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+%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 This package is no longer maintained because it has been replaced by the package piicrgmms.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 This package is no longer maintained and has been 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