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diff --git a/python-unidip.spec b/python-unidip.spec new file mode 100644 index 0000000..7e00917 --- /dev/null +++ b/python-unidip.spec @@ -0,0 +1,194 @@ +%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 <Python_Bot@openeuler.org> - 0.1.1-1 +- Package Spec generated |