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diff --git a/python-gmmclusteringalgorithms.spec b/python-gmmclusteringalgorithms.spec new file mode 100644 index 0000000..f91ee01 --- /dev/null +++ b/python-gmmclusteringalgorithms.spec @@ -0,0 +1,392 @@ +%global _empty_manifest_terminate_build 0 +Name: python-GMMClusteringAlgorithms +Version: 0.1.28 +Release: 1 +Summary: OBSOLETE. This package is no longer maintained because it has been replaced by the package piicrgmms. +License: MIT License +URL: https://pypi.org/project/GMMClusteringAlgorithms/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/92/93/10712cdd5c0167e051fa50f5ab31cef5456640f6ade68c25c88437f85a35/GMMClusteringAlgorithms-0.1.28.tar.gz +BuildArch: noarch + +Requires: python3-scikit-learn +Requires: python3-pandas +Requires: python3-matplotlib +Requires: python3-lmfit +Requires: python3-joblib +Requires: python3-tqdm +Requires: python3-pillow +Requires: python3-webcolors + +%description +# GMMClusteringAlgorithms + +OBSOLETE. This package is no longer maintained and has been +replaced by the package +[piicrgmms](https://pypi.org/project/piicrgmms/), which retains +all the same capabilities. +GMMClusteringAlgorithms was a package for implementing Gaussian +Mixture Models as a data analysis tool in PI-ICR Mass +Spectrometry experiments. It was +first developed in the Fall of 2020 to be used in PI-ICR +experiments at the Canadian Penning Trap (CPT) mass +spectrometer at Argonne National Laboratory (Lemont, IL, U.S.). +It has since been transferred to the package 'piicrgmms'. +At its core is a modified version of the ['mixture' module +from the package scikit-learn.](https://scikit-learn.org/stable/modules/mixture.html) +The modified version, *sklearn_mixture_piicr*, retains all +the same components as the +original version. In addition, it contains two classes with +restricted fitting algorithms: a GMM fit where the phase +dimension of the component means is _not_ a parameter, and a +BGM fit where the number of components is _not_ a parameter. +The rest of the package facilitates +quick, intuitive use of the GMM algorithms through the use +of 4 classes, and visualization methods for debugging. + +#### 1. DataFrame +* This class is responsible for processing the .lmf + file and phase shifts. As attributes, it holds the + processed data for easy access, as well as any data + cuts. + +#### 2. GaussianMixtureModel +* This class fits Gaussian Mixture Models to the + DataFrame object. As parameters, it takes: + 1. Cartesian/Polar coordinates + 2. Number of components to use + 3. Covariance matrix type + 4. Information criterion +* Allows for 'strict' fits, i.e. fits where the number + of components is specified. + +#### 3. BayesianGaussianModel +* Exact same as the GaussianMixtureModel class, but + uses the BayesianGaussianModel class from scikit-learn + instead of the GaussianMixtureModel class. + +#### 4. PhaseFirstGaussianModel +* Implements a fit where the phase dimension is fit to + first, followed by a GMM fit to both spatial dimensions + in which the phase dimension of the component means is + fixed. This type of fit was found to work especially + well with data sets in which there were many species, + like the 168Ho data. + +* Only works with Polar coordinates + +Each model class also includes the ability to visualize +results in several ways (clustering results, One-dimensional +histograms, Probability density function) and the ability to +copy fit results to the clipboard for pasting into an Excel +spreadsheet. + +### Installation +#### Dependencies +GMMClusteringAlgorithms requires: +* Python (>=3.6) +* scikit-learn (>=0.23.2) +* pandas (>=1.2.0) +* matplotlib (>=3.3.0) +* lmfit (>=1.0.0) +* joblib (>=1.0.0) +* tqdm (>=4.56.0) +* pillow (>=8.1.0) +* webcolors(>=1.11.1) + +#### User Installation +This package is now obsolete. Please see the package +[piicrgmms](https://pypi.org/project/piicrgmms/), which is +current. + +Assuming Python and `pip` have already been installed, decide +whether you want a system-wide or local installation, and +which Python distribution (e.g. Anaconda) you want to +install under. Then, open the Command Prompt (for regular +Python distribution) or the Prompt for another distribution +(e.g. Anaconda Prompt for Anaconda), and run either: +* `pip install GMMClusteringAlgorithms` for a system-wide +installation (works for regular Python distributions