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diff --git a/python-changepointdetectorevt.spec b/python-changepointdetectorevt.spec new file mode 100644 index 0000000..d85acfd --- /dev/null +++ b/python-changepointdetectorevt.spec @@ -0,0 +1,129 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ChangePointDetectorEVT +Version: 0.0.18 +Release: 1 +Summary: This module takes a date time series and returns: (a) the underlaying linear trend and (b) the times where there is a change in the trend +License: MIT License +URL: https://github.com/mhaupt63/ChangePointDetector +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1a/fd/d1cbdda9b01a4cdc1f69df8936add62f6792076171ec8e7174e9fe77fd80/ChangePointDetectorEVT-0.0.18.tar.gz +BuildArch: noarch + + +%description +ChangePointDetector + +This module returns change points in a date time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf + +1. from ChangePointDetector import ChangePointDetector +2. Prepare your time series as data plus Panda dates +3. Create the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.: + Session=ChangePointDetector.ChangePointDetectorSession(data,dates). + - 'SeasonalityPeriods' is an optional input, e.g if your data is sequential months, 12 = calendar month seasonality + - 'ForecastPeriods' is another optional input, indicating how many periods to forecast. Default = 3 +4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction() + This will return a "Results" object that contains the following: + - ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints + - Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 dates + - PredictionVariance. Variance of the smoothed actuals and forecast + - ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made) + - Trend. This is the linear change factor + - TrendVariance. Variance of the trend + + + +%package -n python3-ChangePointDetectorEVT +Summary: This module takes a date time series and returns: (a) the underlaying linear trend and (b) the times where there is a change in the trend +Provides: python-ChangePointDetectorEVT +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ChangePointDetectorEVT +ChangePointDetector + +This module returns change points in a date time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf + +1. from ChangePointDetector import ChangePointDetector +2. Prepare your time series as data plus Panda dates +3. Create the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.: + Session=ChangePointDetector.ChangePointDetectorSession(data,dates). + - 'SeasonalityPeriods' is an optional input, e.g if your data is sequential months, 12 = calendar month seasonality + - 'ForecastPeriods' is another optional input, indicating how many periods to forecast. Default = 3 +4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction() + This will return a "Results" object that contains the following: + - ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints + - Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 dates + - PredictionVariance. Variance of the smoothed actuals and forecast + - ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made) + - Trend. This is the linear change factor + - TrendVariance. Variance of the trend + + + +%package help +Summary: Development documents and examples for ChangePointDetectorEVT +Provides: python3-ChangePointDetectorEVT-doc +%description help +ChangePointDetector + +This module returns change points in a date time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf + +1. from ChangePointDetector import ChangePointDetector +2. Prepare your time series as data plus Panda dates +3. Create the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.: + Session=ChangePointDetector.ChangePointDetectorSession(data,dates). + - 'SeasonalityPeriods' is an optional input, e.g if your data is sequential months, 12 = calendar month seasonality + - 'ForecastPeriods' is another optional input, indicating how many periods to forecast. Default = 3 +4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction() + This will return a "Results" object that contains the following: + - ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints + - Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 dates + - PredictionVariance. Variance of the smoothed actuals and forecast + - ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made) + - Trend. This is the linear change factor + - TrendVariance. Variance of the trend + + + +%prep +%autosetup -n ChangePointDetectorEVT-0.0.18 + +%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-ChangePointDetectorEVT -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.18-1 +- Package Spec generated |