021) C. Le Losq and co.
lelosq@ipgp.fr
[](https://travis-ci.org/charlesll/rampy) [](https://doi.org/10.5281/zenodo.1168729) [](https://mybinder.org/v2/gh/charlesll/rampy.git/master)
Rampy is a Python library that aims at helping processing spectroscopic data, such as Raman, Infrared or XAS spectra. It offers, for instance, functions to subtract baselines as well as to stack, resample or smooth spectra. It aims at facilitating the use of Python in processing spectroscopic data. It integrates within a workflow that uses Numpy/Scipy as well as optimisation libraries such as lmfit or emcee, for instance.
The /examples/ folder contain various examples.
# REQUIREMENTS
Rampy is tested on Python 3.8 (see Travis badge; no garantee that it works on other Python versions)
The following libraries are required and indicated in setup.cfg:
- Scipy
- Numpy >= 1.12
- sklearn
- pandas & xlrd
Optional dependencies:
- gcvspline (you need a working FORTRAN compiler for its installation. To avoid this problem under Windows, wheels for Python 2.7, 3.4 and 3.6 are provided for 64 bit Windows, and a wheel for Python 3.6 is provided for Windows 32 bits. If installation fails, please check if is due to a fortran compiler issue.)
- xlrd and matplotlib
*Installation of gcvspline as well as matplotlib and xlrd are necessary for use of the `rampy.rameau()` class.*
- cvxpy v 1.1 or higher. As for gcvspline, the installation of cvxpy can cause problems for Windows users due to missing compiler. See instructions from cvxpy in this case.
*Installation of cvxpy is necessary for use of the `rampy.mixing()` class.*
Additional libraries for model fitting may be wanted:
- lmfit & aeval (http://cars9.uchicago.edu/software/python/lmfit/)
- emcee
# INSTALLATION
Install with pip:
`pip install rampy`
If you want to use gcvspline and cvxpy, also install it:
`pip install gcvspline`
`pip install cvxpy`
# EXAMPLES
Given a signal [x y] containing a peak, and recorded in a text file myspectrum.txt.
You can import it, remove a automatic background, plot the result, and print the centroid of the peak as:
```
import matplotlib.pyplot as plt
import numpy as np
import rampy as rp
spectrum = np.genfromtxt("myspectrum.txt")
bir = np.array([[0,100., 200., 1000]]) # the frequency regions devoid of signal, used by rp.baseline()
y_corrected, background = rp.baseline(spectrum[:,0],spectrum[:,1],bir,"arPLS",lam=10**10)
plt.figure()
plt.plot(spectrum[:,0],spectrum[:,1],"k",label="raw data")
plt.plot(spectrum[:,0],background,"k",label="background")
plt.plot(spectrum[:,0],y_corrected,"k",label="corrected signal")
plt.show()
print("Signal centroid is %.2f" % rp.centroid(spectrum[:,0],y_corrected))
```
See the /example folder for further examples.
# Other packages
rampy can be used also to analyse the output of the [RADIS](https://radis.readthedocs.io/en/latest/) package.
See for instance https://github.com/charlesll/rampy/issues/13
Updated September 2021
%package help
Summary: Development documents and examples for rampy
Provides: python3-rampy-doc
%description help
Copyright (2015-2021) C. Le Losq and co.
lelosq@ipgp.fr
[](https://travis-ci.org/charlesll/rampy) [](https://doi.org/10.5281/zenodo.1168729) [](https://mybinder.org/v2/gh/charlesll/rampy.git/master)
Rampy is a Python library that aims at helping processing spectroscopic data, such as Raman, Infrared or XAS spectra. It offers, for instance, functions to subtract baselines as well as to stack, resample or smooth spectra. It aims at facilitating the use of Python in processing spectroscopic data. It integrates within a workflow that uses Numpy/Scipy as well as optimisation libraries such as lmfit or emcee, for instance.
The /examples/ folder contain various examples.
# REQUIREMENTS
Rampy is tested on Python 3.8 (see Travis badge; no garantee that it works on other Python versions)
The following libraries are required and indicated in setup.cfg:
- Scipy
- Numpy >= 1.12
- sklearn
- pandas & xlrd
Optional dependencies:
- gcvspline (you need a working FORTRAN compiler for its installation. To avoid this problem under Windows, wheels for Python 2.7, 3.4 and 3.6 are provided for 64 bit Windows, and a wheel for Python 3.6 is provided for Windows 32 bits. If installation fails, please check if is due to a fortran compiler issue.)
- xlrd and matplotlib
*Installation of gcvspline as well as matplotlib and xlrd are necessary for use of the `rampy.rameau()` class.*
- cvxpy v 1.1 or higher. As for gcvspline, the installation of cvxpy can cause problems for Windows users due to missing compiler. See instructions from cvxpy in this case.
*Installation of cvxpy is necessary for use of the `rampy.mixing()` class.*
Additional libraries for model fitting may be wanted:
- lmfit & aeval (http://cars9.uchicago.edu/software/python/lmfit/)
- emcee
# INSTALLATION
Install with pip:
`pip install rampy`
If you want to use gcvspline and cvxpy, also install it:
`pip install gcvspline`
`pip install cvxpy`
# EXAMPLES
Given a signal [x y] containing a peak, and recorded in a text file myspectrum.txt.
You can import it, remove a automatic background, plot the result, and print the centroid of the peak as:
```
import matplotlib.pyplot as plt
import numpy as np
import rampy as rp
spectrum = np.genfromtxt("myspectrum.txt")
bir = np.array([[0,100., 200., 1000]]) # the frequency regions devoid of signal, used by rp.baseline()
y_corrected, background = rp.baseline(spectrum[:,0],spectrum[:,1],bir,"arPLS",lam=10**10)
plt.figure()
plt.plot(spectrum[:,0],spectrum[:,1],"k",label="raw data")
plt.plot(spectrum[:,0],background,"k",label="background")
plt.plot(spectrum[:,0],y_corrected,"k",label="corrected signal")
plt.show()
print("Signal centroid is %.2f" % rp.centroid(spectrum[:,0],y_corrected))
```
See the /example folder for further examples.
# Other packages
rampy can be used also to analyse the output of the [RADIS](https://radis.readthedocs.io/en/latest/) package.
See for instance https://github.com/charlesll/rampy/issues/13
Updated September 2021
%prep
%autosetup -n rampy-0.4.9
%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-rampy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.9-1
- Package Spec generated
|