only), + **OR** +* `pip install -U GMMClusteringAlgorithms` for a local + installation. + +If you want to install in a virtual environment instead, +then navigate to the virtual environment's directory, activate +the virtual environment, and install with the commands above. + +### Source code +You can check the latest source code with the command +`git clone https://github.com/colinweber27/GMMClusteringAlgorithms` + + + + + + + +%package -n python3-GMMClusteringAlgorithms +Summary: OBSOLETE. This package is no longer maintained because it has been replaced by the package piicrgmms. +Provides: python-GMMClusteringAlgorithms +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-GMMClusteringAlgorithms +# GMMClusteringAlgorithms + +OBSOLETE. This package is no longer maintained and has been +replaced by the package +[piicrgmms](https://pypi.org/project/piicrgmms/), which retains +all the same capabilities. +GMMClusteringAlgorithms was a package for implementing Gaussian +Mixture Models as a data analysis tool in PI-ICR Mass +Spectrometry experiments. It was +first developed in the Fall of 2020 to be used in PI-ICR +experiments at the Canadian Penning Trap (CPT) mass +spectrometer at Argonne National Laboratory (Lemont, IL, U.S.). +It has since been transferred to the package 'piicrgmms'. +At its core is a modified version of the ['mixture' module +from the package scikit-learn.](https://scikit-learn.org/stable/modules/mixture.html) +The modified version, *sklearn_mixture_piicr*, retains all +the same components as the +original version. In addition, it contains two classes with +restricted fitting algorithms: a GMM fit where the phase +dimension of the component means is _not_ a parameter, and a +BGM fit where the number of components is _not_ a parameter. +The rest of the package facilitates +quick, intuitive use of the GMM algorithms through the use +of 4 classes, and visualization methods for debugging. + +#### 1. DataFrame +* This class is responsible for processing the .lmf + file and phase shifts. As attributes, it holds the + processed data for easy access, as well as any data + cuts. + +#### 2. GaussianMixtureModel +* This class fits Gaussian Mixture Models to the + DataFrame object. As parameters, it takes: + 1. Cartesian/Polar coordinates + 2. Number of components to use + 3. Covariance matrix type + 4. Information criterion +* Allows for 'strict' fits, i.e. fits where the number + of components is specified. + +#### 3. BayesianGaussianModel +* Exact same as the GaussianMixtureModel class, but + uses the BayesianGaussianModel class from scikit-learn + instead of the GaussianMixtureModel class. + +#### 4. PhaseFirstGaussianModel +* Implements a fit where the phase dimension is fit to + first, followed by a GMM fit to both spatial dimensions + in which the phase dimension of the component means is + fixed. This type of fit was found to work especially + well with data sets in which there were many species, + like the 168Ho data. + +* Only works with Polar coordinates + +Each model class also includes the ability to visualize +results in several ways (clustering results, One-dimensional +histograms, Probability density function) and the ability to +copy fit results to the clipboard for pasting into an Excel +spreadsheet. + +### Installation +#### Dependencies +GMMClusteringAlgorithms requires: +* Python (>=3.6) +* scikit-learn (>=0.23.2) +* pandas (>=1.2.0) +* matplotlib (>=3.3.0) +* lmfit (>=1.0.0) +* joblib (>=1.0.0) +* tqdm (>=4.56.0) +* pillow (>=8.1.0) +* webcolors(>=1.11.1) + +#### User Installation +This package is now obsolete. Please see the package +[piicrgmms](https://pypi.org/project/piicrgmms/), which is +current. + +Assuming Python and `pip` have already been installed, decide +whether you want a system-wide or local installation, and +which Python distribution (e.g. Anaconda) you want to +install under. Then, open the Command Prompt (for regular +Python distribution) or the Prompt for another distribution +(e.g. Anaconda Prompt for Anaconda), and run either: +* `pip install GMMClusteringAlgorithms` for a system-wide +installation (works for regular Python distributions only), + **OR** +* `pip install -U GMMClusteringAlgorithms` for a local + installation. + +If you want to install in a virtual environment instead, +then navigate to the virtual environment's directory, activate +the virtual environment, and install with the commands