%global _empty_manifest_terminate_build 0 Name: python-unidip Version: 0.1.1 Release: 1 Summary: Python port of the UniDip clustering algorithm License: GPL URL: http://github.com/BenjaminDoran/unidip Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1c/22/e2b39fd524297ecc6c439748c4e20d56a97a8829f2b2b1897365a0f23a19/unidip-0.1.1.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-matplotlib %description # UniDip Python Port See reference paper: http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noise UniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality. ## Install coming soon... ``` pip3.6 install unidip ``` ## Examples ### Basic Usage ```python from unidip import UniDip # create bi-modal distribution dat = np.concatenate([np.random.randn(200)-3, np.random.randn(200)+3]) # sort data so returned indices are meaningful dat = np.msort(dat) # get start and stop indices of peaks intervals = UniDip(dat).run() ``` ### Advanced Options * **alpha**: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise. * **mrg_dst**: Defines how close intervals must be before they are merged. * **ntrials**: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer. ```python intervals = UniDip(dat, alpha=0.001, ntrials=1000, mrg_dst=5).run() ``` %package -n python3-unidip Summary: Python port of the UniDip clustering algorithm Provides: python-unidip BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-unidip # UniDip Python Port See reference paper: http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noise UniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality. ## Install coming soon... ``` pip3.6 install unidip ``` ## Examples ### Basic Usage ```python from unidip import UniDip # create bi-modal distribution dat = np.concatenate([np.random.randn(200)-3, np.random.randn(200)+3]) # sort data so returned indices are meaningful dat = np.msort(dat) # get start and stop indices of peaks intervals = UniDip(dat).run() ``` ### Advanced Options * **alpha**: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise. * **mrg_dst**: Defines how close intervals must be before they are merged. * **ntrials**: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer. ```python intervals = UniDip(dat, alpha=0.001, ntrials=1000, mrg_dst=5).run() ``` %package help Summary: Development documents and examples for unidip Provides: python3-unidip-doc %description help # UniDip Python Port See reference paper: http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noise UniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality. ## Install coming soon... ``` pip3.6 install unidip ``` ## Examples ### Basic Usage ```python from unidip import UniDip # create bi-modal distribution dat = np.concatenate([np.random.randn(200)-3, np.random.randn(200)+3]) # sort data so returned indices are meaningful dat = np.msort(dat) # get start and stop indices of peaks intervals = UniDip(dat).run() ``` ### Advanced Options * **alpha**: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise. * **mrg_dst**: Defines how close intervals must be before they are merged. * **ntrials**: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer. ```python intervals = UniDip(dat, alpha=0.001, ntrials=1000, mrg_dst=5).run() ``` %prep %autosetup -n unidip-0.1.1 %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-unidip -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.1.1-1 - Package Spec generated