above. + +### Source code +You can check the latest source code with the command +`git clone https://github.com/colinweber27/GMMClusteringAlgorithms` + + + + + + + +%package help +Summary: Development documents and examples for GMMClusteringAlgorithms +Provides: python3-GMMClusteringAlgorithms-doc +%description help +# GMMClusteringAlgorithms + +OBSOLETE. This package is no longer maintained and has been +replaced by the package +[piicrgmms](https://pypi.org/project/piicrgmms/), which retains +all the same capabilities. +GMMClusteringAlgorithms was a package for implementing Gaussian +Mixture Models as a data analysis tool in PI-ICR Mass +Spectrometry experiments. It was +first developed in the Fall of 2020 to be used in PI-ICR +experiments at the Canadian Penning Trap (CPT) mass +spectrometer at Argonne National Laboratory (Lemont, IL, U.S.). +It has since been transferred to the package 'piicrgmms'. +At its core is a modified version of the ['mixture' module +from the package scikit-learn.](https://scikit-learn.org/stable/modules/mixture.html) +The modified version, *sklearn_mixture_piicr*, retains all +the same components as the +original version. In addition, it contains two classes with +restricted fitting algorithms: a GMM fit where the phase +dimension of the component means is _not_ a parameter, and a +BGM fit where the number of components is _not_ a parameter. +The rest of the package facilitates +quick, intuitive use of the GMM algorithms through the use +of 4 classes, and visualization methods for debugging. + +#### 1. DataFrame +* This class is responsible for processing the .lmf + file and phase shifts. As attributes, it holds the + processed data for easy access, as well as any data + cuts. + +#### 2. GaussianMixtureModel +* This class fits Gaussian Mixture Models to the + DataFrame object. As parameters, it takes: + 1. Cartesian/Polar coordinates + 2. Number of components to use + 3. Covariance matrix type + 4. Information criterion +* Allows for 'strict' fits, i.e. fits where the number + of components is specified. + +#### 3. BayesianGaussianModel +* Exact same as the GaussianMixtureModel class, but + uses the BayesianGaussianModel class from scikit-learn + instead of the GaussianMixtureModel class. + +#### 4. PhaseFirstGaussianModel +* Implements a fit where the phase dimension is fit to + first, followed by a GMM fit to both spatial dimensions + in which the phase dimension of the component means is + fixed. This type of fit was found to work especially + well with data sets in which there were many species, + like the 168Ho data. + +* Only works with Polar coordinates + +Each model class also includes the ability to visualize +results in several ways (clustering results, One-dimensional +histograms, Probability density function) and the ability to +copy fit results to the clipboard for pasting into an Excel +spreadsheet. + +### Installation +#### Dependencies +GMMClusteringAlgorithms requires: +* Python (>=3.6) +* scikit-learn (>=0.23.2) +* pandas (>=1.2.0) +* matplotlib (>=3.3.0) +* lmfit (>=1.0.0) +* joblib (>=1.0.0) +* tqdm (>=4.56.0) +* pillow (>=8.1.0) +* webcolors(>=1.11.1) + +#### User Installation +This package is now obsolete. Please see the package +[piicrgmms](https://pypi.org/project/piicrgmms/), which is +current. + +Assuming Python and `pip` have already been installed, decide +whether you want a system-wide or local installation, and +which Python distribution (e.g. Anaconda) you want to +install under. Then, open the Command Prompt (for regular +Python distribution) or the Prompt for another distribution +(e.g. Anaconda Prompt for Anaconda), and run either: +* `pip install GMMClusteringAlgorithms` for a system-wide +installation (works for regular Python distributions only), + **OR** +* `pip install -U GMMClusteringAlgorithms` for a local + installation. + +If you want to install in a virtual environment instead, +then navigate to the virtual environment's directory, activate +the virtual environment, and install with the commands above. + +### Source code +You can check the latest source code with the command +`git clone https://github.com/colinweber27/GMMClusteringAlgorithms` + + + + + + + +%prep +%autosetup -n GMMClusteringAlgorithms-0.1.28 + +%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-GMMClusteringAlgorithms -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.28-1 +- Package Spec generated |